Abstract

The application discloses an enterprise carbon emission monitoring system and method, and relates to the technical field of carbon emission. The invention provides a new scheme capable of automatically acquiring, analyzing and displaying carbon emission information of a manufacturing enterprise in real time, namely a data acquisition layer, a data transmission layer, an edge calculation layer, a data management fusion layer, a data analysis modeling layer and a data interaction application layer which are arranged from bottom to top, wherein the data acquisition layer comprises a plurality of data acquisition devices which are arranged in the manufacturing enterprise, the calculation accuracy can be greatly improved through an automatic acquisition mode of the data acquisition devices, errors and errors of manual filling are avoided, inaccuracy of manual estimation data can be avoided, meanwhile, data reported by the acquisition devices and heterogeneous data intelligently combined from a multi-heterogeneous system are directly utilized for carbon check, real-time and accurate data can be well guaranteed, the enterprise operation checking efficiency is also improved, and the cost is reduced.

Description

Enterprise carbon emission monitoring system and method

Technical Field

The invention belongs to the technical field of carbon emission, and particularly relates to an enterprise carbon emission monitoring system and method.

Background

As global climate warms, extreme climate frequency becomes worse, greenhouse effect becomes worse, finding low-carbon and efficient sustainable economy has inevitably become a hot spot for emergent development in the world today, and in order to actively respond to national double-carbon (i.e. carbon peak and carbon neutral short) policies and targets, part of manufacturing enterprises also start to take carbon check as a part of self development, and meanwhile, because of rapid development of big data, cloud computing and internet technology, information management of enterprise carbon emission has become more and more popular.

At present, aiming at the carbon emission monitoring and management work of a manufacturing enterprise, the following four problems are mainly existed:

(1) The existing carbon emission collection mode is still behind in comparison with the traditional mode, and for carbon emission data generated by enterprises, the mode of manual recording and manual summarization is still adopted, so that the correctness of source data cannot be guaranteed, namely, as is well known, the manual recording is slow, the manual recording is very dependent on the patience and the care of staff, the problem of recording errors can occur when the staff is careless, and sometimes, the problem of errors occurs only in a decimal point, so that a lot of troubles can be brought. Although some manufacturing enterprises use internet technology, the method only provides various carbon emission data input on line, does not have automatic data acquisition, and cannot well realize automatic calculation of the data and automatic real-time measurement of the carbon emission data of various types, so that the method is easy to cause inaccurate and intelligent data due to manual input of users, and has probability of causing big errors of the data caused by a decimal point.

(2) The existing carbon emission collection mode is that documents are directly used for circulation in a company, the working efficiency is low, and the data utilization rate is low; meanwhile, when a user finds that a certain data calculation is wrong, the user needs to find out a problem from the data, and after modification, the user needs to make a calculation again, so that time and effort are wasted, namely, in paper or an EXCEL file, if the data needs to be modified, the user needs to make a effort to find out the wrong data, and after modification, the user needs to make a manual calculation again, which leads to repeated work and greatly wastes human resources. And the carbon emission data is circulated in the company by using the form of the EXCEL file, whether the current latest version is circulated or not is checked and confirmed step by step, the working efficiency is low, the accuracy is not guaranteed, and the utilization rate of various data is low.

(3) Existing methods and systems do not have in-depth methods and applications, such as in-depth solutions to industry characteristics, for example, in the production environment of a factory or the operational environment of a building.

(4) The main functions of the existing method and system are limited to simple data summarizing calculation, and the carbon neutralization transformation bottleneck cannot be accurately identified according to the characteristics of the industry, so that a targeted carbon reduction scheme cannot be provided.

Disclosure of Invention

The invention aims to provide a novel enterprise carbon emission monitoring system and method, which are used for solving the problem that the accuracy and instantaneity of carbon emission data cannot be ensured in the prior art.

In order to achieve the above purpose, the present invention adopts the following technical scheme:

in a first aspect, an enterprise carbon emission monitoring system is provided, which comprises a data acquisition layer, a data transmission layer, an edge calculation layer, a data management fusion layer, a data analysis modeling layer and a data interaction application layer which are arranged from bottom to top, wherein the data acquisition layer comprises a plurality of data acquisition devices distributed in a manufacturing enterprise;

the data acquisition equipment comprises direct exhaust gas acquisition equipment, direct exhaust water dissolution acquisition equipment and/or indirect exhaust acquisition equipment, wherein the direct exhaust gas acquisition equipment is used for detecting, acquiring and reporting greenhouse gas content data in a gas environment, the direct exhaust water dissolution acquisition equipment is used for detecting, acquiring and reporting carbon-containing compound content data in a water environment, and the indirect exhaust acquisition equipment is used for detecting, acquiring and reporting production resource use data of the manufacturing enterprises;

The data transmission layer is used for transmitting the reported data from the data acquisition equipment to the edge calculation layer;

the edge computing layer is used for distributively accessing data uploaded nearby by the data acquisition equipment and preprocessing the accessed data to obtain preprocessing result data, wherein the preprocessing comprises sequentially performing data conversion processing, exception analysis processing, data cleaning processing and data integration processing, the data conversion processing is used for converting received data with different formats into data which can be processed subsequently, the exception analysis processing is used for identifying whether abnormal data which does not match preset data specifications and is abnormal due to obvious transmission errors and/or abnormal due to obvious equipment function problems and/or abnormal due to obvious network communication problems exists in the data subjected to the data conversion processing, the data cleaning processing is used for deleting the identified abnormal data, the data integration processing is used for carrying out data gathering, identical data accumulation and/or different integration processing on the data which are subjected to the data cleaning processing and come from a plurality of different data acquisition equipment based on the dimension of actual production equipment, the dimension of working procedure dimension, the dimension of the process dimension of the production line dimension and/or the dimension of the workshop dimension, and obtaining the preprocessing result data;

The data management fusion layer is configured to receive the preprocessing result data from each edge computing device in the edge computing layer and/or heterogeneous data related to energy consumption and carbon from heterogeneous systems, and sequentially perform pattern matching processing, data assembling processing and data fusion processing on the received data to obtain carbon emission fusion result data, where the heterogeneous systems include a supplier relation management system, a manufacturing execution system, an energy management system, an enterprise resource planning system, a logistics management system, an office automation system, a data acquisition and monitoring control system and/or a supply chain management system of the manufacturing enterprise, the pattern matching processing refers to performing data identification matching processing on actually produced product types according to each subdivision industry and/or each subdivision process type on the basis of organizing carbon emission and product LCA carbon emission models constructed by machine learning technology, the data assembling refers to performing data matching processing on each effectively received data into corresponding carbon emission organization carbon emission and product carbon emission and carbon emission model so as to obtain carbon emission fusion result data based on the carbon emission calculation result data of the carbon emission and calculation result fusion control system;

The data analysis modeling layer is used for applying the carbon emission fusion result data to an organization carbon emission data statistical analysis module, a product LCA carbon emission data statistical analysis module, an employee carbon data statistical analysis module and/or a digital twin function module according to different statistical analysis requirements to form carbon emission monitoring data of the manufacturing enterprise;

and the data interaction application layer is used for visually displaying the carbon emission monitoring data.

Based on the above-mentioned invention, a new scheme capable of automatically acquiring, analyzing and displaying the carbon emission information of manufacturing enterprises in real time is provided, namely, the scheme comprises a data acquisition layer, a data transmission layer, an edge calculation layer, a data management fusion layer, a data analysis modeling layer and a data interaction application layer which are arranged from bottom to top, wherein the data acquisition layer comprises a plurality of data acquisition devices which are arranged in the manufacturing enterprises, the calculation accuracy can be greatly improved by the automatic acquisition mode of the data acquisition devices, errors and errors of manual filling are avoided, inaccuracy of manual estimation data can be avoided, meanwhile, data reported by the acquisition devices and heterogeneous data obtained from intelligent combination of a multi-heterogeneous system are directly utilized for carbon check, real-time and accurate data can be well ensured, the operation checking efficiency of the enterprises is also improved, the cost is reduced, and the practical application and popularization are facilitated.

In one possible design, when the data acquisition device comprises a direct exhaust gas acquisition device, the direct exhaust gas acquisition device comprises a fixed greenhouse gas detection sensor and/or an unmanned aerial vehicle-mounted greenhouse gas detection sensor, wherein the fixed greenhouse gas detection sensor is used for detecting, acquiring and reporting greenhouse gas content data in the gas environment at a fixed position, and the unmanned aerial vehicle-mounted greenhouse gas detection sensor is used for detecting, acquiring and reporting greenhouse gas content data in the gas environment at a fixed airspace by means of an unmanned aerial vehicle;

and/or when the data acquisition equipment comprises direct discharge water dissolution acquisition equipment, the direct discharge water dissolution acquisition equipment comprises an in-water oil and hydrocarbon online analyzer and/or a carbon dioxide water quality sensor, wherein the in-water oil and hydrocarbon online analyzer is used for detecting, acquiring and reporting content data of hydrocarbon, oil and volatile organic compounds in the water environment, and the carbon dioxide water quality sensor is used for detecting, acquiring and reporting content data of dissolved carbon dioxide in the water environment;

And/or when the data acquisition equipment comprises indirect emission acquisition equipment, the indirect emission acquisition equipment comprises an ammeter, a water meter and/or a gas meter, wherein the ammeter is used for detecting, acquiring and reporting the electricity resource use data of the manufacturing type enterprise, the water meter is used for detecting, acquiring and reporting the water resource use data of the manufacturing type enterprise, and the gas meter is used for detecting, acquiring and reporting the natural gas resource use data of the manufacturing type enterprise.

In one possible design, the data transmission layer includes an internet of things device and/or a remote access device;

the internet of things gateway device is configured to access data reported by the data acquisition device in a local access manner, convert the reported data into a data format identifiable by an upper system, and finally transmit the reported data after format conversion to the edge computing layer, where the local access manner includes a WiFi wireless network access manner, a remote radio network access manner, an ethernet port access manner, a serial port access manner and/or a USB port expansion access manner;

the remote access device is configured to provide, when a certain data acquisition device cannot access the internet of things gateway device through a local access manner, access to the certain data acquisition device in a wide area network manner so as to transmit data reported by the certain data acquisition device to the edge computing layer, where the wide area network manner includes a narrowband internet of things manner, a 4G/5G network manner, a Beidou satellite communication network manner and/or an internet network manner.

In one possible design, the edge computation layer receives data uploaded by the data acquisition device in proximity through the data transport layer based on a message queue telemetry transport protocol, a hypertext transport protocol, a restricted application protocol, a transmission control protocol, a user datagram protocol, and/or an advanced message queue protocol.

In one possible design, receiving the preprocessing result data from each edge computing device in the edge computing layer includes: and receiving the preprocessing result data from each edge computing device in the edge computing layer based on the Kafka distributed message queue, and forwarding the preprocessing result data to a computing module for sequentially performing pattern matching processing, data assembling processing and data fusion processing on the received data.

In one possible design, the pattern matching process, the data assembling process and the data fusion process are sequentially performed on the received data to obtain carbon emission fusion result data, including: and carrying out pattern matching processing, data assembling processing and data fusion processing on the received data in sequence based on the Spark cluster operation framework to obtain carbon emission fusion result data.

In one possible design, the carbon emission monitoring data includes organized carbon emission monitoring data, product lifecycle assessment carbon emission monitoring data, product lifecycle inventory carbon emission monitoring data, employee carbon emission monitoring data, and/or digital twin data, wherein the organized carbon emission monitoring data is monitoring data of carbon emission organized according to country division phase ranges and emission types, the product lifecycle assessment carbon emission monitoring data is monitoring data of carbon emission at each phase in the product full lifecycle, the product lifecycle inventory carbon emission monitoring data is carbon emission inventory data of scene, input and output of each production activity in the product full lifecycle, the employee carbon emission monitoring data is related carbon emission monitoring data generated by employee behavior, and the digital twin data is data obtained by digitally modeling factory buildings and production lines of a factory with corresponding carbon emission data.

In one possible design, the data interaction application layer is further configured to perform data analysis and classification on the carbon emission monitoring data through a random forest algorithm and a support vector machine algorithm, automatically match historical conditions and expert suggestions given under the historical conditions according to a data classification result and a previous data precipitation result, and finally obtain carbon neutralization optimization suggestions conforming to enterprise data and current states.

In one possible design, the data interaction application layer is further used for predicting and dynamically simulating key indexes and data affecting the peak of carbon, and continuously giving optimization adjustment suggestions in the whole course so as to provide references and guidance for strategic targets and management of the manufacturing enterprises.

In a second aspect, there is also provided an enterprise carbon emission monitoring method, including:

the method comprises the steps that data are collected through a plurality of data collection devices distributed in a manufacturing enterprise, wherein the data collection devices comprise direct discharge gas collection devices, direct discharge water dissolution collection devices and/or indirect discharge collection devices, the direct discharge gas collection devices are used for detecting, collecting and reporting greenhouse gas content data in a gas environment, the direct discharge water dissolution collection devices are used for detecting, collecting and reporting carbon-containing compound content data in a water environment, and the indirect discharge collection devices are used for detecting, collecting and reporting production resource use data of the manufacturing enterprise;

the method comprises the steps of distributing and accessing data uploaded nearby by data acquisition equipment through edge computing equipment, preprocessing the accessed data to obtain preprocessing result data, wherein preprocessing comprises sequentially performing data conversion processing, exception analysis processing, data cleaning processing and data integration processing, the data conversion processing is used for converting received data with different formats into data which can be processed subsequently, the exception analysis processing is used for identifying whether abnormal data which does not match preset data specifications and is abnormal due to obvious transmission errors, abnormal data due to obvious equipment function problems and/or abnormal data due to obvious network communication problems exists in the data which are processed through the data conversion processing, the data cleaning processing is used for deleting the identified abnormal data, the data integration processing is used for carrying out data gathering, identical data accumulation calculation and/or different data integration processing on the data which are processed through the data cleaning processing and come from a plurality of different data acquisition equipment based on actual production equipment dimension, procedure dimension, process dimension, production line dimension and/or workshop dimension, and obtaining preprocessing results;

Receiving the preprocessing result data from each edge computing device and/or heterogeneous data which are related to energy consumption and carbon and come from heterogeneous systems, and sequentially carrying out pattern matching processing, data assembling processing and data fusion processing on the received data to obtain carbon emission fusion result data, wherein the heterogeneous systems comprise a supplier relation management system, a manufacturing execution system, an energy management system, an enterprise resource planning system, a logistics management system, an office automation system, a data acquisition and monitoring control system and/or a supply chain management system of the manufacturing type enterprise, the pattern matching processing refers to evaluating LCA (liquid crystal display) record data according to product life cycles related to various industrial production processes, carrying out data identification matching processing on actually produced product types by combining the received data on the basis of a carbon emission and product LCA carbon emission model constructed by a machine learning technology according to various subdivision industries and/or various subdivision process types, so as to obtain effective received data, the data assembling processing refers to arranging each effective received data into corresponding carbon emission and product LCA carbon emission of the organization so as to form a carbon emission model, and calculating the carbon emission data of the enterprise carbon emission and the carbon emission model based on the obtained carbon emission data, and the carbon emission data of the manufactured by the manufacturing type is fused with the obtained carbon emission data based on the calculation result data of the carbon emission model;

According to different statistical analysis requirements, the carbon emission fusion result data is applied to an organization carbon emission data statistical analysis module, a product LCA carbon emission data statistical analysis module, an employee carbon data statistical analysis module and/or a digital twin function module to form carbon emission monitoring data of the manufacturing enterprise;

the carbon emission monitoring data is visually displayed.

The beneficial effects are that:

(1) The invention provides a new scheme capable of automatically acquiring, analyzing and displaying carbon emission information of a manufacturing enterprise in real time, namely a data acquisition layer, a data transmission layer, an edge calculation layer, a data management fusion layer, a data analysis modeling layer and a data interaction application layer which are arranged from bottom to top, wherein the data acquisition layer comprises a plurality of data acquisition devices which are arranged in the manufacturing enterprise, the calculation accuracy can be greatly improved through the automatic acquisition mode of the data acquisition devices, errors and errors of manual filling are avoided, inaccuracy of manual estimation data can be avoided, meanwhile, the data reported by the acquisition devices and heterogeneous data intelligently combined from a multi-heterogeneous system are directly utilized for carbon check, the real-time and accurate data can be well ensured, the efficiency of enterprise operation checking is also improved, and the cost is reduced, so that the method is convenient for practical application and popularization;

(2) For the later data maintenance, whether editing, inquiring or deleting is more convenient, and errors in the manual aspect are not easy to occur;

(3) The method can also help users to make carbon reduction prediction, and well help enterprises to realize carbon neutralization as early as possible.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic structural diagram of an enterprise carbon emission monitoring system provided by the present invention.

Fig. 2 is a schematic flow chart of an enterprise carbon emission monitoring method provided by the invention.

Detailed Description

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the embodiments of the present application will be briefly described below with reference to the drawings and the description of the embodiments or the prior art, and it is apparent that the following descriptions about the structures of the drawings are only some embodiments of the present application, and other embodiment descriptions can be obtained according to the descriptions of the embodiments without inventive effort for those skilled in the art. It should be noted that the description of these examples is for aiding in understanding the present invention, but is not intended to limit the present invention.

It should be understood that although the terms first and second, etc. may be used herein to describe various objects, these objects should not be limited by these terms. These terms are only used to distinguish one object from another. For example, a first object may be referred to as a second object, and similarly a second object may be referred to as a first object, without departing from the scope of example embodiments of the present application.

It should be understood that for the term "and/or" that may appear herein, it is merely one association relationship that describes an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: three cases of A alone, B alone or both A and B exist; as another example, A, B and/or C, can represent the presence of any one of A, B and C or any combination thereof; for the term "/and" that may appear herein, which is descriptive of another associative object relationship, it means that there may be two relationships, e.g., a/and B, it may be expressed that: the two cases of A and B exist independently or simultaneously; in addition, for the character "/" that may appear herein, it is generally indicated that the context associated object is an "or" relationship.

Example 1

As shown in fig. 1, the system for monitoring carbon emissions of enterprises provided in this embodiment includes, but is not limited to, a data collection layer, a data transmission layer, an edge calculation layer, a data administration fusion layer, a data analysis modeling layer, and a data interaction application layer, which are arranged from bottom to top, where the data collection layer includes a plurality of data collection devices arranged in the manufacturing enterprises, the data collection devices include, but are not limited to, a direct emission gas collection device, a direct emission water dissolution collection device, and/or an indirect emission collection device, where the direct emission gas collection device is used for detecting, collecting, and reporting greenhouse gas content data in the gas environment (which directly reflects how much greenhouse gas is generated in the industrial production process and directly emitted to the air), and the direct emission water dissolution collection device is used for detecting, collecting, and reporting carbon content data in the water environment (which directly reflects how much oil and hydrocarbons are dissolved in the water and include a water-soluble greenhouse gas portion), and the indirect emission device is used for detecting, collecting, and reporting how much greenhouse gas content data in the water environment (which directly reflects how much carbon is emitted to the manufacturing enterprises use the carbon emissions). The aforementioned greenhouse gases include, but are not limited to, seven types of greenhouse gases including carbon dioxide, nitrous oxide, methane, sulfur hexafluoride, nitrogen trifluoride, hydrofluorocarbon, and perfluorocarbon (according to the teachings of "general rules for accounting and reporting of greenhouse gas emissions" of industrial enterprises, GB/T32150-2015, and "kyoto protocol" by United nations).

The data acquisition equipment can be arranged after knowing the products produced by enterprises and the process characteristics thereof, and can be combined with public auxiliary systems (such as a water system, a gas system, a drainage system, a thermodynamic system, a ventilation and dust removal system and the like) or combined with the characteristics of the geographic position, the size, the shape and the like of related buildings/production workshops to identify and arrange specific acquisition points. After the acquisition point is identified, the system can be controlled in a scattered manner through Internet of things IoT (Internet of Things) equipment, a programmable logic controller (Programmable Logic Controller, PLC)Systems (Distributed Control System, DCS) orData acquisitionAnd a monitoring control system (Supervisory Control And Data Acquisition, SCADA) and the like, and according to the industry characteristics of manufacturing enterprises, one of the acquisition methods is selected according to local conditions, and after the off-line data acquisition equipment is well installed, the energy consumption data related to the carbon emission source can be automatically stored. Specifically, when the data acquisition device includes a direct exhaust gas acquisition device, the direct exhaust gas acquisition device includes, but is not limited to, a fixed greenhouse gas detection sensor and/or an unmanned aerial vehicle-mounted greenhouse gas detection sensor, wherein the fixed greenhouse gas detection sensor is used for detecting, acquiring and reporting greenhouse gas content data in a gas environment where the fixed greenhouse gas detection sensor is located at a fixed position, and the unmanned aerial vehicle-mounted greenhouse gas detection sensor is used for detecting, acquiring and reporting greenhouse gas content data in the gas environment where the unmanned aerial vehicle is located by means of unmanned aerial vehicle inspection in a fixed airspace. When the data acquisition equipment comprises direct discharge water dissolution acquisition equipment, the direct discharge water dissolution acquisition equipment comprises, but is not limited to, an in-water oil and hydrocarbon online analyzer and/or a carbon dioxide water quality sensor, wherein the in-water oil and hydrocarbon online analyzer is used for detecting, acquiring and reporting content data of hydrocarbon, oil and Volatile Organic Compounds (VOCs) in the water environment, and the carbon dioxide water quality sensor is used for detecting, acquiring and reporting content data of dissolved carbon dioxide in the water environment. And when the data acquisition equipment comprises indirect emission acquisition equipment, the indirect emission acquisition equipment comprises, but is not limited to, an ammeter, a water meter, a gas meter and/or the like, wherein the ammeter is used for detecting, acquiring and reporting the electricity resource use data of the manufacturing enterprise, the water meter is used for detecting, acquiring and reporting the water resource use data of the manufacturing enterprise, and the gas meter is used for detecting, acquiring and reporting the natural gas resource use data of the manufacturing enterprise.

The data transmission layer is used for transmitting the reported data from the data acquisition equipment to the edge calculation layer. Specifically, the data transmission layer includes, but is not limited to, internet of things related devices and/or remote access devices, etc.; the internet of things gateway device is configured to access data reported by the data acquisition device through a local access manner, convert the reported data into a recognizable data format of an upper layer system (i.e., an edge computing layer and above), and finally transmit the reported data after format conversion to the edge computing layer, where the local access manner includes, but is not limited to, a WiFi wireless network access manner (e.g., based on a transmission control protocol, a TCP protocol/user datagram protocol, or a UDP protocol, etc.), a Long Range Radio (LORA) network access manner (e.g., by docking a LORA base station to access a data acquisition device supporting the LORA technology), an ethernet port access manner (e.g., based on a transmission control protocol, a TCP protocol/user datagram protocol, or a UDP protocol, etc.), a serial port access manner (e.g., based on a serial communication protocol or a MODBUS 485 protocol), and/or a USB (Universal Serial Bus, abbreviated as a universal serial bus) port expansion access manner, etc.; the remote access device is configured to provide, when a certain data acquisition device cannot access the Internet of things gateway device through a local access manner, access to a wide area network manner for the certain data acquisition device so as to transmit data reported by the certain data acquisition device to the edge computing layer, where the wide area network manner includes, but is not limited to, a narrowband Internet of things (NB-IoT) manner, a 4G/5G network manner, a beidou satellite communication network manner, and/or an Internet network manner (for example, access to the Internet through a WIFI/ethernet manner). Further, the data transmission layer may transmit data in, but not limited to, JSON (JavaScript object notation ), DL/T (power industry standard), ASCII, or IEEE754 (binary floating point arithmetic standard).

The edge computing layer is used for distributively accessing data uploaded nearby by the data acquisition equipment and preprocessing the accessed data to obtain preprocessing result data, wherein the preprocessing comprises, but is not limited to, sequentially performing data conversion processing, exception analysis processing, data cleaning processing, data integration processing and the like, the data conversion processing is used for converting received data with different formats into data which can be processed subsequently, the exception analysis processing is used for identifying whether abnormal data which does not match preset data specifications and is abnormal due to obvious transmission errors, abnormal data due to obvious equipment function problems and/or abnormal data due to obvious network communication problems exists in the data subjected to the data conversion processing, the data cleaning processing is used for deleting the identified abnormal data, the data integration processing is used for carrying out data gathering, same data calculation and accumulation or different preprocessing results on the data which are obtained from a plurality of different data acquisition equipment and are subjected to the data cleaning processing based on actual production equipment dimension, process dimension, production line dimension and/or workshop dimension and the like, and the comparison result is obtained. As shown in fig. 1, the edge computing layer includes a plurality of edge computing devices, so as to perform the preprocessing in a distributed manner, and solve the access pressure of the mass data acquisition device, which may be disposed near the internet of things device and the data acquisition device, so as to perform the preprocessing on the data uploaded nearby by the data acquisition device. The aim of data cleaning is to reduce the noise of the data and realize the aim of disambiguation operation; the foregoing data gathering, same data accumulation calculation and/or different data integration and comparison processing manner specifically includes that multiple collected data of the same service data are combined and arranged, for example, in actual production work, accumulation calculation of the same data can be performed for multiple collected data of different devices in the same workshop, and gathering statistics of a certain collected data consumption of the workshop level is carried out, and integration and comparison for different collected data are supported, so that the combination and arrangement of the collected data are completed. Specifically, the edge computation layer is based on a message queue telemetry transport protocol (Message Queuing Telemetry Transport, MQTT), a hypertext transport protocol (Hyper Text Transfer Protocol, HTTP, specific data formats are generally Json, and XML and custom data formats are also supported), a limited application protocol (Constrained Application Protocol, coAP, applied to a wireless sensor network protocol), a transmission control protocol (Transmission Control Protocol, TCP), a user datagram protocol (User Datagram Protocol, UDP), and/or an advanced message queue protocol (Advanced Message Queuing Protocol, AMQP) to receive data uploaded by the data acquisition device in the vicinity through the data transport layer.

The data governance fusion layer is configured to receive the preprocessing result data from each edge computing device in the edge computing layer and/or heterogeneous data related to energy consumption and carbon from heterogeneous systems, and sequentially perform pattern matching processing, data assembling processing and data fusion processing on the received data to obtain carbon emission fusion result data, where the heterogeneous systems include, but are not limited to, a vendor relationship management system (i.e., SRM system) of the manufacturing enterprise to obtain heterogeneous data including vendor information and raw material information, a manufacturing execution system (i.e., MES system) to obtain heterogeneous data including product production schedule information, process information and process related execution information, etc.), an energy management system (i.e., EMS system to obtain heterogeneous data including energy consumption information, etc.), an enterprise resource planning system (i.e., ERP system to obtain heterogeneous data including enterprise product information, sales information and transportation information, etc.), a logistics management system (i.e., LMS system to obtain heterogeneous data including raw material information, product cargo information and transportation related information, etc.), an automation system (i.e., OA system to obtain heterogeneous data including low-carbon information, OA and low-carbon information, i.e., sca system to obtain heterogeneous data including sca chain control system and/or complete customer control system to obtain the complete data of the supply chain of the customer, the pattern matching process refers to data identification matching process for actually produced product types based on the data recorded according to product life cycle evaluation LCA (Life Cycle Assessment) related to various industrial production processes and constructed by machine learning technology according to various subdivision industries and/or various subdivision process types, so as to obtain effective received data, the data assembling process refers to arranging and assembling each effective received data into the corresponding organized carbon emission and product LCA carbon emission model, so as to form a calculation basis of enterprise organized carbon emission data and product LCA carbon emission data, and the data fusion process refers to calculation of assembled data so as to obtain carbon emission fusion result data for enabling carbon emission data obtained based on the preprocessing result data to be consistent with carbon emission data obtained based on the heterogeneous data.

Specifically, the preprocessing result data from each edge computing device in the edge computing layer is received, including but not limited to: the preprocessing result data from each edge computing device in the edge computing layer is received based on a Kafka distributed message queue (a distributed message queue developed by the Apache software foundation and having the capability of persisting messages), and the preprocessing result data is forwarded to a computing module for sequentially performing pattern matching processing, data assembling processing and data fusion processing on the received data. And sequentially performing pattern matching processing, data assembling processing and data fusion processing on the received data to obtain carbon emission fusion result data, including but not limited to: and carrying out pattern matching processing, data assembly processing and data fusion processing on the received data in sequence based on a Spark cluster operation framework (a cluster operation framework developed by Apache software foundation) in a distributed manner to obtain carbon emission fusion result data.

The main purpose of the foregoing pattern matching process is to integrate the data of each heterogeneous system to extract effective data, where the specific manner of obtaining the organized carbon emission and product LCA carbon emission model by machine learning techniques according to each subdivision industry and/or each subdivision process type is implemented by performing data identification matching process on the actually produced product type by combining the received data on the basis of the model, so as to obtain the effective received data.

Specific examples of the foregoing data assembling process include: in all carbon emission models for performing carbon emission calculation on energy consumption data (namely, a specific effective receiving data), the energy consumption data can be arranged and assembled into the carbon emission model corresponding to the energy consumption type according to different energy consumption types, so as to form a calculation basis of the carbon emission data for the energy consumption type.

In the foregoing data fusion processing, the carbon emission fusion result data that makes the carbon emission data obtained based on the pretreatment result data coincide with the carbon emission data obtained based on the heterogeneous data may be calculated using a form in which the fitting data is multiplied by its corresponding carbon emission factor (the selection manner of which may be selected by matching according to an actual accounting standard and/or accounting) at the time of the specific calculation; the foregoing two-carbon emission data coincidence means that the phase difference range of the two-carbon emission data belongs to a preset range (for example, the phase difference absolute value is smaller than or equal to a preset threshold value), and may specifically, but not be limited to, taking the average value of the two-carbon emission data as the carbon emission fusion result data; in addition, if the foregoing two carbon emission data do not agree, it is necessary to reselect an appropriate carbon emission factor for recalculation until the two carbon emission data agree, and when only the pretreatment result data or the heterogeneous data is received, it may be directly determined that the data agree and the carbon emission data obtained based on the pretreatment result data or the carbon emission data obtained based on the heterogeneous data is taken as the carbon emission fusion result data.

In detail, the carbon emission factors can be selected according to factors in a built-in massive carbon emission factor library (the specific selection method can be based on factor quality, the data quality is preferably selected to be higher, and the factors of the system are classified into the following 6 classes according to the descending order of the data quality, namely the emission factors, namely the carbon emission factors obtained based on a measurement/material balance method, the carbon emission factors obtained based on experience coefficients of the same process/equipment, the carbon emission factors provided by equipment manufacturers, the carbon emission factors provided by areas, the carbon emission factors provided by countries, the carbon emission factors provided by internationally and the like, and factors can be recommended based on actual equipment, process and system conditions to enable a user to more quickly complete factor matching) to participate in calculation of carbon emission data; and modeling of the LCA product can be adopted, so that an enterprise can perform modeling according to a production process and the like, a carbon emission factor which completely accords with the actual situation of the enterprise is customized, the accuracy and the practicability of data are better, the working efficiency of related operators is improved, the carbon calculation is provided through the data, and according to different countries and different industries, the carbon calculation is performed according to the required calculation boundary of the organizations by referring to the national or international standards, and the carbon calculation of the organizations which accords with the national and enterprise standards is performed.

In addition, the data collected by the data collecting device (this is metering data) and the heterogeneous data obtained in each heterogeneous system (this is calculation data) are considered to have uncertainty, so that the data governance fusion layer is preferably further used for carrying out uncertainty calculation on the carbon emission fusion result data according to a method of merging uncertainty (namely, uncertainty parameters are involved in calculation in the process of calculating carbon emission data), so as to obtain an uncertainty calculation result.

The data analysis modeling layer is used for applying the carbon emission fusion result data to an organization carbon emission data statistical analysis module, a product LCA carbon emission data statistical analysis module, an employee carbon emission data statistical analysis module, a digital twin function module and the like according to different statistical analysis requirements to form carbon emission monitoring data of the manufacturing enterprise.

Specifically, the carbon emission monitoring data includes, but is not limited to, organized carbon emission monitoring data, product life cycle assessment (Life Cycle Assessment, LCA) carbon emission monitoring data, product life cycle inventory (Life Cycle Inventory, LCI) carbon emission monitoring data, employee carbon emission monitoring data, and/or digital twin data, wherein the organized carbon emission monitoring data refers to monitoring data of carbon emission organized according to country division phase ranges and emission types, the product life cycle assessment carbon emission monitoring data refers to monitoring data of carbon emission at each phase in a product full life cycle, the product life cycle inventory carbon emission monitoring data refers to carbon emission data inventory data (which is a further refinement of the product life cycle assessment carbon emission monitoring data) of each production activity in a product full life cycle, the employee carbon emission monitoring data refers to related carbon emission monitoring data generated due to employee behaviors, and the digital twin data refers to data obtained by digitally modeling a factory building and a production line and corresponding carbon emission data. In addition, the organization carbon emission data statistical analysis module, the product LCA carbon emission data statistical analysis module, the employee carbon emission data statistical analysis module and/or the digital twin function module and the like can be obtained by conventional modification in advance based on a corresponding existing statistical analysis method or an existing digital modeling mode.

And the data interaction application layer is used for visually displaying the carbon emission monitoring data. The specific display mode is, but not limited to, visual display by combining the digital twin data, and the carbon layout condition of factories and buildings is displayed by combining a simulation system with 3D, so that enterprises can acquire key information rapidly.

Preferably, for data application, such as carbon emission prediction, optimization suggestion or intelligent control, the data interaction application layer is further configured to perform data analysis and classification on the carbon emission monitoring data through a random forest algorithm and a support vector machine algorithm, automatically match historical conditions with expert suggestions given under the historical conditions according to the data classification result and the previous data precipitation result, and finally obtain carbon neutralization optimization suggestions conforming to enterprise data and current states. Of course, the future carbon emission can be predicted through data analysis; the carbon reduction prediction can be performed only by inputting parameters by a user, so that enterprises can be well helped to finish the carbon emission reduction stage targets; and the intelligent remote control can be performed on related equipment, namely, when a user is not in an enterprise, the equipment can be ensured to operate normally, and the carbon emission is controllable. The data interaction application layer is also used for predicting key indexes and data influencing the carbon arrival peak and dynamically simulating the future, and continuously giving out optimization adjustment suggestions in the whole process so as to provide references and guidance for strategic targets and management of the manufacturing type enterprises and ensure that the enterprises can achieve the double-carbon targets. In addition, the carbon emission monitoring data and the like can be fed back to a company database and a third party database so as to serve as historical reference data and reference data of related industries, data are laid for intelligent suggestions of enterprises in other stages and other same industries, and meanwhile, the matching degree coefficient with factor library data can be automatically built according to actual data, so that the enterprises can more efficiently, accurately and accord with actual production conditions when selecting carbon emission factors of a certain substance from a mass factor library.

In summary, the enterprise carbon emission monitoring system provided by the embodiment has the following technical effects:

(1) The embodiment provides a new scheme capable of automatically acquiring, analyzing and displaying carbon emission information of a manufacturing enterprise in real time, namely a data acquisition layer, a data transmission layer, an edge calculation layer, a data management fusion layer, a data analysis modeling layer and a data interaction application layer which are arranged from bottom to top, wherein the data acquisition layer comprises a plurality of data acquisition devices which are arranged in the manufacturing enterprise, the calculation accuracy can be greatly improved through an automatic acquisition mode of the data acquisition devices, errors and errors of manual filling are avoided, inaccuracy of manual estimation data can be avoided, meanwhile, the data reported by the acquisition devices and heterogeneous data intelligently combined from a multi-heterogeneous system are directly utilized for carbon check, real-time and accurate data can be well ensured, the efficiency of enterprise operation checking is also improved, and the cost is reduced, so that the method is convenient for practical application and popularization;

(2) For the later data maintenance, whether editing, inquiring or deleting is more convenient, and errors in the manual aspect are not easy to occur;

(3) The method can also help users to make carbon reduction prediction, and well help enterprises to realize carbon neutralization as early as possible.

Example two

As shown in fig. 2, the present embodiment further provides an enterprise carbon emission monitoring method based on the technical scheme of the enterprise carbon emission monitoring system provided in the first embodiment, including, but not limited to, the following steps S1 to S5.

S1, collecting data through a plurality of data collecting devices distributed in a manufacturing enterprise, wherein the data collecting devices comprise direct discharge gas collecting devices, direct discharge water dissolution collecting devices and/or indirect discharge collecting devices, the direct discharge gas collecting devices are used for detecting, collecting and reporting greenhouse gas content data in a gas environment, the direct discharge water dissolution collecting devices are used for detecting, collecting and reporting carbon-containing compound content data in a water environment, and the indirect discharge collecting devices are used for detecting, collecting and reporting production resource use data of the manufacturing enterprise.

S2, data uploaded nearby by the data acquisition equipment are accessed in a distributed mode through edge computing equipment, preprocessing is carried out on the accessed data to obtain preprocessing result data, wherein preprocessing comprises data conversion processing, anomaly analysis processing, data cleaning processing and data integration processing which are sequentially carried out, the data conversion processing is to convert received data with different formats into data which can be processed subsequently, the anomaly analysis processing is to identify whether abnormal data which does not match preset data specifications and is abnormal in data due to obvious transmission errors and/or abnormal in data due to obvious equipment function problems and/or abnormal in data due to obvious network communication problems exists in the data subjected to the data conversion processing, the data cleaning processing is to delete the identified abnormal data, the data integration processing is to carry out data gathering, identical data accumulation calculation and/or different integration processing on the data which are from a plurality of different data acquisition equipment and are subjected to the data cleaning processing based on actual production equipment dimension, process dimension, production line dimension and/or workshop dimension, and the preprocessing result is obtained.

S3, receiving preprocessing result data from each edge computing device and/or heterogeneous data which are related to energy consumption and carbon and come from heterogeneous systems, and sequentially carrying out pattern matching processing, data assembling processing and data fusion processing on the received data to obtain carbon emission fusion result data, wherein the heterogeneous systems comprise a supplier relation management system, a manufacturing execution system, an energy management system, an enterprise resource planning system, a logistics management system, an office automation system, a data acquisition and monitoring control system and/or a supply chain management system of the manufacturing enterprise, the pattern matching processing refers to carrying out data identification matching processing on actually produced product types according to each subdivision industry and/or each subdivision process type on the basis of a carbon emission organization and a carbon emission model of the product, the data assembling processing refers to carrying out carbon emission calculation on the data of the corresponding carbon emission organization and the carbon emission organization, the data is based on the data of the carbon emission fusion model of the product, and the data is obtained by carrying out calculation on the carbon emission fusion result data, and the carbon emission fusion data is based on the obtained carbon emission fusion result data.

S4, according to different statistical analysis requirements, the carbon emission fusion result data is applied to an organization carbon emission data statistical analysis module, a product LCA carbon emission data statistical analysis module, an employee carbon data statistical analysis module and/or a digital twin function module to form carbon emission monitoring data of the manufacturing enterprise.

And S5, visually displaying the carbon emission monitoring data.

The specific technical details and technical effects of the foregoing monitoring method provided in this embodiment may be derived by referring to the enterprise carbon emission monitoring system described in the first embodiment, and are not described herein.

Finally, it should be noted that: the foregoing description is only of the preferred embodiments of the invention and is not intended to limit the scope of the invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Thanks for the team work and contribution from Yunjin Tong and Shengtao Gao at CarbonSense Technologies.