Pollution reduction and carbon reduction management method and system based on multi-chain cooperation

Abstract

The invention relates to a pollution and carbon reduction management method and system based on multi-chain cooperation. According to the method, a multi-chain collaborative database can be constructed on the basis of total element data corresponding to an energy value chain, a material supply chain and a production process chain, so that multi-chain collaborative analysis is carried out in combination with the multi-chain collaborative database; thirdly, innovatively constructing a multi-chain interactive quantitative coupling model and carbon emission models corresponding to different collaborative modes based on data types contained in all total element data; respectively acquiring carbon emission data corresponding to an energy value chain, carbon emission data corresponding to a material supply chain and carbon emission data corresponding to a production process chain, so as to determine management data based on the carbon emission data, a multi-chain collaborative database, a multi-chain interactive quantitative coupling model, carbon emission models and a dynamic multi-target optimization algorithm; multi-chain collaborative intelligent emission reduction is realized to dynamically generate an optimal management scheme, and the pollution reduction and carbon reduction efficiency and the comprehensive pollution control cost of the industrial park can be improved.

Description

Technical Field

[0001] The embodiments in this specification belong to the engineering and cross-disciplinary field of industrial systems, and in particular relate to a pollution reduction and carbon reduction management method and system based on multi-chain collaboration. Background Technology

[0002] Pollution reduction and carbon reduction technologies in industrial parks refer to the key pathways for achieving green, low-carbon, and high-quality development by using systematic and integrated technical means and management measures to synergistically reduce pollutant and greenhouse gas emissions.

[0003] Currently, pollution reduction and carbon reduction technologies in industrial parks mainly utilize traditional optimization models to manage single chains, such as energy-saving renovations of the energy value chain or end-of-pipe treatment management of the production process chain. These models suffer from insufficient support for optimization decisions and difficulty in balancing the conflicting objectives of economic efficiency, pollution reduction, and carbon reduction, thereby impacting the efficiency of pollution reduction and carbon reduction in industrial parks. Summary of the Invention

[0004] The embodiments of this disclosure propose a pollution reduction and carbon reduction management method and system based on multi-chain collaboration.

[0005] In a first aspect of this disclosure, a pollution reduction and carbon reduction management method based on multi-chain collaboration is provided. The method includes determining full-factor data corresponding to the energy value chain, the material supply chain, and the production process chain, and constructing a multi-chain collaborative database based on all full-factor data. The method also includes constructing a three-chain interaction quantitative coupling model based on the data types contained in all full-factor data. This three-chain interaction quantitative coupling model has parameter constraints corresponding to the energy value chain, the material supply chain, and the production process chain. Furthermore, based on the three-chain interaction quantitative coupling model, the method also includes constructing carbon emission models corresponding to the energy priority collaboration mode, the material recycling collaboration mode, and the low-carbon process collaboration mode, respectively. In addition, the method includes acquiring carbon emission data corresponding to the energy value chain, the material supply chain, and the production process chain, and determining pollution reduction and carbon reduction management data based on the duration of each carbon emission data, the multi-chain collaborative database, the three-chain interaction quantitative coupling model, and all carbon emission models.

[0006] In a second aspect of this disclosure, a pollution reduction and carbon reduction management system based on multi-chain collaboration is provided. The system includes a database establishment module configured to determine full-factor data corresponding to the energy value chain, the material supply chain, and the production process chain, and to construct a multi-chain collaborative database based on all full-factor data. The system also includes a coupling model establishment module configured to construct a three-chain interaction quantitative coupling model based on the data types contained in all full-factor data. This three-chain interaction quantitative coupling model has parameter constraints corresponding to the energy value chain, the material supply chain, and the production process chain. The system further includes a collaboration mode determination module configured to construct carbon emission models corresponding to an energy-priority collaboration mode, a material recycling collaboration mode, and a low-carbon process collaboration mode, respectively, based on the three-chain interaction quantitative coupling model. In addition, the system also includes a management data determination module, which is configured to acquire carbon emission data corresponding to the energy value chain, carbon emission data corresponding to the material supply chain, and carbon emission data corresponding to the production process chain, and determine pollution reduction and carbon reduction management data based on the duration of each carbon emission data, the multi-chain collaborative database, the three-chain interaction quantitative coupling model, and all carbon emission models.

[0007] In a third aspect of this disclosure, a computer program product is provided, comprising a computer program that is executed by a processor to implement the method according to the first aspect.

[0008] In a fourth aspect of this disclosure, a machine-readable storage medium is provided. The machine-readable storage medium stores machine-executable instructions, which are executed by a processor to implement the method provided according to a first aspect of this disclosure.

[0009] It should be understood that the description in the Summary of the Invention section is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0010] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein: Figure 1 A schematic diagram of an example environment in which some embodiments of this disclosure may be implemented is shown; Figure 2 A flowchart illustrating a pollution reduction and carbon reduction management method based on multi-chain collaboration, representing some embodiments of this disclosure, is shown. Figure 3A block diagram of a multi-chain collaborative pollution reduction and carbon reduction management system based on some embodiments of this disclosure is shown; and Figure 4 A block diagram of an electronic device that can implement several embodiments of the present disclosure is shown. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0012] The terms “comprising” and “having”, and any variations thereof, in this specification, claims, and the foregoing drawings are intended to cover a non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. Depending on the context, the word “if” as it applies herein may be interpreted as “when”, “when”, “in response to determination”, or “in response to detection”.

[0013] As mentioned above, current pollution reduction and carbon reduction technologies in industrial parks mainly utilize traditional optimization models to manage single chains, such as energy-saving retrofits of the energy value chain or end-of-pipe treatment management of the production process chain, lacking quantitative analysis of the synergistic effects of multiple chains. Furthermore, existing optimization algorithms used for managing single chains have failed to effectively integrate heterogeneous data from multiple sources, such as energy consumption, material flow, and production processes. This results in insufficient dynamic optimization decision support and difficulty in balancing conflicting objectives such as economic efficiency, pollution reduction, and carbon reduction, thus impacting the efficiency of pollution reduction and efficiency improvement in industrial parks.

[0014] To address this, embodiments of this disclosure propose a pollution reduction and carbon reduction management method based on multi-chain collaboration. The method includes determining full-factor data corresponding to the energy value chain, the material supply chain, and the production process chain, and constructing a multi-chain collaborative database based on all full-factor data. The method further includes constructing a three-chain interaction quantitative coupling model based on the data types contained in all full-factor data. This model has parameter constraints corresponding to the energy value chain, the material supply chain, and the production process chain. The method also includes constructing carbon emission models corresponding to the energy priority collaboration mode, the material recycling collaboration mode, and the low-carbon process collaboration mode, respectively, based on the three-chain interaction quantitative coupling model. Furthermore, the method includes acquiring carbon emission data corresponding to the energy value chain, the material supply chain, and the production process chain, and determining pollution reduction and carbon reduction management data based on the duration of each carbon emission data, the multi-chain collaborative database, the three-chain interaction quantitative coupling model, and all carbon emission models.

[0015] In this way, multi-chain collaborative analysis can be carried out based on the full-element data corresponding to the energy value chain, material supply chain and production process chain, respectively. It innovatively constructs a multi-chain interactive quantitative coupling model and carbon emission models corresponding to different collaborative modes. Based on multiple carbon emission data and dynamic multi-objective optimization algorithms, management data is determined. This not only realizes intelligent emission reduction through multi-chain collaboration and dynamically generates the optimal management plan, but also improves the efficiency of pollution reduction and carbon reduction in industrial parks and reduces the overall pollution control cost.

[0016] Figure 1 Schematic diagrams are shown illustrating example environments in which some embodiments of this disclosure can be implemented. For example... Figure 1As shown, the example environment 100 may include a data acquisition terminal 101, which is used to collect multi-source data related to energy consumption, material flow and production processes generated by the industrial park during the process of pollution reduction and carbon reduction. Here, the data acquisition terminal 101 can include an energy management system, a production execution system (ERP), an environmental monitoring system, and equipment sensors installed on the equipment within the industrial park. By focusing on the needs of the three chains (energy chain, production chain, and industrial chain) collaborative optimization, the energy management system can collect core data of all elements, including data on gas turbine power generation, purchased electricity, green electricity substitution and emission reduction, energy storage system charging and discharging, refrigeration system power consumption, carbon emissions, and carbon emissions. The production execution system can collect core data of all elements, including data on time-of-use electricity prices, total raw material input, raw material carbon footprint, solid waste reuse rate, solid waste end-of-life treatment volume, actual output material volume, and solid waste treatment cost. The environmental monitoring system can collect core data of all elements, including data on process-level energy consumption intensity, pollutant generation, pollutant emission factors, end-of-life treatment efficiency, carbon emission intensity, and total emissions. The equipment sensors can collect core data of all elements, including data on the efficiency of energy conversion equipment such as boilers and waste heat generator sets, equipment start-up and shutdown status timing data, gas turbine operating load, and end-of-life treatment equipment operating parameters, to ensure comprehensive coverage of key parameters of the three chains' operation.

[0017] It is understood that the data acquisition terminal 101 can acquire all-element core data collected by the energy management system, production execution system, environmental monitoring system and equipment sensors according to the production cycle of the industrial park (such as hourly). By establishing a communication connection with the processing terminal 102 in the example environment 100, it can send the all-element core data collected by the energy management system, production execution system, environmental monitoring system and equipment sensors under multiple historical production cycles to the processing terminal 102. Of course, it can also send the all-element core data (i.e., real-time data) collected by the energy management system, production execution system, environmental monitoring system and equipment sensors under the current production cycle to the processing terminal 102, and is not limited to this.

[0018] Furthermore, after acquiring the core data of all elements collected by the energy management system, production execution system, environmental monitoring system, and equipment sensors from multiple historical production cycles, the processing terminal 102 can define the collaborative scope of the energy chain (such as covering energy procurement, conversion, storage, and consumption), material chain (such as raw material input, production flow, solid waste recycling, and end-of-pipe treatment), and process chain (such as including the operating parameters and pollutant emission nodes of each production process). It can then divide the core data of all elements collected by the energy management system, production execution system, environmental monitoring system, and equipment sensors from multiple historical production cycles into core data of all elements corresponding to the energy value chain, core data of all elements corresponding to the material supply chain, and core data of all elements corresponding to the production process chain. Based on all the core data of all elements, a multi-chain collaborative database covering the entire process of energy, materials, and processes can be constructed. Here, the total element data corresponding to the energy value chain can include data types such as time-of-use electricity prices, gas turbine power generation, purchased electricity, energy storage system charging and discharging, production process power consumption, refrigeration system power consumption, carbon emissions, and the efficiency of energy conversion equipment such as boilers and waste heat generators, as well as historical datasets corresponding to each data type. The total element data corresponding to the material supply chain can include data types such as total raw material input, raw material carbon footprint, solid waste reuse rate, solid waste end-of-life treatment volume, actual output material volume, and solid waste treatment cost, as well as historical datasets corresponding to each data type. The total element data corresponding to the production process chain can include data types such as process-level energy consumption intensity, pollutant generation, pollutant emission factors, end-of-life treatment efficiency, carbon emission intensity, equipment start-up and shutdown status time-series data, and total emissions, as well as historical datasets corresponding to each data type.

[0019] In addition, the processing terminal 102 can fully capture the dynamic correlation and feedback mechanism between the energy chain, material chain and process chain by utilizing the application of system dynamics theory, based on the data types contained in all the full-element data. Furthermore, by analyzing the correlation between energy conversion efficiency and power consumption of process equipment, the coupling between material recycling cost and energy consumption, and the mutual influence between process emission intensity and material purity, a three-chain interactive quantitative coupling model is constructed, which includes energy chain balance, material chain recycling, and process chain pollution-carbon emission coupling. Here, the three-chain interactive quantitative coupling model can be understood as including model equations corresponding to the energy chain balance model, model equations corresponding to the material chain circulation model, and model equations corresponding to the process chain pollution-carbon emission coupling model. Each model equation has corresponding parameter constraints. For example, the model equation corresponding to the energy chain balance model has energy chain parameter constraints, the model equation corresponding to the material chain circulation model has material chain parameter constraints, and the model equation corresponding to the process chain pollution-carbon emission coupling model has process chain parameter constraints. In addition, the three-chain interactive quantitative coupling model also needs to meet time constraints. For example, the data types collected by the equipment sensors need to be updated in real time according to the production cycle of the industrial park (including other data types) to ensure that the three-chain interactive quantitative coupling model is synchronized with the actual production rhythm of the industrial park.

[0020] Furthermore, the processing terminal 102 can also construct a multi-chain collaborative operation mode based on the three-chain interaction quantitative coupling model, building upon the traditional single-chain operation strategy. By quantifying the multi-chain interaction characteristics under different collaborative modes, carbon emission models corresponding to the energy-priority collaborative mode, the material recycling collaborative mode, and the process low-carbon collaborative mode can be constructed respectively. Here, each carbon emission model has a corresponding model equation, and each model equation must satisfy time-series dynamic constraints and data boundary constraints. For example, each carbon emission model needs to be updated in real time on an hourly basis, excluding non-production carbon emissions from the park's office area and living area (only involving three-chain production-related aspects); also, the carbon emission model equation corresponding to the energy-priority collaborative mode needs to consider the carbon emissions of gas-fired internal combustion engines, the end-of-pipe treatment volume in the carbon emission model equation corresponding to the material recycling collaborative mode needs to be calculated separately for solid and liquid types, and the process emission intensity in the carbon emission model equation corresponding to the process low-carbon collaborative mode needs to be calculated according to different production process periods.

[0021] Furthermore, the processing terminal 102 can acquire carbon emission data corresponding to the energy value chain, the material supply chain, and the production process chain, respectively. Based on the duration of each carbon emission data, a multi-chain collaborative database, a three-chain interactive quantitative coupling model, and all carbon emission models, it utilizes a multi-objective optimization algorithm to determine pollution reduction and carbon reduction management data. Understandably, the processing terminal 102 can feed back the determined pollution reduction and carbon reduction management data to the industrial park's management personnel in real time, enabling them to promptly optimize pollution reduction and carbon reduction management, thereby maximizing the overall pollution reduction and carbon reduction efficiency of the industrial park while minimizing comprehensive pollution control costs.

[0022] Of course, the processing terminal 102 in some embodiments of this disclosure can also directly establish communication connections with the energy management system, production execution system (i.e., ERP), environmental monitoring system and equipment sensors installed on the equipment in the industrial park to obtain full-element core data collected by the energy management system, production execution system, environmental monitoring system and equipment sensors under different production cycles, and is not limited thereto.

[0023] In this way, multi-chain collaborative analysis can be carried out based on the full-element data corresponding to the energy value chain, material supply chain and production process chain, respectively. It innovatively constructs a multi-chain interactive quantitative coupling model and carbon emission models corresponding to different collaborative modes. Based on multiple carbon emission data and dynamic multi-objective optimization algorithms, management data is determined. This not only realizes intelligent emission reduction through multi-chain collaboration and dynamically generates the optimal management plan, but also improves the efficiency of pollution reduction and carbon reduction in industrial parks and reduces the overall pollution control cost.

[0024] It should be understood that the architecture and functionality in example environment 100 are described for illustrative purposes only and do not imply any limitation on the scope of this disclosure. Embodiments of this disclosure can also be applied to other environments with different architectures and / or functionalities.

[0025] Figure 2 A flowchart of a pollution reduction and carbon reduction management method based on multi-chain collaboration according to some embodiments of the present disclosure is shown. Method 200 may, for example, be derived from... Figure 1 The processing terminal in the example environment shown executes the commands. Figure 2 As shown in box 202, method 200 can determine the total factor data corresponding to the energy value chain, the total factor data corresponding to the material supply chain, and the total factor data corresponding to the production process chain, and construct a multi-chain collaborative database based on all the total factor data. Here, the processing terminal can be... Figure 1The example environment shown uses a data acquisition terminal to obtain core data on all elements collected by the energy management system, production execution system, environmental monitoring system, and equipment sensors from multiple historical production cycles. By clarifying the collaborative scope of the energy chain (e.g., covering energy procurement, conversion, storage, and consumption), material chain (e.g., raw material input, production flow, solid waste recycling, and end-of-pipe treatment), and process chain (e.g., including operating parameters and pollutant emission points of each production process), the core data on all elements collected by the energy management system, production execution system, environmental monitoring system, and equipment sensors from multiple historical production cycles is divided into core data on all elements corresponding to the energy value chain, core data on all elements corresponding to the material supply chain, and core data on all elements corresponding to the production process chain. Based on all this core data, a multi-chain collaborative database covering the entire process of energy, materials, and processes is constructed. Understandably, the total data corresponding to the energy value chain can include data types such as time-of-use electricity prices, gas turbine power generation, purchased electricity, energy storage system charging and discharging, production process power consumption, refrigeration system power consumption, carbon emissions, and the efficiency of energy conversion equipment such as boilers and waste heat generators, as well as historical datasets corresponding to each data type. The total data corresponding to the material supply chain can include data types such as total raw material input, raw material carbon footprint, solid waste reuse rate, solid waste end-of-life treatment volume, actual output material volume, and solid waste treatment cost, as well as historical datasets corresponding to each data type. The total data corresponding to the production process chain can include data types such as process-level energy consumption intensity, pollutant generation, pollutant emission factors, end-of-life treatment efficiency, carbon emission intensity, equipment start-up and shutdown status time-series data, and total emissions, as well as historical datasets corresponding to each data type.

[0026] By constructing a multi-chain collaborative database covering the entire process of energy, materials, and technology, comprehensive data support can be provided for subsequent quantitative analysis, ensuring the accuracy and timeliness of optimization decisions and solving the problems of data fragmentation and incomplete coverage in traditional single-chain optimization.

[0027] In some implementations, when constructing a multi-chain collaborative database based on all full-factor data, the processing terminal can determine the standard deviation and mean corresponding to each data type based on the historical datasets corresponding to each data type within all full-factor data. Here, the historical datasets corresponding to each data type can include all data for that data type across all historical production cycles. The corresponding mean is obtained by substituting all data into the mean calculation formula shown below: In the above formula, This corresponds to the mean, and n corresponds to the total number of data points. This corresponds to the i-th data.

[0028] Furthermore, the standard deviation can also be obtained by substituting all the data and the mean into the standard deviation calculation formula shown below: In the above formula, This corresponds to the standard deviation. This corresponds to the mean, and n corresponds to the total number of data points. This corresponds to the i-th data.

[0029] Subsequently, the processing terminal can perform outlier removal on the historical dataset corresponding to each data type based on the standard deviation and mean of each data type. Here, the corresponding outlier intervals can be obtained by substituting the standard deviation and mean of each data type into the outlier determination formula shown below: In the above formula, Corresponding to outliers, This corresponds to the standard deviation. This corresponds to the mean.

[0030] It is understandable that if any data in the historical dataset corresponding to each data type falls within the outlier range mentioned above, then that data can be considered an outlier and removed; if any data in the historical dataset corresponding to each data type does not fall within the outlier range mentioned above, then that data can be retained.

[0031] Subsequently, the processing terminal can determine the set of extreme values ​​from each historical dataset after outlier removal, and normalize the corresponding historical datasets after outlier removal based on each set of extreme values. It is understandable that, since each set of all-factor data contains data of different dimensions, such as data related to energy consumption, solid waste treatment costs, and total emissions, in order to provide complete and accurate data support for the subsequent establishment of the three-chain interaction quantitative coupling model and dynamic optimization decision-making, the maximum and minimum values ​​can be determined from each historical dataset after outlier removal. These maximum and minimum values ​​can then be used as the set of extreme values. The data and the set of extreme values ​​from each historical dataset after outlier removal are then substituted into the normalization calculation formula shown below to obtain the normalized data corresponding to the corresponding data: In the above formula, The corresponding data is normalized, and x represents each data point in each historical dataset after outlier removal. This corresponds to the maximum value in the set of extreme values. This corresponds to the minimum value in the set of extreme values.

[0032] Subsequently, the processing terminal can construct a multi-chain collaborative database based on all historical datasets after normalization. It is understandable that, to ensure the real-time performance and reliability of the multi-chain collaborative database, it can also be updated according to the production cycle of the industrial park, and is not limited to this.

[0033] In box 204, method 200 can construct a three-chain interactive quantitative coupling model based on the data types contained in all full-element data. This model has parameter constraints corresponding to the energy value chain, the material supply chain, and the production process chain. Understandably, the processing terminal fully captures the dynamic correlations and feedback mechanisms between the energy chain, material chain, and process chain by utilizing system dynamics theory. It constructs a three-chain interactive quantitative coupling model that includes energy chain balance, material chain circulation, and process chain pollution-carbon emission coupling by analyzing the correlation between energy conversion efficiency and process equipment power consumption, the coupling between material recycling costs and energy consumption, and the mutual influence between process emission intensity and material purity. This model encompasses energy chain balance, material chain circulation, and process chain pollution-carbon emission coupling, forming a coupled equation system covering energy-carbon footprint mapping, material-environmental cost transmission, and process-emission intensity feedback. This not only overcomes the static analysis limitations of traditional single-chain models but also provides a quantitative benchmark for cross-chain synergistic effects in multi-objective optimization. Here, the three-chain interactive quantitative coupling model can be understood as including model equations corresponding to the energy chain balance model, model equations corresponding to the material chain circulation model, and model equations corresponding to the process chain pollution-carbon emission coupling model. Each model equation has corresponding parameter constraints. For example, the model equation corresponding to the energy chain balance model has energy chain parameter constraints, the model equation corresponding to the material chain circulation model has material chain parameter constraints, and the model equation corresponding to the process chain pollution-carbon emission coupling model has process chain parameter constraints. In addition, the three-chain interactive quantitative coupling model also needs to meet time series constraints. For example, the data types collected by equipment sensors (time-of-use electricity price and equipment start-up and shutdown status time series data, etc.) need to be updated in real time according to the production cycle of the industrial park (including other data types) to ensure that the three-chain interactive quantitative coupling model is synchronized with the actual production rhythm of the industrial park.

[0034] In some implementations, the three-chain interaction quantitative coupling model includes an energy chain balance model, a material chain circulation model, and a process chain pollution-carbon emission coupling model. The energy chain balance model includes data types such as gas turbine power generation, purchased electricity, energy storage system charging and discharging, production process power consumption, and refrigeration system power consumption. The material chain circulation model includes data types such as total raw material input, solid waste reuse rate, end-of-pipe treatment volume, and total output material volume. The process chain pollution-carbon emission coupling model includes data types such as pollutant generation, end-of-pipe treatment efficiency, carbon emission intensity, and total emissions. In one example, the model equation corresponding to the energy chain balance model can be expressed as shown in the following expression: In the above formula, This corresponds to the power generation of the gas turbine unit. This corresponds to purchased electricity. This corresponds to the charging and discharging capacity of the energy storage system. This corresponds to the power consumption of the production process. This corresponds to the power consumption of the refrigeration system. The energy chain parameter constraints can include the constraint expressions shown below: In the above constraint expression, This corresponds to the rated power generation capacity of the gas turbine unit (to avoid overload operation). This corresponds to the upper limit of the discharge capacity of the energy storage device (expressed as a negative value). This corresponds to the upper limit of the charging capacity of energy storage devices (represented as a positive value). This corresponds to the minimum operating power of the production process.

[0035] The model equations corresponding to the material chain circulation model can be expressed as shown in the following expression: In the above formula, This corresponds to the total amount of raw material input. This corresponds to the solid waste recycling rate. This corresponds to the amount of data processed at the end of the process. This corresponds to the total output material quantity. The material chain parameter constraints can include the constraint expressions shown below: The model equations corresponding to the process chain pollution-carbon emission coupling model can be expressed as shown in the following expression: In the above formula, Corresponding to the amount of pollutants generated, This corresponds to end-of-pipe treatment efficiency. Corresponding to carbon emission intensity, This corresponds to the total emissions. The process chain parameter constraints can include the constraint expressions shown below: In box 206, method 200 can construct carbon emission models corresponding to the energy-priority collaborative mode, the material circulation collaborative mode, and the process low-carbon collaborative mode, respectively, based on the three-chain interaction quantification coupling model. It can be understood that, to address the lack of synergistic effects caused by the fragmentation of energy, material, and process chains under the traditional single-chain optimization mode, the processing terminal constructs a multi-chain collaborative operation mode based on the traditional single-chain operation strategy. By quantifying the multi-chain interaction characteristics under different collaborative modes, it constructs the energy-priority collaborative mode, the material circulation collaborative mode, and the process low-carbon collaborative mode, respectively. These three collaborative modes do not have pre-defined fixed applicable scenarios and can be freely triggered for switching. Carbon emission models corresponding to the energy-priority collaborative mode, the material circulation collaborative mode, and the process low-carbon collaborative mode can be established respectively. By quantifying the impact of energy consumption, material flow, and process operation on carbon emissions under different modes, model support is provided for quantifying the emission reduction effect after real-time switching.

[0036] Here, each carbon emission model has a corresponding model equation, and each model equation must satisfy both time-series dynamic constraints and data boundary constraints. The time-series dynamic constraints mean that each carbon emission model must update its carbon emission calculation results according to the industrial park's production cycle (in hours) to avoid data lag. The data boundary constraints mean that only the production-related links of the three-chain system need to be covered, excluding non-production carbon emissions from the park's office and living areas. Furthermore, in the carbon emission model equations corresponding to the energy priority coordination model, it is necessary to focus on calculating carbon emissions from the entire energy chain (including indirect carbon emissions from purchased electricity, direct carbon emissions from gas combustion, and carbon emissions from energy storage system losses). Additionally, it is necessary to include gas... The carbon emissions from internal combustion engines are included in the total life-cycle carbon emissions of gas combustion (including gas extraction and transportation). In the carbon emission model equations corresponding to the material recycling synergy model, it is necessary to distinguish between the "emission reduction in the recycling and utilization stage" and the "carbon emissions in the end-of-life disposal stage" of solid waste and calculate them separately. At the same time, the end-of-life treatment volume needs to be calculated separately for solid and liquid types. In the carbon emission model equations corresponding to the low-carbon process synergy model, it is necessary to calculate process emissions in segments according to the production process (such as calculating carbon emissions separately for the raw material pretreatment stage, the core reaction stage, and the finished product processing stage). For example, the process emission intensity needs to be calculated in segments according to different production stages, so as to ensure that the carbon emission data of each mode is accurately matched with the monitoring parameters of the corresponding chain.

[0037] In one example, the carbon emission model equation corresponding to the Energy Priority Convergence (EPC) model can be expressed as shown in the following expression: In the above formula, This corresponds to the carbon emissions of electricity purchased from the external power grid. This corresponds to the carbon emissions of gas turbine units. This corresponds to the carbon emissions of the energy storage system. This corresponds to the emission reduction from green electricity substitution. This corresponds to the carbon emission threshold.

[0038] The carbon emission model equation corresponding to the Material Cyclic Co-operation (MCC) model can be expressed as shown in the following expression: In the above formula, This corresponds to the solid waste recycling rate. This corresponds to the total amount of raw material input. This corresponds to the amount of data processed at the end of the process. This corresponds to the total output material quantity. This corresponds to the resource utilization threshold.

[0039] The carbon emission model equation corresponding to the process low-carbon synergy mode (PLC) can be expressed as shown in the following expression: In the above formula, Corresponding to process emission intensity, This corresponds to the power consumption of the production process. This corresponds to end-of-pipe treatment efficiency. Corresponding to the amount of pollutants generated, This corresponds to the carbon emission intensity threshold.

[0040] In box 208, method 200 acquires carbon emission data corresponding to the energy value chain, the material supply chain, and the production process chain, respectively. Based on the duration of each carbon emission data, a multi-chain collaborative database, a three-chain interactive quantitative coupling model, and all carbon emission models, it determines pollution reduction and carbon reduction management data. Here, the processing terminal can acquire the carbon emission data corresponding to the energy value chain, the material supply chain, and the production process chain in real time through the aforementioned data acquisition terminal. The carbon emission data corresponding to the energy value chain can be the total carbon emissions (which can be directly acquired by the data acquisition terminal or calculated based on the carbon emissions from purchased grid electricity, gas turbine units, energy storage systems, and green electricity substitution, etc., collected by the data acquisition terminal). The carbon emission data corresponding to the material supply chain can be the solid waste recycling rate, and the carbon emission data corresponding to the production process chain can be the amount of pollutants generated. Understandably, the duration of each carbon emission data point can be used to determine whether a certain collaborative mode's continuous exceedance standard has been triggered. After determining whether a particular collaborative mode's continuous exceedance standard has been triggered, a multi-objective optimization algorithm can be used to generate corresponding pollution reduction and carbon reduction management data. For example, when the continuous exceedance standard of the energy priority collaborative mode is triggered, the corresponding pollution reduction and carbon reduction management data may include the proportion of purchased electricity, the operating load of gas turbine units, the amount of green electricity purchased, and the total carbon emissions after regulation. When the continuous exceedance standard of the material recycling collaborative mode is triggered, the corresponding pollution reduction and carbon reduction management data may include the solid waste classification ratio (the percentage of different types of solid waste, such as metal / non-metal each accounting for %), the investment in reuse facilities, the resource utilization rate after regulation, and the amount of end-of-pipe treatment after regulation. When the continuous exceedance standard of the process low-carbon collaborative mode is triggered, the corresponding pollution reduction and carbon reduction management data may include key parameters of the production process, operating parameters of end-of-pipe treatment equipment, the emission intensity of the process after regulation, and the total emissions after regulation.

[0041] In addition, the processing terminal can also feed back the determined pollution reduction and carbon reduction management data to the management personnel of the industrial park in real time, so that the management personnel can optimize the pollution reduction and carbon reduction management in a timely manner, thereby achieving the goal of maximizing the overall pollution reduction and carbon reduction efficiency of the industrial park and minimizing the comprehensive pollution control cost.

[0042] In some implementations, when determining pollution reduction and carbon reduction management data based on the duration of each carbon emission data, a multi-chain collaborative database, a three-chain interactive quantitative coupling model, and all carbon emission models, the processing terminal can determine the total carbon emission threshold, solid waste recycling rate threshold, and pollutant generation threshold based on the multi-chain collaborative database and preset adjustment coefficients. Here, the multi-chain collaborative database includes historical datasets corresponding to total carbon emissions, historical datasets corresponding to solid-liquid recycling rates, and historical datasets corresponding to pollutant generation. The total carbon emissions can be calculated from historical datasets corresponding to carbon emissions from purchased grid electricity, gas turbine units, energy storage systems, and green electricity substitution, which are included in the multi-chain collaborative database. Alternatively, it can be directly obtained from the energy management system included in the data acquisition terminal; it is not limited to these methods.

[0043] In one example, when the processing terminal determines the thresholds for total carbon emissions, solid waste recycling rate, and pollutant generation based on a multi-chain collaborative database and preset adjustment coefficients, it can determine the mean and standard deviation corresponding to total carbon emissions, solid waste recycling rate, and pollutant generation, respectively, based on the multi-chain collaborative database. It is understood that historical datasets corresponding to total carbon emissions, solid waste recycling rate, and pollutant generation from the multi-chain collaborative database can be substituted into the aforementioned mean and standard deviation calculation formulas to obtain the mean and standard deviation corresponding to total carbon emissions, solid waste recycling rate, and pollutant generation, respectively.

[0044] Next, the processing terminal can determine the total carbon emission threshold based on a preset adjustment coefficient and the mean and standard deviation corresponding to the total carbon emissions. Here, the preset adjustment coefficient, the mean and standard deviation corresponding to the total carbon emissions can be substituted into the threshold calculation formula shown below to obtain the total carbon emission threshold: In the above formula, T corresponds to the total carbon emission threshold. This corresponds to the standard deviation. The value corresponds to the mean, and k corresponds to the preset adjustment coefficient.

[0045] It should be noted that the preset adjustment coefficients in some embodiments of this disclosure can refer to the statistical confidence interval of a normal distribution to cover a reasonable fluctuation range of historical data in the park. Commonly used values ​​are between 1 and 2.5. Different preset adjustment coefficients have corresponding matching historical data distribution characteristics. For example, when the preset adjustment coefficient is 1.0, it corresponds to a 68.27% confidence interval, covering approximately 67% of the historical data; when the preset adjustment coefficient is 2.0, it corresponds to a 95.45% confidence interval, covering approximately 95% of the historical data; and when the preset adjustment coefficient is 2.5, it corresponds to a 98.76% confidence interval, covering approximately 99% of the historical data.

[0046] Next, the processing terminal can determine the solid waste recycling rate threshold based on a preset adjustment coefficient and the mean and standard deviation corresponding to the solid waste recycling rate; and determine the pollutant generation threshold based on a preset adjustment coefficient and the mean and standard deviation corresponding to the pollutant generation amount. It is understood that the process of determining the solid waste recycling rate threshold and the pollutant generation threshold can be found above, and will not be elaborated upon here.

[0047] Subsequently, the treatment terminal can determine the target coordination mode among energy-priority coordination mode, material recycling coordination mode, and process low-carbon coordination mode based on the duration of total carbon emissions, the total carbon emissions threshold, the duration of solid waste recycling rate, the solid waste recycling rate threshold, the duration of pollutant generation, and the pollutant generation threshold. In one example, the treatment terminal can continuously obtain the total carbon emissions threshold, solid waste recycling rate, and solid waste recycling rate according to the industrial park's production cycle (e.g., hourly), and determine whether the duration of total carbon emissions exceeding the total carbon emissions threshold reaches 2 hours, the duration of solid waste recycling rate being less than the solid waste recycling rate threshold reaches 12 hours, and the duration of pollutant generation exceeding the pollutant generation threshold reaches 4 hours. Understandably, if the total carbon emissions exceed the total carbon emissions threshold for 2 hours, it indicates that the current energy priority synergy mode's continuous exceedance standard has been triggered; otherwise, the current energy priority synergy mode's continuous exceedance standard has not been triggered. If the solid waste recycling rate is less than the solid waste recycling rate threshold for 12 hours, it indicates that the current material recycling synergy mode's continuous exceedance standard has been triggered; otherwise, the current material recycling synergy mode's continuous exceedance standard has not been triggered. If the pollutant generation exceeds the pollutant generation threshold for 4 hours, it indicates that the current process low-carbon synergy mode's continuous exceedance standard has been triggered; otherwise, the current process low-carbon synergy mode's continuous exceedance standard has not been triggered.

[0048] Next, in response to the determination that the total carbon emissions exceed the total carbon emissions threshold for a duration of 2 hours, the treatment terminal can set the energy-priority coordination mode as the target coordination mode (that is, switch the target coordination mode to the energy-priority coordination mode); or, in response to the determination that the solid waste recycling rate is less than the solid waste recycling rate threshold for a duration of 12 hours, the treatment terminal can set the material recycling coordination mode as the target coordination mode (that is, switch the target coordination mode to the material recycling coordination mode); or, in response to the determination that the pollutant generation exceeds the pollutant generation threshold for a duration of 4 hours, the treatment terminal can set the process low-carbon coordination mode as the target coordination mode (that is, switch the target coordination mode to the process low-carbon coordination mode), thereby effectively avoiding the potential for false triggering caused by instantaneous fluctuations.

[0049] The preset adjustment coefficients in some embodiments of this disclosure, by being set between 1 and 2.5, can also meet the continuous exceedance standards for triggering the material circulation synergy mode and the continuous exceedance standards for triggering the low-carbon synergy mode of the process, that is, ensure that the threshold is higher than the average value (matching the "exceedance trigger" logic); of course, they can also meet the continuous exceedance standards for triggering the material circulation synergy mode, that is, ensure that the threshold is lower than the average value (matching the "insufficient compliance trigger" logic).

[0050] Subsequently, the processing terminal can construct a local optimization objective function based on the target collaborative mode and the corresponding carbon emission model. It can be understood that by constructing a local optimization objective function corresponding to the target collaborative mode, the parameter values ​​under different collaborative modes can be differentiated, thereby achieving the goal of making targeted dynamic control strategies for different production conditions of the enterprise.

[0051] Here, when the target coordination mode is the energy-first coordination mode, a local optimization objective function can be constructed based on the carbon emission model equations corresponding to the energy-first coordination mode mentioned above, as shown below: In the above formula, This corresponds to the carbon emissions of electricity purchased from the external power grid. This corresponds to the carbon emissions of gas turbine units. Corresponding to the emission reduction from green electricity substitution, the objective function of this local optimization needs to minimize the difference between the sum of carbon emissions from purchased grid electricity and carbon emissions from gas turbine units and the emission reduction from green electricity substitution.

[0052] When the target coordination mode is the material recycling coordination mode, the local optimization objective function shown below can be constructed based on the carbon emission model equation corresponding to the material recycling coordination mode mentioned above: In the above formula, This corresponds to the solid waste recycling rate. Corresponding to the end-of-life treatment volume, the objective function of this local optimization needs to maximize the sum of the solid waste recycling rate and the end-of-life treatment volume.

[0053] When the target collaborative mode is a process low-carbon collaborative mode, a local optimization objective function can be constructed based on the carbon emission model equations corresponding to the process low-carbon collaborative mode mentioned above, as shown below: In the above formula, Corresponding to process emission intensity, Corresponding to end-of-pipe treatment efficiency, the objective function of this local optimization must satisfy the minimum sum of process emission intensity and end-of-pipe treatment efficiency.

[0054] Subsequently, the processing terminal can determine pollution reduction and carbon reduction management data based on the local optimization objective function, the three-chain interactive quantitative coupling model, preset control execution constraints, and multi-objective optimization algorithms. Here, the preset control execution constraints specifically include: each adjustment of the purchased electricity ratio should not exceed 10%, each adjustment of the gas turbine unit's operating load should not exceed 5% of the rated load, and each adjustment of the solid waste classification ratio should not exceed 15%, to avoid production instability caused by sudden parameter changes. Furthermore, corresponding response time constraints and coordination constraints can be set for the target coordination mode. For example, when the target coordination mode is the energy priority coordination mode, the corresponding response time constraint can be that the parameter adjustment time is less than or equal to 30 minutes, and the corresponding coordination constraint can be the model equation corresponding to the energy chain balance model in the three-chain interactive quantitative coupling model (see the expression shown above). For example, when the target collaboration mode is the material circulation collaboration mode, the corresponding response time constraint can be that the parameter adjustment time is less than or equal to 24 hours (i.e., matching the solid waste recycling cycle), and the corresponding collaboration constraint can be the model equation corresponding to the material chain circulation model in the three-chain interaction quantitative coupling model (see the expression shown above). For example, when the target collaborative mode is the process low-carbon collaborative mode, the corresponding response time constraint can be that the parameter adjustment time is less than or equal to 48 hours (i.e., matching the process parameter adjustment cycle), and the corresponding collaborative constraint can be the model equation corresponding to the process chain pollution-carbon emission coupling model in the three-chain interaction quantitative coupling model (see the expression shown above). ).

[0055] Understandably, when the target collaboration mode is the energy-first collaboration mode, the processing terminal can acquire in real time all-factor data corresponding to the energy value chain, all-factor data corresponding to the material supply chain, and all-factor data corresponding to the production process chain. Among them, the all-factor data corresponding to the energy value chain can include time-of-use electricity prices, gas turbine power generation, purchased electricity, energy storage system charging and discharging, production process power consumption, refrigeration system power consumption, carbon emissions, and the efficiency of energy conversion equipment such as boilers and waste heat generator sets, the proportion of purchased electricity (if there is a preset correspondence between them), and the operating load of gas turbine units. (For example, there is a preset correspondence between the power generation of the gas turbine unit and the energy priority coordination mode), and guided by the local optimization objective function corresponding to the energy priority coordination mode (with corresponding response time constraints and coordination constraints), combined with the adjustment of the proportion of purchased electricity, the operating load of the gas turbine unit, and the amount of green electricity purchased, a linkage control strategy to reduce carbon emissions in the energy chain is adopted. A multi-objective optimization algorithm is used to solve the local optimization objective function corresponding to the energy priority coordination mode, the three-chain interaction quantification coupling model, the preset control execution constraints, and all the above-mentioned real-time acquired full-element data, thereby obtaining the corresponding pollution reduction and carbon reduction management data. Here, the pollution reduction and carbon reduction management data may include the proportion of purchased electricity, the operating load of the gas turbine unit, the amount of green electricity purchased, and the total carbon emissions after regulation, but is not limited to these.

[0056] When the target collaboration mode is the material recycling collaboration mode, the processing terminal can acquire in real time the full-element data corresponding to the energy value chain, the full-element data corresponding to the material supply chain, and the full-element data corresponding to the production process chain. Among them, the full-element data corresponding to the material supply chain can include the total amount of raw material input, raw material carbon footprint, solid waste reuse rate, solid waste end-of-life treatment volume, actual output material volume, solid waste treatment cost, solid-liquid classification ratio (if it has a preset correspondence with the solid waste reuse rate), and the investment in reuse facilities (if it has a preset correspondence with the solid waste treatment cost). Guided by the local optimization objective function corresponding to the material recycling collaboration mode (with corresponding response time constraints and collaboration constraints), and combined with the linkage control strategy of optimizing the solid waste classification and recycling process, increasing the investment in reuse facilities, and improving resource utilization, the multi-objective optimization algorithm is used to solve the local optimization objective function corresponding to the material recycling collaboration mode, the three-chain interaction quantitative coupling model, the preset control execution constraints, and all the full-element data acquired in real time, thereby obtaining the corresponding pollution reduction and carbon reduction management data. Here, pollution reduction and carbon reduction management data may include the proportion of solid waste classification, the amount of investment in reuse facilities, the resource utilization rate after regulation, and the amount of end-of-pipe treatment after regulation, but are not limited to these.

[0057] When the target collaborative mode is the process low-carbon collaborative mode, the processing terminal can acquire in real time all-factor data corresponding to the energy value chain, the material supply chain, and the production process chain. The all-factor data corresponding to the production process chain can include process-level energy consumption intensity, pollutant generation, pollutant emission factors, end-of-pipe treatment efficiency, carbon emission intensity, equipment start-up and shutdown status time-series data, and end-of-pipe treatment equipment operating parameters (such as having a preset correspondence with end-of-pipe treatment efficiency). Guided by the local optimization objective function corresponding to the process low-carbon collaborative mode (with corresponding response time constraints and collaborative constraints), and combined with the linkage control strategy of improving production process parameters, upgrading end-of-pipe treatment equipment, and reducing emission intensity, the multi-objective optimization algorithm is used to solve the local optimization objective function corresponding to the process low-carbon collaborative mode, the three-chain interaction quantitative coupling model, the preset control execution constraints, and all the above-mentioned real-time acquired all-factor data, thereby obtaining the corresponding pollution reduction and carbon reduction management data. Here, pollution reduction and carbon reduction management data may include key parameters of the production process (such as process-level energy consumption intensity or equipment start-up and shutdown status timing data, etc.), end-of-pipe treatment equipment operating parameters, process emission intensity after regulation, and total emission after regulation, but is not limited to these.

[0058] In some implementations, when the processing terminal determines pollution reduction and carbon reduction management data based on the duration of each carbon emission data, a multi-chain collaborative database, a three-chain interactive quantitative coupling model, and all carbon emission models, it can also construct a multi-indicator objective function in response to determining that the duration of total carbon emissions exceeding the total carbon emission threshold has not reached 2 hours, the duration of ...

[0059] Here, the multi-indicator objective function includes an economic objective function constructed from an economic perspective, which can be expressed as shown in the following expression: In the above formula, This corresponds to energy procurement costs (such as having a pre-defined correspondence with the time-of-use electricity price and purchased electricity mentioned above). This corresponds to the cost of solid waste treatment. Corresponding to equipment operation and maintenance costs (such as those related to the power generation of the gas turbine unit and the time-series data of equipment start-up and shutdown status mentioned above), the economic objective function must minimize the sum of energy procurement costs, solid waste treatment costs, and equipment operation and maintenance costs. The economic constraints of the economic objective function can include the constraint expressions shown below: In the above constraint expression, This corresponds to the lowest total system operating cost. This corresponds to the minimum energy procurement cost. This corresponds to the minimum maintenance cost of the equipment, in order to avoid malfunctions caused by excessively compressing maintenance.

[0060] The multi-indicator objective function also includes an environmental benefit objective function constructed from the perspective of environmental benefits, which can be expressed as shown in the following expression: In the above formula, This corresponds to the amount of pollutant reduction (calculated from the amount of pollutant generated). Corresponding to carbon emission reduction (calculated from carbon emissions), this environmental benefit objective function must maximize the sum of pollutant reduction and carbon emission reduction. The environmental benefit constraints of the objective function can include the constraint expressions shown below: In the above formula, This corresponds to the minimum pollutant reduction required by local environmental protection authorities. This corresponds to the minimum amount required by the national carbon emission reduction policy.

[0061] The multi-indicator objective function also includes a technical feasibility objective function constructed from a technical feasibility perspective, which can be expressed as shown in the following expression: In the above formula, represents the actual operating parameters of the equipment (such as the operating load of the gas turbine unit and the operating parameters of the end-of-pipe treatment equipment), and represents the ideal operating parameters of the equipment. The technical feasibility objective function must minimize the difference between the actual operating parameters and the ideal operating parameters of the equipment. The technical feasibility constraints of the technical feasibility objective function can include the constraint expressions shown below: In the above formula, This corresponds to the product of the equipment's rated operating parameters and the minimum percentage (e.g., 30% of the rated load of a gas turbine unit). This corresponds to the product of the equipment's rated operating parameters and the maximum percentage (e.g., 100% of the rated load of a gas turbine unit).

[0062] Subsequently, the processing terminal can determine multi-data type management data based on a multi-index objective function, a three-chain interactive quantitative coupling model, and a multi-objective optimization algorithm. It is understood that, when determining that a continuous exceedance standard for any collaborative mode has not been triggered, the processing terminal can acquire in real-time all-factor data corresponding to the energy value chain, the material supply chain, and the production process chain. Guided by the aforementioned technical feasibility objective function (with economic constraints), environmental benefit objective function (with environmental benefit constraints), and technical feasibility objective function (with technical feasibility constraints), the terminal uses a multi-objective optimization algorithm to solve the three-chain interactive quantitative coupling model and all the real-time acquired all-factor data, thereby obtaining the corresponding multi-data type management data. Here, the multi-data type management data may include, but is not limited to, gas turbine power generation, purchased electricity, energy storage system charging and discharging, production process power consumption, refrigeration system power consumption, total material input, solid waste reuse rate, end-of-pipe treatment volume, total output material volume, pollutant generation, end-of-pipe treatment efficiency, carbon emission intensity, and total emissions.

[0063] It should also be noted that the multi-objective optimization algorithms of some embodiments of this disclosure are data optimization techniques well known in the art, and their processing procedures will not be described in detail here.

[0064] Figure 3 A block diagram of a multi-chain collaborative pollution reduction and carbon reduction management system according to some embodiments of this disclosure is shown. The various embodiments in this specification are described in a progressive manner, and similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for the apparatus embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and relevant parts can be referred to the description of the method embodiments. Figure 3As shown, the pollution reduction and carbon reduction management system 300 based on multi-chain collaboration can include at least a database establishment module 302, configured to determine the full-element data corresponding to the energy value chain, the full-element data corresponding to the material supply chain, and the full-element data corresponding to the production process chain, and construct a multi-chain collaborative database based on all full-element data. The multi-chain collaborative pollution reduction and carbon reduction management system 300 also includes a coupling model establishment module 304, configured to construct a three-chain interaction quantitative coupling model based on the data types contained in all full-element data. The three-chain interaction quantitative coupling model has parameter constraints corresponding to the energy value chain, the material supply chain, and the production process chain. The multi-chain collaborative pollution reduction and carbon reduction management system 300 also includes a collaboration mode determination module 306, configured to construct a carbon emission model corresponding to the energy priority collaboration mode, the material recycling collaboration mode, and the low-carbon process collaboration mode based on the three-chain interaction quantitative coupling model. In addition, the pollution reduction and carbon reduction management system 300 based on multi-chain collaboration also includes a management data determination module 308, which is configured to acquire carbon emission data corresponding to the energy value chain, carbon emission data corresponding to the material supply chain, and carbon emission data corresponding to the production process chain, and determine pollution reduction and carbon reduction management data based on the duration of each carbon emission data, the multi-chain collaborative database, the three-chain interaction quantitative coupling model, and all carbon emission models.

[0065] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this specification are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Versatile Discs (DVDs)), or semiconductor media (e.g., Solid State Disks (SSDs)).

[0066] Figure 4 Block diagrams of electronic devices that can implement various embodiments of the present disclosure are shown. For example... Figure 4 As shown, the electronic device 400 includes a processor 401, which can perform various appropriate actions and processes based on computer program instructions loaded into random access memory (RAM) 403 according to computer program instructions stored in read-only memory (ROM) 402. The RAM 403 may also store various programs and data required for the operation of the electronic device 400. The processor 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0067] The various processes and procedures described above, such as method 200, can be executed by processor 401. For example, in some embodiments, method 200 may be implemented as a software program tangibly contained in a machine-readable medium. In some embodiments, part or all of the software program may be loaded into and / or installed onto electronic device 400 via ROM 402. When the software program is loaded into RAM 403 and executed by processor 401, one or more actions of method 200 described above may be performed.

[0068] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload programmable logic devices (CPLDs), and so on.

[0069] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0070] This disclosure can be a method, apparatus, system, and / or program product. The program product may include a machine-readable storage medium on which machine-readable program instructions for performing various aspects of this disclosure are loaded. The machine-readable program instructions described herein can be downloaded from the machine-readable storage medium to various computing / processing devices, or downloaded via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the machine-readable program instructions from the network and forwards them to the machine-readable storage medium in the respective computing / processing device.

[0071] Machine program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. Machine-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as a standalone software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the machine-readable program instructions to implement various aspects of this disclosure.

[0072] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. Furthermore, although operations are depicted in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the foregoing discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented individually or in any suitable sub-combination in multiple implementations.

[0073] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A multi-chain coordination-based pollution reduction and carbon reduction management method, characterized in that, The method comprises the following steps: determining total factor data corresponding to an energy value chain, total factor data corresponding to a material supply chain, and total factor data corresponding to a production process chain, respectively, and constructing a multi-chain coordination database based on all the total factor data; constructing a three-chain interaction quantization coupling model based on the data types contained in all the total factor data, the three-chain interaction quantization coupling model having parameter constraints corresponding to the energy value chain, parameter constraints corresponding to the material supply chain, and parameter constraints corresponding to the production process chain; constructing a carbon emission model corresponding to an energy priority coordination mode, a carbon emission model corresponding to a material recycling coordination mode, and a carbon emission model corresponding to a process low-carbon coordination mode, respectively, based on the three-chain interaction quantization coupling model; and obtaining carbon emission data corresponding to the energy value chain, carbon emission data corresponding to the material supply chain, and carbon emission data corresponding to the production process chain, respectively, and determining pollution reduction and carbon reduction management data based on the duration of each of the carbon emission data, the multi-chain coordination database, the three-chain interaction quantization coupling model, and all the carbon emission models.

2. The method of claim 1, wherein, The method of constructing a multi-chain coordination database based on all the total factor data comprises the following steps: determining the standard deviation and the mean value corresponding to each data type based on the historical data set corresponding to each data type in all the total factor data; performing outlier removal processing on the historical data set corresponding to each data type based on the standard deviation and the mean value corresponding to each data type; determining a maximum value set from each of the historical data sets after outlier removal processing, and performing normalization processing on the historical data set corresponding to each data type after outlier removal processing based on each of the maximum value sets; and constructing a multi-chain coordination database based on all the historical data sets after normalization processing.

3. The method according to claim 1 or 2, characterized in that, The carbon emission data corresponding to the energy value chain is the total carbon emission, the carbon emission data corresponding to the material supply chain is the solid waste recycling rate, and the carbon emission data corresponding to the production process chain is the pollutant generation amount; The method of determining pollution reduction and carbon reduction management data based on the duration of each of the carbon emission data, the multi-chain coordination database, the three-chain interaction quantization coupling model, and all the carbon emission models comprises the following steps: determining a carbon emission threshold, a solid waste recycling rate threshold, and a pollutant generation amount threshold, respectively, based on the multi-chain coordination database and a preset adjustment coefficient; determining a target coordination mode among the energy priority coordination mode, the material recycling coordination mode, and the process low-carbon coordination mode based on the duration of the total carbon emission, the total carbon emission threshold, the duration of the solid waste recycling rate, the solid waste recycling rate threshold, the duration of the pollutant generation amount, and the pollutant generation amount threshold; constructing a local optimization objective function based on the target coordination mode and the carbon emission model corresponding to the target coordination mode; and determining pollution reduction and carbon reduction management data based on the local optimization objective function, the three-chain interaction quantization coupling model, a preset control execution constraint, and a multi-objective optimization algorithm.

4. The method of claim 3, wherein, The method further comprises: determining, based on the multi-chain synergy database and a preset adjustment coefficient, a total carbon emission threshold, a solid waste recycling rate threshold, and a pollutant generation amount threshold, respectively, including: determining, based on the multi-chain synergy database, a mean value and a standard deviation corresponding to the total carbon emission, a mean value and a standard deviation corresponding to the solid waste recycling rate, and a mean value and a standard deviation corresponding to the pollutant generation amount, respectively; determining, based on the preset adjustment coefficient, the mean value, and the standard deviation corresponding to the total carbon emission, a total carbon emission threshold, the preset adjustment coefficient being between 1 and 2.5; determining, based on the preset adjustment coefficient, the mean value, and the standard deviation corresponding to the solid waste recycling rate, a solid waste recycling rate threshold; and 5. The method of claim 3, wherein, determining, based on the preset adjustment coefficient, the mean value, and the standard deviation corresponding to the pollutant generation amount, a pollutant generation amount threshold. The method further comprises: determining, based on the duration of the total carbon emission, the total carbon emission threshold, the duration of the solid waste recycling rate, the solid waste recycling rate threshold, the duration of the pollutant generation amount, and the pollutant generation amount threshold, a target synergy mode in the energy priority synergy mode, the material circulation synergy mode, and the process low-carbon synergy mode, including: determining whether the duration of the total carbon emission greater than the total carbon emission threshold reaches 2 hours, whether the duration of the solid waste recycling rate less than the solid waste recycling rate threshold reaches 12 hours, and whether the duration of the pollutant generation amount greater than the pollutant generation amount threshold reaches 4 hours, respectively; in response to determining that the duration of the total carbon emission greater than the total carbon emission threshold reaches 2 hours, determining the energy priority synergy mode as the target synergy mode; or 6. The method of claim 5, wherein, in response to determining that the duration of the solid waste recycling rate less than the solid waste recycling rate threshold reaches 12 hours, determining the material circulation synergy mode as the target synergy mode; or in response to determining that the duration of the pollutant generation amount greater than the pollutant generation amount threshold reaches 4 hours, determining the process low-carbon synergy mode as the target synergy mode. The method further comprises: in response to determining that the duration of the total carbon emission greater than the total carbon emission threshold does not reach 2 hours, determining that the duration of the solid waste recycling rate less than the solid waste recycling rate threshold does not reach 12 hours, and determining that the duration of the pollutant generation amount greater than the pollutant generation amount threshold does not reach 4 hours, constructing a multi-index objective function, the multi-index objective function having a multi-index constraint; and determining, based on the multi-index objective function, the three-chain interaction quantization coupling model, and a multi-objective optimization algorithm, a multi-data type management data.

7. The method of claim 1, wherein, The three-chain interactive quantification coupling model comprises an energy chain balance model, a material chain circulation model and a process chain pollution-carbon emission coupling model, the energy chain balance model has data types including power generation of gas units, purchased power, charging and discharging capacity of a power storage system, power consumption of a production process and power consumption of a refrigeration system, the material chain circulation model has data types including total raw material input, solid waste recycling rate, end-of-pipe treatment capacity and total output material, and the process chain pollution-carbon emission coupling model has data types including pollution generation, end-of-pipe treatment efficiency, carbon emission intensity and total emission.

8. A multi-chain coordination based pollution and carbon reduction management system, characterized in that, Comprise: a database establishment module configured to determine full-factor data corresponding to an energy value chain, full-factor data corresponding to a material supply chain and full-factor data corresponding to a production process chain respectively, and to construct a multi-chain coordination database based on all the full-factor data; a coupling model establishment module configured to construct a three-chain interactive quantification coupling model based on data types contained in all the full-factor data, the three-chain interactive quantification coupling model having parameter constraints corresponding to the energy value chain, parameter constraints corresponding to the material supply chain and parameter constraints corresponding to the production process chain; a coordination mode determination module configured to construct a carbon emission model corresponding to an energy priority coordination mode, a carbon emission model corresponding to a material circulation coordination mode and a carbon emission model corresponding to a low-carbon process coordination mode based on the three-chain interactive quantification coupling model; and a management data determination module configured to obtain carbon emission data corresponding to the energy value chain, carbon emission data corresponding to the material supply chain and carbon emission data corresponding to the production process chain respectively, and to determine pollution reduction and carbon reduction management data based on a duration of each of the carbon emission data, the multi-chain coordination database, the three-chain interactive quantification coupling model and all the carbon emission models. 9.A computer readable storage medium having stored thereon a computer program, the computer readable storage medium having stored therein instructions which, when executed on a computer or processor, cause the computer or processor to perform the steps of the method of any one of claims 1-7.

10. An electronic device, comprising: Comprise: one or more processors, and a memory associated with the one or more processors, the memory for storing program instructions which, when read and executed by the one or more processors, perform the steps of the method of any one of claims 1-7.

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