Industrial park-oriented energy-carbon collaborative management method, system, device and medium

Abstract

The application relates to an industrial park-oriented carbon-energy collaborative management method, system, device and medium, which comprises the following steps: obtaining and preprocessing multi-source carbon-energy data of an industrial park to obtain a carbon-energy data set; constructing a park carbon-energy flow hypergraph model based on the carbon-energy data set to depict the correlation between energy flow and carbon emission flow among multiple subjects in the park; extracting carbon-energy evaluation indexes of each subject, calculating a comprehensive score value by using a comprehensive scoring model of the fusion of an analytic hierarchy process and an entropy weight method, determining a carbon quota allocation coefficient, and realizing reasonable allocation of the carbon quota; identifying output subjects and demand subjects of waste heat and surplus energy based on the carbon-energy flow hypergraph model, determining a waste heat and surplus energy sharing path and a sharing amount, and generating a waste heat and surplus energy sharing transaction scheme; and performing carbon-energy collaborative management on multiple subjects in the park according to the carbon quota allocation result and the waste heat and surplus energy sharing transaction scheme and generating a management report. The application has the effect of improving the collaborative efficiency of carbon-energy management of multiple subjects in the industrial park.

Description

Technical Field

[0001] This application relates to the technical field of energy management and carbon asset management in industrial parks, and in particular to a method, system, equipment and medium for coordinated energy and carbon management in industrial parks. Background Technology

[0002] Currently, industrial parks typically encompass multiple enterprises, public facilities, and various energy equipment, characterized by diverse stakeholders, complex energy categories, and a close coupling relationship between energy flows and carbon emission flows. To achieve overall energy efficiency improvement and carbon emission control within the park, unified management and collaborative optimization of the energy use and carbon emission status of all stakeholders are necessary.

[0003] Existing industrial park energy and carbon management systems mostly adopt a decentralized management model. Each enterprise typically conducts energy use monitoring and carbon emission accounting independently, with park management relying solely on statistical reports or simple data summaries to manage overall energy consumption and carbon emissions. In terms of data modeling, current technologies often use traditional topologies or single network models to describe energy flow, making it difficult to simultaneously depict the complex energy transmission relationships and carbon emission correlations among multiple entities within the park. Furthermore, existing carbon quota allocation methods mostly employ average or static allocation models, lacking a comprehensive evaluation of factors such as enterprise energy intensity, emission intensity, and emission reduction potential, resulting in irrational quota allocation. In addition, there is a lack of effective channels for sharing waste heat and energy between enterprises within the park and between enterprises and public facilities, leading to the underutilization of a large amount of usable energy and hindering the synergistic utilization of energy and carbon emission reduction.

[0004] The existing technical solutions mentioned above have the following drawbacks: the existing energy and carbon management methods in industrial parks lack the ability to unify and integrate energy and carbon data of multiple entities within the park and to model the relationship between multiple entities, multiple energy categories, and energy flow and carbon flow. As a result, they cannot support energy and carbon collaborative optimization and dynamic management at the park level, and therefore there is room for improvement. Summary of the Invention

[0005] To improve the collaborative efficiency of energy and carbon management among multiple stakeholders in industrial parks, this application provides a method, system, equipment, and medium for collaborative energy and carbon management in industrial parks.

[0006] The above-mentioned objective of this application is achieved through the following technical solution:

[0007] A method for coordinated energy and carbon management in industrial parks, comprising:

[0008] Acquire multi-source energy carbon data within the industrial park and obtain the total carbon emission quota of the industrial park. Perform preprocessing operations on the multi-source energy carbon data to obtain a preprocessed energy carbon dataset.

[0009] A hypergraph model of energy and carbon flow in the park is constructed based on the preprocessed energy and carbon dataset.

[0010] Based on the preprocessed energy and carbon dataset, energy and carbon assessment indicators for each entity within the industrial park are extracted. These energy and carbon assessment indicators include energy consumption intensity, carbon emission intensity, emission reduction potential, and industry benchmark indicators.

[0011] Based on the energy and carbon evaluation indicators of each entity, a comprehensive scoring model based on the analytic hierarchy process and the entropy weight method is used to calculate the comprehensive score value of each entity, and the carbon quota allocation coefficient of each entity is determined based on the comprehensive score value.

[0012] The total carbon emission quota is allocated according to the carbon quota allocation coefficient to obtain the carbon quota of each entity, and the carbon quota allocation result is obtained by summarizing.

[0013] Based on the energy-carbon flow hypergraph model, the producers and consumers of waste heat and energy are identified, and the waste heat and energy sharing path and sharing amount are determined according to the energy flow connection relationship between the producers and consumers, so as to form a waste heat and energy sharing trading scheme.

[0014] Based on the carbon quota allocation results and the waste heat and energy sharing and trading scheme, energy and carbon collaborative management is carried out on multiple entities within the industrial park, and a park energy and carbon collaborative management report is generated.

[0015] By adopting the above technical solution, and by acquiring multi-source energy carbon data within the industrial park and obtaining the total carbon emission quota of the industrial park, preprocessing the multi-source energy carbon data to obtain a preprocessed energy carbon dataset, it is possible to achieve unified integration and standardized processing of energy carbon data from multiple entities within the park, such as enterprises, public facilities, and energy storage equipment. This improves the consistency and usability of the park's energy carbon data, providing a reliable data foundation for subsequent collaborative energy carbon analysis. Furthermore, by constructing a park energy carbon flow hypergraph model based on the preprocessed energy carbon dataset, it is possible to characterize the complex relationships between energy flow and carbon emission flow among multiple entities within the park, thereby enhancing the expressive power and analytical accuracy of the overall energy carbon flow relationships within the park. By utilizing a model based on… A comprehensive scoring model integrating the analytic hierarchy process (AHP) and entropy weight method calculates the comprehensive score of each entity and determines the carbon quota allocation coefficient. This model comprehensively considers multi-dimensional indicators such as enterprise energy intensity, emission intensity, and emission reduction potential, thereby achieving a more scientific and reasonable carbon quota allocation mechanism and improving the fairness and incentive effect of carbon quota management. By identifying the producers and consumers of waste heat and energy based on the energy-carbon flow hypergraph model and determining the sharing path and sharing amount to form a waste heat and energy sharing trading scheme, this model can promote the coordinated use of energy among different entities in the park, thereby reducing waste heat and energy and improving overall energy utilization efficiency. Ultimately, it enables coordinated energy and carbon management at the park level and generates management reports, providing data support for park management decisions.

[0016] In one example, this application can be further configured as follows: acquiring multi-source energy carbon data within an industrial park, and performing preprocessing operations on the multi-source energy carbon data to obtain a preprocessed energy carbon dataset, specifically includes:

[0017] Acquire enterprise-side data within the industrial park, including energy consumption data, carbon emission data, energy intensity data, emission intensity data, waste heat and energy output data, and waste heat and energy demand data for each production stage.

[0018] Acquire public facility data within the industrial park, including energy consumption and carbon emission data for lighting, heating, and water supply systems.

[0019] Acquire data from energy storage devices within the industrial park. This data includes charging and discharging data, energy storage capacity data, and energy consumption loss data of the energy storage devices.

[0020] The multi-source energy carbon data is obtained by aggregating the enterprise-side data, the public facility-side data, and the energy storage device-side data.

[0021] The multi-source energy carbon data is cleaned, deduplicated, standardized, and normalized to unify data caliber and accounting standards, resulting in the preprocessed energy carbon dataset.

[0022] By adopting the above technical solution, and by acquiring and aggregating enterprise-side data, public facility-side data, and energy storage device-side data within the industrial park to form multi-source energy carbon data, it is possible to comprehensively cover the energy use and carbon emission information of different entities within the park, thereby improving the completeness of energy carbon data collection in the park. By performing data cleaning, deduplication, standardization, and normalization processing on the multi-source energy carbon data, it is possible to eliminate the differences in caliber between different data sources and improve data quality, thereby providing a unified and reliable data foundation for subsequent energy carbon flow modeling and evaluation analysis.

[0023] In one example, this application can be further configured as follows: the construction of the park energy-carbon flow hypergraph model based on the preprocessed energy-carbon dataset specifically includes:

[0024] Enterprises, public facilities, and energy storage equipment within the industrial park are defined as hypergraph nodes, and energy categories, carbon quotas, and carbon emission-related attributes determined based on the preprocessed energy and carbon dataset are defined as attribute nodes, in order to construct a node set for the energy and carbon flow of the park.

[0025] Based on the preprocessed energy and carbon dataset, a variety of hyperedges are constructed, including energy transmission hyperedges, carbon flow association hyperedges, and waste heat and waste energy interaction hyperedges.

[0026] A campus energy-carbon flow hypergraph model is constructed based on the node set and the multi-type hyperedges, and an energy-carbon flow coupling coefficient is introduced. The edge weight parameters in the campus energy-carbon flow hypergraph model are updated based on the energy-carbon coupling coefficient.

[0027] By adopting the above technical solutions, defining enterprises, public facilities, and energy storage equipment as hypergraph nodes and constructing a node set by defining energy categories, carbon quotas, and carbon emission-related attributes as attribute nodes, the relationship between the park's main entities and energy and carbon attributes can be described from a multi-dimensional perspective, thereby improving the expressive power of energy and carbon system structure modeling. By constructing multiple types of hyperedges, including energy transmission hyperedges, carbon flow correlation hyperedges, and waste heat and energy interaction hyperedges, the relationships of energy flow, carbon emission correlation, and waste heat and energy exchange can be simultaneously characterized, thus reflecting the park's energy and carbon flow structure more comprehensively. By constructing a park energy and carbon flow hypergraph model based on the node set and multiple types of hyperedges and introducing energy and carbon flow coupling coefficients to update edge weight parameters, the coupling relationship between energy consumption and carbon emissions can be quantified, thereby improving the accuracy of energy and carbon relationship modeling and providing a basic model for park energy and carbon collaborative optimization.

[0028] In one example, this application can be further configured as follows: the introduction of an energy-carbon flow coupling coefficient, and the updating of the edge weight parameters in the park's energy-carbon flow hypergraph model based on the energy-carbon flow coupling coefficient, specifically includes:

[0029] Energy consumption data and carbon emission data are extracted from the preprocessed energy and carbon dataset;

[0030] The energy-carbon flow coupling coefficient is calculated based on the energy consumption data and carbon emission data to quantify the correlation between energy consumption and carbon emissions.

[0031] The edge weight parameters of the corresponding hyperedges in the energy and carbon flow hypergraph model of the park are updated according to the energy and carbon flow coupling coefficient to reflect the coupling relationship between energy flow and carbon emission flow among different entities.

[0032] By adopting the above technical solution, energy consumption data and carbon emission data are extracted from the preprocessed energy and carbon dataset, and the energy-carbon flow coupling coefficient is calculated. This allows for the quantification of the correlation between energy consumption and carbon emissions, thus more accurately reflecting the coupling characteristics of different entities in energy utilization and carbon emissions. By updating the edge weight parameters of the corresponding hyperedges in the park's energy-carbon flow hypergraph model according to the energy-carbon flow coupling coefficient, the strength of the energy-carbon flow relationship between different entities can be dynamically reflected, thereby improving the energy-carbon flow model's ability to express the actual operating state.

[0033] In one example, this application can be further configured as follows: The step of calculating a comprehensive score value for each entity based on its energy and carbon assessment indicators using a comprehensive scoring model that integrates the analytic hierarchy process (AHP) and the entropy weight method, and determining the carbon quota allocation coefficient for each entity based on the comprehensive score value, specifically includes:

[0034] An energy and carbon evaluation index system is constructed based on the aforementioned energy and carbon evaluation indexes, and the subjective weights of each energy and carbon evaluation index are determined using the analytic hierarchy process (AHP).

[0035] Based on the preprocessed energy and carbon dataset, the information entropy value of each energy and carbon evaluation index is calculated, and the objective weight of each energy and carbon evaluation index is determined using the entropy weight method according to the information entropy value.

[0036] The subjective weights and objective weights are integrated and calculated to obtain the comprehensive weights of each energy and carbon evaluation index, and the comprehensive score of each subject is calculated based on the comprehensive weights.

[0037] The carbon quota allocation coefficient for each entity is determined based on the comprehensive score.

[0038] By adopting the above technical solutions, and by constructing an evaluation index system based on energy and carbon assessment indicators and using the analytic hierarchy process (AHP) to determine subjective weights, the importance of indicators can be judged by combining expert experience, thereby improving the rationality of the evaluation system. By using the entropy weight method to calculate the information entropy value of each indicator and determine the objective weight, the information contribution of each indicator can be reflected according to data changes, thereby improving the objectivity of the evaluation process. By integrating subjective and objective weights to obtain a comprehensive weight and calculating the comprehensive score value of each subject, a comprehensive evaluation of each subject can be conducted based on both experience judgment and data characteristics, thereby improving the accuracy of the evaluation results. By determining the carbon quota allocation coefficient based on the comprehensive score value, the carbon quota allocation can be more in line with the actual energy consumption and emissions of each subject, thereby promoting enterprises to actively carry out energy conservation and emission reduction.

[0039] In one example, this application can be further configured to: calculate the emission reduction potential, specifically including:

[0040] Based on the preprocessed energy and carbon dataset, extract the energy consumption data and carbon emission data of each entity, and obtain the energy consumption benchmark value and carbon emission benchmark value from the preset industry benchmark database.

[0041] The energy consumption intensity and carbon emission intensity of each entity are calculated based on the energy consumption data and the carbon emission data.

[0042] The energy consumption intensity and carbon emission intensity are compared and analyzed with the industry benchmark values ​​to obtain the energy consumption deviation and carbon emission deviation of each entity.

[0043] The theoretical emission reductions for each entity are calculated based on the energy consumption deviation and the carbon emission deviation, and the theoretical emission reductions are determined as the emission reduction potential indicators for that entity.

[0044] By adopting the above technical solutions, and extracting energy consumption and carbon emission data from the preprocessed energy and carbon dataset and obtaining industry benchmark values, a basis for comparison between the actual energy and carbon performance of enterprises and industry standards can be established, thus providing a reference for assessing emission reduction potential. By calculating the energy intensity and carbon emission intensity of each entity and comparing them with industry benchmark values ​​to obtain the deviation, the gaps between each entity in energy utilization efficiency and carbon emission levels can be identified, thus providing a basis for subsequent emission reduction decisions. By calculating the theoretical emission reduction amount based on the energy consumption deviation and carbon emission deviation and determining the emission reduction potential indicators, the potential emission reduction space that each entity may achieve in the future can be quantified, thus providing a reference for the overall carbon emission control and emission reduction planning of the park.

[0045] In one example, this application can be further configured as follows: the step of identifying the producers and consumers of waste heat and energy based on the energy-carbon flow hypergraph model, and determining the waste heat and energy sharing path and sharing amount according to the energy flow connection relationship between the producers and consumers, in order to form a waste heat and energy sharing trading scheme, specifically includes:

[0046] Based on the waste heat and energy interaction hyperedge in the energy carbon flow hypergraph model, identify the main body with waste heat and energy output, and determine the corresponding waste heat and energy output amount.

[0047] Based on the waste heat and energy interaction hyperedge in the energy carbon flow hypergraph model, identify the entities with waste heat and energy needs and determine the corresponding waste heat and energy needs.

[0048] Based on the waste heat and energy output and the waste heat and energy demand, supply and demand are matched to obtain candidate waste heat and energy sharing entities;

[0049] Based on the energy flow connection relationship between the main entities in the energy-carbon flow hypergraph model, the sharing path between the candidate waste heat and waste energy sharing main entities is determined.

[0050] The amount of waste heat and energy shared is calculated based on the waste heat and energy output, the waste heat and energy demand, and the shared path, and a waste heat and energy sharing transaction scheme is generated.

[0051] By adopting the above technical solutions, the system identifies entities that produce waste heat and energy and determines their output based on the waste heat and energy interaction hyperedge in the energy-carbon flow hypergraph model. Simultaneously, it identifies entities that require waste heat and energy and determines their demand. This allows for accurate identification of available waste heat and energy resources and their demand within the park, providing fundamental information for energy sharing. By matching supply and demand based on waste heat and energy output and demand to obtain candidate sharing entity pairs, the system achieves a reasonable match between waste heat resources and demand resources among park entities, thereby reducing energy waste. By determining sharing paths and calculating sharing quantities based on the energy flow connection relationships between entities in the energy-carbon flow hypergraph model, the system optimizes the transmission and utilization of waste heat and energy within the park's energy network structure, thereby improving the overall energy efficiency of the park. Finally, by generating waste heat and energy sharing trading schemes, the system promotes collaborative energy utilization and optimal resource allocation among entities within the park, thus driving overall energy conservation, emission reduction, and low-carbon development within the park.

[0052] The second objective of this invention is achieved through the following technical solution:

[0053] An energy and carbon co-management system for industrial parks, comprising:

[0054] The data acquisition and preprocessing module is used to acquire multi-source energy carbon data in the industrial park and obtain the total carbon emission quota of the industrial park. It performs preprocessing operations on the multi-source energy carbon data to obtain a preprocessed energy carbon dataset.

[0055] The hypergraph model construction module is used to construct a hypergraph model of energy and carbon flow in the park based on the preprocessed energy and carbon dataset.

[0056] The evaluation index extraction module is used to extract energy and carbon evaluation indicators for each entity in the industrial park based on the preprocessed energy and carbon dataset. The energy and carbon evaluation indicators include energy consumption intensity, carbon emission intensity, emission reduction potential, and industry benchmark indicators.

[0057] The comprehensive scoring calculation module is used to calculate the comprehensive score value of each entity based on the energy and carbon evaluation indicators of each entity, using a comprehensive scoring model based on the analytic hierarchy process and the entropy weight method, and to determine the carbon quota allocation coefficient of each entity based on the comprehensive score value.

[0058] The carbon quota allocation module is used to allocate the total carbon emission quota according to the carbon quota allocation coefficient, obtain the carbon quota of each entity, and summarize the carbon quota allocation results.

[0059] The waste heat sharing scheme generation module is used to identify the producers and consumers of waste heat and energy based on the energy-carbon flow hypergraph model, and to determine the waste heat and energy sharing path and sharing amount according to the energy flow connection relationship between the producers and consumers, so as to form a waste heat and energy sharing transaction scheme.

[0060] The collaborative management and control generation module is used to conduct collaborative energy and carbon management and control of multiple entities in the industrial park based on the carbon quota allocation results and the waste heat and waste energy sharing and trading scheme, and to generate a collaborative energy and carbon management and control report for the park.

[0061] By adopting the above technical solution, and by acquiring multi-source energy carbon data within the industrial park and obtaining the total carbon emission quota of the industrial park, preprocessing the multi-source energy carbon data to obtain a preprocessed energy carbon dataset, it is possible to achieve unified integration and standardized processing of energy carbon data from multiple entities within the park, such as enterprises, public facilities, and energy storage equipment. This improves the consistency and usability of the park's energy carbon data, providing a reliable data foundation for subsequent collaborative energy carbon analysis. Furthermore, by constructing a park energy carbon flow hypergraph model based on the preprocessed energy carbon dataset, it is possible to characterize the complex relationships between energy flow and carbon emission flow among multiple entities within the park, thereby enhancing the expressive power and analytical accuracy of the overall energy carbon flow relationships within the park. By utilizing a model based on… A comprehensive scoring model integrating the analytic hierarchy process (AHP) and entropy weight method calculates the comprehensive score of each entity and determines the carbon quota allocation coefficient. This model comprehensively considers multi-dimensional indicators such as enterprise energy intensity, emission intensity, and emission reduction potential, thereby achieving a more scientific and reasonable carbon quota allocation mechanism and improving the fairness and incentive effect of carbon quota management. By identifying the producers and consumers of waste heat and energy based on the energy-carbon flow hypergraph model and determining the sharing path and sharing amount to form a waste heat and energy sharing trading scheme, this model can promote the coordinated use of energy among different entities in the park, thereby reducing waste heat and energy and improving overall energy utilization efficiency. Ultimately, it enables coordinated energy and carbon management at the park level and generates management reports, providing data support for park management decisions.

[0062] The above-mentioned objective three of this application is achieved through the following technical solution:

[0063] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described energy and carbon synergistic management method for industrial parks.

[0064] The fourth objective of this application is achieved through the following technical solution:

[0065] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described energy and carbon synergistic management method for industrial parks.

[0066] In summary, this application includes the following beneficial technical effects:

[0067] 1. By acquiring multi-source energy carbon data and the total carbon emission quota of the industrial park, preprocessing the multi-source energy carbon data to obtain a preprocessed energy carbon dataset, it is possible to achieve unified integration and standardized processing of energy carbon data from multiple entities within the park, such as enterprises, public facilities, and energy storage equipment. This improves the consistency and usability of the park's energy carbon data, providing a reliable data foundation for subsequent collaborative energy carbon analysis. By constructing a hypergraph model of energy carbon flows within the park based on the preprocessed energy carbon dataset, it is possible to characterize the complex relationships between energy flows and carbon emission flows among multiple entities within the park, thereby enhancing the expressive power and analytical accuracy of the overall energy carbon flow relationships within the park. Furthermore, by utilizing an analytic hierarchy process (AHP)... The comprehensive scoring model, integrated with the entropy weight method, calculates the comprehensive score value of each entity and determines the carbon quota allocation coefficient. It can comprehensively consider multi-dimensional indicators such as enterprise energy consumption intensity, emission intensity, and emission reduction potential, thereby achieving a more scientific and reasonable carbon quota allocation mechanism and improving the fairness and incentive effect of carbon quota management. By identifying the producers and consumers of waste heat and energy based on the energy-carbon flow hypergraph model and determining the sharing path and sharing amount to form a waste heat and energy sharing trading scheme, it can promote the coordinated use of energy among different entities in the park, thereby reducing waste of waste heat and energy and improving the overall energy utilization efficiency. Ultimately, it can realize the coordinated management and control of energy and carbon at the park level and generate management and control reports, providing data support for park management decisions.

[0068] 2. By using a comprehensive scoring model based on the analytic hierarchy process and the entropy weight method to calculate the comprehensive score of each subject and determine the carbon quota allocation coefficient, it is possible to comprehensively consider multi-dimensional indicators such as enterprise energy intensity, emission intensity and emission reduction potential, thereby achieving a more scientific and reasonable carbon quota allocation mechanism and improving the fairness and incentive effect of carbon quota management.

[0069] 3. By identifying the producers and consumers of waste heat and energy based on the energy and carbon flow hypergraph model and determining the sharing paths and sharing quantities to form a waste heat and energy sharing and trading scheme, it is possible to promote the coordinated use of energy among different entities in the park, thereby reducing waste of waste heat and energy and improving overall energy utilization efficiency. Ultimately, it can achieve coordinated energy and carbon management at the park level and generate management reports to provide data support for park management decisions. Attached Figure Description

[0070] Figure 1 This is a flowchart of an energy and carbon synergistic management method for industrial parks according to one embodiment of this application;

[0071] Figure 2 This is a schematic diagram of a principle block diagram of an energy and carbon collaborative management and control system for industrial parks according to one embodiment of this application;

[0072] Figure 3 This is a schematic diagram of a device according to one embodiment of this application. Detailed Implementation

[0073] The present application will be further described in detail below with reference to the accompanying drawings.

[0074] In one embodiment, such as Figure 1 As shown, this application discloses a method for coordinated energy and carbon management in industrial parks, which specifically includes the following steps:

[0075] S10: Obtain multi-source energy carbon data within the industrial park and obtain the total carbon emission quota of the industrial park. Perform preprocessing operations on the multi-source energy carbon data to obtain the preprocessed energy carbon dataset.

[0076] Specifically, the data on energy consumption and corresponding carbon emissions generated by various entities within the industrial park during their production and operation processes are obtained. The annual or phased total carbon emission quotas for the park, issued by the park management or approved by regulatory agencies, are also acquired. This multi-source energy and carbon data can come from enterprise energy management platforms, electricity and gas metering terminals, production equipment energy consumption records, public facility operation records, and the park's energy metering platform. For example, the data on a manufacturing company's electricity consumption, natural gas usage, and the corresponding production line's operating load can be obtained. Simultaneously, energy usage data for park public facilities such as central air conditioning rooms or park lighting equipment can be acquired. Then, a unified preprocessing operation is performed on the acquired multi-source data, including completing missing data, identifying and correcting abnormal energy consumption values, converting data of different energy types to a unified unit of measurement, and performing time alignment processing on data of different time granularities. This results in a preprocessed energy and carbon dataset with a unified structure, continuous time, and usable for subsequent calculations and analyses.

[0077] S20: Construct a supergraph model of energy and carbon flow in the park based on the preprocessed energy and carbon dataset.

[0078] Specifically, based on the preprocessed energy and carbon dataset, various entities involved in energy consumption and carbon emission activities within the park are identified, and each entity is abstracted as a node in the energy and carbon flow network. At the same time, the connection relationship between nodes is established according to the supply, consumption, or exchange relationship of energy between different entities to form an energy and carbon flow structure model. The energy type can include different energy forms such as electricity, steam, natural gas, or waste heat, and carbon emissions are identified according to the energy usage and emission factors. During the construction process, the flow relationship of the same energy among multiple entities can be expressed in the form of hyperedges to describe the complex relationship of one energy source supplying energy to multiple entities at the same time or multiple entities jointly participating in energy exchange. For example, when the steam generated by the centralized boiler in the park is supplied to the production of multiple enterprises at the same time, the boiler node can be connected to multiple enterprise nodes through hyperedges, thereby forming an energy and carbon flow hypergraph model that can characterize the coupling relationship between energy flow and carbon emission flow among multiple entities in the park.

[0079] S30: Extract energy and carbon assessment indicators for each entity in the industrial park based on the preprocessed energy and carbon dataset. These indicators include energy intensity, carbon emission intensity, emission reduction potential, and industry benchmark indicators.

[0080] Specifically, the total energy consumption, production output or product output, and corresponding carbon emissions of each entity within the statistical period are read from the preprocessed energy and carbon dataset. Energy intensity indicators are obtained by calculating energy consumption per unit of output or per unit of product. At the same time, the total carbon emissions are calculated based on energy consumption and corresponding emission factors, and carbon emissions per unit of output are further calculated to obtain carbon emission intensity indicators. Then, combined with historical operating data or phased energy use trends, the potential energy saving and emission reduction space that each entity may achieve under the conditions of improved energy use efficiency or process optimization is analyzed to estimate emission reduction potential indicators. Industry benchmark indicators are determined based on publicly available industry statistics or industry energy consumption benchmark values. For example, a steel company can obtain its energy and carbon performance level in the park by comparing its energy consumption per unit of steel production with the industry average level, thereby forming a set of energy and carbon evaluation indicators that can reflect the energy and carbon performance of each entity.

[0081] S40: Based on the energy and carbon assessment indicators of each entity, calculate the comprehensive score value of each entity using a comprehensive scoring model that integrates the analytic hierarchy process and the entropy weight method, and determine the carbon quota allocation coefficient of each entity based on the comprehensive score value.

[0082] Specifically, a comprehensive evaluation model is constructed using the energy intensity, carbon emission intensity, emission reduction potential, and industry benchmark indicators of each entity as evaluation input indicators. First, the relative importance and weight relationships between each indicator are determined using the analytic hierarchy process (AHP) to reflect the subjective decision-making factors in the evaluation system. Simultaneously, the objective weights of each indicator are calculated based on the dispersion of data among different entities using the entropy weight calculation method to reflect the amount of information contained in the indicators. Then, the subjective and objective weights are integrated to form a comprehensive weight coefficient, and a weighted summation method is used to calculate the comprehensive score value of each entity. The score value can reflect the comprehensive performance of each entity in the park's energy and carbon management. For example, enterprises with lower energy intensity and greater emission reduction potential may receive higher scores. Then, the carbon quota allocation coefficient corresponding to each entity is calculated based on the relative proportion of the score values ​​among all entities to provide a basis for subsequent carbon quota allocation.

[0083] S50: Allocate the total carbon emission quota according to the carbon quota allocation coefficient to obtain the carbon quota of each entity, and summarize the carbon quota allocation results.

[0084] Specifically, the carbon emission allowances that each entity can obtain are calculated based on the total carbon emission allowance of the park and the carbon allowance allocation coefficient corresponding to each entity. The total carbon allowance is then allocated to each entity proportionally through a product calculation method. For example, when the total annual carbon emission allowance of the park is a certain value, the annual allowable emission allowance of each entity can be calculated based on the allocation coefficient of each entity. The carbon allowances allocated to each entity are then summarized and compiled into a carbon allowance allocation result table at the park level to record the carbon emission allowance allocation of each enterprise, public facility or energy operator in the current management cycle, thereby providing a basis for subsequent energy and carbon control and carbon emission management in the park.

[0085] S60: Based on the energy-carbon flow hypergraph model, identify the producers and consumers of waste heat and energy, and determine the waste heat and energy sharing path and sharing amount according to the energy flow connection relationship between the producers and consumers, so as to form a waste heat and energy sharing trading scheme.

[0086] Specifically, the energy input and output relationships of each entity are analyzed in the energy-carbon flow hypergraph model, and nodes with recyclable waste heat or waste energy in the energy output are identified. At the same time, the entity nodes in the park that have demand for heat or other energy are identified. Then, based on the energy connection relationship in the model, reachable paths between output nodes and demand nodes are searched, and the amount of energy that can be shared is calculated by combining factors such as energy transmission distance, energy form and demand scale. Thus, the sharing path and sharing amount of waste heat and waste energy are determined. For example, when the steam waste heat generated in the production process of a chemical enterprise can be transmitted to a neighboring enterprise through the park's pipeline network for heating process, the corresponding path can be identified in the model and the amount of steam that can be shared can be calculated. Based on this, a waste heat and waste energy sharing trading scheme including energy supply entities, energy demand entities and energy sharing scale can be formed.

[0087] S70: Based on the carbon quota allocation results and the waste heat and energy sharing and trading scheme, conduct energy and carbon collaborative management of multiple entities within the industrial park and generate a park energy and carbon collaborative management report.

[0088] Specifically, the carbon emissions of each entity during operation are monitored based on the carbon quota allocation results and compared with their allocated quotas. At the same time, the energy utilization of different entities within the park is coordinated and scheduled in conjunction with the waste heat and energy sharing trading scheme to guide entities with surplus energy to provide energy to entities in need and reduce overall energy waste. The overall energy and carbon management effect of the park is statistically analyzed based on changes in energy consumption, carbon emissions, and energy sharing implementation during the operating cycle. For example, the energy consumption reduction of each entity after the implementation of sharing and the overall carbon emission change trend of the park can be recorded. Then, a park energy and carbon collaborative management report is compiled, which includes the energy consumption indicators, carbon emission indicators, quota execution status, and energy sharing implementation status of each entity, to provide a basis for decision-making in subsequent park energy optimization and carbon emission reduction management.

[0089] Furthermore, after completing the energy and carbon collaborative management calculations and generating the waste heat and energy sharing trading scheme, the overall energy and carbon operation status of the park can be visualized and managed. By reading the energy consumption data, carbon emission data, carbon quota execution status, and waste heat and energy sharing data of each entity in the park, and summarizing, analyzing, and graphically processing various types of data, a park energy and carbon operation status map, an energy consumption distribution map, a carbon emission trend map, and a waste heat and energy sharing network map can be generated. This will intuitively display the energy flow relationship and carbon emission correlation between enterprises, public facilities, and energy storage equipment in the park. For example, the energy consumption level and carbon emission level of each enterprise node can be marked on the park topology map, and the waste heat and energy sharing path and sharing scale can be displayed by connecting lines. At the same time, the carbon quota usage and remaining quota changes of each entity can be dynamically displayed, enabling managers to grasp the park's energy and carbon operation status in real time. On this basis, energy consumption change trend analysis, carbon emission change trend analysis, and energy utilization efficiency analysis results can be generated based on historical operation data, and the overall energy saving and emission reduction effect of the park can be presented in a visual way. This will assist managers in making park energy and carbon collaborative management decisions and further optimize energy dispatch and carbon emission control strategies.

[0090] In one embodiment, step S10 involves acquiring multi-source energy carbon data within the industrial park and performing preprocessing operations on the multi-source energy carbon data to obtain a preprocessed energy carbon dataset, specifically including:

[0091] S11: Obtain enterprise-side data within the industrial park. Enterprise-side data includes energy consumption data, carbon emission data, energy intensity data, emission intensity data, waste heat and energy output data, and waste heat and energy demand data for each production stage.

[0092] Specifically, the system reads energy use and emission data generated by enterprises within the industrial park during their production operations. It extracts electricity consumption, natural gas usage, steam usage, or other energy usage data for each production stage from enterprise production management platforms, energy metering terminals, or production equipment operation records. Simultaneously, it calculates carbon emission data based on the emission factors of the corresponding energy sources. Furthermore, it combines enterprise output value or product output to calculate energy intensity data and carbon emission intensity data. While acquiring basic energy consumption and emission data, the system further identifies waste heat and energy generated during enterprise production processes, as well as enterprises' demand for heat or energy. For example, in steel or chemical production processes, it can identify high-temperature flue gas waste heat output data, and in food processing or material manufacturing processes, it can identify steam or hot water demand data. This forms enterprise-side data that reflects the energy use status and energy supply and demand relationship of enterprises.

[0093] S12: Obtain data on public facilities within the industrial park, including energy consumption and carbon emission data for lighting, heating, and water supply systems.

[0094] Specifically, the system reads the energy consumption records generated during the operation of public facilities in the industrial park, and obtains energy usage data for lighting, heating, and water supply facilities from the park's public facility operation management platform or energy metering equipment. For example, it can read the electricity consumption of park road lighting fixtures or public building lighting equipment, as well as the fuel consumption of centralized heating equipment or boiler equipment during operation and the electricity consumption of water supply pump stations or water treatment equipment. Then, it calculates the corresponding carbon emission data based on various energy consumptions and corresponding emission factors. For example, it calculates the indirect emissions of electricity based on lighting electricity consumption and the emissions of gas combustion based on boiler gas consumption, thereby forming public facility-side data that can reflect the energy consumption and carbon emissions of the park's public infrastructure operation.

[0095] S13: Obtain data from energy storage devices within the industrial park. This data includes charging and discharging data, energy storage capacity data, and energy consumption loss data.

[0096] Specifically, the system reads the operation records of energy storage devices within the park during operation, and obtains charging and discharging power data of energy storage units at different time periods from the energy storage device monitoring platform or device control terminal. It also reads the rated energy storage capacity and current available capacity of the energy storage devices, and further records the energy losses during energy conversion. For example, it records the energy conversion loss ratio or energy loss amount of battery energy storage units during charging and discharging. For instance, in lithium battery energy storage devices configured in the park, it can read the charging amount during low-load periods at night and the discharging amount during high-load periods during the day, while also recording changes in energy storage capacity and conversion losses. This forms energy storage device-side data that reflects the operating status and energy regulation capabilities of the energy storage devices.

[0097] S14: Summarize enterprise-side data, public facility-side data, and energy storage device-side data to obtain multi-source energy carbon data.

[0098] Specifically, the acquired enterprise-side data, public facility-side data, and energy storage device-side data are uniformly integrated and processed. Data from different sources are integrated and categorized according to a unified data structure. Data from various entities are correlated with the subject type according to the time dimension. For example, within the same time period, enterprise production energy consumption data, public facility operation energy consumption data, and energy storage device charging and discharging data are summarized and uniformly recorded according to the subject identifier. This forms a data set that simultaneously contains enterprise operation energy consumption information, public facility operation energy consumption information, and energy storage device operation information, enabling data from different sources to be expressed under a unified data framework and forming multi-source energy carbon data.

[0099] S15: Perform data cleaning, deduplication, standardization, and normalization on the multi-source energy carbon data to unify data caliber and accounting standards, and obtain the preprocessed energy carbon dataset.

[0100] Specifically, data quality processing operations are performed on the aggregated multi-source energy carbon data, including identifying and correcting abnormal energy consumption data, deduplicating duplicate records, and completing missing data. At the same time, the measurement units of different energy types are uniformly converted, such as converting natural gas consumption to standard energy units and different electricity measurement units to a unified measurement unit. Then, carbon emission data are recalculated or calibrated according to a unified accounting standard, and data with different dimensions are mapped to a unified numerical range through normalization processing, such as converting energy consumption data and emission data into comparable standardized values. This results in a preprocessed energy carbon dataset with a unified structure, consistent data caliber, and usability for subsequent analysis and calculation.

[0101] In one embodiment, step S20, namely constructing a campus energy and carbon flow hypergraph model based on the preprocessed energy and carbon dataset, specifically includes:

[0102] S21: Define enterprises, public facilities and energy storage equipment in the industrial park as hypergraph nodes, and define energy categories, carbon quotas and carbon emission-related attributes determined based on the preprocessed energy and carbon dataset as attribute nodes to construct a node set for the energy and carbon flow in the park.

[0103] Specifically, based on the preprocessed energy and carbon dataset, the main entities involved in energy use and carbon emission activities within the industrial park are identified. Each enterprise, public facility, and energy storage device is abstracted as a main node in a hypergraph. Simultaneously, energy type information, carbon emission information, and carbon quota-related data are extracted from the preprocessed energy and carbon dataset, and information such as energy category, carbon quota, and carbon emission are abstracted as attribute nodes. Then, the main nodes are associated with their corresponding attribute nodes to construct a node relationship structure. For example, a manufacturing enterprise node can be associated with an electric energy node, a steam energy node, and a carbon emission node. Similarly, an energy storage device node can be associated with an electric energy node and an energy storage capacity attribute node. At the same time, the park's public lighting facility node can be associated with an electric energy node and its corresponding carbon emission node, thereby forming a set of nodes that can reflect the relationship between the main entities and energy and carbon attributes, thus constructing the basic node structure of the park's energy and carbon flow.

[0104] S22: Construct multi-type hyperedges based on the preprocessed energy and carbon dataset. The multi-type hyperedges include energy transmission hyperedges, carbon flow association hyperedges, and waste heat and waste energy interaction hyperedges.

[0105] Specifically, based on the preprocessed energy and carbon dataset, the energy flow relationships and carbon emission correlations among different entities within the park are analyzed. Various types of hyperedges are constructed according to different types of correlations. For example, when energy is transferred from one entity to multiple entities or multiple entities participate in energy exchange, an energy transmission hyperedge is constructed to connect related nodes. For instance, the park's centralized power supply node can be connected to multiple enterprise energy-consuming nodes via an energy transmission hyperedge. Simultaneously, carbon flow correlation hyperedges are constructed based on the correlations formed between carbon emissions among different entities. For instance, when multiple enterprises share the same energy source and generate carbon emissions, their carbon emission correlations can be represented by carbon flow correlation hyperedges. Furthermore, waste heat and waste energy interaction hyperedges are constructed based on the supply and demand relationships between different entities. For instance, when the waste heat generated during the production process of a chemical enterprise can be supplied to neighboring enterprises, the waste heat producing entity node and the waste heat demanding entity node can be connected via a waste heat and waste energy interaction hyperedge. This forms a multi-type hyperedge structure capable of expressing complex energy flow and carbon emission relationships among multiple entities.

[0106] S23: Construct a campus energy-carbon flow hypergraph model based on the node set and multiple types of hyperedges, and introduce an energy-carbon flow coupling coefficient. Update the edge weight parameters in the campus energy-carbon flow hypergraph model based on the energy-carbon flow coupling coefficient.

[0107] Specifically, a hypergraph model of energy and carbon flow in the park is constructed by combining a set of nodes with multiple types of hyperedges. The coupling relationship parameters between energy flow and carbon emission flow are calculated by centrally recording energy consumption and carbon emission data in the energy and carbon data set to obtain the energy-carbon flow coupling coefficient. Then, the edge weights in the hypergraph model are dynamically adjusted according to the energy-carbon flow coupling coefficient to reflect the degree of influence of energy flow on carbon emissions. For example, when the carbon emission corresponding to a unit energy consumption of a certain enterprise is high, the carbon emission weight of the corresponding energy transmission edge can be increased, while when a certain energy source is low-carbon energy, its corresponding edge weight can be decreased. Thus, the edge weights in the hypergraph model can simultaneously reflect the scale of energy flow and the degree of influence of carbon emissions, thereby obtaining a hypergraph model of energy and carbon flow in the park that can reflect the coupling relationship between energy flow and carbon flow among multiple entities in the park.

[0108] In one embodiment, step S23, namely, introducing an energy-carbon flow coupling coefficient and updating the edge weight parameters in the park's energy-carbon flow hypergraph model based on the energy-carbon flow coupling coefficient, specifically includes:

[0109] S231: Extract energy consumption data and carbon emission data from the preprocessed energy and carbon dataset.

[0110] Specifically, energy consumption records and carbon emission records for each entity within the statistical period are read from the preprocessed energy and carbon dataset. These records are then extracted according to entity type and time dimension to obtain basic data reflecting energy use and emissions. Energy consumption data may include electricity consumption, natural gas consumption, steam consumption, or other energy consumption. Carbon emission data can be calculated based on energy consumption and corresponding emission factors. For example, the electricity consumption data and corresponding carbon emissions of a manufacturing enterprise during a certain production cycle can be read. At the same time, the energy consumption and carbon emission information of public facilities in the park during operation can also be read. The extracted energy consumption data and carbon emission data are then linked and organized according to entity identifiers to form a basic data set for subsequent calculation of energy-carbon coupling relationships.

[0111] S232: Calculate the energy-carbon flow coupling coefficient based on energy consumption data and carbon emission data to quantify the correlation between energy consumption and carbon emissions.

[0112] Specifically, the correlation between energy consumption and carbon emission is established based on the extracted energy consumption and carbon emission data. The energy-carbon flow coupling coefficient is obtained by calculating the proportional relationship or correlation coefficient between energy consumption and corresponding carbon emission, so as to quantify the degree of impact of energy use on carbon emissions. For example, the degree of energy-carbon coupling can be represented by calculating the carbon emission corresponding to a unit of energy consumption. When an entity uses high-carbon emission energy, its corresponding coupling coefficient is relatively high, while when an entity mainly uses low-carbon energy, its corresponding coupling coefficient is relatively low. For example, when an enterprise uses a large amount of coal-fired steam, its carbon emission corresponding to a unit of energy consumption is high, while when an enterprise mainly uses clean electricity, its carbon emission is relatively low. Thus, the energy-carbon flow coupling coefficient corresponding to different entities or different energy flows can be calculated.

[0113] S233: Update the edge weight parameters of the corresponding hyperedges in the energy and carbon flow hypergraph model of the park according to the energy and carbon flow coupling coefficient, so as to reflect the coupling relationship between energy flow and carbon emission flow among different entities.

[0114] Specifically, the hyperedge weight parameters in the park's energy-carbon flow hypergraph model are adjusted based on the calculated energy-carbon flow coupling coefficient. This ensures that the energy flow relationships in the hypergraph can simultaneously reflect the energy scale and the degree of carbon emission impact. During the update process, the coupling coefficient is used as an edge weight adjustment factor and combined with the original energy flow weights. For example, when the energy-carbon coupling coefficient of a certain energy transmission path is high, the edge weight parameter of that path in the hypergraph can be increased to reflect its higher carbon emission impact. Conversely, when a certain energy source is low-carbon energy and the coupling coefficient is low, the edge weight parameter of that path can be reduced accordingly. For instance, in the park's power supply path, if some enterprises mainly rely on high-carbon energy for power generation, the corresponding edge weight can be increased accordingly. If some enterprises use clean energy for power supply, the corresponding edge weight can be appropriately reduced. This allows the updated hypergraph model to more accurately express the coupling relationship between energy flow and carbon emission flow between different entities.

[0115] In one embodiment, step S40 involves calculating the comprehensive score of each entity based on its energy and carbon assessment indicators using a comprehensive scoring model that integrates the analytic hierarchy process (AHP) and the entropy weight method, and determining the carbon quota allocation coefficient for each entity based on the comprehensive score. Specifically, this includes:

[0116] S41: Construct an energy and carbon evaluation index system based on energy and carbon evaluation indicators, and use the analytic hierarchy process (AHP) to determine the subjective weights of each energy and carbon evaluation index.

[0117] Specifically, an evaluation index system reflecting the energy consumption level and carbon emission performance of the main body is established based on the extracted energy and carbon evaluation indicators. A hierarchical structure model is constructed according to the hierarchical relationship between the evaluation objectives and evaluation indicators. The energy and carbon performance of the main body of the park is taken as the overall evaluation objective layer, and energy consumption intensity, carbon emission intensity, emission reduction potential, and industry benchmark indicators are taken as the indicator layer. Then, a judgment matrix is ​​constructed according to the importance of the indicators to compare the degree of influence of different indicators on the overall evaluation objective. The weight value of each indicator is calculated based on the judgment matrix to obtain the subjective weight. For example, in the evaluation process, carbon emission intensity can be considered to have a greater impact on carbon quota allocation based on the actual needs of energy and carbon management, and thus given a higher weight. The emission reduction potential indicator is used to reflect the future energy-saving space and thus given an appropriate weight. The subjective weight of each energy and carbon evaluation indicator is determined in the above way.

[0118] S42: Calculate the information entropy value of each energy carbon evaluation index based on the preprocessed energy carbon dataset, and determine the objective weight of each energy carbon evaluation index using the entropy weight method based on the information entropy value.

[0119] Specifically, the data for each entity in terms of energy intensity, carbon emission intensity, emission reduction potential, and industry benchmark indicators are read from the preprocessed energy and carbon dataset. The data for each indicator is then normalized to eliminate differences in dimensions. The distribution probability of each indicator among different entities is calculated based on the normalized data, and the corresponding information entropy value is further calculated to reflect the information dispersion of each indicator. When an indicator differs significantly among different entities, its lower information entropy value indicates that the indicator provides a higher amount of information. Conversely, when an indicator differs slightly among different entities, its higher information entropy value indicates a lower information discrimination. The objective weight of each indicator is then calculated based on the information entropy value. For example, when there are significant differences in carbon emission intensity among different enterprises in the park, the objective weight of that indicator is relatively high, thus obtaining objective weights that reflect the degree of objective differences in the data.

[0120] S43: Combine subjective and objective weights to obtain the comprehensive weight of each energy and carbon evaluation index, and calculate the comprehensive score of each subject based on the comprehensive weight.

[0121] Specifically, the subjective weights obtained through the analytic hierarchy process (AHP) and the objective weights obtained through the entropy weight method are integrated. The comprehensive weights of each indicator are then calculated using a weighted average or proportional fusion method. This ensures that the comprehensive weights reflect both the evaluator's perception of the importance of the indicators and the objective differences reflected in the data distribution. The comprehensive weights are then used to weight and sum the values ​​of each entity on each energy and carbon evaluation indicator to obtain a comprehensive score. For example, a company with low energy intensity and carbon emission intensity lower than the industry benchmark can obtain a higher score in the comprehensive score calculation, while a company with high energy intensity and high emission intensity will have a relatively lower score. Through the above calculations, a comprehensive score that can comprehensively reflect the energy and carbon performance level of each entity is obtained.

[0122] S44: Determine the carbon quota allocation coefficient for each entity based on the comprehensive score.

[0123] Specifically, all entities within the park are ranked according to their comprehensive scores, and the proportion of each score in the overall score is calculated. Then, the carbon quota allocation coefficient for each entity is determined based on the score proportion. Entities with higher scores have performed better in carbon emission control and can obtain a relatively reasonable quota ratio, while entities with lower scores correspond to a lower allocation ratio. For example, when a company reduces energy consumption through energy-saving renovations and obtains a higher comprehensive score, its carbon quota allocation coefficient can be relatively increased, while high-energy-consuming and high-emission entities correspond to a lower allocation coefficient. The allocation coefficient of each entity in the total carbon emission quota of the park is obtained through the above method, thus providing a basis for subsequent carbon quota allocation.

[0124] In one embodiment, step S30, namely the calculation of emission reduction potential, specifically includes:

[0125] S301: Extract energy consumption data and carbon emission data of each entity based on the preprocessed energy and carbon dataset, and obtain energy consumption benchmark values ​​and carbon emission benchmark values ​​from the preset industry benchmark database.

[0126] Specifically, the energy consumption and carbon emission records of each entity within the industrial park during the statistical period are read from the preprocessed energy and carbon dataset. Data is extracted according to the entity identifier to form an entity energy and carbon dataset. The energy consumption data may include electricity consumption, natural gas usage, steam consumption, or other energy usage. The carbon emission data can be calculated based on energy consumption and emission factors. At the same time, the energy consumption benchmark value and carbon emission benchmark value corresponding to the industry type of each entity are read from the preset industry benchmark database. For example, when the entity is a steel production enterprise, the energy consumption benchmark value and carbon emission benchmark value per unit product of the steel industry can be read. When the entity is a food processing enterprise, the energy consumption benchmark value and carbon emission benchmark value per unit output value of the food processing industry can be read. The extracted entity energy and carbon data are matched and organized with the corresponding industry benchmark data to form the dataset required for subsequent calculation and analysis.

[0127] S302: Calculate the energy intensity and carbon emission intensity of each entity based on energy consumption data and carbon emission data.

[0128] Specifically, the extracted energy consumption and carbon emission data are combined with the production scale information of each entity within the statistical period to calculate intensity indicators that reflect energy utilization efficiency and emission levels. Energy consumption intensity can be calculated by energy consumption per unit output or energy consumption per unit product, and carbon emission intensity can be calculated by carbon emissions per unit output or carbon emissions per unit product. For example, when a manufacturing enterprise consumes a certain amount of electricity and produces a corresponding number of products within the statistical period, the energy consumption per unit product can be calculated by dividing the total energy consumption by the product output. At the same time, the carbon emission intensity per unit product can be obtained by dividing the carbon emissions by the product output. Thus, the energy consumption intensity index and carbon emission intensity index that reflect the energy utilization efficiency and carbon emission levels of each entity are obtained.

[0129] S303: Compare and analyze energy consumption intensity and carbon emission intensity with industry benchmarks to obtain the energy consumption deviation and carbon emission deviation of each entity.

[0130] Specifically, the energy intensity index calculated by each entity is compared with the corresponding industry energy consumption benchmark value to obtain the degree of deviation of the entity's energy consumption level from the industry benchmark. At the same time, the carbon emission intensity index of the entity is compared with the corresponding industry carbon emission benchmark value to obtain the degree of deviation of the entity's carbon emission level from the industry benchmark. The deviation can be expressed as the ratio of the difference between the entity's index value and the industry benchmark value. For example, when a company's energy consumption per unit of product is higher than the industry average, a higher energy consumption deviation can be calculated, while when a company's carbon emission intensity is lower than the industry benchmark, a lower carbon emission deviation can be calculated. Through the above comparative analysis, the deviation of each entity from the industry benchmark in terms of energy utilization efficiency and carbon emission level is obtained.

[0131] S304: Calculate the theoretical emission reduction of each entity based on the energy consumption deviation and carbon emission deviation, and determine the theoretical emission reduction as the emission reduction potential indicator of the entity.

[0132] Specifically, based on the energy consumption deviation and carbon emission deviation calculated by each entity, the potential reduction in energy consumption and carbon emissions that an entity could achieve if it reaches the industry benchmark level is estimated. The theoretical emission reduction is determined by calculating the difference between the entity's current carbon emissions and the benchmark emission level, thereby reflecting the entity's potential space for energy conservation and carbon reduction. For example, if an enterprise's current carbon emission intensity is significantly higher than the industry benchmark value, the corresponding reduction in carbon emissions can be calculated by adjusting its emission intensity to the benchmark level. If an enterprise's energy consumption level is slightly higher than the industry average, the energy consumption and carbon emissions that it can reduce under the condition of improved energy efficiency can be calculated. The calculated theoretical emission reduction is used as the entity's emission reduction potential indicator for subsequent energy and carbon assessment and quota allocation analysis.

[0133] In one embodiment, step S60 involves identifying the producers and consumers of waste heat and energy based on the energy-carbon flow hypergraph model, and determining the waste heat and energy sharing path and amount based on the energy flow connection relationship between the producers and consumers to form a waste heat and energy sharing trading scheme. Specifically, this includes:

[0134] S61: Identify entities with waste heat and energy output based on the waste heat and energy interaction hyperedge in the energy carbon flow hypergraph model, and determine the corresponding waste heat and energy output.

[0135] Specifically, in the energy-carbon flow hypergraph model, the hyperedges marked as having waste heat and waste energy interaction relationships are read, and the entities that may generate waste heat and waste energy are identified based on the entity nodes connected by the hyperedges. Then, the energy usage and production operation data recorded in the preprocessed energy-carbon dataset are combined to analyze the energy output of each entity in the production process, thereby determining the entities with waste heat and waste energy output. At the same time, the waste heat or waste energy data generated by the corresponding entity within the statistical period is read and its usable output is calculated. For example, a steel company generates a large amount of high-temperature flue gas waste heat during high-temperature smelting, and the recoverable waste heat can be estimated by reading its energy output records. Another example is a chemical company that releases steam waste heat during the reaction process, and the corresponding waste heat output can be obtained through energy balance calculation, thereby determining the waste heat and waste energy output of each entity.

[0136] S62: Identify entities with waste heat and energy requirements based on the waste heat and energy interaction hyperedge in the energy carbon flow hypergraph model, and determine the corresponding waste heat and energy requirements.

[0137] Specifically, in the energy-carbon flow hypergraph model, the hyperedges related to waste heat and waste energy interactions are read and the principal nodes connected to them are analyzed. By identifying principals with high energy input demands or those requiring thermal support during production, the principals with waste heat and waste energy demands are determined. Then, based on the energy usage data centrally recorded in the preprocessed energy-carbon dataset, the thermal energy demand or energy demand scale of each principal is analyzed. The waste heat and waste energy demand is calculated by reading historical energy consumption records or production operation data. For example, a food processing company needs steam for heating and sterilization processes during production, and the corresponding thermal energy demand can be calculated based on its steam consumption records. Similarly, a textile company needs hot water or hot air for processing during production, and the corresponding waste heat demand can be estimated based on its energy consumption data. Thus, the waste heat and waste energy demand corresponding to each principal is determined.

[0138] S63: Match supply and demand based on the amount of waste heat and energy produced and the amount of waste heat and energy demanded to obtain candidate waste heat and energy sharing entities.

[0139] Specifically, a supply-demand matching analysis is conducted based on the identified waste heat and energy producing entities and their corresponding outputs, and the waste heat and energy demanding entities and their demands. Potential sharing relationships are established by comparing the waste heat production capacity and demand scale of each entity. When the waste heat production of an entity can meet or partially meet the energy demand of another entity, it is identified as a candidate sharing entity pair. For example, when the steam waste heat generated by a chemical enterprise is greater than the steam demand of a food processing enterprise, a corresponding sharing entity pair can be formed. Similarly, when the high-temperature waste heat generated by a steel enterprise can partially meet the heating demand of a neighboring enterprise, a candidate sharing entity pair can also be formed. Through the above supply-demand matching process, a set of candidate waste heat and energy sharing entity pairs with energy sharing potential is obtained.

[0140] S64: Determine the sharing path between candidate waste heat and waste energy sharing entities based on the energy flow connection relationship between entities in the energy carbon flow hypergraph model.

[0141] Specifically, the energy flow connection relationship between the main nodes is read in the energy-carbon flow hypergraph model, and the feasible path for the transmission of waste heat and waste energy between different main entities is determined by analyzing the connection structure between the nodes. Then, based on the node positions of the candidate sharing main entity pairs, the connection path is searched in the hypergraph model and the shared path that can realize energy transmission is determined. For example, when there is a steam pipeline connection between the waste heat producing entity and the demanding entity, the pipeline can be identified as an energy transmission path. Another example is when the two entities are connected through the park's centralized heating network, the heating network can be identified as a shared path. At the same time, the path length or connection relationship is combined to screen the path that can realize stable energy transmission, thereby determining the shared path between the candidate waste heat and waste energy sharing main entity pairs.

[0142] S65: Calculate the amount of waste heat and energy shared based on the waste heat and energy output, waste heat and energy demand, and shared path, and generate a waste heat and energy sharing transaction plan.

[0143] Specifically, the waste heat and energy output of the waste heat producing entity is matched with the waste heat and energy demand of the demanding entity. The actual amount of energy that can be shared is determined by combining the energy transmission capacity that the shared path can carry. Then, the energy allocation relationship between the sharing entities is determined based on the matching results. For example, when the waste heat generated by a certain entity is greater than the demand of the demanding entity, the sharing scale can be determined according to the demand. When the output is less than the demand, the allocation can be based on the output. The waste heat and energy sharing transaction scheme is formed based on the sharing entity, the sharing path, and the scale of shared energy. For example, information records containing waste heat providing entities, waste heat using entities, shared energy types, and shared quantities can be generated, thereby forming a waste heat and energy sharing transaction scheme that can guide the energy sharing and collaborative utilization among the entities in the park.

[0144] Furthermore, after the waste heat and energy sharing trading scheme is generated, the waste heat sharing transmission path can be dynamically adjusted according to the real-time changes in waste heat supply and demand during the park's operation and the operating status of the energy transmission network. During operation, the waste heat and energy output data and waste heat and energy demand data of each entity are continuously read, and the operating status information of the park's energy transmission network is obtained simultaneously, such as the pressure status of the steam pipeline network, the flow status of the hot water pipeline, or the available capacity of the energy transmission channel. Then, based on the updated supply and demand relationship, the matching relationship between the waste heat supply entity and the demand entity is recalculated, and the feasibility and transmission capacity of each candidate sharing path are re-evaluated in the energy-carbon flow hypergraph model. When the original sharing path experiences changes in supply and demand or a decrease in transmission capacity, a new feasible path is selected for replacement. For example, when the waste heat output of a certain enterprise increases, some waste heat can be redistributed to nearby demand entities, while when a certain pipeline network is overloaded or out of service for maintenance, waste heat can be transmitted through other energy connection paths. This achieves dynamic adjustment of the waste heat sharing transmission path to improve the waste heat and energy utilization efficiency of the park and maintain the stability of energy collaborative scheduling.

[0145] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0146] In one embodiment, an energy and carbon co-management system for industrial parks is provided, which corresponds one-to-one with the energy and carbon co-management method for industrial parks described in the above embodiments. For example... Figure 2 As shown, this energy and carbon collaborative management system for industrial parks includes a data acquisition and preprocessing module, a hypergraph model construction module, an evaluation index extraction module, a comprehensive scoring calculation module, a carbon quota allocation module, a waste heat sharing scheme generation module, and a collaborative management generation module. Detailed descriptions of each functional module are as follows:

[0147] The data acquisition and preprocessing module is used to acquire multi-source energy carbon data in the industrial park and obtain the total carbon emission quota of the industrial park. It performs preprocessing operations on the multi-source energy carbon data to obtain the preprocessed energy carbon dataset.

[0148] The hypergraph model building module is used to build a hypergraph model of energy and carbon flow in the park based on the preprocessed energy and carbon dataset.

[0149] The evaluation index extraction module is used to extract energy and carbon evaluation indicators for each entity in the industrial park based on the preprocessed energy and carbon dataset. The energy and carbon evaluation indicators include energy consumption intensity, carbon emission intensity, emission reduction potential, and industry benchmark indicators.

[0150] The comprehensive scoring calculation module is used to calculate the comprehensive score value of each entity based on the energy and carbon evaluation indicators of each entity, using a comprehensive scoring model that integrates the analytic hierarchy process and the entropy weight method, and to determine the carbon quota allocation coefficient of each entity based on the comprehensive score value.

[0151] The carbon quota allocation module is used to allocate the total carbon emission quota according to the carbon quota allocation coefficient, obtain the carbon quota of each entity, and summarize the carbon quota allocation results.

[0152] The waste heat sharing scheme generation module is used to identify the producers and consumers of waste heat and energy based on the energy-carbon flow hypergraph model, and to determine the waste heat and energy sharing path and sharing amount according to the energy flow connection relationship between the producers and consumers, so as to form a waste heat and energy sharing trading scheme.

[0153] The collaborative management and control generation module is used to conduct collaborative energy and carbon management and control of multiple entities in the industrial park based on the carbon quota allocation results and the waste heat and waste energy sharing and trading scheme, and to generate a collaborative energy and carbon management and control report for the park.

[0154] Optionally, the data acquisition preprocessing module includes:

[0155] The enterprise data acquisition submodule is used to acquire enterprise-side data within the industrial park. Enterprise-side data includes energy consumption data, carbon emission data, energy intensity data, emission intensity data, waste heat and energy output data, and waste heat and energy demand data for each production stage.

[0156] The public facilities data acquisition submodule is used to acquire public facilities data within the industrial park. Public facilities data includes energy consumption data and carbon emission data for lighting systems, heating systems, and water supply systems.

[0157] The energy storage equipment data acquisition submodule is used to acquire data from energy storage equipment in the industrial park. The energy storage equipment data includes charging and discharging data, energy storage capacity data, and energy consumption loss data of the energy storage equipment.

[0158] The multi-source data aggregation submodule is used to aggregate enterprise-side data, public facility-side data, and energy storage device-side data to obtain multi-source energy carbon data.

[0159] The data preprocessing submodule is used to perform data cleaning, deduplication, standardization, and normalization on multi-source energy and carbon data to unify data caliber and accounting standards, and obtain preprocessed energy and carbon datasets.

[0160] Optionally, the hypergraph model building modules include:

[0161] The node construction submodule is used to define enterprises, public facilities and energy storage equipment in the industrial park as hypergraph nodes, and to define energy categories, carbon quotas and carbon emission related attributes determined according to the preprocessed energy and carbon dataset as attribute nodes, so as to construct a node set of energy and carbon flow in the park.

[0162] The hyperedge construction submodule is used to construct multiple types of hyperedges based on the preprocessed energy and carbon dataset. These types of hyperedges include energy transmission hyperedges, carbon flow association hyperedges, and waste heat and waste energy interaction hyperedges.

[0163] The hypergraph model generation submodule is used to construct a campus energy and carbon flow hypergraph model based on the node set and multiple types of hyperedges, and introduces the energy and carbon flow coupling coefficient, and updates the edge weight parameters in the campus energy and carbon flow hypergraph model based on the energy and carbon coupling coefficient.

[0164] Optionally, the hypergraph model generation submodule includes:

[0165] The data extraction unit is used to extract energy consumption data and carbon emission data from the preprocessed energy and carbon dataset.

[0166] The coupling coefficient calculation unit is used to calculate the energy-carbon flow coupling coefficient based on energy consumption data and carbon emission data, so as to quantify the degree of correlation between energy consumption and carbon emissions.

[0167] The edge weight update unit is used to update the edge weight parameters of the corresponding hyperedges in the energy and carbon flow hypergraph model of the park according to the energy and carbon flow coupling coefficient, so as to reflect the coupling relationship between energy flow and carbon emission flow between different entities.

[0168] Optional, the comprehensive score calculation module includes:

[0169] The indicator system construction submodule is used to construct an energy carbon evaluation indicator system based on energy carbon evaluation indicators, and to determine the subjective weight of each energy carbon evaluation indicator using the analytic hierarchy process.

[0170] The objective weight calculation submodule is used to calculate the information entropy value of each energy carbon evaluation index based on the preprocessed energy carbon dataset, and to determine the objective weight of each energy carbon evaluation index using the entropy weight method based on the information entropy value.

[0171] The comprehensive weight calculation submodule is used to integrate subjective and objective weights to obtain the comprehensive weight of each energy and carbon assessment index, and calculate the comprehensive score of each subject based on the comprehensive weight.

[0172] The quota coefficient determination submodule is used to determine the carbon quota allocation coefficient for each entity based on the comprehensive score.

[0173] Optionally, the energy and carbon co-management system for industrial parks may also include:

[0174] The data extraction module is used to extract energy consumption data and carbon emission data of each entity based on the preprocessed energy and carbon dataset, and to obtain energy consumption benchmark values ​​and carbon emission benchmark values ​​from the preset industry benchmark database.

[0175] The intensity calculation module is used to calculate the energy intensity and carbon emission intensity of each entity based on energy consumption data and carbon emission data.

[0176] The deviation analysis module is used to compare and analyze energy consumption intensity and carbon emission intensity with industry benchmark values ​​to obtain the energy consumption deviation and carbon emission deviation of each entity.

[0177] The emission reduction potential calculation module is used to calculate the theoretical emission reduction of each entity based on the energy consumption deviation and carbon emission deviation, and to determine the theoretical emission reduction as the emission reduction potential index of the entity.

[0178] Optionally, the waste heat sharing scheme generation module includes:

[0179] The output entity identification submodule is used to identify entities with waste heat and waste energy output based on the waste heat and waste energy interaction hyperedge in the energy carbon flow hypergraph model, and to determine the corresponding waste heat and waste energy output.

[0180] The demand subject identification submodule is used to identify subjects with waste heat and waste energy demand based on the waste heat and waste energy interaction hyperedge in the energy carbon flow hypergraph model, and to determine the corresponding waste heat and waste energy demand amount.

[0181] The supply and demand matching submodule is used to match the supply and demand based on the waste heat and energy output and the waste heat and energy demand to obtain candidate waste heat and energy sharing entities.

[0182] The shared path determination submodule is used to determine the shared path between candidate waste heat and waste energy sharing entities based on the energy flow connection relationship between entities in the energy carbon flow hypergraph model.

[0183] The shared quantity calculation submodule is used to calculate the shared quantity of waste heat and energy based on the waste heat and energy output, waste heat and energy demand, and shared path, and generate a waste heat and energy sharing transaction scheme.

[0184] Specific limitations regarding the energy and carbon co-management system for industrial parks can be found in the above description of the energy and carbon co-management method for industrial parks, and will not be repeated here. Each module in the aforementioned energy and carbon co-management system for industrial parks can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0185] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for coordinated energy and carbon management in industrial parks.

[0186] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0187] Acquire multi-source energy carbon data within the industrial park and obtain the total carbon emission quota of the industrial park. Perform preprocessing operations on the multi-source energy carbon data to obtain the preprocessed energy carbon dataset.

[0188] A hypergraph model of energy and carbon flow in the park was constructed based on the preprocessed energy and carbon dataset.

[0189] Based on the preprocessed energy and carbon dataset, energy and carbon assessment indicators for each entity in the industrial park are extracted. These indicators include energy intensity, carbon emission intensity, emission reduction potential, and industry benchmark indicators.

[0190] Based on the energy and carbon assessment indicators of each entity, a comprehensive scoring model based on the analytic hierarchy process and the entropy weight method is used to calculate the comprehensive score value of each entity, and the carbon quota allocation coefficient of each entity is determined based on the comprehensive score value.

[0191] The total carbon emission quota is allocated according to the carbon quota allocation coefficient to obtain the carbon quota of each entity, and the carbon quota allocation result is obtained by summing them up.

[0192] Based on the energy-carbon flow hypergraph model, the producers and consumers of waste heat and energy are identified, and the waste heat and energy sharing paths and sharing amounts are determined according to the energy flow connection relationship between the producers and consumers, so as to form a waste heat and energy sharing trading scheme.

[0193] Based on the carbon quota allocation results and the waste heat and energy sharing and trading scheme, energy and carbon collaborative management is carried out on multiple entities within the industrial park, and a park energy and carbon collaborative management report is generated.

[0194] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0195] Acquire multi-source energy carbon data within the industrial park and obtain the total carbon emission quota of the industrial park. Perform preprocessing operations on the multi-source energy carbon data to obtain the preprocessed energy carbon dataset.

[0196] A hypergraph model of energy and carbon flow in the park was constructed based on the preprocessed energy and carbon dataset.

[0197] Based on the preprocessed energy and carbon dataset, energy and carbon assessment indicators for each entity in the industrial park are extracted. These indicators include energy intensity, carbon emission intensity, emission reduction potential, and industry benchmark indicators.

[0198] Based on the energy and carbon assessment indicators of each entity, a comprehensive scoring model based on the analytic hierarchy process and the entropy weight method is used to calculate the comprehensive score value of each entity, and the carbon quota allocation coefficient of each entity is determined based on the comprehensive score value.

[0199] The total carbon emission quota is allocated according to the carbon quota allocation coefficient to obtain the carbon quota of each entity, and the carbon quota allocation result is obtained by summing them up.

[0200] Based on the energy-carbon flow hypergraph model, the producers and consumers of waste heat and energy are identified, and the waste heat and energy sharing paths and sharing amounts are determined according to the energy flow connection relationship between the producers and consumers, so as to form a waste heat and energy sharing trading scheme.

[0201] Based on the carbon quota allocation results and the waste heat and energy sharing and trading scheme, energy and carbon collaborative management is carried out on multiple entities within the industrial park, and a park energy and carbon collaborative management report is generated.

[0202] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0203] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0204] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for coordinated energy and carbon management in industrial parks, characterized in that, The energy and carbon synergistic management method for industrial parks includes: Acquire multi-source energy carbon data within the industrial park and obtain the total carbon emission quota of the industrial park. Perform preprocessing operations on the multi-source energy carbon data to obtain a preprocessed energy carbon dataset. A hypergraph model of energy and carbon flow in the park is constructed based on the preprocessed energy and carbon dataset. Based on the preprocessed energy and carbon dataset, energy and carbon assessment indicators for each entity within the industrial park are extracted. These energy and carbon assessment indicators include energy consumption intensity, carbon emission intensity, emission reduction potential, and industry benchmark indicators. Based on the energy and carbon evaluation indicators of each entity, a comprehensive scoring model based on the analytic hierarchy process and the entropy weight method is used to calculate the comprehensive score value of each entity, and the carbon quota allocation coefficient of each entity is determined based on the comprehensive score value. The total carbon emission quota is allocated according to the carbon quota allocation coefficient to obtain the carbon quota of each entity, and the carbon quota allocation result is obtained by summarizing. Based on the energy-carbon flow hypergraph model, the producers and consumers of waste heat and energy are identified, and the waste heat and energy sharing path and sharing amount are determined according to the energy flow connection relationship between the producers and consumers, so as to form a waste heat and energy sharing trading scheme. Based on the carbon quota allocation results and the waste heat and energy sharing and trading scheme, energy and carbon collaborative management is carried out on multiple entities within the industrial park, and a park energy and carbon collaborative management report is generated.

2. The energy and carbon synergistic management method for industrial parks according to claim 1, characterized in that, The process of acquiring multi-source energy carbon data within the industrial park and performing preprocessing operations on the multi-source energy carbon data to obtain a preprocessed energy carbon dataset specifically includes: Acquire enterprise-side data within the industrial park, including energy consumption data, carbon emission data, energy intensity data, emission intensity data, waste heat and energy output data, and waste heat and energy demand data for each production stage. Acquire public facility data within the industrial park, including energy consumption and carbon emission data for lighting, heating, and water supply systems. Acquire data from energy storage devices within the industrial park. This data includes charging and discharging data, energy storage capacity data, and energy consumption loss data of the energy storage devices. The multi-source energy carbon data is obtained by aggregating the enterprise-side data, the public facility-side data, and the energy storage device-side data. The multi-source energy carbon data is cleaned, deduplicated, standardized, and normalized to unify data caliber and accounting standards, resulting in the preprocessed energy carbon dataset.

3. The energy and carbon synergistic management method for industrial parks according to claim 1, characterized in that, The construction of the park's energy and carbon flow hypergraph model based on the preprocessed energy and carbon dataset specifically includes: Enterprises, public facilities, and energy storage equipment within the industrial park are defined as hypergraph nodes, and energy categories, carbon quotas, and carbon emission-related attributes determined based on the preprocessed energy and carbon dataset are defined as attribute nodes, in order to construct a node set for the energy and carbon flow of the park. Based on the preprocessed energy and carbon dataset, a variety of hyperedges are constructed, including energy transmission hyperedges, carbon flow association hyperedges, and waste heat and waste energy interaction hyperedges. A campus energy-carbon flow hypergraph model is constructed based on the node set and the multi-type hyperedges, and an energy-carbon flow coupling coefficient is introduced. The edge weight parameters in the campus energy-carbon flow hypergraph model are updated based on the energy-carbon coupling coefficient.

4. The energy and carbon synergistic management method for industrial parks according to claim 3, characterized in that, The introduction of an energy-carbon flow coupling coefficient, and the updating of edge weight parameters in the park's energy-carbon flow hypergraph model based on the energy-carbon flow coupling coefficient, specifically includes: Energy consumption data and carbon emission data are extracted from the preprocessed energy and carbon dataset; The energy-carbon flow coupling coefficient is calculated based on the energy consumption data and carbon emission data to quantify the correlation between energy consumption and carbon emissions. The edge weight parameters of the corresponding hyperedges in the energy and carbon flow hypergraph model of the park are updated according to the energy and carbon flow coupling coefficient to reflect the coupling relationship between energy flow and carbon emission flow among different entities.

5. The energy and carbon synergistic management method for industrial parks according to claim 1, characterized in that, The process involves calculating a comprehensive score for each entity based on its energy and carbon assessment indicators using a comprehensive scoring model that integrates the analytic hierarchy process (AHP) and the entropy weight method, and determining the carbon quota allocation coefficient for each entity based on the comprehensive score. Specifically, this includes: An energy and carbon evaluation index system is constructed based on the aforementioned energy and carbon evaluation indexes, and the subjective weights of each energy and carbon evaluation index are determined using the analytic hierarchy process (AHP). Based on the preprocessed energy and carbon dataset, the information entropy value of each energy and carbon evaluation index is calculated, and the objective weight of each energy and carbon evaluation index is determined using the entropy weight method according to the information entropy value. The subjective weights and objective weights are integrated and calculated to obtain the comprehensive weights of each energy and carbon evaluation index, and the comprehensive score of each subject is calculated based on the comprehensive weights. The carbon quota allocation coefficient for each entity is determined based on the comprehensive score.

6. The energy and carbon synergistic management method for industrial parks according to claim 1, characterized in that, The calculation of the emission reduction potential specifically includes: Based on the preprocessed energy and carbon dataset, extract the energy consumption data and carbon emission data of each entity, and obtain the energy consumption benchmark value and carbon emission benchmark value from the preset industry benchmark database. The energy consumption intensity and carbon emission intensity of each entity are calculated based on the energy consumption data and the carbon emission data. The energy consumption intensity and carbon emission intensity are compared and analyzed with the industry benchmark values ​​to obtain the energy consumption deviation and carbon emission deviation of each entity. The theoretical emission reductions for each entity are calculated based on the energy consumption deviation and the carbon emission deviation, and the theoretical emission reductions are determined as the emission reduction potential indicators for that entity.

7. The energy and carbon synergistic management method for industrial parks according to claim 1, characterized in that, The process of identifying the producers and consumers of waste heat and energy based on the energy-carbon flow hypergraph model, and determining the waste heat and energy sharing path and amount based on the energy flow connection relationship between the producers and consumers to form a waste heat and energy sharing trading scheme, specifically includes: Based on the waste heat and energy interaction hyperedge in the energy carbon flow hypergraph model, identify the main body with waste heat and energy output, and determine the corresponding waste heat and energy output amount. Based on the waste heat and energy interaction hyperedge in the energy carbon flow hypergraph model, identify the entities with waste heat and energy needs and determine the corresponding waste heat and energy needs. Based on the waste heat and energy output and the waste heat and energy demand, supply and demand are matched to obtain candidate waste heat and energy sharing entities; Based on the energy flow connection relationship between the main entities in the energy-carbon flow hypergraph model, the sharing path between the candidate waste heat and waste energy sharing main entities is determined. The amount of waste heat and energy shared is calculated based on the waste heat and energy output, the waste heat and energy demand, and the shared path, and a waste heat and energy sharing transaction scheme is generated.

8. A coordinated energy and carbon management system for industrial parks, characterized in that, The energy and carbon co-management system for industrial parks includes: The data acquisition and preprocessing module is used to acquire multi-source energy carbon data in the industrial park and obtain the total carbon emission quota of the industrial park. It performs preprocessing operations on the multi-source energy carbon data to obtain a preprocessed energy carbon dataset. The hypergraph model construction module is used to construct a hypergraph model of energy and carbon flow in the park based on the preprocessed energy and carbon dataset. The evaluation index extraction module is used to extract energy and carbon evaluation indicators for each entity in the industrial park based on the preprocessed energy and carbon dataset. The energy and carbon evaluation indicators include energy consumption intensity, carbon emission intensity, emission reduction potential, and industry benchmark indicators. The comprehensive scoring calculation module is used to calculate the comprehensive score value of each entity based on the energy and carbon evaluation indicators of each entity, using a comprehensive scoring model based on the analytic hierarchy process and the entropy weight method, and to determine the carbon quota allocation coefficient of each entity based on the comprehensive score value. The carbon quota allocation module is used to allocate the total carbon emission quota according to the carbon quota allocation coefficient, obtain the carbon quota of each entity, and summarize the carbon quota allocation results. The waste heat sharing scheme generation module is used to identify the producers and consumers of waste heat and energy based on the energy-carbon flow hypergraph model, and to determine the waste heat and energy sharing path and sharing amount according to the energy flow connection relationship between the producers and consumers, so as to form a waste heat and energy sharing transaction scheme. The collaborative management and control generation module is used to conduct collaborative energy and carbon management and control of multiple entities in the industrial park based on the carbon quota allocation results and the waste heat and waste energy sharing and trading scheme, and to generate a collaborative energy and carbon management and control report for the park.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the energy and carbon synergistic management method for industrial parks as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the energy and carbon synergistic management method for industrial parks as described in any one of claims 1 to 7.

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