Collaborative carbon reduction method among park enterprises, storage medium and computer program product

Abstract

The application discloses a park enterprise inter-coordinated carbon reduction method, a storage medium and a computer program product, relates to the technical field of carbon management, and the method comprises the following steps: acquiring supply-side data and demand-side data reported by park enterprises, and matching a priority weight for each supply-side data; constructing a space-time coupling factor of potential supply pairs existing in the supply-side data and the demand-side data, and calculating an actual schedulable coordination amount of the potential supply pairs according to the space-time coupling factor; calculating an expected carbon emission reduction amount according to the actual schedulable coordination amount and the priority weight, and generating a candidate coordinated carbon reduction scheme based on the expected carbon emission reduction amount; inputting the candidate coordinated carbon reduction scheme into a pre-constructed digital twin simulation unit for verification to obtain a coordinated carbon reduction scheme, so as to improve the coordinated carbon reduction effect and the scheme execution success rate among park enterprises.

Description

Technical Field

[0001] This application relates to the field of carbon management technology, and in particular to collaborative carbon reduction methods, storage media and computer program products among enterprises in a park. Background Technology

[0002] Collaborative carbon reduction among enterprises in industrial parks aims to establish a park-level energy-sharing network to match surplus energy or low-cost clean energy from supply-side enterprises to demand-side enterprises, thereby replacing the high-carbon-emission self-sufficiency of demand-side enterprises and achieving overall carbon emission reduction in the park. Currently, some parks are attempting to collect enterprise energy consumption data through energy management centers and generate scheduling schemes using preset thresholds or offline optimization algorithms. However, generating scheduling schemes using preset thresholds or offline optimization algorithms is difficult to accurately quantify the potential for collaborative carbon reduction among enterprises and generate highly feasible implementation plans in the complex and ever-changing operating environment of industrial parks.

[0003] Therefore, how to improve the collaborative carbon reduction effect among enterprises in the park and the success rate of solution implementation has become a technical problem that this application urgently needs to solve. Summary of the Invention

[0004] The main purpose of this application is to provide a collaborative carbon reduction method, storage medium, and computer program product among enterprises in a park, aiming to solve the technical problem of how to improve the collaborative carbon reduction effect and the success rate of the solution implementation among enterprises in the park.

[0005] To achieve the above objectives, this application proposes a collaborative carbon reduction method among enterprises in a park, the method comprising: Obtain supply-side and demand-side data reported by enterprises in the park, and assign priority weights to each type of supply-side data. Construct a spatiotemporal coupling factor for potential supply pairs existing in the supply-side data and the demand-side data, and calculate the actual schedulable coordination amount of the potential supply pairs based on the spatiotemporal coupling factor; The expected carbon emission reduction is calculated based on the actual schedulable collaborative amount and the priority weight, and candidate collaborative carbon reduction schemes are generated based on the expected carbon emission reduction. The candidate synergistic carbon reduction schemes are input into a pre-constructed digital twin simulation unit for verification to obtain the synergistic carbon reduction schemes.

[0006] In one embodiment, the step of assigning priority weights to each of the supply-side data includes: The supply-side data is standardized and labeled with attributes according to a preset labeling dimension to obtain a supply-side attribute vector corresponding to the supply-side data. Calculate the initial priority weights of the supply-side attribute vector; Collect the operational health parameters of the energy output link corresponding to the supply-side data, and calculate the priority weight based on the operational health parameters and the initial priority weight.

[0007] In one embodiment, the step of constructing the spatiotemporal coupling factor of potential supply pairs existing in the supply-side data and the demand-side data includes: The supply-side data and the demand-side data are paired to obtain the potential supply pairs; Extract the temporal and spatial feature parameters of the potential supply pairs respectively; The overlapping supply periods are determined based on the time-series characteristic parameters, and the power curve matching ratio of the potential supply pairs within the overlapping supply periods is calculated. The time coupling factor is obtained by performing a time-weighted average calculation on the power curve matching ratio; The loss threshold of the potential supply pair is calculated based on the spatial characteristic parameters, and the spatial coupling factor is calculated based on the loss threshold. The spatiotemporal coupling factor is calculated based on the time coupling factor and the spatial coupling factor.

[0008] In one embodiment, the step of calculating the actual schedulable cooperative quantity based on the spatiotemporal coupling factor includes: Collect historical supply-side power time-series curves and historical demand-side power time-series curves within the overlapping supply periods; Calculate the Pearson correlation coefficient between the historical supply-side power time-series curve and the historical demand-side power time-series curve; The spatiotemporal coupling factor is corrected based on the Pearson correlation coefficient; Calculate the theoretical maximum schedulable quantity during the overlapping supply period, and calculate the actual schedulable cooperative quantity based on the corrected spatiotemporal coupling factor and the theoretical maximum schedulable quantity.

[0009] In one embodiment, the step of calculating the expected carbon emission reduction based on the actual schedulable coordination amount and the priority weight includes: Obtain the marginal carbon emission factors and baseline emission factors reported by enterprises in the park; The baseline emissions for each potential supply pair are calculated based on the marginal carbon emission factor and the baseline emission factor. The allocation coefficient for each potential supply pair is determined based on the priority weight, and the allocation coordination quantity is calculated based on the actual schedulable coordination quantity and the allocation coefficient. Calculate the collaborative carbon emissions under collaborative operation conditions based on the allocated collaborative amount; The difference between the synergistic carbon emissions and the baseline emissions is calculated, and the difference is corrected according to a preset confidence correction function to obtain the initial expected carbon emission reduction. The initial expected carbon emission reduction is calibrated using a preset time decay factor to obtain the expected carbon emission reduction.

[0010] In one embodiment, the step of generating candidate synergistic carbon reduction schemes based on the expected carbon emission reduction includes: A bidirectional matching graph is constructed using the potential supply pairs as edges and the corresponding supply-side nodes and demand-side nodes as vertices; wherein the weight of each edge is the corresponding expected carbon emission reduction. Conflict domain detection is performed on the bidirectional matching graph to generate candidate collaborative carbon reduction schemes.

[0011] In one embodiment, the step of inputting the candidate synergistic carbon reduction scheme into a pre-constructed digital twin simulation unit for verification to obtain the synergistic carbon reduction scheme further includes: Collect static asset information of enterprises in the park, and establish an energy directed graph model based on the static asset information; The transmission loss function is fitted into the energy directed graph model, and the operating data of the park enterprises is incorporated to obtain the digital twin simulation unit.

[0012] In one embodiment, the step of inputting the candidate synergistic carbon reduction scheme into a pre-constructed digital twin simulation unit for verification to obtain the synergistic carbon reduction scheme includes: The candidate synergistic carbon reduction scheme is input into a pre-constructed digital twin simulation unit, and the digital twin simulation unit performs time-series simulation of the candidate synergistic carbon reduction scheme to obtain the actual simulated carbon emission reduction. Based on the actual simulated carbon emission reduction, the candidate collaborative carbon reduction schemes are screened to obtain collaborative carbon reduction schemes.

[0013] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the collaborative carbon reduction method among enterprises in the industrial park as described above.

[0014] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the collaborative carbon reduction method among enterprises in the industrial park as described above.

[0015] One or more technical solutions proposed in this application have at least the following technical effects: First, the system acquires supply-side and demand-side data reported by enterprises in the park and assigns priority weights to each supply-side data point. These priority weights ensure that subsequent scheduling prioritizes stable, reliable supply resources with low transmission losses, reducing the failure rate of the scheme from the source. Second, it constructs a spatiotemporal coupling factor and calculates the actual schedulable collaborative quantity. Constraints from both temporal and spatial perspectives are incorporated into a unified quantitative framework, making the carbon reduction potential assessment closer to physical reality. Further, based on the actual schedulable collaborative quantity and priority weights, the system calculates the expected carbon emission reduction and generates candidate schemes. Through priority weight-guided allocation and constraints from the actual schedulable collaborative quantity, multiple sets of high-efficiency, low-conflict candidate feasible scheduling combinations are generated. Finally, the candidate schemes are input into a digital twin simulation unit for time-series simulation and perturbation verification, exposing the defects of the candidate schemes in advance. This ensures that the final output collaborative carbon reduction scheme possesses both high efficiency under ideal operating conditions and robustness under complex perturbations, thereby significantly improving the actual achievement effect and execution success rate of collaborative carbon reduction among enterprises in the park. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the first embodiment of the collaborative carbon reduction method for enterprises in the industrial park as described in this application; Figure 2 A flowchart illustrating the second embodiment of the collaborative carbon reduction method for enterprises in the industrial park as described in this application; Figure 3 A flowchart illustrating the third embodiment of the collaborative carbon reduction method for enterprises in the industrial park as described in this application; Figure 4 A flowchart illustrating the fourth embodiment of the collaborative carbon reduction method for enterprises in the industrial park as described in this application; Figure 5 This is a schematic diagram of the modular structure of the collaborative carbon reduction device for enterprises in the industrial park, as described in this application embodiment. Figure 6 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the collaborative carbon reduction method for enterprises in the industrial park in this embodiment of the application.

[0019] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0021] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0022] The main solution of this application embodiment is as follows: Obtain supply-side data and demand-side data reported by enterprises in the park, and match priority weights for each supply-side data; construct a spatiotemporal coupling factor for potential supply pairs existing in the supply-side data and demand-side data, and calculate the actual schedulable collaborative amount of the potential supply pairs based on the spatiotemporal coupling factor; calculate the expected carbon emission reduction based on the actual schedulable collaborative amount and the matching priority weights, and generate candidate collaborative carbon reduction schemes based on the expected carbon emission reduction; input the candidate collaborative carbon reduction schemes into a pre-constructed digital twin simulation unit for verification to obtain the collaborative carbon reduction scheme.

[0023] In this embodiment, for ease of description, the following description will focus on the collaborative carbon reduction system of park enterprises.

[0024] This application's embodiments take into account that: since collaborative carbon reduction among enterprises in industrial parks aims to establish a park-level energy-sharing network, it will dispatch and match surplus energy or low-cost clean energy from supply-side enterprises to demand-side enterprises, thereby replacing the high-carbon-emission self-sufficiency of demand-side enterprises and achieving overall carbon emission reduction in the park. Currently, some parks are attempting to collect enterprise energy consumption data through energy management centers and generate dispatch schemes using preset thresholds or offline optimization algorithms. However, generating dispatch schemes using preset thresholds or offline optimization algorithms is difficult to accurately quantify the potential for collaborative carbon reduction among enterprises and generate highly feasible implementation schemes in the complex and ever-changing operating environment of industrial parks.

[0025] Therefore, this application provides a solution. First, it acquires supply-side and demand-side data reported by enterprises in the park and assigns priority weights to each supply-side data. The priority weights ensure that subsequent scheduling prioritizes stable, reliable, and low-transmission-loss supply resources, reducing the failure rate of the scheme from the source. Second, it constructs a spatiotemporal coupling factor and calculates the actual schedulable collaborative quantity. Constraints from both temporal and spatial perspectives are incorporated into a unified quantitative framework, making the carbon reduction potential assessment closer to physical reality. Furthermore, it calculates the expected carbon emission reduction based on the actual schedulable collaborative quantity and priority weights and generates candidate schemes. Through priority weight-guided allocation and constraints of the actual schedulable collaborative quantity, multiple sets of high-efficiency, low-conflict candidate feasible scheduling combinations are generated. Finally, the candidate schemes are input into a digital twin simulation unit for time-series simulation and disturbance verification, exposing the defects of the candidate schemes in advance, ensuring that the final output collaborative carbon reduction scheme has both high efficiency under ideal operating conditions and robustness under complex disturbances, thereby significantly improving the actual achievement effect and execution success rate of collaborative carbon reduction among enterprises in the park.

[0026] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a collaborative carbon reduction system for enterprises in a park. The following description uses a collaborative carbon reduction system for enterprises in a park as an example to illustrate this embodiment and the subsequent embodiments.

[0027] Based on this, the embodiments of this application provide a collaborative carbon reduction method for enterprises in a park, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the collaborative carbon reduction method for enterprises in the industrial park presented in this application.

[0028] In this embodiment, the collaborative carbon reduction method for enterprises in the industrial park includes steps S10 to S40: Step S10: Obtain the supply-side data and demand-side data reported by enterprises in the park, and match priority weights for each of the supply-side data. It should be noted that, in this embodiment of the application, supply-side data refers to the energy type, maximum output power, available supply period, output carbon emission factor, and other related data reported by enterprises in the park that have surplus energy production capacity; demand-side data refers to the energy type, demand power, demand period, self-supplied energy carbon emission factor, and other related data reported by enterprises in the park that have additional energy use needs.

[0029] Priority weight refers to the quantitative value used to characterize the comprehensive indicators such as energy supply reliability, carbon reduction benefits, and transmission losses corresponding to different supply-side data. The higher the value, the higher the priority scheduling level of the supply-side resource.

[0030] Specifically, the collaborative carbon reduction system for enterprises in the industrial park first opens a data reporting channel to all enterprises in the park through a pre-set standardized data collection interface. It collects supply-side data reported by supply-side enterprises and demand-side data reported by demand-side enterprises according to pre-set field rules, and performs outlier cleaning and normalization on all reported data. Second, it performs standardized attribute labeling on each piece of supply-side data according to pre-set labeling dimensions to generate a corresponding supply-side attribute vector, and obtains the initial priority weight by weighted summation. Finally, it collects the operational health parameters of the energy output link corresponding to the supply-side data, and multiplies the initial priority weight by the weighted summation of the operational health parameters to obtain the final priority weight.

[0031] In one possible implementation, the collaborative carbon reduction system for enterprises in the industrial park can also access real-time data from the park's carbon emission monitoring platform to dynamically verify the carbon emission factors reported by supply-side enterprises. If the deviation between the reported value and the real-time monitoring value exceeds a preset threshold, the priority weight of the supply-side data will be lowered accordingly, thereby further improving the accuracy of priority weight matching.

[0032] Step S20: Construct the spatiotemporal coupling factor of the potential supply pairs existing in the supply-side data and the demand-side data, and calculate the actual schedulable coordination amount of the potential supply pairs based on the spatiotemporal coupling factor; It should be noted that, in the embodiments of this application, a potential supply pair refers to a supply and demand combination formed by pairing supply-side data with demand-side data; the spatiotemporal coupling factor refers to a comprehensive indicator used to quantify the supply and demand matching degree of potential supply pairs in the time dimension and the transmission accessibility in the spatial dimension.

[0033] The actual schedulable synergy quantity refers to the total amount of energy that can be actually transmitted from the potential supply after considering comprehensive spatiotemporal constraints.

[0034] Specifically, all possible potential supply pairs are extracted from supply-side and demand-side data. For each potential supply pair, a time coupling factor and a spatial coupling factor are constructed. In the time dimension, the overlap between the sustainable supply period on the supply side and the demand-side load occurrence period is determined. The matching ratio between the power output of the supply side and the energy consumption of the demand side within the overlapping period is calculated, and a time-weighted average is performed with the urgency of the demand side as the weight to obtain the time coupling factor.

[0035] Furthermore, in the spatial dimension, the shortest transmission path is identified based on the park's energy pipeline network topology. The transmission loss rate and the constraint coefficient of the remaining available capacity of the pipeline network under this path are calculated. After normalization and loss threshold verification, the spatial coupling factor is obtained. Multiplying the temporal coupling factor and the spatial coupling factor yields the spatiotemporal coupling factor, which comprehensively reflects the feasibility of coordination between supply and demand in the spatiotemporal dimensions. Multiplying the theoretically schedulable energy by this spatiotemporal coupling factor and the minimum transmission efficiency of the path, the actual schedulable coordination amount of the potential supply pair is obtained.

[0036] Step S30: Calculate the expected carbon emission reduction based on the actual schedulable collaborative amount and the priority weight, and generate candidate collaborative carbon reduction schemes based on the expected carbon emission reduction. It should be noted that, in the embodiments of this application, the expected carbon emission reduction refers to the total carbon emission reduction that can be achieved by the potential supply pair after completing the energy transmission of the actual dispatchable coordinated amount, compared to the independent energy supply scenario of the supply and demand sides; the candidate coordinated carbon reduction scheme refers to a preliminary carbon reduction scheduling scheme formed by combining multiple potential supply pairs without resource conflicts, which can be implemented simultaneously.

[0037] Specifically, the marginal carbon emission factor reported by the supply side and the baseline emission factor of the self-supplied energy mode on the demand side are obtained, and the theoretical dispatchable energy of the potential supply pair is combined to calculate the baseline total carbon emissions without coordination.

[0038] Furthermore, based on the allocation coefficient of each potential supply pair determined according to the priority weight, the higher the priority weight, the higher the allocation coefficient. The allocation coordination amount of each potential supply pair is calculated based on the allocation coefficient. The allocation coordination amount is multiplied by the carbon reduction per unit energy to obtain the initial expected carbon emission reduction. The initial expected carbon emission reduction is calibrated by a preset confidence correction function and time decay factor to obtain the final expected carbon emission reduction.

[0039] Finally, a bidirectional matching graph is constructed with each potential supply pair as an edge and the corresponding supply-side node and demand-side node as vertices. The expected carbon emission reduction is used as the weight of the edge. Conflict domain detection is performed on the bidirectional matching graph to eliminate conflicting combinations where the same supply resource is occupied by multiple demand groups or the same demand is repeatedly matched by multiple supply groups. All conflict-free combinations are the candidate collaborative carbon reduction schemes.

[0040] Step S40: Input the candidate synergistic carbon reduction scheme into a pre-constructed digital twin simulation unit for verification to obtain the synergistic carbon reduction scheme.

[0041] It should be noted that, in this application embodiment, the digital twin simulation unit refers to a digital simulation model built based on the real energy infrastructure and operation data of the park, which can simulate the real operation status of the park's energy system; the collaborative carbon reduction scheme refers to the final carbon reduction scheduling scheme that has been verified by simulation to meet the operation requirements and achieve the carbon reduction benefits, and can be directly implemented.

[0042] Additionally, it should be noted that the collaborative carbon reduction system for enterprises in the park pre-collects static information on the energy assets of all enterprises in the park (including parameters of energy production equipment, transmission pipeline parameters, parameters of energy storage equipment, etc.), constructs a directed graph model of energy in the park based on the static asset information, fits transmission loss functions under different transmission links and different ambient temperatures in the model, connects to the park's real operating data to complete model calibration, and obtains a digital twin simulation unit.

[0043] Furthermore, all candidate collaborative carbon reduction schemes are sequentially input into the digital twin simulation unit. The simulation unit performs full-time time-series simulations of the schemes, simulating typical disturbance scenarios including sudden equipment failures, energy demand fluctuations, and changes in transmission pressure. It outputs core indicators such as the actual simulated carbon emission reduction, operational failure rate, and link load rate for each candidate scheme. Finally, the candidate schemes are sorted according to the rules of actual simulated carbon emission reduction from high to low and operational failure rate from low to high, and the final collaborative carbon reduction scheme is selected.

[0044] This embodiment provides a collaborative carbon reduction method for enterprises in a park. First, it acquires supply-side and demand-side data reported by enterprises in the park and assigns priority weights to each supply-side data. Priority weights ensure that subsequent scheduling prioritizes stable, reliable supply resources with low transmission losses, reducing the failure rate of the scheme from the source. Second, it constructs a spatiotemporal coupling factor and calculates the actual schedulable collaborative quantity. Constraints from both time and space perspectives are incorporated into a unified quantitative framework, making the carbon reduction potential assessment closer to physical reality. Furthermore, it calculates the expected carbon emission reduction based on the actual schedulable collaborative quantity and priority weights and generates candidate schemes. Through priority weight-guided allocation and constraints of the actual schedulable collaborative quantity, multiple sets of high-efficiency, low-conflict candidate feasible scheduling combinations are generated. Finally, the candidate schemes are input into a digital twin simulation unit for time-series simulation and disturbance verification, exposing the defects of the candidate schemes in advance, ensuring that the final output collaborative carbon reduction scheme has both high efficiency under ideal operating conditions and robustness under complex disturbances, thereby significantly improving the actual achievement effect and execution success rate of collaborative carbon reduction among enterprises in the park.

[0045] In one feasible implementation, step S10, which matches priority weights to each of the supply-side data, may include steps S11 to S13: Step S11: Standardize the attributes of the supply-side data according to the preset tagging dimensions to obtain the supply-side attribute vector corresponding to the supply-side data. It is understandable that the preset labeling dimensions need to ensure a comprehensive portrayal of supply-side resources, and enterprises in each park can preset different labeling dimensions according to their actual situation. For example, in one feasible implementation, the labeling dimensions include at least the following five items: The type of energy supplied identifies the types of secondary or surplus energy that the supply side can provide, such as electricity, high-temperature steam, medium-temperature hot water, chilled water, compressed air, and by-product hydrogen. Rated supply power: The maximum power that the supply side can stably output under rated operating conditions; Sustainable supply duration refers to the length of time that the supply side can maintain continuous output within the current production cycle or planned operating window. Supply parameter fluctuation threshold: During the stable output process on the supply side, the maximum permissible percentage deviation of key parameters (such as voltage, frequency, pressure, and temperature) from their rated values. Physical distance from the supply side to the access point of the park's shared energy transmission network: The physical distance measured along the pipeline route from the metering outlet on the supply side to the access point of the park's public pipeline / bus.

[0046] Because the dimensions and numerical ranges of each dimension differ significantly, a mapping function or hierarchical scoring table needs to be pre-defined for each dimension to transform the original physical quantities into standard scores with uniform dimensions and comparability. Taking sustainable supply duration on a percentage scale as an example, the standard score for sustainable supply duration is:

[0047] in, This represents a standardized score representing the duration of sustainable supply after conversion. For sustainable supply duration, The maximum reference duration is preset.

[0048] Furthermore, based on the standard score mapped to the score corresponding to each supply-side data labeling dimension, a five-dimensional feature vector, namely the supply-side attribute vector, is constructed. Each component in the supply-side attribute vector is the standard score of the corresponding labeling dimension.

[0049] Step S12: Calculate the initial priority weights of the supply-side attribute vector; For the defined labeling dimensions, a set of basic weight coefficients are pre-set. These weight coefficients can be made available to park managers for fine-tuning according to phased carbon reduction strategies. For example, during the summer when pipeline capacity is tight, the value of the basic weight coefficient corresponding to physical distance can be appropriately increased to prioritize the absorption of nearby resources.

[0050] Furthermore, the initial priority weights of the supply-side data are obtained by multiplying the standard scores of each dimension in the supply-side attribute vector with the corresponding basic weight coefficients and then summing them.

[0051] Step S13: Collect the operational health parameters of the energy output link corresponding to the supply-side data, and calculate the priority weight based on the operational health parameters and the initial priority weight.

[0052] For each supply side, a dedicated output link (which can be a power cable segment, steam / hot water pipeline segment, or a hybrid path) is determined between it and the access point of the park's public energy transmission network. The operational health parameters of link failure rate and transmission loss rate are collected by connecting to the park's deployed carbon management emission system and energy pipeline monitoring system through API interface.

[0053] The link failure rate is defined as the ratio of the number of unplanned outages or serious anomalies occurring on the link within a unit statistical period to the total operating time, and is used to measure the reliability of the link. The transmission loss rate is defined as the average proportion of energy lost per unit of transmitted energy when passing through the dedicated link, and is used to measure the energy efficiency of the link.

[0054] For example, the near real-time meter reading sequences of the first end (supply-side exit) and the last end (public network access point) of the link are obtained from the energy metering system within the past 24 hours. The cumulative output energy at the first end and the cumulative received energy at the last end are calculated respectively. The transmission loss rate is obtained by dividing the two.

[0055] The operational health parameters, namely link failure rate and transmission loss rate, are normalized and then multiplied by the initial priority weight to obtain the priority weight.

[0056] Based on the first embodiment of this application, a second embodiment of this application is proposed. In the second embodiment of this application, content that is the same as or similar to that in the first embodiment described above can be referred to the above description and will not be repeated hereafter.

[0057] Based on this, please refer to Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of this application, as shown below. Figure 2 As shown, step S20, which constructs the spatiotemporal coupling factor of potential supply pairs existing in the supply-side data and the demand-side data, may include steps S21 to S26: Step S21: Pair the supply-side data and the demand-side data to obtain the potential supply pair; The collaborative carbon reduction system for enterprises in the park first retrieves all supply-side and demand-side datasets that have completed standardization and cleaning and energy type attribute labeling, and performs Cartesian product operation on the two types of data to achieve full pairwise pairwise matching.

[0058] Furthermore, the system performs an initial screening of all paired combinations, directly eliminating combinations where the energy types on the supply and demand sides do not match (e.g., combinations of photovoltaic power supply and steam demand are directly eliminated). Each remaining supply and demand combination is a potential supply pair, and the system generates a unique identifier code for each valid potential supply pair to facilitate subsequent calculations and traceability.

[0059] Step S22: Extract the temporal and spatial feature parameters of the potential supply pair, respectively; For each effective potential supply pair, two types of feature parameters are extracted: First, time-series characteristic parameters: including the available supply period, supply power time-series curve, and allowable range of supply power fluctuations on the supply side, and the demand period, demand power time-series curve, and tolerance range of demand power fluctuations on the demand side; Second, spatial characteristic parameters: including the location of the access point for the supply side to access the park's public energy network, the location of the access point for the demand side to access the park's public energy network, the type of public transmission link (electricity / heat / hybrid) between the two access points, and the link length.

[0060] Step S23: Determine the overlapping supply period based on the time-series characteristic parameters, and calculate the power curve matching ratio of the potential supply pair within the overlapping supply period; The overlapping time period is the time period during which the supply side of the potential supply pair can supply and the demand side can demand. If the intersection is empty, the potential supply pair is marked as invalid and the subsequent coupling factor is uniformly counted as 0.

[0061] If the overlapping supply period is valid, it is divided into several consecutive time slices. Within each time slice, the output power value on the supply side and the demand power value on the demand side are taken. The output power value is divided by the demand power value to obtain the power curve matching ratio corresponding to that time slice.

[0062] Step S24: Calculate the time-weighted average of the power curve matching ratio to obtain the time coupling factor; Assign a weighting coefficient to each time slot: if the time slot belongs to the peak energy consumption period of the park (such as 9:00-11:00 and 14:00-16:00 when industrial production is concentrated), the weighting coefficient is set to 1.2; if it belongs to the flat energy consumption period, the weighting coefficient is set to 1.0; if it belongs to the low energy consumption period, the weighting coefficient is set to 0.8. Then, multiply the power curve matching ratio of each time slot by the corresponding weighting coefficient, sum them up, and then divide by the sum of the weighting coefficients of all time slots. The value in the range of 0-1 is the time coupling factor. The closer the value is to 1, the higher the supply and demand time matching degree.

[0063] Step S25: Calculate the loss threshold of the potential supply pair based on the spatial characteristic parameters, and calculate the spatial coupling factor based on the loss threshold; First, based on the type, length, current load rate, and historical loss data of the public transmission link for the potential supply pair, calculate the theoretical transmission loss rate, i.e., the loss threshold, for energy transmission from the supply-side access point to the demand-side access point. Then, retrieve the preset maximum allowable loss thresholds for different energy types (e.g., 5% maximum allowable loss for electricity transmission, 8% maximum allowable loss for steam transmission, and 6% maximum allowable loss for hot water transmission), and calculate the spatial coupling factor in the form of (maximum allowable loss threshold - actual loss threshold) / maximum allowable loss threshold. If the calculation result is less than 0, the spatial coupling factor is directly set to 0, indicating that the transmission link loss exceeds the allowable range and the potential supply pair is not feasible.

[0064] Step S26: Calculate the spatiotemporal coupling factor based on the time coupling factor and the spatial coupling factor.

[0065] According to the preset weight allocation rules (the default weight of time coupling factor is 60% and the weight of spatial coupling factor is 40%, and the weight ratio can be flexibly adjusted if there are special scenarios such as tight transmission link resources or large fluctuations in supply and demand in the park), the time coupling factor and spatial coupling factor are multiplied by their corresponding weights and then summed. The value in the range of 0-1 is the final time-space coupling factor of the potential supply pair. The higher the value, the better the comprehensive matching degree of the supply and demand combination.

[0066] In this embodiment, potential supply pairs are initially screened by first enumerating all supply and demand combinations. Then, the time-series features of supply and demand are extracted from the time dimension, and the power curve matching degree within overlapping periods is calculated to obtain the time coupling factor. The transmission link features are extracted from the spatial dimension, and the transmission loss threshold is calculated to obtain the spatial coupling factor. Finally, the quantified values ​​of the two dimensions are integrated to obtain the comprehensive spatiotemporal coupling factor. This fully realizes the unified quantification of time and space physical constraints in the supply and demand matching assessment process. It effectively solves the problem that the assessment results of traditional supply and demand matching, which rely solely on static supply and demand difference calculations, deviate too much from the actual operation scenario of the park. It can accurately screen out high-value potential supply combinations with strong time adaptability and low transmission loss, thereby improving the feasibility and actual carbon reduction benefits of subsequent collaborative carbon reduction schemes.

[0067] Furthermore, in a feasible implementation, step S20, which calculates the actual schedulable cooperative quantity based on the spatiotemporal coupling factor, may further include steps A21 to A24: Step A21: Collect the historical supply-side power time-series curve and the historical demand-side power time-series curve within the overlapping supply period; Based on the overlapping supply periods of the current potential supply pairs, a time backtracking window for historical data collection is determined. This backtracking window is selected by extracting time intervals from the past M consecutive natural days that have the same start and end times as the overlapping supply periods, forming M historical time period instances.

[0068] For each historical time period instance, power output time-series data recorded at fixed sampling intervals on the supply side are read from the park's historical energy metering database to form a historical supply-side power time-series curve; simultaneously, energy load time-series data recorded at the same sampling interval on the demand side are read to form a historical demand-side power time-series curve. Each historical curve collected undergoes data integrity verification and outlier removal to ensure the accuracy of subsequent correlation coefficient calculations.

[0069] Step A22: Calculate the Pearson correlation coefficient between the historical supply-side power time-series curve and the historical demand-side power time-series curve; Based on the collected M sets of historical power time series curves, the degree of consistency between supply-side and demand-side fluctuations in similar past periods is calculated.

[0070] For example, for the m-th historical curve pair, the historical supply-side power sequence of each sampling time within the overlapping period of the curve pair is first extracted. Historical demand-side power timing ， in, This indicates the sampling point number. Further, the correlation coefficient of this set of historical curve pairs is calculated according to the definition of the Pearson correlation coefficient. :

[0071] in, ， These represent the arithmetic mean of the supply-side power and demand-side power of the historical curves during the overlapping period, respectively.

[0072] After obtaining the M sets of correlation coefficients, calculate the median of the M sets of correlation coefficients. To reduce the interference of individual abnormal operating days on the evaluation results, the median was used. The Pearson correlation coefficient, which serves as the historical power time series curve for the supply side and the historical power time series curve for the demand side, ranges from [-1, 1]. The closer the value is to +1, the more the historical power fluctuation trends on the supply side and the demand side tend to be in the same direction. Conversely, the closer the value is to +1, the more the fluctuations lack coordination or show opposite changes.

[0073] Step A23: Correct the spatiotemporal coupling factor based on the Pearson correlation coefficient; The calculated Pearson correlation coefficient is incorporated into the calculation framework of the spatiotemporal coupling factor to dynamically correct the static spatiotemporal matching degree calculated solely based on the current planned or nominal values. First, a correction function is constructed based on the physical meaning of the Pearson correlation coefficient, and the correlation coefficient is then... Mapped to a dynamic correction coefficient acting on the spatiotemporal coupling factor In one specific implementation, the correction coefficient The calculation formula is:

[0074] The above formula linearly maps the correlation coefficient, which takes values ​​in the range [-1, 1], to the range [0, 1]. When historical data shows a high positive correlation between supply and demand fluctuations (…), this is particularly relevant when… p median The correction coefficient is close to 1, basically maintaining the original spatiotemporal coupling factor unchanged; when historical data shows a weak or negative correlation between supply and demand fluctuations ( p median (If the value is close to 0 or negative), the correction coefficient decreases accordingly, thereby lowering the evaluation value of the spatiotemporal matching quality.

[0075] Furthermore, the correction factor Multiplying this by the initial spatiotemporal coupling factor yields the corrected spatiotemporal coupling factor.

[0076] Step A24: Calculate the theoretical maximum schedulable quantity within the overlapping supply period, and calculate the actual schedulable cooperative quantity based on the corrected spatiotemporal coupling factor and the theoretical maximum schedulable quantity.

[0077] Calculate the theoretical maximum schedulable quantity during overlapping supply periods. This value represents the maximum energy that the supply side can provide to the demand side under ideal transmission conditions and the assumption of perfect timing matching. The calculation method is as follows: within the overlapping time period, the smaller of the power output from the supply side and the power demanded by the demand side at each sampling moment is integrated over time.

[0078] Secondly, the minimum rated transmission efficiency on the transmission path is obtained. This efficiency value is obtained by multiplying or taking the minimum value of the rated efficiencies of each energy conversion and transmission device on the path. It represents the minimum unavoidable loss ratio in the process of energy being transported from the supply side outlet to the demand side inlet.

[0079] The minimum rated transmission efficiency, the corrected spatiotemporal coupling factor, and the theoretical maximum schedulable quantity are considered. Multiply to obtain the actual schedulable coordination quantity. .

[0080] Based on the first and / or second embodiments of this application, a third embodiment of this application is proposed. In the third embodiment of this application, content that is the same as or similar to the first and / or second embodiments described above can be referred to the above description and will not be repeated hereafter.

[0081] Based on this, please refer to Figure 3 , Figure 3 This is a schematic diagram of the process of the third embodiment of this application, as shown below. Figure 3 As shown, step S30, which calculates the expected carbon emission reduction based on the actual schedulable coordination quantity and the priority weight, may include steps S31 to S36: Step S31: Obtain the marginal carbon emission factor and baseline emission factor reported by enterprises in the park; Specifically, the system obtains two types of key emission factors from the park's carbon management platform or the enterprise's energy management system through a data interface. The first type is the supply-side marginal carbon emission factor, defined as the equivalent carbon emissions generated by the unit of energy provided by the supply side if it is not utilized in conjunction with other energy sources and is disposed of through its own conventional disposal methods (such as venting, combustion, or inefficient use), or defined as the carbon emission coefficient of the park's externally purchased energy replaced by the supply side. For example, for the waste heat steam supply side, the marginal carbon emission factor can be taken as 0.32 kgCO2 / kWh, which is the emission factor for replacing steam production from a coal-fired boiler.

[0082] The second category is the demand-side baseline emission factor, which is defined as the carbon emission coefficient generated if the demand side produces an equivalent amount of energy through its own energy supply facilities (such as gas boilers, electric chillers, or electricity purchased from the grid) without obtaining coordinated energy supply. It can be pre-set in the system configuration table according to international or local greenhouse gas accounting standards, or it can be updated periodically by enterprises based on measured data.

[0083] Step S32: Calculate the baseline emissions for each potential supply pair based on the marginal carbon emission factor and the baseline emission factor; The baseline emissions are calculated based on the total carbon emissions of both parties operating independently without coordinated scheduling. First, the baseline emissions consist of two parts: emissions generated if the supply side does not provide energy to the demand side, and emissions generated if the demand side produces the same amount of energy itself. The calculation formula is as follows:

[0084] in, This represents the theoretical maximum schedulable quantity. To supply side marginal carbon emission factors, This is the demand-side baseline emission factor.

[0085] Step S33: Determine the allocation coefficient for each potential supply pair according to the priority weight, and calculate the allocation coordination amount based on the actual schedulable coordination amount and the allocation coefficient; When multiple supply sides within a park can serve the same demand side, the actual schedulable collaborative quantity needs to be competitively allocated based on priority weights.

[0086] Specifically, for a certain demand side Identify all related to Let the potential supply pairs that have been matched and passed the spatiotemporal feasibility check be the supply-side set. The priority weights for each supply side are: Calculate the first i The normalized allocation coefficient of the supply side in the total supply on the demand side. :

[0087] Furthermore, the normalized allocation coefficient With actual schedulable coordination quantity Multiplying these together yields the weighted allocation synergy of the potential supply under competitive conditions. .

[0088] Step S34: Calculate the collaborative carbon emissions under collaborative operation based on the allocated collaborative amount; This step calculates the actual change in carbon emissions generated by the park system after implementing the coordinated scheduling corresponding to this potential supply pair. Under coordinated operation, this includes the incremental emissions from the supply side due to the output of coordinated energy. for:

[0089] in, To allocate coordination quantity, As an incremental emission factor in the coordinated operation of the supply side, it includes waste heat, waste pressure, and other originally waste energy sources. Typically, a value of 0 or a minimum is used; for energy storage release or distributed energy, the emission factor is taken over its entire life cycle. This represents the reduction in self-generated energy emissions on the demand side due to the use of collaborative energy supply. for:

[0090] Furthermore, coordinated carbon emissions .

[0091] Step S35: Calculate the difference between the synergistic carbon emissions and the baseline emissions, and correct the difference according to a preset confidence correction function to obtain the initial expected carbon emission reduction. Calculate the difference between baseline emissions and co-emissions. This yields the uncorrected carbon emission reduction. Since the actual effectiveness of the scheduling scheme is affected by the reliability implied by the supply-side priority weights, higher priority weights typically have more stable supply quality and lower failure probabilities. Therefore, a confidence correction function is introduced to adjust the above difference to reflect the confidence level of the emission reduction expectation. In one specific implementation, the confidence correction function adopts an exponential form:

[0092] in, This represents the confidence level correction function. As the final priority weight of the supply side, Given a preset sensitivity coefficient (e.g., 0.05), the characteristics of this confidence function are: When the value approaches 0, the function approaches 0, indicating that the expected emission reductions from the supply side with low reliability should be significantly reduced; when the function approaches 1, it indicates that the expected emission reductions from the supply side with high reliability are basically credible.

[0093] Furthermore, the initial expected carbon emission reductions for:

[0094] Step S36: The initial expected carbon emission reduction is calibrated using a preset time decay factor to obtain the expected carbon emission reduction.

[0095] Considering the long-term uncertainties brought about by changes in production plans and equipment start-ups / shutdowns of companies in the industrial park, the probability of achieving emission reduction targets for collaborative solutions whose execution time is far removed from the current decision-making time decays over time. A time decay factor is introduced. The initial expected carbon emission reductions are calibrated. The time decay factor is defined as:

[0096] in, Set a preset attenuation constant (e.g., 0.05h). -1 ), The time interval from the current decision-making moment to the planned start time of the collaborative scheme is defined by this exponential form. This ensures that the closer the scheme is to the execution window, the closer its decay factor is to 1. The emission reduction of the long-term scheme is moderately reduced, thus giving preference to the more certain scheme that can be implemented in the near future when ranking the candidate schemes.

[0097] Furthermore, the expected carbon emission reductions R= × .

[0098] In this embodiment, by sequentially completing the calculation of carbon reduction per unit of energy, allocation of supply resources based on priority weights, calibration of expected carbon emission reductions, construction of a two-way supply-demand matching graph, resource conflict detection, and elimination of invalid combinations, a pool of conflict-free candidate collaborative carbon reduction schemes is finally selected. Priority weight calibration ensures that highly reliable and efficient supply resources are prioritized for scheduling, while conflict detection completely avoids execution contradictions such as duplicate occupation of supply resources and duplicate matching of demand resources. At the same time, fairness constraints can be flexibly incorporated to ensure the balance of participation of enterprises in collaborative carbon reduction in the park. This provides high-quality alternative schemes that take into account both carbon reduction benefits and feasibility for subsequent digital twin simulation verification. From the scheme generation stage, the execution risk of subsequent scheme implementation is greatly reduced, and the overall efficiency and actual benefits of collaborative carbon reduction are improved.

[0099] Furthermore, in a feasible implementation, step S30, which generates candidate synergistic carbon reduction schemes based on the expected carbon emission reductions, may further include steps A31-A32: Step A31: Construct a bidirectional matching graph with the potential supply pair as edges and the corresponding supply-side node and demand-side node as vertices; wherein the weight of the edge is the corresponding expected carbon emission reduction. All supply-side entities participating in the collaboration are abstracted into a supply-side node set V1, and all demand-side entities are abstracted into a demand-side node set V2. The two types of nodes together constitute the graph vertex set V.

[0100] For each valid potential supply pair, if its expected carbon emission reduction is greater than zero, an undirected edge is established between the supply-side node and the demand-side node. This edge carries two core attributes: the corresponding expected carbon emission reduction and the priority weight.

[0101] Step A32: Detect conflict domains in the bidirectional matching graph to generate candidate collaborative carbon reduction schemes.

[0102] Based on the constructed bidirectional matching graph, multiple non-conflicting feasible collaborative carbon reduction schemes are generated by identifying and resolving resource use conflicts.

[0103] First, traverse all edges of the graph and detect three types of conflicts based on the scheduling time period and occupied pipeline path information attached to each edge: Supply-side conflict: Multiple edges connected to the same supply-side node are considered a conflict edge set if their scheduling time periods overlap and the cumulative allocated coordination amount exceeds the rated supply capacity of that supply side. Demand-side conflict: Multiple edges connected to the same demand-side node are considered a conflict edge set if their scheduling time periods overlap and the cumulative received coordination amount exceeds the actual demand limit of that demand side. Pipeline capacity conflict: Multiple edges whose corresponding scheduling paths share the same pipeline segment or line, and whose cumulative transmission power exceeds the remaining available capacity of that pipeline segment during the overlapping time period, are considered a conflict edge set.

[0104] Furthermore, all edges in the graph are sorted in descending order of carbon reduction benefit per unit of collaborative effort. Next, edges are selected from highest to lowest priority and added to the candidate solution set. For each added edge, it is checked whether it causes any of the three types of conflicts mentioned above with already added edges. If there is no conflict, it is retained; if there is a conflict, it is handled according to a preset strategy. For example, the preset strategy could be: comparing the priority weight or carbon reduction benefit of conflicting edges, selecting the best one to retain, and adding the remaining edges to the candidate set.

[0105] Based on the above embodiments of this application, a fourth embodiment of this application is proposed. In this fourth embodiment, content that is the same as or similar to that in the above embodiments can be referred to the above description, and will not be repeated hereafter.

[0106] Based on this, please refer to Figure 4 , Figure 4 This is a flowchart illustrating the fourth embodiment of this application, as shown below. Figure 4 As shown, before step S40, which involves inputting the candidate synergistic carbon reduction scheme into a pre-constructed digital twin simulation unit for verification, steps S341-S342 are also included: Step S341: Collect the static asset information of the enterprises in the park, and establish an energy directed graph model based on the static asset information; Collect static information on energy infrastructure from the park's asset management system or engineering drawings, including but not limited to: the geographical location, rated capacity, and efficiency curves of energy production equipment (boilers, chillers, photovoltaic arrays, etc.); and the topological connections of energy transmission networks (power lines, steam pipes, hot water pipes, etc.).

[0107] Furthermore, based on the aforementioned static asset information, a directed graph model describing the energy flow relationships within the park is established. The node set includes energy production nodes, energy consumption nodes, pipeline branch nodes, and energy conversion nodes; the directed edge set represents the permitted directions and paths of energy flow, with edge attributes including length, transmission medium type, maximum design capacity, and impedance or thermal resistance per unit length. This directed graph model constitutes the spatial topological framework of the digital twin simulation unit.

[0108] Step S342: Fit the transmission loss function into the directed energy graph model and input the operating data of the park enterprises to obtain the digital twin simulation unit.

[0109] For different energy transmission media, transmission loss calculation functions driven by physical mechanisms or historical operating data are established. For example, for power lines, a line loss function is fitted. ΔΡ = f (I, R, X) ,in, I For current, R and X These are the line resistance and reactance parameters.

[0110] Furthermore, after fitting the transmission loss function, a real-time or near-real-time data channel is established through a standard industrial data interface. The dynamic data accessed includes: real-time power on the supply and demand sides, pressure / temperature / flow rate at key nodes in the pipeline network, valve opening degree, circuit breaker status, etc. The accessed data is used, on the one hand, to drive the initial state alignment of the simulation model, and on the other hand, as the basis for updating boundary conditions during the simulation process.

[0111] After completing model construction and data access, a digital twin simulation unit is encapsulated. This simulation unit has a built-in time-stepping simulation engine that can receive scheduling command sequences as input, iteratively solve the energy flow distribution state at each moment on the virtual time axis, and output the time-series curves of the state variables of each node and branch. At this point, the digital twin simulation unit is complete and can be used to verify candidate synergistic carbon reduction schemes.

[0112] In this embodiment, by transforming the discrete supply and demand matching problem into a standardized weighted directed graph problem, structured data support is provided for subsequent rapid detection of conflict domains and screening of optimal carbon reduction combinations under multiple constraints, ensuring the accuracy of candidate solution generation.

[0113] Based on the above embodiments of this application, a fifth embodiment of this application is proposed. In this fifth embodiment, content that is the same as or similar to that in the above embodiments can be referred to the above description, and will not be repeated hereafter.

[0114] In this embodiment, step S40, which involves inputting the candidate synergistic carbon reduction scheme into a pre-constructed digital twin simulation unit for verification to obtain the synergistic carbon reduction scheme, may include steps S41 to S42: Step S41: Input the candidate synergistic carbon reduction scheme into the pre-constructed digital twin simulation unit, and perform time-series simulation of the candidate synergistic carbon reduction scheme through the digital twin simulation unit to obtain the actual simulated carbon emission reduction. The supply and demand pairings, scheduling power curves, and execution periods in the candidate schemes are analyzed and converted into a sequence of scheduling instructions that can be recognized by the digital twin simulation unit, including the power setpoint on the supply side and the action sequence of pipeline valves / switches.

[0115] Furthermore, the actual operating conditions of the current park energy system (supply-side output, demand-side load, and pipeline pressure and temperature distribution) are obtained from the real-time database and loaded into the digital twin model as the initial state for simulation.

[0116] The simulation engine is launched and progresses along the time axis in fixed steps. Within each step, scheduling instructions are executed, energy flow distribution is solved, transmission losses are calculated, and the state of each node is updated. Simultaneously, a disturbance channel can be selectively run in parallel, that is, random supply and demand fluctuations that conform to the historical statistical characteristics of the park are injected into the simulation process to evaluate the robustness of the scheme under non-ideal operating conditions.

[0117] After the simulation is completed, the time-series integral values ​​of the actual output energy on the supply side and the actual received energy on the demand side are extracted, the actual carbon emission reduction in the collaborative process is recalculated, and any violations such as pipeline capacity exceeding limits, insufficient supply capacity, or substandard energy quality are recorded during the simulation.

[0118] Step S42: Based on the actual simulated carbon emission reduction, the candidate collaborative carbon reduction schemes are screened to obtain collaborative carbon reduction schemes.

[0119] First, candidate solutions that violate any hard safety constraints during the simulation process (such as line overload, pipeline overpressure, or insufficient supply leading to demand-side power outages) are eliminated, as such solutions are not practically feasible.

[0120] For schemes that pass the safety constraint filtering, their comprehensive verification score is calculated. The score comprehensively considers factors such as the achievement rate of actual simulated carbon emission reductions and expected carbon emission reductions, the stability of emission reductions under the channel, and the efficiency of pipeline resource utilization. The candidate scheme with the highest comprehensive score is selected as the recommended implementation scheme.

[0121] In this embodiment, by using a digital twin simulation unit, operational risks that cannot be detected at the theoretical level are identified in advance, ensuring that the final carbon reduction solution has both ideal carbon reduction benefits and practical feasibility from the verification stage.

[0122] This application also provides a collaborative carbon reduction device for enterprises in an industrial park; please refer to [reference needed]. Figure 5 The collaborative carbon reduction device for enterprises in the industrial park includes: Matching module 10 is used to obtain supply-side data and demand-side data reported by enterprises in the park, and to match priority weights for each of the supply-side data. The coupling factor calculation module 20 is used to construct the spatiotemporal coupling factor of the potential supply pairs existing in the supply-side data and the demand-side data, and to calculate the actual schedulable coordination amount of the potential supply pairs based on the spatiotemporal coupling factor. The scheme generation module 30 is used to calculate the expected carbon emission reduction based on the actual schedulable collaborative amount and the priority weight, and generate candidate collaborative carbon reduction schemes based on the expected carbon emission reduction. The verification module 40 is used to input the candidate synergistic carbon reduction scheme into a pre-constructed digital twin simulation unit for verification, so as to obtain the synergistic carbon reduction scheme.

[0123] The collaborative carbon reduction device for industrial parks provided in this application, employing the collaborative carbon reduction method for industrial parks in the above embodiments, can solve the technical problems of collaborative carbon reduction for industrial parks. Compared with the prior art, the beneficial effects of the collaborative carbon reduction device for industrial parks provided in this application are the same as those of the collaborative carbon reduction method for industrial parks provided in the above embodiments, and other technical features in the collaborative carbon reduction device for industrial parks are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0124] This application provides a collaborative carbon reduction device for enterprises in a park. The collaborative carbon reduction device for enterprises in a park includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the collaborative carbon reduction method for enterprises in the park described in Embodiment 1 above.

[0125] The following is for reference. Figure 6 The diagram illustrates a structural schematic suitable for implementing collaborative carbon reduction equipment for park enterprises in the embodiments of this application. The collaborative carbon reduction equipment for park enterprises in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The collaborative carbon reduction equipment for enterprises in the industrial park shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0126] like Figure 6As shown, the collaborative carbon reduction equipment for enterprises in the industrial park may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the collaborative carbon reduction equipment for enterprises in the industrial park. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows collaborative carbon reduction equipment for enterprises within the industrial park to communicate wirelessly or wiredly with other devices to exchange data. While the diagram shows collaborative carbon reduction equipment for industrial park enterprises with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0127] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0128] The collaborative carbon reduction equipment for industrial parks provided in this application, employing the collaborative carbon reduction method for industrial parks in the above embodiments, can solve the technical problems of collaborative carbon reduction for industrial parks. Compared with the prior art, the beneficial effects of the collaborative carbon reduction equipment for industrial parks provided in this application are the same as the beneficial effects of the collaborative carbon reduction method for industrial parks provided in the above embodiments, and other technical features of the collaborative carbon reduction equipment for industrial parks are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0129] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0130] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0131] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the collaborative carbon reduction method for park enterprises in the above embodiments.

[0132] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0133] The aforementioned computer-readable storage medium may be included in the collaborative carbon reduction equipment of enterprises in the park; or it may exist independently and not be installed in the collaborative carbon reduction equipment of enterprises in the park.

[0134] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the collaborative carbon reduction equipment of the park enterprises, the collaborative carbon reduction equipment of the park enterprises enables the following actions: acquiring supply-side data and demand-side data reported by the park enterprises, and matching priority weights to each of the supply-side data; constructing a spatiotemporal coupling factor for potential supply pairs existing in the supply-side data and demand-side data, and calculating the actual schedulable collaborative amount of the potential supply pairs based on the spatiotemporal coupling factor; calculating the expected carbon emission reduction based on the actual schedulable collaborative amount and the priority weight, and generating candidate collaborative carbon reduction schemes based on the expected carbon emission reduction; and inputting the candidate collaborative carbon reduction schemes into a pre-constructed digital twin simulation unit for verification to obtain the collaborative carbon reduction scheme.

[0135] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0136] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0137] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0138] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., computer programs) for executing the above-described collaborative carbon reduction method for enterprises in industrial parks, thereby solving the technical problems of collaborative carbon reduction for enterprises in industrial parks. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the collaborative carbon reduction method for enterprises in industrial parks provided in the above embodiments, and will not be repeated here.

[0139] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described collaborative carbon reduction method for park enterprises.

[0140] The computer program product provided in this application can solve the technical problem of collaborative carbon reduction among enterprises in industrial parks. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the collaborative carbon reduction method for enterprises in industrial parks provided in the above embodiments, and will not be repeated here.

[0141] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A collaborative carbon reduction method among enterprises in an industrial park, characterized in that, The collaborative carbon reduction methods among enterprises in the industrial park include: Obtain supply-side and demand-side data reported by enterprises in the park, and assign priority weights to each type of supply-side data. Construct a spatiotemporal coupling factor for potential supply pairs existing in the supply-side data and the demand-side data, and calculate the actual schedulable coordination amount of the potential supply pairs based on the spatiotemporal coupling factor; The expected carbon emission reduction is calculated based on the actual schedulable collaborative amount and the priority weight, and candidate collaborative carbon reduction schemes are generated based on the expected carbon emission reduction. The candidate synergistic carbon reduction schemes are input into a pre-constructed digital twin simulation unit for verification to obtain the synergistic carbon reduction schemes. The step of constructing the spatiotemporal coupling factor of potential supply pairs existing in the supply-side data and the demand-side data, and calculating the actual schedulable coordination amount of the potential supply pairs based on the spatiotemporal coupling factor, includes: The supply-side data and the demand-side data are paired to obtain the potential supply pairs; Extract the temporal and spatial feature parameters of the potential supply pairs respectively; The overlapping supply periods are determined based on the time-series characteristic parameters, and the power curve matching ratio of the potential supply pairs within the overlapping supply periods is calculated. The time coupling factor is obtained by performing a time-weighted average calculation on the power curve matching ratio; The loss threshold of the potential supply pair is calculated based on the spatial characteristic parameters, and the spatial coupling factor is calculated based on the loss threshold. The spatiotemporal coupling factor is calculated based on the time coupling factor and the spatial coupling factor; Collect historical supply-side power time-series curves and historical demand-side power time-series curves within the overlapping supply periods; Calculate the Pearson correlation coefficient between the historical supply-side power time-series curve and the historical demand-side power time-series curve; The spatiotemporal coupling factor is corrected based on the Pearson correlation coefficient; Calculate the theoretical maximum schedulable quantity during the overlapping supply period, and calculate the actual schedulable cooperative quantity based on the corrected spatiotemporal coupling factor and the theoretical maximum schedulable quantity.

2. The collaborative carbon reduction method among enterprises in the industrial park as described in claim 1, characterized in that, The step of matching priority weights for each of the supply-side data includes: The supply-side data is standardized and labeled with attributes according to a preset labeling dimension to obtain a supply-side attribute vector corresponding to the supply-side data. Calculate the initial priority weights of the supply-side attribute vector; Collect the operational health parameters of the energy output link corresponding to the supply-side data, and calculate the priority weight based on the operational health parameters and the initial priority weight.

3. The collaborative carbon reduction method among enterprises in the industrial park as described in claim 1, characterized in that, The step of calculating the expected carbon emission reduction based on the actual schedulable coordination quantity and the priority weight includes: Obtain the marginal carbon emission factors and baseline emission factors reported by enterprises in the park; The baseline emissions for each potential supply pair are calculated based on the marginal carbon emission factor and the baseline emission factor. The allocation coefficient for each potential supply pair is determined based on the priority weight, and the allocation coordination quantity is calculated based on the actual schedulable coordination quantity and the allocation coefficient. Calculate the collaborative carbon emissions under collaborative operation conditions based on the allocated collaborative amount; The difference between the synergistic carbon emissions and the baseline emissions is calculated, and the difference is corrected according to a preset confidence correction function to obtain the initial expected carbon emission reduction. The initial expected carbon emission reduction is calibrated using a preset time decay factor to obtain the expected carbon emission reduction.

4. The collaborative carbon reduction method among enterprises in the industrial park as described in claim 1, characterized in that, The step of generating candidate synergistic carbon reduction schemes based on the expected carbon emission reduction includes: A bidirectional matching graph is constructed using the potential supply pairs as edges and the corresponding supply-side nodes and demand-side nodes as vertices; wherein the weight of each edge is the corresponding expected carbon emission reduction. Conflict domain detection is performed on the bidirectional matching graph to generate candidate collaborative carbon reduction schemes.

5. The collaborative carbon reduction method among enterprises in the industrial park as described in claim 1, characterized in that, Before the step of inputting the candidate synergistic carbon reduction scheme into a pre-constructed digital twin simulation unit for verification to obtain the synergistic carbon reduction scheme, the following steps are also included: Collect static asset information of enterprises in the park, and establish an energy directed graph model based on the static asset information; The transmission loss function is fitted into the energy directed graph model, and the operating data of the park enterprises is incorporated to obtain the digital twin simulation unit.

6. The collaborative carbon reduction method among enterprises in the industrial park as described in claim 1, characterized in that, The step of inputting the candidate synergistic carbon reduction scheme into a pre-constructed digital twin simulation unit for verification to obtain the synergistic carbon reduction scheme includes: The candidate synergistic carbon reduction scheme is input into a pre-constructed digital twin simulation unit, and the digital twin simulation unit performs time-series simulation of the candidate synergistic carbon reduction scheme to obtain the actual simulated carbon emission reduction. Based on the actual simulated carbon emission reduction, the candidate collaborative carbon reduction schemes are screened to obtain collaborative carbon reduction schemes.

7. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the collaborative carbon reduction method among enterprises in the park as described in any one of claims 1 to 6.

8. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the collaborative carbon reduction method among enterprises in the industrial park as described in any one of claims 1 to 6.

Images