Electricity-carbon measurement detection method and device based on electricity-carbon coupling model, and medium

Abstract

The invention relates to an electric carbon metering detection method and device based on an electric carbon coupling model and a medium, and the method comprises the following steps: collecting electric carbon data of an electric power system, and constructing an initial electric carbon coupling model based on the electric carbon data of the electric power system; expanding the initial electricity-carbon coupling model, and optimizing the expanded electricity-carbon coupling model based on a fuzzy logic reasoning mechanism to obtain an optimized electricity-carbon coupling model; generating a simulation scene based on the optimized power-carbon coupling model according to the collected power system power-carbon data and different preset operation conditions; and performing matching analysis on the simulation scene and the power system electricity and carbon data to obtain comprehensive evaluation parameters, and judging the precision of the power system electricity and carbon metering equipment based on the comprehensive evaluation parameters.

Description

Technical Field

[0001] The invention relates to an electric-carbon metering detection method, equipment and medium based on an electric-carbon coupling model, and belongs to the technical field of measurement and detection. Background Art

[0002] As the global demand for carbon emission control and efficient energy utilization continues to increase, the measurement of electric energy and the assessment of carbon emissions in power systems have gradually become an important technical requirement. In the actual operation of power systems, the coupling relationship between electric energy and carbon emissions is complex and changeable, involving multiple dynamic parameters such as voltage, current, active power, reactive power, as well as key carbon emission parameters such as carbon emission factor and carbon emissions per unit of electricity. These parameters not only change dynamically over time, but are also affected by factors such as load fluctuations, changes in network topology, and external environmental disturbances, making it difficult for traditional linear measurement models to meet the high-precision requirements under complex working conditions.

[0003] Existing electricity-carbon metering methods mainly rely on static or linear models, which simply couple electricity parameters with carbon emission parameters and lack in-depth characterization of dynamic change characteristics and nonlinear relationships. In addition, most metering methods fail to fully consider the impact of multidimensional disturbances on metering results. Under complex dynamic conditions, the system metering error is large and the stability is poor. At the same time, the generation methods of simulation scenarios are limited, usually only covering a small number of typical conditions, and cannot fully reflect the complex dynamic conditions in actual operation. This makes the match between simulation scenarios and actual data insufficient, further limiting the existing methods' ability to fully evaluate metering performance.

[0004] However, the above technology has the following technical problems: under complex dynamic operating conditions, the evaluation of the electro-carbon coupling relationship in electro-carbon detection is inaccurate and the electro-carbon metering detection is inaccurate. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention proposes an electric-carbon metering detection method, equipment and medium based on an electric-carbon coupling model.

[0006] The technical solution of the present invention is as follows: On the one hand, the present invention provides an electric-carbon measurement detection method based on an electric-carbon coupling model, comprising the following steps: Collect the electricity-carbon data of the power system and build an initial electricity-carbon coupling model based on the electricity-carbon data of the power system; The initial electric-carbon coupling model is expanded, and the expanded electric-carbon coupling model is optimized based on the fuzzy logic reasoning mechanism to obtain the optimized electric-carbon coupling model; Generate simulation scenarios based on the optimized electricity-carbon coupling model according to the collected electricity-carbon data of the power system and different preset operating conditions; The simulation scenarios and the electric carbon data of the power system are matched and analyzed to obtain comprehensive evaluation parameters, and the accuracy of the electric carbon metering equipment of the power system is judged based on the comprehensive evaluation parameters.

[0007] As a preferred embodiment of the present invention, the electric carbon data of the power system specifically includes active power, reactive power, current and carbon emission factor of the corresponding power node.

[0008] As a preferred embodiment of the present invention, the initial electric-carbon coupling model is specifically shown in the following formula: ; in: express Output of the initial electric-carbon coupling model at time instant; , , , represents the initial weight coefficient; Represents a power node Active power at the moment; Represents a power node Reactive power at the moment; Represents a power node The current at the moment; Represents a power node Carbon emission factor at the time.

[0009] As a preferred embodiment of the present invention, the initial electric-carbon coupling model is expanded based on the time-space decomposition method, and the specific steps are as follows: Based on the spatiotemporal decomposition method, the electric carbon data of the power system is decomposed into static baseline values, short-term fluctuations, long-term trends and external disturbance forms, and constructed as dynamic input variables, as shown in the following formula: ; in: express Dynamic input variables at the moment; Represents the static benchmark value of the electric carbon data of the power system; express The short-term fluctuation of electricity carbon data in the power system at the moment; express Long-term trend of electricity and carbon data in the power system at the moment; express External disturbances in the electricity and carbon data of the power system at the moment; Substituting the dynamic input variables into the initial electric-carbon coupling model, the extended electric-carbon coupling model is obtained, as shown in the following formula: ; in: express Always expand the output of the electric-carbon coupling model; Indicates Weight coefficients; Indicates the carbon data of the power system Static benchmark value of class data samples; Indicates the carbon data of the power system Class data samples are Short-term fluctuations at different times; Indicates the carbon data of the power system Class data samples are Long-term trend at the moment; Indicates the carbon data of the power system Class data samples are External disturbances at any time.

[0010] As a preferred embodiment of the present invention, the specific steps of optimizing the extended electric-carbon coupling model based on the fuzzy logic reasoning mechanism are as follows: For each dynamic input variable, a fuzzy set is defined, including three fuzzy labels: low, medium, and high, and the membership is represented by an asymmetric Gaussian distribution function, as shown in the following formula: ; ; ; in: , , Respectively represent the fuzzy membership of the input variable to the fuzzy label low, medium, and high; represents input variables; , represents the expansion coefficient of the asymmetric Gaussian distribution function; , Represents the central value of the asymmetric Gaussian distribution function; represents a natural constant; A fuzzy rule base is constructed based on the fuzzy membership of each dynamic input variable. The output membership of the fuzzy rule is calculated by the following formula: ; in: Indicates The output membership of the fuzzy rules; It represents the logical result of all dynamic input variables after fuzzy membership mapping; Indicates In the fuzzy rules, the dynamic input variable In the fuzzy set The degree of membership in ; Indicates the number of dynamic input variables; Indicates the dynamic input variable quantity index; The defuzzification formula is constructed based on the output membership of fuzzy rules and the extended electric-carbon coupling model, as shown in the following formula: ; in: The extended electric-carbon coupling model after defuzzification is Output results at the moment; represents the total number of fuzzy rules; Indicates The central value of the fuzzy rules; represents the residual correction factor; The defuzzified output results are recursively dynamically fused and optimized. The recursive dynamic fusion optimization formula is: ; in: The extended electric-carbon coupling model after defuzzification is The output result at time Output after layer-by-layer recursive dynamic fusion optimization; Indicates the number of recursive layers; Indicates Time decay factor for layer recursion; Indicates Layer recursive weight function; Indicates The time interval between layer recursions; Set the stopping condition for recursive dynamic fusion optimization: ; in: represents the recursive convergence threshold; An adaptive deviation correction mechanism is introduced to correct the deviation of the recursive dynamic fusion optimization output, as shown in the following formula: ; in: express The deviation correction result at the moment; Indicates the number of correction factors; Indicates The strength of the correction factor; Indicates The sensitivity of the correction factor; Indicates The calibration reference value of the calibration factor; High-dimensional perturbation optimization is introduced to construct an optimized electric-carbon coupling model, as shown in the following formula: ; in: Indicates that the optimized electric-carbon coupling model is Output results at the moment; represents the disturbance amplitude factor; represents the main frequency of disturbance; express The dynamic change value of external disturbance at every moment; represents the disturbance smoothing factor.

[0011] As a preferred embodiment of the present invention, the steps of generating the simulation scene are: Based on the collected electric carbon data of the power system, a simulation scenario generation formula is constructed, as shown in the following formula: ; in: Indicates based on Simulation data corresponding to the simulation scenario generated by the electric carbon data of the power system at the moment; Indicates the fluctuation range of the current simulation scenario preset; Indicates the preset fluctuation frequency of the current simulation scene; Indicates the random disturbance intensity preset in the current simulation scenario; represents a random disturbance.

[0012] As a preferred embodiment of the present invention, the simulation data corresponding to the generated simulation scenario is input into the optimized electric-carbon coupling model to obtain the simulation output, which is specifically shown in the following formula: ; in: express Simulation output at the moment; The collected electricity-carbon data of the power system is used as the input of the optimized electricity-carbon coupling model to obtain the corresponding actual output; A multi-dimensional comparison and matching analysis algorithm is introduced to perform comparison and matching analysis on the simulation output and the actual output to generate comprehensive evaluation parameters.

[0013] As a preferred embodiment of the present invention, the specific calculation steps of the comprehensive evaluation parameters are: A recursive dynamic mapping formula is constructed to adjust the simulation output. The recursive dynamic mapping formula is shown as follows: ; in: Indicates passing After layer recursion Simulate the output mapping value at each moment; Indicates the number of recursive levels of the recursive dynamic mapping; Indicates Dynamic weights of layer recursion; express The actual output at the moment; The multi-dimensional matching error index between the simulation output and the actual output is calculated based on the recursive dynamic mapping output results, as shown in the following formula: ; in: express Multi-dimensional matching error index between simulation output and actual output at each moment; Indicates the total number of input dimensions; express The simulation output mapping value at time The components of the input dimensions; express The actual output at the moment The components of the input dimensions; Indicates The standard deviation of the input dimensions; The comprehensive evaluation parameters are calculated based on the multi-dimensional matching error index, as shown in the following formula: ; in: represents comprehensive evaluation parameters; Indicates the time frame of the assessment; express Dynamic matching weights based on simulation output mapping values ​​at all times.

[0014] On the other hand, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in any embodiment of the present invention is implemented.

[0015] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any embodiment of the present invention.

[0016] The present invention has the following beneficial effects: 1. The present invention introduces a dynamic fuzzy recursive fusion algorithm and makes full use of the real-time collected electric-carbon data to construct an accurate electric-carbon coupling model. The optimized electric-carbon coupling model can dynamically adapt to the temporal and spatial changes of the input parameters, including the combined effects of short-term fluctuations, long-term trends and external disturbances, which significantly improves the accuracy and adaptability of the model; 2. The present invention adopts fuzzy logic reasoning mechanism to deal with the uncertainty and nonlinear characteristics of input variables; by designing Gaussian membership function and fuzzy rule base, the nonlinear coupling relationship between power parameters and carbon emission parameters is efficiently analyzed, effectively reducing the impact of measurement errors under complex working conditions; 3. The present invention realizes layer-by-layer matching between simulation data and actual data through multi-parameter simulation scene generation technology combined with a recursive dynamic mapping algorithm. The diversity and dynamic optimization capabilities of simulation scenes enhance the applicability of metrology detection under complex operating conditions and can effectively quantify the performance of the system under various working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0020] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0021] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0022] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0023] Embodiment 1: See also Figure 1 , an electric-carbon measurement detection method based on an electric-carbon coupling model, comprising the following steps: Collect the electricity-carbon data of the power system and build an initial electricity-carbon coupling model based on the electricity-carbon data of the power system; The initial electric-carbon coupling model is expanded, and the expanded electric-carbon coupling model is optimized based on the fuzzy logic reasoning mechanism to obtain the optimized electric-carbon coupling model; Generate simulation scenarios based on the optimized electricity-carbon coupling model according to the collected electricity-carbon data of the power system and different preset operating conditions; The simulation scenarios and the electric carbon data of the power system are matched and analyzed to obtain comprehensive evaluation parameters, and the accuracy of the electric carbon metering equipment of the power system is judged based on the comprehensive evaluation parameters.

[0024] As a preferred implementation of this embodiment, the power system electric carbon data specifically includes active power, reactive power, current and carbon emission factor of the corresponding power node.

[0025] As a preferred implementation of this embodiment, the initial electric-carbon coupling model is specifically shown in the following formula: ; in: express Output of the initial electric-carbon coupling model at time instant; , , , represents the initial weight coefficient; Represents a power node Active power at the moment; Represents a power node Reactive power at the moment; Represents a power node The current at the moment; Represents a power node Carbon emission factor at the time.

[0026] As a preferred implementation of this embodiment, the initial electric-carbon coupling model is expanded based on the time-space decomposition method, and the specific steps are as follows: Based on the spatiotemporal decomposition method, the electric carbon data of the power system is decomposed into static baseline values, short-term fluctuations, long-term trends and external disturbance forms, and constructed as dynamic input variables, as shown in the following formula: ; in: express Dynamic input variables at the moment; Represents the static benchmark value of the electric carbon data of the power system; express The short-term fluctuation of the electric carbon data of the power system at the moment indicates the fast dynamic change part of the input variable; express The long-term trend of the electricity carbon data of the power system at the moment reflects the slow dynamic changes; express Changes caused by external disturbances in the electricity and carbon data of the power system at the moment; Substituting the dynamic input variables into the initial electric-carbon coupling model, the extended electric-carbon coupling model is obtained, as shown in the following formula: ; in: express Always expand the output of the electric-carbon coupling model; Indicates Weight coefficients; Indicates the carbon data of the power system Static benchmark value of class data samples; Indicates the carbon data of the power system Class data samples are Short-term fluctuations at different times; Indicates the carbon data of the power system Class data samples are Long-term trend at the moment; Indicates the carbon data of the power system Class data samples are External disturbances at any time.

[0027] As a preferred implementation of this embodiment, the specific steps of optimizing the extended electric-carbon coupling model based on the fuzzy logic reasoning mechanism are as follows: For each dynamic input variable, a fuzzy set is defined, including three fuzzy labels: low, medium, and high, and the membership is represented by an asymmetric Gaussian distribution function, as shown in the following formula: ; ; ; in: , , Respectively represent the fuzzy membership of the input variable to the fuzzy label low, medium, and high; represents input variables; , represents the expansion coefficient of the asymmetric Gaussian distribution function; , Represents the central value of the asymmetric Gaussian distribution function; represents a natural constant; A fuzzy rule base is constructed based on the fuzzy membership of each dynamic input variable (for example, if High and high, the electric-carbon coupling is strong; if Zhongqie The output membership of the fuzzy rule is calculated by the following formula: ; in: Indicates The output membership of the fuzzy rules; Generated by fuzzy logic rule reasoning, it represents the logical result of all dynamic input variables after being mapped by fuzzy membership; Indicates In the fuzzy rules, the dynamic input variable In the fuzzy set The degree of membership in ; Indicates the number of dynamic input variables; Indicates the dynamic input variable quantity index; The defuzzification formula is constructed based on the output membership of fuzzy rules and the extended electric-carbon coupling model, as shown in the following formula: ; in: The extended electric-carbon coupling model after defuzzification is Output results at the moment; represents the total number of fuzzy rules; Indicates The central value of the fuzzy rules; Represents the residual correction factor, which is used to enhance the adaptability of fuzzy logic and is determined based on expert experience; The defuzzified output results are recursively dynamically fused and optimized. The recursive dynamic fusion optimization formula is: ; in: The extended electric-carbon coupling model after defuzzification is The output result at time Output after layer-by-layer recursive dynamic fusion optimization; Indicates the number of recursive layers; Indicates The time decay factor of the layer recursion, , represents the time attenuation coefficient, Indicates the recursive optimization process The time point referenced by the layer recursion, ; Indicates The layer-recursive weight function is used to balance the data contribution of different historical layers. , represents the frequency factor of the dynamic weight function, represents the phase shift of the dynamic weight function, represents the dynamic weight perturbation term; Indicates The time interval between layer recursions; Set the stopping condition for recursive dynamic fusion optimization: ; in: represents the recursive convergence threshold, which is determined according to the expert experience method; An adaptive deviation correction mechanism is introduced to correct the deviation of the recursive dynamic fusion optimization output, as shown in the following formula: ; in: express The deviation correction result at the moment; represents the number of correction factors (i.e., the total number of key states, external disturbance dimensions, input variable categories, or time periods that need correction); Indicates The strength of the correction factor; Indicates The sensitivity of the correction factor; Indicates The calibration reference value of the calibration factor; High-dimensional perturbation optimization is introduced to construct an optimized electric-carbon coupling model, as shown in the following formula: ; in: Indicates that the optimized electric-carbon coupling model is Output results at the moment; Represents the disturbance amplitude factor, which is used to adjust the sensitivity of disturbance optimization; It represents the main frequency of disturbance, reflecting the dominant frequency of change of external disturbance; express The dynamic change value of the external disturbance at every moment is obtained through the sensor; Represents the disturbance smoothing factor, which controls the smoothness of the disturbance response.

[0028] As a preferred implementation of this embodiment, the steps for generating a simulation scene are: Based on the collected electric carbon data of the power system, a simulation scenario generation formula is constructed, as shown in the following formula: ; in: Indicates based on Simulation data corresponding to the simulation scenario generated by the electric carbon data of the power system at the moment; Indicates the fluctuation range of the current simulation scenario preset; Indicates the preset fluctuation frequency of the current simulation scene; Indicates the random disturbance intensity preset in the current simulation scenario; represents a random disturbance.

[0029] As a preferred implementation of this embodiment, the simulation data corresponding to the generated simulation scenario is input into the optimized electric-carbon coupling model to obtain the simulation output, which is specifically shown in the following formula: ; in: express Simulation output at the moment; The collected electricity-carbon data of the power system is used as the input of the optimized electricity-carbon coupling model to obtain the corresponding actual output; A multi-dimensional comparison and matching analysis algorithm is introduced to perform comparison and matching analysis on the simulation output and the actual output to generate comprehensive evaluation parameters.

[0030] As a preferred implementation of this embodiment, the specific calculation steps of the comprehensive evaluation parameters are: A recursive dynamic mapping formula is constructed to adjust the simulation output so that it approaches the actual data in each dimension. The recursive dynamic mapping formula is shown as follows: ; in: Indicates passing After layer recursion Simulate the output mapping value at each moment; Indicates the number of recursive levels of the recursive dynamic mapping; Indicates Dynamic weights of layer recursion, , represents the weight condition factor; express The actual output at the moment; The multi-dimensional matching error index between the simulation output and the actual output is calculated based on the recursive dynamic mapping output results, as shown in the following formula: ; in: express The multi-dimensional matching error index between the simulation output and the actual output at each moment reflects the matching degree between the mapping value and the actual value in the multi-dimensional space; Indicates the total number of input dimensions; express The simulation output mapping value at time The components of the input dimensions; express The actual output at the moment The components of the input dimensions; Indicates The standard deviation of the input dimensions; The comprehensive evaluation parameters are calculated based on the multi-dimensional matching error index, as shown in the following formula: ; in: It represents a comprehensive evaluation parameter, which indicates the performance score of the electric carbon metering equipment. It is an important indicator used to quantify the performance of the electric carbon metering detection system. Its value range is between [0,1]. The closer the value is to 1, the higher the matching accuracy and the better the metering performance; the closer the value is to 0, the larger the error and the poorer the dynamic adaptability. Indicates the time frame of the assessment; express Dynamic matching weights based on simulation output mapping values ​​at all times; According to the comprehensive evaluation parameters The numerical range of the electric carbon metering test results is divided into the following five performance levels: 1) Excellent performance, : It has extremely high measurement accuracy, the matching error between simulation data and actual data is extremely small, the dynamic weight distribution is stable, and it is suitable for complex working conditions; 2) Good performance, , indicating that the overall performance is high, with only slight fluctuations in error under specific working conditions, suitable for general dynamic conditions; 3) Medium performance, , indicating that the measurement accuracy is relatively general, the error fluctuation is obvious, and the ability to adapt to dynamic changes is limited; 4) Poor performance, , indicating that the matching accuracy is low, the error is large, and it is suitable for static conditions; 5) Failure performance , indicating that the measurement error is too large and the dynamic adaptability is extremely poor, and recalibration or replacement is required.

[0031] Embodiment 2: This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present invention when executing the program.

[0032] Embodiment three: This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in any embodiment of the present invention is implemented.

[0033] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0034] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0035] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0036] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0037] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An electric-carbon measurement detection method based on an electric-carbon coupling model, characterized in that: The following steps are involved: Collect the electricity-carbon data of the power system and build an initial electricity-carbon coupling model based on the electricity-carbon data of the power system; The initial electric-carbon coupling model is expanded, and the expanded electric-carbon coupling model is optimized based on the fuzzy logic reasoning mechanism to obtain the optimized electric-carbon coupling model; Generate simulation scenarios based on the optimized electricity-carbon coupling model according to the collected electricity-carbon data of the power system and different preset operating conditions; The simulation scenarios and the electric carbon data of the power system are matched and analyzed to obtain comprehensive evaluation parameters, and the accuracy of the electric carbon metering equipment of the power system is judged based on the comprehensive evaluation parameters.

2. The electric-carbon measurement detection method based on the electric-carbon coupling model according to claim 1 is characterized in that: The electric power system electric carbon data specifically includes the active power, reactive power, current and carbon emission factor of the corresponding power node.

3. The electric-carbon measurement detection method based on the electric-carbon coupling model according to claim 2 is characterized in that: The initial electric-carbon coupling model is specifically shown in the following formula: ； in: express Output of the initial electric-carbon coupling model at time instant; , , , represents the initial weight coefficient; Represents a power node Active power at the moment; Represents a power node Reactive power at the moment; Represents a power node The current at the moment; Represents a power node Carbon emission factor at the time.

4. The electric-carbon measurement detection method based on the electric-carbon coupling model according to claim 3 is characterized in that: The initial electric-carbon coupling model is expanded based on the time-space decomposition method. The specific steps are as follows: Based on the spatiotemporal decomposition method, the electric carbon data of the power system is decomposed into static baseline values, short-term fluctuations, long-term trends and external disturbance forms, and constructed as dynamic input variables, as shown in the following formula: ； in: express Dynamic input variables at the moment; Represents the static benchmark value of the electric carbon data of the power system; express The short-term fluctuation of electricity carbon data in the power system at the moment; express Long-term trend of electricity and carbon data in the power system at the moment; express External disturbances in the electricity and carbon data of the power system at the moment; Substituting the dynamic input variables into the initial electric-carbon coupling model, the extended electric-carbon coupling model is obtained, as shown in the following formula: ； in: express Always expand the output of the electric-carbon coupling model; Indicates Weight coefficients; Indicates the carbon data of the power system Static benchmark value of class data samples; Indicates the carbon data of the power system Class data samples are Short-term fluctuations at different times; Indicates the carbon data of the power system Class data samples are Long-term trend at the moment; Indicates the carbon data of the power system Class data samples are External disturbances at any time.

5. The electric-carbon measurement detection method based on the electric-carbon coupling model according to claim 4 is characterized in that: The specific steps of optimizing the extended electric-carbon coupling model based on the fuzzy logic reasoning mechanism are as follows: For each dynamic input variable, a fuzzy set is defined, including three fuzzy labels: low, medium, and high, and the membership is represented by an asymmetric Gaussian distribution function, as shown in the following formula: ； ； ； in: , , Respectively represent the fuzzy membership of the input variable to the fuzzy label low, medium, and high; represents input variables; , represents the expansion coefficient of the asymmetric Gaussian distribution function; , Represents the central value of the asymmetric Gaussian distribution function; represents a natural constant; A fuzzy rule base is constructed based on the fuzzy membership of each dynamic input variable. The output membership of the fuzzy rule is calculated by the following formula: ； in: Indicates The output membership of the fuzzy rules; It represents the logical result of all dynamic input variables after fuzzy membership mapping; Indicates In the fuzzy rules, the dynamic input variable In the fuzzy set The degree of membership in ; Indicates the number of dynamic input variables; Indicates the dynamic input variable quantity index; The defuzzification formula is constructed based on the output membership of fuzzy rules and the extended electric-carbon coupling model, as shown in the following formula: ； in: The extended electric-carbon coupling model after defuzzification is Output results at the moment; represents the total number of fuzzy rules; Indicates The central value of the fuzzy rules; represents the residual correction factor; The defuzzified output results are recursively dynamically fused and optimized. The recursive dynamic fusion optimization formula is: ； in: The extended electric-carbon coupling model after defuzzification is The output result at time Output after layer-by-layer recursive dynamic fusion optimization; Indicates the number of recursive layers; Indicates Time decay factor for layer recursion; Indicates Layer recursive weight function; Indicates The time interval between layer recursions; Set the stopping condition for recursive dynamic fusion optimization: ； in: represents the recursive convergence threshold; An adaptive deviation correction mechanism is introduced to correct the deviation of the recursive dynamic fusion optimization output, as shown in the following formula: ； in: express The deviation correction result at the moment; Indicates the number of correction factors; Indicates The strength of the correction factor; Indicates The sensitivity of the correction factor; Indicates The calibration reference value of the calibration factor; High-dimensional perturbation optimization is introduced to construct an optimized electric-carbon coupling model, as shown in the following formula: ； in: Indicates that the optimized electric-carbon coupling model is Output results at the moment; represents the disturbance amplitude factor; represents the main frequency of disturbance; express The dynamic change value of external disturbance at every moment; represents the disturbance smoothing factor.

6. The electric-carbon measurement detection method based on the electric-carbon coupling model according to claim 5 is characterized in that: The steps to generate the simulation scenario are: Based on the collected electric carbon data of the power system, a simulation scenario generation formula is constructed, as shown in the following formula: ； in: Indicates based on Simulation data corresponding to the simulation scenario generated by the electric carbon data of the power system at the moment; Indicates the fluctuation range of the current simulation scenario preset; Indicates the preset fluctuation frequency of the current simulation scene; Indicates the random disturbance intensity preset in the current simulation scenario; represents a random disturbance.

7. The electric-carbon measurement detection method based on the electric-carbon coupling model according to claim 6 is characterized in that: The simulation data corresponding to the generated simulation scenario is input into the optimized electric-carbon coupling model to obtain the simulation output, as shown in the following formula: ； in: express Simulation output at the moment; The collected electricity-carbon data of the power system is used as the input of the optimized electricity-carbon coupling model to obtain the corresponding actual output; A multi-dimensional comparison and matching analysis algorithm is introduced to perform comparison and matching analysis on the simulation output and the actual output to generate comprehensive evaluation parameters.

8. The electric-carbon measurement detection method based on the electric-carbon coupling model according to claim 7 is characterized in that: The specific calculation steps of the comprehensive evaluation parameters are: A recursive dynamic mapping formula is constructed to adjust the simulation output. The recursive dynamic mapping formula is shown as follows: ； in: Indicates passing After layer recursion Simulate the output mapping value at each moment; Indicates the number of recursive levels of the recursive dynamic mapping; Indicates Dynamic weights of layer recursion; express The actual output at the moment; The multi-dimensional matching error index between the simulation output and the actual output is calculated based on the recursive dynamic mapping output results, as shown in the following formula: ； in: express Multi-dimensional matching error index between simulation output and actual output at each moment; Indicates the total number of input dimensions; express The simulation output mapping value at time The components of the input dimensions; express The actual output at the moment The components of the input dimensions; Indicates The standard deviation of the input dimensions; The comprehensive evaluation parameters are calculated based on the multi-dimensional matching error index, as shown in the following formula: ； in: represents comprehensive evaluation parameters; Indicates the time frame of the assessment; express Dynamic matching weights based on simulation output mapping values ​​at all times.

9. An electronic 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 program, the method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

Images