Energy carbon control value measurement method, device, electronic equipment and storage medium

Abstract

The present application discloses a method, device, electronic device and storage medium for measuring the value of energy carbon control, which relates to the field of carbon reduction and consumption reduction. The method includes: subdividing the measurement boundary of energy carbon control based on a first preset dimension to obtain multiple sub-boundaries, the measurement boundary is used to characterize the energy carbon control range to be measured within the measurement period; obtaining energy carbon control data and baseline data within each sub-boundary, the baseline data is used to characterize the reference level of energy carbon control; based on a preset value measurement model, obtaining the actual value corresponding to the energy carbon control data and the baseline value corresponding to the baseline data, the value measurement model is used to characterize the mapping relationship between input data and value; based on the actual value and baseline value within each sub-boundary, the total value of energy carbon control within the measurement boundary is obtained. The present application can finely measure the effect value of the control action and greatly optimize the value measurement accuracy of energy carbon control.

Description

Technical Field

[0001] The present application relates to the technical field of carbon reduction and consumption reduction, and specifically to a method, device, electronic device and storage medium for measuring the value of energy carbon management. Background Art

[0002] Carbon reduction and energy consumption reduction is a complex and long-term task. In order to meet the challenges of carbon reduction and energy consumption reduction, each carbon reduction and energy consumption reduction entity can implement corresponding carbon control measures through the carbon control system to control energy consumption and manage carbon emissions. In order to evaluate the effect of carbon control, it is necessary to measure the added value generated by carbon control measures. However, the commonly used value measurement method is based on the comparison of statistical values ​​of carbon control data within a certain period of time, which can only evaluate the effect of carbon control in a relatively rough manner, and the value measurement accuracy is low. Summary of the invention

[0003] The embodiments of the present application provide a method, device, electronic device and storage medium for measuring the energy carbon management value to optimize the measurement accuracy of the energy carbon management value.

[0004] In order to solve the above technical problems, the embodiments of the present application disclose the following technical solutions:

[0005] In the first aspect, a method for measuring the value of energy and carbon management is provided, including:

[0006] Subdividing the measurement boundary of energy carbon management and control based on the first preset dimension to obtain a plurality of sub-boundaries, wherein the measurement boundary is used to characterize the energy carbon management and control range to be measured within the measurement period;

[0007] Acquire energy carbon management data and baseline data within each of the sub-boundaries, wherein the baseline data is used to characterize a reference level of energy carbon management;

[0008] Based on a preset value measurement model, the actual value corresponding to the energy carbon management data and the baseline value corresponding to the baseline data are obtained, and the value measurement model is used to characterize the mapping relationship between input data and value;

[0009] Based on the actual value and the baseline value within each of the sub-boundaries, the total energy and carbon management value within the measurement boundary is obtained.

[0010] In some embodiments, obtaining the energy and carbon management data and baseline data within each of the sub-boundaries includes:

[0011] Acquire actual energy and carbon data and actual control data within each of the sub-boundaries;

[0012] Obtaining baseline energy and carbon data and baseline control data within each of the sub-boundaries;

[0013] Fusion of the actual energy-carbon data within each of the sub-boundaries with the actual control data to obtain the energy-carbon control data within each of the sub-boundaries;

[0014] The baseline energy and carbon data within each of the sub-boundaries are integrated with the baseline control data to obtain the baseline data within each of the sub-boundaries.

[0015] In some embodiments, the fusing the actual energy-carbon data within each of the sub-boundaries with the actual control data to obtain the energy-carbon control data within each of the sub-boundaries includes:

[0016] Subdividing the actual energy-carbon data within each of the sub-boundaries based on a second preset dimension to obtain a plurality of actual unit data, wherein the second preset dimension is configured as a metering frequency or a detection cycle;

[0017] Based on the time dimension, the plurality of actual unit data are merged with the actual control data in the corresponding sub-boundaries to obtain the energy and carbon control data in each sub-boundary.

[0018] In some embodiments, the fusing the actual energy-carbon data within each of the sub-boundaries with the actual control data to obtain the energy-carbon control data within each of the sub-boundaries includes:

[0019] Subdividing the actual control data within each of the sub-boundaries based on a third preset dimension to obtain a plurality of control unit data, wherein the third preset dimension is configured as a control frequency or an instruction issuing cycle;

[0020] Based on the time dimension, the plurality of control unit data are merged with the actual energy-carbon data in the corresponding sub-boundary to obtain the energy-carbon control data in each sub-boundary.

[0021] In some embodiments, the step of obtaining the actual control data and the baseline control data includes:

[0022] Acquire the actual control action log or actual control time series data within each of the sub-boundaries to obtain the actual control data within the corresponding sub-boundaries;

[0023] Obtain the baseline control action log or baseline control time series data within each of the sub-boundaries to obtain the baseline control data within the corresponding sub-boundaries.

[0024] In some embodiments, the first preset dimension includes at least one of a system operation dimension, a business feature dimension, a time dimension, and a management and control action dimension.

[0025] In some embodiments, the method for obtaining the baseline data includes obtaining historical control data within the baseline year, obtaining simulated control data obtained based on simulation under baseline conditions, and obtaining reference control data obtained by controlling based on a control strategy different from the current energy and carbon control strategy.

[0026] In some embodiments, obtaining the total energy and carbon management value within the measurement boundary based on the actual value and the baseline value within each sub-boundary includes:

[0027] Determine the difference between the actual value and the baseline value within each sub-boundary, and determine the sum of the differences corresponding to all sub-boundaries as the total energy and carbon management value within the measurement boundary;

[0028] Alternatively, the difference between the first value and the second value is determined as the total energy and carbon management value within the measurement boundary, the first value is the sum of the actual values ​​within all the sub-boundaries, and the second value is the sum of the baseline values ​​within all the sub-boundaries.

[0029] In some embodiments, the value measurement model includes a financial value measurement model, a brand value measurement model, and a social value measurement model;

[0030] The obtaining, based on a preset value measurement model, the actual value corresponding to the energy carbon management data and the baseline value corresponding to the baseline data includes:

[0031] Inputting the energy carbon management data into the financial value measurement model, the brand value measurement model and the social value measurement model respectively, to obtain the actual financial value, actual brand value and actual social value corresponding to the energy carbon management data;

[0032] The baseline data are respectively input into the financial value measurement model, the brand value measurement model and the social value measurement model to obtain the baseline financial value, baseline brand value and baseline social value corresponding to the baseline data.

[0033] In some embodiments, obtaining the total energy and carbon management value within the measurement boundary based on the actual value and the baseline value within each sub-boundary includes:

[0034] Determining the energy and carbon management financial value within the measurement boundary based on the actual financial value and the baseline financial value within each of the sub-boundaries;

[0035] Determine the energy and carbon management brand value within the measurement boundary based on the actual brand value and the baseline brand value within each of the sub-boundaries;

[0036] Determine the social value of energy and carbon management within the measurement boundary based on the actual social value and the baseline social value within each of the sub-boundaries;

[0037] Based on preset coefficients, the financial value of energy carbon management, the brand value of energy carbon management, and the social value of energy carbon management are weighted and summed, and the weighted sum result is determined as the total value of energy carbon management.

[0038] In the second aspect, a device for measuring the value of energy carbon management is provided, comprising:

[0039] A boundary subdivision module, used to subdivide the measurement boundary of energy carbon control based on the first preset dimension to obtain multiple sub-boundaries, wherein the measurement boundary is used to characterize the energy carbon control range to be measured within the measurement period;

[0040] A data acquisition module, used to acquire energy carbon management data and baseline data within each of the sub-boundaries, wherein the baseline data is used to characterize a reference level of energy carbon management;

[0041] A value measurement module, used to obtain the actual value corresponding to the energy carbon management data and the baseline value corresponding to the baseline data based on a preset value measurement model, wherein the value measurement model is used to characterize the mapping relationship between input data and value;

[0042] The value integration module is used to obtain the total energy and carbon management value within the measurement boundary based on the actual value and the baseline value within each of the sub-boundaries.

[0043] According to a third aspect, an electronic device is provided, the electronic device comprising:

[0044] at least one processor; and a memory communicatively coupled to the at least one processor;

[0045] In which, the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the energy carbon management value measurement method as described in any one of the first aspects, or so that the processor can configure the energy carbon management value measurement device as described in the second aspect.

[0046] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the energy carbon management value measurement method as described in any one of the first aspects when executed.

[0047] The present application provides a method for measuring the value of energy carbon control, including: subdividing the measurement boundary of energy carbon control based on a first preset dimension to obtain multiple sub-boundaries, the measurement boundary is used to characterize the energy carbon control range to be measured within the measurement period; obtaining the energy carbon control data and baseline data within each sub-boundary, the baseline data is used to characterize the reference level of energy carbon control; based on a preset value measurement model, obtaining the actual value corresponding to the energy carbon control data and the baseline value corresponding to the baseline data, the value measurement model is used to characterize the mapping relationship between input data and value; based on the actual value and baseline value within each sub-boundary, obtaining the total value of energy carbon control within the measurement boundary. The energy carbon control value measurement method provided in the present application can subdivide the measurement boundary according to preset dimensions, and respectively measure the value of the energy carbon control data and the baseline data within the sub-boundary to determine the total value of energy carbon control within the measurement boundary, and can finely measure the effect value of the control action, greatly optimizing the value measurement accuracy of energy carbon control.

[0048] A carbon management value measurement device of the present application can subdivide the measurement boundary into preset dimensions, and respectively finely measure the value of the carbon management data and the baseline data within the sub-boundary to determine the total value of the carbon management within the measurement boundary. It can finely measure the effect value of the control action and greatly optimize the value measurement accuracy of carbon management. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0050] Figure 1 This is a schematic diagram of the overall process of the energy carbon management value measurement method of the embodiment of the present application;

[0051] Figure 2 An exemplary schematic diagram of the metering boundary subdivision of an embodiment of the present application;

[0052] Figure 3 Another exemplary schematic diagram of metering boundary subdivision according to an embodiment of the present application;

[0053] Figure 4 This is an example schematic diagram of a data structure in a sub-boundary of an embodiment of the present application;

[0054] Figure 5 A schematic diagram of the structure of a display device according to an embodiment of the present application;

[0055] Figure 6 This is a schematic diagram of a specific structural example of the energy carbon management value measurement device according to an embodiment of the present application;

[0056] Figure 7 A schematic diagram of the structure of an electronic device according to an embodiment of the present application;

[0057] Reference numerals:

[0058] 40-electronic device; 41-processor; 42-read-only memory; 43-random access memory; 44-bus; 45-input / output interface; 46-input unit; 47-output unit; 48-storage unit; 49-communication unit; 51-boundary subdivision module; 52-data acquisition module; 521-actual data acquisition unit; 522-baseline data acquisition unit; 5221-first acquisition subunit; 5222-second acquisition unit; 5223-simulation subunit; 53-value measurement module; 531-first measurement module; 532-second measurement module; 54-value integration module; 55-value measurement model. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0060] In the description of the present application, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first" and "second" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more features. In the description of the present application, "multiple" means two or more, and at least one means one, two or more, unless otherwise clearly and specifically defined.

[0061] The embodiments of the present application can be applied to carbon reduction and consumption reduction scenarios, including but not limited to energy carbon management and control in factories, parks, hospitals, schools, transportation, buildings and other scenarios, as well as supply chain carbon management, organizational carbon management, product carbon management, SBTi management and other scenarios. The carbon reduction and consumption reduction subjects may include all governments at all levels, various organizations, enterprises, families and individuals who need to carry out carbon reduction and consumption reduction work (such as dual carbon work). It should be noted that, for the sake of ease of description, in some embodiments, the carbon reduction and consumption reduction work subject is specifically described by taking enterprises as an example.

[0062] Carbon reduction and consumption reduction is a complex and long-term task. In order to meet the challenges of carbon reduction and consumption reduction, each carbon reduction and consumption reduction subject can implement corresponding carbon reduction and consumption reduction measures through the carbon reduction and consumption reduction system to control energy consumption and manage carbon emissions. As a collaborative platform, the carbon reduction and consumption reduction system can coordinate and optimize management to meet the various carbon reduction and consumption reduction tasks and the multi-level and multi-dimensional needs of all parties (including but not limited to carbon consulting, carbon verification and certification, carbon reduction management, carbon asset operation management, energy asset operation management, green financial services, etc.). In order to evaluate the effect of carbon reduction and consumption reduction, it is necessary to measure the added value generated by carbon reduction and consumption reduction measures. Among them, the added value refers to the added value brought to the carbon reduction and consumption reduction subject through the application and optimization of the carbon reduction and consumption reduction system. For example, the added value may include economic value and social value. Economic value may involve cost reduction, income increase, etc., and social value may involve improving the environment, enhancing brand value, etc. However, the commonly used value measurement method is based on the comparison of the statistical values ​​of carbon reduction and consumption reduction data within a certain period of time. It can only evaluate the effect of carbon reduction and consumption reduction in a rough way, and it is impossible to accurately know whether each control action is useful. Therefore, the value measurement accuracy is low, and the value measurement accuracy problem has always been a technical problem that needs to be solved in the industry.

[0063] In view of this, an embodiment of the present application provides a method for measuring the value of energy carbon management. By setting measurement boundaries and subdividing the measurement boundaries, the value of energy carbon management data and baseline data within each sub-boundary is measured respectively to determine the total value of energy carbon management within the measurement boundary, thereby being able to finely measure the effect value of the control action and optimize the value measurement accuracy of energy carbon management, thereby solving at least part of the above-mentioned technical problems.

[0064] See also Figure 1 , Figure 1 This is a schematic diagram of the overall process of the energy carbon management value measurement method of the embodiment of the present application. The energy carbon management value measurement method includes the following steps:

[0065] Step 101: subdividing the measurement boundary of energy carbon management and control based on a first preset dimension to obtain a plurality of sub-boundaries, where the measurement boundary is used to characterize the energy carbon management and control range to be measured within a measurement period.

[0066] Specifically, the measurement boundary can define the scope of energy carbon management, including the facilities, departments, processes, activities, energy (such as electricity, natural gas, pipeline steam, gasoline, etc.) and carbon emissions (organizational carbon emissions, activity carbon emissions, product carbon footprint) that should be included, to ensure that all sources of carbon emissions and energy consumption related to energy carbon management are covered.

[0067] In some examples, the measurement boundary may include an energy-carbon boundary and a time boundary. The energy-carbon boundary includes the energy-carbon range associated with energy and carbon in the entire process of production, use, storage, etc. The time boundary is the measurement period, which is used to determine the time period for data collection and reporting, such as annual, quarterly or monthly. The selection of the time period should be consistent with management objectives and regulatory requirements.

[0068] In some examples, after the sub-boundaries are determined, the sub-boundaries may also be identified, that is, specific monitoring points and data collection requirements corresponding to each subdivided sub-boundary may be determined.

[0069] In some examples, the first preset dimension may include at least one of a system operation dimension, a business feature dimension, a time dimension, and a control action dimension. For example, the first preset dimension may be a time dimension or a control action dimension or a combination of the two. The system operation dimension may include operating conditions, operating time periods, etc., and the business feature dimension may include geographic location, business unit, product line, etc. It is understandable that the first preset dimension may be set according to specific management needs and actual operation conditions, and multiple dimensions may also be combined for more refined management and monitoring.

[0070] See also Figure 2 , Figure 2 Schematic diagram of an example of metering boundary subdivision in an embodiment of the present application. In some examples, the first preset dimension includes operating conditions and control strategies. Then after subdividing the metering boundary A, multiple sub-boundaries a can be obtained. 11 ~a mn , sub-boundary a mn Corresponding to the energy-carbon control range that simultaneously satisfies the operating condition m and the control strategy n, m and n are positive integers.

[0071] See also Figure 3 , Figure 3 Another example schematic diagram of the measurement boundary subdivision of the embodiment of the present application. In other examples, the first preset dimension includes the operation period and the control strategy. Then after subdividing the measurement boundary A, multiple sub-boundaries b can be obtained. 11 ~b tn , sub-boundary b tn Corresponding to the energy-carbon control range that simultaneously satisfies the operating period t and the control strategy n, t and n are positive integers.

[0072] It is understandable that the first preset dimension may also include other dimensions, which will not be described in detail in the embodiments of the present application.

[0073] Step 102: Obtain energy carbon management data and baseline data within each sub-boundary, where the baseline data is used to characterize a reference level of energy carbon management.

[0074] Specifically, baseline data refers to reference data used to evaluate the effect of energy carbon management and control, which can be used as the initial level of carbon emissions and energy use. In other words, the energy carbon management measurement value represents the added value of energy carbon management data compared to baseline data. Energy carbon management data refers to the actual data obtained after the implementation of the current energy carbon management strategy, which can include energy carbon data and various management actions taken to control energy carbon.

[0075] In some embodiments, the method for obtaining baseline data includes obtaining historical control data within the baseline year, obtaining simulated control data obtained based on simulation under baseline conditions, and obtaining reference control data obtained by controlling based on a control strategy different from the current energy and carbon control strategy.

[0076] The setting of the baseline year is usually based on the completeness and representativeness of the available data. For example, the year before the current energy carbon control year can be selected as the baseline year, or any year can be selected as the baseline year. The historical control data can be the historical control data of the same carbon reduction and consumption reduction subject as the current energy carbon control, or the historical control data of the carbon reduction and consumption reduction subject similar to the current energy carbon control. In this way, the value-added effect of energy carbon control compared with historical control can be measured, and a better longitudinal comparison can be made.

[0077] If it is impossible to obtain reference data or parameter data does not meet the requirements and thus it is impossible to obtain baseline data, a simulation method can also be used to obtain energy carbon data of simulated operation under benchmark conditions. It is understandable that the benchmark conditions may be not adopting an energy carbon management strategy, or may be adopting other energy carbon management strategies, which can be specifically set according to actual needs, and the embodiments of the present application are not limited to this. In addition, the simulation software can be implemented using existing more mature simulation software, which is not described in detail in the embodiments of the present application. In this way, baseline data can be obtained in scenarios where there is no historical control data or the quality of historical control data is not high, thereby ensuring that the energy carbon management effect can be accurately measured.

[0078] In addition, the baseline data can also be the reference control data obtained by controlling a control strategy different from the current energy carbon control strategy. For example, the data of energy carbon control strategy A can be used as the baseline data of the data of energy carbon control strategy B. In this way, a horizontal comparison of the control effects of different energy carbon control strategies can be achieved, thereby increasing the measurement dimension of the energy carbon control value and making the evaluation more comprehensive.

[0079] Through the above method, baseline data can be derived from historical data, which is convenient for longitudinal comparison of management and control effects; baseline data can also be derived from simulation reference data, case comparison data, and forecast data, so that it can be applicable to new projects and scenarios where the quality of historical data is not high. In addition, it can also improve the problem of the lag of historical data affecting the accuracy of baseline value assessment.

[0080] In some embodiments, step 102 may be implemented by:

[0081] Step 1: Obtain the actual energy and carbon data and actual control data within each sub-boundary.

[0082] Step 2: Obtain baseline energy and carbon data and baseline control data within each sub-boundary.

[0083] Specifically, both actual energy carbon data and baseline energy carbon data are energy carbon data, the difference is that the data sources are different. The actual energy carbon data comes from the real-time collection and acquisition of the current metering period. The actual energy carbon data in each sub-boundary can be obtained by deploying automated data collection systems such as smart meters, flow meters, temperature sensors, carbon emission sensors, etc. For the baseline system in operation, an automated data collection system can be deployed to collect baseline energy carbon data. For the baseline system that is not in operation, the historical data or simulation data of the baseline year can be used as the baseline energy carbon data. Energy carbon data can include energy consumption data, carbon emission data, economic impact data and other data, which can be collected and collected through methods such as automatic monitoring systems, financial reports, operation logs, energy markets, carbon markets, etc.

[0084] For example, after obtaining the actual energy carbon data and the baseline energy carbon data, data integration tools, such as data warehouses and ETL (Extract Transform Load) tools, can be used to integrate the collected data into a unified database or data platform. Data cleaning and preprocessing can then be performed to verify the integrity and accuracy of the data, ensure that there are no errors or omissions, and unify the data from different sources and formats to facilitate subsequent processing and analysis.

[0085] Specifically, actual control data and baseline control data are both control data. The difference lies in the different data sources. The actual control data are the control actions performed in the current energy and carbon control, and the baseline control data are the control actions performed by energy and carbon control during the baseline metering period or the simulated metering period.

[0086] In some examples, the actual control data and baseline control data within a sub-boundary can be implemented in the following ways:

[0087] The first step is to obtain the actual control action log or actual control time series data within each sub-boundary to obtain the actual control data within the corresponding sub-boundary.

[0088] Specifically, both the actual control action log and the actual control time series data can be used to represent the various control actions corresponding to the current energy and carbon control strategy. It can be understood that the control action can include control instructions and device response conditions.

[0089] The second step is to obtain the baseline control action log or baseline control time series data within each sub-boundary to obtain the baseline control data within the corresponding sub-boundary.

[0090] Specifically, both the baseline control action log and the baseline control time series data can be used to represent each control action corresponding to the energy and carbon control strategy during the baseline period.

[0091] It should be noted that there is no specific order limit for step one and step two. The control data can be obtained first and then the energy carbon data, or the energy carbon data can be obtained first and then the control data, or both can be obtained at the same time. The embodiment of the present application does not make specific limitations on this.

[0092] Step three: integrate the actual energy and carbon data within each sub-boundary with the actual control data to obtain the energy and carbon control data within each sub-boundary.

[0093] In some embodiments, step three may be implemented in the following manner:

[0094] In the first step, the actual energy-carbon data within each sub-boundary is subdivided based on a second preset dimension to obtain a plurality of actual unit data, and the second preset dimension is configured as a metering frequency or a detection cycle.

[0095] The second step is to integrate multiple actual unit data with the actual control data within the corresponding sub-boundaries based on the time dimension to obtain the energy and carbon control data within each sub-boundary.

[0096] Specifically, the time dimension can be set according to requirements, for example, it can be accurate to minutes.

[0097] For example, taking the metering period corresponding to the sub-boundary as one week, the metering period can be further refined into daytime and nighttime according to the metering frequency or detection cycle, and the actual unit data of the daytime can be integrated with the control actions made for the operating conditions corresponding to the sub-boundary at the corresponding time point, and the actual unit data of the night can be integrated with the control actions made for the operating conditions corresponding to the sub-boundary at the corresponding time point, so as to finally obtain the energy and carbon management data within the entire sub-boundary.

[0098] In some other embodiments, step three may also be implemented in the following manner:

[0099] In the first step, the actual control data within each sub-boundary is subdivided based on the third preset dimension to obtain multiple control unit data. The third preset dimension is configured as the control frequency or the instruction issuance cycle.

[0100] The second step is to integrate the data of multiple control units with the actual energy and carbon data in the corresponding sub-boundaries based on the time dimension to obtain the energy and carbon control data in each sub-boundary.

[0101] Similarly, the time dimension can be set according to requirements, for example, it can be accurate to minutes.

[0102] For example, taking the metering period corresponding to the sub-boundary as one week, the metering period can be further refined into daytime and nighttime according to the control frequency or instruction issuance cycle, and the control unit data of the daytime can be integrated with the actual energy and carbon data within the sub-boundary at the corresponding time point, and the control unit data of the night can be integrated with the actual energy and carbon data within the sub-boundary at the corresponding time point, so as to finally obtain the energy and carbon control data within the entire sub-boundary.

[0103] In other embodiments, the actual energy-carbon data and the actual control data may not be subdivided, and the actual energy-carbon data and the actual control data may be directly fused based on the time dimension; the actual energy-carbon data and the actual control data may also be subdivided separately and then fused based on the time dimension, which is not limited to the embodiments of the present application.

[0104] Through the above scheme, the actual energy carbon data and / or actual control data can be subdivided and associated, so as to clearly reflect the relationship between the changes in energy carbon data and the control actions, action status and effects, thereby facilitating the evaluation of the effects of control actions according to different granularities and further realizing refined measurement.

[0105] Step 4: Integrate the baseline energy and carbon data within each sub-boundary with the baseline control data to obtain the baseline data within each sub-boundary.

[0106] Specifically, the method of integrating the baseline energy and carbon data and the baseline control data within each sub-boundary can refer to the above-mentioned method of integrating the actual energy and carbon data and the actual control data within each sub-boundary, that is, the baseline energy and carbon data within each sub-boundary can be first subdivided based on the same second preset dimension and / or the baseline control data within each sub-boundary can be subdivided based on the same third preset dimension, and then integrated. No further details will be given here.

[0107] In other embodiments, the baseline energy carbon data and the baseline control data may not be subdivided, and the baseline energy carbon data and the baseline control data may be directly fused based on the time dimension; the baseline energy carbon data and the baseline control data may also be subdivided separately and then fused based on the time dimension, which is not limited to the embodiments of the present application.

[0108] Through the above scheme, the baseline energy carbon data and / or baseline control data can be subdivided and associated, so as to clearly reflect the relationship between the changes in energy carbon data and the control actions, action status and effects, thereby facilitating the evaluation of the baseline control action effects according to different granularities and further realizing refined measurement.

[0109] For example, carbon reduction and consumption reduction entities can use integrated information systems such as Enterprise Resource Planning (ERP) systems and Environmental Management Information Systems (EMIS) to automatically collect and process relevant data.

[0110] For example, see Figure 4 , Figure 4 Schematic diagram of an example of a data structure in a sub-boundary of an embodiment of the present application. Figure 2 Taking the subdivision method shown as an example, the data structure used by the data in the sub-boundary can be working conditions, strategies, material and / or energy impact, benefits (or benefits, income, expenditure). In addition, other data structures can also be used, which is not limited in the embodiments of the present application.

[0111] Step 103: Based on a preset value measurement model, the actual value corresponding to the carbon management data and the baseline value corresponding to the baseline data are obtained. The value measurement model is used to characterize the mapping relationship between input data and value.

[0112] Specifically, the value measurement model can output its corresponding value based on the input data. When constructing the value measurement model, the key parameters of value measurement can be defined first, such as cost savings, increased revenue, reduced risks, and enhanced brand value. Then, a measurement model containing relevant factors is constructed. The model should be able to convert the impact of carbon management activities (such as reduced carbon emissions, reduced energy consumption, improved energy efficiency, etc.) into corresponding values, such as financial value, brand value, and social value. Data analysis and financial modeling tool software (such as Excel, R, Python, etc.) can be used when constructing and running the value measurement model. The parameters of the value measurement model can be adjusted according to actual needs to ensure that it reflects the latest business environment and policy changes.

[0113] In some examples, embodiments of the present application may directly adopt existing value measurement models.

[0114] In other examples, embodiments of the present application can retrain existing value measurement models. For example, energy-saving, energy consumption reduction, carbon emission reduction, price reduction, energy efficiency improvement and other energy and carbon management data can be added as new inputs to the model to affect the output of the model.

[0115] For example, for an existing value measurement model with a clear rule mechanism, such as a financial value model or an energy-saving benefit model, the existing value measurement model can be modified by modifying the transfer function, adding sub-boundary input variables to the existing value measurement model, adding a total value calculation function, etc.

[0116] For existing value measurement models trained by deep learning and other methods with unclear rules, incremental training methods can be used to retrain the existing value measurement models. The specific training methods are as follows:

[0117] The first step is to analyze the incremental training requirements.

[0118] Specifically, first, a scenario analysis is performed, that is, whether the incremental training is due to the expansion of the data set scale, the adaptation of the new scenario, or the update of the model parameters. The model of the embodiment of the present application needs to adapt to the scenario of the sub-boundary. Then, data classification is performed, that is, the existing data and the newly added data are distinguished, and the impact of the newly added data on the original model is clarified. In the embodiment of the present application, the newly added data is the energy carbon management data and the baseline data within the sub-boundary. Finally, the data is further subdivided. The energy carbon management data includes the actual energy carbon data and the actual management data within the sub-boundary, and the baseline data includes the baseline energy carbon data and the baseline management data within the sub-boundary. For example, in the energy saving of the cold and hot system, the energy carbon data includes the power, power consumption, and carbon emissions of the cold and hot system of the cold and hot energy saving, and the management data includes the control of the chiller power, the adjustment of the start and stop, and the cooling tower fan frequency and start and stop. For example, in the optimization of light storage and load, the energy carbon data includes the energy storage system power, charge, discharge, state, carbon emissions, load power, power consumption, state, and carbon emissions; the management data includes the control of the energy storage equipment power, the charging and discharging mode, the start and stop, and the control of the load power and start and stop.

[0119] The second step is to prepare the data.

[0120] Specifically, the newly added data can be cleaned first to ensure that the data distribution of the newly added data is consistent with that of the original model (or a mechanism to combat distribution changes can be designed). Then the data is sharded, that is, the newly added data is divided into training sets, validation sets, and test sets. Finally, an appropriate balance fusion mechanism is established between the new and old data, such as weighted sampling and reconstruction of the training set.

[0121] The third step is to design an incremental training strategy.

[0122] Specifically, you can freeze the underlying feature extraction layer and only update the high-level decision-making part (especially in deep learning models). This way, you can retain the performance of the original model through knowledge distillation and use the output of the original model to guide the training of the new model. In addition, you can adjust the training rate. For new data, you can set a smaller learning rate to avoid overfitting the model. If the new data is very different, you can quickly adapt to the new task through transfer learning.

[0123] The fourth step is to adjust the model structure.

[0124] Specifically, a new module can be added to the model structure to specifically process the features of the newly added data. Alternatively, a dynamic model (such as dynamic weight allocation) can be used to achieve real-time adaptation of the model to the newly added data.

[0125] The fifth step is to implement incremental training.

[0126] Specifically, fine-tuning can be used, that is, fine-tuning training with new data based on the original model weights. Specifically, frameworks that support incremental training can be used, such as PyTorch and TensorFlow (transfer learning and continual learning). A small amount of original data can be mixed during training to avoid forgetting the characteristics of the original data (to prevent "catastrophic forgetting").

[0127] Step 6: Model verification.

[0128] Specifically, we first conduct a baseline test, that is, verify the performance of the new model on the original data set to ensure that it has not significantly decreased due to incremental training. Then we conduct a test on the newly added data to ensure that the model performs better than the original model on the newly added data set. Finally, we can also conduct a comprehensive evaluation, using a mixed data set to test the overall performance to evaluate the universality of the model.

[0129] Through the above scheme, by retraining the existing value measurement model, the value measurement model can better adapt to the scenarios of each sub-boundary, so that the added value of carbon control measures can be measured more accurately.

[0130] In some examples, the value measurement model may include a financial value measurement model, a brand value measurement model, and a social value measurement model. Among them, the financial value measurement model is used to measure the financial value of energy carbon management data, for example, the energy storage regulation model can be used to evaluate the financial value brought by the difference in charging and discharging electricity prices; the brand value measurement model is used to measure the brand value of energy carbon management data, for example, the brand valuation model (Brand Valuation Models), brand asset valuation model (Brand Asset Valuation Models), brand premium model (Brand Premium Models), market structure model, Kemin model, Interbrand value assessment model, Ganjia brand value assessment model or brand strength model (Brand Strength Models); the social value measurement model is used to measure the social value of energy carbon management data, for example, the cost-benefit analysis (CBA) model, social return on investment (SROI) model, social accounting (Social Accounting) model or social capital assessment (Social Capital Assessment) model can be used.

[0131] In this example, step 103 can be implemented by the following steps:

[0132] The energy and carbon management data are input into the financial value measurement model, brand value measurement model and social value measurement model respectively to obtain the actual financial value, actual brand value and actual social value corresponding to the energy and carbon management data.

[0133] The baseline data is input into the financial value measurement model, brand value measurement model and social value measurement model respectively to obtain the baseline financial value, baseline brand value and baseline social value corresponding to the baseline data.

[0134] In other examples, the value measurement model can also calculate and generate financial value, brand value, and social value based on a unified model. This embodiment of the present application does not specifically limit this.

[0135] Step 104: Based on the actual value and baseline value in each sub-boundary, the total value of energy and carbon management within the measurement boundary is obtained.

[0136] Specifically, the value can be measured by the control effect and the value coefficient, for example, it can be determined by the following formula (1):

[0137] V = S × P (1);

[0138] In formula (1), V is value, S is control effect, and P is value coefficient.

[0139] In some embodiments, step 104 may be implemented by:

[0140] Determine the difference between the actual value and the baseline value within each sub-boundary, and determine the sum of the differences corresponding to all sub-boundaries as the total energy and carbon management value within the measurement boundary.

[0141] For example, taking the number of sub-boundaries as N, then V Si is the actual value within the ith sub-boundary, V Bi is the baseline value within the ith sub-boundary, where V Si =Si×P Si , V Bi =Bi×P Bi , value coefficient P Si With P Bi It may be the same or different.

[0142] Accordingly, the total value of energy and carbon management within the measurement boundary can be determined by the following formula (2):

[0143]

[0144] In formula (2), V is the total value of energy and carbon control within the measurement boundary, V Si is the actual value within the ith sub-boundary, V Bi is the baseline value within the ith subboundary, and N is the total number of subboundaries.

[0145] In some other embodiments, step 104 may also be implemented by the following steps:

[0146] The difference between the first value and the second value is determined as the total value of energy and carbon management within the measurement boundary. The first value is the sum of the actual values ​​within all sub-boundaries, and the second value is the sum of the baseline values ​​within all sub-boundaries.

[0147] The total value of energy and carbon management within the measurement boundary can be determined by the following formula (3):

[0148]

[0149] In formula (3), V is the total value of energy and carbon control within the measurement boundary, V1 is the first value, V2 is the second value, and V Si is the actual value within the ith sub-boundary, V Bi is the baseline value within the ith subboundary, and N is the total number of subboundaries.

[0150] In other embodiments, step 104 may also be implemented by calculating the total value of energy and carbon management within the measurement boundary by first determining the difference between the actual value and the baseline value within each sub-boundary for a portion of the sub-boundaries and then summing the sub-boundaries, and by using the difference between the first value and the second value for another portion of the sub-boundaries. This is not specifically limited in the embodiments of the present application.

[0151] In some examples, when the value measurement model includes a financial value measurement model, a brand value measurement model, and a social value measurement model, step 104 can be implemented by the following steps:

[0152] Based on the actual financial value and baseline financial value within each sub-boundary, the financial value of energy and carbon management within the measurement boundary is determined.

[0153] Based on the actual brand value and baseline brand value within each sub-boundary, determine the energy and carbon management brand value within the measurement boundary.

[0154] Based on the actual social value and baseline social value within each sub-boundary, the social value of energy and carbon management within the measurement boundary is determined.

[0155] Based on the preset coefficients, the financial value of energy carbon control, the brand value of energy carbon control, and the social value of energy carbon control are weighted and summed, and the weighted sum result is determined as the total value of energy carbon control.

[0156] Specifically, the weighting coefficients may be set according to actual business needs or focus, and the sum of the weighting coefficients is 1.

[0157] It is understandable that the financial value of carbon energy control, the brand value of carbon energy control, and the social value of carbon energy control can also be considered separately, that is, the financial value of carbon energy control, the brand value of carbon energy control, and the social value of carbon energy control can be determined as the total value of carbon energy control for consideration in different dimensions without weighted summation. The embodiments of the present application are not limited to this.

[0158] Through the above scheme, the calculated actual value can be compared with the baseline value to analyze the economic benefits of carbon management measures, and then evaluate the increase or decrease in economic value brought about by the management measures, as well as its impact on the overall economic situation and brand value of the organization.

[0159] In some embodiments, after executing step 104, detailed analysis reports and dashboards can also be created to intuitively display the total value and key performance indicators (KPIs). Generating detailed reports can intuitively display the comparison between the actual value and the baseline value, as well as the economic and social impacts derived therefrom. Specifically, business intelligence (BI) tools and data visualization software (such as Tableau, Power BI, etc.) can be used to analyze and display data. In this way, data support and analysis results can be provided to help management make data-based decisions.

[0160] In addition, the method of the embodiment of the present application can regularly compare the actual data with the baseline data to regularly evaluate the effects of energy and carbon management measures, and display trends and differences through data visualization tools. In this way, by analyzing the trend of total value changes over time, it is also helpful to identify key influencing factors.

[0161] It can be understood that the measurement boundary subdivision, value measurement calculation and data integration algorithm in the embodiments of the present application are dynamic and can be carried out continuously. When the carbon management data, calculation model and integration method change, they can be updated and iteratively upgraded at any time.

[0162] It can be understood that the method of the embodiment of the present application can accurately measure the value of the effect of the control action by subdividing and associating the actual energy-carbon data and the actual control data, and by comparing and subtracting them with the baseline data, thereby achieving a refined measurement of the energy-carbon control value. Moreover, the baseline data can be derived from the simulation data of the new control algorithm, which is also convenient for fine measurement of the potential optimization space.

[0163] Accordingly, see Figure 5 , Figure 5 The overall structure diagram of the energy carbon management value measurement device of the embodiment of the present application is shown in FIG. The energy carbon management value measurement device provided by the embodiment of the present application includes a boundary segmentation module 51 , a data acquisition module 52 , a value measurement module 53 and a value integration module 54 .

[0164] The boundary subdivision module 51 is used to subdivide the measurement boundary of energy carbon control based on the first preset dimension to obtain multiple sub-boundaries, and the measurement boundary is used to characterize the energy carbon control range to be measured within the measurement period.

[0165] The data acquisition module 52 is used to acquire energy carbon management data and baseline data within each sub-boundary, and the baseline data is used to characterize the reference level of energy carbon management.

[0166] The value measurement module 53 is used to obtain the actual value corresponding to the carbon management data and the baseline value corresponding to the baseline data based on a preset value measurement model 55. The value measurement model 55 is used to characterize the mapping relationship between input data and value.

[0167] The value integration module 54 is used to obtain the total energy and carbon management value within the measurement boundary based on the actual value and baseline value within each sub-boundary.

[0168] See also Figure 6 , Figure 6 This is a schematic diagram of a specific structural example of the energy carbon management value measurement device of an embodiment of the present application. In some embodiments, the data acquisition module 52 may include:

[0169] The actual data acquisition unit 521 is used to acquire the actual energy-carbon data and the actual control data in each sub-boundary, and fuse the actual energy-carbon data and the actual control data in each sub-boundary to obtain the energy-carbon control data in each sub-boundary.

[0170] The baseline data acquisition unit 522 is used to acquire the baseline energy and carbon data and the baseline management and control data within each sub-boundary, and fuse the baseline energy and carbon data and the baseline management and control data within each sub-boundary to obtain the baseline data within each sub-boundary.

[0171] In some embodiments, the actual data acquisition unit 521 is specifically used to:

[0172] The actual energy-carbon data within each sub-boundary is subdivided based on a second preset dimension to obtain a plurality of actual unit data, and the second preset dimension is configured as a metering frequency or a detection cycle.

[0173] Based on the time dimension, multiple actual unit data are integrated with the actual control data in the corresponding sub-boundaries to obtain the energy and carbon control data in each sub-boundary.

[0174] In some embodiments, the actual data acquisition unit 521 is specifically used to:

[0175] The actual control data within each sub-boundary is subdivided based on a third preset dimension to obtain multiple control unit data, and the third preset dimension is configured as a control frequency or an instruction issuance cycle.

[0176] Based on the time dimension, the data of multiple control units are integrated with the actual energy and carbon data in the corresponding sub-boundaries to obtain the energy and carbon control data in each sub-boundary.

[0177] In some embodiments, the steps of the actual data acquisition unit 521 for acquiring the actual management and control data include:

[0178] Obtain the actual control action log or actual control time series data within each sub-boundary to obtain the actual control data within the corresponding sub-boundary.

[0179] The steps of the baseline data acquisition unit 522 for acquiring baseline management and control data include:

[0180] Obtain the baseline control action log or baseline control time series data within each sub-boundary to obtain the baseline control data within the corresponding sub-boundary.

[0181] In some embodiments, the baseline data acquisition unit 522 may include at least one of a first acquisition subunit 5221, a second acquisition unit 5222 and a simulation subunit 5223. The first acquisition subunit 5221 is used to acquire baseline data based on historical control data within the baseline year, the simulation subunit 5223 is used to acquire simulated control data obtained based on simulation under benchmark conditions, and the second acquisition unit 5222 is used to acquire baseline data based on reference control data obtained through control based on a control strategy different from the current energy and carbon control strategy.

[0182] In some embodiments, the value measurement module 53 includes a first measurement module 531 and a second measurement module 532. The first measurement module 531 is used to obtain the actual value corresponding to the carbon management data based on a preset value measurement model, and the second measurement module 532 is used to obtain the baseline value corresponding to the baseline data based on a preset value measurement model.

[0183] In some embodiments, the value integration module 54 is specifically used to:

[0184] Determine the difference between the actual value and the baseline value within each sub-boundary, and determine the sum of the differences corresponding to all sub-boundaries as the total energy and carbon management value within the measurement boundary.

[0185] Alternatively, the difference between the first value and the second value is determined as the total value of energy and carbon management within the measurement boundary, the first value is the sum of the actual values ​​within all sub-boundaries, and the second value is the sum of the baseline values ​​within all sub-boundaries.

[0186] In some embodiments, the value measurement model includes a financial value measurement model, a brand value measurement model, and a social value measurement model. The value measurement module 53 is specifically used to:

[0187] The energy and carbon management data are input into the financial value measurement model, brand value measurement model and social value measurement model respectively to obtain the actual financial value, actual brand value and actual social value corresponding to the energy and carbon management data.

[0188] The baseline data is input into the financial value measurement model, brand value measurement model and social value measurement model respectively to obtain the baseline financial value, baseline brand value and baseline social value corresponding to the baseline data.

[0189] In some embodiments, the value integration module 54 is specifically used to:

[0190] Based on the actual financial value and baseline financial value within each sub-boundary, the financial value of energy and carbon management within the measurement boundary is determined.

[0191] Based on the actual brand value and baseline brand value within each sub-boundary, determine the energy and carbon management brand value within the measurement boundary.

[0192] Based on the actual social value and baseline social value within each sub-boundary, the social value of energy and carbon management within the measurement boundary is determined.

[0193] Based on the preset coefficients, the financial value of energy carbon control, the brand value of energy carbon control, and the social value of energy carbon control are weighted and summed, and the weighted sum result is determined as the total value of energy carbon control.

[0194] It can be understood that the device of the embodiment of the present application can subdivide the measurement boundary into preset dimensions through a refined measurement structure, and can respectively refine the value of the energy carbon management data and the baseline data within the sub-boundary to determine the total value of energy carbon management within the measurement boundary, and can refine the measurement of the effect value of the control action, thereby greatly optimizing the value measurement accuracy of energy carbon management.

[0195] Accordingly, the present application embodiment also provides an electronic device. Figure 7 , Figure 7 Schematic diagram of the structure of an electronic device of an embodiment of the present application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0196] The electronic device 40 includes at least one processor 41, and a memory connected to the at least one processor 41 in communication, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by the at least one processor 41, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44. Multiple components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0197] The processor 41 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 41 executes the various methods described in the aforementioned embodiments, such as the energy carbon management value measurement method, or the processor 41 can configure the energy carbon management value measurement device described in the aforementioned embodiments.

[0198] In some embodiments, the energy carbon management value measurement method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the energy carbon management value measurement method described in the aforementioned embodiment may be executed, or the processor 41 may be enabled to configure the energy carbon management value measurement device of the aforementioned embodiment. Alternatively, in other embodiments, the processor 41 may be configured to execute the energy carbon management value measurement method in any other appropriate manner (e.g., by means of firmware).

[0199] In the context of the present application embodiment, computer readable storage medium can be a tangible medium, which can contain or store a computer program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer readable storage medium can include but is not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the above. Alternatively, computer readable storage medium can be a machine readable signal medium. More specific examples of machine readable storage medium can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CDROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0200] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0201] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0202] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in the embodiments of the present application can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present application can be achieved, and this document is not limited here.

[0203] The above is a detailed introduction to a carbon management value measurement method, device, electronic device and storage medium provided in the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the technical solution and core idea of ​​the present application; ordinary technicians in this field should understand that they can still modify the technical solutions recorded in the aforementioned embodiments, or replace some of the technical features therein with equivalents; and these modifications or replacements do not make the essence of the corresponding technical solution deviate from the scope of the technical solution of the embodiments of the present application.

Claims

1. A method for measuring the value of energy and carbon management, characterized in that: include: Subdividing the measurement boundary of energy carbon management and control based on the first preset dimension to obtain a plurality of sub-boundaries, wherein the measurement boundary is used to characterize the energy carbon management and control range to be measured within the measurement period; Acquire energy carbon management data and baseline data within each of the sub-boundaries, wherein the baseline data is used to characterize a reference level of energy carbon management; Based on a preset value measurement model, the actual value corresponding to the energy carbon management data and the baseline value corresponding to the baseline data are obtained, and the value measurement model is used to characterize the mapping relationship between input data and value; Based on the actual value and the baseline value within each of the sub-boundaries, the total energy and carbon management value within the measurement boundary is obtained.

2. The method for measuring the energy carbon management value according to claim 1, characterized in that: The obtaining of energy and carbon management data and baseline data within each of the sub-boundaries includes: Acquire actual energy and carbon data and actual control data within each of the sub-boundaries; Obtaining baseline energy and carbon data and baseline control data within each of the sub-boundaries; Fusion of the actual energy-carbon data within each of the sub-boundaries with the actual control data to obtain the energy-carbon control data within each of the sub-boundaries; The baseline energy and carbon data within each of the sub-boundaries are integrated with the baseline control data to obtain the baseline data within each of the sub-boundaries.

3. The method for measuring the energy and carbon management value according to claim 2 is characterized in that: The fusing the actual energy-carbon data within each of the sub-boundaries with the actual control data to obtain the energy-carbon control data within each of the sub-boundaries includes: Subdividing the actual energy-carbon data within each of the sub-boundaries based on a second preset dimension to obtain a plurality of actual unit data, wherein the second preset dimension is configured as a metering frequency or a detection cycle; Based on the time dimension, the multiple actual unit data are merged with the actual control data in the corresponding sub-boundary to obtain the energy and carbon control data in each sub-boundary.

4. The method for measuring the energy and carbon management value according to claim 2 is characterized in that: The fusing the actual energy-carbon data within each of the sub-boundaries with the actual control data to obtain the energy-carbon control data within each of the sub-boundaries includes: Subdividing the actual control data within each of the sub-boundaries based on a third preset dimension to obtain a plurality of control unit data, wherein the third preset dimension is configured as a control frequency or an instruction issuing cycle; Based on the time dimension, the plurality of control unit data are merged with the actual energy-carbon data in the corresponding sub-boundary to obtain the energy-carbon control data in each sub-boundary.

5. The method for measuring the energy and carbon management value according to claim 2, characterized in that: The step of obtaining the actual control data and the baseline control data includes: Acquire the actual control action log or actual control time series data within each of the sub-boundaries to obtain the actual control data within the corresponding sub-boundaries; Obtain the baseline control action log or baseline control time series data within each of the sub-boundaries to obtain the baseline control data within the corresponding sub-boundaries.

6. The method for measuring the energy carbon management value according to claim 1, characterized in that: The first preset dimension includes at least one of a system operation dimension, a business feature dimension, a time dimension, and a management and control action dimension.

7. The method for measuring the energy carbon management value according to claim 1, characterized in that: The method for obtaining the baseline data includes obtaining historical control data within the baseline year, obtaining simulated control data obtained based on simulation under baseline conditions, and obtaining reference control data obtained by controlling based on a control strategy different from the current energy and carbon control strategy.

8. The method for measuring the energy and carbon management value according to claim 1, characterized in that: The total energy and carbon management value within the measurement boundary is obtained based on the actual value and the baseline value within each sub-boundary, including: Determine the difference between the actual value and the baseline value within each sub-boundary, and determine the sum of the differences corresponding to all sub-boundaries as the total energy and carbon management value within the measurement boundary; Alternatively, the difference between the first value and the second value is determined as the total energy and carbon management value within the measurement boundary, the first value is the sum of the actual values ​​within all the sub-boundaries, and the second value is the sum of the baseline values ​​within all the sub-boundaries.

9. The method for measuring the energy and carbon management value according to claim 1, characterized in that: The value measurement model includes a financial value measurement model, a brand value measurement model, and a social value measurement model; The obtaining, based on a preset value measurement model, the actual value corresponding to the energy carbon management data and the baseline value corresponding to the baseline data includes: Inputting the energy carbon management data into the financial value measurement model, the brand value measurement model and the social value measurement model respectively, to obtain the actual financial value, actual brand value and actual social value corresponding to the energy carbon management data; The baseline data are respectively input into the financial value measurement model, the brand value measurement model and the social value measurement model to obtain the baseline financial value, baseline brand value and baseline social value corresponding to the baseline data.

10. The method for measuring the energy and carbon management value according to claim 9, characterized in that: The total energy and carbon management value within the measurement boundary is obtained based on the actual value and the baseline value within each sub-boundary, including: Determining the energy and carbon management financial value within the measurement boundary based on the actual financial value and the baseline financial value within each of the sub-boundaries; Determine the energy and carbon management brand value within the measurement boundary based on the actual brand value and the baseline brand value within each of the sub-boundaries; Determining the social value of energy and carbon management within the measurement boundary based on the actual social value and the baseline social value within each of the sub-boundaries; Based on preset coefficients, the financial value of energy carbon management, the brand value of energy carbon management, and the social value of energy carbon management are weighted and summed, and the weighted sum result is determined as the total value of energy carbon management.

11. A carbon management value measurement device, characterized in that: include: A boundary subdivision module, used to subdivide the measurement boundary of energy carbon control based on the first preset dimension to obtain multiple sub-boundaries, wherein the measurement boundary is used to characterize the energy carbon control range to be measured within the measurement period; A data acquisition module, used to acquire energy carbon management data and baseline data within each of the sub-boundaries, wherein the baseline data is used to characterize a reference level of energy carbon management; A value measurement module, used to obtain the actual value corresponding to the energy carbon management data and the baseline value corresponding to the baseline data based on a preset value measurement model, wherein the value measurement model is used to characterize the mapping relationship between input data and value; The value integration module is used to obtain the total energy and carbon management value within the measurement boundary based on the actual value and the baseline value within each of the sub-boundaries.

12. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; In which, the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the energy carbon management value measurement method as described in any one of claims 1 to 10, or so that the processor can configure the energy carbon management value measurement device as described in claim 11.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the energy carbon management value measurement method according to any one of claims 1 to 10 when executed.

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