Energy efficiency evaluation method and device for high-energy-consumption enterprise under low-carbon target

Abstract

The invention provides an energy efficiency evaluation method and device for a high-energy-consumption enterprise under a low-carbon target, and belongs to the field of data processing. The method comprises the steps of collecting current operation data of a target high-energy-consumption enterprise; the current operation data comprises environment data; the environmental data comprises water pollution data, carbon emission, light pollution data, vegetation area and noise pollution data; constructing a simulation operation model according to the current operation data; calculating simulation operation data; and calculating a carbon emission index value based on the simulation operation data and the carbon emission evaluation index, and further performing energy efficiency evaluation according to the simulation operation data when the carbon emission index value satisfies a preset condition. According to the method, the environmental indexes directly or indirectly influencing the carbon absorption amount can be comprehensively considered, and a new carbon emission evaluation index is generated based on the influence factors, so that simulation data of a high-energy-consumption enterprise under a low-carbon target is obtained, energy efficiency evaluation is performed on the high-energy-consumption enterprise, and green transformation of the enterprise is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to an energy efficiency evaluation method and device for high-energy-consuming enterprises under the low-carbon goal. Background Art

[0002] In the context of current global climate change and environmental issues, achieving low-carbon development has become a global consensus. Especially in high-energy-consuming industries such as steel, chemical, and non-ferrous metal smelting, due to their high energy consumption and high emission characteristics during the production process, their impact on the environment and climate change is particularly significant. Therefore, developing an energy efficiency evaluation method for high-energy-consuming enterprises under the low-carbon goal is of great significance for promoting the green transformation of these industries and achieving sustainable development.

[0003] Current evaluation methods usually directly rely on the operation data of high-energy-consuming enterprises, but they ignore whether these high-energy-consuming enterprises are under the "low-carbon" goal, resulting in the evaluation results being true and effective but unable to promote the green transformation of enterprises. Summary of the Invention

[0004] Embodiments of the present invention provide an energy efficiency evaluation method and device for high-energy-consuming enterprises under the low-carbon goal to solve the problem that the current energy efficiency evaluation method cannot promote the green transformation of enterprises.

[0005] In a first aspect, embodiments of the present invention provide an energy efficiency evaluation method for high-energy-consuming enterprises under the low-carbon goal, including:

[0006] Collect the current operation data of the target high-energy-consuming enterprise; wherein, the current operation data includes current equipment operation data and current environmental data; the environmental data includes water pollution data, carbon emissions, light pollution data, vegetation area, and noise pollution data;

[0007] Construct a simulation operation model based on the current operation data; under the constraint conditions of the environmental data, perform dynamic simulation on the simulation operation model to obtain simulation operation data;

[0008] Calculate the carbon emission index value based on the simulation operation data and the carbon emission evaluation index. When the carbon emission index value meets the preset conditions, obtain the current simulation operation data; wherein, the carbon emission evaluation index is determined by the historical environmental data of the target high-energy-consuming enterprise within a preset time period;

[0009] Perform energy efficiency evaluation on the target high-energy-consuming enterprise based on the current operation simulation data.

[0010] In a possible implementation manner, the determination process of the carbon emission evaluation index includes:

[0011] Obtain the historical environmental data of the target high-energy-consuming enterprise within a preset time period;

[0012] Determine the water pollution curve based on the water pollution data;

[0013] Determine the light pollution curve based on the light pollution data;

[0014] Determine the noise pollution curve based on the noise pollution data;

[0015] Fit the water pollution curve, the light pollution curve, the noise pollution curve and the vegetation area to obtain the vegetation impact coefficient;

[0016] Construct a carbon emission evaluation index based on the water pollution data, the carbon emission, the light pollution data, the vegetation area, the noise pollution data and the vegetation impact coefficient.

[0017] In a possible implementation, determining the water pollution curve based on the water pollution data includes:

[0018] Determine the water pollution degree corresponding to each collection moment within a preset time period based on the water pollution data, and arrange the water pollution degrees corresponding to each collection moment within the preset time period in time sequence to determine the water pollution curve;

[0019] Determine the light pollution curve based on the light pollution data, including:

[0020] Determine the light pollution degree corresponding to each collection moment within a preset time period based on the light pollution data, and arrange the light pollution degrees corresponding to each collection moment within the preset time period in time sequence to determine the light pollution curve;

[0021] Determine the noise pollution curve based on the noise pollution data, including:

[0022] Determine the noise pollution degree corresponding to each collection moment within a preset time period based on the noise pollution data, and arrange the noise pollution degrees corresponding to each collection moment within the preset time period in time sequence to determine the noise pollution curve.

[0023] In a possible implementation, the water pollution data, the carbon emission, the light pollution data, the vegetation area, the noise pollution data, the vegetation impact coefficient and the carbon emission evaluation index satisfy the following relationship:

[0024]

[0025] Where C 净 is the carbon emission evaluation index, C 排 is the carbon emission, C is the carbon absorption rate of vegetation per unit area, S is the vegetation area, k is the vegetation influence coefficient, and A, B, and C are the daily maximum water pollution degree, the daily maximum light pollution degree, and the daily maximum noise pollution degree, respectively; the daily maximum water pollution degree is determined from water pollution data; the daily maximum light pollution degree is determined from light pollution data; the daily maximum noise pollution degree is determined from noise pollution data.

[0026] In a possible implementation, the environmental data constraint conditions include water pollution degree constraint conditions, light pollution degree constraint conditions, and noise pollution degree constraint conditions;

[0027] The water pollution degree constraint conditions include water pollution area constraint conditions and water pollution category constraint conditions;

[0028] The light pollution degree constraint conditions include light exposure time constraint conditions, light intensity constraint conditions, and light area constraint conditions;

[0029] The noise pollution degree constraint conditions include noise decibel constraint conditions, noise range constraint conditions, and noise duration constraint conditions.

[0030] In a possible implementation, the simulation operation model is a digital twin model;

[0031] Constructing a simulation operation model based on current operation data includes:

[0032] Discretizing the current device operation data and the current environmental data to obtain a typical data set;

[0033] Based on the typical data set, obtaining initial digital twin data;

[0034] Based on the relationship between the data in the typical data set and the corresponding current device operation data and current environmental data, correcting the initial digital twin data; and constructing a digital twin model based on the corrected initial digital twin data.

[0035] In a possible implementation, the simulation operation model also includes cost constraints, energy conversion rate constraints, energy consumption constraints, and risk constraints;

[0036] Under the environmental data constraint conditions, dynamically simulating the simulation operation model to obtain simulation operation data, including:

[0037] Using a Markov chain, dynamically simulating the digital twin model under the environmental data constraint conditions, cost constraints, energy conversion rate constraints, energy consumption constraints, and risk constraints to obtain initial simulation operation data;

[0038] Based on the initial simulation operation data and the current operation data, adjusting the digital twin model to obtain simulation operation data.

[0039] In a possible implementation manner, based on the initial simulation operation data and the current operation data, the digital twin model is adjusted to obtain the simulation operation data, including:

[0040] Evaluate the current operation data to obtain a first analysis result;

[0041] Evaluate the initial simulation operation data to obtain a second analysis result;

[0042] Compare the first analysis result and the second analysis result to determine the data to be optimized, and based on the data to be optimized, adjust the digital twin model to obtain the simulation operation data.

[0043] In a possible implementation manner, the method further includes:

[0044] When the carbon emission index value does not meet the preset condition, based on the equipment operation constraint conditions, return the step of dynamically simulating the simulation operation model under the environmental data constraint conditions to obtain the simulation operation data.

[0045] In a second aspect, an energy efficiency evaluation device for high-energy-consuming enterprises under a low-carbon target provided by an embodiment of the present invention includes:

[0046] An acquisition module, configured to acquire the current operation data of the target high-energy-consuming enterprise and the historical environmental data within a preset time period; wherein, the current operation data includes the current equipment operation data and the current environmental data; the environmental data includes water pollution data, carbon emissions, light pollution data, vegetation area, and noise pollution data;

[0047] An operation module, configured to construct a simulation operation model according to the current operation data; perform simulation operation on the simulation operation model under the environmental data constraint conditions to obtain the simulation operation data;

[0048] An optimization module, configured to calculate the carbon emission index value based on the simulation operation data and the carbon emission evaluation index, and obtain the current simulation operation data when the carbon emission index value meets the preset condition; wherein, the carbon emission evaluation index is determined by the historical environmental data of the target high-energy-consuming enterprise within a preset time period;

[0049] An evaluation module, configured to perform energy efficiency evaluation on the target high-energy-consuming enterprise based on the current operation simulation data.

[0050] The embodiment of the present invention provides an energy efficiency evaluation method and device for high - energy - consuming enterprises under the low - carbon target. First, the operation data of high - energy - consuming enterprises is simulated to ensure that the obtained simulated operation data meets the low - carbon target, and the energy efficiency evaluation is carried out based on the simulated operation data. Through this method, each high - energy - consuming enterprise can understand that even if the low - carbon target is implemented, it will not reduce its energy efficiency, but instead can reduce environmental pollution, so as to promote the green transformation of enterprises. In addition, compared with the traditional method that only takes carbon emissions as the evaluation index of low - carbon operation, the embodiment of the present invention comprehensively considers environmental indicators that directly or indirectly affect carbon absorption, and generates a new carbon emission evaluation index based on these influencing factors; moreover, when running the simulation operation model, water pollution data, carbon emissions, light pollution data, vegetation area, and noise pollution data that affect net carbon emissions are all used as constraint conditions, making the obtained simulated operation data more reasonable and more standardized. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0052] Figure 1 is the implementation flowchart of the energy efficiency evaluation method for high - energy - consuming enterprises under the low - carbon target provided by the embodiment of the present invention;

[0053] Figure 2 is the structural schematic diagram of the energy efficiency evaluation device for high - energy - consuming enterprises under the low - carbon target provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well - known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0055] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will be described through specific embodiments with reference to the drawings.

[0056] Figure 1 is the implementation flowchart of the energy efficiency evaluation method for high - energy - consuming enterprises under the low - carbon target provided by the embodiment of the present invention. As Figure 1 shown, the method includes:

[0057] Step 110: Collect the current operation data of the target high-energy-consuming enterprise; among them, the current operation data includes current equipment operation data and current environmental data; the environmental data includes water pollution data, carbon emissions, light pollution data, vegetation area, and noise pollution data.

[0058] In this embodiment, the current equipment may include energy storage equipment and various loads. The energy storage equipment may include storage batteries, photovoltaic energy storage equipment, wind power generation equipment, etc.; various loads may include equipment such as air conditioners, power supplies, and light sources; correspondingly, the current equipment operation data may include the capacity data, input data, output data of each storage battery, the light intensity, light time, and photovoltaic output data received by the photovoltaic energy storage equipment; the wind speed, air volume, output power, etc. received by the wind power generation equipment; the operation time, rated power, maximum power data, etc. of various load energy storage equipment.

[0059] The water pollution data may include pollution area, pollution degree, propagation speed, and pollution category, etc.; the light pollution data may include light time, light intensity, light area, and light source flicker degree, etc.; the noise pollution data may include noise magnitude, noise range, and noise duration, etc. In addition, the environmental data may also include radioactive pollution data, nuclear pollution, soil pollution, etc., which are not limited here.

[0060] Step 120: Construct a simulation operation model according to the current operation data; under the constraint conditions of the environmental data, perform dynamic simulation on the simulation operation model to obtain simulation operation data.

[0061] In this embodiment, when traditional simulation methods perform simulation operations, they only use carbon emissions as the only environmental constraint condition, while this solution comprehensively considers various types of environmental data. Specifically, water pollution and soil pollution will directly affect the area of vegetation and the quality of surviving vegetation, and when the area of vegetation decreases, the amount of carbon emissions absorbed will also decrease; light pollution will cause the vegetation growth cycle to be chaotic, which is not conducive to plant growth, and in addition, light pollution will cause a reduction in insects responsible for pollinating plants, and over time, the area of vegetation will decrease; similarly, noise pollution will affect the growth of plants and the insects that pollinate plants; therefore, in this embodiment, water pollution data, light pollution data, noise pollution data, and soil pollution data in the environmental data, etc. are used as constraint conditions to obtain simulation operation data.

[0062] Specifically, the environmental data constraint conditions may include water pollution degree constraint conditions, light pollution degree constraint conditions, noise pollution degree constraint conditions, and soil pollution constraint conditions, etc. Among them, the water pollution degree constraint conditions include water pollution area constraint conditions and water pollution category constraint conditions; the light pollution degree constraint conditions include light duration constraint conditions, light intensity constraint conditions, and light area constraint conditions; the noise pollution degree constraint conditions include noise decibel constraint conditions, noise range constraint conditions, and noise duration constraint conditions; the soil pollution constraint conditions may include soil pollution area constraint conditions and soil pollution depth constraint conditions, etc., which are not limited herein.

[0063] In some alternative embodiments, the simulation operation model further includes cost constraint, energy conversion rate constraint, energy consumption constraint, and risk constraint; in step 120, under the environmental data constraint conditions, the simulation operation model is dynamically simulated to obtain simulation operation data, which may include:

[0064] Step 121: Using a Markov chain, under the environmental data constraint conditions, cost constraint, energy conversion rate constraint, energy consumption constraint, and risk constraint, the digital twin model is dynamically simulated to obtain initial simulation operation data; among them, the environmental data constraint conditions include water pollution degree constraint conditions, carbon emission constraint conditions, light pollution degree constraint conditions, vegetation area constraint conditions, and noise pollution degree constraint conditions.

[0065] Step 122: Based on the initial simulation operation data and the current operation data, the digital twin model is adjusted to obtain simulation operation data.

[0066] In this embodiment, the simulation operation model may be a digital twin model. Considering that the Markov chain can make good predictions on current data, therefore, based on the Markov chain, under the preset environmental data constraint conditions, the digital twin model is dynamically simulated to obtain initial simulation operation data. Based on the initial simulation operation data and the current operation data, the abnormal parameters that need to be adjusted between the current data and the target data, that is, the initial simulation operation data, are determined, and the parameters in the digital twin model are adjusted based on these abnormal parameters. Using the Markov chain, under the environmental data constraint conditions, the adjusted digital twin model is dynamically simulated to obtain simulation operation data.

[0067] In addition, when performing the simulation operation, in addition to achieving the goal of low carbon, it is also necessary to ensure that the benefits of each enterprise will not decline, no new risks will be added, and the cost will not be increased. Therefore, in the embodiment of the present invention, when performing the simulation operation, cost constraint, energy conversion rate constraint, energy consumption constraint, and risk constraint should also be added.

[0068] In some alternative embodiments, constructing a simulation operation model based on the current operation data in step 121 may include:

[0069] Discretize the current device operation data and the current environment data to obtain a typical data set.

[0070] Based on the typical data set, obtain initial digital twin data.

[0071] Based on the relationship between the data in the typical data set and the corresponding current device operation data and current environment data, correct the initial digital twin data; and based on the corrected initial digital twin data, construct a digital twin model.

[0072] In this embodiment, to remove abnormal data and obtain typical data, the current device operation data and the current environment data may be discretized to obtain a typical data set. Analyze and sort out the data in the typical data set to obtain initial digital twin data.

[0073] To improve the accuracy of the constructed digital twin model, it is necessary to correct the initial digital twin data based on the relationship between the data in the typical data set and the corresponding current device operation data and current environment data, and construct a digital twin model based on the corrected initial digital twin data.

[0074] In some alternative embodiments, adjusting the digital twin model based on the initial simulation operation data and the current operation data in step 122 to obtain simulation operation data may include:

[0075] Evaluate the current operation data to obtain a first analysis result.

[0076] Evaluate the initial simulation operation data to obtain a second analysis result.

[0077] Compare the first analysis result and the second analysis result to determine the data to be optimized, and based on the data to be optimized, adjust the digital twin model to obtain simulation operation data.

[0078] In this embodiment, evaluate the current device operation data and the current environment data in the previous operation data respectively to obtain a first analysis result; wherein, the first analysis result includes a first device analysis result and a first environment analysis result; the first device analysis result includes the operation indicators of each device, specifically including output and load balance indicators, device working temperature indicators, etc.; the first environment analysis result includes the operation indicators of each environment, specifically including temperature indicators, humidity indicators, visibility indicators, etc.

[0079] Evaluate the initial simulation device operation data and the initial simulation environment data in the initial simulation operation data respectively to obtain a second analysis result; wherein, the second analysis result may include a second device analysis result and a second environment analysis result.

[0080] Compare each index in the first analysis result and the second analysis result respectively to determine abnormal indexes. When abnormal data appears, it indicates that the simulation result of the current digital twin model is inaccurate and the model needs to be adjusted. Therefore, determine the data to be optimized based on the abnormal indexes, and based on the data to be optimized, adjust the digital twin model to re-perform dynamic simulation on the adjusted digital twin model to obtain simulation operation data.

[0081] Step 130: Calculate the carbon emission index value based on the simulation operation data and the carbon emission evaluation index. When the carbon emission index value meets the preset conditions, obtain the current simulation operation data; wherein, the carbon emission evaluation index is determined by the historical environment data of the target high-energy-consuming enterprise within a preset time period.

[0082] In this embodiment, this embodiment analyzes the historical environment data of the target high-energy-consuming enterprise within a preset time period, establishes a new carbon emission evaluation index, and according to the new carbon emission evaluation index and the simulation operation data, inputs the environmental simulation data in the simulation operation data into the carbon emission evaluation index to calculate the carbon emission index value corresponding to the current simulation operation data; wherein, the environmental simulation data may include carbon emission simulation data, vegetation area simulation data, water pollution simulation data, light pollution simulation data, and noise pollution simulation data.

[0083] When the carbon emission index value meets the preset conditions, obtain the current simulation operation data of the target high-energy-consuming enterprise. Among them, the preset conditions may be that the average value of the carbon emission index value is less than the preset threshold; or the growth rate of the carbon emission index value is less than the preset growth rate, etc., which are not limited here. Among them, the carbon emission evaluation index is:

[0084]

[0085] Among them, C 净 is the carbon emission evaluation index, C 排 is the carbon emission, is the carbon absorption rate of vegetation per unit area, S is the vegetation area, k is the vegetation influence coefficient, A, B, and C are the daily maximum water pollution degree, the daily maximum light pollution degree, and the daily maximum noise pollution degree respectively; the daily maximum water pollution degree is determined by the water pollution data; the daily maximum light pollution degree is determined by the light pollution data; the daily maximum noise pollution degree is determined by the noise pollution data.

[0086] In some alternative embodiments, the determination process of the carbon emission evaluation index may include:

[0087] Obtain the historical environmental data of the target high-energy-consuming enterprise within a preset time period.

[0088] Determine the water pollution curve according to the water pollution data.

[0089] Determine the light pollution curve according to the light pollution data.

[0090] Determine the noise pollution curve according to the noise pollution data.

[0091] Fit the water pollution curve, light pollution curve, noise pollution curve and vegetation area to obtain the vegetation impact coefficient.

[0092] Construct a carbon emission evaluation index based on the water pollution data, carbon emissions, light pollution data, vegetation area, noise pollution data and vegetation impact coefficient.

[0093] In this embodiment, considering that the historical environmental data of the target high-energy-consuming enterprise within a preset time period is time series data, correspondingly, the water pollution data, carbon emissions, light pollution data, vegetation area, noise pollution data, etc. will all change with time. Among them, the preset time period can be 30 days, 40 days, 50 days, etc., which is not limited here.

[0094] For the water pollution data, the water pollution degree corresponding to each collection moment can be determined according to the water pollution data at each collection moment, and the water pollution degrees corresponding to each collection moment are sorted according to the time series to obtain the water pollution time series data, and the water pollution curve is drawn according to the water pollution time series data.

[0095] For the light pollution data, the light pollution degree corresponding to each collection moment can be determined according to the light pollution data at each collection moment, and the light pollution degrees corresponding to each collection moment are sorted according to the time series to obtain the light pollution time series data, and the light pollution curve is drawn according to the light pollution time series data.

[0096] For the noise pollution data, the noise pollution degree corresponding to each collection moment can be determined according to the noise pollution data at each collection moment, and the noise pollution degrees corresponding to each collection moment are sorted according to the time series to obtain the noise pollution time series data, and the noise pollution curve is drawn according to the noise pollution time series data.

[0097] Considering that the water pollution data, light pollution data, and noise pollution data will all affect the vegetation area, in order to obtain the impact coefficients of the water pollution data, light pollution data, and noise pollution data on the vegetation area, the water pollution curve, light pollution curve, noise pollution curve and vegetation area (curve) can be fitted to obtain the vegetation impact coefficient.

[0098] Construct a carbon emission evaluation index based on water pollution data, carbon emissions, light pollution data, vegetation area, noise pollution data, and vegetation impact coefficient.

[0099] In some alternative embodiments, the method may further include:

[0100] When the carbon emission index value does not meet the preset conditions, based on the equipment operation constraint conditions, return the step of dynamically simulating the simulation operation model under the environmental data constraint conditions to obtain simulation operation data.

[0101] In this embodiment, when the carbon emission index value does not meet the preset conditions, it indicates that the currently obtained simulation operation data does not meet the requirements and needs to be run again to obtain new simulation operation data. Specifically, considering that the direct data affecting environmental data in the target high-energy-consuming enterprise is equipment operation data, therefore, equipment operation constraint conditions are added in the subsequent adjustment, and the step of dynamically simulating the simulation operation model under the environmental data constraint conditions to obtain simulation operation data is returned until the obtained carbon emission index value meets the preset conditions. Among them, no equipment operation constraint conditions were added in the first run to reduce the amount of calculation. However, if the normal operation of the enterprise is not considered during the subsequent operation process, equipment operation deviations will gradually occur, and the finally obtained simulation operation data of the target high-energy-consuming enterprise may be the optimal environmental operation strategy, but it may not be able to maintain the normal operation of the target high-energy-consuming enterprise.

[0102] Step 140: Conduct an energy efficiency evaluation of the target high-energy-consuming enterprise based on the current operation simulation data.

[0103] In this embodiment, the current operation simulation data can be used as the optimal operation data of the target high-energy-consuming enterprise after implementing low-carbon management, and an energy efficiency evaluation is performed based on this data.

[0104] In summary, in the embodiment of the present invention, the operation data of high-energy-consuming enterprises is first simulated to ensure that the obtained simulation operation data meets the low-carbon target, and an energy efficiency evaluation is performed based on the simulation operation data. Through this method, each high-energy-consuming enterprise can be made to understand that even if the low-carbon target is implemented, it will not reduce its energy efficiency, but instead can reduce environmental pollution, thereby promoting the green transformation of enterprises. In addition, compared with the traditional method that only uses carbon emissions as the evaluation index for low-carbon operation, the embodiment of the present invention comprehensively considers environmental indicators that directly or indirectly affect carbon absorption, and generates a new carbon emission evaluation index based on these influencing factors; and when running the simulation operation model, water pollution data, carbon emissions, light pollution data, vegetation area, and noise pollution data that affect net carbon emissions are all used as constraint conditions, making the obtained simulation operation data more reasonable and more standardized.

[0105] It should be understood that the sequence numbers of the steps in the above embodiments do not indicate the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0106] The following is an embodiment of the device of the present invention. For the details not described in detail, reference may be made to the corresponding method embodiment above.

[0107] Figure 2 The structural schematic diagram of the energy efficiency evaluation device for high-energy-consuming enterprises under the target provided by the embodiment of the present invention is shown. For the convenience of description, only the parts related to the embodiment of the present invention are shown and are described in detail as follows:

[0108] As Figure 2 shown, the energy efficiency evaluation device 2 for high-energy-consuming enterprises under the target includes:

[0109] A collection module 21, configured to collect the current operation data of the target high-energy-consuming enterprise and the historical environmental data within a preset time period; wherein, the current operation data includes the current device operation data and the current environmental data; the environmental data includes water pollution data, carbon emissions, light pollution data, vegetation area, and noise pollution data;

[0110] An operation module 22, configured to construct a simulation operation model according to the current operation data; under the constraint conditions of the environmental data, perform simulation operation on the simulation operation model to obtain simulation operation data;

[0111] An optimization module 23, configured to calculate a carbon emission index value based on the simulation operation data and the carbon emission evaluation index, and obtain the current simulation operation data when the carbon emission index value meets the preset conditions; wherein, the carbon emission evaluation index is determined by the historical environmental data of the target high-energy-consuming enterprise within a preset time period;

[0112] An evaluation module 24, based on the current operation simulation data, performs an energy efficiency evaluation on the target high-energy-consuming enterprise.

[0113] In a possible implementation manner, the optimization module 23 is specifically configured to:

[0114] Obtain the historical environmental data of the target high-energy-consuming enterprise within a preset time period;

[0115] Determine a water pollution curve according to the water pollution data;

[0116] Determine a light pollution curve according to the light pollution data;

[0117] Determine a noise pollution curve according to the noise pollution data;

[0118] Fit the water pollution curve, the light pollution curve, the noise pollution curve, and the vegetation area to obtain a vegetation influence coefficient;

[0119] Construct a carbon emission evaluation index based on water pollution data, carbon emissions, light pollution data, vegetation area, noise pollution data, and vegetation impact coefficient.

[0120] In a possible implementation, the optimization module 23 is specifically configured to:

[0121] Determine the water pollution degree corresponding to each collection moment within a preset time period based on the water pollution data, and arrange the water pollution degrees corresponding to each collection moment within the preset time period in chronological order to determine the water pollution curve;

[0122] Determine the light pollution curve according to the light pollution data, including:

[0123] Determine the light pollution degree corresponding to each collection moment within a preset time period based on the light pollution data, and arrange the light pollution degrees corresponding to each collection moment within the preset time period in chronological order to determine the light pollution curve;

[0124] Determine the noise pollution curve according to the noise pollution data, including:

[0125] Determine the noise pollution degree corresponding to each collection moment within a preset time period based on the noise pollution data, and arrange the noise pollution degrees corresponding to each collection moment within the preset time period in chronological order to determine the noise pollution curve.

[0126] In a possible implementation, the water pollution data, carbon emissions, light pollution data, vegetation area, noise pollution data, vegetation impact coefficient, and carbon emission evaluation index satisfy the following relationship:

[0127]

[0128] Wherein, C 净 is the carbon emission evaluation index, C 排 is the carbon emissions, is the carbon absorption rate of vegetation per unit area, S is the vegetation area, k is the vegetation impact coefficient, A, B, and C are the daily maximum water pollution degree, daily maximum light pollution degree, and daily maximum noise pollution degree respectively; the daily maximum water pollution degree is determined by the water pollution data; the daily maximum light pollution degree is determined by the light pollution data; the daily maximum noise pollution degree is determined by the noise pollution data.

[0129] In a possible implementation, the environmental data constraint conditions include water pollution degree constraint conditions, light pollution degree constraint conditions, and noise pollution degree constraint conditions;

[0130] The water pollution degree constraint conditions include water pollution area constraint conditions and water pollution category constraint conditions;

[0131] The light pollution degree constraint conditions include illumination time constraint conditions, illumination intensity constraint conditions, and illumination area constraint conditions;

[0132] The noise pollution degree constraint conditions include noise decibel constraint conditions, noise range constraint conditions, and noise duration constraint conditions.

[0133] In a possible implementation, the simulation operation model is a digital twin model;

[0134] The operation module 22 is specifically configured to:

[0135] Discretize the current device operation data and the current environmental data to obtain a typical data set;

[0136] Based on the typical data set, obtain initial numerical twin data;

[0137] Based on the relationship between the data in the typical data set and the corresponding current device operation data and current environmental data, correct the initial numerical twin data; and based on the corrected initial numerical twin data, construct a digital twin model.

[0138] In a possible implementation, the simulation operation model further includes cost constraint, energy conversion rate constraint, energy consumption constraint, and risk constraint;

[0139] The operation module 22 is specifically configured to:

[0140] Adopt a Markov chain to dynamically simulate the digital twin model under the environmental data constraint conditions, cost constraint, energy conversion rate constraint, energy consumption constraint, and risk constraint to obtain initial simulation operation data;

[0141] Based on the initial simulation operation data and the current operation data, adjust the digital twin model to obtain simulation operation data.

[0142] In a possible implementation, the operation module 22 is specifically configured to:

[0143] Evaluate the current operation data to obtain a first analysis result;

[0144] Evaluate the initial simulation operation data to obtain a second analysis result;

[0145] Compare the first analysis result and the second analysis result to determine the data to be optimized, and based on the data to be optimized, adjust the digital twin model to obtain simulation operation data.

[0146] In a possible implementation, the optimization module 23 is further configured to:

[0147] When the carbon emission index value does not meet the preset conditions, then based on the device operation constraint conditions, return to the step of dynamically simulating the simulation operation model under the environmental data constraint conditions to obtain simulation operation data.

[0148] In the above embodiments, the descriptions of the respective embodiments have their own focuses. For parts not detailed or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0149] Those of ordinary skill in the art can realize that the templates, units, and algorithm steps of the various examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0150] If the module / unit 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, to implement all or part of the processes in the above method embodiments of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above method embodiments for evaluating the energy efficiency of high-energy-consuming enterprises under various low-carbon goals can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0151] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. An energy efficiency evaluation method for high-energy-consuming enterprises under the low-carbon goal, characterized in that, Including: Collecting the current operation data of the target high-energy-consuming enterprise; wherein, the current operation data includes current equipment operation data and current environmental data; the environmental data includes water pollution data, carbon emissions, light pollution data, vegetation area, and noise pollution data; Constructing a simulation operation model based on the current operation data; under the constraint conditions of environmental data, dynamically simulating the simulation operation model to obtain simulation operation data; Calculating the carbon emission index value based on the simulation operation data and the carbon emission evaluation index. When the carbon emission index value meets the preset conditions, obtaining the current simulation operation data; wherein, the carbon emission evaluation index is determined by the historical environmental data of the target high-energy-consuming enterprise within a preset time period; Conducting an energy efficiency evaluation of the target high-energy-consuming enterprise based on the current operation simulation data.

2. The energy efficiency evaluation method for high-energy-consuming enterprises under the low-carbon target according to claim 1, wherein The determination process of the carbon emission evaluation index includes: Obtaining the historical environmental data of the target high-energy-consuming enterprise within a preset time period; Determining the water pollution curve according to the water pollution data; Determining the light pollution curve according to the light pollution data; Determining the noise pollution curve according to the noise pollution data; Fitting the water pollution curve, the light pollution curve, the noise pollution curve, and the vegetation area to obtain the vegetation impact coefficient; Constructing a carbon emission evaluation index based on the water pollution data, the carbon emissions, the light pollution data, the vegetation area, the noise pollution data, and the vegetation impact coefficient.

3. The energy efficiency evaluation method for high-energy-consuming enterprises under the low-carbon target according to claim 2, wherein The determination of the water pollution curve according to the water pollution data includes: Determining the water pollution degree corresponding to each collection moment within the preset time period based on the water pollution data, and arranging the water pollution degrees corresponding to each collection moment within the preset time period in chronological order to determine the water pollution curve; The determination of the light pollution curve according to the light pollution data includes: Determining the light pollution degree corresponding to each collection moment within the preset time period based on the light pollution data, and arranging the light pollution degrees corresponding to each collection moment within the preset time period in chronological order to determine the light pollution curve; The determination of the noise pollution curve according to the noise pollution data includes: Determining the noise pollution degree corresponding to each collection moment within the preset time period based on the noise pollution data, and arranging the noise pollution degrees corresponding to each collection moment within the preset time period in chronological order to determine the noise pollution curve.

4. The energy efficiency evaluation method for high-energy-consuming enterprises under the low-carbon target according to claim 2, characterized in that, The water pollution data, the carbon emissions, the light pollution data, the vegetation area, the noise pollution data, the vegetation impact coefficient, and the carbon emission evaluation index satisfy the following relationship: Among them, C 净 is the carbon emission evaluation index, C 排 is the carbon emission, is the carbon absorption rate of vegetation per unit area, S is the vegetation area, k is the vegetation influence coefficient, A, B, and C are the daily maximum water pollution degree, the daily maximum light pollution degree, and the daily maximum noise pollution degree respectively; the daily maximum water pollution degree is determined by the water pollution data; the daily maximum light pollution degree is determined by the light pollution data; the daily maximum noise pollution degree is determined by the noise pollution data.

5. The energy efficiency evaluation method for high-energy-consuming enterprises under the low-carbon target according to claim 1, characterized in that The environmental data constraint conditions include water pollution degree constraint conditions, light pollution degree constraint conditions, and noise pollution degree constraint conditions; The water pollution degree constraint conditions include water pollution area constraint conditions and water pollution category constraint conditions; The light pollution degree constraint conditions include lighting time constraint conditions, lighting intensity constraint conditions, and lighting area constraint conditions; The noise pollution degree constraint conditions include noise decibel constraint conditions, noise range constraint conditions, and noise duration constraint conditions.

6. The energy efficiency evaluation method for high-energy-consuming enterprises under the low-carbon target according to claim 1, characterized in that, The simulation operation model is a digital twin model; Building a simulation operation model based on the current operation data includes: Discretizing the current device operation data and the current environmental data to obtain a typical data set; Based on the typical data set, obtaining initial digital twin data; Based on the relationship between the data in the typical data set and the corresponding current device operation data and current environmental data, correcting the initial digital twin data; and building a digital twin model based on the corrected initial digital twin data.

7. The energy efficiency evaluation method for high-energy-consuming enterprises under the low-carbon target according to claim 1, characterized in that The simulation operation model also includes cost constraints, energy conversion rate constraints, energy consumption constraints, and risk constraints; Under the environmental data constraint conditions, dynamically simulating the simulation operation model to obtain simulation operation data, including: Using a Markov chain, dynamically simulating the digital twin model under environmental data constraint conditions, the cost constraint, the energy conversion rate constraint, the energy consumption constraint, and the risk constraint to obtain initial simulation operation data; Based on the initial simulation operation data and the current operation data, adjusting the digital twin model to obtain simulation operation data.

8. The energy efficiency evaluation method for high-energy-consuming enterprises under the low-carbon goal according to claim 7, characterized in that, Based on the initial simulation operation data and the current operation data, adjusting the digital twin model to obtain simulation operation data, including: Evaluating the current operation data to obtain a first analysis result; Evaluating the initial simulation operation data to obtain a second analysis result; Comparing the first analysis result and the second analysis result to determine the data to be optimized, and adjusting the digital twin model based on the data to be optimized to obtain simulation operation data.

9. The energy efficiency evaluation method for high-energy-consuming enterprises under the low-carbon target according to claim 1, characterized in that, The method further includes: When the carbon emission index value does not meet the preset conditions, based on the device operation constraint conditions, returning to the step of dynamically simulating the simulation operation model under the environmental data constraint conditions to obtain simulation operation data.

10. An energy efficiency evaluation device for high-energy-consuming enterprises under a low-carbon target, characterized in that, Including: A collection module for collecting the current operation data of the target high-energy-consuming enterprise and the historical environmental data within a preset time period; wherein the current operation data includes current device operation data and current environmental data; the environmental data includes water pollution data, carbon emissions, light pollution data, vegetation area, and noise pollution data; An operation module for building a simulation operation model according to the current operation data; dynamically simulating the simulation operation model under the environmental data constraint conditions to obtain simulation operation data; An optimization module for calculating a carbon emission index value based on the simulation operation data and a carbon emission evaluation index, and obtaining the current simulation operation data when the carbon emission index value meets the preset conditions; wherein the carbon emission evaluation index is determined by the historical environmental data of the target high-energy-consuming enterprise within a preset time period; An evaluation module for performing an energy efficiency evaluation on the target high-energy-consuming enterprise based on the current operation simulation data.

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