Big data analysis-based low-carbon industrial park construction carbon emission regulation and control method and platform

Abstract

The invention provides a low-carbon industrial park construction carbon emission regulation and control method and platform based on big data analysis, and relates to the technical field of carbon emission regulation and control, and the method comprises the steps: carrying out the zero-carbon fitness analysis of a plurality of devices of an industrial park; performing zero-carbon regulation and control analysis on the zero-carbon equipment cluster according to the current carbon emission data set of the industrial park; performing scene application carbon emission mining according to the non-zero carbon equipment cluster; performing emission reduction target identification on the non-zero carbon equipment cluster according to the expected carbon emission data set and the current carbon emission data set; performing a carbon emission sensitivity test according to the control index set of the emission reduction target equipment; based on the emission reduction target, optimizing and adjusting the emission reduction target equipment according to the emission reduction regulation first factor and the emission reduction regulation second factor; and performing adaptive carbon emission regulation and control on the industrial park according to the zero carbon regulation and control strategy and the emission reduction regulation and control strategy. According to the invention, the technical effects of accurate regulation and control according to the actual carbon emission condition of the equipment and improvement of the overall carbon emission efficiency of the park can be achieved.

Description

Technical Field

[0001] The present application relates to the field of carbon emission control technology, and in particular to a carbon emission control method and platform for the construction of low-carbon industrial parks based on big data analysis. Background Art

[0002] Carbon emission management within industrial parks has become a crucial component in reducing their overall carbon footprint. Currently, many industrial parks have begun adopting clean energy, energy efficiency optimization, and carbon emission monitoring to reduce carbon emissions. However, existing technologies for carbon emission regulation still have numerous shortcomings, making them unable to meet the low-carbon operation requirements of industrial parks in complex scenarios.

[0003] Currently, existing technologies primarily rely on traditional carbon emission monitoring platforms, which typically focus on a single device or a fixed area and lack the ability to comprehensively analyze carbon emissions across the entire industrial park. Furthermore, traditional carbon emission monitoring technologies typically employ periodic sampling, resulting in delayed data updates and difficulty in achieving real-time carbon emission control. Furthermore, traditional methods for carbon emission optimization often rely on empirical rules or fixed thresholds, lacking the ability to dynamically adapt to the equipment's operating environment and actual emissions. This makes it difficult to precisely align carbon emission reduction measures with actual needs. Furthermore, current low-carbon management approaches in industrial parks fail to effectively integrate zero-carbon and non-zero-carbon equipment, preventing differentiated carbon emission control. Some industrial parks have introduced zero-carbon equipment, such as photovoltaic power generation and energy storage platforms, but in actual operation, the potential of these devices has not been fully tapped, and their ability to coordinate optimization with other high-carbon-emitting equipment is insufficient. Furthermore, carbon emission optimization for non-zero-carbon equipment is often limited to the individual device level, lacking a comprehensive carbon management strategy for its entire lifecycle and preventing the development of a platform-based carbon emission optimization approach.

[0004] In summary, the existing technology has a technical problem that due to the lack of the ability to analyze the overall carbon emissions of equipment in the entire park, carbon emission optimization measures are difficult to implement accurately, further affecting the efficiency and effectiveness of low-carbon management in industrial parks. Summary of the Invention

[0005] The purpose of this application is to provide a carbon emission control method and platform for the construction of low-carbon industrial parks based on big data analysis, in order to solve the technical problem in the existing technology that due to the lack of the ability to analyze the overall carbon emissions of equipment in the entire park, carbon emission optimization measures are difficult to implement accurately, which further affects the efficiency and effectiveness of low-carbon management of industrial parks.

[0006] In view of the above problems, this application provides a carbon emission control method and platform for the construction of low-carbon industrial parks based on big data analysis.

[0007] In the first aspect, the present application provides a carbon emission control method for the construction of a low-carbon industrial park based on big data analysis, which is implemented through a carbon emission control platform for the construction of a low-carbon industrial park based on big data analysis, including: performing zero-carbon adaptability analysis on multiple equipment in the industrial park to obtain a zero-carbon equipment cluster and a non-zero-carbon equipment cluster; performing zero-carbon control analysis on the zero-carbon equipment cluster based on the current carbon emission data set of the industrial park to obtain a zero-carbon control strategy; performing scenario application carbon emission mining based on the non-zero-carbon equipment cluster to determine an expected carbon emission data set; identifying emission reduction targets for the non-zero-carbon equipment cluster based on the expected carbon emission data set and the current carbon emission data set to determine the emission reduction targets corresponding to the emission reduction target equipment; performing carbon emission sensitivity testing based on the control indicator set of the emission reduction target equipment to determine a first emission reduction control factor and a second emission reduction control factor; based on the emission reduction target, optimizing the emission reduction target equipment according to the first emission reduction control factor and the second emission reduction control factor to obtain an emission reduction control strategy; and performing adaptive carbon emission control on the industrial park according to the zero-carbon control strategy and the emission reduction control strategy.

[0008] In the second aspect, the present application also provides a carbon emission control platform for the construction of low-carbon industrial parks based on big data analysis, which is used to execute the carbon emission control method for the construction of low-carbon industrial parks based on big data analysis as described in the first aspect, including: a zero-carbon fitness analysis module, which is used to perform zero-carbon fitness analysis on multiple equipment in the industrial park to obtain a zero-carbon equipment cluster and a non-zero-carbon equipment cluster; a zero-carbon control analysis module, which is used to perform zero-carbon control analysis on the zero-carbon equipment cluster based on the current carbon emission data set of the industrial park to obtain a zero-carbon control strategy; a scenario application carbon emission mining module, which is used to perform scenario application carbon emission mining based on the non-zero-carbon equipment cluster to determine the expected carbon emission data set; an emission reduction target identification module, which is used to identify the target based on the current carbon emission data set of the industrial park. The emission reduction target of the non-zero carbon equipment cluster is identified according to the expected carbon emission data set and the current carbon emission data set, and the emission reduction target corresponding to the emission reduction target equipment is determined; a carbon emission sensitivity test module is used to perform a carbon emission sensitivity test according to the control indicator set of the emission reduction target equipment, and determine the first emission reduction control factor and the second emission reduction control factor; an optimal adjustment module is used to perform optimal adjustment on the emission reduction target equipment based on the emission reduction target and the first emission reduction control factor and the second emission reduction control factor to obtain an emission reduction control strategy; an adaptive carbon emission control module is used to perform adaptive carbon emission control on the industrial park according to the zero-carbon control strategy and the emission reduction control strategy.

[0009] The technical solution provided in this application has at least the following technical effects or advantages: by achieving the technical goal of carbon emission management and dynamic optimization and regulation of all equipment in the park, precise regulation is achieved according to the actual carbon emissions of the equipment, the overall carbon emission efficiency of the park is improved, and the carbon emissions of key equipment in different scenarios are minimized or zero.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the description, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically listed below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in this application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without creative work.

[0012] Figure 1 A flow chart of the carbon emission control method for the low-carbon industrial park constructed for this application of big data analysis;

[0013] Figure 2 Schematic diagram of the structure of the carbon emission control platform built for the low-carbon industrial park applying big data analysis.

[0014] Explanation of the accompanying symbols: zero-carbon adaptability analysis module 11, zero-carbon regulation analysis module 12, scenario application carbon emission mining module 13, emission reduction target identification module 14, carbon emission sensitivity testing module 15, optimization adjustment module 16, adaptive carbon emission regulation module 17. DETAILED DESCRIPTION

[0015] This application solves the technical problem in the existing technology of carbon emission control measures being difficult to accurately implement due to the lack of the ability to analyze the overall carbon emissions of equipment in the entire park, which further affects the efficiency and effectiveness of low-carbon management in the industrial park by providing a carbon emission control method and platform for the construction of low-carbon industrial parks based on big data analysis. It achieves the technical goal of carbon emission management and dynamic optimization and control of all equipment in the park, achieving the technical effect of accurate control based on the actual carbon emissions of the equipment, improving the overall carbon emission efficiency of the park, and ensuring that the carbon emissions of key equipment in different scenarios are minimized or zero.

[0016] Below, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited to the example embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the convenience of description, only the parts related to this application, rather than all of them, are shown in the accompanying drawings.

[0017] For example, see the attached Figure 1 This application provides a carbon emission control method for low-carbon industrial park construction based on big data analysis, which is applied to a carbon emission control platform for low-carbon industrial park construction based on big data analysis, and specifically includes the following steps:

[0018] S1: Conduct zero-carbon adaptability analysis on multiple equipment in the industrial park to obtain zero-carbon equipment clusters and non-zero-carbon equipment clusters.

[0019] Further, S1 includes: S11: collecting current application scenario information of the multiple devices to obtain multiple device application scenarios; S12: performing zero-carbon fitness evaluation on the multiple device application scenarios to obtain multiple scene zero-carbon fitness; S13: extracting the first scene zero-carbon fitness based on the multiple scene zero-carbon fitness, and judging whether the first scene zero-carbon fitness is greater than or equal to the predetermined zero-carbon fitness; S14: if the first scene zero-carbon fitness is greater than or equal to the predetermined zero-carbon fitness, the device corresponding to the first scene zero-carbon fitness is recorded as a zero-carbon device, and the zero-carbon device is added to the zero-carbon device cluster; S15: if the first scene zero-carbon fitness is less than the predetermined zero-carbon fitness, the device corresponding to the first scene zero-carbon fitness is recorded as a non-zero-carbon device, and the non-zero-carbon device is added to the non-zero-carbon device cluster.

[0020] Specifically, in an industrial park, many devices are involved in production, office work, or other functions, and their energy consumption patterns and carbon emissions vary. Zero-carbon adaptability analysis involves assessing the adaptability of multiple devices in a low-carbon or zero-carbon environment, specifically whether they can operate on clean energy or have low carbon emissions. To conduct a zero-carbon adaptability analysis, it's important to first understand the actual usage of multiple devices, specifically their application scenarios, to ensure the assessment is based on real operating data, thereby improving the accuracy of the analysis. Application scenarios refer to the device's operating mode, frequency of use, and energy consumption structure.

[0021] Each application scenario is evaluated for zero-carbon adaptability, resulting in multiple scenarios with zero-carbon adaptability. Zero-carbon adaptability refers to whether the device's carbon emissions in that scenario meet zero-carbon requirements. For example, if a device should produce no carbon emissions in specific scenarios such as standby, idling, or shutdown, or if it should not produce carbon emissions in the device application scenario, its zero-carbon adaptability will be high. On the other hand, if a device should produce carbon emissions in the device application scenario, its zero-carbon adaptability will be low. Through this assessment, each device will receive a adaptability score in different scenarios to facilitate subsequent classification and decision-making.

[0022] From the fitness results for multiple scenarios, the zero-carbon fitness of one scenario is randomly extracted, referred to as the first scenario zero-carbon fitness. This first scenario zero-carbon fitness is then compared with the predetermined zero-carbon fitness, which serves as a benchmark for determining whether a device is zero-carbon. For example, if the predetermined zero-carbon fitness is 80%, and a device's first scenario zero-carbon fitness is 90%, it indicates that the device has good zero-carbon capabilities in this scenario.

[0023] If the zero-carbon adaptability of the first scenario is greater than or equal to the predetermined zero-carbon adaptability, the device has good zero-carbon capabilities. The device corresponding to the zero-carbon adaptability of the first scenario is recorded as a zero-carbon device and classified into a zero-carbon device cluster. A zero-carbon device cluster is a collection of multiple zero-carbon devices. For example, if a wind turbine's zero-carbon adaptability reaches 100%, it will be classified as a zero-carbon device cluster because it operates entirely on clean energy and has no carbon emissions.

[0024] If the zero-carbon adaptability of the first scenario is less than the predetermined zero-carbon adaptability, it indicates that the device still has high carbon emissions or relies on traditional energy. The device corresponding to the zero-carbon adaptability of the first scenario is recorded as a non-zero-carbon device and classified into a non-zero-carbon device cluster. A non-zero-carbon device cluster is a collection of multiple non-zero-carbon devices. For example, a diesel generator may have a zero-carbon adaptability of only 30%, so it is classified into a non-zero-carbon device cluster for subsequent modification or optimization.

[0025] S2: performing zero-carbon regulation analysis on the zero-carbon equipment cluster according to the current carbon emission data set of the industrial park to obtain a zero-carbon regulation strategy.

[0026] Further, S2 includes: S21: performing feature identification on the current carbon emission data set according to the zero-carbon equipment cluster to obtain multiple zero-carbon equipment carbon emission data; S22: judging whether the multiple zero-carbon equipment carbon emission data are zero; S23: if any zero-carbon equipment carbon emission data among the multiple zero-carbon equipment carbon emission data is not 0, obtaining a zero-carbon control device; S24: performing zero-carbon control on the zero-carbon control device to generate the zero-carbon control strategy.

[0027] Specifically, zero-carbon regulation and analysis is performed on the zero-carbon equipment cluster based on the current carbon emission data set of the industrial park. The current carbon emission data set includes the current carbon emission data corresponding to each device in the industrial park. Among them, feature recognition is performed on the current carbon emission data set based on the zero-carbon equipment cluster to obtain multiple zero-carbon equipment carbon emission data. Feature recognition refers to extracting information related to zero-carbon equipment from the data set, such as the power consumption, energy source and actual carbon emissions of these devices. Zero-carbon equipment carbon emission data refers to the carbon emission data generated by these devices during operation. For example, the carbon emissions of a wind turbine may be close to zero, while an electric forklift may still have a small amount of carbon emissions due to the use of some fossil energy when charging. Through feature recognition, the carbon emission information of zero-carbon equipment can be accurately screened out to provide data support for subsequent analysis.

[0028] Determine whether the carbon emission data for multiple zero-carbon devices is zero, and whether their carbon emission values are truly zero. Ideally, zero-carbon devices should produce no carbon emissions, but in practice, some carbon emissions may occur due to factors such as the device's operating environment and energy structure. For example, a device may have zero carbon emissions when using solar energy during the day, but may emit a small amount of carbon at night due to the use of backup power from the grid. Therefore, the purpose of this step is to confirm whether the operation of the zero-carbon device has truly achieved a zero-carbon state, or whether some carbon emissions still exist.

[0029] If the carbon emission data for any of the multiple zero-carbon devices is non-zero, the judgment result shows that some zero-carbon devices still emit carbon. These devices need to be further screened out to identify zero-carbon control devices. Zero-carbon control devices refer to those that should theoretically achieve zero carbon emissions but still emit carbon in actual operation. The purpose of identifying zero-carbon control devices is to provide targets for the next step of the zero-carbon control strategy, so as to optimize their operating mode or adjust their energy source.

[0030] After identifying zero-carbon control equipment, these devices need to be zero-carbon controlled. Zero-carbon control refers to reducing or even eliminating carbon emissions from these devices by optimizing equipment operation, adjusting energy structure, or introducing new technologies, thereby generating a zero-carbon control strategy.

[0031] S3: Perform scenario application carbon emission mining based on the non-zero carbon equipment cluster to determine an expected carbon emission data set.

[0032] Further, S3 includes: S31: taking the equipment application scenario corresponding to the nth non-zero carbon equipment in the non-zero carbon equipment cluster as the nth target application scenario, where n is a positive integer; S32: performing normal carbon emission sample retrieval on the nth non-zero carbon equipment according to the nth target application scenario to obtain the normal carbon emission sample set of the nth equipment; S33: performing centralized value calculation based on the normal carbon emission sample set of the nth equipment to obtain the expected carbon emission data of the nth equipment, and adding the expected carbon emission data of the nth equipment to the expected carbon emission data set.

[0033] Specifically, scenario application carbon emission mining is carried out based on the non-zero carbon equipment cluster. Scenario application carbon emission mining refers to analyzing the carbon emission patterns of these devices in their actual application scenarios to determine the expected carbon emission data set. Each device in the non-zero carbon equipment cluster has its own specific application scenario. For example, boilers are used for heating, forklifts are used for material handling, and air conditioners are used for temperature control. The device application scenario corresponding to the n-th non-zero carbon device in the non-zero carbon equipment cluster is taken as the n-th target application scenario. The n-th non-zero carbon device refers to a random device in this device cluster. Where n is a positive integer. The device application scenario corresponding to the device is defined as the n-th target application scenario, that is, the usage environment of the device when it is operating under specific conditions. The specific usage scenario of each device is clarified to facilitate the subsequent analysis of its carbon emissions.

[0034] After determining the nth target application scenario, it is necessary to conduct a normal carbon emission sample search for the carbon emissions of the equipment in this scenario to obtain the normal carbon emission sample set of the nth equipment. Normal carbon emission sample retrieval refers to finding carbon emission data that matches the normal operating status of the equipment in historical operating data. For example, the carbon emission data of a gas boiler in full load operation, half load operation and standby state may be different. When searching, it is necessary to filter out the carbon emission data in the target scenario to ensure the accuracy of the data. The normal carbon emission sample set of the nth equipment refers to the multiple carbon emission sample data collected in this process, such as the set of carbon emission records of the equipment under the same environment in the past month. These data can reflect the actual carbon emission level of the equipment and provide a basis for further calculations.

[0035] Based on the normal carbon emission sample set for the nth device, a centralized value calculation is performed. This statistical method, such as calculating the mean, median, or mode, is used to determine a value that represents the device's carbon emission characteristics. This is used to obtain the expected carbon emission data for the nth device. The expected carbon emission data for the nth device refers to the expected carbon emission value calculated based on the normal carbon emission sample set for the device's target application scenario. The expected carbon emission data for the nth device is added to the expected carbon emission data set to assess whether the device's carbon emissions exceed a reasonable range.

[0036] S4: Identifying emission reduction targets for the non-zero carbon device cluster based on the expected carbon emission dataset and the current carbon emission dataset, and determining emission reduction targets corresponding to the emission reduction target devices.

[0037] Further, S4 includes: S41: extracting the carbon emission data of the nth device corresponding to the nth non-zero carbon device based on the current carbon emission data set; S42: judging whether the carbon emission data of the nth device meets the expected carbon emission data of the nth device; S43: if the carbon emission data of the nth device does not meet the expected carbon emission data of the nth device, adding the nth non-zero carbon device to the emission reduction target device, and performing a difference analysis between the expected carbon emission data of the nth device and the carbon emission data of the nth device to generate the emission reduction target.

[0038] Specifically, based on the expected and current carbon emission datasets, emission reduction targets are identified for non-zero-carbon device clusters, and emission reduction targets corresponding to target devices are determined. Target devices are devices whose actual carbon emissions are higher than their expected emissions, and emission reduction targets are specific emission reduction requirements set for these devices, such as reducing energy consumption or optimizing emission control strategies.

[0039] Based on the current carbon emission dataset, extract the nth device carbon emission data corresponding to the nth non-zero carbon device. The nth non-zero carbon device is a random device in the non-zero carbon device cluster and is used to identify different devices. The nth device carbon emission data refers to the carbon emissions of the device in its current operating state. For example, the carbon emission data for a gas boiler may be 250 kilograms, while the carbon emission data for a diesel generator may be 350 kilograms. The purpose of extracting this data is to subsequently compare it with the expected carbon emission data to determine whether the device has achieved the desired carbon emission level.

[0040] Determine whether the carbon emissions data for the nth device meet the expected carbon emissions data for the nth device. After obtaining the carbon emissions data for the nth device, compare it with the corresponding expected carbon emissions data. Meeting the expected carbon emissions data means that the actual carbon emissions of the device are within a reasonable range and do not exceed the set target value. If not, it means that the carbon emissions of the device exceed the target and additional measures are needed to optimize it.

[0041] If the carbon emissions data for the nth device do not meet the expected carbon emissions data for the nth device, the nth non-zero carbon device will be added to the emission reduction target devices. If the actual carbon emissions of a device are higher than its expected carbon emissions data, the device needs to be classified as an emission reduction target device, that is, it needs to be included in the key emission reduction management scope. A difference analysis is performed on the expected carbon emissions data of the nth device and the carbon emissions data of the nth device. The difference between the actual carbon emissions data of the device and the expected carbon emissions data is analyzed, which is called difference analysis. This is used to assess the specific circumstances of the carbon emissions exceeding the standard and generate emission reduction targets.

[0042] S5: Perform a carbon emission sensitivity test according to the control indicator set of the emission reduction target equipment to determine a first emission reduction control factor and a second emission reduction control factor.

[0043] Further, S5 includes: S51: performing a carbon emission sensitivity test on the control indicator set according to a predetermined step sequence to obtain multiple carbon emission sensitivity test sequences; S52: performing centralized value calculations on the multiple carbon emission sensitivity test sequences respectively to obtain multiple indicator carbon emission sensitivity coefficients; S53: classifying the control indicator set according to the multiple indicator carbon emission sensitivity coefficients to obtain the first emission reduction control factor greater than or equal to the predetermined carbon emission sensitivity coefficient, and the second emission reduction control factor less than the predetermined carbon emission sensitivity coefficient.

[0044] Further, S51 includes: S511: reading the current control decision of the device and the current carbon emission data of the device corresponding to the emission reduction target device; S512: extracting the first control indicator based on the control indicator set; S513: taking the first control indicator as a floating variable, randomly perturbing the current control decision of the device according to the predetermined step sequence, and obtaining multiple disturbance control decisions; S514: predicting the carbon emissions of the emission reduction target device according to the multiple disturbance control decisions, and obtaining multiple carbon emission prediction data; S515: evaluating the carbon emission change intensity of the multiple carbon emission prediction data respectively according to the current carbon emission data of the device, and obtaining multiple carbon emission change intensities; S516: outputting the multiple carbon emission change intensities as a first carbon emission sensitive test sequence, and adding the first carbon emission sensitive test sequence to the multiple carbon emission sensitive test sequences.

[0045] Specifically, the current control decision and carbon emission data of the target device are read. The current control decision of the device refers to the operating parameters or control strategy of the device in its current state, such as the gas valve opening of a gas boiler, the speed of an electric motor, or the temperature set point of an air conditioner.

[0046] Emission reduction target equipment refers to equipment whose actual carbon emissions exceed target values and for which reduction measures are required. Current equipment carbon emissions data refers to the carbon emissions value of the equipment under current operating conditions. Once this data is obtained, equipment operations can be adjusted to optimize carbon emissions.

[0047] Extract the first control indicator from the control indicator set. A control indicator set refers to a collection of key parameters that affect equipment operation. For example, a gas boiler may include multiple control indicators such as gas flow rate, combustion temperature, and exhaust gas concentration. The first control indicator is the first parameter selected for optimization and control. For example, gas flow rate can be selected as the optimization target. The purpose of extracting this indicator is to subsequently adjust equipment operating parameters and analyze their impact on carbon emissions.

[0048] The first control indicator is taken as a floating variable. A floating variable refers to a variable that can be adjusted within a certain range. For example, the gas flow rate of a gas boiler can vary between zero and one hundred kilograms per hour. The current control decision of the device is randomly perturbed according to a predetermined step sequence to obtain multiple disturbance control decisions. A predetermined step sequence refers to a set of numerical values for adjusting the control variable at fixed intervals. For example, if the step size is set to ten kilograms per hour, the gas flow rate can vary between multiple values such as fifty kilograms per hour, sixty kilograms per hour, and seventy kilograms per hour. Random perturbation refers to adding certain random changes to these fixed step sizes to simulate different control strategies. For example, the gas flow rate can be adjusted to sixty-five kilograms per hour or seventy-five kilograms per hour. In this way, multiple disturbance control decisions, that is, different control parameter combinations, can be obtained for subsequent carbon emission predictions.

[0049] Based on multiple disturbance control decisions, carbon emissions are predicted for target equipment, generating multiple carbon emission prediction data sets. Carbon emission prediction uses mathematical models or data analysis methods to predict a device's carbon emissions under different control parameters. For example, if the gas flow rate increases to 65 kilograms per hour, carbon emissions are predicted to be 250 kilograms, while if it decreases to 55 kilograms per hour, carbon emissions are predicted to be 220 kilograms. Multiple carbon emission prediction data sets are a collection of carbon emission results under different disturbance conditions and are used to evaluate which control strategies are most effective in reducing emissions.

[0050] Based on the current carbon emission data of the equipment, the carbon emission change intensity is evaluated for multiple carbon emission prediction data respectively to obtain multiple carbon emission change intensities. The current carbon emission data of the equipment is the carbon emission value under the actual operating state, while the carbon emission prediction data is the expected carbon emission value calculated based on different disturbance control decisions. Carbon emission change intensity evaluation refers to the calculation of the degree of change in carbon emissions before and after the disturbance. For example, if the current carbon emission data of the equipment is 250 kilograms, and under different control decisions, the carbon emission prediction data are 240 kilograms, 230 kilograms, and 260 kilograms respectively, then the corresponding carbon emission change intensity can be expressed as a decrease of 10 kilograms, a decrease of 20 kilograms, and an increase of 10 kilograms. Multiple carbon emission change intensities refer to the set of carbon emission change degrees calculated under all disturbance schemes, which are used to analyze the impact of different control decisions on carbon emissions.

[0051] Output multiple carbon emission change intensities as the first carbon emission sensitive test sequence. The carbon emission sensitive test sequence refers to a series of sensitivity analysis results on the carbon emissions of the equipment to changes in control parameters. The first carbon emission sensitive test sequence is a set of test data currently calculated, which indicates how different adjustment schemes of a certain control variable affect carbon emissions, such as the increase or decrease in carbon emissions when the gas flow is adjusted. Add the first carbon emission sensitive test sequence to multiple carbon emission sensitive test sequences. Multiple carbon emission sensitive test sequences refer to multiple data sets obtained after similar analysis of different control variables. For example, in addition to gas flow, the effects of combustion temperature and air supply on carbon emissions can also be tested. By collecting all sensitive test data, a complete equipment carbon emission characteristic analysis system can be established to assist in formulating the optimal emission reduction strategy.

[0052] A centralized value calculation is performed on multiple carbon emission sensitivity test sequences to obtain carbon emission sensitivity coefficients for multiple indicators. Central value calculation refers to the statistical analysis of these data to obtain a numerical value that represents the overall trend of change, such as an average, median, or weighted mean. The carbon emission sensitivity coefficients for multiple indicators refer to the calculated impact of each control indicator on carbon emissions. For example, if a change in gas flow results in an average reduction of 30 kilograms in carbon emissions, while a change in combustion temperature results in a reduction of 20 kilograms in carbon emissions, the corresponding carbon emission sensitivity coefficients are 30 and 20, respectively. These coefficients are used to assess the importance of each control parameter in the emission reduction process.

[0053] The control indicator set is categorized based on the carbon sensitivity coefficients of multiple indicators. This yields a first emission reduction control factor greater than or equal to the predetermined carbon sensitivity coefficient, and a second emission reduction control factor less than the predetermined carbon sensitivity coefficient. A control indicator set refers to the collection of all parameters that affect equipment operation, such as a boiler's gas flow rate, combustion temperature, and air supply. These control indicators are categorized based on their carbon sensitivity coefficients, or the degree of their impact on carbon emissions. The predetermined carbon sensitivity coefficient is a set threshold, such as 25. If the carbon sensitivity coefficient of a control indicator is greater than or equal to 25, it indicates a significant impact on carbon emissions and requires focused control, known as the first emission reduction control factor. If it is less than 25, it indicates a minor impact and can be considered a secondary control parameter, known as the second emission reduction control factor. For example, if the carbon sensitivity coefficient of gas flow is 30 and that of combustion temperature is 20, then gas flow would be the first factor, and combustion temperature the second factor. This categorization helps determine the primary emission reduction control direction, thereby optimizing equipment operation strategies. Table 1 shows the latest carbon emission sensitivity test record of the emission reduction target equipment, which calculates the carbon emission change intensity through disturbance control decision and carbon emission prediction results based on the current control decision of the equipment corresponding to the emission reduction target equipment and the current carbon emission data of the equipment.

[0054] Table 1: The most recent carbon emission sensitivity test record of emission reduction target equipment

[0055] Device ID Device 1 Device 2 Device 3 Device 4 Device 5 Current control decision 37.454012 95.071431 73.199394 59.865848 15.601864 Current carbon emissions data 7.799726 2.904181 43.308807 30.055751 35.403629 Disturbance Control Decision 1 42.865702 38.536350 41.783364 39.618688 42.865702 Disturbance Control Decision 2 119.469628 99.951070 114.589988 104.830709 119.469628 Disturbance Control Decision 3 94.848247 77.529165 90.518476 81.858935 94.848247 Disturbance Control Decision 4 69.112631 61.715205 67.263274 63.564561 69.112631 Disturbance Control Decision 5 24.238363 17.329164 22.511064 19.056464 24.238363 Forecast carbon emission data 1 21.597251 18.318092 29.620728 47.444277 34.211651 Forecast carbon emission data 2 14.561457 22.803499 2.322521 48.281602 22.007625 Forecast carbon emission data 3 30.592645 39.258798 30.377243 40.419867 6.101912 Forecast carbon emission data 4 6.974693 9.983689 8.526206 15.230688 24.758846 Forecast carbon emission data 5 14.607232 25.711722 3.252580 4.883606 1.719426 Carbon emission change intensity1 13.797525 15.413912 13.688079 17.388526 1.191978 Carbon emission change intensity 2 6.761731 19.899319 40.986287 18.225851 13.396004 Carbon emission change intensity 3 22.792919 36.354617 12.931565 10.364117 29.301717 Carbon emission change intensity 4 0.825033 7.079508 34.782601 14.825062 10.644783 Carbon emission change intensity 5 6.807506 22.807541 40.056228 25.172145 33.684203

[0056] Among them, the numerical units of different columns in the table are: current control decision, disturbance control decision: no unit (indicating the numerical value of the control parameter); current carbon emission data, predicted carbon emission data: kilograms of carbon dioxide; carbon emission change intensity: kilograms of carbon dioxide.

[0057] S6: Based on the emission reduction target, the emission reduction target device is optimized and adjusted according to the first emission reduction control factor and the second emission reduction control factor to obtain an emission reduction control strategy.

[0058] Further, S6 includes: S61: taking the equipment application scenario corresponding to the emission reduction target equipment as the emission reduction control constraint scenario; S62: taking the first emission reduction control factor as a non-fixed variable, and the second emission reduction control factor as a fixed variable; S63: based on the emission reduction control constraint scenario, randomly adjusting the current control decision of the equipment according to the non-fixed variables and the fixed variables to obtain the emission reduction control space; S64: performing optimization analysis on the emission reduction control space according to the emission reduction target to generate the emission reduction control strategy.

[0059] Further, S64 includes: S641: extracting the first emission reduction regulation decision based on the emission reduction regulation space; S642: predicting carbon emissions for the emission reduction target device based on the first emission reduction regulation decision to obtain a first carbon emission prediction result; S643: performing a difference analysis between the expected carbon emission data of the device corresponding to the emission reduction target device and the first carbon emission prediction result to obtain a first emission reduction target; S644: if the first emission reduction target meets the emission reduction target, outputting the first emission reduction regulation decision as the emission reduction regulation strategy; S645: if the first emission reduction target does not meet the emission reduction target, eliminating the first emission reduction regulation decision, and continuing to iteratively optimize the emission reduction regulation space according to the emission reduction target until the emission reduction regulation strategy is generated.

[0060] Specifically, the equipment application scenario corresponding to the target equipment for emission reduction is the emission reduction control constraint scenario. The target equipment for emission reduction refers to the equipment whose carbon emissions exceed the expected value and for which emission reduction measures need to be taken. The equipment application scenario refers to the specific environment and purpose in which the equipment operates. For example, an industrial boiler may be used for heating, steam production, or auxiliary power supply. The emission reduction control constraint scenario means that when optimizing the emission reduction strategy, the actual use environment of the equipment needs to be considered to ensure that the adjustment parameters will not affect the normal function of the equipment. For example, if the boiler is used for heating, its minimum heating temperature cannot be lower than a certain set value, otherwise it will affect production or residents' lives. The purpose of using the equipment application scenario as the emission reduction control constraint scenario is to ensure that the emission reduction plan is feasible in the actual environment without affecting the normal operation of the equipment.

[0061] The first emission reduction control factor is a non-fixed variable, while the second emission reduction control factor is a fixed variable. The first emission reduction control factor refers to the control parameter with the greatest impact on carbon emissions, such as the gas flow rate of a gas-fired boiler. Its carbon sensitivity coefficient is high, and therefore it serves as the primary control parameter. A non-fixed variable is a parameter that can be adjusted within a certain range, such as gas flow rate, which can vary between 50 kilograms per hour and 100 kilograms per hour. The second emission reduction control factor is a control parameter with less impact on carbon emissions, such as the air supply to the boiler. Its carbon sensitivity coefficient is low, and therefore it serves as a secondary control parameter. Fixed variables are parameters that remain unchanged during the optimization process, such as the air supply being fixed at 800 cubic meters per hour, while the gas flow rate is optimized as an adjustable parameter. This ensures that the optimization process focuses on the most important variables, improving emission reduction efficiency.

[0062] Based on the emission reduction control constraint scenario, the current control decision of the device is randomly adjusted according to the non-fixed and fixed variables to obtain the emission reduction control space. The emission reduction control constraint scenario specifies the operating conditions that must be observed during the optimization process, such as the minimum heating temperature of the boiler must not fall below 60 degrees Celsius. The current control decision of the device refers to the control strategy of the device under the current operating state, such as setting the gas flow rate to 70 kilograms per hour and the air supply to 800 cubic meters per hour. Random adjustment refers to randomly adjusting the non-fixed variables within a certain range. For example, the gas flow rate can be increased or decreased to 75 kilograms per hour to observe the impact on carbon emissions. The emission reduction control space refers to all possible control parameter combinations. For example, the gas flow rate can be set to 60, 65, 70, or 75 kilograms per hour, while the air supply is fixed at 800 cubic meters per hour. These combinations constitute an optimization space for finding the optimal emission reduction solution.

[0063] Extract the first emission reduction control decision based on the emission reduction control space. The emission reduction control space refers to the optimal range formed by combining different control parameters. The first emission reduction control decision is a specific control solution selected within this space. The purpose of extracting this decision is to select a feasible control solution for subsequent carbon emission forecasting and to evaluate its emission reduction effect.

[0064] Based on the first emission reduction control decision, carbon emissions are predicted for the target equipment, yielding a first carbon emissions prediction result. Based on the selected control decision, carbon emissions prediction estimates the equipment's carbon emissions level under the new control parameters through calculation or simulation. For example, if the gas flow rate is set at 70 kilograms per hour, carbon emissions may be predicted to be 330 kilograms. The first carbon emissions prediction result is a preliminary estimate obtained during the prediction process and is used for subsequent analysis.

[0065] Perform a difference analysis between the expected carbon emissions data for the target device and the first carbon emissions forecast result to determine the first emissions reduction target. The expected carbon emissions data for the device refers to the carbon emissions level that the device should ideally achieve, for example, 300 kilograms. Difference analysis involves comparing the first carbon emissions forecast result with the expected carbon emissions data and calculating the difference between them. For example, if the first carbon emissions forecast result is 330 kilograms and the expected carbon emissions data is 300 kilograms, the difference is 30 kilograms. The first emissions reduction target is the result of this difference calculation and is used to assess whether the current regulatory decision meets the emission reduction requirements.

[0066] If the first emission reduction target is achieved, the first emission reduction control decision is output as the emission reduction control strategy. The emission reduction target refers to the carbon emission standard that the equipment must meet, for example, it must be reduced to less than 300 kilograms. If the first emission reduction target is achieved, indicating that the first carbon emission forecast result is less than or equal to the equipment's expected carbon emissions data, for example, the forecast result is 290 kilograms, which meets the target requirement, the current first emission reduction control decision is considered a valid solution and is used as the final emission reduction control strategy, directly used for equipment operation optimization.

[0067] If the first emission reduction target is not met, the first emission reduction control decision is eliminated, and iterative optimization continues within the emission reduction control space based on the emission reduction target until an emission reduction control strategy is generated. If the first emission reduction target indicates that the current first emission reduction control decision cannot meet the emission reduction target, for example, if the predicted carbon emissions are still 330 kilograms, 300 kilograms higher than the target, the current decision is eliminated. Iterative optimization involves continuing to adjust parameters within the emission reduction control space, such as reducing the gas flow rate to 65 kilograms per hour and re-evaluating carbon emissions, until an optimal solution that meets the emission reduction target is found. For example, if the gas flow rate is reduced to 65 kilograms per hour, the predicted carbon emissions will drop to 295 kilograms, meeting the target, and the control solution is ultimately selected as the emission reduction control strategy.

[0068] S7: Adaptively control carbon emissions of the industrial park according to the zero-carbon control strategy and the emission reduction control strategy.

[0069] Specifically, a zero-carbon control strategy optimizes the operating methods of equipment that has already met zero-carbon emission standards to ensure that its carbon emissions remain at zero. For example, a photovoltaic power station's power generation equipment relies on solar energy during the day and adjusts its energy storage device usage strategy at night to ensure continuous zero-carbon operation. An emission reduction control strategy optimizes control parameters for equipment whose carbon emissions do not meet expected standards, gradually reducing carbon emissions. For example, adjusting the gas flow rate of a gas-fired boiler to reduce carbon emissions to meet target requirements. An industrial park is an area consisting of multiple industrial, commercial, or public facilities, such as a manufacturing plant, logistics center, and office buildings. It contains a wide variety of equipment and has complex carbon emissions. Adaptive carbon emission control combines zero-carbon control strategies with emission reduction control strategies during a dynamic adjustment process to achieve optimal carbon emissions for the entire industrial park. For example, during peak hours, zero-carbon equipment is prioritized for power supply, and appropriate power limits are imposed on high-energy-consuming equipment. During low-carbon emission periods, the operation of high-energy-consuming equipment is optimized to minimize carbon emissions.

[0070] To sum up, the carbon emission control method for the construction of low-carbon industrial parks based on big data analysis provided in this application has the following technical effects: by achieving the technical goal of carbon emission management and dynamic optimization and control of all equipment in the park, precise control can be achieved based on the actual carbon emissions of the equipment, thereby improving the overall carbon emission efficiency of the park and ensuring that the carbon emissions of key equipment in different scenarios are minimized or zero.

[0071] Example 2: Based on the same inventive concept as the carbon emission control method for low-carbon industrial park construction based on big data analysis in the above embodiment, this application also provides a carbon emission control platform for low-carbon industrial park construction based on big data analysis. Please refer to the attached Figure 2, including: a zero-carbon adaptability analysis module 11, used to perform zero-carbon adaptability analysis on multiple devices in the industrial park to obtain a zero-carbon device cluster and a non-zero-carbon device cluster; a zero-carbon regulation analysis module 12, used to perform zero-carbon regulation analysis on the zero-carbon device cluster according to the current carbon emission data set of the industrial park to obtain a zero-carbon regulation strategy; a scenario application carbon emission mining module 13, used to perform scenario application carbon emission mining on the non-zero-carbon device cluster to determine an expected carbon emission data set; an emission reduction target identification module 14, used to perform scenario application carbon emission mining on the non-zero-carbon device cluster according to the expected carbon emission data set and the current carbon emission data set. Identify emission reduction targets and determine the emission reduction targets corresponding to the emission reduction target devices; a carbon emission sensitivity test module 15 is used to perform carbon emission sensitivity testing based on the control indicator set of the emission reduction target device to determine the first emission reduction control factor and the second emission reduction control factor; an optimal adjustment module 16 is used to perform optimal adjustment on the emission reduction target device based on the emission reduction target and the first emission reduction control factor and the second emission reduction control factor to obtain an emission reduction control strategy; an adaptive carbon emission control module 17 is used to perform adaptive carbon emission control on the industrial park based on the zero-carbon control strategy and the emission reduction control strategy.

[0072] Furthermore, the carbon emission control platform for the construction of a low-carbon industrial park based on big data analysis is also used to: collect current application scenario information of the multiple devices to obtain multiple device application scenarios; perform zero-carbon adaptability evaluation on the multiple device application scenarios to obtain multiple scenario zero-carbon adaptabilities; extract the zero-carbon adaptability of the first scenario based on the zero-carbon adaptability of the multiple scenarios, and determine whether the zero-carbon adaptability of the first scenario is greater than or equal to a predetermined zero-carbon adaptability; if the zero-carbon adaptability of the first scenario is greater than or equal to the predetermined zero-carbon adaptability, the device corresponding to the zero-carbon adaptability of the first scenario is recorded as a zero-carbon device, and the zero-carbon device is added to the zero-carbon device cluster; if the zero-carbon adaptability of the first scenario is less than the predetermined zero-carbon adaptability, the device corresponding to the zero-carbon adaptability of the first scenario is recorded as a non-zero-carbon device, and the non-zero-carbon device is added to the non-zero-carbon device cluster.

[0073] Furthermore, the carbon emission control platform for building a low-carbon industrial park based on big data analysis is also used to: perform carbon emission sensitivity testing on the control indicator set according to a predetermined step sequence to obtain multiple carbon emission sensitivity test sequences; perform centralized value calculations on the multiple carbon emission sensitivity test sequences respectively to obtain multiple indicator carbon emission sensitivity coefficients; classify the control indicator set according to the multiple indicator carbon emission sensitivity coefficients to obtain the first emission reduction control factor greater than or equal to the predetermined carbon emission sensitivity coefficient, and the second emission reduction control factor less than the predetermined carbon emission sensitivity coefficient.

[0074] Furthermore, the carbon emission control platform for low-carbon industrial park construction based on big data analysis is also used to: read the current control decision of the equipment and the current carbon emission data of the equipment corresponding to the emission reduction target equipment; extract the first control indicator according to the control indicator set; use the first control indicator as a floating variable, and randomly perturb the current control decision of the equipment according to the predetermined step sequence to obtain multiple disturbance control decisions; predict the carbon emissions of the emission reduction target equipment according to the multiple disturbance control decisions to obtain multiple carbon emission prediction data; evaluate the carbon emission change intensity of the multiple carbon emission prediction data according to the current carbon emission data of the equipment, and obtain multiple carbon emission change intensities; output the multiple carbon emission change intensities as a first carbon emission sensitive test sequence, and add the first carbon emission sensitive test sequence to the multiple carbon emission sensitive test sequences.

[0075] Furthermore, the carbon emission control platform for building a low-carbon industrial park based on big data analysis is also used to: use the equipment application scenario corresponding to the emission reduction target equipment as the emission reduction control constraint scenario; use the first emission reduction control factor as a non-fixed variable, and the second emission reduction control factor as a fixed variable; based on the emission reduction control constraint scenario, randomly adjust the current control decision of the equipment according to the non-fixed variables and the fixed variables to obtain the emission reduction control space; perform optimization analysis on the emission reduction control space according to the emission reduction target to generate the emission reduction control strategy.

[0076] Furthermore, the carbon emission control platform for building a low-carbon industrial park based on big data analysis is also used to: extract a first emission reduction control decision based on the emission reduction control space; predict carbon emissions for the emission reduction target equipment based on the first emission reduction control decision to obtain a first carbon emission prediction result; perform a difference analysis between the expected carbon emission data of the equipment corresponding to the emission reduction target equipment and the first carbon emission prediction result to obtain a first emission reduction target; if the first emission reduction target meets the emission reduction target, output the first emission reduction control decision as the emission reduction control strategy; if the first emission reduction target does not meet the emission reduction target, eliminate the first emission reduction control decision, and continue to iteratively optimize the emission reduction control space based on the emission reduction target until the emission reduction control strategy is generated.

[0077] Furthermore, the carbon emission control platform for building a low-carbon industrial park based on big data analysis is also used to: perform feature recognition on the current carbon emission data set according to the zero-carbon equipment cluster to obtain multiple zero-carbon equipment carbon emission data; determine whether the multiple zero-carbon equipment carbon emission data are zero; if any one of the multiple zero-carbon equipment carbon emission data is not zero, obtain a zero-carbon control device; perform zero-carbon control on the zero-carbon control device to generate the zero-carbon control strategy.

[0078] Furthermore, the carbon emission control platform for building a low-carbon industrial park based on big data analysis is also used to: take the equipment application scenario corresponding to the nth non-zero carbon equipment in the non-zero carbon equipment cluster as the nth target application scenario, where n is a positive integer; perform normal carbon emission sample retrieval on the nth non-zero carbon equipment according to the nth target application scenario to obtain a normal carbon emission sample set for the nth equipment; perform centralized value calculation based on the normal carbon emission sample set for the nth equipment to obtain expected carbon emission data for the nth equipment, and add the expected carbon emission data for the nth equipment to the expected carbon emission data set.

[0079] Furthermore, the carbon emission control platform for building a low-carbon industrial park based on big data analysis is also used to: extract the carbon emission data of the nth device corresponding to the nth non-zero carbon device based on the current carbon emission data set; determine whether the carbon emission data of the nth device meets the expected carbon emission data of the nth device; if the carbon emission data of the nth device does not meet the expected carbon emission data of the nth device, add the nth non-zero carbon device to the emission reduction target device, and perform a difference analysis between the expected carbon emission data of the nth device and the carbon emission data of the nth device to generate the emission reduction target.

[0080] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The carbon emission control method for low-carbon industrial park construction based on big data analysis and the specific examples in the aforementioned embodiment one are also applicable to the carbon emission control platform for low-carbon industrial park construction based on big data analysis in this embodiment. Through the aforementioned detailed description of the carbon emission control method for low-carbon industrial park construction based on big data analysis, those skilled in the art can clearly understand the carbon emission control platform for low-carbon industrial park construction based on big data analysis in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0081] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0082] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A carbon emission control method for low-carbon industrial park construction based on big data analysis, characterized in that: include: Conduct zero-carbon adaptability analysis on multiple equipment in the industrial park to obtain zero-carbon equipment clusters and non-zero-carbon equipment clusters; Performing zero-carbon regulation analysis on the zero-carbon equipment cluster based on the current carbon emission data set of the industrial park to obtain a zero-carbon regulation strategy; Perform scenario application carbon emission mining based on the non-zero carbon equipment cluster to determine an expected carbon emission data set; Identifying emission reduction targets for the non-zero carbon device cluster based on the expected carbon emission dataset and the current carbon emission dataset, and determining emission reduction targets corresponding to the emission reduction target devices; Performing a carbon emission sensitivity test based on the control indicator set of the emission reduction target equipment to determine a first emission reduction control factor and a second emission reduction control factor; Based on the emission reduction target, optimizing and adjusting the emission reduction target device according to the first emission reduction control factor and the second emission reduction control factor to obtain an emission reduction control strategy; The industrial park is adaptively regulated in carbon emissions according to the zero-carbon regulation strategy and the emission reduction regulation strategy.

2. The carbon emission control method for low-carbon industrial park construction based on big data analysis according to claim 1 is characterized in that: We conducted zero-carbon adaptability analysis on multiple equipment in the industrial park and obtained zero-carbon equipment clusters and non-zero-carbon equipment clusters, including: Collecting current application scenario information of the multiple devices to obtain multiple device application scenarios; Performing zero-carbon adaptability evaluation on the multiple device application scenarios to obtain zero-carbon adaptability of the multiple scenarios; Extracting a first scene zero-carbon fitness value based on the multiple scene zero-carbon fitness values, and determining whether the first scene zero-carbon fitness value is greater than or equal to a predetermined zero-carbon fitness value; If the zero-carbon fitness of the first scenario is greater than or equal to the predetermined zero-carbon fitness, the device corresponding to the zero-carbon fitness of the first scenario is recorded as a zero-carbon device, and the zero-carbon device is added to the zero-carbon device cluster; If the zero-carbon fitness of the first scenario is less than the predetermined zero-carbon fitness, the device corresponding to the zero-carbon fitness of the first scenario is recorded as a non-zero-carbon device, and the non-zero-carbon device is added to the non-zero-carbon device cluster.

3. The carbon emission control method for low-carbon industrial park construction based on big data analysis according to claim 1 is characterized in that: Performing a carbon emission sensitivity test based on the control indicator set of the emission reduction target equipment to determine a first emission reduction control factor and a second emission reduction control factor, including: Performing a carbon emission sensitivity test on the control indicator set according to a predetermined step length sequence to obtain multiple carbon emission sensitivity test sequences; performing centralized value calculations on the multiple carbon emission sensitivity test sequences respectively to obtain multiple indicator carbon emission sensitivity coefficients; The control indicator set is classified according to the carbon emission sensitivity coefficients of the multiple indicators to obtain the first emission reduction regulation factor greater than or equal to the predetermined carbon emission sensitivity coefficient and the second emission reduction regulation factor less than the predetermined carbon emission sensitivity coefficient.

4. The carbon emission control method for low-carbon industrial park construction based on big data analysis according to claim 3 is characterized in that: Performing a carbon emission sensitivity test on the control indicator set according to a predetermined step length sequence to obtain multiple carbon emission sensitivity test sequences, including: Read the current control decision and current carbon emission data of the device corresponding to the emission reduction target device; extracting a first control indicator according to the control indicator set; Taking the first control indicator as a floating variable, randomly perturbing the current control decision of the device according to the predetermined step sequence to obtain multiple disturbance control decisions; Performing carbon emission prediction on the emission reduction target equipment according to the multiple disturbance control decisions to obtain multiple carbon emission prediction data; performing carbon emission change intensity evaluation on the plurality of carbon emission prediction data according to the current carbon emission data of the device to obtain a plurality of carbon emission change intensities; The multiple carbon emission change intensities are output as a first carbon emission sensitive test sequence, and the first carbon emission sensitive test sequence is added to the multiple carbon emission sensitive test sequences.

5. The carbon emission control method for low-carbon industrial park construction based on big data analysis according to claim 1, characterized in that: Based on the emission reduction target, optimizing and adjusting the emission reduction target device according to the first emission reduction control factor and the second emission reduction control factor to obtain an emission reduction control strategy includes: The equipment application scenario corresponding to the emission reduction target equipment is used as the emission reduction control constraint scenario; The first emission reduction control factor is used as a non-fixed variable, and the second emission reduction control factor is used as a fixed variable; Based on the emission reduction control constraint scenario, randomly adjusting the current control decision of the device according to the non-fixed variables and the fixed variables to obtain an emission reduction control space; An optimization analysis is performed on the emission reduction control space according to the emission reduction target to generate the emission reduction control strategy.

6. The carbon emission control method for low-carbon industrial park construction based on big data analysis according to claim 5 is characterized in that: Performing an optimization analysis on the emission reduction control space according to the emission reduction target to generate the emission reduction control strategy includes: Extracting a first emission reduction regulation decision according to the emission reduction regulation space; Performing carbon emission prediction on the emission reduction target equipment according to the first emission reduction control decision to obtain a first carbon emission prediction result; Performing a difference analysis between the expected carbon emission data of the device corresponding to the emission reduction target device and the first carbon emission prediction result to obtain a first emission reduction target; If the first emission reduction achievement target satisfies the emission reduction target, outputting the first emission reduction control decision as the emission reduction control strategy; If the first emission reduction target does not meet the emission reduction target, the first emission reduction control decision is eliminated, and the emission reduction control space is continuously iterated and optimized according to the emission reduction target until the emission reduction control strategy is generated.

7. The carbon emission control method for low-carbon industrial park construction based on big data analysis according to claim 1, characterized in that: Performing zero-carbon regulation analysis on the zero-carbon equipment cluster based on the current carbon emission data set of the industrial park to obtain a zero-carbon regulation strategy, including: Performing feature recognition on the current carbon emission data set according to the zero-carbon equipment cluster to obtain a plurality of zero-carbon equipment carbon emission data; Determining whether the carbon emission data of the plurality of zero-carbon devices are zero; If any one of the carbon emission data of the plurality of zero-carbon devices is not zero, obtaining a zero-carbon control device; Perform zero-carbon regulation on the zero-carbon regulation device to generate the zero-carbon regulation strategy.

8. The carbon emission control method for low-carbon industrial park construction based on big data analysis according to claim 1, characterized in that: Scenario application carbon emission mining is performed based on the non-zero carbon equipment cluster to determine the expected carbon emission data set, including: The device application scenario corresponding to the nth non-zero carbon device in the non-zero carbon device cluster is taken as the nth target application scenario, where n is a positive integer; Performing a normal carbon emission sample search for the nth non-zero carbon device according to the nth target application scenario to obtain a normal carbon emission sample set for the nth device; A centralized value calculation is performed based on the normal carbon emission sample set of the nth device to obtain expected carbon emission data of the nth device, and the expected carbon emission data of the nth device is added to the expected carbon emission data set.

9. The carbon emission control method for low-carbon industrial park construction based on big data analysis according to claim 1, characterized in that: Identifying emission reduction targets for the non-zero carbon device cluster based on the expected carbon emission dataset and the current carbon emission dataset, and determining emission reduction targets corresponding to the emission reduction target devices, including: Extracting the nth device carbon emission data corresponding to the nth non-zero carbon device according to the current carbon emission data set; Determining whether the carbon emission data of the nth device meets the expected carbon emission data of the nth device; If the carbon emission data of the nth device does not meet the expected carbon emission data of the nth device, the nth non-zero carbon device is added to the emission reduction target device, and a difference analysis is performed between the expected carbon emission data of the nth device and the carbon emission data of the nth device to generate the emission reduction target.

10. A carbon emission control platform is built in the low-carbon industrial park based on big data analysis, which is characterized by: The steps for implementing the carbon emission control method for low-carbon industrial park construction based on big data analysis according to any one of claims 1 to 9 include: The zero-carbon adaptability analysis module is used to perform zero-carbon adaptability analysis on multiple equipment in the industrial park to obtain zero-carbon equipment clusters and non-zero-carbon equipment clusters; A zero-carbon regulation and analysis module, configured to perform zero-carbon regulation and analysis on the zero-carbon equipment cluster according to the current carbon emission data set of the industrial park to obtain a zero-carbon regulation strategy; A scenario application carbon emission mining module is used to perform scenario application carbon emission mining based on the non-zero carbon equipment cluster to determine an expected carbon emission data set; an emission reduction target identification module, configured to identify emission reduction targets for the non-zero carbon device cluster based on the expected carbon emission dataset and the current carbon emission dataset, and determine emission reduction targets corresponding to the emission reduction target devices; A carbon emission sensitivity test module, configured to perform a carbon emission sensitivity test based on the control indicator set of the emission reduction target equipment to determine a first emission reduction control factor and a second emission reduction control factor; An optimization adjustment module is used to optimize and adjust the emission reduction target device based on the emission reduction target and the first emission reduction adjustment factor and the second emission reduction adjustment factor to obtain an emission reduction adjustment strategy; An adaptive carbon emission control module is used to perform adaptive carbon emission control on the industrial park according to the zero-carbon control strategy and the emission reduction control strategy.

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