A carbon emission analysis method and system for smart parks

Abstract

The present invention provides a carbon emission analysis method and system for a smart park, which belongs to the field of data processing technology, and specifically includes: determining the abnormal energy consumption dates of a region in different date groups based on the deviation from a reference energy consumption amount, and determining that the park does not need to perform carbon emission optimization processing according to the distribution data of the abnormal energy consumption dates in different date groups and the electric energy self-sufficiency data of different date groups; determining the carbon emission optimization target in the energy consumption equipment based on the deviation of the energy consumption data of the energy consumption equipment between different dates in different date groups, obtaining the energy consumption data of different carbon emission optimization targets on different dates, and determining whether the park needs to perform carbon emission optimization processing in combination with the new energy self-sufficiency data on different dates, thereby realizing differentiated processing of the park's carbon emissions.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and in particular relates to a carbon emission analysis method and system for a smart park. Background Art

[0002] Industrial parks have great potential for reducing carbon emissions. Therefore, in order to analyze and process the carbon emission data of industrial parks and reduce the carbon emissions of industrial parks, the invention patent application CN202311588727.1 "A method and system for predicting carbon emissions in industrial parks" collects various energy consumption-related data such as energy consumed in the industrial production process of the park and energy consumption of transportation vehicles through a statistical processing unit, and establishes formulas to calculate carbon output, so as to simply and accurately calculate the total carbon output forecast value of the park. However, the above technical solutions all have the following technical problems: When analyzing and processing the carbon emissions of smart parks, existing technical solutions ignore the determination of energy-saving and carbon-reduction control targets based on changes in carbon emission data. In the actual operation process, due to equipment aging, control strategies and other reasons, the energy consumption of some energy-consuming equipment may gradually become abnormal. Therefore, how to identify and process energy-consuming equipment with abnormal energy consumption changes and then generate carbon emission control strategies has become a technical problem that needs to be solved urgently.

[0003] In response to the above technical problems, the present application specifically provides a carbon emission analysis method and system for a smart park. Summary of the invention

[0004] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: In a first aspect, the present application provides a carbon emission analysis method for a smart park, specifically comprising: S1 divides the dates into different date groups based on weather data of different dates, and when it is determined that the self-sufficiency of the park does not meet the requirements by using the self-sufficiency data of new energy power generation of different date groups, the reference energy consumption of the park in different date groups is determined by using the setting data of the energy consumption equipment in the park; S2 determines the abnormal energy consumption dates of the park in different date groups based on the deviation from the reference energy consumption, and when it is determined that the park does not need to perform carbon emission optimization processing according to the distribution data of the abnormal energy consumption dates in different date groups and the electric energy self-sufficiency data of different date groups, proceeds to the next step; S3 determines the carbon emission optimization target of the energy-consuming equipment based on the deviation of the energy consumption data of the energy-consuming equipment between different dates in different date groups; S4 obtains energy consumption data of different carbon emission optimization targets on different dates, and determines whether the park needs to perform carbon emission optimization processing in combination with new energy self-sufficiency data on different dates.

[0005] The beneficial effects of the present invention are: Based on the deviation of energy consumption data of energy-consuming equipment in different date groups and between different dates, the carbon emission optimization target of the energy-consuming equipment is determined, which reduces the impact of differences in weather data on energy consumption data, realizes the deviation of energy consumption data between different dates, and screens carbon emission optimization targets with worse energy consumption. It also lays the foundation for determining the carbon emission optimization strategy of the park that uses differentiated carbon emission optimization targets.

[0006] Whether the park needs to carry out carbon emission optimization is determined by using energy consumption data and new energy self-sufficiency data on different dates with different carbon emission optimization targets. This not only takes into account the impact of carbon emission optimization targets on the park's carbon emission intensity due to differences in energy consumption, but also takes into account the differentiated impact of differences in new energy self-sufficiency data on the park's carbon emission intensity, laying the foundation for further reducing the park's carbon emission intensity.

[0007] A further technical solution is to divide the dates into different date groups, specifically including: The dates whose deviations of weather data in different dimensions are within the preset weather data deviation range are grouped into the same date group.

[0008] A further technical solution is that the dimensional weather data includes humidity, temperature, wind speed, precipitation and cloud cover.

[0009] A further technical solution is that the new energy power generation self-sufficiency data is determined based on the deviation between the power generation data of the new energy equipment within the park and the energy consumption of the energy consumption equipment within the park.

[0010] A further technical solution is that the new energy equipment includes wind turbines and photovoltaics within the park.

[0011] A further technical solution is to determine that the self-sufficiency of the park does not meet the requirements, specifically including: Determine the self-sufficiency rate of the park on different dates in different date groups using the new energy power generation self-sufficiency data of different date groups; Determine the group self-sufficiency rate for different date groups by taking the average of the self-sufficiency rates for different dates in different date groups; The self-sufficiency rate deviation group is determined according to the group self-sufficiency rate of different date groups, and whether the self-sufficiency of the park meets the requirements is determined according to the sum of the date quantity proportions of the different self-sufficiency rate deviation groups.

[0012] A further technical solution is that when the sum of the percentages of the number of dates of different self-sufficiency rate deviation groups is greater than a set value of the percentage of the number of dates, it is determined that the self-sufficiency of the park does not meet the requirements.

[0013] A further technical solution is to determine whether the park needs to be treated for carbon emission optimization, including: Determine the energy consumption of the carbon emission optimization target on different dates based on the energy consumption data of different carbon emission optimization targets, and determine the total energy consumption on different dates based on the energy consumption of the carbon emission optimization target on different dates; Based on the total energy consumption of the carbon emission optimization target on different dates, determine the ratio of the total energy consumption on different dates to the new energy power generation within the park, and use the ratio to determine the energy consumption optimization demand date on the date; Whether the park needs to perform carbon emission optimization processing is determined based on the quantity ratio of the energy consumption optimization demand dates.

[0014] A further technical solution is that the energy consumption optimization required date is a date when the ratio is greater than a preset ratio.

[0015] A further technical solution is that when the proportion of the number of energy consumption optimization demand dates is greater than the proportion of the number of preset dates, it is determined that the park needs to perform carbon emission optimization processing.

[0016] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned carbon emission analysis method for a smart park when running the computer program.

[0017] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings; Figure 1 It is a flow chart of a carbon emission analysis method for a smart park; Figure 2 It is a flow chart to determine that the park's self-sufficiency does not meet the requirements; Figure 3 It is a flow chart to determine that the park does not need to carry out carbon emission optimization treatment; Figure 4 It is a flow chart of a method for determining a carbon emission optimization target in an energy-consuming device; Figure 5 It is a flow chart to determine whether the park needs to carry out carbon emission optimization treatment; Figure 6 It is a framework diagram of a computer system. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0021] In this application, the energy consumption of different energy-consuming equipment in the park and the self-sufficiency of new energy power generation within the park are utilized to determine the park that needs carbon emission optimization, thereby reducing the carbon emission intensity of the park.

[0022] The dates whose deviations of weather data in different dimensions are within the preset weather data deviation range are grouped into the same date group.

[0023] According to the average value of the self-sufficiency rate of new energy power generation in different date groups, it is determined whether the self-sufficiency of the park meets the requirements. When the average value of the self-sufficiency rate of new energy equipment in different date groups is greater than 0.9, it is determined that the self-sufficiency of the park meets the requirements.

[0024] The reference energy consumption is determined according to the sum of average values ​​of energy consumption of different energy consuming devices in the corresponding date group.

[0025] The energy consumption abnormal date is a date whose deviation from the reference energy consumption of the corresponding date group is not within a preset deviation range.

[0026] The weight coefficients of different date groups are determined by the average values ​​of their self-sufficiency rates. The group optimization coefficients of different date groups are determined by multiplying the proportion of abnormal energy consumption dates in different date groups by the weight coefficient. The comprehensive optimization coefficient is determined based on the sum of the group optimization coefficients of different date groups. When the comprehensive optimization coefficient is less than 0.3, it is determined that the park does not need to perform carbon emission optimization treatment.

[0027] The difference between the maximum energy consumption and the minimum energy consumption of different energy-consuming equipment in different date groups is used to determine the optimized date group of the energy-consuming equipment. When the sum of the proportions of the number of dates in different optimized date groups is greater than 0.4, the energy-consuming equipment is determined as a carbon emission optimization target.

[0028] According to the ratio of energy consumption data on different dates to the power generation of new energy power generation equipment based on the carbon emission optimization target, the energy consumption proportion on different dates is determined. When the average value of the energy consumption proportion on different dates is greater than 0.2, it is determined that the park needs to carry out carbon emission optimization.

[0029] Carbon emission optimization processing includes but is not limited to optimizing the setting temperature of air conditioners, reducing unnecessary energy-consuming equipment, and replacing aging energy-consuming equipment.

[0030] Embodiment 1 like Figure 1 As shown, the present application provides a first aspect, which provides a carbon emission analysis method for a smart park, specifically comprising: S1 divides the dates into different date groups based on weather data of different dates, and when it is determined that the self-sufficiency of the park does not meet the requirements by using the self-sufficiency data of new energy power generation of different date groups, the reference energy consumption of the park in different date groups is determined by using the setting data of the energy consumption equipment in the park; Furthermore, the dates are divided into different date groups, specifically including: The dates whose deviations of weather data in different dimensions are within the preset weather data deviation range are grouped into the same date group.

[0031] It should be noted that the dimensional weather data includes humidity, temperature, wind speed, precipitation and cloud cover.

[0032] It can be understood that the new energy power generation self-sufficiency data is determined based on the deviation between the power generation data of the new energy equipment within the park and the energy consumption of the energy consumption equipment within the park.

[0033] Specifically, the new energy equipment includes wind turbines and photovoltaics within the park.

[0034] Further, such as Figure 2 As shown, it is determined that the self-sufficiency of the park does not meet the requirements, including: Determine the self-sufficiency rate of the park on different dates in different date groups using the new energy power generation self-sufficiency data of different date groups; Determine the group self-sufficiency rate for different date groups by taking the average of the self-sufficiency rates for different dates in different date groups; The self-sufficiency rate deviation group is determined according to the group self-sufficiency rate of different date groups, and whether the self-sufficiency of the park meets the requirements is determined according to the sum of the date quantity proportions of the different self-sufficiency rate deviation groups.

[0035] It should be noted that when the sum of the percentages of the number of dates of different self-sufficiency rate deviation groups is greater than the set value of the percentage of the number of dates, it is determined that the self-sufficiency of the park does not meet the requirements.

[0036] Furthermore, when the self-sufficiency of the park meets the requirements, it is determined that the park does not need to perform carbon emission optimization processing.

[0037] In another possible embodiment, determining that the self-sufficiency of the park does not meet the requirements specifically includes: Determine the self-sufficiency rate of the park on different dates in different date groups using the new energy power generation self-sufficiency data of different date groups; Based on the self-sufficiency rates at different dates, determine the date of deviation of the self-sufficiency rate at the said date; Based on the percentage of the self-sufficiency rate deviation dates, determine whether the self-sufficiency of the park meets the requirements.

[0038] Furthermore, the self-sufficiency rate deviation date is a date when the self-sufficiency rate is less than a preset self-sufficiency rate.

[0039] It is understandable that when the number of the self-sufficiency rate deviation dates does not meet the requirements, it is determined that the self-sufficiency of the park does not meet the requirements.

[0040] Optionally, determining that the self-sufficiency of the park does not meet the requirements specifically includes: Determine the self-sufficiency rate of the park on different dates in different date groups using the new energy power generation self-sufficiency data of different date groups; When the self-sufficiency rate of the park on different dates is greater than the preset self-sufficiency rate, it is determined that the self-sufficiency of the park meets the requirements; When there is a date when the self-sufficiency rate is not greater than the preset self-sufficiency rate: the date when the self-sufficiency rate is not greater than the preset self-sufficiency rate is taken as the self-sufficiency rate deviation date, and when the number of the self-sufficiency rate deviation dates does not meet the requirement, it is determined that the self-sufficiency situation of the park does not meet the requirement; When the number of sufficiency rate deviation dates meets the requirement: Based on the self-sufficiency rates of different self-sufficiency rate deviation dates and in combination with the number ratio of the self-sufficiency rate deviation dates, a self-sufficiency rate deviation coefficient is determined. When the self-sufficiency rate deviation coefficient does not meet the requirements, it is determined that the self-sufficiency situation of the park does not meet the requirements. When the self-sufficiency rate deviation coefficient meets the requirements: When the self-sufficiency rate deviation coefficient is within a preset self-sufficiency rate deviation coefficient range, it is determined that the self-sufficiency of the park meets the requirements; When the self-sufficiency rate deviation coefficient is not within the preset self-sufficiency rate deviation coefficient range: Determine the group self-sufficiency rate of different date groups by taking the average of the self-sufficiency rates of different dates in different date groups, and when there is no date group whose group self-sufficiency rate does not meet the requirement, determine that the self-sufficiency of the park meets the requirement; When there are date groups whose group self-sufficiency rate does not meet the requirements: The date group whose group self-sufficiency rate does not meet the requirement is used as the self-sufficiency rate deviation group. When the total number of dates corresponding to the self-sufficiency rate deviation group does not meet the requirement, it is determined that the self-sufficiency of the park does not meet the requirement; When the total number of dates corresponding to the self-sufficiency rate deviation group meets the requirements: According to the sum of the proportions of the number of dates in different self-sufficiency rate deviation groups, it is determined whether the self-sufficiency of the park meets the requirements.

[0041] Furthermore, the energy-consuming equipment includes central air conditioning, lighting devices, monitoring systems, charging equipment, elevators, and lifting equipment.

[0042] Specifically, the method for determining the reference energy consumption in the date group is: Based on the setting data of the energy-consuming equipment in the park, determine the average value of energy consumption of the energy-consuming equipment in the park on different dates in the date group, and use it as a reference value of energy consumption of the energy-consuming equipment; The reference energy consumption in the date group is determined based on the sum of the energy consumption reference values ​​of different energy consuming devices.

[0043] It should be noted that the abnormal energy consumption date is a date when the deviation between the energy consumption and the reference energy consumption does not meet the requirements.

[0044] S2 determines the abnormal energy consumption dates of the park in different date groups based on the deviation from the reference energy consumption, and when it is determined that the park does not need to perform carbon emission optimization processing according to the distribution data of the abnormal energy consumption dates in different date groups and the electric energy self-sufficiency data of different date groups, proceeds to the next step; It is understandable that if Figure 3 As shown, it is determined that the park does not need to perform carbon emission optimization treatment, specifically including: Determine the number ratio of the energy consumption abnormal dates in the date group based on the distribution data of the energy consumption abnormal dates in the date group, and use it as the number ratio of abnormal dates; According to the electric energy self-sufficiency data of the date group, determining the average value of the electric energy self-sufficiency rate of different dates in the date group, and using it as the mean value of the self-sufficiency rate; The group optimization demand values ​​of different date groups are determined by multiplying the mean self-sufficiency rate of different date groups by the proportion of abnormal dates, and the average value of the group optimization demand values ​​of different date groups is used to determine whether the park needs to perform carbon emission optimization treatment.

[0045] Furthermore, when the average value of the group optimization demand values ​​of different date groups is greater than a preset group demand value threshold, it is determined that the park needs to perform carbon emission optimization processing.

[0046] In another possible embodiment, determining that the park does not need to perform carbon emission optimization processing specifically includes: Determine the number ratio of the energy consumption abnormal dates in the date group based on the distribution data of the energy consumption abnormal dates in the date group, and use it as the number ratio of abnormal dates; According to the electric energy self-sufficiency data of the date group, determining the average value of the electric energy self-sufficiency rate of different dates in the date group, and using it as the mean value of the self-sufficiency rate; The group optimization demand values ​​of different date groups are determined by multiplying the mean self-sufficiency rate of different date groups by the proportion of abnormal dates, and the group optimization demand values ​​of different date groups are used to determine the optimization demand date groups in the date groups, and the proportion of the number of dates in the optimization demand date groups is used to determine whether the park needs to perform carbon emission optimization processing.

[0047] Furthermore, the optimized demand date group is a date group whose group optimization demand value is greater than a preset demand value.

[0048] It should be noted that when the proportion of the number of dates in the optimization demand date group does not meet the requirements, it is determined that the park needs to perform carbon emission optimization processing.

[0049] Optionally, determining that the park does not need to perform carbon emission optimization treatment specifically includes: Determine the proportion of the number of abnormal energy consumption dates based on the distribution data of the abnormal energy consumption dates in the date group, and when the proportion of the number of abnormal energy consumption dates does not meet the requirement, determine that the park needs to perform carbon emission optimization processing; When the percentage of abnormal energy consumption dates meets the requirements: Determine the proportion of energy consumption abnormal dates in the date group, and use it as the proportion of abnormal date quantity. When the proportion of abnormal quantity in different date groups meets the requirements, it is determined that the park does not need to perform carbon emission optimization processing; When there are date groups whose abnormal quantity ratio does not meet the requirements: The date groups whose abnormal number ratios do not meet the requirements are regarded as abnormal date groups. When the total number of dates in different abnormal date groups does not meet the requirements, it is determined that the park needs to be processed for carbon emission optimization; When the total number of dates in different abnormal date groups meets the requirement: According to the electric energy self-sufficiency data of the date group, the average value of the electric energy self-sufficiency rate of different dates in the date group is determined, and it is used as the mean value of the self-sufficiency rate. The group optimization demand values ​​of different date groups are determined by multiplying the mean value of the self-sufficiency rate of different date groups by the proportion of the number of abnormal dates. When it is determined by using the group optimization demand values ​​of different date groups that there is no optimization demand date group in the date group, it is determined that the park does not need to perform carbon emission optimization processing; When there is an optimization requirement date group: Obtaining the total number of dates corresponding to the optimization demand date group, and when the total number of dates corresponding to the optimization demand group does not meet the requirement, determining that the park needs to perform carbon emission optimization processing; When the total number of dates corresponding to the optimization demand group meets the requirements: Whether the park needs to perform carbon emission optimization treatment is determined by taking the average value of the group optimization demand values ​​of different date groups.

[0050] S3 determines the carbon emission optimization target of the energy-consuming equipment based on the deviation of the energy consumption data of the energy-consuming equipment between different dates in different date groups; Specifically, Figure 4 As shown, the method for determining the carbon emission optimization target in the energy-consuming equipment is: Determine the deviation of energy consumption data between different dates in different date groups and the date with the smallest energy consumption data in the date groups based on the deviation of energy consumption data between different dates in different date groups of the energy consuming equipment; According to the deviation amount of the energy consumption data, the energy consumption deviation date in different date groups is performed; Based on the proportion of the number of energy consumption deviation dates in different date groups, it is determined whether the energy consuming equipment is a carbon emission optimization target.

[0051] Furthermore, the energy consumption deviation date is a date when the deviation amount of the energy consumption data does not meet the requirements.

[0052] Optionally, when there is a date group whose number ratio of energy consumption deviation dates does not meet the requirements, the energy-consuming equipment is determined to be a carbon emission optimization target.

[0053] S4 obtains energy consumption data of different carbon emission optimization targets on different dates, and determines whether the park needs to perform carbon emission optimization processing in combination with new energy self-sufficiency data on different dates.

[0054] Specifically, Figure 5 As shown, determining whether the park needs to be optimized for carbon emissions includes: Determine the energy consumption of the carbon emission optimization target on different dates based on the energy consumption data of different carbon emission optimization targets, and determine the total energy consumption on different dates based on the energy consumption of the carbon emission optimization target on different dates; Based on the total energy consumption of the carbon emission optimization target on different dates, determine the ratio of the total energy consumption on different dates to the new energy power generation within the park, and use the ratio to determine the energy consumption optimization demand date on the date; Whether the park needs to perform carbon emission optimization processing is determined based on the proportion of the number of energy consumption optimization demand dates.

[0055] Furthermore, the energy consumption optimization required date is a date when the ratio is greater than a preset ratio.

[0056] It can be understood that when the proportion of the number of energy consumption optimization demand dates is greater than the proportion of the number of preset dates, it is determined that the park needs to perform carbon emission optimization processing.

[0057] Optionally, determining whether the park needs to perform carbon emission optimization treatment specifically includes: S41 determines the energy consumption of the carbon emission optimization target on different dates based on the energy consumption data of different carbon emission optimization targets on different dates, and determines the electric energy self-sufficiency rate on different dates based on the new energy self-sufficiency data on different dates; S42 determines the preset energy consumption coefficients corresponding to different dates according to the energy consumption of the carbon emission optimization target on different dates, and determines the target optimization demand values ​​of different carbon emission optimization targets based on the average value of the product of the preset energy consumption coefficients corresponding to different dates and the electric energy self-sufficiency rate; S43 determines the optimization demand coefficient of the park based on the sum of the target optimization demand values ​​of different carbon emission optimization targets, and uses the optimization demand coefficient to determine whether the park needs to perform carbon emission optimization processing.

[0058] Furthermore, when the optimized demand coefficient is greater than a preset demand coefficient threshold, it is determined that the park needs to perform carbon emission optimization processing.

[0059] Optionally, the above step S41 includes the following contents: S411 determines the energy consumption of the carbon emission optimization target on different dates based on the energy consumption data of different carbon emission optimization targets on different dates. When the average value of the sum of the energy consumption of the carbon emission optimization target on different dates is greater than the preset energy consumption threshold, it is determined that the park needs to perform carbon emission optimization processing. When the average value of the sum of the energy consumption of the carbon emission optimization target on different dates is not greater than the preset energy consumption threshold, proceed to step S412. S412 takes the average value of the sum of the energy consumption of the carbon emission optimization target on different dates as the optimization target energy consumption, and determines the average value of the new energy power generation on different dates according to the new energy self-sufficiency data on different dates. When the ratio of the optimization target energy consumption to the average value of the new energy power generation is greater than a preset ratio threshold, it is determined that the park needs to perform carbon emission optimization processing. When the ratio of the optimization target energy consumption to the average value of the new energy power generation is not greater than the preset ratio threshold, proceed to step S413; S413 determines the high-proportion dates based on the ratio of the sum of energy consumption on different dates to the new energy power generation. When the quantitative proportion of the high-proportion dates does not meet the requirements, it is determined that the park needs to perform carbon emission optimization processing. When the quantitative proportion of the high-proportion dates meets the requirements, the electricity self-sufficiency rate of different dates is determined based on the new energy self-sufficiency data on different dates, and then proceeds to step S42.

[0060] Optionally, the above step S42 includes the following contents: S421 determines the preset energy consumption coefficients corresponding to different dates according to the energy consumption of the carbon emission optimization target on different dates, and determines the target optimization demand values ​​of different carbon emission optimization targets based on the average value of the product of the preset energy consumption coefficients corresponding to different dates and the electric energy self-sufficiency rate. When there is a carbon emission optimization target with a target optimization demand value greater than the preset target demand threshold, the process proceeds to step S423; when there is no carbon emission optimization target with a target optimization demand value greater than the preset target demand threshold, the process proceeds to step S422; S422: When the target number of the carbon emission optimization target is within the preset target number interval, it is determined that the park does not need to perform carbon emission optimization processing; when the target number of the carbon emission optimization target is not within the preset target number interval, the process proceeds to step S43; S423 When the number of carbon emission optimization targets whose target optimization demand value is greater than the preset target demand threshold is greater than the preset optimization target number, it is determined that the park needs to perform carbon emission optimization processing; when the number of carbon emission optimization targets whose target optimization demand value is greater than the preset target demand threshold is not greater than the preset optimization target number, proceed to step S43.

[0061] Embodiment 2 Second, as Figure 6As shown, the present invention provides a computer system, comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned carbon emission analysis method for a smart park when running the computer program.

[0062] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0063] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0064] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A carbon emission analysis method for a smart park, characterized in that: Specifically include: Divide the dates into different date groups based on weather data of different dates, and when determining that the self-sufficiency of the park does not meet the requirements by using the self-sufficiency data of new energy power generation of different date groups, determine the reference energy consumption of the park in different date groups by using the setting data of energy consumption equipment in the park; Based on the deviation from the reference energy consumption, determine the abnormal energy consumption dates of the park in different date groups, and when it is determined that the park does not need to perform carbon emission optimization processing according to the distribution data of the abnormal energy consumption dates in different date groups and the electric energy self-sufficiency data of different date groups, proceed to the next step; Determine the carbon emission optimization target of the energy-consuming equipment based on the deviation of the energy consumption data of the energy-consuming equipment between different dates in different date groups; The energy consumption data of different carbon emission optimization targets on different dates are obtained, and combined with the new energy self-sufficiency data on different dates, it is determined whether the park needs to perform carbon emission optimization treatment.

2. The carbon emission analysis method for a smart park according to claim 1, characterized in that: Divide the dates into different date groups, including: The dates whose deviations of weather data in different dimensions are within the preset weather data deviation range are grouped into the same date group.

3. The carbon emission analysis method for a smart park according to claim 2, characterized in that: The dimensional weather data includes humidity, temperature, wind speed, precipitation, and cloud cover.

4. The carbon emission analysis method for a smart park according to claim 1, characterized in that: The new energy power generation self-sufficiency data is determined according to the deviation between the power generation data of the new energy equipment within the park and the energy consumption of the energy consumption equipment within the park.

5. The carbon emission analysis method for a smart park according to claim 1, characterized in that: Determine that the self-sufficiency of the park does not meet the requirements, including: Determine the self-sufficiency rate of the park on different dates in different date groups using the self-sufficiency data of renewable energy power generation in different date groups; Determine the group self-sufficiency rate for different date groups by taking the average of the self-sufficiency rates for different dates in different date groups; The self-sufficiency rate deviation group is determined according to the group self-sufficiency rate of different date groups, and whether the self-sufficiency of the park meets the requirements is determined according to the sum of the date quantity proportions of the different self-sufficiency rate deviation groups.

6. The carbon emission analysis method for a smart park according to claim 5, characterized in that: When the sum of the percentages of the number of dates of different self-sufficiency rate deviation groups is greater than the set value of the percentage of the number of dates, it is determined that the self-sufficiency of the park does not meet the requirements.

7. The carbon emission analysis method for a smart park according to claim 1, characterized in that: When the self-sufficiency of the park meets the requirements, it is determined that the park does not need to perform carbon emission optimization processing.

8. The carbon emission analysis method for a smart park according to claim 1, characterized in that: Divide the dates into different date groups to determine whether the park needs to be treated for carbon emission optimization, including: Determine the energy consumption of the carbon emission optimization target on different dates based on the energy consumption data of different carbon emission optimization targets, and determine the total energy consumption on different dates based on the energy consumption of the carbon emission optimization target on different dates; Based on the total energy consumption of the carbon emission optimization target on different dates, determine the ratio of the total energy consumption on different dates to the new energy power generation within the park, and use the ratio to determine the energy consumption optimization demand date on the date; Whether the park needs to perform carbon emission optimization processing is determined based on the quantity ratio of the energy consumption optimization demand dates.

9. The carbon emission analysis method for a smart park according to claim 8, characterized in that: The dates are divided into different date groups, and the energy consumption optimization demand date is a date whose ratio is greater than a preset ratio.

10. A computer system comprising: A memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, characterized in that when the processor runs the computer program, a carbon emission analysis method for a smart park as described in any one of claims 1-9 is executed.

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