Treatment decision-making method and system for pollution reduction and carbon reduction collaborative management and control of park

Abstract

The invention provides a governance decision-making method and system for pollution reduction and carbon reduction collaborative management and control of a park. The method comprises the following steps: determining a charging target function value based on charging parameters of a lead-acid battery pack; determining a final charging rate based on the charging target function value, and performing first-stage charging on the lead-acid battery pack based on the final charging rate; whether the electric quantity of the lead-acid battery pack charged at the first stage is larger than a first preset value or not is judged, and if the electric quantity of the lead-acid battery pack charged at the first stage is larger than the first preset value, the lead-acid battery pack continues to be charged at the second stage; and judging whether the pulse current of the lead-acid battery pack after the second-stage charging is less than a second preset value, and if the pulse current of the lead-acid battery pack after the second-stage charging is less than the second preset value, continuously performing third-stage charging on the lead-acid battery pack until the charging process is completed, according to the invention, the safety of the battery is ensured, and the charging speed is effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of pollution reduction and carbon emission reduction, and particularly relates to a governance decision-making method and system for collaborative control of pollution reduction and carbon emission reduction in a park. Background Art

[0002] For existing production parks, it is very necessary to reduce carbon emissions in the park for environmental protection, and at the same time, it is also very necessary to reduce pollutant emissions in the production park. Therefore, for current parks, pollution reduction and carbon emission reduction in the park are usually carried out simultaneously to further reduce carbon emissions. In the prior art, the collaborative control of pollution reduction and carbon emission reduction is usually completed manually. However, for a park, its area is large and its industries and equipment are diverse, and there are significant differences in the effects brought by different decision-making schemes for pollution reduction and carbon emission reduction. Therefore, in the prior art, there are situations where the decision-making scheme is not accurate enough and the pollution reduction and carbon emission reduction are not obvious. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention provides a governance decision-making method and system for collaborative control of pollution reduction and carbon emission reduction in a park, which is used to solve the technical problems in the prior art.

[0004] On the one hand, the present invention provides the following technical solution. A governance decision-making method for collaborative control of pollution reduction and carbon emission reduction in a park includes: Decompose the target park into several park areas, calculate the regional influence degree of the park areas, and sort the several park areas based on the regional influence degree to obtain a first sorted area group; Obtain the pollution reduction and emission parameters corresponding to the park areas, calculate the pollution reduction comparison value of each park area based on the pollution reduction and emission parameters, and sort the several park areas based on the pollution reduction comparison value to obtain a second sorted area group; Determine a final sorted area group based on the first sorted area group and the second sorted area group; Calculate the index importance of the influencing factors of each sorted area in the final sorted area group, and sort each influencing factor within the sorted area based on the index importance to obtain a sorted index group; Determine a corresponding decision-making scheme set from a preset governance decision-making scheme library based on the final sorted area group and the sorted index group, calculate the governance degree value of each decision-making scheme in the decision-making scheme set, and determine the best decision-making scheme for each influencing factor in the sorted area based on the governance degree value.

[0005] Compared with the prior art, the beneficial effects of the present invention are as follows: First, the target park is decomposed into several park areas, the regional influence degree of the park areas is calculated, and several park areas are sorted based on the regional influence degree to obtain the first sorted area group; then the pollution reduction emission parameters corresponding to the park areas are obtained, and the pollution reduction comparison value of each park area is calculated based on the pollution reduction emission parameters, and several park areas are sorted based on the pollution reduction comparison value to obtain the second sorted area group; then the final sorted area group is determined based on the first sorted area group and the second sorted area group; then the index importance of the influencing factors of each sorted area in the final sorted area group is calculated, and each influencing factor within the sorted area is sorted based on the index importance to obtain the sorted index group; finally, the corresponding decision plan set is determined from the preset governance decision plan library based on the final sorted area group and the sorted index group, the governance degree value of each decision plan in the decision plan set is calculated, and the best decision plan is determined for each influencing factor in the sorted area based on the governance degree value. By determining the best decision plan for different influencing factors corresponding to different park areas, the present invention can improve the accuracy of decision-making and also enhance the effect of coordinated control of pollution reduction and carbon emission reduction.

[0006] Preferably, the step of calculating the regional influence degree of the park area and sorting several park areas based on the regional influence degree to obtain the first sorted area group includes: Calculating the first regional influence degree of the park area : ; In the formula, represents the number of influencing factors of carbon emission in the th park area, represents the average value of the carbon emissions corresponding to the influencing factors in the th park area, , respectively represent the carbon emissions corresponding to the th influencing factor and the th influencing factor in the th park area; Calculating the second regional influence degree of the park area : ; In the formula, represents the number of park areas, represents the number of influencing factors of carbon emission in the th park area, represents the average value of the carbon emissions corresponding to the influencing factors in the th park area, represents the The first The carbon emissions corresponding to each influencing factor are: It represents the average carbon emissions of all park areas; Calculate the third area influence of the park area : ; Based on the first area influence , Second Region Influence , Third Region Influence Calculate regional influence : ; The regional influence corresponding to each park area is determined, and the plurality of park areas are arranged in descending order according to the size of the regional influence to obtain a first sorted area group.

[0007] Preferably, the step of calculating the pollution reduction comparison value of each of the park areas based on the pollution reduction emission parameters, and sorting the plurality of park areas based on the pollution reduction comparison values ​​to obtain a second sorted area group comprises: Determine the pollutant emissions of each of the park areas, the pollutant emissions include at least SO 2 Emissions, No x Emissions, PM2.5 emissions, PM10 emissions; Calculate a first pollution reduction comparison value based on the pollutant emission amount Compared with the second pollution reduction value : ; ; In the formula, Indicates The park area before and after pollution reduction Emission difference, Indicates Park area Emissions, Indicates Carbon emissions in each park area, , , , Respectively represent SO in the park area 2 Emissions, No x Emissions, PM2.5 emissions, PM10 emissions, , , , respectively represent the SO emission difference, No 2 emission difference, PM2.5 emission difference, and PM10 emission difference of the x th park area; Based on the first pollution reduction comparison value and the second pollution reduction comparison value calculate the pollution reduction comparison value : ; Determine the pollution reduction comparison value corresponding to each park area, and arrange several park areas in descending order according to the magnitude of the pollution reduction comparison value to obtain the second sorted area group.

[0008] Preferably, the step of determining the final sorted area group based on the first sorted area group and the second sorted area group includes: Mark the park areas in the first sorted area group and the second sorted area; Assign weights to each park area in the first sorted area group in the order of the first sorted area group and in an arithmetic decreasing manner, so that each park area in the first sorted area group has a corresponding first weight; Assign weights to each park area in the second sorted area group in the order of the second sorted area group and in an arithmetic decreasing manner, so that each park area in the second sorted area group has a corresponding second weight; Calculate the sum of the first weight and the second weight corresponding to the park areas with the same area mark, and arrange several park areas in descending order according to the magnitude of the sum of the first weight and the second weight to obtain the final sorted area group.

[0009] Preferably, the step of calculating the index importance of the influencing factors of each sorted area in the final sorted area group and sorting each influencing factor within the sorted area based on the index importance to obtain the sorted index group includes: Calculate the object weight of the influencing factors of each sorted area in the final sorted area group : ; In the formula, represents the number of influencing factors in the sorted area, represents the number of objects corresponding to the influencing factors in the sorted area, represents the th object data and the th object data represents the th influencing factor corresponding to the The normalized value of the object data, represents the average value of all object data among the th influencing factors; Based on the index weight Calculate the first influence value and the second influence value : ; ; In the formula, , respectively represent the optimal and worst normalized data of the th object data; Based on the first influence value and the second influence value calculate the index importance : ; Determine the index importance of the influencing factors corresponding to each sorting area, and arrange each influencing factor in the sorting area in descending order according to the magnitude of the index importance to obtain a sorting index group.

[0010] Preferably, the step of determining the corresponding decision plan set from the preset governance decision plan library based on the final sorting area group and the sorting index group is specifically: Determine a decision plan group with decreasing decision strength according to the sorting relationship of the final sorting area group, and determine a decision plan set with decreasing decision strength for each influencing factor according to the sorting relationship in the sorting index group.

[0011] Preferably, the step of calculating the governance degree value of each decision plan in the decision plan set and determining the best decision plan for each influencing factor in the sorting area based on the governance degree value is specifically: Calculate the governance degree value of each decision plan in the decision plan set : ; In the formula, respectively represent the first Bayesian additive regression model and the second Bayesian additive regression model, represents the th decision plan in the governance plan set, represents the th propensity score of the decision plan, represents the noise; Select the decision plan with the largest governance degree value in the decision plan set as the best decision plan, and make a governance decision on the collaborative control of pollution reduction and carbon emission reduction in the park according to the best decision plan.

[0012] In a second aspect, the present invention provides the following technical solution: a governance decision-making system for collaborative management and control of pollution reduction and carbon emission reduction in a park, the system comprising: A decomposition module, configured to decompose a target park into a plurality of park areas, calculate the regional influence degree of the park areas, and sort the plurality of park areas based on the regional influence degree to obtain a first sorted area group; A first sorting module, configured to obtain pollution reduction emission parameters corresponding to the park areas, calculate a pollution reduction comparison value for each of the park areas based on the pollution reduction emission parameters, and sort the plurality of park areas based on the pollution reduction comparison value to obtain a second sorted area group; A second sorting module, configured to determine a final sorted area group based on the first sorted area group and the second sorted area group; A third sorting module, configured to calculate the index importance of influencing factors of each sorted area in the final sorted area group, and sort each influencing factor within the sorted area based on the index importance to obtain a sorted index group; A decision-making module, configured to determine a corresponding decision-making plan set from a preset governance decision-making plan library based on the final sorted area group and the sorted index group, calculate a governance degree value of each decision-making plan in the decision-making plan set, and determine an optimal decision-making plan for each influencing factor in the sorted area based on the governance degree value.

[0013] In a third aspect, the present invention provides the following technical solution: a computer, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the computer program, the governance decision-making method for collaborative management and control of pollution reduction and carbon emission reduction in a park as described above is implemented.

[0014] In a fourth aspect, the present invention provides the following technical solution: a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the governance decision-making method for collaborative management and control of pollution reduction and carbon emission reduction in a park as described above is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0016] Figure 1 It is a flowchart of the governance decision-making method for collaborative management and control of pollution reduction and carbon emission reduction in a park provided in Embodiment 1 of the present invention; Figure 2 It is a structural block diagram of a governance decision-making system for collaborative control of pollution reduction and carbon emission reduction in a park provided in the second embodiment of the present invention; Figure 3 It is a schematic diagram of the hardware structure of a computer provided in another embodiment of the present invention.

[0017] The embodiments of the present invention will be further described below in conjunction with the accompanying drawings. Specific embodiments

[0018] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the embodiments of the present invention and should not be construed as a limitation of the present invention.

[0019] In the description of the embodiments of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the embodiments of the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0020] In addition, the terms "first" and "second" are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present invention, "a plurality" means two or more unless otherwise specifically defined.

[0021] In the embodiments of the present invention, unless otherwise clearly defined and limited, the terms "installed", "connected", "connected", "fixed", etc. should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the internal communication of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific circumstances.

[0022] Embodiment 1 In the first embodiment of the present invention, as Figure 1 shown, a governance decision-making method for collaborative control of pollution reduction and carbon emission reduction in a park includes: S1. Decompose the target park into several park areas, calculate the regional influence degree of the park areas, and sort the several park areas based on the regional influence degree to obtain the first sorted area group; Specifically, for a large target park, various enterprises are usually settled in it, resulting in different types of industries in the park. Therefore, before the actual sorting process, the target park can be decomposed into several park areas according to the industrial type or the settled enterprises as the classification conditions, and then the first sorting is carried out according to the regional influence degree.

[0023] Among them, the step S1 includes: S11. Calculate the first regional influence degree of the park area : ; In the formula, represents the number of influencing factors of carbon emissions in the th park area, represents the average value of carbon emissions corresponding to the influencing factors in the th park area, , respectively represent the carbon emissions corresponding to the th influencing factor and the th influencing factor in the th park area.

[0024] S12. Calculate the second regional influence degree of the park area : ; In the formula, represents the number of park areas, represents the number of influencing factors of carbon emissions in the th park area, represents the average value of carbon emissions corresponding to the influencing factors in the th park area, represents the carbon emissions corresponding to the th influencing factor in the th park area, represents the average value of carbon emissions of all park areas.

[0025] S13. Calculate the third regional influence degree of the park area : .

[0026] S14. Based on the first regional influence degree , the second regional influence degree , the third regional influence degree Calculation of regional influence degree :[[]] .[[]]

[0027] S15. Determine the regional influence degree corresponding to each park area, and sort several park areas in descending order according to the magnitude of the regional influence degree to obtain the first sorted regional group; Specifically, during the first sorting process, as long as the first sorting is based on the degree of influence of each park area on carbon emissions and the influence relationship between park areas, and the sorting is in descending order, among the first sorted regional groups, the decision-making power of the park areas ranked higher should be stronger.

[0028] S2. Obtain the pollution reduction emission parameters corresponding to the park areas, calculate the pollution reduction comparison value for each park area based on the pollution reduction emission parameters, and sort several park areas based on the pollution reduction comparison value to obtain the second sorted regional group; Among them, the step S2 includes: S21. Determine the pollutant emissions of each park area, and the pollutant emissions at least include SO 2 emissions, No x emissions, PM2.5 emissions, PM10 emissions.

[0029] S22. Calculate the first pollution reduction comparison value and the second pollution reduction comparison value :[[]] ; ; In the formula, represents the emission difference before and after pollution reduction of the th park area, represents the emissions of the th park area, represents the carbon emissions of the , , , respectively represent the SO emissions, No 2 emissions, PM2.5 emissions, PM10 emissions of the x th park area, , , , respectively represent the SO of a park area 2 emission difference, No x emission difference, PM2.5 emission difference, PM10 emission difference.

[0030] S23. Based on the first pollution reduction comparison value and the second pollution reduction comparison value calculate the pollution reduction comparison value : .

[0031] S24. Determine the pollution reduction comparison value corresponding to each park area, and sort several park areas in descending order according to the size of the pollution reduction comparison value to obtain the second sorted area group; Specifically, the second sorting is mainly to sort the park areas according to the influence degree of pollution reduction on the area. In the second sorted area group, the decision-making power of the park areas ranked higher should be stronger.

[0032] S3. Determine the final sorted area group based on the first sorted area group and the second sorted area group; Specifically, in the first sorted area group and the second sorted area group, if the order and position of each park area in the two sorted area groups are the same, there is no need to rearrange, and one of them can be selected as the final sorted area group. However, in actual situations, the order of each park area in the first sorted area group and the second sorted area group may be different. Therefore, it is necessary to re-sort by weighted combining the orders in the two sorted area groups to obtain the final sorted area group.

[0033] Among them, the step S3 includes: S31. Mark the park areas in the first sorted area group and the second sorted area; Specifically, the purpose of adding area marks is to represent the position and name of each park area for the subsequent re-sorting process.

[0034] S32. Assign weights to each park area in the first sorted area group in an arithmetic decreasing manner according to the order in the first sorted area group, so that each park area in the first sorted area group has a corresponding first weight.

[0035] S33. Assign weights to each park area in the second sorted area group in an arithmetic decreasing manner according to the order in the second sorted area group, so that each park area in the second sorted area group has a corresponding second weight; Specifically, the weight assignment process in step S32 is the same as that in step S33. For example, assuming there are 4 park areas, according to the order relationship in the first sorted area group, 0.4 can be assigned as the first weight to the park area ranked first, 0.3 as the first weight to the park area ranked second, 0.2 as the first weight to the park area ranked third, and 0.1 as the first weight to the park area ranked fourth. At the same time, the process of assigning the second weight is also the same.

[0036] S34. Calculate the sum of the first weight and the second weight corresponding to the park areas with the same area label, and sort several park areas in descending order according to the sum of the first weight and the second weight to obtain the final sorted area group; Specifically, the first weight and the second weight can respectively reflect the order relationship of a park area in the two sorted area groups. Therefore, by calculating the sum of the first weight and the second weight of the same park area, and using the sum of the first weight and the second weight as a new sorting relationship factor to re-sort each park area, the final sorted area group can be obtained.

[0037] S4. Calculate the index importance of the influencing factors of each sorted area in the final sorted area group, and sort each influencing factor within the sorted area based on the index importance to obtain a sorted index group; Specifically, the sorted area here is the park area after re-sorting. After determining the final sorted area group, since there are many influencing factors for pollution reduction and carbon reduction within the sorted area, and the degree to which each influencing factor ultimately affects pollution reduction and carbon reduction is different, the influencing factors are sorted to achieve a better decision-making effect.

[0038] Among them, step S4 includes: S41. Calculate the object weight of the influencing factors of each sorted area in the final sorted area group : ; In the formula, represents the number of influencing factors in the sorted area, represents the number of objects corresponding to the influencing factors in the sorted area, represents the th object data and the th object data between the correlation coefficient, represents the th influencing factor corresponding to the th object data normalized value, represents the th influencing factor in all object data average value.

[0039] S42. Calculate the first influence value based on the index weight and the second influence value : : ; ; In the formula, , respectively represent the optimal and worst normalized data of the th object data

[0040] S43. Calculate the index importance based on the first influence value and the second influence value : : .

[0041] S44. Determine the index importance of the influencing factors corresponding to each sorting area, and sort each influencing factor in the sorting area in descending order according to the size of the index importance to obtain a sorted index group; Specifically, after sorting the influencing factors, different decision-making strengths can be determined according to the order relationship of the influencing factors

[0042] S5. Based on the final sorting area group and the sorted index group, determine the corresponding decision-making plan set from the preset governance decision plan library, calculate the governance degree value of each decision-making plan in the decision-making plan set, and determine the best decision-making plan for each influencing factor in the sorting area based on the governance degree value; Specifically, there are several different decision-making plan sets in the preset decision-making governance plan library, and the decision-making strength of each decision-making plan set and the degree of change in the object data corresponding to the influencing indicators are different. Therefore, the best decision-making plan needs to be determined for each influencing factor in each sorting area

[0043] Among them, the step S5 includes: Determine a decision-making plan group with decreasing decision-making strength according to the sorting relationship of the final sorting area group, and determine a decision-making plan set with decreasing decision-making strength for each influencing factor according to the sorting relationship in the sorted index group; Specifically, in the decision-making plan set, assuming that for a single influencing factor, there are many value ranges, and each range represents a decision-making strength. Therefore, the same is true for the sorting area

[0044] Among them, the step S5 includes: S51. Calculate the governance degree value of each decision-making plan in the decision-making plan set : ; In the formula, respectively represent the first Bayesian additive regression model and the second Bayesian additive regression model, represents the th decision-making scheme in the set of governance schemes, represents the th propensity score of the decision-making scheme, represents noise; Among them, the above-mentioned Bayesian additive regression model is specifically a BART model, a Bayesian non-parametric machine learning method, which stacks several decision trees. Each tree only explains a part of the unknown function, but when combined, it can capture the non-linear relationship and complex interactions between variables in the data, so as to determine the best decision-making scheme. The propensity score in the above steps is the propensity score estimated by the model incorporating covariates and treatment variables.

[0045] S52. Select the decision-making scheme with the largest governance degree value in the set of decision-making schemes as the best decision-making scheme, and make a governance decision on the collaborative control of pollution reduction and carbon emission reduction in the park according to the best decision-making scheme.

[0046] The governance decision-making method for the collaborative control of pollution reduction and carbon emission reduction in the park provided in Embodiment 1 of the present invention first decomposes the target park into several park areas, calculates the regional influence degree of the park areas, and sorts the several park areas based on the regional influence degree to obtain the first sorted area group; then obtains the pollution reduction emission parameters corresponding to the park areas, and calculates the pollution reduction comparison value of each park area based on the pollution reduction emission parameters, and sorts the several park areas based on the pollution reduction comparison value to obtain the second sorted area group; then determines the final sorted area group based on the first sorted area group and the second sorted area group; then calculates the index importance of the influencing factors of each sorted area in the final sorted area group, and sorts each influencing factor within the sorted area based on the index importance to obtain the sorted index group; finally, determines the corresponding decision-making scheme set from the preset governance decision-making scheme library based on the final sorted area group and the sorted index group, calculates the governance degree value of each decision-making scheme in the decision-making scheme set, and determines the best decision-making scheme for each influencing factor in the sorted area based on the governance degree value. The present invention determines the best decision-making scheme for different influencing factors corresponding to different park areas, thereby improving the accuracy of decision-making and also enhancing the effect of the collaborative control of pollution reduction and carbon emission reduction.

[0047] Embodiment 2 As Figure 2 shown, Embodiment 2 of the present invention provides a governance decision-making system for the collaborative control of pollution reduction and carbon emission reduction in a park. The system includes: The decomposition module 1 is used to decompose the target park into several park areas, calculate the regional influence degree of the park areas, and sort the several park areas based on the regional influence degree to obtain the first sorted area group; The first sorting module 2 is used to obtain the pollution reduction emission parameters corresponding to the park areas, calculate the pollution reduction comparison value of each park area based on the pollution reduction emission parameters, and sort the several park areas based on the pollution reduction comparison value to obtain the second sorted area group; The second sorting module 3 is used to determine the final sorted area group based on the first sorted area group and the second sorted area group; The third sorting module 4 is used to calculate the index importance of the influencing factors of each sorted area in the final sorted area group, and sort each influencing factor within the sorted area based on the index importance to obtain the sorted index group; The decision-making module 5 is used to determine the corresponding decision-making plan set from the preset governance decision-making plan library based on the final sorted area group and the sorted index group, calculate the governance degree value of each decision-making plan in the decision-making plan set, and determine the best decision-making plan for each influencing factor in the sorted area based on the governance degree value; Among them, the decomposition module 1 includes: The first influence degree calculation sub-module is used to calculate the first regional influence degree of the park area : ; In the formula, represents the number of influencing factors of carbon emissions in the th park area, represents the average value of carbon emissions corresponding to the influencing factors in the th park area, , respectively represent the carbon emissions corresponding to the rd influencing factor and the th influencing factor in the th park area; The second influence degree calculation sub-module is used to calculate the second regional influence degree of the park area : ; In the formula, represents the number of park areas, represents the number of influencing factors of carbon emissions in the th park area, represents the average value of carbon emissions corresponding to the influencing factors in the th park area, represents the the carbon emissions corresponding to the th influencing factor in a park area, representing the average value of the carbon emissions of all park areas; A third influence degree calculation sub-module, used to calculate the third area influence degree of the park area : ; A fourth influence degree calculation sub-module, used to calculate the area influence degree based on the first area influence degree , the second area influence degree , the third area influence degree : : ; A first sorting sub-module, used to determine the area influence degree corresponding to each park area, and descendingly sort a number of park areas according to the magnitude of the area influence degree to obtain a first sorted area group.

[0048] The first sorting module 2 includes: An emission determination sub-module, used to determine the pollutant emissions of each park area, and the pollutant emissions at least include SO 2 emissions, No x emissions, PM2.5 emissions, PM10 emissions; A comparison value sub-module, used to calculate a first pollution reduction comparison value and a second pollution reduction comparison value : ; ; In the formula, represents the emission difference before and after pollution reduction of the th park area, represents the emissions of the th park area, represents the carbon emissions of the , , , respectively represent the SO 2 emissions, No x emissions, PM2.5 emissions, PM10 emissions of the , , , respectively represent the SO emission difference, No 2 emission difference, PM2.5 emission difference, and PM10 emission difference of the x th park area; The pollution reduction sub-module is used to calculate the pollution reduction comparison value based on the first pollution reduction comparison value and the second pollution reduction comparison value : ; The second sorting sub-module is used to determine the pollution reduction comparison value corresponding to each park area, and arrange several park areas in descending order according to the magnitude of the pollution reduction comparison value to obtain the second sorted area group.

[0049] The second sorting module 3 includes: The marking sub-module is used to perform area marking on the park areas in the first sorted area group and the second sorted area; The first weight sub-module is used to assign weights to each park area in the first sorted area group in an arithmetic decreasing manner according to the order in the first sorted area group, so that each park area in the first sorted area group has a corresponding first weight; The second weight sub-module is used to assign weights to each park area in the second sorted area group in an arithmetic decreasing manner according to the order in the second sorted area group, so that each park area in the second sorted area group has a corresponding second weight; The third sorting sub-module is used to calculate the sum of the first weight and the second weight corresponding to the park areas with the same area mark, and arrange several park areas in descending order according to the magnitude of the sum of the first weight and the second weight to obtain the final sorted area group.

[0050] The third sorting module 4 includes: The object weight sub-module is used to calculate the object weight of the influencing factor of each sorted area in the final sorted area group : ; In the formula, represents the number of influencing factors in the sorted area, represents the number of objects corresponding to the influencing factors in the sorted area, represents the th object data and the th object data, represents the th influencing factor corresponding to the th object data normalization value,​ represents the average value of all object data among the th influencing factors; The influence value sub-module is used to calculate the first influence value based on the index weight and the second influence value : ; ; ; In the formula, , respectively represent the optimal and worst normalized data of the th object data; The importance sub-module is used to calculate the index importance based on the first influence value and the second influence value : ; ; The fourth sorting sub-module is used to determine the index importance of the influencing factors corresponding to each sorting area, and sort each influencing factor in the sorting area in descending order according to the size of the index importance to obtain a sorted index group.

[0051] The decision-making module 5 is specifically used for: Determining a decision-making plan group with decreasing decision-making strength according to the sorting relationship of the final sorting area group, and determining a decision-making plan set with decreasing decision-making strength for each influencing factor according to the sorting relationship in the sorted index group.

[0052] The decision-making module 5 includes: The degree value calculation sub-module is used to calculate the governance degree value of each decision-making plan in the decision-making plan set : ; In the formula, respectively represent the first Bayesian additive regression model and the second Bayesian additive regression model, represents the th decision-making plan in the governance plan set, represents the th propensity score of the decision-making plan, represents the noise; The decision-making sub-module is used to select the decision-making plan with the largest governance degree value in the decision-making plan set as the best decision-making plan, and make a governance decision on the collaborative control of pollution reduction and carbon emission reduction in the park according to the best decision-making plan.

[0053] In some other embodiments of the present invention, the embodiments of the present invention provide the following technical solution. A computer includes a memory 102, a processor 101, and a computer program stored on the memory 102 and executable on the processor 101. When the processor 101 executes the computer program, the governance decision-making method for collaborative management and control of pollution reduction and carbon emission reduction in the park as described above is implemented.

[0054] Specifically, the above-mentioned processor 101 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0055] Among them, the memory 102 may include a mass storage for data or instructions. By way of example and not limitation, the memory 102 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disc, a magneto-optical disc, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In a suitable case, the memory 102 may include removable or non-removable (or fixed) media. In a suitable case, the memory 102 may be internal or external to the data processing device. In a particular embodiment, the memory 102 is a non-volatile memory. In a particular embodiment, the memory 102 includes a read-only memory (ROM) and a random access memory (RAM). In a suitable case, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM), or a flash memory, or a combination of two or more of these. In a suitable case, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM may be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0056] The memory 102 can be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 101.

[0057] The processor 101 reads and executes the computer program instructions stored in the memory 102 to implement the above-mentioned governance decision-making method for collaborative control of pollution reduction and carbon emission reduction in the park.

[0058] In some of the embodiments, the computer may further include a communication interface 103 and a bus 100. Among them, as Figure 3 shown, the processor 101, the memory 102, and the communication interface 103 are connected through the bus 100 and complete communication with each other.

[0059] The communication interface 103 is used to implement communication between the various modules, devices, units, and / or devices in the embodiments of the present invention. The communication interface 103 can also implement data communication with other components such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations.

[0060] Bus 100 includes hardware, software, or both, and couples components of a computer device to each other. Bus 100 includes, but is not limited to, at least one of the following: Data Bus, Address Bus, Control Bus, Expansion Bus, Local Bus. By way of example and not limitation, Bus 100 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable bus or a combination of two or more of these. In suitable cases, Bus 100 may include one or more buses. Although embodiments of the present invention describe and illustrate specific buses, the present invention contemplates any suitable bus or interconnect.

[0061] The computer may execute the governance decision-making method for collaborative control of pollution reduction and carbon emission reduction in the park according to the obtained governance decision-making system for collaborative control of pollution reduction and carbon emission reduction in the park, so as to implement the governance decision-making for collaborative control of pollution reduction and carbon emission reduction in the park.

[0062] In some further embodiments of the present invention, in combination with the above-mentioned governance decision-making method for collaborative control of pollution reduction and carbon emission reduction in the park, embodiments of the present invention provide the following technical solution: a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned governance decision-making method for collaborative control of pollution reduction and carbon emission reduction in the park is implemented.

[0063] Those skilled in the art will understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or used in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0064] More specific examples (a non-exhaustive list) of readable media include the following: an electrical connection portion (electronic device) having one or more wirings, a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, a computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other appropriate processing as necessary, and then stored in a computer memory.

[0065] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0066] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as within the scope described in this specification.

[0067] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the appended claims.

Claims

1. A governance decision-making method for coordinated management and control of pollution reduction and carbon reduction in a park, characterized in that: include: Decomposing the target park into a plurality of park areas, calculating the regional influences of the park areas, and sorting the plurality of park areas based on the regional influences to obtain a first sorted area group; Acquire the pollution reduction emission parameters corresponding to the park area, calculate the pollution reduction comparison value of each park area based on the pollution reduction emission parameters, and sort the park areas based on the pollution reduction comparison values ​​to obtain a second sorted area group; Determine a final sorting region group based on the first sorting region group and the second sorting region group; Calculating the index importance of the influencing factors of each sorting area in the final sorting area group, and sorting each influencing factor in the sorting area based on the index importance to obtain a sorting index group; Based on the final sorting area group and the sorting index group, a corresponding decision solution set is determined from a preset governance decision solution library, a governance degree value of each decision solution in the decision solution set is calculated, and based on the governance degree value, an optimal decision solution is determined for each influencing factor in the sorting area.

2. The governance decision-making method for coordinated management and control of pollution reduction and carbon reduction in a park according to claim 1 is characterized in that: The step of calculating the regional influence of the park area and sorting the plurality of park areas based on the regional influence to obtain a first sorted area group includes: Calculate the first regional influence of the park area : ； In the formula, Indicates The number of factors affecting carbon emissions in each park area, Indicates The average carbon emissions corresponding to the influencing factors in each park area, , Respectively represent The first The influencing factors Carbon emissions corresponding to each influencing factor; Calculate the second area influence of the park area : ； In the formula, Indicates the number of park areas, Indicates The number of factors affecting carbon emissions in each park area, Indicates The average carbon emissions corresponding to the influencing factors in each park area, Indicates The first The carbon emissions corresponding to each influencing factor are: It represents the average carbon emissions of all park areas; Calculate the third area influence of the park area : ； Based on the first area influence , Second Region Influence , the third regional influence Calculate regional influence : ； The regional influence corresponding to each park area is determined, and the plurality of park areas are arranged in descending order according to the size of the regional influence to obtain a first sorted area group.

3. The governance decision-making method for coordinated management and control of pollution reduction and carbon reduction in a park according to claim 1 is characterized in that: The step of calculating the pollution reduction comparison value of each of the park areas based on the pollution reduction emission parameters, and sorting the plurality of park areas based on the pollution reduction comparison values ​​to obtain a second sorted area group comprises: Determine the pollutant emissions of each park area, the pollutant emissions at least include SO2 emissions, No x Emissions, PM2.5 emissions, PM10 emissions; Calculate a first pollution reduction comparison value based on the pollutant emission amount Compared with the second pollution reduction value : ； ； In the formula, Indicates The park area before and after pollution reduction Emission difference, Indicates Park area Emissions, Indicates Carbon emissions in each park area, , , , Respectively represent SO2 emissions in each park area, No x Emissions, PM2.5 emissions, PM10 emissions, , , , Respectively represent The difference in SO2 emissions in the park area, No. x Emission difference, PM2.5 emission difference, PM10 emission difference; Based on the first pollution reduction comparison value Compared with the second pollution reduction value Calculate the pollution reduction comparison value : ； The pollution reduction comparison value corresponding to each park area is determined, and the plurality of park areas are arranged in descending order according to the pollution reduction comparison values ​​to obtain a second sorting area group.

4. The governance decision-making method for coordinated management and control of pollution reduction and carbon reduction in a park according to claim 1 is characterized in that: The step of determining a final sorting region group based on the first sorting region group and the second sorting region group comprises: Marking the park areas in the first sorting area group and the second sorting area; Assigning a weight to each park area in the first sorting area group according to the order in the first sorting area group and in an arithmetic decreasing manner, so that each of the first sorting area groups has a corresponding first weight; Assigning a weight to each park area in the second sorting area group according to the order in the second sorting area group and in an arithmetic decreasing manner, so that each of the second sorting area groups has a corresponding second weight; The sum of the first weight and the second weight corresponding to the park areas with the same area mark is calculated, and the plurality of park areas are arranged in descending order according to the sum of the first weight and the second weight to obtain a final sorted area group.

5. The governance decision-making method for coordinated management and control of pollution reduction and carbon reduction in a park according to claim 1 is characterized in that: The step of calculating the index importance of the influencing factors of each sorting area in the final sorting area group, and sorting each influencing factor in the sorting area based on the index importance to obtain a sorting index group includes: Calculate the object weight of the influencing factor of each sorting area in the final sorting area group : ； In the formula, Indicates the number of influencing factors in the sorting area, Indicates the number of objects corresponding to the influencing factors in the sorting area, Indicates The object data and The correlation coefficient between the object data, Indicates The influencing factors correspond to The normalized value of the object data, Indicates The average value of all object data in the influencing factors; Based on indicator weight Calculate the first impact value The second impact value : ； ； In the formula, , Respectively represent The best and worst normalized data of each object data; Based on the first impact value The second impact value Calculate the importance of indicators : ； Determine the index importance of the influencing factors corresponding to each sorting area, and arrange each influencing factor in the sorting area in descending order according to the size of the index importance to obtain a sorting index group.

6. The governance decision-making method for coordinated management and control of pollution reduction and carbon reduction in a park according to claim 1 is characterized in that: The step of determining a corresponding decision solution set from a preset governance decision solution library based on the final sorting region group and the sorting index group is specifically as follows: A group of decision schemes with decreasing decision strengths is determined according to the ranking relationship of the final ranking area group, and a set of decision schemes with decreasing decision strengths is determined for each influencing factor according to the ranking relationship in the ranking indicator group.

7. The governance decision-making method for coordinated management and control of pollution reduction and carbon reduction in a park according to claim 1 is characterized in that: The step of calculating the governance degree value of each decision solution in the decision solution set and determining the best decision solution for each influencing factor in the sorting area based on the governance degree value is specifically as follows: Calculate the governance degree value of each decision solution in the decision solution set : ； In the formula, They represent the first Bayesian additive regression model and the second Bayesian additive regression model respectively. Indicates the governance solution concentration decision making options, Indicates The propensity score of a decision option, Indicates noise; The decision plan with the largest governance degree value is selected from the decision plan set as the best decision plan, and the governance decision for the coordinated management of pollution reduction and carbon reduction in the park is made based on the best decision plan.

8. A governance decision-making system for coordinated management and control of pollution reduction and carbon reduction in a park, characterized in that: The system comprises: A decomposition module, used for decomposing the target park into a plurality of park areas, calculating the regional influence of the park areas, and sorting the plurality of park areas based on the regional influence to obtain a first sorted area group; A first sorting module is used to obtain the pollution reduction emission parameters corresponding to the park area, calculate the pollution reduction comparison value of each park area based on the pollution reduction emission parameters, and sort the plurality of park areas based on the pollution reduction comparison value to obtain a second sorting area group; a second sorting module, configured to determine a final sorting region group based on the first sorting region group and the second sorting region group; A third sorting module is used to calculate the index importance of the influencing factors of each sorting area in the final sorting area group, and sort each influencing factor in the sorting area based on the index importance to obtain a sorting index group; A decision module is used to determine a corresponding decision solution set from a preset governance decision solution library based on the final sorting area group and the sorting indicator group, calculate the governance degree value of each decision solution in the decision solution set, and determine the best decision solution for each influencing factor in the sorting area based on the governance degree value.

9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the governance decision-making method for coordinated management and control of pollution reduction and carbon reduction in a park as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the governance decision-making method for coordinated management of pollution reduction and carbon reduction in a park as described in any one of claims 1 to 7.

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