A multi-water source joint dispatching method, system, device and medium

Abstract

The application relates to the technical field of water resource management, and particularly relates to a multi-water-source joint scheduling method, system, device and medium. The method comprises the following steps: acquiring historical water consumption data of a region to be scheduled, and constructing a plurality of subsystems based on the historical water consumption data; calling a fuzzy evaluation standard matrix of each subsystem; constructing an index factor membership degree matrix and a factor level membership degree matrix for each subsystem based on a subsystem matrix and the fuzzy evaluation standard matrix of the plurality of subsystems; and determining a water resource scheduling suggestion for the region to be scheduled based on the index factor membership degree matrix and the factor level membership degree matrix of the plurality of subsystems. The application realizes accurate scheduling of water resources, and is suitable for a complex water resource management scene of multi-index and multi-region cooperation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water resource management, and in particular to a multi-water source joint scheduling method, system, device and medium. BACKGROUND

[0002] With the acceleration of urbanization and the intensification of climate change, the contradiction between water supply and demand is increasingly prominent, and multi-water source joint scheduling has become a key means to improve water resource utilization efficiency and ensure regional water supply safety. Related technologies mostly rely on single water source scheduling models or simple combination methods, and make scheduling decisions through linear correlation or empirical formula based on historical water consumption data, without fully considering the complex coupling relationship between different water sources, regions and indicators, resulting in scheduling recommendations that are difficult to adapt to the dynamic changes of multi-dimensional and multi-level scenarios. SUMMARY

[0003] In order to solve the problem that the existing multi-water joint scheduling method is difficult to adapt to different scenarios, the present application provides a multi-water source joint scheduling method, system, device and medium.

[0004] In the first aspect, the present application provides a multi-water source joint scheduling method, which adopts the following technical scheme:

[0005] A multi-water source joint scheduling method, comprising:

[0006] Obtaining historical water consumption data of a region to be scheduled, and constructing a plurality of subsystems based on the historical water consumption data; wherein each subsystem is represented as a subsystem matrix, the rows of the subsystem matrix represent indicators, the columns represent regional factors, and the elements represent indicator values;

[0007] Retrieving a fuzzy evaluation standard matrix of each subsystem; wherein the rows of the fuzzy evaluation standard matrix represent subsystem indicators, and the columns represent evaluation standards of different levels;

[0008] Based on the subsystem matrix and the fuzzy evaluation standard matrix of the plurality of subsystems, constructing an indicator factor membership degree matrix and a factor level membership degree matrix for each subsystem; wherein the indicator factor membership degree matrix of any subsystem includes the membership degree of each element in the corresponding subsystem matrix for each level, and the factor level membership degree matrix of any subsystem includes the membership degree of each regional factor in the corresponding subsystem matrix for each level;

[0009] Based on the indicator factor membership degree matrix and the factor level membership degree matrix of the plurality of subsystems, determining a water resource scheduling recommendation for the region to be scheduled.

[0010] By adopting the technical solution, the subsystem matrix with indexes and regional factors as dimensions is constructed based on historical water consumption data, the complex water consumption system is modularized, and the resolvability of the data structure is improved; the fuzzy evaluation standard matrix is introduced to quantify the index level threshold, and subjective experience is converted into calculable standardized rules; the multi-dimensional fuzzy evaluation of the water consumption state is realized by calculating the membership degree matrix of the index factor and the regional factor, and the data uncertainty problem is effectively handled; the scheduling suggestion is dynamically generated in combination with the membership degree analysis result and the resource constraint, and the resource allocation is optimized under the condition of meeting the system limitation; through the fusion of matrix modeling and fuzzy mathematics, the accuracy and flexibility of the scheduling scheme are considered, and the method is especially suitable for complex water resource management scenes with multiple indexes and multiple regional coordination.

[0011] In a preferred example, the application can be further configured to: based on the subsystem matrix and the fuzzy evaluation standard matrix of the multiple subsystems, construct an index factor membership degree matrix and a factor level membership degree matrix for each subsystem, including:

[0012] Based on the subsystem matrix and the fuzzy evaluation standard matrix of the target subsystem, an index factor membership degree matrix of the target subsystem is constructed; wherein the target subsystem is any one of the multiple subsystems;

[0013] Based on the fuzzy evaluation standard matrix of the target subsystem, a standard level membership degree matrix of the target subsystem is constructed; wherein the standard level membership degree matrix includes the membership degree of each index in the subsystem matrix of the target subsystem for each level;

[0014] Based on the index factor membership degree matrix and the standard level membership degree matrix, a factor level membership degree matrix of the target subsystem is constructed.

[0015] By adopting the above technical solution, the index factor membership degree matrix is generated by comparing the subsystem matrix elements and the fuzzy evaluation standard, the belonging degree of each index to each level is quantified, the problem that single-point data is difficult to reflect the overall state is solved; the global mapping of indexes and evaluation standards is established by integrating the level membership relationship of each index through the standard level membership degree matrix; the factor level membership degree matrix is generated by fusing the two types of matrices, and the comprehensive rating of the regional factor is realized. Through hierarchical fuzzy calculation, discrete data is converted into continuous probabilistic evaluation, which not only retains the detailed features of the original data, but also eliminates the one-sidedness of a single index through membership aggregation.

[0016] In a preferred example, the application can be further configured to: based on the subsystem matrix and the fuzzy evaluation standard matrix of the target subsystem, construct an index factor membership degree matrix of the target subsystem, including:

[0017] determining a positive or negative type of a target element; wherein the target element is any element in a subsystem matrix of the target subsystem;

[0018] calling a membership degree calculation formula corresponding to the positive or negative type, calling a target level evaluation criterion from a fuzzy evaluation criterion matrix of the target subsystem, and substituting an index value of the target element and the target level evaluation criterion into the membership degree calculation formula to obtain a membership degree of the target element for the target level; wherein the target level is any level in the fuzzy evaluation criterion matrix;

[0019] integrating the membership degrees of each element in the subsystem matrix of the target subsystem for the target level into an index factor membership degree matrix of the target subsystem for the target level, wherein the index factor membership degree matrix of the target subsystem comprises an index factor membership degree matrix of the target subsystem for each level.

[0020] By using the above technical solution, the positive and negative characteristics of the index are distinguished, the evaluation direction is ensured to be consistent with the business logic, the membership degrees of each level of a single index are calculated in combination with the fuzzy standard threshold, the absolute value is converted into a continuous probability distribution, the membership degrees of all indexes are integrated into a matrix to construct a complete index level evaluation system, and through differential modeling and fuzzy conversion, the physical meaning of the index is retained and the influence of different dimensions and standards is eliminated, so that various water indexes can be compared and analyzed under a unified framework.

[0021] In a preferred example, the application can be further configured to: based on the fuzzy evaluation criterion matrix of the target subsystem, constructing a standard level membership degree matrix of the target subsystem, comprising:

[0022] determining respective evaluation criteria of a lowest level, a target level and a highest level corresponding to a target index from the fuzzy evaluation criterion matrix of the target subsystem; wherein the target index is any index in the target subsystem;

[0023] calculating a membership degree of the target index for the target level based on the respective evaluation criteria of the lowest level, the target level and the highest level;

[0024] constructing the membership degrees of all indexes in the target subsystem for each level into the standard level membership degree matrix of the target subsystem.

[0025] By adopting the technical scheme, the evaluation standard values corresponding to the minimum, current and maximum levels of each index are extracted to establish a dynamic reference system; the transition membership degrees of the indexes to a specific level are calculated based on the threshold values, and the absolute values are converted into relative level probabilities; and the membership degrees of all indexes are integrated to form a standard level matrix, thereby effectively solving the problem of boundary mutation in traditional grading evaluation while maintaining the rigid boundaries of the evaluation standards of different levels and realizing smooth transition between the levels through fuzzy calculation.

[0026] In a preferred example, the application can be further configured to: based on the index factor membership degree matrix and the standard level membership degree matrix, construct a factor level membership degree matrix of the target subsystem, including:

[0027] retrieve a first membership degree array corresponding to the target regional factor from the index factor membership degree matrix of the target subsystem for the target level;

[0028] calculate the weight of each element in the target subsystem for the target level based on the first membership degree array to obtain a weight array;

[0029] retrieve a second membership degree array corresponding to the target level from the standard level membership degree matrix of the target subsystem;

[0030] calculate the membership degree of the target regional factor for the target level based on the first membership degree array, the weight array and the second membership degree array;

[0031] construct the factor level membership degree matrix of the target subsystem based on the membership degree of each regional factor in the target subsystem for each level.

[0032] By adopting the technical scheme, the membership degree distribution (first array) of each index under the regional factor is extracted and the weight thereof is calculated to reflect the importance difference of different indexes; the comprehensive membership degree of the regional factor is generated through weighted aggregation in combination with the reference provided by the standard level membership degree (second array); the index layer fuzzy evaluation is combined with the standard layer reference system, the actual contribution of each index to the regional evaluation is considered, and the consistency of the evaluation result and the preset standard system is ensured, and finally the factor level membership degree matrix output can accurately reflect the comprehensive state of each region under different evaluation dimensions.

[0033] In a preferred example, the application can be further configured to: based on the index factor membership degree matrix and the factor level membership degree matrix of the plurality of subsystems, determine a water resource scheduling suggestion for the region to be dispatched, including:

[0034] determine a region to be adjusted from the factor level membership degree matrix of the target subsystem;

[0035] determining a target to-be-adjusted index corresponding to the to-be-adjusted region from the index factor membership degree matrix;

[0036] determining whether the target to-be-adjusted index is a resource type index; wherein the target to-be-adjusted index is any to-be-adjusted index corresponding to the to-be-adjusted region;

[0037] when the target to-be-adjusted index is the resource type index, determining an adjustment amount of the target to-be-adjusted index and a sum of adjustment amounts of the to-be-adjusted indexes;

[0038] determining whether the sum of adjustment amounts meets a resource constraint, and if not, determining a resource reduction type index and a reduction amount of the resource reduction type index from the index factor membership degree matrix;

[0039] generating the water resource scheduling suggestion based on the adjustment amounts of the resource type indexes and the reduction amounts of the resource reduction type indexes.

[0040] By adopting the above technical solution, the region and key index that need to be preferentially scheduled are accurately identified based on the membership degree matrix, a two-way adjustment mechanism is established by distinguishing between resource type indexes and reduction type indexes, resource gaps are calculated and system carrying capacity is dynamically evaluated, an efficiency optimization scheme is automatically triggered when demand exceeds constraints, a balanced scheduling strategy that takes into account supply increase and loss reduction is generated, fuzzy evaluation results are converted into quantifiable schemes that can be operated, and both key regional resource supply and system balance through optimized water use efficiency are ensured.

[0041] In a second aspect, a multi-water source joint scheduling system is used to execute the multi-water source joint scheduling method of any one of the first aspect, and adopts the following technical solution:

[0042] A multi-water source joint scheduling system includes:

[0043] A first construction module is used to obtain historical water use data of a to-be-scheduled region, and construct a plurality of subsystems based on the historical water use data; wherein each subsystem is represented as a subsystem matrix, a row of the subsystem matrix represents an index, a column represents a region factor, and an element represents an index value;

[0044] A retrieval module is used to retrieve a fuzzy evaluation standard matrix of each subsystem; wherein a row of the fuzzy evaluation standard matrix represents a subsystem index, and a column represents an evaluation standard of different levels;

[0045] a second constructing module configured to construct, for each of the plurality of subsystems, an index factor membership degree matrix and a factor level membership degree matrix based on the subsystem matrix and the fuzzy evaluation criterion matrix, wherein the index factor membership degree matrix of any one of the subsystems comprises a membership degree of each element in the corresponding subsystem matrix with respect to each level, and the factor level membership degree matrix of any one of the subsystems comprises a membership degree of each regional factor in the corresponding subsystem matrix with respect to each level;

[0046] a determining module configured to determine, based on the index factor membership degree matrix and the factor level membership degree matrix of the plurality of subsystems, a water resource dispatching suggestion for the region to be dispatched.

[0047] In a third aspect, the present application provides an electronic device, which adopts the technical scheme as follows:

[0048] at least one processor;

[0049] a memory;

[0050] at least one application program, wherein the at least one application program is stored in the memory and configured to be executed by the at least one processor, and the at least one application program is configured to execute the multi-water-source joint dispatching method according to any one of the first aspect.

[0051] In a fourth aspect, the present application provides a computer readable storage medium, which adopts the technical scheme as follows:

[0052] A computer readable storage medium, which stores a computer program, and when the computer program is executed in a computer, the computer is caused to execute the multi-water-source joint dispatching method according to any one of the first aspect.

[0053] In a fifth aspect, the present application provides a computer program product, which adopts the technical scheme as follows:

[0054] A computer program product, which comprises a computer program, and when the computer program is executed by a processor, the multi-water-source joint dispatching method according to any one of the first aspect is realized.

[0055] In summary, the present application has the following beneficial technical effects:

[0056] The application constructs a subsystem matrix with indexes and regional factors as dimensions based on historical water consumption data, modularizes the complex water consumption system, and improves the solvability of the data structure; introduces a fuzzy evaluation standard matrix to quantify the index level threshold, converts subjective experience into calculable standardized rules; realizes multi-dimensional fuzzy evaluation of water consumption state by calculating the membership matrix of index factors and regional factors, effectively handles data uncertainty problems; combines the membership analysis result with resource constraints to dynamically generate scheduling suggestions, optimizes resource allocation under the condition of meeting system constraints; through the fusion of matrix modeling and fuzzy mathematics, the accuracy and flexibility of the scheduling scheme are considered, which is especially suitable for complex water resource management scenarios with multiple indexes and multiple regional coordination. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 is a flowchart of a multi-water source joint scheduling method provided by an embodiment of the application;

[0058] Figure 2 is a structural diagram of a multi-water source joint scheduling system provided by an embodiment of the application;

[0059] Figure 3 is a structural diagram of an electronic device provided by an embodiment of the application. DETAILED DESCRIPTION

[0060] The following will be described in detail with reference to the accompanying drawings. Figure 1 to the accompanying Figure 3 The application will be further described in detail.

[0061] The specific embodiments are only an explanation of the application, and are not a limitation of the application. Those skilled in the art can make modifications to the embodiments without creative contribution after reading the specification, and the modifications are protected by the patent law as long as they are within the scope of the claims of the application.

[0062] In order to make the objects, technical solutions and advantages of the embodiments of the application clearer, the technical solutions in the embodiments of the application will be described clearly and completely in combination with the drawings in the embodiments of the application. Obviously, the described embodiments are some of the embodiments of the application, but not all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative contribution are within the protection scope of the application.

[0063] In addition, the term "and / or" in this paper is only to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the following three cases: A exists alone, A and B exist together, and B exists alone. In addition, the character " / " in this paper generally represents an "or" relationship between the associated objects unless otherwise specified.

[0064] It should be noted that in the optional embodiments of the present application, the data related to the object information, when the embodiments of the present application are applied to specific products or technologies, need to obtain the permission or consent of the object, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of the country and region. That is, if the embodiments of the present application involve data related to the object, the data needs to be obtained with the authorization and consent of the object, the authorization and consent of the relevant department, and in compliance with the relevant laws, regulations and standards of the country and region. If the embodiments involve personal information, the consent of the individual needs to be obtained, and if the embodiments involve sensitive information, the separate consent of the information subject needs to be obtained, and the embodiments also need to be implemented with the authorization and consent of the object.

[0065] The embodiments of the present application provide a multi-water source joint scheduling method, as shown in the following table: Figure 1 The method provided in the embodiments of the present application is executed by an electronic device, which can be a server or a terminal device. The server can be a physical server, a server cluster composed of multiple physical servers, or a distributed system, and can also be a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc., but is not limited to this. The terminal device and the server can be directly or indirectly connected through wired or wireless communication, and the embodiments of the present application do not limit this. The method includes steps S101-S104, wherein:

[0066] S101, obtain historical water consumption data of a region to be scheduled, and construct multiple subsystems based on the historical water consumption data; wherein each subsystem is represented as a subsystem matrix, the rows of the subsystem matrix represent indexes, the columns represent regional factors, and the elements represent index values.

[0067] Specifically, the subsystems can be set based on actual water resource utilization requirements. Any subsystem is represented as X k , k = 1, 2, …, b. Optionally, the multiple subsystems include a water resource development and utilization status subsystem, a water resource conservation subsystem, and a water ecological protection subsystem, and b = 3. Each subsystem includes multiple index data, represented as a subsystem matrix. Any subsystem matrix is X k = (x i,j ), i represents the i-th index of the k-th subsystem, i = 1, 2, …, m, and m values of different subsystems are different; j is the j-th regional factor of the k-th subsystem, the regional factor is the regional division in the region to be scheduled, which can be a county or a district, j = 1, 2, …, n; and the element x i,j is the index value corresponding to the i-th index and the j-th regional factor.

[0068] The historical water data is original data, and the index values of each subsystem are calculated in combination with the data of the population and economic subsystem. The data of the population and economic subsystem includes: urban population, rural population, total population, GDP (first industry, second industry, third industry), industrial added value, irrigation area, and cultivated land area.

[0069] For the water resources development and utilization status subsystem, the subsystem matrix indexes include but are not limited to: per capita water resources, surface water development and utilization rate, groundwater utilization degree, and external water utilization rate. For the water resources saving subsystem, the subsystem matrix indexes include but are not limited to: total water intake proportion of controlled water, per ten thousand yuan GDP water intake decrease (which can be one year as a cycle), agricultural irrigation per mu water index, efficient water-saving irrigation area proportion, per ten thousand yuan industrial added value water intake decrease, urban life comprehensive water index, and rural life comprehensive water index. For the water resources environment status subsystem, the subsystem matrix indexes include but are not limited to: overexploited pore water area proportion of national land area, overexploited pore water area mining coefficient, overexploited groundwater area proportion of national land area in hilly areas, groundwater utilization coefficient in hilly areas, excess urban sewage discharge rate, and excess mine water discharge rate.

[0070] S102, retrieve the fuzzy evaluation standard matrix of each subsystem; wherein the rows of the fuzzy evaluation standard matrix represent the subsystem indexes, and the columns represent the evaluation standards of different levels.

[0071] Specifically, the fuzzy evaluation standard matrix is a matrix of evaluation results of different subsystems, different indexes, and different fuzzy levels, that is, the matrix of the membership degree of each index of each regional factor to the standard evaluation of different fuzzy levels of the index conforming to a fuzzy concept of a certain level.

[0072] The fuzzy evaluation standard matrix of the target subsystem is: Y k = (y i,h ), i represents the ith index of the kth subsystem, i = 1, 2, …, m; h is the fuzzy evaluation level, h = 1, 2, …, c, and c is the total number of levels, which can be selected as c = 3. The higher the level, the more harmonious it is. The three levels from low to high are disharmonious, less harmonious, and harmonious; y i,h is the evaluation standard of the ith index of the kth subsystem corresponding to the hth fuzzy level, which is represented as a numerical value in the fuzzy evaluation standard matrix.

[0073] S103, constructing, for each subsystem, an index factor membership degree matrix and a factor level membership degree matrix based on the subsystem matrix of the plurality of subsystems and the fuzzy evaluation standard matrix; wherein the index factor membership degree matrix of any subsystem comprises the membership degree of each element in the corresponding subsystem matrix for each level, and the factor level membership degree matrix of any subsystem comprises the membership degree of each regional factor in the corresponding subsystem matrix for each level.

[0074] Specifically, taking the target subsystem as an example, the subsystem matrix of the target subsystem is represented as a target subsystem matrix. Based on the target subsystem matrix and the fuzzy evaluation standard matrix, the index factor membership degree matrix and the factor level membership degree matrix of the target subsystem are constructed, including: based on the target subsystem matrix and the fuzzy evaluation standard matrix, constructing the index factor membership degree matrix of the target subsystem. The index factor membership degree matrix of the target subsystem can include c sub-matrices, each sub-matrix corresponding to a level, the rows of the sub-matrix representing the indexes of the target subsystem, the columns of the sub-matrix representing the regional factors of the target subsystem, and the elements of the sub-matrix representing the degree to which the index value of the corresponding index and regional factor belongs to the level.

[0075] Based on the fuzzy evaluation standard matrix of the target subsystem, a standard level membership degree matrix of the target subsystem is constructed. The rows of the standard level membership degree of the target subsystem represent indexes, the columns represent levels, and the elements represent the degree to which the corresponding index belongs to the corresponding level, i.e., the elements represent the membership degree of the corresponding index for the corresponding level.

[0076] Based on the index factor membership degree matrix and the standard level membership degree matrix of the target subsystem, a factor level membership degree matrix of the target subsystem is constructed. The rows of the factor level membership degree matrix represent levels, the columns represent regional factors, and the elements represent the degree to which the corresponding regional factor belongs to the corresponding level, i.e., the elements represent the membership degree of the corresponding regional factor for the corresponding level.

[0077] S104, determining a water resource scheduling suggestion for the to-be-scheduled region based on the index factor membership degree matrix and the factor level membership degree matrix of the plurality of subsystems.

[0078] Specifically, a to-be-adjusted region is determined from a factor level membership matrix of a target subsystem; a to-be-adjusted index corresponding to the to-be-adjusted region is determined from an index factor membership matrix. Any to-be-adjusted index corresponding to the to-be-adjusted region is taken as a target to-be-adjusted index, and it is determined whether the target to-be-adjusted index is a resource type index. When the target to-be-adjusted index is a resource type index, an adjustment difference of the target to-be-adjusted index is determined, and a sum of adjustment differences of the to-be-adjusted indexes is determined. It is determined whether the sum of adjustment differences meets a resource constraint. If not, a resource reduction type index and a reduction difference of the resource reduction type index are determined from the index factor membership matrix. A water resource scheduling suggestion is generated based on the adjustment differences of the resource type indexes and the reduction differences of the resource reduction type indexes.

[0079] The embodiment is based on historical water consumption data to construct a subsystem matrix with indexes and regional factors as dimensions, modularize a complex water consumption system, and improve the solvability of a data structure. A fuzzy evaluation standard matrix is introduced to quantize index level threshold values, convert subjective experience into calculable standardized rules, realize multi-dimensional fuzzy evaluation of water consumption states through calculation of index factor and regional factor membership matrices, effectively handle data uncertainty problems, dynamically generate scheduling suggestions in combination with membership analysis results and resource constraints, optimize resource allocation under the condition of meeting system limitations, and balance the accuracy and flexibility of scheduling schemes through the fusion of matrix modeling and fuzzy mathematics, and is especially suitable for complex water resource management scenarios with multiple indexes and multiple regional coordination.

[0080] In a possible implementation of the embodiment, index factor membership matrices and factor level membership matrices are constructed for each subsystem based on subsystem matrices and fuzzy evaluation standard matrices of multiple subsystems, including:

[0081] An index factor membership matrix of a target subsystem is constructed based on a subsystem matrix and a fuzzy evaluation standard matrix of the target subsystem; the target subsystem is any of the multiple subsystems;

[0082] A standard level membership matrix of the target subsystem is constructed based on the fuzzy evaluation standard matrix of the target subsystem; the standard level membership matrix includes the membership of each index in the subsystem matrix of the target subsystem for each level;

[0083] A factor level membership matrix of the target subsystem is constructed based on the index factor membership matrix and the standard level membership matrix.

[0084] The embodiment generates an index factor membership matrix by comparing the subsystem matrix elements with the fuzzy evaluation criteria, quantifies the membership degree of each index to each level, and solves the problem that single-point data is difficult to reflect the overall state; integrates the level membership of each index through the standard level membership matrix, establishes the global mapping of the index and the evaluation criteria; and generates a factor level membership matrix by fusing the two types of matrices, to realize the comprehensive evaluation of regional factors. Through hierarchical fuzzy calculation, discrete data is converted into continuous probabilistic evaluation, which not only retains the detailed characteristics of the original data, but also eliminates the one-sidedness of a single index through membership aggregation.

[0085] In a possible implementation of the embodiment, an index factor membership matrix of a target subsystem is constructed based on a subsystem matrix of the target subsystem and a fuzzy evaluation criteria matrix, and includes the following steps:

[0086] determining the positive and negative types of a target element, wherein the target element is any element in the subsystem matrix of the target subsystem;

[0087] retrieving a membership calculation formula corresponding to the positive and negative types, retrieving an evaluation criteria of a target level from the fuzzy evaluation criteria matrix of the target subsystem, and substituting the index value of the target element and the evaluation criteria of the target level into the membership calculation formula to obtain the membership of the target element to the target level; wherein the target level is any level in the fuzzy evaluation criteria matrix;

[0088] integrating the membership of each element in the subsystem matrix of the target subsystem to the target level into an index factor membership matrix of the target subsystem to the target level, and the index factor membership matrix of the target subsystem includes an index factor membership matrix of the target subsystem to each level.

[0089] In the embodiment, the positive and negative types of the target element include positive and negative, and the positive index represents an index whose value is larger and the level is more harmonious (for example, per capita water resources, water-saving irrigation rate, efficient water-saving irrigation rate, and urban and rural ecological water proportion outside the river head), and the negative index represents an index whose value is smaller and the level is more harmonious. The row of the subsystem matrix of the target subsystem represents an index, the column represents a regional factor, and the element represents an index value of the corresponding regional factor and index. The size of the fuzzy evaluation criteria matrix of the target subsystem is mx c, the row represents an index of the target subsystem, the column represents each level, and the element represents an evaluation criteria value of the corresponding level and index.

[0090] In a possible case, the target element is positive, and the membership calculation formula of the target element to the target level is:

[0091]

[0092] In another possible case, the target element is negative, and the membership degree calculation formula of the target element for the target level is:

[0093]

[0094] wherein, x i,j is the index value of the target element in the target subsystem matrix; y i,h is the evaluation standard value corresponding to the ith index and the hth level in the fuzzy evaluation standard matrix of the target subsystem; y i,h-1 is the evaluation standard value corresponding to the ith index and the h-1th level in the fuzzy evaluation standard matrix of the target subsystem.

[0095] Further, referring to the calculation process of the membership degree of the target element for the target level, the membership degree of each element in the target subsystem for the target level is calculated, and all results are integrated into a sub-matrix corresponding to the target level. The sub-matrix corresponding to the target level has the same size as the target subsystem matrix, both being m*n. The row represents the index, the column represents the regional factor, and the element represents the degree to which the index and regional factor corresponding to the element in the target subsystem belong to the target level. Referring to the above process of the target level, the sub-matrix under each level is determined, and each level corresponds to a sub-matrix. The index factor membership degree matrix of the target subsystem includes c sub-matrices.

[0096] When the number of subsystems is 3 and the number of levels is 3, the number of index factor membership degree matrices is 9, which are respectively: the matrix of the water resource development and utilization subsystem belonging to the fuzzy levels of harmony, sub-harmony and harmony, the matrix of the water resource conservation subsystem belonging to the fuzzy levels of harmony, sub-harmony and harmony, and the matrix of the water ecological protection subsystem belonging to the fuzzy levels of harmony, sub-harmony and harmony.

[0097] The embodiment distinguishes the positive and negative characteristics of the index, ensures that the evaluation direction is consistent with the business logic, calculates the membership degree of each level for a single index in combination with the fuzzy standard threshold, converts the absolute value into a continuous probability distribution, integrates the membership degrees of all indexes to form a matrix, constructs a complete index level evaluation system, and through differential modeling and fuzzy conversion, the physical meaning of the index is retained, and the influence of different dimensions and standards is eliminated, so that various water indexes can be compared and analyzed under a unified framework.

[0098] In a possible implementation of the embodiment, a standard level membership degree matrix of the target subsystem is constructed based on the fuzzy evaluation standard matrix of the target subsystem, including:

[0099] The evaluation standards of the lowest level, the target level and the highest level corresponding to the target index are determined from the fuzzy evaluation standard matrix of the target subsystem, wherein the target index is any index in the target subsystem;

[0100] Calculate the membership of the target indicator to the target level based on the evaluation criteria of the lowest level, the target level and the highest level respectively;

[0101] Construct the standard level membership matrix of the target subsystem by the membership of all indicators in the target subsystem to each level.

[0102] In this embodiment, the formula for calculating the membership of the target indicator to the target level is:

[0103]

[0104] Wherein, s i,h is the membership of the target indicator i to the target level h; y i,h is the evaluation criteria of the lowest level corresponding to the target indicator; y i,1 is the evaluation criteria of the target level corresponding to the target indicator; y i,c is the evaluation criteria of the highest level corresponding to the target indicator.

[0105] Referring to the above process, the membership of each indicator in the target subsystem to each level is determined to form the standard level membership matrix of the target subsystem. The standard level membership matrix of the target subsystem is an m×c matrix, wherein the rows represent indicators, the columns represent levels, and the elements represent the membership of the corresponding indicator to the corresponding level.

[0106] This embodiment establishes a dynamic reference system by extracting the evaluation criteria values of each indicator at the lowest, current and highest levels; calculates the transition membership of the indicator to a specific level based on these threshold values, converts absolute numerical values into relative level probabilities; integrates the membership of all indicators to form a standard level matrix; both maintains the rigid boundaries of the evaluation criteria of each level and realizes smooth transition between levels through fuzzy calculation, effectively solving the problem of boundary mutation in traditional grading evaluation.

[0107] In one possible implementation of the embodiment of the present application, a factor level membership matrix of the target subsystem is constructed based on the indicator factor membership matrix and the standard level membership matrix, including:

[0108] From the indicator factor membership matrix of the target subsystem to the target level, the first membership array corresponding to the target area factor is called;

[0109] Based on the first membership array, the weight of each element in the target subsystem to the target level is calculated to obtain a weight array;

[0110] From the standard level membership matrix of the target subsystem, the second membership array corresponding to the target level is called;

[0111] Based on the first membership array, the weight array and the second membership array, the membership of the target area factor to the target level is calculated.

[0112] Based on the membership of each area factor in the target subsystem to each level, a factor-level membership matrix of the target subsystem is constructed.

[0113] In the embodiment, the indicator factor membership matrix of the target subsystem to the target level is represented as:

[0114]

[0115] The target area factor is represented as j, and the first membership array corresponding to the target area factor is called, and the first membership array is represented as:

[0116]

[0117] Any element x i,j The weight w of the target level i,j The weight calculation formula is:

[0118]

[0119] Referring to the above process, the weight of each element corresponding to the target area factor in the target subsystem to the target level is calculated to obtain the weight array corresponding to the target level, and the weight array can be represented as:

[0120]

[0121] The standard level membership matrix of the target subsystem can be represented as:

[0122]

[0123] The target level is represented as h, and the second membership array corresponding to the target level h is called, and the second membership array is represented as:

[0124]

[0125] Based on the first membership array, the weight array and the second membership array, the membership u of the target area factor j to the target level h is calculated h,j The calculation formula is:

[0126]

[0127] Wherein, p is a distance parameter, and optionally, p=2.

[0128] Referring to the above process, the factor level membership matrix of the target subsystem is constructed, in which the rows represent the levels, the columns represent the regional factors, and the elements represent the membership of the corresponding regional factor to the corresponding level.

[0129] The embodiment reflects the importance difference of different indexes by extracting the membership distribution of each index under the regional factor (the first array) and calculating the weight thereof; the comprehensive membership of the regional factor is generated by weighted aggregation in combination with the benchmark reference provided by the standard level membership (the second array); the index layer fuzzy evaluation is combined with the standard layer reference system, the actual contribution of each index to the regional evaluation is considered, and the consistency of the evaluation result and the preset standard system is ensured, and finally the factor level membership matrix output can accurately reflect the comprehensive state of each region under different evaluation dimensions.

[0130] In one possible implementation of the embodiment, the water resource scheduling suggestion for the region to be dispatched is determined based on the index factor membership matrix and the factor level membership matrix of the plurality of subsystems, and includes:

[0131] The region to be adjusted is determined from the factor level membership matrix of the target subsystem;

[0132] The index to be adjusted corresponding to the region to be adjusted is determined from the index factor membership matrix;

[0133] It is judged whether the target index to be adjusted is a resource type index; wherein the target index to be adjusted is any index to be adjusted corresponding to the region to be adjusted;

[0134] When the target index to be adjusted is a resource type index, the adjustment amount of the target index to be adjusted is determined, and the sum of the adjustment amounts of the index to be adjusted is determined;

[0135] It is judged whether the sum of the adjustment amounts meets the resource constraint, if not, the resource reduction type index and the reduction amount of the resource reduction type index are determined from the index factor membership matrix;

[0136] The water resource scheduling suggestion is generated based on the adjustment amount of the resource type index and the reduction amount of the resource reduction type index in the index to be adjusted.

[0137] In the embodiment, the rows of the factor level membership matrix of the target subsystem represent the levels, the columns represent the regional factors, and the elements represent the membership. For each regional factor, the level with the highest membership is selected, for example, the membership of regional factor j in level 1 (disharmony), level 2 (less harmony) and level 3 (harmony) is 0.4, 0.5 and 0.7 respectively, and level 3 is selected as the membership of regional factor j, and the corresponding relationship between each region and the level with the highest membership is obtained.

[0138] All regional factors in the target subsystem are sorted according to the rule that the highest level of the corresponding membership degree is arranged from low to high. When there are two regional factors corresponding to the highest level of the membership degree which are the same and are level 1, the membership degree of the regional factor for the highest level of the membership degree is sorted from high to low; when there are two regional factors corresponding to the highest level of the membership degree which are the same and are level 2, the regional factor with higher level 1 membership degree is arranged in front; when there are two regional factors corresponding to the highest level of the membership degree which are the same and are level 3, the membership degree of the regional factor for the highest level of the membership degree is sorted from low to high.

[0139] In the arranged regional factors, the earlier indicates that the harmony degree of the regional factor is lower. A fixed number or a fixed proportion of regional factors can be selected from the sorted regional factor list as the to-be-adjusted region. The fixed number and the fixed proportion can be flexibly set according to actual needs, and the embodiment is not limited.

[0140] The index factor membership degree matrix of the target subsystem includes c sub-matrices, c represents the number of levels, each sub-matrix corresponds to a level, and the size of the sub-matrix is m x n. The row represents an index, and the column represents a regional factor.

[0141] Any to-be-adjusted region corresponds to m indexes in the target subsystem matrix. Any index corresponding to any to-be-adjusted region corresponds to an element in each sub-matrix. Each index corresponding to each to-be-adjusted region corresponds to c elements in the c sub-matrices. The index corresponding to the element value of the to-be-adjusted region in the sub-matrix corresponding to level 1 higher than the preset threshold can be used as the to-be-adjusted index.

[0142] The resource type index represents an index directly related to resource allocation, and the index optimization needs to increase resources (such as water supply); the reduction type index represents that the amount of use can be reduced by scheduling to compensate for the resource type index with poor harmony. For each resource type index, the adjustment difference = target value - current value. The target value of any resource type index can be the evaluation standard of the middle level in the fuzzy evaluation standard matrix.

[0143] For any to-be-adjusted index, the sum of all adjustment differences is accumulated, and the total consumption of the index corresponding to the nearest period in the historical water consumption data from the current time is summarized. The sum of the total consumption and the adjustment difference is used as the total demand of the next period. The resource constraint condition (i.e. the total amount of resources available in the next period) of the to-be-adjusted index is obtained. If the total demand does not exceed the resource constraint condition, a resource increase suggestion for the to-be-adjusted index is generated.

[0144] If the total demand exceeds the resource constraint condition, the difference between the total demand and the resource constraint condition is calculated as the total reduction amount, and the reduction type index is selected. From the index factor membership matrix corresponding to the index factor of the highest level, the index whose membership degree exceeds the preset reduction threshold is selected as the reduction type index. The ratio of the total reduction amount to the number of reduction type indexes is used as the reduction difference of each reduction type index. In addition, the index value of the reduction type index in the target subsystem matrix can be used as a reference to reduce the value in proportion to the size of the value. This embodiment is not limited.

[0145] According to the above process, the water resource scheduling plan of each subsystem is realized.

[0146] The embodiment is based on the accurate identification of the region and key index that needs to be prioritized in the membership matrix. A two-way adjustment mechanism is established by distinguishing between resource type and reduction type indexes. The resource gap is calculated and the system carrying capacity is dynamically evaluated. When the demand exceeds the constraint, an efficiency optimization scheme is automatically triggered. A balanced scheduling strategy that takes into account supply increase and loss reduction is generated. The fuzzy evaluation result is converted into a quantifiable scheme that can be operated. This ensures the supply of resources in key areas and optimizes water use efficiency to achieve system balance.

[0147] In the embodiment of the application, a multi-water source joint scheduling system is provided, as shown in Figure 2 The system includes a first construction module 201, a calling module 202, a second construction module 203, and a determination module 204, wherein:

[0148] The first construction module 201 is configured to obtain historical water consumption data of a region to be scheduled, and construct a plurality of subsystems based on the historical water consumption data. Each subsystem is represented as a subsystem matrix, wherein the rows of the subsystem matrix represent indexes, the columns represent regional factors, and the elements represent index values.

[0149] The calling module 202 is configured to call a fuzzy evaluation standard matrix of each subsystem. The rows of the fuzzy evaluation standard matrix represent subsystem indexes, and the columns represent evaluation standards of different levels.

[0150] The second construction module 203 is configured to construct an index factor membership matrix and a factor level membership matrix for each subsystem based on the subsystem matrix and the fuzzy evaluation standard matrix of the plurality of subsystems. The index factor membership matrix of any subsystem includes the membership degree of each element in the corresponding subsystem matrix for each level, and the factor level membership matrix of any subsystem includes the membership degree of each regional factor in the corresponding subsystem matrix for each level.

[0151] The determination module 204 is configured to determine a water resource scheduling suggestion for the region to be scheduled based on the index factor membership matrix and the factor level membership matrix of the plurality of subsystems.

[0152] An electronic device is provided in embodiments of the present application, such as Figure 3 As shown in FIG. 3, Figure 3 The electronic device 300 shown in FIG. 3 includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 can also include a transceiver 304. It should be noted that the transceiver 304 is not limited to one in actual applications, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0153] The processor 301 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, modules, and circuits described in connection with the present disclosure. The processor 301 can also be a combination of computing functions, such as one or more microprocessor combinations, combinations of DSP and microprocessor, etc.

[0154] The bus 302 can include a path for transmitting information between the above-mentioned components. The bus 302 can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 302 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 In the figure, only one thick line is used to represent the bus, but it does not mean that there is only one bus or one type of bus.

[0155] The memory 303 can be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, a magnetic disk storage or other magnetic storage devices, or any other medium capable of storing desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto.

[0156] The memory 303 is configured to store application program codes for implementing the solutions of the present application, and the processor 301 is configured to control the execution of the application program codes. The processor 301 is configured to execute the application program codes stored in the memory 303 to implement the content shown in the foregoing multi-water-source joint dispatching method embodiments.

[0157] Figure 3 The electronic device shown is merely an example, and should not impose any limitation on the functions and use range of the embodiments of the present application.

[0158] The embodiments of the present application provide a computer readable storage medium, which stores a computer program. When the computer program is run on a computer, the computer can execute the content shown in the foregoing multi-water-source joint dispatching method embodiments.

[0159] It should be understood that, although each step in the flowchart of the accompanying drawings is shown in sequence according to the direction of the arrow, these steps are not necessarily executed in sequence according to the direction of the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and they can be executed in other sequences. Moreover, at least part of the steps in the flowchart of the accompanying drawings can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or sub-steps or stages of other steps.

[0160] The embodiments of the present application provide a computer program product, which includes a computer program. When the computer program is executed by a processor, the content shown in the foregoing multi-water-source joint dispatching method embodiments is implemented.

[0161] The above merely provides part of the embodiments of the present application, and it should be pointed out that, for those skilled in the art, some improvements and refinements can be made without departing from the principles of the present application, and these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A multi-source joint scheduling method, characterized in that, The method comprises the following steps: acquiring historical water consumption data of a region to be dispatched, and constructing a plurality of subsystems based on the historical water consumption data; each subsystem is represented as a subsystem matrix, the rows of the subsystem matrix represent indexes, the columns represent regional factors, and the elements represent index values; calling a fuzzy evaluation standard matrix of each subsystem; the rows of the fuzzy evaluation standard matrix represent subsystem indexes, and the columns represent evaluation standards of different levels; constructing an index factor membership degree matrix and a factor level membership degree matrix for each subsystem based on the subsystem matrix and the fuzzy evaluation standard matrix of the plurality of subsystems; the index factor membership degree matrix of any subsystem comprises the membership degree of each element in the corresponding subsystem matrix for each level, and the factor level membership degree matrix of any subsystem comprises the membership degree of each regional factor in the corresponding subsystem matrix for each level; determining a water resource dispatching suggestion for the region to be dispatched based on the index factor membership degree matrix and the factor level membership degree matrix of the plurality of subsystems; the step of constructing the index factor membership degree matrix and the factor level membership degree matrix for each subsystem based on the subsystem matrix and the fuzzy evaluation standard matrix of the plurality of subsystems comprises the following steps: constructing an index factor membership degree matrix of a target subsystem based on the subsystem matrix and the fuzzy evaluation standard matrix of the target subsystem; the target subsystem is any subsystem in the plurality of subsystems; constructing a standard level membership degree matrix of the target subsystem based on the fuzzy evaluation standard matrix of the target subsystem; the standard level membership degree matrix comprises the membership degree of each index in the subsystem matrix of the target subsystem for each level; constructing a factor level membership degree matrix of the target subsystem based on the index factor membership degree matrix and the standard level membership degree matrix.

2. The multi-water source co-scheduling method of claim 1, wherein, the step of constructing the index factor membership degree matrix of the target subsystem based on the subsystem matrix and the fuzzy evaluation standard matrix of the target subsystem comprises the following steps: determining the positive and negative types of a target element; the target element is any element in the subsystem matrix of the target subsystem; calling a membership degree calculation formula corresponding to the positive and negative types, calling an evaluation standard of a target level from the fuzzy evaluation standard matrix of the target subsystem, substituting the index value of the target element and the evaluation standard of the target level into the membership degree calculation formula to obtain the membership degree of the target element for the target level; the target level is any level in the fuzzy evaluation standard matrix; integrating the membership degree of each element in the subsystem matrix of the target subsystem for the target level into an index factor membership degree matrix of the target subsystem for the target level; the index factor membership degree matrix of the target subsystem comprises the index factor membership degree matrix of the target subsystem for each level.

3. The method of claim 1, wherein, the step of constructing the standard level membership degree matrix of the target subsystem based on the fuzzy evaluation standard matrix of the target subsystem comprises the following steps: determining the lowest level, the target level and the highest level of the target index from the fuzzy evaluation criterion matrix of the target subsystem; wherein the target index is any index in the target subsystem; calculating the membership degree of the target index for the target level based on the respective evaluation criterion of the lowest level, the target level and the highest level; constructing the membership degree of all indexes in the target subsystem for each level into the standard level membership matrix of the target subsystem.

4. The method of claim 1, wherein, The factor level membership matrix of the target subsystem is constructed based on the index factor membership matrix and the standard level membership matrix, including: retrieve the first membership degree array corresponding to the target area factor from the index factor membership matrix of the target subsystem for the target level; calculate the weight array of each element in the target subsystem for the target level based on the first membership degree array; retrieve the second membership degree array corresponding to the target level from the standard level membership matrix of the target subsystem; calculate the membership degree of the target area factor for the target level based on the first membership degree array, the weight array and the second membership degree array; construct the factor level membership matrix of the target subsystem based on the membership degree of each area factor in the target subsystem for each level.

5. The method of claim 1, wherein, The water resource scheduling suggestion for the region to be dispatched is determined based on the index factor membership matrix and the factor level membership matrix of the plurality of subsystems, including: determining the region to be adjusted from the factor level membership matrix of the target subsystem; determining the target index to be adjusted corresponding to the region to be adjusted from the index factor membership matrix; determining whether the target index to be adjusted is a resource type index; wherein the target index to be adjusted is any index corresponding to the region to be adjusted; when the target index to be adjusted is the resource type index, determining the adjustment amount of the target index to be adjusted and the sum of the adjustment amounts of the target index to be adjusted; determining whether the sum of the adjustment amounts meets the resource constraint, and if not, determining the resource reduction type index and the reduction amount of the resource reduction type index from the index factor membership matrix; generating the water resource scheduling suggestion based on the adjustment amount of the resource type index and the reduction amount of the resource reduction type index.

6. A multi-water source joint dispatching system for performing the multi-water source joint dispatching method of any one of claims 1-5, characterized in that, including: The first construction module is used for acquiring historical water consumption data of the region to be dispatched, and constructing a plurality of subsystems based on the historical water consumption data; wherein each subsystem is represented as a subsystem matrix, the row of the subsystem matrix represents an index, the column represents an area factor, and the element represents an index value; The retrieval module is used for retrieving the fuzzy evaluation criterion matrix of each subsystem; wherein the row of the fuzzy evaluation criterion matrix represents a subsystem index, and the column represents evaluation criteria of different levels; a second constructing module, configured to construct, based on the subsystem matrix and the fuzzy evaluation criterion matrix of each of the plurality of subsystems, an index factor membership degree matrix and a factor level membership degree matrix for each of the plurality of subsystems; wherein the index factor membership degree matrix of any one of the plurality of subsystems comprises the membership degree of each element in the corresponding subsystem matrix with respect to each level, and the factor level membership degree matrix of any one of the plurality of subsystems comprises the membership degree of each region factor in the corresponding subsystem matrix with respect to each level; a determining module, configured to determine, based on the index factor membership degree matrix and the factor level membership degree matrix of the plurality of subsystems, a water resource scheduling suggestion for the region to be scheduled; the second constructing module, when constructing, based on the subsystem matrix and the fuzzy evaluation criterion matrix of each of the plurality of subsystems, the index factor membership degree matrix and the factor level membership degree matrix for each of the plurality of subsystems, is specifically configured to: construct, based on the subsystem matrix and the fuzzy evaluation criterion matrix of a target subsystem, an index factor membership degree matrix of the target subsystem; wherein the target subsystem is any one of the plurality of subsystems; construct, based on the fuzzy evaluation criterion matrix of the target subsystem, a standard level membership degree matrix of the target subsystem; wherein the standard level membership degree matrix comprises the membership degree of each index in the subsystem matrix of the target subsystem with respect to each level; construct, based on the index factor membership degree matrix and the standard level membership degree matrix, a factor level membership degree matrix of the target subsystem.

7. An electronic device, comprising: comprise: at least one processor; a memory; at least one application program, wherein the at least one application program is stored in the memory and configured to be executed by the at least one processor, and the at least one application program is configured to implement the multi-water-source joint scheduling method according to any one of claims 1-5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed in the computer, the computer is caused to implement the multi-water-source joint scheduling method according to any one of claims 1-5.

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