Fuzzy set-based water system emergency decision method and device

Abstract

The application discloses a water conservancy system emergency decision-making method and device based on a fuzzy set, and the method comprises the following steps: determining a region range, and acquiring hydrological information in the region range, wherein the hydrological information comprises rainfall information, water flow information and basin information; optimizing the hydrological information to obtain optimized information; calculating distance values of the hydrological information in each decision result in a decision set according to the optimized information and the decision set, wherein the decision set is composed of historical hydrological information in the region range and corresponding decision results, and the distance values are determined by the membership degrees of the hydrological information and the decision results and the non-membership degrees of the hydrological information and the decision results; and obtaining a current decision result of the region range according to the distance values, improving the tendency of the decision result, and improving the timeliness and accuracy of the water conservancy system emergency decision-making.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of emergency decision-making, and in particular to a water conservancy system emergency decision-making method and device based on fuzzy sets. BACKGROUND

[0002] As an information-intensive industry, water conservancy not only undertakes the work of water resource allocation and management, but also undertakes the work of providing valuable hydrological information to society, such as rainfall, flood disaster, typhoon, mountain disaster, and tide, and at the same time, these information also provides decision support for government and water conservancy administrative decision-making departments to carry out flood control and drought resistance, water resource development and utilization, and water resource management. At present, in order to timely process a large amount of water conservancy information, the existing water conservancy system emergency decision-making method adopts a distance measurement method based on fuzzy sets to calculate the distance value between water conservancy information and a decision set, which can accurately indicate and distinguish, and avoid the results that are contrary to human cognitive common sense. However, this distance measurement method does not consider the influence of the decision maker's tendency on the measurement results in the actual situation, and cannot quickly and accurately obtain accurate decision results according to hydrological information, which affects the timeliness and accuracy of water conservancy system emergency decision-making. SUMMARY

[0003] To solve the above problems, the purpose of the present application is to provide a water conservancy system emergency decision-making method and device based on fuzzy sets and a storage medium thereof, which improves the tendency of the decision results by calculating the membership degree and non-membership degree between hydrological information and a decision set, and improves the timeliness and accuracy of water conservancy system emergency decision-making.

[0004] The technical solution adopted by the present application to solve the problems is as follows:

[0005] In a first aspect, the present application provides a water conservancy system emergency decision-making method based on fuzzy sets, which comprises: determining a region range, and obtaining hydrological information in the region range, wherein the hydrological information comprises rainfall information, water flow information, and basin information; optimizing the hydrological information to obtain optimized information; calculating distance values of the hydrological information in each decision result in a decision set according to the optimized information and the decision set, wherein the decision set is composed of historical hydrological information in the region range and corresponding decision results of the historical hydrological information, and the distance values are determined by the membership degree of the hydrological information and the decision result and the non-membership degree of the hydrological information and the decision result; and obtaining a current decision result of the region range according to the distance values.

[0006] In a second aspect, the embodiments of the present application provide a fuzzy set-based emergency decision device for a water conservancy system, comprising: an acquisition module configured to determine a region range and acquire hydrological information in the region range, wherein the hydrological information comprises rainfall information, water flow information and basin information; an optimization module configured to perform optimization processing on the hydrological information to obtain optimization information; a calculation module configured to calculate distance values of each decision result of the hydrological information in a decision set according to the optimization information and the decision set, wherein the decision set is composed of historical hydrological information in the region range and corresponding decision results of the historical hydrological information, and the distance values are determined by membership degrees of the hydrological information and the decision results and non-membership degrees of the hydrological information and the decision results; and a decision module configured to obtain a current decision result of the region range according to the distance values.

[0007] In a third aspect, the embodiments of the present application provide an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the fuzzy set-based emergency decision method for a water conservancy system as described above when executing the computer program.

[0008] In a fourth aspect, the embodiments of the present application provide a computer readable storage medium storing a computer program, wherein the computer program is executable by a processor to implement the fuzzy set-based emergency decision method for a water conservancy system as described above.

[0009] In the embodiments of the present application, the region range is determined, and the hydrological information in the region range is acquired, wherein the hydrological information comprises rainfall information, water flow information and basin information; the hydrological information is optimized to obtain optimization information; distance values of each decision result of the hydrological information in a decision set are calculated according to the optimization information and the decision set, wherein the decision set is composed of historical hydrological information in the region range and corresponding decision results of the historical hydrological information, and the distance values are determined by membership degrees of the hydrological information and the decision results and non-membership degrees of the hydrological information and the decision results; and a current decision result of the region range is obtained according to the distance values, so that the tendency of the decision result is improved, and the timeliness and accuracy of the emergency decision for the water conservancy system are improved.

[0010] Additional aspects and advantages of the application will be described in the description that follows, and partly become apparent from the description, or be understood by those skilled in the art from the practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0011] Figure 1 A flowchart of the fuzzy set-based emergency decision method for a water conservancy system according to the embodiments of the present application is shown in FIG. 2;

[0012] Figure 2 A flowchart of step S2000 in FIG. 2 is shown in FIG. 3; Figure 1

[0013] ​Figure 3 For Figure 1 the flowchart of step S3000 in the embodiment;

[0014] Figure 4 For Figure 3 the flowchart of step S3300 in the embodiment;

[0015] Figure 5 For Figure 3 the flowchart of step S3400 in the embodiment;

[0016] Figure 6 For Figure 5 the flowchart of step S3440 in the embodiment;

[0017] Figure 7 For Figure 1 the flowchart of step S4000 in the embodiment;

[0018] Figure 8 For the structure diagram of the water conservancy system emergency decision device based on the fuzzy set in the embodiment of the application;

[0019] Figure 9 is a structural schematic diagram of an electronic device provided by the embodiment of the application. DETAILED DESCRIPTION

[0020] The embodiments of the present application will be described in detail below, examples of which are shown in the drawings, wherein the same or similar notations represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only, and are used only for explaining the present application, and cannot be understood as a limitation of the present application.

[0021] In the description of the present application, it should be understood that the orientation description, such as the orientation or position relationship indicated by up, down, front, back, left, right, etc. is based on the orientation or position relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element indicated must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0022] In the description of the present application, the meaning of several is one or more, and the meaning of multiple is two or more. Greater than, less than, more than, etc. are understood as not including the number, and above, below, etc. are understood as including the number. If it is described as first, second, etc., it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or the order of indicated technical features.

[0023] In the description of the present application, unless otherwise explicitly defined, the words such as arrangement, installation, connection and the like should be understood broadly, and the person skilled in the art can determine the specific meaning of the above words in the present application in combination with the specific content of the technical solution.

[0024] The embodiment of the present application relates to a fuzzy set-based water conservancy system emergency decision method and device and a storage medium thereof, and the method comprises the steps of determining a region range, and acquiring hydrological information in the region range, wherein the hydrological information comprises rainfall information, water flow information and basin information; the hydrological information is optimized to obtain optimization information; according to the optimization information and a decision set, the distance value of each decision result of the hydrological information in the decision set is calculated, wherein the decision set is composed of historical hydrological information in the region range and corresponding decision results, and the distance value is determined by the membership degree of the hydrological information and the decision result and the non-membership degree of the hydrological information and the decision result; and according to the distance value, the current decision result of the region range is obtained, the tendency of the decision result is improved, and the timeliness and accuracy of the water conservancy system emergency decision are improved.

[0025] At present, a large number of water conservancy projects such as water gates and pumping stations have been built in river networks, and reasonable regulation and control of these projects is one of the effective measures to solve water problems. The interwoven water system and dense project group jointly constitute a complex system, and in the face of different water safety problems and emergencies, corresponding decision results and corresponding project joint scheduling schemes need to be quickly given according to hydrological information. As an information-intensive industry, water conservancy needs to refer to a large amount of hydrological information in the decision-making process. In order to quickly and accurately obtain the decision result from the hydrological information with large data volume and multiple reference dimensions, the existing water conservancy system emergency decision system introduces a data processing method of fuzzy set.

[0026] A fuzzy set is a set used to express a fuzzy concept, also known as a fuzzy set or a fuzzy subset. A general set refers to the totality of objects with certain attributes. Fuzzy set theory takes the object to be studied and the fuzzy concept reflecting it as a certain fuzzy set, establishes an appropriate membership function, and analyzes the fuzzy object through fuzzy set operations and transformations. Fuzzy set theory is based on fuzzy mathematics and studies related non-precise phenomena. In the objective world, there are many fuzzy phenomena. There are many fuzzy concepts in people's thinking, such as young, large, warm, and evening. The attributes of the objects described by these concepts cannot be simply answered by "yes" or "no", and a fuzzy set refers to the totality of objects with certain fuzzy concept attributes. Since the concept itself is not clear and the boundary is clear, the membership of the object to the set is also not clear and non-binary.

[0027] Intuitionistic fuzzy sets (IFSs) are an extension of fuzzy sets (FSs), and the distance measure between IFSs is an important research problem in IFSs theory. Based on the study of existing distance measures of IFSs, a new distance measure method based on normal distribution function is proposed based on the voting model. This method can solve the distance measure problem between several special intuitionistic fuzzy sets, overcome the defects of existing distance measure methods, and is very suitable for the distance measure between language variables, providing a new method for the application of IFSs in intelligent decision-making. However, this calculation method does not consider the influence of the decision maker's inclination on the measurement results in actual situations, and cannot quickly and accurately obtain accurate decision results according to hydrological information, affecting the timeliness and accuracy of the emergency decision-making of the water conservancy system.

[0028] Based on the above, the embodiments of the present application provide a fuzzy set-based emergency decision-making method and device for a water conservancy system and a storage medium thereof. The regional range is determined, and the hydrological information in the regional range is obtained, wherein the hydrological information includes rainfall information, water flow information and basin information. The hydrological information is optimized to obtain optimized information. The distance value of the hydrological information in each decision result in the decision set is calculated according to the optimized information and the decision set, wherein the decision set is composed of historical hydrological information in the regional range and the corresponding decision results, and the distance value is determined by the membership degree of the hydrological information and the decision result and the non-membership degree of the hydrological information and the decision result. The current decision result of the regional range is obtained according to the distance value, the inclination of the decision result is improved, and the timeliness and accuracy of the emergency decision-making of the water conservancy system are improved.

[0029] Please refer to Figure 1 , Figure 1 The flow of the fuzzy set-based emergency decision-making method for a water conservancy system provided by the embodiments of the present application is shown. As shown in Figure 1 The fuzzy set-based emergency decision-making method for a water conservancy system provided by the embodiments of the present application includes the following steps:

[0030] Step S1000, determine the regional range, and obtain the hydrological information in the regional range, wherein the hydrological information includes rainfall information, water flow information and basin information.

[0031] It can be understood that the hydrological information is the general term of the measured hydrological data and the results obtained by analyzing and calculating. Specifically, the hydrological information is an important basis for carrying out basin management, engineering planning and management, flood prevention and drought resistance, and formulating social and economic development plans. In practical application, the hydrological information includes but is not limited to rainfall information, water flow information and basin information. Among them, the rainfall information refers to the total amount of rainfall (snow) within 12 or 24 hours, and also refers to the depth of liquid or solid (after melting) water falling from the sky to the ground without evaporation, infiltration, loss and accumulation on the horizontal plane. The water flow information refers to the change of the water level and flow of the river over time. The water level and flow have daily, monthly, seasonal and annual changes, which are important hydrological characteristics of the river and one of the important bases for river classification and hydrological zoning. The general river regime also includes the change of river ice regime, river sediment and water quality. The basin information includes the basin area, river network density, basin shape, basin height, basin direction or main stream direction of the river in the current region. By obtaining the hydrological information in the region, the current hydrological situation in the region can be accurately mastered, and the decision result of the region can be quickly and accurately obtained according to the current rainfall and water flow.

[0032] Step S2000, the hydrological information is optimized to obtain optimized information.

[0033] It can be understood that in the detection and collection of hydrological information, error values may be generated due to sudden situations, instrument failure, transmission error and other reasons, that is, there are noise values in the hydrological information. In order to ensure the accuracy of the decision result and obtain high information content and useful knowledge, the ideal situation is that the hydrological information does not contain noise correct data. The hydrological information is the basis of the fuzzy set-based emergency decision method of water conservancy system, in order to avoid the situation that the decision result has too large error, the hydrological information must be optimized.

[0034] Please refer to Figure 2 , Figure 2 The specific implementation process diagram of another embodiment of the above step S2000 is shown. As Figure 2 shown, step S2000 at least includes the following steps:

[0035] Step S2100, respectively calculating the average value of rainfall information, water flow information and basin information.

[0036] It can be understood that, in order to avoid the change of hydrological information in a period of time is large, affecting the authenticity and reference value of the data, it is necessary to calculate the reference value of rainfall information, flow information and basin information, so as to screen the rainfall information, flow information and basin information. In practical application, the square average value of rainfall information, flow information and basin information is calculated as the reference value to determine whether the collected rainfall information, flow information and basin information have reference value. In some embodiments, the arithmetic mean, geometric mean, harmonic mean or weighted mean of rainfall information, flow information and basin information can also be calculated as the reference value, which is not limited here.

[0037] Step S2200, screening the hydrological information according to the average value to obtain optimized information.

[0038] It can be understood that, after obtaining the average value of rainfall information, flow information and basin information, by comparing the difference between the current rainfall information, flow information and basin information and the corresponding average value, it is determined whether to perform cleaning operation on the current hydrological information. Data cleaning refers to the process of re-examining and verifying data, aiming to find and correct errors in data files, and ensure the consistency and accuracy of data. This process includes removing duplicate information, correcting errors, and processing invalid values and missing values to ensure the accuracy of hydrological information and avoid the impact of false hydrological information on decision results. In practical application, by judging whether the difference between the current hydrological information and the average value is greater than the preset threshold, it is determined whether to perform cleaning operation on the current hydrological information, so as to achieve debugging and optimization of hydrological information.

[0039] In other embodiments, the optimization processing of hydrological information also includes data mapping, data merging, data splitting and other operations to ensure data quality and consistency and provide a reliable basis for subsequent analysis.

[0040] Step S3000, calculating the distance value of hydrological information in each decision result in the decision set according to the optimized information and the decision set, wherein the decision set is composed of historical hydrological information in the region and its corresponding decision result, and the distance value is determined by the membership degree of hydrological information and decision result and the non-membership degree of hydrological information and decision result.

[0041] It can be understood that, in order to calculate the distance value of hydrological information in each decision result in the decision set, the intuitionistic fuzzy set is introduced to measure the distance between two data sets. By the membership degree of hydrological information and decision result, the non-membership degree of hydrological information and decision result, the distance value of hydrological information in each decision result in the decision set is determined.

[0042] In practical applications, the decision set is composed of historical hydrological information in the region and its corresponding decision results. For example, when the rainfall is greater than the preset rainfall threshold, the gate is opened for drainage; when the water flow is greater than the preset warning flow value, the corresponding waterlogging warning operation is performed. The specific decision set is flexibly set and adjusted according to the current historical data in the region, which is not described here.

[0043] It should be noted that there are two mappings u A :X→[0,1] and v A :X→[0,1] on the domain X, such that x∈X|→u A (x)∈[0,1] and x∈X|→v A (x)∈[0,1], and satisfy 0≤u A (x)+v A (x)≤1, u A and v A are called a intuitionistic fuzzy set A on the domain X, denoted as A={<x,u A ,v A >|x∈X}, where u A and v A are called the membership function and non-membership function of the intuitionistic fuzzy set A respectively, u A (x) and v A (x) are the membership degree and non-membership degree of element x belonging to A.

[0044] Please refer to Figure 3 , Figure 3 shows the specific implementation process diagram of another embodiment of the above step S3000. As Figure 3 shown, step S3000 at least includes the following steps:

[0045] Step S3100, calculating the membership degree of hydrological information and decision result.

[0046] It can be understood that according to the above-mentioned step, the preset intuitionistic fuzzy set A is the hydrological information, and the intuitionistic fuzzy set B is the decision set. As known from the above, A={<x,u A (x i ),v A (x i )>}, B={<x,u B (x i ),v B (x i )>}. At this time, the membership degree of the hydrological information is u A (x i ), and the membership degree of the decision result is u B (x i ).

[0047] Step S3200, calculating the non-membership degree of hydrological information and decision result.

[0048] It can be understood that, according to the preset intuitionistic fuzzy set A in the above steps, the hydrological information is the intuitionistic fuzzy set B, and the decision set. The non-membership degree of the hydrological information is v A (x i ), and the non-membership degree of the decision result is v B (x i ).

[0049] Step S3300, calculating the hesitation degree of hydrological information and decision result according to the membership degree and the non-membership degree.

[0050] It can be understood that, after obtaining the membership degree and the non-membership degree of the hydrological information and the decision result, in order to ensure that the distance between the hydrological information and the decision result can be accurately reflected, the hesitation degree of the hydrological information and the decision result is introduced here, which represents a fuzzy degree of measurement of whether x belongs to the hydrological information and the decision result.

[0051] Please refer to Figure 4 , Figure 4 for another embodiment of the specific implementation process of the above step S3300. As Figure 4 shown, step S3300 at least includes the following steps:

[0052] Step S3310, calculating the sum of the membership degrees of the hydrological information and the decision result according to the membership degree and the non-membership degree.

[0053] It can be understood that, the sum of the membership degrees of the hydrological information T A and the sum of the membership degrees of the decision result T B are as follows:

[0054] T A =u A (x)+v A (x)

[0055] T B =u B (x)+v B (x)

[0056] Step S3320, obtaining the hesitation degree of the hydrological information and the decision result according to the sum of the membership degrees.

[0057] It can be understood that, the hesitation degree of the hydrological information π A (x) and the hesitation degree of the decision result π B (x) are as follows:

[0058] π A (x)=1-u A(x) = 1 - u A (x)

[0059] π B (x) = 1 - u B (x) = 1 - u B (x)

[0060] It can be understood that, for the hesitation degree of hydrological information and decision result, 0≤π A (x)≤1, 0≤π B (x)≤1.

[0061] Step S3400, according to the membership, non-membership and hesitation, the distance value of hydrological information and decision result is obtained.

[0062] It can be understood that, by the above steps S3100 to S3300, the membership, non-membership and hesitation are obtained, and the distance value of hydrological information and decision result is obtained by logarithmic operation.

[0063] Please refer to Figure 5 , Figure 5 The specific implementation process diagram of another embodiment of the above step S3400 is shown. As Figure 5 shown, step S3400 further includes at least the following steps:

[0064] Step S3410, the membership between hydrological information and decision result is calculated and logarithmic operation is performed, and the membership influence value of hydrological information and decision result is obtained.

[0065] It can be understood that, by calculating the sum of the membership of hydrological information and the membership of decision set, the total value of membership is obtained, and the ratio of the membership of hydrological information to the total value of membership is calculated, which can more accurately obtain the distance measurement between hydrological information and decision set. Then, the ratio of the membership of hydrological information to the total value of membership is logarithmically processed. The logarithmic operation is the inverse operation of the exponential operation, and by using the logarithm, larger numbers can be more flexibly processed. After the membership between hydrological information and decision result is calculated and logarithmic operation is performed, the membership between hydrological information and decision result is added according to the logarithmic result, and specifically, the membership influence value U i As follows:

[0066]

[0067] Step S3420, the non-membership between hydrological information and decision result is calculated and logarithmic operation is performed, and the non-membership influence value of hydrological information and decision result is obtained.

[0068] It can be understood that, consistent with the above step S3410, the non-membership influence value V iAs shown below:

[0069]

[0070] Step S3430, calculate the hesitation degree between the hydrological information and the decision result and perform logarithmic operation to obtain the hesitation degree influence value of the hydrological information and the decision result.

[0071] It can be understood that, consistent with the above step S3410, the hesitation degree influence value π i As shown in the following formula:

[0072]

[0073] Step S3440, according to the membership degree influence value, the non-membership degree influence value and the hesitation degree influence value, obtain the distance value of the hydrological information and the decision result.

[0074] It can be understood that, after obtaining the membership degree influence value, the non-membership degree influence value and the hesitation degree influence value, in order to better consider the influence of the decision maker's tendency on the distance value in the actual situation, the tendency coefficient of membership degree, non-membership degree and hesitation degree needs to be introduced, so as to obtain a more reference value of the decision result.

[0075] Please refer to Figure 6 , Figure 6 The specific implementation process diagram of another embodiment of the above step S3440 is shown. As Figure 6 shown, step S3440 further includes at least the following steps:

[0076] Step S3441, according to the membership degree influence value and the preset membership degree tendency coefficient, obtain the membership degree reference value.

[0077] It can be understood that, the membership degree influence value U i and the preset membership degree tendency coefficient α are multiplied to obtain the membership degree reference value U' i As shown in the following formula:

[0078] U' i = α·U i

[0079] Step S3442, according to the non-membership degree influence value and the preset non-membership degree tendency coefficient, obtain the non-membership degree reference value.

[0080] It can be understood that, the non-membership degree influence value V i and the preset non-membership degree tendency coefficient β are multiplied to obtain the membership degree reference value U' i As shown in the following formula:

[0081] V i '= β·V i

[0082] Step S3443, obtaining a hesitation reference value according to the hesitation influence value and a preset hesitation tendency coefficient, wherein the sum of the membership tendency coefficient, the non-membership tendency coefficient and the hesitation tendency coefficient is a fixed value.

[0083] It can be understood that the hesitation influence value π i is multiplied by the preset hesitation tendency coefficient γ to obtain the hesitation reference value π' i , as shown in the following formula:

[0084] π' i = γ·π i

[0085] Wherein, α+β+γ=2.

[0086] Step S3444, summing the membership reference value, the non-membership reference value and the hesitation reference value to obtain the distance value of the hydrological information and the decision result.

[0087] It can be understood that the membership reference value, the non-membership reference value and the hesitation reference value obtained according to the above steps are summed to obtain the distance value d(A, B) of the hydrological information and the decision result, as shown in the following formula:

[0088]

[0089] Specifically, the sum of the membership reference value, the non-membership reference value and the hesitation reference value is square root processed by substituting the calculation results of the above steps, and the specific calculation formula is as shown below:

[0090]

[0091] Step S4000, obtaining the current decision result of the region range according to the distance value.

[0092] It can be understood that the distance value of the hydrological information and the decision result of each decision result in the decision set of the region range is obtained through the calculation of the distance value of the above steps. In actual application, the decision result with the smallest distance value of the hydrological information of the region range is used as the current decision result, so that the decision result is more consistent with the hydrological information of the current region range.

[0093] Please refer to Figure 7 , Figure 7 a specific implementation process diagram of another embodiment of the above step S4000 is shown. As Figure 7 shown, step S4000 further includes at least the following steps:

[0094] Step S4100, sorting the decision result corresponding to the optimization information according to the distance value to obtain the decision sorting result.

[0095] It can be understood that after the distance values of the hydrological information and each decision result in the decision set are obtained, the decision results corresponding to the optimization information are sorted by comparing the size of the distance values to obtain a decision sorting result. In actual application, the distance values can be sorted according to the order from small to large to quickly obtain the decision result with the minimum distance value to the optimization information.

[0096] In step S4200, a current decision result of the region range is obtained according to the decision sorting result.

[0097] It can be understood that after the decision sorting result is obtained, the decision result with the minimum distance value to the optimization information can be quickly obtained, and at this time, the decision result corresponding to the minimum distance value can be directly output as the current decision result. In actual application, there can be decision results with the same distance value to the current hydrological information, and at this time, the decision results with the same distance value need to be output according to the decision sorting result and provided to the user for selection.

[0098] Referring to Figure 8 , Figure 8 is a structural schematic diagram of the fuzzy set-based water conservancy system emergency decision device 600 provided in the embodiments of the present application. The entire process of the fuzzy set-based water conservancy system emergency decision method provided in the embodiments of the present application involves the following modules in the fuzzy set-based water conservancy system emergency decision device: an acquisition module 610, an optimization module 620, a calculation module 630, and a decision module 640.

[0099] The acquisition module 610 is configured to determine a region range and acquire hydrological information in the region range, where the hydrological information includes rainfall information, water flow information, and watershed information.

[0100] The optimization module 620 is configured to perform optimization processing on the hydrological information to obtain optimization information.

[0101] The calculation module 630 is configured to calculate distance values of the hydrological information to each decision result in a decision set according to the optimization information and the decision set, where the decision set is composed of historical hydrological information in the region range and corresponding decision results thereof, and the distance values are determined by the membership degrees of the hydrological information to the decision results and the non-membership degrees of the hydrological information to the decision results.

[0102] The decision module 640 is configured to obtain a current decision result of the region range according to the distance values.

[0103] It should be noted that the information interaction, execution process, and the like between the modules of the above device are based on the same concept as the method embodiments of the present application, and the specific functions and the technical effects brought by the same can be referred to the method embodiments part, which will not be described here.

[0104] Figure 9 An electronic device 600 provided by an embodiment of the present application is shown. The electronic device 600 includes but is not limited to:

[0105] a memory 601, configured to store a program;

[0106] a processor 602, configured to execute the program stored in the memory 601, and when the processor 602 executes the program stored in the memory 601, the processor 602 is configured to perform the above-mentioned fuzzy set-based emergency decision method for water conservancy system.

[0107] The processor 602 and the memory 601 can be connected by a bus or other means.

[0108] The memory 601, as a kind of non-transient computer readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs, such as the fuzzy set-based emergency decision method for water conservancy system described in any embodiment of the present application. The processor 602 realizes the above-mentioned fuzzy set-based emergency decision method for water conservancy system by running the non-transient software programs and instructions stored in the memory 601.

[0109] The memory 601 can include a program storage area and a data storage area, wherein the program storage area can store an operating system and application programs required by at least one function; the data storage area can store the fuzzy set-based emergency decision method for water conservancy system described above. In addition, the memory 601 can include a high-speed random access memory, and can also include a non-transient memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 601 can optionally include a memory remotely arranged with respect to the processor 602, and these remote memories can be connected to the processor 602 through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0110] The non-transient software programs and instructions required to realize the above-mentioned fuzzy set-based emergency decision method for water conservancy system are stored in the memory 601, and when executed by one or more processors 602, the fuzzy set-based emergency decision method for water conservancy system provided by any embodiment of the present application is executed.

[0111] The present application also provides a storage medium having computer executable instructions stored therein, and the computer executable instructions are used to execute the above-mentioned fuzzy set-based emergency decision method for water conservancy system.

[0112] In an embodiment, the storage medium stores computer executable instructions which are executed by one or more control processors 602, such as by one processor 602 in the electronic device 600, to cause the one or more processors 602 to perform the method for fuzzy set based emergency decision of water conservancy system according to any embodiment of the present application.

[0113] The above-described embodiments are merely illustrative for the present application and the units described as separated components can or can not be physically separated, i.e., can be located in one place or distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments.

[0114] Those skilled in the art can understand that all or some steps in the above disclosed method and system can be implemented as software, firmware, hardware and appropriate combination thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software can be distributed on a computer readable medium, which can include computer storage media (or non-transitory media) and communication media (or transitory media). As known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, it is known to those skilled in the art that communication media typically includes computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and can include any information delivery media.

Claims

1. A water conservancy system emergency decision-making method based on fuzzy sets, characterized in that, Includes the following steps: The area is defined, and hydrological information within the area is obtained, wherein the hydrological information includes rainfall information, water flow information, and watershed information; The hydrological information is optimized to obtain optimized information; Based on the optimized information and decision set, the distance value between the hydrological information and each decision result in the decision set is calculated. The decision set consists of historical hydrological information within the region and its corresponding decision results. The distance value is determined by the membership degree and non-membership degree between the hydrological information and the decision results. This includes: calculating the membership degree between the hydrological information and the decision results; calculating the non-membership degree between the hydrological information and the decision results; and calculating the hesitation degree between the hydrological information and the decision results based on the membership degree and the non-membership degree, including: calculating the sum of the membership degrees between the hydrological information and the decision results based on the membership degree and the non-membership degree; and obtaining the distance value between the hydrological information and the decision results based on the sum of the membership degrees. The degree of hesitation in the decision outcome; based on the membership degree, the non-membership degree, and the degree of hesitation, the distance value between the hydrological information and the decision outcome is obtained, including: calculating the membership degree between the hydrological information and the decision outcome and performing a logarithmic operation to obtain the membership degree influence value between the hydrological information and the decision outcome; calculating the non-membership degree between the hydrological information and the decision outcome and performing a logarithmic operation to obtain the non-membership degree influence value between the hydrological information and the decision outcome; calculating the degree of hesitation between the hydrological information and the decision outcome and performing a logarithmic operation to obtain the degree of hesitation influence value between the hydrological information and the decision outcome; and obtaining the distance value between the hydrological information and the decision outcome based on the membership degree influence value, the non-membership degree influence value, and the degree of hesitation influence value. Based on the distance value, the current decision result for the area is obtained.

2. The emergency decision-making method for water conservancy systems based on fuzzy sets according to claim 1, characterized in that, The optimization processing of the hydrological information to obtain optimized information includes: Calculate the average values ​​of the rainfall information, the water flow information, and the watershed information, respectively; The optimized information is obtained by filtering out the hydrological information based on the average value.

3. The emergency decision-making method for water conservancy systems based on fuzzy sets according to claim 1, characterized in that, The step of obtaining the distance value between the hydrological information and the decision result based on the membership degree influence value, the non-membership degree influence value, and the hesitation degree influence value includes: Based on the membership degree influence value and the preset membership degree tendency coefficient, a membership degree reference value is obtained; Based on the non-membership influence value and the preset non-membership tendency coefficient, the non-membership reference value is obtained; Based on the hesitation influence value and the preset hesitation tendency coefficient, a hesitation reference value is obtained, wherein the sum of the membership tendency coefficient, the non-membership tendency coefficient, and the hesitation tendency coefficient is a fixed value; The distance between the hydrological information and the decision result is obtained by summing the membership reference value, the non-membership reference value, and the hesitation reference value.

4. The emergency decision-making method for water conservancy systems based on fuzzy sets according to claim 1, characterized in that, The step of obtaining the current decision result for the region based on the distance value includes: The decision results corresponding to the optimization information are sorted according to the distance values ​​to obtain the decision ranking results; Based on the decision ranking results, the current decision result for the region is obtained.

5. An emergency decision-making device for water conservancy systems based on fuzzy sets, characterized in that, include: The acquisition module is used to determine the area range and acquire hydrological information within the area range, wherein the hydrological information includes rainfall information, water flow information and watershed information; The optimization module is used to optimize the hydrological information to obtain optimized information; The calculation module is used to calculate the distance value of the hydrological information to each decision result in the decision set based on the optimization information and the decision set, wherein the decision set consists of historical hydrological information within the region and its corresponding decision results, and the distance value is determined by the membership degree between the hydrological information and the decision results and the non-membership degree between the hydrological information and the decision results; The decision module is used to obtain the current decision result for the area range based on the distance value.

6. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the fuzzy set-based emergency decision-making method for water conservancy systems as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The system contains a computer program that, when executed by a processor, implements the fuzzy set-based emergency decision-making method for water conservancy systems as described in any one of claims 1 to 4.

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