Water conservancy system emergency decision-making method and device based on fuzzy set

Abstract

The invention discloses a water conservancy system emergency decision-making method and device based on a fuzzy set, and the method comprises the following steps: determining an area range, and obtaining hydrological information in the area range, the hydrological information including rainfall information, water flow information and drainage basin information; performing optimization processing on the hydrological information to obtain optimization information; according to the optimization information and a decision set, the distance value of the hydrological information in each decision result in the decision set is calculated, the decision set is composed of historical hydrological information in the regional range and the corresponding decision result, 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; according to the distance value, the current decision result of the area range is obtained, the tendency of the decision result is improved, and the timeliness and accuracy of emergency decision of the water conservancy system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency decision-making, and in particular to a method and device for emergency decision-making of a water conservancy system based on a fuzzy set. Background Art

[0002] As an information-intensive industry, water conservancy not only undertakes the work of water resource allocation and management, but also provides valuable hydrological and water conservancy information to society, such as rainfall, flood disasters, typhoons, mountain disasters, and tides. At the same time, this information also provides decision-making support for the government and water conservancy administrative decision-making departments in flood control and drought relief, water resource development and utilization, and water resource management. Currently, in order to process a large amount of water conservancy information in a timely manner, the existing emergency decision-making method for a water conservancy system uses a distance measurement method based on a fuzzy set to calculate the distance value between the water conservancy information and the decision set, which can accurately make an indication and discrimination and avoid results that are contrary to people's cognitive common sense. However, this distance measurement method does not consider the influence of the decision maker's tendency on the measurement result in the actual situation, and cannot quickly and accurately obtain a precise decision result based on the hydrological information, which affects the timeliness and accuracy of the emergency decision-making of the water conservancy system. Summary of the Invention

[0003] To solve the above problems, an object of the present invention is to provide a method, device, and storage medium for emergency decision-making of a water conservancy system based on a fuzzy set, which can improve the tendency of the decision result and the timeliness and accuracy of the emergency decision-making of the water conservancy system by calculating the membership degree and non-membership degree between the hydrological information and the decision set.

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

[0005] In a first aspect, an embodiment of the present application provides a method for emergency decision-making of a water conservancy system based on a fuzzy set. The method includes: determining a regional scope and obtaining hydrological information within the regional scope, where the hydrological information includes rainfall information, water flow information, and basin information; performing optimization processing on the hydrological information to obtain optimized information; calculating, according to the optimized information and a decision set, the distance value of each decision result of the hydrological information in the decision set, where the decision set is composed of historical hydrological information within the regional scope and its 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 obtaining the current decision result of the regional scope according to the distance value.

[0006] In a second aspect, an emergency decision-making device for a water conservancy system based on a fuzzy set provided by an embodiment of the present application includes: an acquisition module, configured to determine a regional scope and acquire hydrological information within the regional scope, where the hydrological information includes rainfall information, water flow information, and basin information; an optimization module, configured to perform optimization processing on the hydrological information to obtain optimized information; a calculation module, configured to calculate distance values of the hydrological information for each decision result in a decision set according to the optimized information and the decision set, where the decision set is composed of historical hydrological information within the regional scope and its corresponding decision results, and the distance values are determined by the membership degree of the hydrological information to the decision results and the non-membership degree of the hydrological information to the decision results; and a decision module, configured to obtain a current decision result of the regional scope according to the distance values.

[0007] In a third aspect, an electronic device provided by an embodiment of the present application includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the emergency decision-making method for a water conservancy system based on a fuzzy set as described above is implemented.

[0008] In a fourth aspect, a computer-readable storage medium provided by an embodiment of the present application stores a computer program, and when the computer program is executed by a processor, the emergency decision-making method for a water conservancy system based on a fuzzy set as described above is implemented.

[0009] In the embodiment of the present application, by determining a regional scope and acquiring hydrological information within the regional scope, where the hydrological information includes rainfall information, water flow information, and basin information; performing optimization processing on the hydrological information to obtain optimized information; calculating distance values of the hydrological information for each decision result in a decision set according to the optimized information and the decision set, where the decision set is composed of historical hydrological information within the regional scope and its corresponding decision results, and the distance values are determined by the membership degree of the hydrological information to the decision results and the non-membership degree of the hydrological information to the decision results; and obtaining a current decision result of the regional scope according to the distance values, the tendency of the decision result is improved, and the timeliness and accuracy of the emergency decision-making for the water conservancy system are enhanced.

[0010] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 is a flowchart of the emergency decision-making method for a water conservancy system based on a fuzzy set according to an embodiment of the present invention;

[0012] Figure 2 is Figure 1 a flowchart of step S2000 in [[ID=]]

[0013] Figure 3 is Figure 1 the flowchart of step S3000 in

[0014] Figure 4 is Figure 3 the flowchart of step S3300 in

[0015] Figure 5 is Figure 3 the flowchart of step S3400 in

[0016] Figure 6 is Figure 5 the flowchart of step S3440 in

[0017] Figure 7 is Figure 1 the flowchart of step S4000 in

[0018] Figure 8 is the structural diagram of the emergency decision-making device for water conservancy systems based on fuzzy sets according to the embodiments of the present invention;

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

[0020] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0021] In the description of the present invention, it should be understood that for the orientation descriptions, such as upper, lower, front, rear, left, right, etc., the orientation or positional relationship indicated is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.

[0022] In the description of the present invention, the meaning of several is one or more, the meaning of multiple is two or more, greater than, less than, exceeding, etc. are understood as not including the number itself, and above, below, within, etc. are understood as including the number itself. If there is a description of first and second, it is only for the purpose of distinguishing technical features and should not be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence relationship of the indicated technical features.

[0023] In the description of the present invention, unless otherwise clearly defined, terms such as "setting", "installation", "connection", etc. should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific content of the technical solution.

[0024] The emergency decision-making method, device and storage medium for a water conservancy system based on a fuzzy set according to an embodiment of the present invention determine a regional scope and obtain hydrological information within the regional scope, where the hydrological information includes rainfall information, water flow information and basin information; optimize the hydrological information to obtain optimized information; calculate the distance values of the hydrological information for each decision result in the decision set according to the optimized information and the decision set, where the decision set consists of historical hydrological information within the regional scope and its corresponding decision results, 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; obtain the current decision result of the regional scope according to the distance values, enhance the tendency of the decision result, and improve the timeliness and accuracy of the emergency decision-making for the water conservancy system.

[0025] At present, a large number of water conservancy projects, such as sluices and pumping stations, have been built in the river network. Reasonable regulation of these projects is one of the effective measures to solve water problems. The intertwined water systems and the dense engineering groups together constitute a complex system. In the face of different water safety problems and emergencies, corresponding decision results and corresponding engineering joint dispatching 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 decision results from hydrological information with a large amount of data and many reference dimensions, the existing emergency decision-making system for water conservancy systems introduces the data processing method of fuzzy sets.

[0026] A fuzzy set is a set used to express fuzzy concepts, also known as a fuzzy set or a fuzzy subset. An ordinary set refers to the totality of objects with a certain attribute. Fuzzy set theory regards the object to be investigated and the fuzzy concept reflecting it as a certain fuzzy set, establishes an appropriate membership function, and analyzes the fuzzy object through relevant operations and transformations of the fuzzy set. Fuzzy set theory is based on fuzzy mathematics and studies non-precise phenomena. In the objective world, there are a large number of fuzzy phenomena that are both this and that. There are also many fuzzy concepts in people's thinking, such as young, very large, warm, evening, etc. The object attributes described by these concepts cannot be simply answered with "yes" or "no". A fuzzy set refers to the totality of objects with the attributes described by a certain fuzzy concept. Since the concept itself is not clear and well-defined, the membership relationship of the object to the set is not clear and either-or.

[0027] Intuitionistic Fuzzy Sets (IFSs) are an extension of Fuzzy sets (FSs). The distance measurement between IFSs is an important research issue in the theory of IFSs. Based on the research of existing distance measurement methods for IFSs, a new distance measurement method based on the normal distribution function is proposed based on the voting model. This method can not only solve the distance measurement problems between several special intuitionistic fuzzy sets, but also overcome the defects existing in several existing distance measurement methods. Moreover, it is very suitable for the distance measurement between linguistic 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 preference on the measurement result in the actual situation, and cannot quickly and accurately obtain accurate decision results based on hydrological information, affecting the timeliness and accuracy of emergency decision-making in the water conservancy system.

[0028] Based on the above, the embodiments of the present invention provide an emergency decision-making method, device and storage medium for a water conservancy system based on fuzzy sets. By determining the regional scope and obtaining the hydrological information within the regional scope, where the hydrological information includes rainfall information, water flow information and basin information; optimizing the hydrological information to obtain optimized information; calculating the distance value of the hydrological information for each decision result in the decision set according to the optimized information and the decision set, where the decision set is composed of historical hydrological information within the regional scope and its 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; obtaining the current decision result of the regional scope according to the distance value, enhancing the tendency of the decision result, and improving the timeliness and accuracy of emergency decision-making in the water conservancy system.

[0029] Please refer to Figure 1 , Figure 1 which shows the flow of the emergency decision-making method for a water conservancy system based on fuzzy sets provided by the embodiments of the present invention. As Figure 1 shown, the emergency decision-making method for a water conservancy system based on fuzzy sets according to the embodiments of the present invention includes the following steps:

[0030] Step S1000: Determine the regional scope and obtain the hydrological information within the regional scope, where the hydrological information includes rainfall information, water flow information and basin information.

[0031] It can be understood that hydrological information is the general term for measured hydrological data and the results obtained from its analysis and calculation. Specifically, hydrological information is an important basis for basin management, engineering planning and design, flood control and drought relief, and formulating social and economic development plans. In practical applications, hydrological information includes, but is not limited to, rainfall information, water flow information, and basin information. Among them, rainfall information refers to the total amount of rainfall (snow) within 12 or 24 hours, and can also refer to the depth of liquid or solid (after melting) water that falls from the sky to the ground and accumulates on a horizontal surface without evaporation, infiltration, or runoff. Water flow information refers to the changing situation of river water level, flow rate, etc. over time. The water level and flow rate have daily, monthly, quarterly, annual, and multi-year variations, which are important hydrological characteristics of rivers and one of the important bases for river classification and hydrological zoning. In a broad sense, river water regime also includes changes in river ice regime, river sediment, water quality, etc. Basin information includes the basin area, river network density, basin shape, basin height, basin direction, or main stream direction of the rivers within the current regional scope. By obtaining hydrological information within the regional scope, the hydrological situation within the current regional scope can be accurately grasped, and based on the current rainfall and water flow conditions, the decision-making results for the regional scope can be obtained quickly and accurately.

[0032] Step S2000: Optimize the hydrological information to obtain optimized information.

[0033] It can be understood that during the detection and collection of hydrological information, error values will be generated due to sudden situations, instrument failures, transmission errors, etc., that is, there are noise values in the hydrological information. To ensure the accuracy of decision-making results and obtain high-information-content and useful knowledge, ideally, the hydrological information is correct data without noise. Hydrological information is the basis for the emergency decision-making method of the water conservancy system based on fuzzy sets. To avoid the situation where the decision-making results have too large errors, it is necessary to perform data optimization processing on the hydrological information.

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

[0035] Step S2100: Calculate the average values of rainfall information, water flow information, and basin information respectively.

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

[0037] Step S2200: Screen the hydrological information according to the mean value to obtain optimized information.

[0038] It can be understood that after obtaining the mean values of rainfall information, water flow information, and basin information, by comparing the gap between the current rainfall information, water flow information, and basin information and their corresponding mean values, it is further determined whether to perform a data cleaning operation on the current hydrological information. Data cleaning refers to the process of reexamining and validating data, aiming to discover and correct errors in the data file to ensure data consistency and accuracy. This process includes removing duplicate information, correcting errors, and handling invalid values and missing values to ensure the accuracy of hydrological information and avoid the impact of incorrect hydrological information on the decision-making results. In practical applications, by determining whether the difference between the current hydrological information and the mean value is greater than a preset threshold, it is determined whether to perform a data cleaning operation on the current hydrological information to achieve error correction and optimization of the hydrological information.

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

[0040] Step S3000: Calculate the distance values of the hydrological information for each decision result in the decision set, where the decision set consists of historical hydrological information within the regional scope and its corresponding decision results, and the distance values are determined by the membership degree and non-membership degree of the hydrological information to the decision results.

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

[0042] In practical applications, the decision set consists of historical hydrological information within a region and its corresponding decision results. For example, when the rainfall is greater than a preset rainfall threshold, the sluice gate is opened for drainage; when the water flow is greater than a preset warning flow value, corresponding waterlogging warning operations are carried out. The specific decision set is flexibly set and adjusted according to the historical data of the current region, which will not be elaborated here.

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

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

[0045] Step S3100, calculate the membership degree of hydrological information and decision results.

[0046] It can be understood that according to the intuitionistic fuzzy set A preset in the above steps as hydrological information and the intuitionistic fuzzy set B as the decision set. As known 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 hydrological information is u A (x i ), and the membership degree of decision results is u B (x i ).

[0047] Step S3200: Calculate the non-membership degree between the hydrological information and the decision result.

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

[0049] Step S3300: Calculate the hesitation between the hydrological information and the decision result based on the membership degree and the non-membership degree.

[0050] It can be understood that after obtaining the membership and non-membership of the hydrological information and the decision-making results, in order to ensure that the distance between the hydrological information and the decision-making results can be accurately reflected, the hesitation of the hydrological information and the decision-making results is introduced here to represent a measure of the fuzziness of whether x belongs to the hydrological information and the decision-making results.

[0051] See Figure 4 , Figure 4 FIG. 5 is a schematic diagram showing a specific implementation process of another embodiment of the above step S3300. Figure 4 As shown, step S3300 includes at least the following steps:

[0052] Step S3310: Calculate the sum of the membership degree of the hydrological information and the membership degree of the decision result based on the membership degree and the non-membership degree.

[0053] It is understandable that the sum of hydrological information membership and T A The sum of the membership degree of the decision result and T B As shown in the following formula:

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

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

[0056] Step S3320: Obtain the hesitation between the hydrological information and the decision result based on the sum of the membership degrees.

[0057] It is understandable that the hesitation of hydrological information π A (x) and the hesitation of the decision result π B (x) is shown in the following formula:

[0058] π A (x) = 1 - u A(x)-v A (x)

[0059] π B (x) = 1 - u B (x)-v B (x)

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

[0061] Step S3400: Obtain the distance value between the hydrological information and the decision-making result according to the membership degree, non-membership degree, and hesitation degree.

[0062] It can be understood that through the membership degree, non-membership degree, and hesitation degree obtained in the above steps S3100 to S3300, by performing logarithmic operations on them, the distance value between the hydrological information and the decision-making result is obtained.

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

[0064] Step S3410: Calculate the membership degree between the hydrological information and the decision-making result and perform logarithmic operations to obtain the membership degree influence value of the hydrological information and the decision-making result.

[0065] It can be understood that by calculating the sum of the membership degree of the hydrological information and the membership degree of the decision-making set, the total membership degree is obtained, and by calculating the ratio of the membership degree of the hydrological information to the total membership degree, the distance metric between the hydrological information and the decision-making set can be obtained more accurately. Then, logarithmic processing is performed on the ratio of the membership degree of the hydrological information to the total membership degree. Among them, the logarithmic operation is the inverse operation of the exponential operation, and using logarithms can handle larger numbers more flexibly. After performing logarithmic operations on the membership degree between the hydrological information and the decision-making result, the membership degrees between the hydrological information and the decision-making result are weighted and added according to the logarithmic result. Specifically, the membership degree influence value U of the hydrological information and the decision-making result i is as follows:

[0066]

[0067] Step S3420: Calculate the non-membership degree between the hydrological information and the decision-making result and perform logarithmic operations to obtain the non-membership degree influence value of the hydrological information and the decision-making result.

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

[0069]

[0070] Step S3430: Calculate the hesitancy degree between the hydrological information and the decision result and perform a logarithmic operation to obtain the hesitancy degree influence value of the hydrological information and the decision result.

[0071] It can be understood that, consistent with the above-mentioned step S3410, the hesitancy degree influence value π i is as shown in the following formula:

[0072]

[0073] Step S3440: Obtain the distance value between the hydrological information and the decision result based on the membership degree influence value, non-membership degree influence value, and hesitancy degree influence value.

[0074] It can be understood that after obtaining the membership degree influence value, non-membership degree influence value, and hesitancy degree influence value, in order to better consider the influence of the decision maker's preference on the distance value in the actual situation, it is necessary to introduce the preference coefficients of the membership degree, non-membership degree, and hesitancy degree, so as to obtain a more reference-worthy decision result.

[0075] Please refer to Figure 6 , Figure 6 which shows a schematic diagram of the specific implementation process of another embodiment of the above-mentioned step S3440. As Figure 6 shown, step S3440 at least further includes the following steps:

[0076] Step S3441: Obtain the membership degree reference value based on the membership degree influence value and the preset membership degree preference coefficient.

[0077] It can be understood that multiplying the membership degree influence value U i by the preset membership degree preference coefficient α to obtain the membership degree reference value U′ i is as shown in the following formula:

[0078] U′ i = α·U i

[0079] Step S3442: Obtain the non-membership degree reference value based on the non-membership degree influence value and the preset non-membership degree preference coefficient.

[0080] It can be understood that multiplying the non-membership degree influence value V i by the preset non-membership degree preference coefficient β to obtain the membership degree reference value U′ i is as shown in the following formula:

[0081] V i ′ = β·V i

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

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

[0084] π′ i = γ·π i

[0085] where α + β + γ = 2.

[0086] Step S3444: Sum the membership degree reference value, non-membership degree reference value, and hesitation degree reference value to obtain the distance value between the hydrological information and the decision result.

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

[0088]

[0089] Specifically, substitute the calculation results of the above steps, and perform a square root operation on the sum value of the membership degree reference value, non-membership degree reference value, and hesitation degree reference value to obtain the specific calculation formula as shown in the following:

[0090]

[0091] Step S4000: Obtain the current decision result of the regional scope according to the distance value.

[0092] It can be understood that through the calculation of the distance value in the above steps, the distance values between the hydrological information of the regional scope and each decision result in the decision set are obtained. In practical applications, the decision result with the smallest distance value from the hydrological information of the regional scope is used as the current decision result to make the decision result more in line with the hydrological information of the current regional scope.

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

[0094] Step S4100: Sort the decision results corresponding to the optimization information according to the distance value to obtain a decision sorting result.

[0095] It is understandable that after obtaining the distance values between the hydrological information and each decision result in the decision set, the decision results corresponding to the optimized information are sorted by comparing the magnitudes of the distance values to obtain a decision sorting result. In practical applications, the distance values can be sorted in ascending order to quickly obtain the decision result with the smallest distance value from the optimized information.

[0096] Step S4200: Obtain the current decision result of the regional scope according to the decision sorting result.

[0097] It is understandable that after obtaining the decision sorting result, the decision result with the smallest distance value from the optimized information can be quickly obtained. At this time, the decision result corresponding to the smallest distance value can be directly output as the current decision result. In practical applications, there may be decision results with the same distance value from the current hydrological information. At this time, the decision results with the same distance value need to be output according to the decision sorting result and provided for the user to select.

[0098] See Figure 8 , Figure 8 FIG. is a schematic structural diagram of a water conservancy system emergency decision-making device 600 based on a fuzzy set provided by an embodiment of the present application. The following modules in the water conservancy system emergency decision-making device based on a fuzzy set are involved in the entire process of the water conservancy system emergency decision-making method provided by the embodiment of the present application: an acquisition module 610, an optimization module 620, a calculation module 630, and a decision module 640.

[0099] Among them, the acquisition module 610 is used to determine a regional scope and obtain hydrological information within the regional scope, where the hydrological information includes rainfall information, water flow information, and basin information;

[0100] The optimization module 620 is used to perform optimization processing on the hydrological information to obtain optimized information;

[0101] The calculation module 630 is used to calculate the distance values between the hydrological information and each decision result in the decision set according to the optimized information and the decision set, where the decision set is composed of historical hydrological information within the regional scope and its corresponding decision results, and the distance values are determined by the membership degree and non-membership degree between the hydrological information and the decision results;

[0102] The decision module 640 is used to obtain the current decision result of the regional scope according to the distance values.

[0103] It should be noted that for the information interaction, execution process, etc. between the modules of the above device, since they are based on the same concept as the method embodiment of the present application, their specific functions and the technical effects brought are specifically described in the method embodiment part, and will not be elaborated here.

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

[0105] A memory 601 for storing programs;

[0106] A processor 602 for executing the programs stored in the memory 601. When the processor 602 executes the programs stored in the memory 601, the processor 602 is used to execute the above-mentioned emergency decision-making method for water conservancy systems based on fuzzy sets.

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

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

[0109] The memory 601 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store the execution of the above-mentioned emergency decision-making method for water conservancy systems based on fuzzy sets. In addition, the memory 601 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory 601 may optionally include a memory remotely set relative 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 enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0110] The non-transitory software programs and instructions required to implement the above-mentioned emergency decision-making method for water conservancy systems based on fuzzy sets are stored in the memory 601. When executed by one or more processors 602, they execute the emergency decision-making method for water conservancy systems based on fuzzy sets provided in any embodiment of the present application.

[0111] An embodiment of the present application also provides a storage medium storing computer-executable instructions for executing the above-mentioned emergency decision-making method for water conservancy systems based on fuzzy sets.

[0112] In one embodiment, the storage medium stores computer-executable instructions that are executed by one or more control processors 602, for example, by one of the processors 602 in the aforementioned electronic device 600, enabling the one or more processors 602 to execute the water conservancy system emergency decision-making method based on fuzzy sets provided in any embodiment of the present application.

[0113] The embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0114] Those of ordinary skill in the art can understand that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and their appropriate combinations. Some physical components or all 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 implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include a computer storage medium (or non-transitory medium) and a communication medium (or transitory medium). As is well known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing 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 technologies, CD-ROM, digital versatile disk (DVD), or other optical disk storage, magnetic cassette, tape, magnetic disk storage, or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, as is well known to those of ordinary skill in the art, 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 transmission mechanism, and can include any information delivery medium.

Claims

1. A water conservancy system emergency decision-making method based on fuzzy sets, characterized in that It includes the following steps: Determine the regional scope and obtain the hydrological information within the regional scope, where the hydrological information includes rainfall information, water flow information, and basin information; Perform optimization processing on the hydrological information to obtain optimized information; According to the optimized information and the decision set, calculate the distance values of the hydrological information for each decision result in the decision set, where the decision set consists of the historical hydrological information within the regional scope and its corresponding decision results, and the distance values are determined by the membership degree and non-membership degree of the hydrological information and the decision results; Obtain the current decision result of the regional scope according to the distance values.

2. The emergency decision-making method for a water conservancy system based on a fuzzy set according to claim 1, wherein The performing optimization processing on the hydrological information to obtain optimized information includes: Calculate the average values of the rainfall information, the water flow information, and the basin information respectively; Screen the hydrological information according to the average values to obtain the optimized information.

3. The emergency decision-making method for water conservancy systems based on fuzzy sets according to claim 1, wherein The calculating the distance values of the hydrological information for each decision result in the decision set according to the optimized information and the decision set includes: Calculate the membership degree of the hydrological information and the decision result; Calculate the non-membership degree of the hydrological information and the decision result; Calculate the hesitation degree of the hydrological information and the decision result according to the membership degree and the non-membership degree; Obtain the distance value of the hydrological information and the decision result according to the membership degree, the non-membership degree, and the hesitation degree.

4. The emergency decision-making method for water conservancy systems based on fuzzy sets according to claim 3, characterized in that, The calculating the hesitation degree of the hydrological information and the decision result according to the membership degree and the non-membership degree includes: Calculate the sum of the membership degree of the hydrological information and the decision result according to the membership degree and the non-membership degree; Obtain the hesitation degree of the hydrological information and the decision result according to the sum of the membership degree.

5. The emergency decision-making method for water conservancy systems based on fuzzy sets according to claim 3, wherein The obtaining the distance value of the hydrological information and the decision result according to the membership degree, the non-membership degree, and the hesitation degree includes: Calculate the membership degree between the hydrological information and the decision result and perform logarithmic operation to obtain the membership degree influence value of the hydrological information and the decision result; Calculate the non-membership degree between the hydrological information and the decision result and perform logarithmic operation to obtain the non-membership degree influence value of the hydrological information and the decision result; 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; Obtain the distance value of the hydrological information and the decision result according to the membership degree influence value, the non-membership degree influence value, and the hesitation degree influence value.

6. The emergency decision-making method for a water conservancy system based on a fuzzy set according to claim 5, characterized in that The obtaining the distance value of the hydrological information and the decision result according to the membership degree influence value, the non-membership degree influence value, and the hesitation degree influence value includes: Obtain the membership degree reference value according to the membership degree influence value and the preset membership degree inclination coefficient; Obtain the non-membership degree reference value according to the non-membership degree influence value and the preset non-membership degree inclination coefficient; According to the hesitation degree influence value and a preset hesitation degree tendency coefficient, a hesitation degree reference value is obtained, wherein the sum of the membership degree tendency coefficient, the non-membership degree tendency coefficient and the hesitation degree tendency coefficient is a fixed value; The membership degree reference value, the non-membership degree reference value and the hesitation degree reference value are summed to obtain the distance value between the hydrological information and the decision result.

7. The emergency decision-making method for water conservancy systems based on fuzzy sets according to claim 1, characterized in that, The obtaining the current decision result of the regional scope according to the distance value includes: Sorting the decision results corresponding to the optimization information according to the distance value to obtain a decision sorting result; According to the decision sorting result, the current decision result of the regional scope is obtained.

8. Emergency decision-making device for water conservancy system based on fuzzy set, characterized in that, Including: An acquisition module, configured to determine a regional scope and acquire hydrological information within the regional scope, wherein the hydrological information includes rainfall information, water flow information and basin information; An optimization module, configured to perform optimization processing on the hydrological information to obtain optimized information; A calculation module, configured to calculate the distance value between the hydrological information and 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 within the regional scope and its corresponding decision results, and the distance value is determined by the membership degree between the hydrological information and the decision result and the non-membership degree between the hydrological information and the decision result; A decision module, configured to obtain the current decision result of the regional scope according to the distance value.

9. An electronic device, characterized in that, Including: A memory, a processor and a computer program stored on the memory and executable on the processor, when the processor executes the computer program, implementing the emergency decision-making method for a water conservancy system based on a fuzzy set according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A computer program is stored, when the computer program is executed by a processor, implementing the emergency decision-making method for a water conservancy system based on a fuzzy set according to any one of claims 1 to 7.

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