Ball valve self-excitation risk assessment method and system

Abstract

The invention belongs to the field of hydropower station equipment detection and evaluation, and particularly discloses a ball valve self-excitation risk evaluation method and system, and the method comprises the steps: obtaining ball valve self-excitation risk evaluation indexes, carrying out the arrangement and hierarchical division of the evaluation indexes according to a membership relation, carrying out the combined weighting through an analytic hierarchy process and an improved entropy weight method, and obtaining a risk evaluation result. Determining a weight vector of each level element of the criterion layer and the index layer; obtaining the original data of the operation states of a plurality of monitoring points of the index layer, preprocessing the original data, determining a fuzzy evaluation set of the preprocessed index data, and calculating a fuzzy evaluation result matrix of the fuzzy evaluation set by using a fuzzy function; calculating a fuzzy evaluation result of each level element of the criterion layer based on the fuzzy evaluation result matrix, the first weight vector and the second weight vector; and determining a scoring standard of the self-excitation risk level of the ball valve system to determine the risk level of the ball valve. According to the invention, the accuracy of risk detection and evaluation of the ball valve can be improved.

Description

Technical Field

[0001] The present application belongs to the field of hydropower station equipment detection and evaluation, and more specifically, to a ball valve self-excitation risk assessment method and system. Background Art

[0002] With the increasing use of renewable energy today, pumped storage power stations can effectively solve the intermittent and instability problems of renewable energy. In this context, maintaining the normal operation of pumped storage power stations is essential to ensure the stable, flexible and efficient operation of the power system. Only by maintaining the normal operation of pumped storage power stations can they better play their role in the power system and provide support for energy transformation and sustainable development.

[0003] As an important part of a pumped storage power station, the ball valve is mainly used to control flow and stabilize pressure. When the ball valve seal deteriorates and leaks, it will become a flexible valve and self-excited vibration will occur, which will seriously affect the safe and stable operation of the unit. At present, ball valve risk detection and assessment are usually achieved using sensors. However, sensors may be affected by environmental noise and interference, resulting in inaccurate data, which in turn leads to low accuracy in ball valve risk detection and assessment.

[0004] Therefore, how to improve the accuracy of ball valve risk detection and assessment is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In view of the defects of the prior art, the purpose of the present application is to provide a ball valve self-excitation risk assessment method and system, aiming to solve the problem of low accuracy in current ball valve risk detection and assessment.

[0006] To achieve the above objectives, the present application provides a ball valve self-excitation risk assessment method, comprising:

[0007] S101. Obtaining ball valve self-excitation risk assessment indicators, arranging and hierarchically dividing the assessment indicators according to their affiliation, obtaining a target layer, a criterion layer, and an indicator layer of the ball valve system, and respectively setting the hierarchical elements of the target layer, the criterion layer, and the indicator layer;

[0008] S102. Perform combined weighting using the analytic hierarchy process and the improved entropy weight method to determine the first weight vector of each level element of the criterion layer and the second weight vector of each level element of the indicator layer;

[0009] S103. Obtaining the original data of the operating status of multiple monitoring points of the indicator layer, and preprocessing the original data, determining the fuzzy evaluation set of the indicator data after preprocessing, and calculating the fuzzy evaluation result matrix of the fuzzy evaluation set using a fuzzy function;

[0010] S104. Calculating fuzzy evaluation results of elements of each level of the criterion layer based on the fuzzy evaluation result matrix, the first weight vector and the second weight vector;

[0011] S105. Determine a scoring standard for the self-excitation risk level of the ball valve system, and determine the risk level of the ball valve according to the interval in which the fuzzy evaluation result is within the scoring standard.

[0012] Optionally, the hierarchical element of the target layer is a ball valve system;

[0013] The hierarchical elements of the criteria layer include: system structure, operating status, and operating water filters;

[0014] The hierarchical elements of the indicator layer include: indicators of system structure: closed / open cavity stress area ratio, water diversion pipe length and pressure regulating well setting; indicators of operating status: working seal operation time, water leakage after the working seal is closed, switch cavity pressure difference after the working seal is closed, displacement after the working seal is closed and water pressure change of the water diversion pipe; indicators of operating the water filter: pressure difference before and after the filter and filter cleaning time.

[0015] Optionally, the S103 includes:

[0016] Acquire the original data, and perform data processing on the original data to eliminate invalid information data;

[0017] Standardize the original data of each indicator to obtain standardized data;

[0018] Obtaining a fuzzy evaluation set corresponding to the standardized data, and calculating the fuzzy coefficient of the quantifiable indicator data under each criterion layer and the direct evaluation result corresponding to the non-quantifiable indicator based on the fuzzy evaluation set and the fuzzy function;

[0019] The fuzzy evaluation result matrix is ​​determined based on the fuzzy coefficients and the direct evaluation results.

[0020] Optionally, the process of weighting using the analytic hierarchy process is as follows:

[0021] Compare l indicators in pairs to form an l-order comparison matrix B;

[0022] Determine the maximum eigenvalue r of the comparison matrix B and the eigenvalue w corresponding to the maximum eigenvalue r;

[0023] Performing consistency check based on the maximum characteristic root r, l indicators and a preset random consistency index;

[0024] It is determined that the consistency check passes, the feature vector is normalized to obtain a normalized vector, and an indicator weight vector is obtained according to the normalized vector.

[0025] Optionally, the process of weighting using the improved entropy weight method is as follows:

[0026] Construct a comparison matrix H from m evaluation objects n×m ;

[0027] For the comparison matrix H n×m Perform normalization processing to obtain a standardized evaluation matrix Y;

[0028] Performing a non-negative translation process on the standardized evaluation matrix Y, and obtaining information entropy from the standardized evaluation matrix after the non-negative translation process using information entropy theory;

[0029] Determine the difference coefficient of the j-th evaluation indicator according to the information entropy, and determine the entropy weight of the j-th evaluation indicator according to the proportion of the difference coefficient to the total difference coefficient of the information entropy of all evaluation indicators.

[0030] Among them, m is the number of evaluation objects and n is the number of evaluation indicators.

[0031] Optionally, it also includes:

[0032] According to the indicator weight vector, entropy weight, analytic hierarchy process weight coefficient and entropy weight method weight coefficient, a combined weight vector is obtained.

[0033] Optionally, the evaluation levels of the fuzzy evaluation set include safety, caution, danger and severity; and the fuzzy function is a triangular fuzzy function.

[0034] In a second aspect, the present application also provides a ball valve self-excitation risk assessment system, comprising:

[0035] A hierarchical division module is used to obtain the ball valve self-excitation risk assessment index, arrange and hierarchically divide the assessment index according to the affiliation, obtain the target layer, criterion layer and indicator layer of the ball valve system, and set the hierarchical elements of the target layer, criterion layer and indicator layer respectively;

[0036] A weight determination module, used to perform combined weighting using the hierarchical analysis method and the improved entropy weight method to determine the first weight vector of each level element of the criterion layer and the second weight vector of each level element of the indicator layer;

[0037] A matrix acquisition module, used to acquire the original data of the operating status of multiple monitoring points of the indicator layer, preprocess the original data, determine the fuzzy evaluation set of the indicator data after preprocessing, and calculate the fuzzy evaluation result matrix of the fuzzy evaluation set using a fuzzy function;

[0038] A fuzzy evaluation module, used for calculating fuzzy evaluation results of elements of each level of the criterion layer based on the fuzzy evaluation result matrix, the first weight vector and the second weight vector;

[0039] The risk assessment module is used to determine the scoring standard for the self-excitation risk level of the ball valve system, and determine the risk level of the ball valve according to the interval in which the fuzzy assessment result is within the scoring standard.

[0040] In a third aspect, the present application provides an electronic device comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method described in the first aspect or any possible implementation of the first aspect.

[0041] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.

[0042] In a fifth aspect, the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.

[0043] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0044] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the prior art:

[0045] (1) This application obtains the ball valve self-excitation risk assessment index system and divides the assessment index into levels, ensuring the comprehensiveness and systematicness of the assessment, and can comprehensively analyze the self-excitation risk of the ball valve to improve the accuracy of the risk assessment. In addition, the hierarchical analysis method and the improved entropy weight method are used for combined weighting, which can objectively reflect the importance of each assessment index. The combination of the two weighting methods can reduce the bias of subjective judgment and improve the accuracy of the assessment.

[0046] (2) This application can effectively deal with the uncertainty and ambiguity in the actual data by preprocessing and fuzzy evaluating the original data of the operating status of the monitoring point. The fuzzy evaluation method can further improve the reliability and accuracy of the evaluation results.

[0047] (3) This application provides clear guidance for the operation and maintenance of ball valves by determining the scoring criteria for the self-excitation risk level of the ball valve system and determining the risk level of the ball valve based on the fuzzy evaluation results.

[0048] (4) This application takes into account the system structure of the ball valve and its water supply pipeline, the historical operation and real-time operation status of the ball valve, and combines the subjective evaluation of experts with the objective evaluation of sample data to accurately determine the risk factor of self-excitation of the ball valve, so as to detect abnormalities in time and then arrange maintenance reasonably to ensure the safety of the power station. By establishing a comprehensive assessment method for identifying the risk of self-excitation of ball valves, it provides an accurate and effective basis for subsequent health management research on the status of the ball valve system. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart of a method for assessing the risk of self-excitation of a ball valve provided in an embodiment of the present application;

[0050] Figure 2 is a schematic diagram of a comprehensive evaluation index system of an embodiment of the present application;

[0051] Figure 3 It is a framework diagram of a ball valve self-excitation risk assessment method provided in an embodiment of the present application;

[0052] Figure 4 It is a structural schematic diagram of a ball valve self-excitation risk assessment system provided in an embodiment of the present application;

[0053] Figure 5 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0055] The term "and / or" in this article is a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The symbol " / " in this article indicates that the associated objects are in an or relationship, for example, A / B means A or B.

[0056] The terms "first" and "second" in the specification and claims herein are used to distinguish different objects rather than to describe a specific order of the objects. For example, a first response message and a second response message are used to distinguish different response messages rather than to describe a specific order of the response messages.

[0057] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0058] In the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more than two. For example, multiple processing units refer to two or more processing units, etc.; multiple elements refer to two or more elements, etc.

[0059] Next, the technical solutions provided in the embodiments of the present application are introduced.

[0060] Reference Figure 1 , the present application provides a ball valve self-excitation risk assessment method, comprising:

[0061] S101. Obtain the ball valve self-excitation risk assessment index, arrange and hierarchically divide the assessment index according to the affiliation, obtain the target layer, criterion layer and indicator layer of the ball valve system, and set the hierarchical elements of the target layer, criterion layer and indicator layer respectively;

[0062] S102. Perform combined weighting using the analytic hierarchy process and the improved entropy weight method to determine the first weight vector of each level element of the criterion layer and the second weight vector of each level element of the indicator layer;

[0063] S103. Obtaining the original data of the operating status of multiple monitoring points of the indicator layer, and preprocessing the original data, determining the fuzzy evaluation set of the indicator data after preprocessing, and calculating the fuzzy evaluation result matrix of the fuzzy evaluation set using the fuzzy function;

[0064] S104. Calculate the fuzzy evaluation results of each level element of the criterion layer based on the fuzzy evaluation result matrix, the first weight vector and the second weight vector;

[0065] S105. Determine a scoring standard for the self-excitation risk level of the ball valve system, and determine the risk level of the ball valve according to the interval in which the fuzzy evaluation result falls within the scoring standard.

[0066] Specifically, in the above S101, the principles of purposefulness, operability, comprehensiveness and independence are fully followed, and the steps of indicator decomposition, indicator preliminary selection and indicator screening are strictly followed. According to the regulations and the actual situation on site, a comprehensive evaluation index system for the self-excitation risk of ball valves including the target layer, the criterion layer and the indicator layer is constructed. Figure 2As shown. Set the target layer element as A, and then set the elements of the criterion layer connected to the target layer as B1, B2, ..., B m , let the index layer elements be C1, C2, …, C m ;

[0067] The hierarchical element of the target layer is a ball valve system;

[0068] The hierarchical elements of the criteria layer include: system structure, operating status, and operating water filters;

[0069] The hierarchical elements of the indicator layer include: indicators of system structure: closed / open cavity stress area ratio, water diversion pipe length and pressure regulating well setting; indicators of operating status: working seal operation time, water leakage after the working seal is closed, switch cavity pressure difference after the working seal is closed, displacement after the working seal is closed and water pressure change of the water diversion pipe; indicators of operating the water filter: pressure difference before and after the filter and filter cleaning time.

[0070] Specifically, the system structure index includes the closed / open cavity force area ratio f s , water diversion pipe length L, pressure regulating well setting T; the operating status indicator includes working seal operation time L t , Water leakage after the working seal is closed Q l , the pressure difference of the switch chamber after the working seal is closed ΔP, the displacement L after the working seal is closed s , water pressure change H(t) of the water diversion pipe; the operating water filter index refers to the pressure difference ΔP before and after the filter s , filter cleaning time T gl ;

[0071] In S102, this embodiment uses a combination of the analytic hierarchy process (AHP) and the improved entropy weight method to assign weights to the elements of the criterion layer and the indicator layer, combines subjective judgment and objective data, and improves the accuracy of weight assignment.

[0072] Furthermore, the operation data of each monitoring point of the ball valve is obtained through S103, preprocessed, and the data is evaluated using fuzzy logic to form a fuzzy evaluation result matrix reflecting the fuzzy evaluation status of each indicator.

[0073] Finally, through S104, based on the fuzzy evaluation result matrix and the aforementioned weight vector, the fuzzy evaluation results of the elements at each level of the criterion layer are calculated to comprehensively evaluate the self-excitation risk of the ball valve. According to the fuzzy evaluation results and the established scoring criteria, the risk level of the ball valve is determined to help clarify the specific risk status.

[0074] This application obtains the ball valve self-excitation risk assessment index system and divides the assessment index into levels to ensure the comprehensiveness and systematicness of the assessment, and can comprehensively analyze the self-excitation risk of the ball valve to improve the accuracy of the risk assessment. In addition, the use of the hierarchical analysis method and the improved entropy weight method for combined weighting can objectively reflect the importance of each assessment index. The combination of the two weighting methods can reduce the deviation of subjective judgment and improve the accuracy of the assessment.

[0075] Optionally, S103 specifically includes:

[0076] Acquire the original data, and perform data processing on the original data to eliminate invalid information data;

[0077] Standardize the original data of each indicator to obtain standardized data;

[0078] Obtaining a fuzzy evaluation set corresponding to the standardized data, and calculating the fuzzy coefficient of the quantifiable indicator data under each criterion layer and the direct evaluation result corresponding to the non-quantifiable indicator based on the fuzzy evaluation set and the fuzzy function;

[0079] The fuzzy evaluation result matrix is ​​determined based on the fuzzy coefficients and the direct evaluation results.

[0080] Specifically, the data processing process of this embodiment is as follows:

[0081] Data processing: Eliminate invalid information to ensure data quality.

[0082] Standardization: Convert raw data into standardized data to unify the dimensions.

[0083] Fuzzy evaluation: Based on standardized data, a fuzzy evaluation set is generated, and the fuzzy coefficient of each indicator (quantifiable indicator) or direct evaluation result (non-quantifiable indicator) is calculated.

[0084] Result matrix: The fuzzy coefficients and direct evaluation results are combined to form a fuzzy evaluation result matrix, denoted as D i .

[0085] The embodiments of the present application can ensure the accuracy and consistency of input data by eliminating invalid information data and standardizing processing. The use of fuzzy evaluation methods to process quantifiable indicators and non-quantifiable indicators can effectively handle the uncertainty and ambiguity of data and improve the objectivity and accuracy of evaluation results.

[0086] Furthermore, by combining the fuzzy coefficients and direct assessment results, the risk of ball valve self-excitation can be quantified more finely and a detailed fuzzy assessment result matrix can be generated, thereby providing a more accurate risk assessment.

[0087] Optionally, the process of weighting using the analytic hierarchy process is as follows:

[0088] Compare l indicators in pairs to form an l-order comparison matrix B;

[0089] Among them, the element b of the comparison matrix B pq It represents the result of comparing index p with index q;

[0090] Determine the maximum eigenvalue r of the comparison matrix B and the eigenvalue w corresponding to the maximum eigenvalue r;

[0091] Performing consistency check based on the maximum characteristic root r, l indicators and a preset random consistency index;

[0092] It is determined that the consistency check passes, the feature vector is normalized to obtain a normalized vector, and an indicator weight vector is obtained according to the normalized vector.

[0093] Specifically, the process of weighting using the hierarchical analysis method in this application includes the following steps:

[0094] Step 3.1 Construct a comparison matrix: l indicators are compared in pairs to form an l-order comparison matrix B, where: the element b of B pq represents the result of comparing index p with index q, and b pq =1 / b qp , the value is based on the 1-9 scale;

[0095] Step 3.2, calculate the maximum eigenvalue r of the matrix b and the eigenvalue w corresponding to r;

[0096] Step 3.3: Perform consistency check. The check method is as follows:

[0097]

[0098] Among them, RI is the random consistency index. When CR<0.1, the consistency test passes;

[0099] Step 3.4: If the consistency check in step 3.3 passes, w is normalized to obtain w0;

[0100] Step 3.5: Determine the indicator weight vector

[0101] Step 3.6: If the consistency in step 3.3 fails, the comparison matrix b can be adjusted to make it pass the consistency test and the weight vector can be re-determined.

[0102] Optionally, the process of weighting using the improved entropy weight method is as follows:

[0103] Construct a comparison matrix H from m evaluation objectsn×m ;

[0104] For the comparison matrix H n×m Perform normalization processing to obtain a standardized evaluation matrix Y;

[0105] Performing non-negative translation processing on the standardized evaluation matrix Y, and obtaining information entropy from the standardized evaluation matrix after the non-negative translation processing using information entropy theory;

[0106] Determine the difference coefficient of the j-th evaluation indicator according to the information entropy, and determine the entropy weight of the j-th evaluation indicator according to the proportion of the difference coefficient to the total difference coefficients of the information entropy of all evaluation indicators;

[0107] Among them, m is the number of evaluation objects and n is the number of evaluation indicators.

[0108] Specifically, the method for determining the index weights by improving the entropy weight method includes the following steps:

[0109] Step 4.1: Construct the comparison matrix H through m evaluation objects n×m : m refers to the number of evaluation objects, and n is the number of evaluation indicators;

[0110] Step 4.2: Normalize H n×m The standardized evaluation matrix Y is obtained by processing as follows:

[0111]

[0112] Among them, y ij is the element in the i-th row and j-th column of the standardized evaluation matrix Y; ij is the comparison matrix H n×m The element in the i-th row and j-th column; min j is the comparison matrix H n×m The minimum value of the jth column in j is the comparison matrix H n×m The maximum value of the jth column in .

[0113] Step 4.3: Perform a non-negative translation on the standardized matrix as follows:

[0114]

[0115] Among them, p ij It represents the weight of the i-th evaluation object with respect to the j-th evaluation index value. In order to ensure the integrity and validity of the data and prevent the index from having a value of 0 after standardization, this application adds 0.001 to all the 0 values ​​in the matrix elements obtained by standardization.

[0116] Step 4.4: According to the theory of information entropy, the information entropy of the normalized evaluation matrix after translation can be obtained as follows:

[0117]

[0118] Among them, e j represents the entropy value of the j-th evaluation index;

[0119] Step 4.5: Determine the weight of each indicator according to step 4.4:

[0120]

[0121] Among them, 1-e j represents the difference coefficient of information entropy of the jth evaluation index; w Sj Represents the entropy weight of the j-th evaluation index.

[0122] Optionally, it also includes:

[0123] Obtain a combined weight vector according to the indicator weight vector, entropy weight, analytic hierarchy process weight coefficient and entropy weight method weight coefficient;

[0124] As shown in the following formula:

[0125] w j =αw cj +βw sj

[0126] Among them, w j is the combined weight vector of the jth evaluation index, w cj is the indicator weight vector of the j-th evaluation indicator, w sj is the entropy weight of the j-th evaluation index, α is the weight parameter of the hierarchical analysis method, and β is the weight parameter of the entropy weight method.

[0127] Among them, α+β=1, α and β represent the weight coefficient of the hierarchical analysis method and the weight coefficient of the entropy weight method respectively. When the evaluation focuses on the subjective judgment of experts, α is greater than 0.5, otherwise, it is less than 0.5. If the subjective and objective factors are equally important, both are taken as 0.5.

[0128] Optionally, the evaluation levels of the fuzzy evaluation set include safety, caution, danger and severity; and the fuzzy function is a triangular fuzzy function.

[0129] Reference Figure 3 The complete process of a ball valve self-excitation risk assessment method provided in this application includes:

[0130] Construct a comprehensive evaluation indicator system;

[0131] The weights were determined using the analytic hierarchy process and improved entropy weight method;

[0132] data collection, statistics and calculations;

[0133] Determine the fuzzy evaluation set and fuzzy function;

[0134] Calculate the fuzzy evaluation results of each index;

[0135] Obtain the comprehensive evaluation value of self-excitation risk and determine the risk level.

[0136] Reference Figure 4 , the present application also provides a ball valve self-excitation risk assessment system, comprising:

[0137] The hierarchical division module 410 is used to obtain the ball valve self-excitation risk assessment index, arrange and hierarchically divide the assessment index according to the affiliation, obtain the target layer, criterion layer and indicator layer of the ball valve system, and set the hierarchical elements of the target layer, criterion layer and indicator layer respectively;

[0138] The weight determination module 420 is used to perform combined weighting using the hierarchical analysis method and the improved entropy weight method to determine the first weight vector of each level element of the criterion layer and the second weight vector of each level element of the indicator layer;

[0139] The matrix acquisition module 430 is used to acquire the original data of the operating status of multiple monitoring points of the indicator layer, preprocess the original data, determine the fuzzy evaluation set of the indicator data after preprocessing, and calculate the fuzzy evaluation result matrix of the fuzzy evaluation set using a fuzzy function;

[0140] A fuzzy evaluation module 440, configured to calculate fuzzy evaluation results of elements of each level of a criterion layer based on the fuzzy evaluation result matrix, the first weight vector, and the second weight vector;

[0141] The risk assessment module 450 is used to determine the scoring standard for the self-excitation risk level of the ball valve system, and determine the risk level of the ball valve according to the interval in which the fuzzy assessment result is within the scoring standard.

[0142] Optionally, the hierarchical element of the target layer is a ball valve system;

[0143] The hierarchical elements of the criteria layer include: system structure, operating status, and operating water filters;

[0144] The hierarchical elements of the indicator layer include: indicators of system structure: closed / open cavity stress area ratio, water diversion pipe length and pressure regulating well setting; indicators of operating status: working seal operation time, water leakage after the working seal is closed, switch cavity pressure difference after the working seal is closed, displacement after the working seal is closed and water pressure change of the water diversion pipe; indicators of operating the water filter: pressure difference before and after the filter and filter cleaning time.

[0145] Optionally, the matrix acquisition module is specifically used for:

[0146] Acquire the original data, and perform data processing on the original data to eliminate invalid information data;

[0147] Standardize the original data of each indicator to obtain standardized data;

[0148] Obtaining a fuzzy evaluation set corresponding to the standardized data, and calculating the fuzzy coefficient of the quantifiable indicator data under each criterion layer and the direct evaluation result corresponding to the non-quantifiable indicator based on the fuzzy evaluation set and the fuzzy function;

[0149] The fuzzy evaluation result matrix is ​​determined based on the fuzzy coefficients and the direct evaluation results.

[0150] Optionally, the process of weighting using the analytic hierarchy process is as follows:

[0151] Compare l indicators in pairs to form an l-order comparison matrix B; the element b of the comparison matrix B pq It represents the result of comparing index p with index q;

[0152] Determine the maximum eigenvalue r of the comparison matrix B and the eigenvalue w corresponding to the maximum eigenvalue r;

[0153] Performing consistency check based on the maximum characteristic root r, l indicators and a preset random consistency index;

[0154] It is determined that the consistency check passes, the feature vector is normalized to obtain a normalized vector, and an indicator weight vector is obtained according to the normalized vector.

[0155] Optionally, the process of weighting using the improved entropy weight method is as follows:

[0156] Construct a comparison matrix H from m evaluation objects n×m , where m is the number of evaluation objects and n is the number of evaluation indicators;

[0157] For the comparison matrix H n×m Perform normalization processing to obtain a standardized evaluation matrix Y;

[0158] Performing non-negative translation processing on the standardized evaluation matrix Y, and obtaining information entropy from the standardized evaluation matrix after the non-negative translation processing using information entropy theory;

[0159] The difference coefficient of the j-th evaluation indicator is determined according to the information entropy, and the entropy weight of the j-th evaluation indicator is determined according to the ratio of the difference coefficient to the total difference coefficients of the information entropy of all evaluation indicators.

[0160] Optionally, a vector combination module is further included, for:

[0161] The index weight vector and entropy weight of the j-th evaluation index are combined with the weight coefficient of the hierarchical analysis method and the weight coefficient of the entropy weight method to obtain the combined weight vector, as shown in the following formula:

[0162] w j =αw cj +βw sj

[0163] Among them, w j is the combined weight vector of the jth evaluation index, w cj is the indicator weight vector of the j-th evaluation indicator, w sj is the entropy weight of the j-th evaluation index, α is the weight parameter of the hierarchical analysis method, and β is the weight parameter of the entropy weight method.

[0164] Optionally, the evaluation levels of the fuzzy evaluation set include safety, caution, danger and severity; and the fuzzy function is a triangular fuzzy function.

[0165] It can be understood that the detailed functional implementation of each of the above-mentioned units / modules can be found in the introduction of the aforementioned method embodiment, and will not be repeated here.

[0166] It should be understood that the above-mentioned system is used to execute the methods in the above-mentioned embodiments. The implementation principles and technical effects of the corresponding program modules in the system are similar to those described in the above-mentioned methods. The working process of the system can refer to the corresponding process in the above-mentioned method and will not be repeated here.

[0167] Reference Figure 5 Based on the method in the above embodiment, the embodiment of the present application provides an electronic device, which may include: a processor (processor) 510, a communication interface (Communications Interface) 520, a memory (memory) 530 and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 can call the logic instructions in the memory 530 to execute the method in the above embodiment.

[0168] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.

[0169] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.

[0170] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0171] It is understandable that the processor in the embodiment of the present application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor can be a microprocessor or any conventional processor.

[0172] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0173] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0174] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.

[0175] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A ball valve self-excitation risk assessment method, characterized in that: include: S101. Obtaining ball valve self-excitation risk assessment indicators, arranging and hierarchically dividing the assessment indicators according to their affiliation, obtaining a target layer, a criterion layer, and an indicator layer of the ball valve system, and respectively setting the hierarchical elements of the target layer, the criterion layer, and the indicator layer; S102. Perform combined weighting using the analytic hierarchy process and the improved entropy weight method to determine the first weight vector of each level element of the criterion layer and the second weight vector of each level element of the indicator layer; S103. Obtaining the original data of the operating status of multiple monitoring points of the indicator layer, and preprocessing the original data, determining the fuzzy evaluation set of the indicator data after preprocessing, and calculating the fuzzy evaluation result matrix of the fuzzy evaluation set using a fuzzy function; S104. Calculating fuzzy evaluation results of elements of each level of the criterion layer based on the fuzzy evaluation result matrix, the first weight vector and the second weight vector; S105. Determine a scoring standard for the self-excitation risk level of the ball valve system, and determine the risk level of the ball valve according to the interval in which the fuzzy evaluation result is within the scoring standard.

2. The ball valve self-excitation risk assessment method according to claim 1 is characterized in that: The hierarchical element of the target layer is a ball valve system; The hierarchical elements of the criteria layer include: system structure, operating status, and operating water filters; The hierarchical elements of the indicator layer include: indicators of system structure: closed / open cavity stress area ratio, water diversion pipe length and pressure regulating well setting; indicators of operating status: working seal operation time, water leakage after the working seal is closed, switch cavity pressure difference after the working seal is closed, displacement after the working seal is closed and water pressure change of the water diversion pipe; indicators of operating the water filter: pressure difference before and after the filter and filter cleaning time.

3. The ball valve self-excitation risk assessment method according to claim 1, characterized in that: The S103 includes: Acquire the original data, and perform data processing on the original data to eliminate invalid information data; Standardize the original data of each indicator to obtain standardized data; Obtaining a fuzzy evaluation set corresponding to the standardized data, and calculating the fuzzy coefficient of the quantifiable indicator data under each criterion layer and the direct evaluation result corresponding to the non-quantifiable indicator based on the fuzzy evaluation set and the fuzzy function; The fuzzy evaluation result matrix is ​​determined based on the fuzzy coefficients and the direct evaluation results.

4. The ball valve self-excitation risk assessment method according to claim 1, characterized in that: The process of weighting using the analytic hierarchy process is as follows: Compare l indicators in pairs to form an l-order comparison matrix B; Determine the maximum eigenvalue r of the comparison matrix B and the eigenvalue w corresponding to the maximum eigenvalue r; Performing consistency check based on the maximum characteristic root r, l indicators and a preset random consistency index; It is determined that the consistency check passes, the feature vector is normalized to obtain a normalized vector, and an indicator weight vector is obtained according to the normalized vector.

5. The ball valve self-excitation risk assessment method according to claim 1, characterized in that: The process of weighting using the improved entropy weight method is as follows: Construct a comparison matrix H from m evaluation objects n×m ; For the comparison matrix H n×m Perform normalization processing to obtain a standardized evaluation matrix Y; Performing non-negative translation processing on the standardized evaluation matrix Y, and obtaining information entropy from the standardized evaluation matrix after the non-negative translation processing using information entropy theory; Determine the difference coefficient of the j-th evaluation indicator according to the information entropy, and determine the entropy weight of the j-th evaluation indicator according to the proportion of the difference coefficient to the total difference coefficients of the information entropy of all evaluation indicators; Among them, m is the number of evaluation objects and n is the number of evaluation indicators.

6. The ball valve self-excitation risk assessment method according to claim 5, characterized in that: Also includes: According to the indicator weight vector, entropy weight, analytic hierarchy process weight coefficient and entropy weight method weight coefficient, a combined weight vector is obtained.

7. The ball valve self-excitation risk assessment method according to any one of claims 1 to 6, characterized in that: The evaluation levels of the fuzzy evaluation set include safety, caution, danger and severity; and the fuzzy function is a triangular fuzzy function.

8. A ball valve self-excitation risk assessment system, characterized in that: include: A hierarchical division module is used to obtain the ball valve self-excitation risk assessment index, arrange and hierarchically divide the assessment index according to the affiliation, obtain the target layer, criterion layer and indicator layer of the ball valve system, and set the hierarchical elements of the target layer, criterion layer and indicator layer respectively; A weight determination module, used to perform combined weighting using the hierarchical analysis method and the improved entropy weight method to determine the first weight vector of each level element of the criterion layer and the second weight vector of each level element of the indicator layer; A matrix acquisition module, used to acquire the original data of the operating status of multiple monitoring points of the indicator layer, preprocess the original data, determine the fuzzy evaluation set of the indicator data after preprocessing, and calculate the fuzzy evaluation result matrix of the fuzzy evaluation set using a fuzzy function; A fuzzy evaluation module, used for calculating fuzzy evaluation results of elements of each level of the criterion layer based on the fuzzy evaluation result matrix, the first weight vector and the second weight vector; The risk assessment module is used to determine the scoring standard for the self-excitation risk level of the ball valve system, and determine the risk level of the ball valve according to the interval in which the fuzzy assessment result is within the scoring standard.

9. An electronic device, characterized in that: include: at least one memory for storing a computer program; At least one processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program runs on a processor, the processor is caused to execute the method according to any one of claims 1 to 7.

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