Hydropower station equipment dynamic operation and maintenance management system and method based on variable weight-set pair-extension fusion model

Abstract

The invention provides a hydropower station equipment dynamic operation and maintenance management system and method based on a variable weight-set pair-extension fusion model, and the system comprises a fusion model construction module which is used for constructing the variable weight-set pair-extension fusion model; the strategy determination module is used for determining an operation and maintenance management strategy based on the variable weight-set pair-extension fusion model; and the strategy application module is used for applying the operation and maintenance management strategy. According to the dynamic operation and maintenance management system and method for the hydropower station equipment based on the variable weight-set pair-extension fusion model, the variable weight-set pair-extension fusion model is constructed to carry out safety evaluation on the state of the hydropower station equipment, and the facility safety level and the possible conversion safety trend of the hydropower station equipment are dynamically obtained in time in the management system. The operation and maintenance management strategy is determined and applied according to the safety evaluation level and the conversion trend, the model sets the dynamic index weight, the safety state of the equipment can be accurately evaluated, the timeliness of follow-up hydropower station operation and maintenance management is further improved, and the operation and maintenance management is more suitable.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydropower station equipment management, and particularly relates to a hydropower station equipment dynamic operation and maintenance management system and method based on a variable weight-set pair-extensive fusion model. BACKGROUND

[0002] The occurrence of safety accidents of a hydropower station is a result of the cross action of multiple factors. On the basis of determining a safety evaluation system of hydropower station equipment, the safety level of the hydropower station equipment is divided, so as to quantize the evaluation indexes and perform safety evaluation of the hydropower station equipment.

[0003] The index weight is determined by using a "structure entropy weight method" combining a subjective assignment method and an objective assignment method. The basic idea of the structure entropy weight method is to analyze the system indexes and their mutual relations, decompose them into a plurality of independent hierarchical structures, combine a Delphi expert investigation method of collecting expert opinions and a fuzzy analysis method to form a "typical order" of the importance of the indexes, quantitatively analyze the uncertainty of the "typical order" structure by using an entropy theory, calculate the entropy value and "blinding" analysis, and statistically process the potential deviation data. The relative importance order of each index at the same level is obtained, and the importance value of the same type index at each level is determined, that is, the index weight.

[0004] However, the evaluation index weight of the hydropower station equipment reflects the influence degree of the risk factors on the equipment safety. In the equipment safety evaluation index system, the influence degree of each evaluation index on the equipment safety is not the same compared with other indexes. The fixed index weight cannot accurately determine the safety state of the equipment, and further, it is easy to cause the subsequent operation and maintenance to be not timely and not suitable.

[0005] Therefore, it is urgent to provide a hydropower station equipment dynamic operation and maintenance management system and method based on a variable weight-set pair-extensive fusion model to at least solve the above problems. SUMMARY

[0006] One of the purposes of the present application is to provide a hydropower station equipment dynamic operation and maintenance management system and method based on a variable weight-set pair-extensive fusion model. The safety of the hydropower station equipment is evaluated by constructing a variable weight-set pair-extensive fusion model to obtain a safety evaluation level. The operation and maintenance management strategy is determined and applied according to the safety evaluation level and the operation and maintenance management strategy library. The dynamic index weight of the model can accurately evaluate the safety state of the equipment and further improve the timeliness and suitability of the subsequent operation and maintenance management of the hydropower station.

[0007] The hydropower station equipment dynamic operation and maintenance management system based on the variable weight-set pair-extensive fusion model provided by the embodiment of the present application comprises:

[0008] The fusion model construction module is configured to construct a variable weight-set pair-extensive fusion model.

[0009] a strategy determination module configured to determine the operation and maintenance strategy based on the variable weight-set pair-extensible fusion model, the safety evaluation level of the hydropower station equipment, and the operation and maintenance strategy library;

[0010] a strategy application module configured to apply the operation and maintenance strategy.

[0011] Preferably, the fusion model construction module constructs the variable weight-set pair-extensible fusion model, which comprises the following steps:

[0012] determining the constant weight of the index based on the structural entropy weight method;

[0013] determining the variable weight of the index by dynamically correcting the constant weight based on the variable weight theory;

[0014] coupling the set pair theory and the matter-element extensible method, combining the variable weight, and constructing the variable weight-set pair-extensible coupling evaluation model;

[0015] wherein the index is dynamically updated according to the scoring of experts or staff familiar with the hydropower station equipment and facilities.

[0016] The hydropower station equipment dynamic operation and maintenance management system based on the variable weight-set pair-extensible fusion model provided in the embodiments of the present application further comprises the following:

[0017] an auxiliary operation and maintenance module configured to match the best operation and maintenance personnel and perform auxiliary operation and maintenance based on the corresponding operation and maintenance strategy when manual intervention is performed.

[0018] Preferably, the auxiliary operation and maintenance module matches the best operation and maintenance personnel and performs auxiliary operation and maintenance based on the corresponding operation and maintenance strategy when manual intervention is performed, which comprises the following steps:

[0019] S41: analyzing the operation and maintenance strategy and determining the target operation and maintenance task;

[0020] S42: obtaining the historical operation and maintenance tasks of the idle operation and maintenance personnel in the first time period;

[0021] S43: labeling the first feature of the historical operation and maintenance task of the same task type as the target operation and maintenance task on the preset time axis based on the execution time of the corresponding historical operation and maintenance task, wherein the first feature comprises the safety evaluation level of the hydropower station equipment corresponding to the historical operation and maintenance task and the operation and maintenance evaluation level of the historical operation and maintenance task;

[0022] S44: if the first feature meets the matching trigger condition, obtaining the time axis point existing in each feature distribution corresponding time axis point as the target axis point, wherein the matching trigger condition comprises that the feature distribution of the first feature of the feature type on the time axis meets the standard feature distribution of the feature type;

[0023] S45: If the acquisition is successful, a second feature is extracted according to a historical operation and maintenance task corresponding to the target axis point;

[0024] S46: According to a matching template preset according to the second feature, a second feature corresponding to a maximum matching value is obtained as a best operation and maintenance personnel according to the second feature;

[0025] S47: When the best operation and maintenance personnel intervenes in operation and maintenance, auxiliary operation and maintenance is performed based on a corresponding operation and maintenance management strategy.

[0026] Preferably, the auxiliary operation and maintenance module performs auxiliary operation and maintenance based on a corresponding operation and maintenance management strategy when the best operation and maintenance personnel intervenes in operation and maintenance, including:

[0027] According to the historical operation and maintenance task corresponding to the second feature with the maximum matching value, a pre-play model is constructed;

[0028] According to a pre-play result of the pre-play model and a management intervention time setting rule, a management intervention time is determined;

[0029] A model slice of the management intervention time is obtained;

[0030] According to the model slice, an intervention queue is obtained;

[0031] According to first intervention operation and maintenance live information, a pre-trigger of an intervention management instruction in the intervention queue is performed;

[0032] According to a model slice corresponding to the pre-trigger successful intervention management instruction, second intervention operation and maintenance live information is obtained;

[0033] According to the second intervention operation and maintenance live information, it is determined whether to determine to trigger the pre-trigger successful intervention management instruction;

[0034] If yes, auxiliary operation and maintenance is performed based on the determined to trigger intervention management instruction.

[0035] The water power station equipment dynamic operation and maintenance management method based on the variable weight-set pair-tubulation fusion model provided by the embodiment of the application comprises:

[0036] Step 1: Construct a variable weight-set pair-tubulation fusion model;

[0037] Step 2: Determine an operation and maintenance management strategy based on a safety evaluation level of a water power station equipment state and an operation and maintenance management strategy library of the variable weight-set pair-tubulation fusion model;

[0038] Step 3: Apply the operation and maintenance management strategy.

[0039] The application has the following beneficial effects:

[0040] The application obtains a safety evaluation grade by constructing a variable weight-set pair-extensive fusion model to evaluate the safety of the equipment of the hydropower station. According to the safety evaluation grade and an operation and maintenance management strategy library, an operation and maintenance management strategy is determined and applied. The model sets dynamic index weights, can accurately evaluate the safety state of the equipment, and further improves the timeliness of subsequent operation and maintenance management of the hydropower station and the suitability of the operation and maintenance management.

[0041] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the application. The objects and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof.

[0042] The technical solutions of the application are further described in detail below with reference to the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0043] The accompanying drawings are included to provide a further understanding of the application and constitute a part of the specification, illustrate embodiments of the application and are used to explain the application, and do not constitute a limitation on the application. In the drawings:

[0044] Figure 1 A schematic diagram of a dynamic operation and maintenance management system for equipment of a hydropower station based on a variable weight-set pair-extensive fusion model in an embodiment of the application;

[0045] Figure 2 A set pair-extensive set theory domain relationship diagram in an embodiment of the application;

[0046] Figure 3 A schematic diagram of a dynamic operation and maintenance management method for equipment of a hydropower station based on a variable weight-set pair-extensive fusion model in an embodiment of the application. DETAILED DESCRIPTION

[0047] The preferred embodiments of the application are described below with reference to the accompanying drawings, and it should be understood that the preferred embodiments described herein are only used to illustrate and explain the application, and do not limit the application.

[0048] The embodiment of the application provides a dynamic operation and maintenance management system for equipment of a hydropower station based on a variable weight-set pair-extensive fusion model, as shown in Figure 1 , which comprises:

[0049] A fusion model construction module 1 is configured to construct a variable weight-set pair-extensive fusion model.

[0050] The fusion model construction module is configured to construct a variable weight-set pair-extensive fusion model.

[0051] The structural entropy weight method is used to determine the constant weight of the index.

[0052] The constant weight is dynamically corrected based on the variable weight theory to determine the variable weight of the index;

[0053] The variable weight-set pair-extenics coupling evaluation model is constructed by coupling the set pair theory and the matter element extenics method and combining the variable weight.

[0054] The index is dynamically updated according to the scoring of experts or staff familiar with the equipment and facilities of the hydropower station.

[0055] The strategy determination module 2 is configured to determine the operation and maintenance strategy based on the variable weight-set pair-extenics fusion model, the safety evaluation level of the equipment of the hydropower station and the operation and maintenance strategy library.

[0056] The strategy application module 3 is configured to apply the operation and maintenance strategy.

[0057] The working principle and beneficial effects of the above technical solution are as follows:

[0058] The variable weight-set pair-extenics fusion model is constructed based on the traditional matter element extenics model, the constant weight is dynamically corrected based on the variable weight theory to determine the weight of the index, then the three principles of "same, different and opposite" in the set pair analysis are used to divide the extension set theory domain, the integrated contact membership and the grade characteristic variable are calculated, and the model for determining the safety evaluation level and the grade transition trend of the equipment of the hydropower station is determined. According to the maximum integrated contact membership principle, the safety level of the equipment of the hydropower station is determined, and the construction method of the variable weight-set pair-extenics fusion model is as follows:

[0059] 1. Determination of the constant weight:

[0060] The "structure entropy weight method" combining the subjective assignment method and the objective assignment method is used to determine the index weight. The basic idea of the structure entropy weight method is to analyze the system index and its mutual relationship, decompose it into several independent hierarchical structures, combine the Delphi expert investigation method collecting expert opinions and the fuzzy analysis method, form a "typical order" for the importance of the index, quantitatively analyze the uncertainty of the "typical order" structure by entropy theory, calculate the entropy value and "blindness" analysis, and statistically process the potential bias data. The relative importance order of each index at the same level is obtained, and the importance value of the same index at each level is determined, that is, the weight of the index.

[0061] Suppose that k experts participate in the consultation investigation, and k consultation tables are obtained, each table corresponds to an index set, denoted as U={u1, u2, …, u n}, the "typical order" array corresponding to the index set is denoted as (a i1 , a i2 , …, a in ), and the ordering matrix of the index obtained from the k tables is denoted as A (A=(a ij ) k×n, i = 1, 2, ..., k; j = 1, 2, ..., n, are called the "typical ranking" matrix of the indicators. Where a ij This indicates that the i-th expert's opinion on the j-th indicator u j Evaluation. i1 a i2 , ..., a in Take any number from the natural numbers {1, 2, ..., n}. For example, if you need to sort four indicators, then the indicator "typical sorting" array {a i1 a i2 , ..., a in In the sequence {}, n=4 can take any number from {1, 2, 3, 4}. The qualitative and quantitative transformation of the above "typical sorting" can be defined by the membership function of the qualitative sorting transformation, which can be transformed into:

[0062] μ(I)=ln(mI) / ln(m-1) (1)

[0063] Where I is the qualitative ranking number given by experts after evaluating a certain indicator according to the "typical ranking" format. If A is considered the "first choice" among the four indicators A, B, C, and D, then I is 1; if it is considered the "second choice," then I is 2; and so on. m is a variable defined on [0, 1], μ(I) is the membership function value corresponding to I, I = 1, 2, ..., j, j+1, where j is the actual maximum sequence number. For example, when j = 4, it means that 4 indicators are participating in the ranking, so the maximum sequence number is 4. m is the transformation parameter, which is taken as m = j + 2, i.e., m = 6.

[0064] When I = 1,

[0065] When I = j + 1, take the maximum index. Let I=a i Substituting into equation (1), we can obtain a ij Quantitative conversion value b ij (μ(a ij )=b ij ), b ij The membership degree is called the order number I. The matrix μ(a) ij )=b ij This is called the membership matrix. It considers the k experts' views on the index u. j The "discourse power" is the same, and the calculation of k experts' opinions on indicator u j The "consensus" is called the average level of awareness, denoted as b. j ,make:

[0066] b j =(b 1j +b 2j +…+b kj) / k (2)

[0067] Definition of expert z i For factor u j The uncertainty generated by cognition is called "cognitive blindness" and is denoted as Q j Let:

[0068] Q j = |{[max(b 1j ,b 2j ,…b kj )-b j ]+[min(b 1j ,b 2j ,…b kj )-b j ]} / 2 (3)

[0069] Obviously Q j ≥ 0.

[0070] For each factor u j , the overall cognitive degree x j of k experts about u j is defined as:

[0071] x j = b j (1-Q j ) (4)

[0072] The evaluation vector X = (x1, x2, …, x n ) of the overall k experts about the index u j is obtained from x j .

[0073] To obtain the weight of the index u j , the normalization processing of x j = b j (1-Q j ) is performed, and let:

[0074]

[0075] Obviously, α n (j = 1, 2, …, n) > 0, and (α1, α2, …, α n ) is the overall judgment of the consistency of the importance of the k "expert opinions" about the factor set U = {u1, u2, …, u n}, which conforms to the will or cognition of the k expert groups. W = {α1, α2, …, α n} is called the weight vector of the factor set U = {u1, u2, …, u m}.

[0076] 2. Determining the variable weights:

[0077] The weights of evaluation indicators for hydropower station equipment reflect the degree of impact of risk factors on the safety of hydropower station equipment. In the hydropower station equipment safety evaluation indicator system, since the degree of impact of each evaluation indicator on the safety of hydropower station equipment varies compared to other indicators, different weights must be assigned to each indicator according to its degree of impact. Only by rationally determining the weights of each evaluation indicator for hydropower station equipment can the safety status of the hydropower station equipment be determined more accurately. Considering that in the actual operation of hydropower station equipment, if the safety status of a certain indicator is poor, even if its weight is low, the overall safety status of the hydropower station equipment will decrease. To avoid fixed indicator weights affecting the effectiveness of the evaluation, variable weight theory is introduced. The indicator values ​​are used to determine the state variable weight vector, thereby correcting the constant weights of the indicators, making the determination of indicator weights more scientific, and laying the foundation for the rational determination of the safety status of hydropower station equipment.

[0078] Let X = (x1, x2, ..., x m ) is the factor state variable, W = (w1, w2, ..., w m ) is a constant weighted variable of the factor, S(X)=(S1(X),S2(X),…,S m (X) is the state-variable weight vector, W(X)=(W1(X),W2(X),…,W m (X)) is the variable weight vector. The variable weight W of the i-th index is... i (X) can be represented as:

[0079]

[0080] Compared to empirical and summative state-weighted vectors, exponential state-weighted vectors have advantages such as good fitting properties, clear principles, and easy parameter determination. They can be set as follows:

[0081]

[0082] In the formula, x i α is the value of the i-th evaluation indicator; α is the variable factor. When α > 0, an incentive-type state-weighted vector is generated, which aims to highlight the evaluation indicators with larger values ​​and does not have high requirements for the balance of indicators; when α < 0, a penalty-type state-weighted vector is generated, which aims to highlight the evaluation indicators with smaller values ​​and has certain requirements for the balance of each evaluation indicator; when α = 0, it is a constant weight model.

[0083] In the safety evaluation of hydropower station equipment, the smaller the value of each evaluation index is, the lower the safety degree of the frame body is. In actual engineering, even if the weight of a certain evaluation index is low, but its evaluation value is small, it will also significantly reduce the safety of the hydropower station equipment. Therefore, in order to reflect the balance of the evaluation index in the safety evaluation of the hydropower station equipment and highlight the evaluation index with small index value, the state variable of the punishment type is adopted. Because when α < -1, it means that the evaluator has gone to the extreme, so the present application takes α = -1.

[0084] 3. Establishment of the set pair-extensive coupling model:

[0085] In order to fully understand the overall risk level of the hydropower station equipment, on the basis of the establishment of the evaluation index system, a scientific safety evaluation model must be adopted to comprehensively analyze the risk of the hydropower station equipment, and then the weak links of the project are found through the evaluation results, and finally the safety condition of the hydropower station equipment is determined. From the above evaluation index system, it can be seen that the safety evaluation index of the hydropower station equipment involves many uncertain factors. The previous evaluation model of the hydropower station equipment all converts the uncertainty of the safety evaluation index of the hydropower station equipment into certainty for analysis, without considering the conversion of the two, which has certain limitations. Therefore, in order to effectively depict the uncertainty of the evaluation index and accurately determine the safety grade of the small and medium-sized hydropower station and the trend of changing to other safety grades. In view of the advantage that the same and opposite analysis can be effectively depicted in the set pair theory through the three division principle, the set pair theory and the matter-element extension method are coupled to construct a set pair-extensive coupling evaluation model.

[0086] The matter-element-extensive theory takes the extension set and the matter-element theory as the core. The extension set describes the "dynamic" of things, that is, the change of the nature of the research object, and the matter-element concept overcomes the incompatibility problem in evaluation. Considering that the safety influencing factors of the hydropower station equipment are mostly qualitative indexes and belong to multiple dimensions, the present application uses the matter-element concept to describe the problem to convert the complex multi-dimensional incompatibility problem of the safety evaluation of the hydropower station equipment into a quantifiable and solvable problem model.

[0087] For the safety evaluation of the hydropower station equipment, let the safety grade set of the hydropower station equipment N = [N1, N2, …, N j , …, N n ], the frame body safety evaluation index set C = [C1, C2, …, C i , …, C m ], the classical domain of each safety grade of the hydropower station equipment is determined by the division and quantification of the safety grade of the hydropower station equipment in the above, and the classical domain R is obtained as follows:

[0088]

[0089] In the formula, <a ij , bij >This is evaluation index C i Regarding security level N j The range of values.

[0090] The corresponding evaluation index's domain R m Represented as:

[0091]

[0092] In the formula, N represents all safety levels of the hydropower station equipment to be evaluated; m b m Under all safety levels of hydropower station equipment, evaluation index C m The range of values ​​for .

[0093] The matter element R0 to be evaluated is:

[0094]

[0095] In the formula, N0 represents the level of safety risk to be evaluated for the hydropower station equipment; V i The evaluation index C is the expert evaluation value of the level N0 to be evaluated.

[0096] This paper combines set pair analysis and extension theory. The domain of extension sets is divided using the ternary triad principle (identity, difference, and inverse) of set pair analysis. The relationship between set pair and extension set domains is shown in the diagram below. Figure 2 As shown.

[0097] Determine the relationship between the value V of evaluation index C and the various safety levels of hydropower station equipment: When the value V of evaluation index C... i When the evaluation index C is located within the standard positive region of the extension set of the discussion level j, the relationship between the evaluation index C and the safety level j is identical, indicating that from the perspective of this evaluation index, the hydropower station equipment is at the j-th safety level. The membership formula is as follows:

[0098]

[0099] In the formula, F ij+1 F ij It is the threshold value for the evaluation level; μ j (V i ) represents the membership degree of the framework to the standard positive domain formed by the i index in the safety level j under discussion.

[0100] When evaluation index C is within the adjacent level j-1 (j>1) or j+1 of discussion level j and V i Located in the positive transition region X1 of the extension set = (F ij－1 F ij ) or X2 = (F ij+1 F ij+2 ​) is the difference between the evaluation index C and the standard of the evaluation index i in the jth evaluation level. It indicates that the hydropower station equipment has a tendency to transform into the jth safety level from the perspective of the evaluation index. The membership degree is as follows:

[0101]

[0102] where F ij－1 , F ij+2 are the boundary values of the evaluation levels, and p(V i , V0) is the distance between the value V of the evaluation index C and the standard positive domain and the extension positive domain of the index i in the jth evaluation level. When j = 1, F ij－1 = F ij ; when j = n, F ij+2 = F in .

[0103] When the evaluation index C i falls into the adjacent evaluation level j-2 (> 2) or j+2 of the jth evaluation level or is not in any evaluation level, it is in the negative domain of the extension set of the safety evaluation levels. The relationship between the evaluation index C i and the jth safety level is independence. At this time, the membership degree is as follows:

[0104] μ j (V i ) = -1 (15)

[0105] which indicates that the hydropower station equipment is not in the ith safety level and has no possibility of transforming into the safety level from the perspective of the evaluation index.

[0106] The body integration membership degree μ j of the hydropower station equipment to each safety level N j (j = 1, 2, 3, 4, 5) is calculated as follows:

[0107]

[0108] where w i (X) is the variable weight of the evaluation index.

[0109] On the basis of the calculation of the integration membership degree of the hydropower station equipment, the safety level of the hydropower station equipment is determined according to the maximum integration membership degree principle as follows:

[0110] j = Max{μ1, μ2,..., μ n} (17)

[0111] In actual engineering, the safety state of the hydropower station equipment will change with the safety management of the engineering, in order to more scientifically determine the safety state of the hydropower station equipment, the application uses the integrated contact membership degree of the hydropower station equipment at each safety level to establish the level characteristic variable j of the hydropower station equipment to be evaluated * .

[0112]

[0113] In the formula, n is the number of evaluation levels of the hydropower station equipment, j is the level name, j=(1, 2, 3, 4, 5), and j is used to determine the trend of the hydropower station equipment to the adjacent safety level. * The safety level and the possible safety trend of the hydropower station equipment can be obtained in time and dynamically in the management system.

[0114] The operation and maintenance management strategy library pre-stores a plurality of one-to-one corresponding evaluation levels of the hydropower station equipment and corresponding operation and maintenance management strategies. When determining the operation and maintenance management strategy, the state of the hydropower station equipment (such as the state of the travel switch, the state of the brake plate, the count value of the pressure gauge and the integrated brake valve group Internet of Things data) is input into the variable weight-set pair-extensive fusion model to obtain the evaluation level of the hydropower station equipment (such as the safety level of the brake device is 3). The evaluation level of the hydropower station equipment is matched with the evaluation level of the hydropower station equipment in the operation and maintenance management strategy library to determine the operation and maintenance management strategy (such as the safety level of the brake device is 3, the level description is that the equipment and facility have safety hidden dangers, corresponding control measures need to be formulated and rectified within the time limit, and the operation and maintenance management strategy is to arrange personnel to overhaul the brake device and restore its safety level to 1 within 48 hours).

[0115] After determining the operation and maintenance management strategy, the application is performed. For example, the dispatching maintenance personnel go to the brake device for maintenance, and the time limit for the task is clear.

[0116] The application evaluates the safety of the hydropower station equipment by constructing a variable weight-set pair-extensive fusion model to obtain the safety evaluation level. According to the safety evaluation level and the operation and maintenance management strategy library, the operation and maintenance management strategy is determined and applied, the dynamic index weight of the model is set, the safety state of the equipment can be accurately evaluated, and the timeliness of the subsequent operation and maintenance management of the hydropower station is further improved, and the operation and maintenance management is more suitable.

[0117] In one embodiment, the method for obtaining the state of the hydropower station equipment of the strategy determination module comprises:

[0118] Based on the industrial Internet of Things technology, the data of the hydropower station equipment is obtained;

[0119] All the data of the hydropower station equipment of the same hydropower station equipment is summarized to obtain the state of the hydropower station equipment of the hydropower station equipment.

[0120] The working principle and beneficial effects of the above technical solution are:

[0121] The hydropower station equipment data is data of the hydropower station equipment collected based on the industrial internet of things technology, such as the travel switch state, the brake gate state, the pressure gauge count value and the integrated brake valve group internet of things data. All the hydropower station equipment data of the same hydropower station equipment is summarized to obtain the hydropower station equipment state of the hydropower station equipment, such as the travel switch state, the brake gate state, the pressure gauge count value and the integrated brake valve group internet of things data, which is the hydropower station equipment state of the "brake device" of the hydropower station equipment. The industrial internet of things technology is introduced to obtain the hydropower station equipment state, which is more reasonable.

[0122] The embodiment of the application provides a hydropower station equipment dynamic operation and maintenance management system based on a variable weight-set pair-takable fusion model, and further comprises:

[0123] An auxiliary operation and maintenance module is configured to match the best operation and maintenance personnel, and when manual intervention operation and maintenance is performed, auxiliary operation and maintenance is performed based on the corresponding operation and maintenance management strategy.

[0124] The auxiliary operation and maintenance module performs the following operations:

[0125] S41: Analyzing the operation and maintenance management strategy to determine a target operation and maintenance task.

[0126] S42: Obtaining historical operation and maintenance tasks of the idle operation and maintenance personnel in a first time period.

[0127] S43: Labeling the first feature of the historical operation and maintenance task of the same task type as the target operation and maintenance task on a preset time axis based on the execution time of the corresponding historical operation and maintenance task, and the first feature includes the safety evaluation level of the corresponding hydropower station equipment of the historical operation and maintenance task and the operation and maintenance evaluation level of the historical operation and maintenance task.

[0128] S44: If the first feature meets the matching trigger condition, obtaining a time axis point existing in each feature distribution as a target axis point; the matching trigger condition includes that the feature distribution of the first feature of the feature type on the time axis meets the standard feature distribution of the feature type.

[0129] S45: If the acquisition is successful, extracting the second feature according to the historical operation and maintenance task corresponding to the target axis point.

[0130] S46: Obtaining the idle operation and maintenance personnel corresponding to the second feature with the maximum matching value as the best operation and maintenance personnel according to the second feature and the second feature.

[0131] S47: When the best operation and maintenance personnel intervenes in the operation and maintenance, auxiliary operation and maintenance is performed based on the corresponding operation and maintenance management strategy.

[0132] The working principle and beneficial effects of the above technical solution are:

[0133] Best operator is the operator who is most suitable for the operation and maintenance of the hydropower station equipment according to the operation and maintenance strategy. Auxiliary operation is the process of assisting the best operator in the operation and maintenance of the hydropower station equipment, such as showing the best operator a virtual processing three-dimensional model of the problem to be solved.

[0134] When matching the best operator, first determine the target operation and maintenance task according to the operation and maintenance strategy. The target operation and maintenance task is a task that needs to be executed by the operator, and is the ultimate goal of operation and maintenance, such as overhauling the brake device to adjust its safety level from level 3 to level 1.

[0135] The first period is a period of time from the current time back to the past time. The length of the time interval of this period of time is set by the operator in advance, such as 30 days. The historical operation and maintenance task is all the completed operation and maintenance tasks of the idle operator in this period of time.

[0136] The task type is the type of the task, such as operating and maintaining the brake device, operating and maintaining the speed regulator oil pressure device, etc. The starting point of the preset time axis corresponds to the starting point of the above-mentioned period of time, and the end point of the time axis corresponds to the midpoint of the above-mentioned period of time. The first feature is extracted based on the corresponding feature type preset feature extraction template, such as the feature type is safety evaluation, and the corresponding preset feature extraction template is a template for extracting the safety evaluation level of the related hydropower station equipment for the historical operation and maintenance task. The first feature is labeled on the time axis based on the execution time of the corresponding historical operation and maintenance task.

[0137] The matching trigger condition is that the feature distribution of the first feature of the feature type on the time axis meets the standard feature distribution preset for the feature type. The standard feature distribution preset for the feature type is set by the operator in advance, such as when the feature type is safety evaluation, the standard feature distribution is the distribution of the first feature (historical safety evaluation level) on the time axis that is less than or equal to the safety evaluation level of the hydropower station equipment state; for example, when the feature type is operation and maintenance evaluation, the standard feature distribution is the distribution of the first feature (historical operation and maintenance evaluation level) on the time axis that is greater than or equal to the evaluation level threshold.

[0138] When judging whether the matching trigger condition is met, whether the feature distribution of the first feature of the feature type on the time axis meets the standard feature distribution preset for the feature type is judged, for example: when the feature type is safety evaluation, taking the first feature of the feature type as the y-axis and the time axis as the x-axis, a marking line parallel to the x-axis is drawn according to the safety evaluation level of the hydropower station equipment state, and if there is a marked first feature below the marking line, the feature distribution of the corresponding first feature of the feature type when the feature type is safety evaluation meets the standard feature distribution; for another example: when the feature type is operation and maintenance evaluation, taking the first feature of the feature type as the y-axis and the time axis as the x-axis, a marking line parallel to the x-axis is drawn according to the evaluation level threshold, and if there is a marked first feature above the marking line, the feature distribution of the corresponding first feature of the feature type when the feature type is operation and maintenance evaluation meets the standard feature distribution. When the feature distribution of the first feature of all feature types meets the standard feature distribution preset for the corresponding feature type, the time axis point existing in each feature distribution corresponding time axis point is obtained as the target axis point; if it is successful, according to the historical operation and maintenance task corresponding to the target axis point, the second feature is extracted, and the second feature is: the historical evaluation index value of the historical operation and maintenance task. The introduction of the matching trigger condition triggers the extraction of the second feature, which greatly reduces the extraction resources of the second feature.

[0139] The matching template preset for the second feature is a template for comparing the historical evaluation index value and the evaluation index value of the water power equipment triggering the target operation and maintenance task to calculate the index similarity value, for example: the template of comparing the travel switch state, brake gate state, pressure table count value and integrated brake valve group Internet of Things data in the target axis point corresponding historical operation and maintenance task, and the similarity value (matching value) of the travel switch state, brake gate state, pressure table count value and integrated brake valve group Internet of Things data of the water power equipment in the target operation and maintenance task, adaptively acquires the corresponding matching template according to the second feature, and improves the matching efficiency of the best operation and maintenance personnel.

[0140] After determining the best operation and maintenance personnel, the best operation and maintenance personnel is reminded to go to the corresponding hydropower station equipment needing operation and maintenance, and after the best operation and maintenance personnel accesses operation and maintenance, auxiliary operation and maintenance is performed based on the corresponding operation and maintenance management strategy.

[0141] The first feature of the historical operation and maintenance task of the same task type as the target operation and maintenance task is extracted, and when the first feature meets the matching trigger condition, the second feature is extracted, the second feature is the historical evaluation index value of the historical operation and maintenance task, which includes multiple index types, and directly matching the historical evaluation index value of the historical operation and maintenance task of all historical operation and maintenance tasks will waste a lot of computing power resources, the matching trigger condition is introduced to make a prior condition judgment, which greatly improves the determination efficiency of the best operation and maintenance personnel and consumes less computing power resources.

[0142] In one embodiment, the auxiliary operation and maintenance module further performs the following operations:

[0143] When the first feature does not satisfy the matching trigger condition or the target axis point acquisition fails, the first time period is extended based on a preset time period extension rule to obtain a second time period, the second time period is used to replace the first time period in S42, and S42-S47 are continued to be executed until the best operation and maintenance personnel is matched.

[0144] The working principle and beneficial effects of the above technical solution are:

[0145] When the first feature does not satisfy the matching trigger condition or the target axis point acquisition fails, the first time period is extended based on a preset time period extension rule (such as: extending 30 days each time) to obtain a second time period, the second time period is used to replace the first time period in S42, and S42-S47 are continued to be executed until the best operation and maintenance personnel is matched, thereby improving the comprehensiveness of matching situation identification.

[0146] In one embodiment, the auxiliary operation and maintenance module assists in operation and maintenance when the best operation and maintenance personnel intervenes in operation and maintenance, including:

[0147] According to the historical operation and maintenance task corresponding to the second feature with the largest matching value, a pre-play model is constructed;

[0148] According to the pre-play result of the pre-play model and a management intervention time setting rule, a management intervention time is determined;

[0149] A model slice of the management intervention time is obtained;

[0150] According to the model slice, an intervention queue is obtained;

[0151] According to the first intervention operation and maintenance live information, pre-triggering of the intervention management instruction in the intervention queue is performed;

[0152] According to the model slice corresponding to the intervention management instruction successfully pre-triggered, second intervention operation and maintenance live information is obtained;

[0153] According to the second intervention operation and maintenance live information, it is determined whether the intervention management instruction successfully pre-triggered is determined to be triggered;

[0154] If yes, the operation and maintenance is assisted based on the intervention management instruction determined to be triggered.

[0155] The working principle and beneficial effects of the above technical solution are:

[0156] Generally, when an operation and maintenance personnel executes a historical similar operation and maintenance task, the operation and maintenance personnel will execute the task according to personal experience, but even if the tasks are the same, the management expectations of the operation and maintenance tasks may be different, for example: the historical operation and maintenance task requires 48 hours to be completed, and the current operation and maintenance task needs to be completed within 24 hours.

[0157] Therefore, the pre-performance model is constructed according to the historical operation and maintenance task corresponding to the second feature with the largest matching value, and the pre-performance model is a three-dimensional model of the best operation and maintenance personnel performing the operation and maintenance task of the hydropower station equipment, which is constructed based on the task record (such as the on-site video when the best operation and maintenance personnel performs the task) of the historical operation and maintenance task. The pre-performance result is the task progress of each task stage pre-performed by the pre-performance model.

[0158] The management intervention opportunity setting rule is determined according to the operation and maintenance management strategy, and the operation and maintenance management strategy is used to predict to what extent the operation and maintenance task should be performed in each task stage, such as: personnel arriving at the scene within 2 hours, completing the pre-arrangement of the operation and maintenance work within 3 hours, and completing the initial inspection of the equipment within 5 hours, etc. When the execution degree of the task stage does not reach the management expectation, the stage starting time of the task stage is set as the management intervention opportunity.

[0159] According to the management intervention opportunity, the model slices are extracted from the pre-performance model, and the model slices include all slices of the pre-performance model from the management intervention opportunity to the stage end point of the task stage, which includes all influence factor information affecting the management expectation of the task stage. The intervention queue includes multiple intervention management instructions, each intervention management instruction is associated with the management intervention opportunity, and the intervention management instruction is generated according to the above-mentioned all influence factor information, such as: detecting that the best operation and maintenance personnel in the model completes the initial inspection of the equipment only after 6 hours after the task is assigned, the management intervention opportunity is the stage starting time of the initial inspection of the equipment (such as the time when the pre-arrangement of the operation and maintenance work just ends), and the model slice of the management intervention opportunity (such as the model slice of the initial inspection task stage of the equipment) is obtained. All influence factor information (factors affecting the initial inspection speed, such as idle chat of the operation and maintenance personnel and unreasonable initial inspection strategy of the operation and maintenance personnel) affecting the management expectation of the task stage is extracted from these model slices, and the intervention queue is determined, such as: [sending a reminder when chatting, and planning a reasonable strategy when the initial inspection strategy is not suitable].

[0160] The first intervention operation and maintenance live information is the operation and maintenance live video picture taken by the engineering recorder worn by the best operation and maintenance personnel. Although the hydropower station equipment site has other monitoring equipment, the on-site equipment also needs to consider other monitoring tasks, and cannot be called at all times, so the pre-triggering of the intervention management instruction is performed.

[0161] When the pre-triggering of the intervention management instruction is performed, the pre-triggering factor of the intervention management instruction is obtained, such as: the pre-triggering factor of sending a reminder when chatting is to identify multiple semantics irrelevant to the operation and maintenance scene; and for example: the pre-triggering factor of planning a reasonable strategy when the initial inspection strategy is not suitable is to identify that the wrong detection tool is taken. The pre-triggering factor is collected and identified by the engineering recorder.

[0162] However, the first intervention operation live information collection device is single, which is easy to cause limited pre-trigger factor collection. Therefore, when the intervention management instruction pre-trigger succeeds, the model slice corresponding to the intervention management instruction pre-trigger success is determined, and the corresponding in the model slice corresponding to the intervention management instruction pre-trigger success means that the formulation basis (management expected influence factor information) of the intervention management instruction is derived from the model slice. After the intervention management instruction pre-trigger succeeds, the source device of the construction data of the corresponding model slice is awakened, the configuration information of the source device is obtained according to the model slice corresponding to the intervention management instruction, and the collected data obtained after the configuration is completed is used as the second intervention operation live information. According to the second intervention operation live information, it is judged whether the intervention management instruction pre-trigger success is determined to trigger, if yes, the operation is assisted based on the determined intervention management instruction (such as: through the mobile terminal configured by the operation personnel to remind the operation personnel not to chat frequently), which greatly improves the intervention determination efficiency of the intervention management instruction.

[0163] The embodiment of the present application provides a hydropower station equipment dynamic operation management method based on a variable weight-set pair-takable fusion model, as shown in the figure, comprising: Figure 3

[0164] Step 1: Construct a variable weight-set pair-takable fusion model;

[0165] Step 2: Determine the operation management strategy based on the safety evaluation level of the hydropower station equipment state and the operation management strategy library of the variable weight-set pair-takable fusion model;

[0166] Step 3: Apply the operation management strategy;

[0167] Step 4: Match the best operation personnel, and assist in operation based on the corresponding operation management strategy when manually intervening in operation;

[0168] The step 1: constructing a variable weight-set pair-takable fusion model, comprising:

[0169] Determine the constant weight of the index based on the structural entropy weight method;

[0170] Dynamically correct the constant weight based on the variable weight theory to determine the variable weight of the index;

[0171] Couple the set pair theory and the matter element extension method, combine the variable weight, and construct a variable weight-set pair-takable coupling evaluation model;

[0172] Among them, the index is dynamically updated according to the score of experts or staff familiar with the hydropower station equipment and facilities;

[0173] The step 4: matching the best operation personnel, and assisting in operation based on the corresponding operation management strategy when manually intervening in operation, comprising:

[0174] ​S41: Analyze the operation and maintenance management strategy to determine the target operation and maintenance task;

[0175] S42: Obtain the historical operation and maintenance tasks of the idle operation and maintenance personnel in the first time period;

[0176] S43: Label the first feature of the historical operation and maintenance task of the same task type as the target operation and maintenance task on the preset time axis based on the execution time of the corresponding historical operation and maintenance task, the first feature including: the safety evaluation level of the historical operation and maintenance task corresponding to the hydropower station equipment and the operation and maintenance evaluation level of the historical operation and maintenance task;

[0177] S44: If the first feature meets the matching trigger condition, obtain the time axis point that exists in each feature distribution corresponding time axis point as the target axis point; the matching trigger condition includes: the feature distribution of the first feature of the feature type on the time axis meets the standard feature distribution preset for the feature type;

[0178] S45: If successful, extract the second feature according to the historical operation and maintenance task corresponding to the target axis point;

[0179] S46: According to the second feature, obtain the idle operation and maintenance personnel corresponding to the second feature with the largest matching value as the best operation and maintenance personnel according to the second feature preset matching template;

[0180] S47: When the best operation and maintenance personnel intervenes in operation and maintenance, assist in operation and maintenance based on the corresponding operation and maintenance management strategy;

[0181] The assisting in operation and maintenance based on the corresponding operation and maintenance management strategy when the best operation and maintenance personnel intervenes in operation and maintenance, includes:

[0182] According to the historical operation and maintenance task corresponding to the second feature with the largest matching value, a pre-play model is constructed;

[0183] According to the pre-play result of the pre-play model and the management intervention time setting rule, the management intervention time is determined;

[0184] Obtain the model slice of the management intervention time;

[0185] According to the model slice, obtain the intervention queue;

[0186] According to the first intervention operation and maintenance real-time information, pre-trigger the intervention management instruction in the intervention queue;

[0187] According to the model slice corresponding to the pre-trigger successful intervention management instruction, obtain the second intervention operation and maintenance real-time information;

[0188] According to the second intervention operation and maintenance real-time information, determine whether to trigger the pre-trigger successful intervention management instruction;

[0189] If yes, assist in operation and maintenance based on the determined triggered intervention management instruction.

[0190] In one embodiment, the method for obtaining the state of the hydropower station equipment in step 2 comprises:

[0191] Based on the industrial Internet of Things technology, the hydropower station equipment data is obtained;

[0192] All hydropower station equipment data of the same hydropower station equipment is summarized to obtain the state of the hydropower station equipment.

[0193] In one embodiment, the step 4: matching the best operation and maintenance personnel, when the artificial intervention operation and maintenance is performed, the auxiliary operation and maintenance is performed based on the corresponding operation and maintenance management strategy, further comprises:

[0194] When the first feature does not satisfy the matching trigger condition or the target axis point fails to be obtained, a second time period is obtained by lengthening the first time period based on a preset time period lengthening rule, the first time period in S42 is replaced by the second time period, and the S42-S47 is continued to be executed until the best operation and maintenance personnel is matched.

[0195] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A dynamic operation and maintenance management system for hydropower station equipment based on a variable weight-set pair-extensible fusion model, characterized in that, The method comprises the following steps of: a fusion model construction module is configured to construct a variable weight-set pair-extensible fusion model; a strategy determination module is configured to determine an operation and maintenance strategy based on a safety evaluation level of a hydropower station equipment and the variable weight-set pair-extensible fusion model and a management strategy library; a strategy application module is configured to apply the operation and maintenance strategy.

2. The hydropower station equipment dynamic operation and maintenance management system based on the variable weight-set pair-takagi-sugeno fusion model of claim 1, wherein, The fusion model construction module constructs the variable weight-set pair-extensible fusion model, which comprises the following steps of: determining a constant weight of an index based on a structural entropy weight method; dynamically correcting the constant weight to determine a variable weight of the index based on a variable weight theory; coupling a set pair theory and a matter-element extensible method and combining the variable weight to construct a variable weight-set pair-extensible coupling evaluation model; wherein the index is dynamically updated according to a score of an expert or a staff member familiar with the hydropower station equipment and facilities. 3.The water power station equipment dynamic operation and maintenance management system based on the variable weight-set pair-takagi-sugeno fusion model of claim 1, wherein, The method further comprises the following steps of: an auxiliary operation and maintenance module is configured to match an optimal operation and maintenance staff member and perform auxiliary operation and maintenance based on a corresponding operation and maintenance strategy when manual intervention is performed.

4. The hydropower station equipment dynamic operation and maintenance management system based on the variable weight-set pair-takagi-sugeno fusion model of claim 3, characterized in that, The auxiliary operation and maintenance module matches the optimal operation and maintenance staff member and performs auxiliary operation and maintenance based on the corresponding operation and maintenance strategy when manual intervention is performed, which comprises the following steps of: S41: analyzing the operation and maintenance strategy to determine a target operation and maintenance task; S42: obtaining historical operation and maintenance tasks of an idle operation and maintenance staff member in a first time period; S43: labeling a first feature of a historical operation and maintenance task of a same task type as the target operation and maintenance task on a preset time axis based on an execution time of the corresponding historical operation and maintenance task, the first feature comprising a safety evaluation level of a hydropower station equipment corresponding to the historical operation and maintenance task and an operation and maintenance evaluation level of the historical operation and maintenance task; S44: if the first feature meets a matching trigger condition, obtaining a time axis point existing in all time axis points corresponding to each feature distribution as a target axis point, the matching trigger condition comprising that a feature distribution of the first feature of a feature type on the time axis meets a standard feature distribution of the feature type; S45: if the obtaining is successful, extracting a second feature according to a historical operation and maintenance task corresponding to the target axis point; S46: obtaining an idle operation and maintenance staff member corresponding to the second feature with the largest matching value as the optimal operation and maintenance staff member according to a matching template of the second feature and the second feature; S47: performing auxiliary operation and maintenance based on the corresponding operation and maintenance strategy when the optimal operation and maintenance staff member intervenes in operation and maintenance.

5. The hydropower station equipment dynamic operation and maintenance management system based on the variable weight-set pair-takagi-sugeno fusion model of claim 4, wherein, The auxiliary operation and maintenance module performs auxiliary operation and maintenance based on the corresponding operation and maintenance strategy when the optimal operation and maintenance staff member intervenes in operation and maintenance, which comprises the following steps of: constructing a pre-performance model according to the historical operation and maintenance task corresponding to the second feature with the largest matching value; determining a management intervention time based on a pre-performance result of the pre-performance model and a management intervention time setting rule; obtaining a model slice of the management intervention time; obtaining an intervention queue according to the model slice; pre-triggering an intervention management instruction in the intervention queue according to first intervention operation and maintenance real-time information; obtaining second intervention operation and maintenance real-time information according to a model slice corresponding to the intervention management instruction successfully pre-triggered; determining whether to trigger the intervention management instruction successfully pre-triggered according to the second intervention operation and maintenance real-time information; if yes, performing auxiliary operation and maintenance based on the intervention management instruction determined to be triggered.

6. The method for dynamic operation and maintenance management of hydropower station equipment based on variable weight-set pair-takable fusion model, characterized in that, The method comprises the following steps of: Step 1: constructing a variable weight-set pair-extensible fusion model; Step 2: Determine the operation and maintenance management strategy based on the safety evaluation level of the hydropower station equipment and the operation and maintenance management strategy library based on the variable weight-set pair-extensive fusion model; Step 3: Apply the operation and maintenance management strategy.

7. The hydropower station equipment dynamic operation and maintenance management method based on the variable weight-set pair-takagi-sugeno fusion model according to claim 6, characterized in that, Step 1: Build a variable weight-set pair-extensive fusion model, including: Determine the constant weight of the index based on the structural entropy weight method; Dynamically correct the constant weight based on the variable weight theory to determine the variable weight of the index; Couple the set pair theory and the matter-element extension method, and combine the variable weight to build a variable weight-set pair-extensive coupled evaluation model; Among them, the index is dynamically updated according to the scoring of experts or staff familiar with the hydropower station equipment and facilities.

8. The hydropower station equipment dynamic operation and maintenance management method based on the variable weight-set pair-takagi-sugeno fusion model according to claim 6, characterized in that, Also includes: Step 4: Match the best operation and maintenance personnel, and assist in operation and maintenance based on the corresponding operation and maintenance management strategy when manually intervening in operation and maintenance. 9.The water power station equipment dynamic operation and maintenance management method based on the variable weight-set pair-takagi-sugeno fusion model of claim 8, wherein, Step 4: Match the best operation and maintenance personnel, and assist in operation and maintenance based on the corresponding operation and maintenance management strategy when manually intervening in operation and maintenance, including: S41: Analyze the operation and maintenance management strategy to determine the target operation and maintenance task; S42: Obtain the historical operation and maintenance tasks of the idle operation and maintenance personnel in the first time period; S43: Label the first feature of the historical operation and maintenance task of the same task type as the target operation and maintenance task on the preset time axis based on the execution time of the corresponding historical operation and maintenance task, the first feature including: the safety evaluation level of the hydropower station equipment corresponding to the historical operation and maintenance task and the operation and maintenance evaluation level of the historical operation and maintenance task; S44: If the first feature meets the matching trigger condition, obtain the time axis point that exists in each feature distribution as the target axis point; The matching trigger condition includes: the feature distribution of the first feature of the feature type on the time axis meets the standard feature distribution preset for the feature type; S45: If successful, extract the second feature according to the historical operation and maintenance task corresponding to the target axis point; S46: According to the second feature, obtain the idle operation and maintenance personnel corresponding to the second feature with the maximum matching value as the best operation and maintenance personnel according to the matching template preset for the second feature; S47: Assist in operation and maintenance based on the corresponding operation and maintenance management strategy when the best operation and maintenance personnel intervenes in operation and maintenance.

10. The hydropower station equipment dynamic operation and maintenance management method based on the variable weight-set pair-takagi-sugeno fusion model according to claim 9, characterized in that, When the best operation and maintenance personnel intervenes in operation and maintenance, assist in operation and maintenance based on the corresponding operation and maintenance management strategy, including: Build a rehearsal model according to the historical operation and maintenance task corresponding to the second feature with the maximum matching value; Determine the management intervention time based on the rehearsal result of the rehearsal model and the management intervention time setting rule; Obtain the model slice of the management intervention time; According to the model slice, obtain the intervention queue; Pre-trigger the intervention management instruction in the intervention queue according to the first intervention operation and maintenance real-time information; According to the model slice corresponding to the pre-triggered successful intervention management instruction, obtain the second intervention operation and maintenance real-time information; Determine whether to trigger the pre-triggered successful intervention management instruction according to the second intervention operation and maintenance real-time information; If yes, assist in operation and maintenance based on the determined triggered intervention management instruction.

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