Method, device and equipment for determining operation state of hydropower station and storage medium

Abstract

The present disclosure provides a method, device and equipment for determining the operation state of a hydropower station, and a storage medium, and relates to the technical field of deep learning. The specific scheme is as follows: obtaining basic operation parameters, an evaluation index set and a reference evaluation feature set of a hydropower station to be evaluated; determining an evaluation index adjustment factor according to each reference evaluation feature in the reference evaluation feature set and the basic operation parameters; inputting the adjustment factor into a pre-constructed random forest operation difficulty model to obtain a difficulty coefficient; determining each reference index value corresponding to the hydropower station to be evaluated according to the difficulty coefficient and the evaluation index set; and determining the operation state of the hydropower station to be evaluated according to the each reference index value. Thus, only real-time data related to evaluation indexes need to be collected, and the real-time data is input into the constructed random forest operation difficulty model, so that the operation state of the hydropower station can be evaluated scientifically and accurately in a convenient and fast manner.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of deep learning, in particular to a method and device for determining the operation state of a hydropower station, a computer device and a storage medium. BACKGROUND

[0002] The operation state of a hydropower station is closely related to the specifications of the power station unit, geographical location, and degree of intelligence. A scientific and reasonable examination result of the operation state of a hydropower station can provide guidance for the examination of hydropower enterprises and help to find weakly operating hydropower stations, and thus facilitate the supplement and difference of the hydropower stations.

[0003] In related technologies, the research on the operation of a hydropower station can be converted into the research on the cost of the hydropower station. However, the fine level of the actual operation of the enterprise is not high, and the cost record is relatively rough. If the hydropower station performs the whole examination work, the cycle is relatively long, a large amount of time and manpower is needed, and the real-time performance is poor. Therefore, how to scientifically, reliably and conveniently examine and analyze the operation of a hydropower station is a problem to be solved at present. SUMMARY

[0004] The present disclosure aims to at least partially solve one of the technical problems in the related art.

[0005] The present disclosure provides a method, device, system and storage medium for determining the operation state of a hydropower station.

[0006] According to a first aspect of the present disclosure, a method for determining the operation state of a hydropower station is provided, comprising:

[0007] obtaining basic operation parameters of a hydropower station to be evaluated, an evaluation index set and a reference evaluation feature set;

[0008] determining an evaluation index adjustment factor according to each reference evaluation feature in the reference evaluation feature set and the basic operation parameters;

[0009] inputting the adjustment factor into a pre-constructed random forest operation difficulty model to obtain a difficulty coefficient;

[0010] determining each reference index value corresponding to the hydropower station to be evaluated according to the difficulty coefficient and the evaluation index set;

[0011] determining the operation state of the hydropower station to be evaluated according to the each reference index value.

[0012] According to a second aspect of the present disclosure, a device for determining the operation state of a hydropower station is provided, comprising:

[0013] a first obtaining module configured to obtain basic operation parameters of a hydropower station to be evaluated, an evaluation index set and a reference evaluation feature set;

[0014] a first determining module, configured to determine an evaluation index adjustment factor according to each reference evaluation feature in the set of reference evaluation features and the basic operation parameter;

[0015] a second obtaining module, configured to input the adjustment factor into a pre-constructed random forest operation difficulty model to obtain a difficulty coefficient;

[0016] a second determining module, configured to determine reference index values corresponding to the water power station to be evaluated according to the difficulty coefficient and the set of evaluation indexes;

[0017] a third determining module, configured to determine an operation state of the water power station to be evaluated according to the reference index values.

[0018] According to a third aspect of the present disclosure, an electronic device is provided, comprising:

[0019] at least one processor; and

[0020] a memory connected with the at least one processor in communication; wherein,

[0021] the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of the first aspect.

[0022] According to a fourth aspect of the present disclosure, a non-transitory computer readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to perform the method of any one of the first aspect.

[0023] According to a fifth aspect of the present disclosure, a computer program product is provided, and when an instruction processor in the computer program product executes, the method according to the first aspect of the present disclosure is performed.

[0024] The method, device and equipment for determining the operation state of the water power station provided by the present disclosure have at least the following beneficial effects:

[0025] In the embodiments of the present disclosure, first, basic operation parameters of a hydropower station to be evaluated, an evaluation index set and a reference evaluation feature set are acquired, then according to each reference evaluation feature in the reference evaluation feature set and the basic operation parameters, an evaluation index adjustment factor is determined, then the adjustment factor is input into a random forest operation difficulty model constructed in advance to obtain a difficulty coefficient, then according to the difficulty coefficient and the evaluation index set, each reference index value corresponding to the hydropower station to be evaluated is determined, and finally according to the each reference index value, an operation state of the hydropower station to be evaluated is determined. Thus, only real-time data related to evaluation indexes need to be collected, the real-time data is input into the constructed random forest operation difficulty model, and a scientific and reasonable examination result can be obtained, which can provide guidance for a hydropower enterprise to conduct a preliminary examination and seek optimization and difference supplement.

[0026] Additional aspects and advantages of the present disclosure will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0027] The above and / or additional aspects and advantages of the present disclosure will become apparent and be readily appreciated from the following description, taken in conjunction with the accompanying drawings, in which:

[0028] Figure 1 is a flowchart of a method for determining an operation state of a hydropower station according to an embodiment of the present disclosure;

[0029] Figure 2 is a flowchart of a method for determining an operation state of a hydropower station according to another embodiment of the present disclosure;

[0030] Figure 3 is a structural block diagram of a device for determining an operation state of a hydropower station according to the present disclosure;

[0031] Figure 4 is a block diagram of an electronic device for implementing the method for determining an operation state of a hydropower station according to the present disclosure. DETAILED DESCRIPTION

[0032] Embodiments of the present disclosure are described in detail below with reference to the accompanying drawings, in which the same or similar components have the same reference numbers throughout. The embodiments described below are examples for explaining the present disclosure and are not intended to limit the present disclosure. On the contrary, the embodiments of the present disclosure include all changes, modifications and equivalents falling within the spirit and scope of the appended claims.

[0033] It can be explained that the execution subject of the water power station operation state determination method of the embodiment is a water power station operation state determination device, the device can be realized by software and / or hardware, the device can be configured in a computer equipment, the computer equipment can include but is not limited to terminal, server end and the like, the water power station operation state determination method of the present disclosure will be explained below with the water power station operation state determination device as the execution subject, hereinafter referred to as "the device".

[0034] Figure 1 It is the flowchart of the water power station operation state determination method of an embodiment of the present disclosure.

[0035] As Figure 1 The water power station operation state determination method comprises:

[0036] S101, the basic operation parameters of the water power station to be evaluated, the evaluation index set and the reference evaluation feature set are acquired.

[0037] Each reference evaluation feature in the reference evaluation feature set can be used to evaluate the operation state of the water power station, which can be a plurality of dimensional features, such as unit characteristics, dam type features, water level features, reservoir capacity features, labor force features, regulation performance features, etc., which are not limited here.

[0038] It should be noted that a certain number of water power stations can be selected as water power stations to be evaluated, and in some specific implementation modes, the basic operation data of 65 water power stations located in the southwest regions of Sichuan, Chongqing, Guangxi and Guizhou can be selected, which are not limited here. After acquiring a plurality of basic operation data, the basic operation parameters of each water power station to be evaluated can be obtained by normalizing the basic operation data.

[0039] Optionally, the basic operation parameters can also be divided according to a certain proportion, such as dividing the basic operation parameters into a training set and a test set according to a proportion of 7:3.

[0040] It should be noted that the evaluation index set contains a plurality of evaluation index data for analyzing the operation state of the water power station from various directions. Among them, the evaluation index data can be the total number of employees at the end of the preset evaluation period, the on-grid power, the investment cost and the installed capacity utilization hours, etc., which are not limited here.

[0041] The total number of employees at the end of the preset evaluation period can correspond to the labor density evaluation index, the on-grid power can correspond to the on-grid power density evaluation index, the installed capacity utilization hours can correspond to the utilization hour density, and the investment cost can correspond to the total operation cost of each operation difficulty coefficient power station / enterprise investment to understand the overall cost investment level of the enterprise, which are not limited here.

[0042] S102, determine an evaluation index adjustment factor according to each reference evaluation feature in the reference evaluation feature set and the basic operation parameter.

[0043] Optionally, each reference evaluation data used to describe each reference evaluation feature in the reference evaluation feature set can be determined from the basic operation parameter, and then each reference evaluation data is determined as the evaluation index adjustment factor.

[0044] It should be noted that the reference evaluation data can be data in the basic operation parameter used to form each reference evaluation feature. For example, if the current reference evaluation feature is a water level feature, the corresponding reference evaluation data can be normal water level, dead water level, water level difference, etc., which is not limited here.

[0045] Specifically, for the unit characteristics, dam type features, water level features, reservoir capacity features, labor force features, and regulation performance in the reference evaluation feature set, the installed capacity, the number of units, the normal water level, the dead water level, the total reservoir capacity, the effective reservoir capacity, the dam height, the dam length, the number of employees, the average single machine capacity, the water level difference, the average unit utilization hours, the commissioning length, the dam area, and the regulation performance in the basic operation parameter can be used as reference evaluation data, that is, these feature data are influence factors of the operation state of the hydropower station, and therefore each reference evaluation data can be determined as the evaluation index adjustment factor.

[0046] The evaluation index adjustment factor can be feature data affecting the operation state of the hydropower station, that is, the evaluation index adjustment factor can be obtained by deconstructing the operation state of the hydropower station, so that the operation difficulty of the hydropower station can be evaluated from equipment, materials, technology, and labor, and the operation difficulty of the hydropower station can be quantified and specifically studied.

[0047] S103, input the adjustment factor into a pre-constructed random forest operation difficulty model to obtain a difficulty coefficient.

[0048] It can be understood that the number and corresponding categories of the adjustment factors can be determined first, and then the importance of each adjustment factor is calculated.

[0049] For example, if there are M adjustment factors and the number of categories is k, the importance mark (IM) of each adjustment factor can be quantified based on the Gini index (GI). Then, by recognizing the adjustment factors, different importance degree influence factors can be extracted, and according to their internal correlation with the operation difficulty coefficient, a random forest operation difficulty coefficient model is established, laying a foundation for the realization of benchmarking evaluation.

[0050] Specifically, the importance of different influencing factors can be calculated, and the results can be arranged in descending order, and then the top eight important features are obtained by using the random forest method. The important features are installed capacity, average single machine capacity, number of employees, dam length, dam area, effective storage capacity, total storage capacity, and number of units.

[0051] After extracting the main influencing factors, the random forest regression calculation step can be as follows: first, form a decision tree training set: if the original data set contains n samples, randomly extract n training samples with replacement to form a sampling data set, which is used to form a decision tree, then randomly select decision tree features: if the feature dimension of the original data sample is M, randomly select m features (m < M) from them, and split the node according to the selected m features, without pruning, wherein the splitting criterion is maximum Gini impurity reduction (classification) or minimum mean square error minimization. According to the above steps, the tree nodes are constructed one by one until the stop condition is reached, and then the above steps are repeated to form a forest composed of multiple decision trees, and the number of trees ntree is determined by the specific situation.

[0052] The random forest parameters include the number of features m selected for splitting by the decision tree, and the number of trees ntree. For regression analysis, each decision tree usually selects m = M / 3 features for splitting, and in this disclosure, ntree is searched by fixing m, which can be considered as the optimal value when the MSE is minimized.

[0053] It should be noted that the random forest operation difficulty model can be a hydropower station operation difficulty coefficient model constructed by a random forest classification algorithm, which can be a pre-trained model. By inputting the adjustment factors into the pre-constructed random forest operation difficulty model, the difficulty coefficient can be obtained.

[0054] The difficulty coefficient can be the operation difficulty coefficient of the hydropower station. By inputting each adjustment factor into the model, the finally generated difficulty coefficient can take into account the specifications of the power station unit, geographical location, intelligent degree, etc., and is more accurate and reliable.

[0055] Optionally, the device can input each adjustment factor into the pre-constructed random forest operation difficulty model to obtain a comprehensive difficulty coefficient, or input the labor cost data in the adjustment factor into the pre-constructed random forest operation difficulty model to obtain a personnel difficulty coefficient.

[0056] It can be understood that the personnel difficulty coefficient is a difficulty coefficient determined based on the influence of labor cost data on the operation difficulty of the hydropower station, and the comprehensive difficulty coefficient is a difficulty coefficient determined based on the influence of each cost data and influencing factor on the operation difficulty of the hydropower station.

[0057] S104, determining, according to the difficulty coefficient and the evaluation index set, each reference index value corresponding to the water power station to be evaluated.

[0058] Optionally, each reference index value corresponding to the water power station to be evaluated can be determined according to each evaluation index in the evaluation index set and the current difficulty coefficient. The water power station to be evaluated can be multiple.

[0059] For example, the current evaluation index set contains index A, index B and index C, and the difficulty coefficient is OC, then the reference index value corresponding to index A can be calculated as A / OC, the reference index value corresponding to index B can be calculated as B / OC, and the reference index value corresponding to index C can be calculated as C / OC, which is not limited here.

[0060] The evaluation index set at least includes the total number of employees at the end of the period, the online power, the input cost and the installed capacity utilization hours in the preset evaluation period, which is not limited here.

[0061] Optionally, the first reference index value can be determined according to the total number of employees at the end of the period and the personnel difficulty coefficient in the preset evaluation period.

[0062] The first reference index value can be a labor reference index, such as labor density, and the calculation formula is as follows:

[0063]

[0064] Wherein, the labor density is, the total number of employees at the end of the period is, the personnel difficulty coefficient is.

[0065] Optionally, the second reference index value can be determined according to the input cost in the preset evaluation period and the comprehensive difficulty coefficient.

[0066] The second reference index value can be an input cost reference index, such as the operation difficulty coefficient input cost, and the calculation formula is as follows:

[0067]

[0068] Wherein, the operation difficulty coefficient input cost in the evaluation period is, the total input cost in the evaluation period is, the operation difficulty coefficient is, and the second reference index value can measure the total operation cost input by each operation difficulty coefficient power station / enterprise, so as to understand the overall cost input level of the enterprise.

[0069] Optionally, the third reference index value can be determined according to the online power in the preset evaluation period and the comprehensive difficulty coefficient.

[0070] wherein the third reference index value can be an online power reference index, such as online power density, and the calculation formula is as follows:

[0071]

[0072] wherein, is online power density, and the value is online power of the power station evaluation period and the operation difficulty coefficient.

[0073] Optionally, the fourth reference index value can be determined according to the installed capacity utilization hours and the comprehensive difficulty coefficient in the preset evaluation period.

[0074] wherein the fourth reference index value can be an installed capacity utilization hour reference index, such as utilization hour density, and the calculation formula is as follows:

[0075]

[0076] wherein, is utilization hour density, and the value is installed capacity utilization hours of the power station evaluation period and the operation difficulty coefficient. The fourth reference index value can reflect the unit energy efficiency level under the influence of the power station installed capacity.

[0077] It should be noted that the reference index values of all the hydropower stations to be evaluated can be normalized to the interval [0, 1], which is not limited herein.

[0078] S105, according to each reference index value, determining the operation state of the hydropower station to be evaluated.

[0079] wherein, after determining the reference index value corresponding to each hydropower station to be evaluated, the operation grade, operation score and operation state of the hydropower station to be evaluated can be determined according to each reference index value, which is not limited herein.

[0080] As a possible implementation manner, according to each reference index value, a plurality of reference evaluation intervals corresponding to each reference index value can be determined, then according to each reference index value corresponding to each hydropower station to be evaluated, an evaluation weight corresponding to each reference index value can be determined, then according to the reference evaluation interval and each reference index value corresponding to each hydropower station to be evaluated, an association degree of each reference index value with each reference evaluation interval and a target reference evaluation interval to which each reference index value belongs can be determined, and finally according to the evaluation weight corresponding to each reference index value and the association degree with each reference evaluation interval, the operation state of each hydropower station to be evaluated can be determined.​

[0081] In the embodiments of the present disclosure, first, basic operation parameters of a hydropower station to be evaluated, an evaluation index set and a reference evaluation feature set are acquired, then an evaluation index adjustment factor is determined according to each reference evaluation feature in the reference evaluation feature set and the basic operation parameters, then the adjustment factor is input into a pre-constructed random forest operation difficulty model to obtain a difficulty coefficient, then each reference index value corresponding to the hydropower station to be evaluated is determined according to the difficulty coefficient and the evaluation index set, and finally the operation state of the hydropower station to be evaluated is determined according to the each reference index value. Therefore, only real-time data related to evaluation indexes need to be collected, the real-time data is input into the constructed random forest operation difficulty model, and a scientific and reasonable examination result can be obtained, which can provide guidance for the examination of hydropower enterprises, optimization and difference supplement.

[0082] Figure 2 is a flowchart of a method for determining the operation state of a hydropower station according to another embodiment of the present disclosure.

[0083] As shown in Figure 2 , the method for determining the operation state of a hydropower station comprises:

[0084] S201, acquiring basic operation parameters of a hydropower station to be evaluated, an evaluation index set and a reference evaluation feature set.

[0085] S202, determining an evaluation index adjustment factor according to each reference evaluation feature in the reference evaluation feature set and the basic operation parameters.

[0086] S203, inputting the adjustment factor into a pre-constructed random forest operation difficulty model to obtain a difficulty coefficient.

[0087] S204, determining each reference index value corresponding to the hydropower station to be evaluated according to the difficulty coefficient and the evaluation index set.

[0088] It should be noted that the specific implementation of S201, S202, S203 and S204 can refer to the above embodiments, and will not be repeated here.

[0089] S205, determining a plurality of reference evaluation intervals corresponding to each reference index value according to the each reference index value.

[0090] It should be noted that after the reference index values of each reference index are determined, the reference index values of each water power station to be evaluated can be sorted according to the reference index values, and then graded, for example, each reference index value can be divided into five levels of poor, worse, qualified, better and excellent, that is, the index values of the top 20%, 40%, 60% and 80% are used as the grading nodes, so as to divide the reference index values into multiple reference evaluation intervals.

[0091] Among them, the reference evaluation interval can be a classical field.

[0092] For example, after collecting the index values of the labor reference index of each water power station to be evaluated, the reference index values can be divided, and the labor reference index in the classical field [0, 0.62) can be determined as the "poor" level, the labor reference index in the reference evaluation interval [0.62, 0.69) can be determined as the "worse" level, the labor reference index in the reference evaluation interval [0.69, 0.76) can be determined as the "qualified" level, the labor reference index in the reference evaluation interval [0.76, 0.92) can be determined as the "better" level, and the labor reference index in the reference evaluation interval [0.92, 1] can be determined as the "excellent" level, which is not limited here.

[0093] It should be noted that each reference index value corresponding to the water power station to be evaluated corresponds to a reference evaluation interval.

[0094] S206, based on the entropy weight method, according to the reference index values corresponding to each water power station to be evaluated, the evaluation weight corresponding to each reference index value is determined.

[0095] It should be noted that when determining the evaluation weight corresponding to each reference index value, the entropy weight method can be used, that is, the information entropy weighting method. It can be understood that by selecting the entropy weight method to determine the evaluation weight corresponding to each reference index value, the objective information of the data can be easily understood, and the core of the entropy weight method is information entropy, which can be used to solve the information quantization problem.

[0096] Specifically, an evaluation index can be constructed first , an evaluation object original matrix of evaluation units , and the standardization processing is performed to obtain .

[0097] Among them, the number of evaluation indexes m corresponds to the number of reference indexes, The number of evaluation units is n, that is, n water power stations to be evaluated. Among them, (xij)mn is also evaluation indexes, The element of the i-th evaluation index of the evaluation object original matrix X, the j-th evaluation unit, that is, the i-th row and the j-th column. After determining the evaluation object original matrix X, it can be standardized to obtain As follows:

[0098]

[0099] Further, the probability matrix is constructed

[0100]

[0101] Wherein, .

[0102] Further, the information entropy H(Xi) of the index is calculated:

[0103]

[0104] Finally, the weight Wi of the index is calculated:

[0105]

[0106] Thus, the corresponding evaluation weight of each type of reference index value can be determined according to the corresponding reference index value of each to-be-evaluated hydropower station.

[0107] S207, according to the reference evaluation interval and the corresponding reference index value of each to-be-evaluated hydropower station, the correlation degree of each reference index value and each reference evaluation interval, and the target reference evaluation interval to which each reference index value belongs are determined.

[0108] Wherein, the correlation degree is the degree of association between the reference evaluation value and each reference evaluation interval.

[0109] Optionally, the correlation degree of each reference index value and each reference evaluation interval can be determined by a correlation function.

[0110] It should be noted that the correlation function is related to the definition of distance. For example, the classical field of the i-th evaluation index and the j-th evaluation level can be , , , , , , , , , , , ,and The bit value is calculated as follows:

[0111]

[0112]

[0113] The correlation function extends the distance from a point to an interval based on the membership function in fuzzy comprehensive evaluation, and proposes a definition of distance. The correlation function is the distance from a point to an interval in the object element to be evaluated. Individual indicators For rating levels The degree of correlation is The correlation function takes various forms, with the interval endpoints representing the optimal correlation.

[0114]

[0115] in, This is the lateral distance, which is the distance at point... It reaches its maximum value at that point.

[0116] This type of correlation function can be based on The value of takes the maximum value at any point in the interval. Take at the endpoint or At this time, the correlation function Then it will reach its maximum value at both ends.

[0117]

[0118] For example, as shown in the table above, S11 is the first reference indicator value for the first hydropower station, S21 is the second reference indicator value for the first hydropower station, S31 is the third reference indicator value for the first hydropower station, and S41 is the fourth reference indicator value for the first hydropower station. Among the various correlation degrees corresponding to reference indicator S11, which is 0.729, 0.56 is the largest and therefore considered qualified, meaning its corresponding target reference evaluation interval is qualified.

[0119] It should be noted that the above examples are merely illustrative illustrations of this disclosure and are not intended to limit this disclosure.

[0120] S208. Based on the evaluation weight corresponding to each reference indicator value and the correlation with each reference evaluation interval, determine the operating status of each hydropower station to be evaluated.

[0121] Specifically, the overall correlation and overall affiliation level can be determined based on the evaluation weight corresponding to each reference indicator value and its correlation with each reference evaluation interval.

[0122] For example, in combination with the above table data, for any to-be-evaluated hydropower station, the comprehensive correlation degree of each associated degree corresponding to the poor grade is -0.36.

[0123] That is, -0.36 is the comprehensive correlation degree under the grade.

[0124] By comparing the comprehensive correlation degrees under each grade, the grade with the highest comprehensive correlation degree can be used as the comprehensive membership grade, which is not limited herein.

[0125] It should be noted that the above example is only a schematic illustration of the present disclosure, and does not limit the present disclosure.

[0126] Optionally, after determining the operation state of each to-be-evaluated hydropower station, the grade to which each reference index value belongs can be determined according to the target reference evaluation interval to which each reference index value of each to-be-evaluated hydropower station belongs, and then the guiding operation state of each to-be-evaluated hydropower station can be determined according to the grade to which each reference index value belongs and the evaluation weight corresponding to the reference index value.

[0127] The guiding operation state can further represent the operation state of the to-be-evaluated hydropower station, that is, a more accurate score value, which can be used for benchmark management analysis.

[0128] For example, the weight corresponding to the first reference index value is 0.211, the grade is 3, the weight corresponding to the second reference index value is 0.434, the grade is 4, the weight corresponding to the first reference index value is 0.015, the grade is 2, and the weight corresponding to the fourth reference index value is 0.219, the grade is 3, then the final improved operation state can be calculated as better, and the score is 3.28.

[0129] That is, 0.211x3+0.434x4+0.015x2+0.219x3=3.28.

[0130] The 3.28 is the better grade.

[0131] In this embodiment, the basic operating parameters, evaluation index set, and reference evaluation feature set of the hydropower station to be evaluated are first obtained. Then, based on each reference evaluation feature in the reference evaluation feature set and the basic operating parameters, an evaluation index adjustment factor is determined. The adjustment factor is then input into a pre-constructed random forest operation difficulty model to obtain a difficulty coefficient. Next, based on the difficulty coefficient and the evaluation index set, the values ​​of each reference index corresponding to the hydropower station to be evaluated are determined. Based on each reference index value, multiple reference evaluation intervals corresponding to each reference index value are determined. Then, based on the entropy weight method, the evaluation weight corresponding to each reference index value is determined. Based on the reference evaluation intervals and the reference index values ​​corresponding to each hydropower station to be evaluated, the correlation between each reference index value and each reference evaluation interval, as well as the target reference evaluation interval to which each reference index value belongs, are determined. Finally, based on the evaluation weight corresponding to each reference index value and its correlation with each reference evaluation interval, the operating status of each hydropower station to be evaluated is determined. Therefore, the difficulty coefficient can be determined by constructing a complete random forest operation difficulty model, and the sub-difficulty coefficients of equipment, materials, technology, and labor can be further calculated, making the evaluation results more problem-oriented and more conducive to achieving the goal of benchmarking management analysis gaps and improvement optimization. Through entropy weight method, extension method and correlation function, the operation status of hydropower station can be accurately and scientifically evaluated, with low cost and real-time speed.

[0132] like Figure 3 As shown, the device 300 for determining the operating status of the hydropower station includes: a first acquisition module 310, a first determination module 320, a second acquisition module 330, a second determination module 340, and a third determination module 350.

[0133] The first acquisition module is used to acquire the basic operating parameters, evaluation index set, and reference evaluation feature set of the hydropower station to be evaluated.

[0134] The first determining module is used to determine the evaluation index adjustment factor based on each reference evaluation feature in the reference evaluation feature set and the basic operating parameters.

[0135] The second acquisition module is used to input the adjustment factor into a pre-built random forest operation difficulty model to obtain the difficulty coefficient;

[0136] The second determining module is used to determine the reference index values ​​corresponding to the hydropower station to be evaluated based on the difficulty coefficient and the evaluation index set.

[0137] The third determining module is used to determine the operating status of the hydropower station to be evaluated based on the various reference index values.

[0138] Optionally, the first determining module is specifically configured to:

[0139] determine, from the basic operation parameters, respective reference evaluation data that can be used to describe each reference evaluation feature in the reference evaluation feature set;

[0140] determine the respective reference evaluation data as an evaluation index adjustment factor.

[0141] Optionally, the difficulty coefficient includes a comprehensive difficulty coefficient and a personnel difficulty coefficient, and the second obtaining module is specifically configured to:

[0142] input each adjustment factor into a pre-constructed random forest operation difficulty model to obtain the comprehensive difficulty coefficient;

[0143] and,

[0144] input the artificial cost data in the adjustment factor into a pre-constructed random forest operation difficulty model to obtain the personnel difficulty coefficient.

[0145] Optionally, the difficulty coefficient includes a comprehensive difficulty coefficient and a personnel difficulty coefficient, and the second determining module is specifically configured to:

[0146] wherein the evaluation index set at least includes the total number of employees at the end of a preset evaluation period, online power consumption, input cost, and installed capacity utilization hours;

[0147] determine a first reference index value according to the total number of employees at the end of the preset evaluation period and the personnel difficulty coefficient;

[0148] determine a third reference index value according to the input cost in the preset evaluation period and the comprehensive difficulty coefficient;

[0149] determine a second reference index value according to the online power consumption in the preset evaluation period and the comprehensive difficulty coefficient;

[0150] determine a fourth reference index value according to the installed capacity utilization hours in the preset evaluation period and the comprehensive difficulty coefficient.

[0151] Optionally, the third determining module includes:

[0152] a first determining unit configured to determine, according to the respective reference index values, a plurality of reference evaluation intervals corresponding to each reference index value;

[0153] a second determining unit configured to determine, according to the respective reference index values corresponding to each of the to-be-evaluated hydropower stations, an evaluation weight corresponding to each reference index value based on an entropy weight method;

[0154] a third determining unit, configured to determine, according to the reference evaluation interval and each reference index value corresponding to each of the hydropower stations to be evaluated, a correlation degree of each reference index value with each reference evaluation interval and a target reference evaluation interval to which each reference index value belongs;

[0155] a fourth determining unit, configured to determine, according to an evaluation weight corresponding to each reference index value and the correlation degree with each reference evaluation interval, an operation state of each of the hydropower stations to be evaluated.

[0156] Optionally, the fourth determining unit is further configured to:

[0157] determine a level to which each reference index value belongs according to the target reference evaluation interval to which each reference index value of each of the hydropower stations to be evaluated belongs;

[0158] determine a guidance operation state of each of the hydropower stations to be evaluated according to the level to which each reference index value belongs and the evaluation weight corresponding to the reference index value.

[0159] In the embodiments of the present disclosure, first, basic operation parameters of the hydropower stations to be evaluated, an evaluation index set and a reference evaluation feature set are acquired, then, according to each reference evaluation feature in the reference evaluation feature set and the basic operation parameters, an evaluation index adjustment factor is determined, then the adjustment factor is input into a pre-constructed random forest operation difficulty model to obtain a difficulty coefficient, then according to the difficulty coefficient and the evaluation index set, each reference index value corresponding to each of the hydropower stations to be evaluated is determined, and finally, according to the each reference index value, an operation state of the hydropower stations to be evaluated is determined. Thus, only real-time data related to evaluation indexes need to be collected, the real-time data is input into the constructed random forest operation difficulty model, and a scientific and reasonable examination result can be obtained, which can provide guidance for a hydropower enterprise to conduct a preliminary examination and seek optimization and difference supplement.

[0160] According to the embodiments of the present disclosure, the present disclosure further provides an electronic device, a readable storage medium and a computer program product.

[0161] Figure 4A schematic block diagram of an example electronic device 400 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0162] like Figure 4 As shown, device 400 includes a computing unit 401, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 402 or a computer program loaded from storage unit 408 into random access memory (RAM) 403. RAM 403 may also store various programs and data required for the operation of device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via bus 404. Input / output (I / O) interface 405 is also connected to bus 404.

[0163] Multiple components in device 400 are connected to I / O interface 405, including: input unit 406, such as keyboard, mouse, etc.; output unit 407, such as various types of monitors, speakers, etc.; storage unit 408, such as disk, optical disk, etc.; and communication unit 409, such as network card, modem, wireless transceiver, etc. Communication unit 409 allows device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0164] The computing unit 401 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 401 performs various methods and processes described above, such as the method of determining the operating state of a hydropower station. For example, in some embodiments, the method of determining the operating state of a hydropower station can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the method of determining the operating state of a hydropower station described above can be performed. Alternatively, in other embodiments, the computing unit 401 can be configured to perform the method of determining the operating state of a hydropower station by any other appropriate means, such as by means of firmware.

[0165] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0166] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces a function / operation specified in the flowchart and / or block diagram. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, and partially on a remote machine or a server.

[0167] In the context of this disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0168] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0169] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0170] The computer system can include clients and servers. This relationship can be. The servers are generally remote from the users and can be accessed via the Internet using a communication network. The relationship can be a client-server relationship over a communications network, and as such, the servers can be accessed by the clients using computer programs. The servers can be cloud servers, also known as cloud computing servers or cloud hosts, which are mainframe products in the cloud computing service system, and solve the defects of large management difficulty and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or simply "VPS"). The servers can also be servers of a distributed system, or servers combined with a blockchain.

[0171] It should be understood that the various forms of flow shown above can be reordered, steps added or removed. For example, the steps described in the present disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions of the present disclosure can be achieved, which are not limited herein.

[0172] The above detailed description does not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements within the spirit and principles of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A method for determining the operating status of a hydropower station, characterized in that, include: Obtain the basic operating parameters, evaluation index set, and reference evaluation feature set of the hydropower station to be evaluated; From the basic operating parameters, determine each reference evaluation data that can be used to describe each reference evaluation feature in the reference evaluation feature set, and determine each reference evaluation data as an evaluation index adjustment factor; The adjustment factor is input into a pre-built random forest operation difficulty model to obtain a difficulty coefficient, which is the operation difficulty coefficient of the hydropower station. When generating the difficulty coefficient, the specifications of the power station units, geographical location, and level of intelligence are also considered. Based on the difficulty coefficient and the evaluation index set, determine the reference index values ​​corresponding to the hydropower station to be evaluated; Based on the values ​​of the various reference indicators, the operating status of the hydropower station to be evaluated is determined.

2. The method according to claim 1, characterized in that, The difficulty coefficient includes a comprehensive difficulty coefficient and a personnel difficulty coefficient. The step of inputting the adjustment factor into a pre-built random forest operation difficulty model to obtain the difficulty coefficient includes: Each of the aforementioned adjustment factors is input into a pre-built random forest operation difficulty model to obtain the comprehensive difficulty coefficient; as well as, The labor cost data in the adjustment factor is input into the pre-built random forest operation difficulty model to obtain the personnel difficulty coefficient.

3. The method according to claim 1 or 2, characterized in that, The difficulty coefficient includes a comprehensive difficulty coefficient and a personnel difficulty coefficient. Based on the difficulty coefficient and the evaluation index set, the reference index values ​​corresponding to the hydropower station to be evaluated are determined. include: The evaluation index set includes at least the total number of employees at the end of the preset evaluation period, the amount of electricity generated online, the investment cost, and the number of hours of installed capacity utilization. The first reference indicator value is determined based on the total number of employees at the end of the preset evaluation period and the personnel difficulty coefficient. The second reference index value is determined based on the input cost and comprehensive difficulty coefficient within the preset evaluation period; The third reference index value is determined based on the online electricity consumption and comprehensive difficulty coefficient within the preset evaluation period; The fourth reference index value is determined based on the installed utilization hours and the comprehensive difficulty coefficient within the preset evaluation period.

4. The method according to claim 1, characterized in that, Determining the operating status of the hydropower station to be evaluated based on the various reference index values ​​includes: Based on the various reference index values, multiple reference evaluation intervals are determined for each of the reference index values; Based on the entropy weight method, the evaluation weight corresponding to each of the reference index values ​​corresponding to each of the hydropower stations to be evaluated is determined. Based on the reference evaluation intervals and the reference index values ​​corresponding to each hydropower station to be evaluated, determine the correlation between each reference index value and each reference evaluation interval, as well as the target reference evaluation interval to which each reference index value belongs. The operating status of each hydropower station to be evaluated is determined based on the evaluation weight corresponding to each of the reference index values ​​and the correlation with each of the reference evaluation intervals.

5. The method according to claim 4, characterized in that, After determining the operating status of each of the hydropower stations to be evaluated, the method further includes: Based on the target reference evaluation interval to which each reference index value of each hydropower station to be evaluated belongs, determine the level to which each reference index value belongs; Based on the level to which each reference indicator value belongs and the evaluation weight corresponding to the reference indicator value, the guiding operation status of each hydropower station to be evaluated is determined.

6. A device for determining the operating status of a hydropower station, characterized in that, include: The first acquisition module is used to acquire the basic operating parameters, evaluation index set, and reference evaluation feature set of the hydropower station to be evaluated. The first determining module is used to determine from the basic operating parameters each reference evaluation data that can be used to describe each reference evaluation feature in the reference evaluation feature set, and to determine each reference evaluation data as an evaluation index adjustment factor; The second acquisition module is used to input the adjustment factor into a pre-constructed random forest operation difficulty model to obtain a difficulty coefficient, which is the operation difficulty coefficient of the hydropower station. When generating the difficulty coefficient, the specifications of the power station units, geographical location, and level of intelligence are also considered. The second determining module is used to determine the reference index values ​​corresponding to the hydropower station to be evaluated based on the difficulty coefficient and the evaluation index set. The third determining module is used to determine the operating status of the hydropower station to be evaluated based on the various reference index values.

7. The apparatus according to claim 6, characterized in that, The difficulty coefficient includes a comprehensive difficulty coefficient and a personnel difficulty coefficient. The second acquisition module is specifically used for: Each of the aforementioned adjustment factors is input into a pre-built random forest operation difficulty model to obtain the comprehensive difficulty coefficient; as well as, The labor cost data in the adjustment factor is input into the pre-built random forest operation difficulty model to obtain the personnel difficulty coefficient.

8. The apparatus according to claim 6 or 7, characterized in that, The difficulty coefficient includes a comprehensive difficulty coefficient and a personnel difficulty coefficient. The second determining module is specifically used for: The evaluation index set includes at least the total number of employees at the end of the preset evaluation period, the amount of electricity generated online, the investment cost, and the number of hours of installed capacity utilization. The first reference indicator value is determined based on the total number of employees at the end of the preset evaluation period and the personnel difficulty coefficient. The third reference indicator value is determined based on the input cost and comprehensive difficulty coefficient within the preset evaluation period; The second reference index value is determined based on the online electricity consumption and the comprehensive difficulty coefficient within the preset evaluation period; The fourth reference index value is determined based on the installed utilization hours and the comprehensive difficulty coefficient within the preset evaluation period.

9. The apparatus according to claim 6, characterized in that, The third determining module includes: The first determining unit is used to determine multiple reference evaluation intervals corresponding to each of the reference index values ​​based on the respective reference index values. The second determining unit is used to determine the evaluation weight corresponding to each of the reference index values ​​based on the entropy weight method and according to the reference index values ​​corresponding to each of the hydropower stations to be evaluated. The third determining unit is used to determine the correlation between each reference indicator value and each reference evaluation interval, and the target reference evaluation interval to which each reference indicator value belongs, based on the reference evaluation interval and each reference indicator value corresponding to each hydropower station to be evaluated. The fourth determining unit is used to determine the operating status of each hydropower station to be evaluated based on the evaluation weight corresponding to each of the reference index values ​​and the correlation with each of the reference evaluation intervals.

10. The apparatus according to claim 9, characterized in that, The fourth determining unit is further configured to: Based on the target reference evaluation interval to which each reference index value of each hydropower station to be evaluated belongs, determine the level to which each reference index value belongs; Based on the level to which each reference indicator value belongs and the evaluation weight corresponding to the reference indicator value, the guiding operation status of each hydropower station to be evaluated is determined.

11. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to implement the method for determining the operating status of a hydropower station as described in any one of claims 1 to 5.

12. A computer-readable storage medium, wherein when instructions in the computer-readable storage medium are executed by a processor of a server, the server is able to perform the method for determining the operating state of a hydropower station as described in any one of claims 1 to 5.

13. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the method for determining the operating status of a hydropower station as described in any one of claims 1 to 5.

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