Power plant electrical equipment energy efficiency management and control system based on multi-sensor monitoring

Abstract

The invention discloses a power plant electrical equipment energy efficiency management and control system based on multi-sensor monitoring, relates to the technical field of energy efficiency management and control, and solves the technical problem in the prior art that the importance of operation influence cannot be deduced according to matrix comparison since judgment matrix construction cannot be performed according to division layers. Specifically, the hierarchical division construction unit performs hierarchical division on electrical equipment operation acquisition parameters in a power plant; the matrix construction unit carries out matrix construction on the influence layer and the parameter layer respectively, carries out labeling on construction matrixes of the influence layer and the parameter layer, and carries out importance level distinguishing through labeling; the hierarchical weight acquisition unit performs weight division on matrix elements; and the energy efficiency evaluation unit performs energy efficiency evaluation on the power equipment according to the division weight.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy efficiency management and control, in particular to a power plant electrical equipment energy efficiency management and control system based on multi-sensor monitoring. BACKGROUND

[0002] A power plant is a factory that converts natural primary energy into electric energy, and electric power equipment is a general term for various devices used in the process of power generation, transmission, transformation, distribution and power utilization, which can be divided into primary equipment and secondary equipment; power plant electrical equipment energy efficiency management and control can improve the energy utilization efficiency of power plants, reduce operating costs and reduce environmental pollution.

[0003] However, in the prior art, the power equipment cannot be hierarchically divided according to the fault type and influence parameters, the operation influence of the power equipment cannot be hierarchically constructed, so that the judgment matrix cannot be constructed according to the hierarchical division, which causes that the importance of the operation influence cannot be inferred according to the matrix comparison, in addition, the energy efficiency cannot be effectively evaluated according to the weight division, which reduces the energy efficiency management and control efficiency of the power plant power equipment.

[0004] In view of the above technical defects, a solution is proposed. SUMMARY

[0005] The purpose of the present application is to solve the above-mentioned problems, and a power plant electrical equipment energy efficiency management and control system based on multi-sensor monitoring is proposed.

[0006] The purpose of the present application can be achieved by the following technical solutions:

[0007] The power plant electrical equipment energy efficiency management and control system based on multi-sensor monitoring comprises an energy efficiency management and control platform, and the energy efficiency management and control platform is communicatively connected with a hierarchical division construction unit, a matrix construction unit, a hierarchical weight acquisition unit and an energy efficiency evaluation unit;

[0008] The hierarchical division construction unit is used for hierarchical division of electrical equipment operation collection parameters in the power plant, obtaining fault parameters according to the fault type occurrence time, and obtaining running parameters with floating abnormality of the fault parameters according to the fault parameters, and marking as influence parameters, taking any type of electrical equipment as an energy efficiency evaluation object, and constructing target layer, influence layer and parameter layer for the energy efficiency evaluation object;

[0009] The matrix construction unit is used for matrix construction of the influence layer and the parameter layer respectively, and the constructed matrices of the influence layer and the parameter layer are marked, and the important grades are distinguished by marking;

[0010] The hierarchical weight acquisition unit is used for weight division of the matrix elements;

[0011] An energy efficiency evaluation unit is configured to evaluate the energy efficiency of the power equipment according to the division weight.

[0012] As a preferred embodiment of the present application, the process of the hierarchical division construction unit is as follows:

[0013] The energy efficiency evaluation of the electrical equipment is taken as the target layer of the current energy efficiency evaluation hierarchy;

[0014] The corresponding type of fault of the electrical equipment is obtained according to the target layer of the electrical equipment, the corresponding type of fault is screened according to the type of fault, and after the screening is completed, the corresponding type of fault is taken as the next layer of the target layer and is set as the influence layer;

[0015] After the screening of the type of fault is completed, the influence parameters corresponding to the fault parameters of the type of fault in the influence layer are statistically analyzed, the influence parameters are screened according to the statistical analysis, and the screened influence parameters are taken as the next layer of the corresponding influence layer and are set as the parameter layer.

[0016] As a preferred embodiment of the present application, the screening process of the type of fault is as follows:

[0017] The floating time of the fault parameter corresponding to the type of fault of the electrical equipment and the time of the abnormal energy efficiency of the electrical equipment are collected, and the overlapping frequency of the compared time is inferred. If the time overlapping frequency exceeds the set frequency threshold, it is inferred that the current fault parameter has a direct influence, and the type of fault is marked as a direct influence type. If the time overlapping frequency does not exceed the set frequency threshold, the type of fault is marked as an indirect influence type. Different types of faults are screened, and if the direct influence type appears in the running process, the corresponding direct influence type is listed in the influence layer. If the number of continuous appearances of the indirect influence type in the running process exceeds the set number threshold, the indirect influence type is listed in the influence layer.

[0018] As a preferred embodiment of the present application, the screening process of the influence parameter is as follows:

[0019] The influence parameter of the fault generation time of the type of fault is monitored. If the value of the influence parameter at the fault generation time is the value peak in the current period, the influence parameter type of the value peak in the current period is collected and listed in the parameter layer. If the value of the influence parameter at the fault generation time is the floating peak in the current period, the influence parameter type of the floating peak in the current period is collected and listed in the parameter layer.

[0020] As a preferred embodiment of the present application, the process of the matrix construction unit is as follows:

[0021] Matrix A and matrix B are respectively constructed according to the type of fault of the influence layer and the type of running parameter of the parameter layer;

[0022] The matrix A is: The fault type as element a, n is represented by a natural number, which is the number of fault types in the matrix; the matrix B is: The fault type as element b, m is represented by a natural number, which is the number of influence parameters in the matrix.

[0023] As a preferred embodiment of the present application, the fault types in the matrix A are compared and labeled, and the interval time of the power equipment operation efficiency decline after the fault type occurs is used as the evaluation data;

[0024] If the evaluation data deviation of two fault types is within the deviation range, it is marked as 1;

[0025] If the evaluation data deviation of the fault type exceeds 1.1 times of the peak value of the deviation range, it is marked as 3;

[0026] If the evaluation data deviation of the fault type exceeds 1.3 times of the peak value of the deviation range, it is marked as 5;

[0027] If the evaluation data deviation of the fault type exceeds 1.5 times of the peak value of the deviation range, it is marked as 7;

[0028] If the evaluation data deviation of the fault type exceeds 1.8 times of the peak value of the deviation range, it is marked as 9;

[0029] Similarly, the influence parameters in the matrix B are compared and labeled, and the span of the power equipment fault frequency rise after the influence parameter appears floating is used as the evaluation data.

[0030] As a preferred embodiment of the present application, the process of the hierarchical weight acquisition unit is as follows:

[0031] In the influence layer corresponding matrix, type influence weight information and parameter influence weight information are collected:

[0032] If the type influence weight information exceeds the set quantity ratio threshold, the current fault type is marked as a high weight fault type; if the type influence weight information does not exceed the set quantity ratio threshold, the current fault type is marked as a low weight fault type

[0033] If the parameter influence weight information exceeds the set cumulative increase period threshold, the operating parameter of the current fault type is marked as a high weight parameter type; if the parameter influence weight information does not exceed the set cumulative increase period threshold, the operating parameter of the current fault type is marked as a low weight parameter type.

[0034] As a preferred embodiment of the present application, the type influence weight information is represented by the quantity ratio of the high importance level fault type to the low importance level fault type corresponding to the current importance level fault type;

[0035] The parameter influence weight information collection process is as follows: in the parameter layer corresponding matrix, the numerical value floating span ratio is collected; the power equipment fault operation energy efficiency floating frequency ratio is collected; the numerical value floating span ratio difference and the energy efficiency floating frequency ratio difference are obtained according to the difference calculation; the numerical value floating span ratio difference and the energy efficiency floating frequency ratio difference of the current fault parameter type in the running process are floatingly counted to obtain the cumulative growth period of the numerical value floating span ratio difference and the energy efficiency floating frequency ratio difference, and the cumulative growth period is marked as the parameter influence weight information.

[0036] As a preferred embodiment of the present application, the process of the energy efficiency evaluation unit is as follows:

[0037] The number of high-weight parameter types corresponding to the same high-weight fault type is collected, and if the corresponding number exceeds the number threshold, it is marked as an easy-to-impact fault type, otherwise it is marked as a difficult-to-impact fault type.

[0038] After the easy-to-impact fault type corresponding influence parameter floating control intervention, the increase span of the corresponding influence parameter numerical value floating span peak value is collected; when the difficult-to-impact fault type appears influence parameter floating, the rising span of the difficult-to-impact fault type corresponding influence parameter numerical value floating frequency is collected.

[0039] As a preferred embodiment of the present application, if the increase span of the influence parameter numerical value floating span peak value exceeds the increase span threshold, or the rising span of the influence parameter numerical value floating frequency exceeds the rising span threshold, it is inferred that the energy efficiency of the power equipment is abnormal, and the energy efficiency control platform traces the monitored weight type and performs numerical control; if the increase span of the influence parameter numerical value floating span peak value does not exceed the increase span threshold, and the rising span of the influence parameter numerical value floating frequency does not exceed the rising span threshold, the energy efficiency control platform continuously monitors the monitored weight type.

[0040] Compared with the prior art, the present application has the following advantages:

[0041] 1、In the present application, each fault type parameter is hierarchically refined, and the different influences of different fault operation parameters of electrical equipment in the running process are obtained through hierarchical refinement, so as to achieve the effect of power plant energy efficiency evaluation, and the energy efficiency state is obtained according to the influence analysis of the operation parameter type, and the energy efficiency abnormality can be inferred in time, and the floating of the operation parameter can be inferred in time, which is beneficial to targeted fault repair.

[0042] 2、In the present application, a judgment matrix is constructed according to the hierarchical division of electrical equipment in the power plant, so as to infer the influence degree of the parameters compared with each other in each level, improve the parameter influence detection efficiency in the current running period, and improve the energy efficiency control performance of the power plant.

[0043] 3、The matrix constructed is analyzed for elements and divided by weight in the application, the data influence of the corresponding level is obtained through the weight division, so as to facilitate the energy efficiency prediction according to the data influence and the numerical trend of the actual data, and timely data control can also be carried out according to the data type, so as to avoid the generation of corresponding fault types, thereby improving the operation energy efficiency stability of the power plant power equipment.

[0044] 4、In the application, the running state of the current power equipment is inferred by comparing the elements of each weight type in the matrix, and the current operation energy efficiency can be effectively judged according to the control trend of the risk elements, so as to facilitate timely energy efficiency monitoring and avoid power equipment operation failure caused by abnormal energy efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to facilitate those skilled in the art to understand, the application will be further described below in combination with the drawings.

[0046] Figure 1 The system principle block diagram of the application;

[0047] Figure 2 The method flow block diagram of the level division construction unit in the application. DETAILED DESCRIPTION

[0048] In order to make those skilled in the art better understand the application scheme, the technical scheme in the embodiment of the application will be described clearly and completely below in combination with the drawings in the embodiment of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor belong to the protection scope of the application.

[0049] In this paper, "embodiment" means that the specific features, structures or characteristics described in combination with the embodiment can be contained in at least one embodiment of the application. The phrase appears at various places in the specification does not necessarily refer to the same embodiment, nor is it independent or alternative to other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0050] Please refer to Figure 1 As shown, the power plant electrical equipment energy efficiency management and control system based on multi-sensor monitoring includes an energy efficiency management and control platform, the energy efficiency management and control platform is communicatively connected with a level division construction unit, a matrix construction unit, a level weight acquisition unit and an energy efficiency evaluation unit;

[0051] The electrical equipment in the power plant will change the energy efficiency when the performance of the equipment decays and the running state changes, and the energy efficiency determines the running performance and efficiency of the power plant. Therefore, during the operation of the power plant, the electrical equipment in the power plant needs to be monitored and data collected according to various types of sensors, and the energy efficiency influence is identified according to the monitoring, and targeted control is carried out;

[0052] The hierarchical division construction unit is used for hierarchical division of the running collection parameters of the electrical equipment in the power plant, hierarchical refinement is carried out according to various fault type parameters, different influences of different fault running parameters of the electrical equipment in the running process are obtained through hierarchical refinement, so as to achieve the effect of energy efficiency evaluation of the power plant, and the energy efficiency state is obtained according to the influence analysis of the running parameter type, and the energy efficiency anomaly can be inferred in time, which is beneficial to targeted fault repair;

[0053] It should be further pointed out that the method flow chart of the hierarchical division construction unit is as shown in Figure 2

[0054] The fault type collection and statistics of the running process of the power plant are carried out, the fault parameters such as equipment energy consumption and running efficiency are obtained according to the fault type generation time, and the fault parameter floating abnormal running parameters are obtained according to the fault parameters, and are marked as influence parameters such as temperature and unit time power consumption;

[0055] Any type of electrical equipment is taken as the energy efficiency evaluation object, and the energy efficiency evaluation of the electrical equipment is taken as the target layer of the current energy efficiency evaluation hierarchy;

[0056] According to the target layer of the electrical equipment, the fault types that have occurred in the corresponding type of electrical equipment are obtained, the fault types are screened according to the fault types, and after the screening is completed, the corresponding fault types are taken as the next layer of the target layer, which is set as the influence layer. The screening process of the fault type is as follows:

[0057] The fault parameter floating time corresponding to the fault type of the electrical equipment and the abnormal time of the energy efficiency of the electrical equipment are collected, and the overlapping frequency of the compared inference time is obtained. If the time overlapping frequency exceeds the set frequency threshold, it is inferred that the current fault parameter has a direct influence, and the fault type is marked as a direct influence type, such as equipment running efficiency and other parameters;

[0058] If the time overlapping frequency does not exceed the set frequency threshold, it is inferred that the current fault parameter has an indirect influence, and the fault type is marked as an indirect influence type, such as equipment maintenance cost and other parameters;

[0059] ​According to different fault types, if the direct influence type appears in the running process, the corresponding direct influence type is listed in the influence layer; if the indirect influence type appears continuously in the running process for more than a set number of times, the indirect influence type is listed in the influence layer;

[0060] After the fault type screening is completed, the influence parameters corresponding to the fault parameters of the fault types in the influence layer are statistically analyzed, the influence parameters are screened according to the statistical analysis, and the screened influence parameters are used as the next level of the corresponding influence layer, and are set as the parameter layer; the influence parameter screening process is as follows:

[0061] The influence parameters at the fault occurrence time of the fault type are monitored, if the value of the influence parameter at the fault occurrence time is continuously the value peak in the current period, the influence parameter type of the value peak in the current period is collected and listed in the parameter layer; wherein the current period represents the interval period between the running start time and the fault occurrence time, if there is no fault occurrence time, the current collection time is used as the period end time;

[0062] If the value of the influence parameter at the fault occurrence time is floating, the influence parameter type of the floating peak in the current period is collected and listed in the parameter layer;

[0063] After the hierarchical division is completed, the matrix is substituted;

[0064] It needs to be further explained that the matrix construction unit is used to construct the judgment matrix according to the division level of the electrical equipment in the power plant, so as to deduce the influence degree of the two parameters compared with each other in each level, improve the parameter influence detection efficiency in the current running period, and is beneficial to improve the energy efficiency control performance of the power plant;

[0065] The influence layer and the parameter layer are respectively constructed into matrices;

[0066] Matrices A and B are respectively constructed according to the fault types of the influence layer and the running parameter types of the parameter layer;

[0067] Matrix A is: The fault type is used as element a, it needs to be explained that a12 represents element a1 compared with a2; a21 represents element a2 compared with a1; n represents a natural number, which is the number of fault types in the matrix;

[0068] Matrix B is: The fault type is used as element b; similarly, b12 represents element b1 compared with b2; b21 represents element b2 compared with b1; m represents a natural number, which is the number of influence parameters in the matrix;

[0069] It needs to be explained that the technical solution relates to matrix construction, if the fault type is irrelevant to the influence parameter, the corresponding element is not counted in the analysis;

[0070] At the same time, the elements in matrix A and matrix B are labeled by using the scale method;

[0071] The specific labeling method is as follows:

[0072] The 1-9 labeling method is used, wherein 1 represents that the two elements are equally important, 3 represents that the former is slightly more important than the latter, 5 represents that the former is obviously more important than the latter, 7 represents that the former is strongly more important than the latter, 9 represents that the former is extremely more important than the latter, and 2, 4, 6 and 8 are intermediate transition values;

[0073] The fault types in matrix A are compared and labeled, and the interval time of the decline of the operating efficiency of the power equipment after the fault type occurs is used as the evaluation data, wherein the operating efficiency is the deviation between the preset output and the actual output of the current electrical parameter of the power equipment, such as voltage deviation and current deviation;

[0074] If the evaluation data deviation of the two fault types is within the deviation range, it indicates that the elements are equally important, and is marked as 1; if the evaluation data deviation of the fault type exceeds 1.1 times of the peak value of the deviation range, it indicates that the former is slightly more important than the latter, and is marked as 3; if the evaluation data deviation of the fault type exceeds 1.3 times of the peak value of the deviation range, it indicates that the former is obviously more important than the latter, and is marked as 5; if the evaluation data deviation of the fault type exceeds 1.5 times of the peak value of the deviation range, it indicates that the former is strongly more important than the latter, and is marked as 7; if the evaluation data deviation of the fault type exceeds 1.8 times of the peak value of the deviation range, it indicates that the former is extremely more important than the latter, and is marked as 9; when the former and the latter of the two fault types are interchanged, a negative sign is set based on the original mark; it needs to be explained that the negative sign is used to reflect the opposite importance, and does not affect the positive and negative nature of the mark number;

[0075] The influence parameters in matrix B are also compared and labeled, and the span of the increase of the fault frequency of the power equipment after the influence parameter appears floating is used as the evaluation data;

[0076] After the matrix construction is completed, hierarchical weight analysis is performed;

[0077] It needs to be further explained that the hierarchical weight acquisition unit is used to analyze the elements of the completed matrix and perform weight division, and the data influence of the corresponding level is obtained through the weight division, so as to facilitate the energy efficiency prediction according to the data influence combined with the numerical trend of the actual data, and the timely data control according to the data type can also be performed to avoid the occurrence of the corresponding fault type, thereby improving the operating efficiency stability of the power plant power equipment;

[0078] The importance level of the fault type is divided according to the importance comparison result in the matrix, specifically, 1, 3, 5, 7, and 9 correspond to the same importance level, slightly important level, obvious important level, strong important level, and extreme important level respectively; and if the importance levels are inconsistent during the fault type comparison, the high importance level and the low importance level are divided;

[0079] In the matrix corresponding to the influence layer, the number ratio of the high importance level fault type and the low importance level fault type corresponding to the current importance level fault type is collected, that is, the number of the high importance level fault type and the low importance level fault type corresponding to the current importance level fault type is obtained to infer the importance level influence of the current fault type in the operation stage of the power equipment; and the number ratio of the high importance level fault type and the low importance level fault type corresponding to the current importance level fault type is marked as type influence weight information;

[0080] In the matrix corresponding to the parameter layer, the numerical floating span ratio of the current importance level influence parameter type is collected, wherein the numerical floating span ratio is represented as the ratio of the floating span of any type of influence parameter to the parameter value before floating; the floating influence degree of the influence parameter is judged by the floating span ratio;

[0081] At the same time, after the floating of the current importance level influence parameter type, the power equipment fault operation energy efficiency floating frequency ratio is collected, wherein the energy efficiency floating span ratio is represented as the span value ratio of the electrical parameter output deviation to the electrical parameter output deviation before the floating of the influence parameter type;

[0082] The numerical floating span ratio difference is obtained by difference calculation of the numerical floating span ratio of the high importance level influence parameter type and the numerical floating span ratio of the current influence parameter type; the energy efficiency floating frequency ratio difference is obtained by difference calculation of the energy efficiency floating frequency ratio of the high importance level influence parameter type and the energy efficiency floating frequency ratio of the current influence parameter type; it is explained that the high importance level means that the current influence parameter type is analyzed, and the influence parameter type higher than the importance level of the current influence parameter type;

[0083] During the operation, the numerical floating span ratio difference and the energy efficiency floating frequency ratio difference of the current fault parameter type are floatingly counted to obtain the cumulative growth period of the numerical floating span ratio difference and the energy efficiency floating frequency ratio difference, and the cumulative growth period is marked as parameter influence weight information;

[0084] If the number ratio of the high importance level fault type and the low importance level fault type corresponding to the current importance level fault type exceeds the set number ratio threshold, the current fault type is marked as a high weight fault type;

[0085] If the number ratio of the high importance level fault type to the low importance level fault type corresponding to the current importance level fault type does not exceed the set number ratio threshold, the current fault type is marked as a low-weight fault type

[0086] If the cumulative growth period of the value floating span ratio difference and the energy efficiency floating frequency ratio difference exceeds the set cumulative increase period threshold, the running parameter of the current fault type is marked as a high-weight parameter type;

[0087] If the cumulative growth period of the value floating span ratio difference and the energy efficiency floating frequency ratio difference does not exceed the set cumulative increase period threshold, the running parameter of the current fault type is marked as a low-weight parameter type;

[0088] The energy efficiency management platform conducts targeted monitoring according to the weight type, and adjusts the corresponding threshold according to the value floating trend to ensure that the threshold comparison meets the accuracy of actual value monitoring. It needs to be explained that when the fault type matches the running parameter, that is, the running parameter in the running parameter floating fault type scene, the corresponding fault type is regarded as a high-impact fault type of the target layer when the weight type is high, and the energy efficiency management platform conducts continuous monitoring and priority regulation on it;

[0089] After completing the hierarchical weight analysis, energy efficiency evaluation is performed;

[0090] It needs to be further explained that the energy efficiency evaluation unit is used for energy efficiency evaluation of the power equipment, and the running state of the current power equipment is inferred by comparing the elements of each weight type in the matrix. According to the control trend of the risk element, the current running energy efficiency can be effectively judged, so as to timely perform energy efficiency monitoring and avoid power equipment running failure caused by energy efficiency anomaly;

[0091] The number of matching high-weight parameter types corresponding to the same high-weight fault type is collected. If the corresponding number exceeds the number threshold, it is marked as an easy-impact fault type; otherwise, it is marked as a difficult-impact fault type;

[0092] During the operation of the power equipment, the increase span of the corresponding impact parameter value floating span peak value is collected after the impact parameter floating control intervention of the easy-impact fault type, and the rising span of the impact parameter value floating frequency of the difficult-impact fault type is collected when the impact parameter of the difficult-impact fault type floats;

[0093] If the increase span of the impact parameter value floating span peak value exceeds the increase span threshold, or the rising span of the impact parameter value floating frequency exceeds the rising span threshold, it is inferred that the energy efficiency of the power equipment is abnormal, and the energy efficiency management platform traces back to the monitored weight type and performs value regulation;

[0094] If the increase span of the parameter value floating span peak does not exceed the increase span threshold value, and the rising span of the parameter value floating frequency does not exceed the rising span threshold value, it is inferred that the energy efficiency of the power equipment is normal, and the energy efficiency management and control platform continuously monitors the monitored weight type.

[0095] In use, the hierarchical division construction unit performs hierarchical division on the operation collection parameters of the electrical equipment in the power plant; the matrix construction unit respectively constructs matrices of the influence layer and the parameter layer, and labels the constructed matrices of the influence layer and the parameter layer, and distinguishes the importance levels through the labeling; the hierarchical weight acquisition unit divides the weights of the matrix elements; and the energy efficiency evaluation unit evaluates the energy efficiency of the power equipment according to the divided weights.

[0096] The preferred embodiments of the application disclosed above are only used to help explain the application. The preferred embodiments do not describe all the details, nor limit the application to the specific embodiments. Obviously, many modifications and variations can be made according to the content of the specification. The specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the application, so that those skilled in the art can well understand and utilize the application. The application is limited only by the claims and their full scope and equivalents.

Claims

1. A power plant electrical equipment energy efficiency management and control system based on multi-sensor monitoring, characterized in that, The energy efficiency management platform is connected with a hierarchical division construction unit, a matrix construction unit, a hierarchical weight acquisition unit and an energy efficiency evaluation unit; The hierarchical division construction unit is used for hierarchical division of the operation collection parameters of the electrical equipment in the power plant, obtaining fault parameters according to the fault type generation time, and obtaining fault parameter floating abnormal operation parameters according to the fault parameters, and marking as impact parameters, taking any type of electrical equipment as an energy efficiency evaluation object, and constructing a target layer, an impact layer and a parameter layer for the energy efficiency evaluation object; The matrix construction unit is used for matrix construction of the impact layer and the parameter layer, and the constructed matrices of the impact layer and the parameter layer are labeled, and the important level is distinguished through the labeling; The hierarchical weight acquisition unit is used for weight division of the matrix elements; The energy efficiency evaluation unit is used for energy efficiency evaluation of the electrical equipment according to the divided weights.

2. The power plant electrical equipment energy efficiency management system based on multi-sensor monitoring according to claim 1, characterized in that, The process of the hierarchical division construction unit is as follows: Taking the energy efficiency evaluation of the electrical equipment as the target layer of the current energy efficiency evaluation hierarchy; According to the target layer of the electrical equipment, the corresponding type of electrical equipment historical fault type is obtained, the fault type is screened according to the fault type, and after the screening is completed, the corresponding fault type is taken as the next level of the target layer, and is set as the impact layer; After the fault type screening is completed, the corresponding impact parameters of the fault parameters of the fault type in the impact layer are statistically analyzed, the impact parameters are screened according to the statistical analysis, and the screened impact parameters are taken as the next level of the corresponding impact layer, and are set as the parameter layer.

3. The power plant electrical equipment energy efficiency management system based on multi-sensor monitoring according to claim 2, characterized in that, The screening process of the fault type is as follows: The fault parameter floating time and the electrical equipment energy efficiency abnormal time corresponding to the fault type are collected, and the overlapping frequency of the time is inferred according to the comparison, if the time overlapping frequency exceeds the set frequency threshold, it is inferred that the current fault parameter exists direct influence, and the fault type is marked as direct influence type; If the time overlapping frequency does not exceed the set frequency threshold, the fault type is marked as indirect influence type; different fault types are screened, if the direct influence type appears in the running process, the corresponding direct influence type is listed in the impact layer; If the continuous appearance number of the indirect influence type in the running process exceeds the set number threshold, the indirect influence type is listed in the impact layer.

4. The power plant electrical equipment energy efficiency management system based on multi-sensor monitoring according to claim 2, characterized in that, The impact parameter screening process is as follows: The impact parameter of the fault generation time of the fault type is monitored, if the value of the impact parameter at the fault generation time is the value peak in the current period, the impact parameter type of the value peak in the current period is collected and listed in the parameter layer; If the value of the impact parameter at the fault generation time is the floating peak in the current period, the impact parameter type of the floating peak in the current period is collected and listed in the parameter layer.

5. The power plant electrical equipment energy efficiency management system based on multi-sensor monitoring according to claim 1, characterized in that, The process of the matrix construction unit is as follows: Matrices A and B are constructed according to the fault types of the impact layer and the operation parameter types of the parameter layer respectively; where the matrix A is: The failure type as element a,n is represented by a natural number, the number of failure types within the matrix; the matrix B is: The failure type as element b,m is represented by a natural number, the number of influencing parameters within the matrix.

6. The power plant electrical equipment energy efficiency management system based on multi-sensor monitoring according to claim 5, characterized in that, The fault types in the matrix A are compared and labeled, and the interval time of the power equipment operation energy efficiency decline after the fault type generation is taken as the evaluation data; If the evaluation data deviation of two fault types is within the deviation range, it is marked as 1; If the fault type corresponding to the evaluation data deviation exceeds 1.1 times the peak value of the deviation range, it is marked as 3; If the fault type corresponding to the evaluation data deviation exceeds 1.3 times the peak value of the deviation range, it is marked as 5; If the fault type corresponding to the evaluation data deviation exceeds 1.5 times the peak value of the deviation range, it is marked as 7; If the fault type corresponding to the evaluation data deviation exceeds 1.8 times the peak value of the deviation range, it is marked as 9; Similarly, the influence parameters in matrix B are compared and marked, and the increase span of the fault frequency of the power equipment after the influence parameter appears floating is used as the evaluation data.

7. The power plant electrical equipment energy efficiency management system based on multi-sensor monitoring according to claim 1, characterized in that, The process of the hierarchical weight acquisition unit is as follows: In the influence layer corresponding matrix, type influence weight information and parameter influence weight information are collected: If the type influence weight information exceeds the set quantity ratio threshold, the current fault type is marked as a high weight fault type; if the type influence weight information does not exceed the set quantity ratio threshold, the current fault type is marked as a low weight fault type If the parameter influence weight information exceeds the set cumulative increase period threshold, the operating parameter of the current fault type is marked as a high weight parameter type; If the parameter influence weight information does not exceed the set cumulative increase period threshold, the operating parameter of the current fault type is marked as a low weight parameter type.

8. The power plant electrical equipment energy efficiency management system based on multi-sensor monitoring according to claim 7, characterized in that, The type influence weight information is represented as the quantity ratio of the high importance level fault type to the low importance level fault type corresponding to the current important level fault type; The parameter influence weight information collection process is as follows: in the parameter layer corresponding matrix, the numerical floating span ratio is collected; at the same time, the power equipment fault operating energy efficiency floating frequency ratio is collected; the difference value is calculated to obtain the numerical floating span ratio difference and the energy efficiency floating frequency ratio difference; During operation, the numerical floating span ratio difference and the energy efficiency floating frequency ratio difference of the current fault parameter type are floatingly counted to obtain the cumulative growth period of the numerical floating span ratio difference and the energy efficiency floating frequency ratio difference, and they are marked as parameter influence weight information.

9. The power plant electrical equipment energy efficiency management system based on multi-sensor monitoring according to claim 1, characterized in that, The process of the energy efficiency evaluation unit is as follows: The number of high weight parameter types corresponding to the same high weight fault type is collected, and if the corresponding number exceeds the quantity threshold, it is marked as an easy-to-influence fault type; otherwise, it is marked as a difficult-to-influence fault type; After the influence parameter floating control intervention of the easy-to-influence fault type, the increase span of the corresponding influence parameter numerical floating span peak value is collected; when the influence parameter of the difficult-to-influence fault type appears floating, the increase span of the numerical floating frequency of the influence parameter corresponding to the difficult-to-influence fault type is collected.

10. The power plant electrical equipment energy efficiency management system based on multi-sensor monitoring according to claim 9, characterized in that, If the increase span of the influence parameter numerical floating span peak value exceeds the increase span threshold, or the increase span of the influence parameter numerical floating frequency exceeds the increase span threshold, it is inferred that the energy efficiency of the power equipment is abnormal, and the energy efficiency control platform traces the weight type monitored and performs numerical control; if the increase span of the influence parameter numerical floating span peak value does not exceed the increase span threshold, and the increase span of the influence parameter numerical floating frequency does not exceed the increase span threshold, the energy efficiency control platform continues to monitor the weight type monitored.

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