WO2022133889A1 - 基于设备监理的电站设备质量数据处理方法及装置 - Google Patents

基于设备监理的电站设备质量数据处理方法及装置 Download PDF

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WO2022133889A1
WO2022133889A1 PCT/CN2020/138969 CN2020138969W WO2022133889A1 WO 2022133889 A1 WO2022133889 A1 WO 2022133889A1 CN 2020138969 W CN2020138969 W CN 2020138969W WO 2022133889 A1 WO2022133889 A1 WO 2022133889A1
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quality
power station
matrix
station equipment
target power
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PCT/CN2020/138969
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English (en)
French (fr)
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程阳
杜光利
司广全
刘树昌
付金良
杨百勋
李太江
田晓璇
郝延涛
王宝灵
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华能国际电力股份有限公司
西安热工研究院有限公司
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Priority to PCT/CN2020/138969 priority Critical patent/WO2022133889A1/zh
Publication of WO2022133889A1 publication Critical patent/WO2022133889A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]

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  • the present application relates to the technical field of power station equipment, in particular to a method and device for processing power station equipment quality data based on equipment supervision.
  • the processing methods for quality data mainly include expert evaluation method, statistical investigation method, analytic hierarchy process, and causal analysis method. inaccurate question.
  • the embodiments of the present application provide a method and apparatus for processing power station equipment quality data based on equipment supervision, so as to solve the problem of inaccurate quality data processing.
  • an embodiment of the present application provides a method for processing power station equipment quality data based on equipment supervision, including:
  • the quality model includes a first judgment matrix of the quality of the target power station equipment and its corresponding first quality index, and a second judgment of each first quality index and its corresponding second quality index matrix;
  • Numerical calculation is performed according to the weights corresponding to each of the second judgment matrices and the corresponding membership degree matrices to determine the first quality index corresponding to the quality problem of the target power station equipment, so as to adjust the manufacturing parameters of the target power station equipment.
  • the method for processing power station equipment quality data based on equipment supervision uses the quality model of the template equipment to calculate the corresponding weight, and on this basis, the corresponding first quality index is carried out in combination with the number of equipment of each second quality index.
  • the calculated membership degree matrix can accurately reflect the actual situation of the target power station equipment and improve the accuracy of quality data determination.
  • the corresponding manufacturing parameters are processed The adjustment can ensure that the adjustment of the manufacturing parameters is carried out according to the actual problem, and the quality of the power station equipment produced subsequently is guaranteed.
  • determining the membership matrix corresponding to each first quality indicator based on the number of devices corresponding to each of the second quality indicators includes:
  • the ratio of the number of devices of each of the second quality indicators to the sum of the devices is calculated respectively, and a membership degree matrix corresponding to each of the first quality indicators is obtained.
  • the method for processing power station equipment quality data based on equipment supervision utilizes the ratio of the number of equipment of each second quality index to the sum of the equipment in the calculation of membership degree to ensure the accuracy of the calculated membership degree matrix.
  • the numerical calculation is performed according to the weights corresponding to each of the second judgment matrices and the corresponding membership degree matrices to determine the quality of the target power station equipment.
  • the first quality index corresponding to the problem is used to adjust the manufacturing parameters of the target power station equipment, including:
  • the manufacturing parameter of the first quality index is determined, so as to adjust the manufacturing parameter.
  • the method for processing power station equipment quality data based on equipment supervision uses a quality assessment matrix to determine the first quality index corresponding to the quality problem, so as to adjust the manufacturing parameters in a targeted manner and ensure the manufacturing quality of subsequent power station equipment. .
  • the first quality index corresponding to the quality problem of the target power station equipment is determined based on the quality evaluation matrix corresponding to each of the first quality indexes ,include:
  • the first quality index corresponding to the quality problem of the target power station equipment is determined by using the evaluation level corresponding to each of the first quality indexes.
  • the method further includes:
  • the quality level of the target power station equipment is determined based on the size of each element in the membership degree matrix corresponding to the target power station equipment.
  • the method for processing power station equipment quality data based on equipment supervision determines the quality level of the target power station equipment by calculating the weight corresponding to the first judgment matrix and each membership degree matrix, which can ensure the objectiveness of the determined quality level. sex.
  • the determining the weight corresponding to each of the second judgment matrices includes:
  • the weight corresponding to the second judgment matrix is a subjective weight.
  • the weight of the second judgment matrix can be directly determined in a subjective manner, which can improve the processing efficiency of the quality data. efficiency.
  • the determining the weight corresponding to each of the second judgment matrices includes:
  • the weight corresponding to the second judgment matrix is calculated based on the second judgment matrix.
  • the weight corresponding to the second judgment matrix is calculated on the basis of the second judgment matrix, so as to ensure the second judgment matrix.
  • the accuracy of the weights corresponding to the judgment matrix is not limited to the embodiment of the present application.
  • an embodiment of the present application further provides a power station equipment quality data processing device based on equipment supervision, including:
  • the first obtaining module is used to obtain a quality model of the target power station equipment, the quality model includes a first judgment matrix of the quality of the target power station equipment and its corresponding first quality index, and each first quality index and its corresponding first quality index.
  • a determination module configured to determine the corresponding weights of each of the second judgment matrices
  • a second obtaining module configured to obtain the number of devices corresponding to each of the second quality indicators in the target power station devices
  • a first determination module configured to obtain a membership degree matrix corresponding to each of the first quality indicators based on the number of devices corresponding to each of the second quality indicators;
  • the second determination module is configured to perform numerical calculation according to the weights corresponding to each of the second judgment matrices and the corresponding membership degree matrices, and determine the first quality index corresponding to the quality problem of the target power station equipment, so as to determine the first quality index corresponding to the quality problem of the target power station equipment.
  • the manufacturing parameters of the device are adjusted.
  • an embodiment of the present application provides an electronic device, including: a memory and a processor, the memory and the processor are connected in communication with each other, the memory stores computer instructions, and the processor By executing the computer instructions, the first aspect or the method for processing power station equipment quality data based on equipment supervision described in any implementation manner of the first aspect is executed.
  • an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the first aspect or any one of the first aspect.
  • FIG. 1 is a flowchart of a method for processing power station equipment quality data based on equipment supervision according to an embodiment of the present application
  • FIG. 2 is a flowchart of a method for processing power station equipment quality data based on equipment supervision according to an embodiment of the present application
  • FIG. 3 is a flowchart of a method for processing power station equipment quality data based on equipment supervision according to an embodiment of the present application
  • FIG. 4 is a structural block diagram of a power station equipment quality data processing apparatus based on equipment supervision according to an embodiment of the present application
  • FIG. 5 is a schematic diagram of a hardware structure of an electronic device provided by an embodiment of the present application.
  • an embodiment of a method for processing equipment quality data of a power station based on equipment supervision is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be implemented in a computer system such as a set of computer-executable instructions. and, although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in an order different from that herein.
  • FIG. 1 is a power station equipment quality data processing method based on equipment supervision according to an embodiment of the present application.
  • the flowchart of the data processing method, as shown in Figure 1, includes the following steps:
  • the quality model includes a first judgment matrix of the quality of the target power station equipment and its corresponding first quality index, and a second judgment matrix of each first quality index and its corresponding second quality index.
  • the quality model can be divided into three layers, namely the target layer, the criterion layer and the index layer.
  • the target layer is the manufacturing quality of power station equipment, which is represented by A in the following;
  • the criterion layer includes at least one first quality index, which is represented by Bi in the following, and i is the quantity of the first quality indicator.
  • the indicator layer includes at least one second quality indicator, hereinafter denoted by Cj, and j represents the number of the second quality indicator.
  • the target layer is the manufacturing quality of power station equipment
  • the first quality indicators of the criterion layer include: raw material quality (B 1 ), welding quality (B 2 ), appearance quality (B 3 ), assembly quality (B 4 ), performance test ( B5), anti - corrosion packaging (B6 )
  • the second quality index of the index layer includes quality certification documents ( C1 ), appearance quality (C2 ) , re-inspection of incoming performance (C3 ) , molding quality (C4 ) , flaw detection quality (C 5 ), physical and chemical performance inspection (C 6) , design review (C 7 ), dimension inspection (C 8 ), deformation and scratches (C 9 ), cleanliness (C 10 ), process review (C 11 ) ), leaf assembly process and results (C 12 ), test equipment (C 13 ), test process and results (C 14 ), preservative quality (C 15 ), labeling quality (C 16 ), and packaging quality (C 17 ).
  • C 1 quality certification documents
  • C2 appearance quality
  • C4 re-
  • first quality index and second quality index are only used as an exemplary description, and the protection scope of the present application is not limited thereto, and can be set according to actual conditions.
  • the quality model of the target power station equipment is divided into three levels, and the quality indicators in each level are as follows:
  • B 1 (C 1 , C 2 , C 3 );
  • B 2 (C 4 , C 5 , C 6 );
  • B 3 (C 7 , C 8 , C 9 , C 10 );
  • B 6 (C 15 , C 16 , C 17 ).
  • Scaling meaning 1 Indicates that two factors are of equal importance compared to 3
  • Indicates that the former is strongly more important than the latter 9 Indicates that the former is extremely important compared to the latter
  • 2,4,6,8 Represents the median value of the above adjacent judgments reciprocal If the ratio of the importance of factor i to that of factor i is a ij , then the ratio of the importance of factor j to that of factor i
  • the "1-9 scaling method” is used to construct a judgment matrix A for the manufacturing quality of power station equipment and its corresponding six evaluation indicators, as shown in Table 3:
  • Raw material quality Welding quality Appearance Quality Assembly quality performance test Preservative packaging
  • the "1-9 scale method” is used to construct a judgment matrix B 1 for the raw material quality indicators and their corresponding three evaluation indicators, as shown in Table 4:
  • the "1-9 scale method” is used to construct a judgment matrix B 2 for the welding quality index and its corresponding three evaluation indexes, as shown in Table 5:
  • the "1-9 scaling method” is used to construct a judgment matrix B 3 for the appearance quality index and its corresponding four evaluation indexes, as shown in Table 5:
  • Design review Dimensional inspection Deformation and scratches cleanliness design review a 11 a 12 a 13 a 14 Dimensional inspection a 21 a 22 a 23 a 24 Deformation and scratches a 31 a 32 a 33 a 34 cleanliness a 41 a 42 a 43 a 44
  • the "1-9 scale method” is used to construct a judgment matrix B 6 for anti-corrosion packaging and its corresponding three evaluation indicators, as shown in Table 6 below:
  • Anticorrosion quality Logo quality packaging quality
  • Anticorrosion quality a 11 a 12 a 13 Logo quality a 21 a 22 a 23 packaging quality a 31 a 32 a 33
  • the first judgment matrix A and the second judgment matrices B 1 -B 6 corresponding to each of the first quality indicators can be obtained.
  • the second judgment matrix B1 corresponds to the first quality index-raw material quality
  • the second judgment matrix B2 corresponds to the first quality index-welding quality
  • the second judgment matrix B3 corresponds to the first quality index-appearance quality
  • the second judgment matrix B4 corresponds to the first quality index - assembly quality
  • the second judgment matrix B5 corresponds to the first quality index - performance test
  • the second judgment matrix B6 corresponds to the first quality index - welding quality anti-corrosion packaging .
  • the quality model may be obtained by the electronic device from the outside world, or input into the electronic device by the user through human-computer interaction.
  • the method for obtaining the quality model of the target power station device by the electronic device is not limited herein.
  • the electronic device After acquiring each second judgment matrix, the electronic device can determine the weight corresponding to each second judgment matrix by means of subjective weight assignment, or it can be calculated by using the second judgment matrix.
  • the electronic device can determine the weight corresponding to each second judgment matrix by means of subjective weight assignment, or it can be calculated by using the second judgment matrix.
  • there is no restriction on the way of determining the weight corresponding to the second judgment matrix and it can be specifically set according to the actual situation.
  • the number of equipment in the target power station equipment corresponding to each second quality index can be manually analyzed for multiple target power station equipment to obtain the equipment with each second quality index problem, and the number of the equipment can be counted to obtain the target power station equipment.
  • the number of devices corresponding to each second quality index in .
  • the number of devices may be realized and stored in the electronic device, or may be obtained by the electronic device from the outside world, and so on.
  • the number of devices corresponding to the second quality index in each target power station device can be stored in the electronic device in advance, and the number of devices belonging to each evaluation level can be obtained, thereby obtaining the membership degree matrix corresponding to each first quality index.
  • the electronic device may sequentially multiply the weights corresponding to the second judgment matrices and the corresponding membership degree matrices to obtain the first quality index corresponding to the quality of the target power station equipment. After the first quality index with quality problems is determined, the manufacturing parameters of the target power station equipment corresponding to the first quality index can be adjusted.
  • the first quality index with quality problems in the target power station equipment is welding quality
  • it is necessary to adjust the manufacturing parameters of the welding process if it is determined that the first quality indicators with quality problems in the target power station equipment
  • the index is the assembly quality, and the manufacturing parameters of the assembly process need to be adjusted.
  • the method for processing power station equipment quality data based on equipment supervision uses the quality model of the template equipment to calculate the corresponding weights, and on this basis, combines the number of equipment of each second quality index to calculate the corresponding weight of each first quality index.
  • the membership degree matrix is calculated, so that the calculated membership degree can accurately reflect the actual situation of the target power station equipment, which improves the accuracy of quality data determination.
  • the corresponding manufacturing parameters are adjusted based on the first quality index corresponding to the determined quality problem. , it can ensure that the adjustment of the manufacturing parameters is carried out according to the actual problem, and the quality of the power station equipment produced subsequently is guaranteed.
  • FIG. 2 is a power station equipment quality data processing method based on equipment supervision according to an embodiment of the present application.
  • the flowchart of the data processing method, as shown in Figure 2 includes the following steps:
  • the quality model includes a first judgment matrix of the quality of the target power station equipment and its corresponding first quality index, and a second judgment matrix of each first quality index and its corresponding second quality index.
  • the above S24 may include the following steps:
  • the defects here correspond to the quality evaluation grades of power station equipment, and the manufacturing quality evaluation grades of power station equipment are constructed as shown in Table 7.
  • S242 Calculate the ratio of the number of devices of each second quality index to the sum of the devices, respectively, to obtain a membership degree matrix corresponding to each first quality index.
  • the second quality index Ci is evaluated, and the percentage statistics method is used to perform percentage statistics on the quality problem grade evaluation result as the grade membership degree.
  • the total number of equipment with quality problems corresponding to the second quality index Ci is y pieces, and the total number of equipment with quality problems at Lm level is x pieces. It can be seen that the Lm membership degree of the second quality index Ci is :
  • the level membership matrix corresponding to the first quality index can be obtained
  • the membership matrix corresponding to each first quality index of the above-mentioned criterion layer can be obtained,
  • the size of each element in the membership degree matrix corresponds to the evaluation index evaluation level Lm.
  • the above S25 may include the following steps:
  • S251 Calculate the product of the weight corresponding to each second judgment matrix and the corresponding membership degree matrix, respectively, to obtain a quality evaluation matrix corresponding to each first quality index.
  • the electronic device obtains the weights corresponding to each of the second judgment matrices after the above-mentioned processing of S22; Further, the electronic device can obtain the quality corresponding to each first quality index by calculating the product of the corresponding weight (W B1 -W B6 ) and the corresponding membership degree matrix (M B1 -M B6 ) of each second judgment matrix. Evaluation Matrix.
  • the electronic device can determine the evaluation level to which the first quality index belongs based on the size of each element; and then compare the evaluation levels to which each first quality index belongs. level, the first quality index corresponding to the quality problem of the target power station equipment can be determined.
  • the foregoing S252 may include the following steps:
  • the quality evaluation matrix corresponding to each first quality index corresponds to the evaluation level of each evaluation index.
  • the quality evaluation can be determined by comparing the size of each element in the same quality evaluation matrix. The evaluation level corresponding to the matrix.
  • the electronic equipment can determine which first quality indexes have quality problems by comparing the evaluation levels corresponding to each first quality index, so as to determine the first quality indexes corresponding to the quality problems of the target power station equipment.
  • the electronic device determines the first quality index with quality problems in the target power station equipment, it can determine the manufacturing parameters corresponding to the first quality index, so as to determine which manufacturing parameters need to be adjusted.
  • the method for processing power station equipment quality data based on equipment supervision uses the ratio of the number of equipment of each second quality index to the sum of the equipment in the calculation of membership degree, so as to ensure the accuracy of the calculated membership degree matrix; using quality assessment The matrix determines the second quality index corresponding to the quality problem, so that the manufacturing parameters can be adjusted in a targeted manner, so as to ensure the manufacturing quality of the subsequent power station equipment.
  • FIG. 3 is a power station equipment quality data processing method based on equipment supervision according to an embodiment of the present application.
  • the flowchart of the data processing method, as shown in Figure 3, includes the following steps:
  • the quality model includes a first judgment matrix of the quality of the target power station equipment and its corresponding first quality index, and a second judgment matrix of each first quality index and its corresponding second quality index.
  • the above S32 may include the following steps:
  • the number of elements in the second judgment matrix represents the number of second quality indexes corresponding to each first quality index. The smaller the number, the easier the evaluation of the first quality index can be, and the subjective assignment method can be directly used for weight calculation. Determine the weight corresponding to the second judgment matrix.
  • the subjective weights can be assigned corresponding weights through the expert scoring method, for example, corresponding to the above-mentioned assembly quality and the second judgment matrix corresponding to the performance test, the corresponding weights can be obtained.
  • the electronic device can calculate the weights corresponding to the second judgment matrices by using the analytic hierarchy process,
  • Consistency check index where: ⁇ max is the largest characteristic root, n is the dimension of the construction matrix, The average random consistency index RI is obtained by checking the table below:
  • the electronic device also determines the quality level of the target power station equipment by calculating the weight WA corresponding to the first judgment matrix and the corresponding membership degree matrix.
  • the weight determination method of the first judgment matrix reference may be made to the weight determination method corresponding to the second judgment matrix, and details are not described herein again.
  • the membership matrix corresponding to the target power station equipment can be calculated by the following formula:
  • the electronic device After the electronic device calculates and obtains the membership degree matrix corresponding to the target power station equipment, it can compare the size of each element in the membership degree matrix to determine the quality level of the target power station equipment, and the quality level is one of the above L1-L4. .
  • the weight of the second judgment matrix can be directly determined in a subjective manner, which can improve the efficiency of the quality data processing
  • the weight corresponding to the second judgment matrix is calculated on the basis of the second judgment matrix, so as to ensure the accuracy of the weight corresponding to the second judgment matrix.
  • the above-mentioned method for processing power station equipment quality data based on equipment supervision may include the following steps:
  • Step 1 Obtain the quality model of the target power station equipment, and the obtained first judgment matrix and second judgment matrix are as follows:
  • the first judgment matrix of the target layer A is:
  • each second judgment matrix of the standard layer is:
  • Step 2 Calculate the indicator weight.
  • the weight of each index of the target layer A to the criterion layer is obtained, that is, the matrix W A :
  • Step 3 Consistency check.
  • Consistency Check Indicator In the formula: ⁇ max is the largest characteristic root, n is the dimension of the construction matrix, in,
  • the consistency ratio CR is obtained.
  • Step 5 Calculate the rank membership of each evaluation index. Taking raw material quality B 1 as an example, the membership degrees of the evaluation results of each index level are shown in Table 9 below.
  • Step 6 Calculation and analysis of criterion-level evaluation results.
  • the anti-corrosion packaging is excellent, and the grade membership is 91.1%.
  • Step 7 Calculate and analyze the comprehensive evaluation results of the target layer.
  • the fuzzy subset of the equipment manufacturing quality evaluation level is:
  • the equipment quality of a power station has the highest membership degree to the L2 level, and the level membership degree is 50.9%, and the overall quality evaluation of equipment manufacturing is good.
  • a power station equipment quality data processing apparatus based on equipment supervision is also provided, and the apparatus is used to implement the above-mentioned embodiments and preferred implementations, and what has been described will not be repeated.
  • the term "module” may be a combination of software and/or hardware that implements a predetermined function.
  • the apparatus described in the following embodiments is preferably implemented in software, implementations in hardware, or a combination of software and hardware, are also possible and contemplated.
  • This embodiment provides a power station equipment quality data processing device based on equipment supervision, as shown in FIG. 4 , including:
  • the first obtaining module 41 is configured to obtain a quality model of the target power station equipment, where the quality model includes a first judgment matrix of the quality of the target power station equipment and its corresponding first quality index, and each first quality index and its corresponding first judgment matrix.
  • a determination module 42 configured to determine the corresponding weights of each of the second judgment matrices
  • a second obtaining module 43 configured to obtain the number of devices corresponding to each of the second quality indicators in the target power station devices
  • a first determination module 44 configured to obtain a membership degree matrix corresponding to each of the first quality indicators based on the number of devices corresponding to each of the second quality indicators;
  • the second determination module 45 is configured to perform numerical calculation according to the weights corresponding to each of the second judgment matrices and the corresponding membership degree matrices, and to determine the second quality index corresponding to the quality problem of the target power station equipment, so as to determine the quality of the target power station equipment. The manufacturing parameters of the power station equipment are adjusted.
  • the apparatus for processing power station equipment quality data based on equipment supervision in this embodiment is presented in the form of functional units, where a unit refers to an ASIC circuit, a processor and memory for executing one or more software or fixed programs, and/or Other devices that can provide the above functions.
  • An embodiment of the present application further provides an electronic device, which has the apparatus for processing power station equipment quality data based on equipment supervision shown in FIG. 4 .
  • FIG. 5 is a schematic structural diagram of an electronic device provided by an optional embodiment of the present application.
  • the electronic device may include: at least one processor 51, such as a CPU (Central Processing Unit, central processing unit). processor), at least one communication interface 53, memory 54, at least one communication bus 52.
  • the communication bus 52 is used to realize the connection and communication between these components.
  • the communication interface 53 may include a display screen (Display) and a keyboard (Keyboard), and the optional communication interface 53 may also include a standard wired interface and a wireless interface.
  • the memory 54 may be a high-speed RAM memory (Random Access Memory, volatile random access memory), or may be a non-volatile memory (non-volatile memory), such as at least one disk memory.
  • the memory 54 can optionally also be at least one storage device located away from the aforementioned processor 51 .
  • the processor 51 may be combined with the device described in FIG. 4 , the memory 54 stores application programs, and the processor 51 calls the program codes stored in the memory 54 for executing any of the above method steps.
  • the communication bus 52 may be a peripheral component interconnect (PCI for short) bus or an extended industry standard architecture (EISA for short) bus or the like.
  • PCI peripheral component interconnect
  • EISA extended industry standard architecture
  • the communication bus 52 can be divided into an address bus, a data bus, a control bus, and the like. For ease of presentation, only one thick line is used in FIG. 5, but it does not mean that there is only one bus or one type of bus.
  • the memory 54 may include volatile memory (English: volatile memory), such as random-access memory (English: random-access memory, abbreviation: RAM); the memory may also include non-volatile memory (English: non-volatile memory) memory), such as flash memory (English: flash memory), hard disk (English: hard disk drive, abbreviation: HDD) or solid-state drive (English: solid-state drive, abbreviation: SSD); the memory 54 may also include the above types of combination of memory.
  • volatile memory English: volatile memory
  • RAM random-access memory
  • flash memory English: flash memory
  • hard disk English: hard disk drive, abbreviation: HDD
  • solid-state drive English: solid-state drive, abbreviation: SSD
  • the memory 54 may also include the above types of combination of memory.
  • the processor 51 may be a central processing unit (English: central processing unit, abbreviation: CPU), a network processor (English: network processor, abbreviation: NP), or a combination of CPU and NP.
  • CPU central processing unit
  • NP network processor
  • the processor 51 may further include a hardware chip.
  • the above-mentioned hardware chip may be an application-specific integrated circuit (English: application-specific integrated circuit, abbreviation: ASIC), a programmable logic device (English: programmable logic device, abbreviation: PLD) or a combination thereof.
  • the above-mentioned PLD can be a complex programmable logic device (English: complex programmable logic device, abbreviation: CPLD), a field programmable logic gate array (English: field-programmable gate array, abbreviation: FPGA), a general-purpose array logic (English: generic array logic, abbreviation: GAL) or any combination thereof.
  • memory 54 is also used to store program instructions.
  • the processor 51 may invoke program instructions to implement the method for processing power station equipment quality data based on equipment supervision as shown in the embodiments of FIGS. 1 to 3 of the present application.
  • the embodiments of the present application further provide a non-transitory computer storage medium, where the computer storage medium stores computer-executable instructions, and the computer-executable instructions can execute the equipment monitoring-based power station equipment quality data in any of the foregoing method embodiments Approach.
  • the storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a flash memory (Flash Memory), a hard disk (Hard) Disk Drive, abbreviation: HDD) or solid-state drive (Solid-State Drive, SSD), etc.; the storage medium may also include a combination of the above-mentioned types of memories.

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Abstract

本申请涉及电站设备技术领域，具体涉及基于设备监理的电站设备质量数据处理方法及装置，所述方法包括获取目标电站设备的质量模型；确定各个第二判断矩阵对应的权重；获取目标电站设备中对应于各个所述第二质量指标的设备数量；基于对应于各个所述第二质量指标的设备数量，得到各个第一质量指标对应的隶属度矩阵；根据各个所述第二判断矩阵对应的权重与相应的隶属度矩阵进行数值计算，确定所述目标电站设备的质量问题对应的第二质量指标，以对所述目标电站设备的制造参数进行调整。结合各个第二质量指标的设备数量进行各个第一质量指标对应的隶属度矩阵计算，使得得到的隶属度能够准确反映目标电站设备的实际情况，提高了质量数据确定的准确性。

Description

基于设备监理的电站设备质量数据处理方法及装置 技术领域
本申请涉及电站设备技术领域，具体涉及基于设备监理的电站设备质量数据处理方法及装置。
背景技术
随着电厂机组容量的提升，对电站设备的等级及质量要求也相应提高。近年来，部分电站设备供应商因产能趋于饱和，在供货压力下，制造厂在设备质量管控上有所松懈，以质量换进度的情况时有发生，加之分包及以包代管现象普遍，设备制造质量风险明显增加。如何准确处理设备的质量数据，使电厂准确了解设备在生产过程中的制造质量情况，继而科学指导电厂设备运行、维护及检修工作的开展，成为电厂面临的重要课题。
目前对于质量数据的处理方法主要有专家评价法、统计调查法、层次分析法、因果分析法等，但上述方法在用于电站设备中时，由于电力行业机组设备制造的特点，存在质量数据处理不准确的问题。
发明内容
有鉴于此，本申请实施例提供了一种基于设备监理的电站设备质量数据处理方法及装置，以解决质量数据处理不准确的问题。
根据第一方面，本申请实施例提供了一种基于设备监理的电站设备质量数据处理方法，包括：
获取目标电站设备的质量模型，所述质量模型包括所述目标电站设备的质与其对应的第一质量指标的第一判断矩阵，以及各个第一质量指标与其对应的第二质量指标的第二判断矩阵；
确定各个所述第二判断矩阵对应的权重；
获取所述目标电站设备中对应于各个所述第二质量指标的设备数量；
基于对应于各个所述第二质量指标的设备数量，得到各个第一质量指标对应的隶属度矩阵；
根据各个所述第二判断矩阵对应的权重与相应的隶属度矩阵进行数值计算，确定所述目标电站设备的质量问题对应的第一质量指标，以对所述目标电站设备的制造参数进行调整。
本申请实施例提供的基于设备监理的电站设备质量数据处理方法，利用模板设备的质量模型进行相应权重的计算处理，在此基础上结合各个第二质量指标的设备数量进行各个第一质量指标对应的隶属度矩阵计算，使得计算得到的隶属度能够准确反映目标电站设备的实际情况，提高了质量数据确定的准确性，最后基于确定出的质量问题对应的第一质量指标对相应的制造参数进行调整，可以保证制造参数的调整是针对实际问题进行的，保证了后续生产出的电站设备的质量。
结合第一方面，在第一方面第一实施方式中，所述基于对应于各个所述第二质量指标的设备数量，确定各个第一质量指标对应的隶属度矩阵，包括：
计算对应于所有所述第二质量指标的设备数量的设备总和；
分别计算各个所述第二质量指标的设备数量与所述设备总和的比值，得到各个所述第一质量指标对应的隶属度矩阵。
本申请实施例提供的基于设备监理的电站设备质量数据处理方法，在隶属度计算中利用各个第二质量指标的设备数量与设备总和的比值，使得计算得到的隶属度矩阵的准确性。
结合第一方面第一实施方式，在第一方面第二实施方式中，所述根据各个所述第二判断矩阵对应的权重与相应的隶属度矩阵进行数值计算，确定所述目标电站设备的质量问题对应的第一质量指标，以对所述目标电站设备的制造参数进行调整，包括：
分别计算各个所述第二判断矩阵对应的权重与相应的隶属度矩阵的乘积，得到与各个所述第一质量指标对应的质量评估矩阵；
基于各个所述第一质量指标对应的质量评估矩阵，确定所述目标电站设备的质量问题对应的第一质量指标；
利用所述质量问题对应的第一质量指标，确定所述第一质量指标的制造参数，以对所述制造参数进行调整。
本申请实施例提供的基于设备监理的电站设备质量数据处理方法，利用质量评估矩阵确定质量问题对应的第一质量指标，以便有针对性地对制造参数进行调整，保证了后续电站设备的制造质量。
结合第一方面第二实施方式，在第一方面第三实施方式中，所述基于各个所述第一质量指标对应的质量评估矩阵，确定所述目标电站设备的质量问题对应的第一质量指标，包括：
针对各个所述第一质量指标对应的质量评估矩阵，比较所述质量评估矩阵中各个元素的大小，确定所述质量评估矩阵对应的评估等级；
利用各个所述第一质量指标对应的评估等级，确定所述目标电站设备的质量问题对应的第一质量指标。
结合第一方面，在第一方面第四实施方式中，所述方法还包括：
确定所述第一判断矩阵对应的权重；
计算所述第一判断矩阵对应的权重与各个所述第一质量指标对应的隶属度矩阵，得到所述目标电站设备对应的隶属度矩阵；
基于所述目标电站设备对应的隶属度矩阵中各个元素的大小，确定所述目标电站设备的质量等级。
本申请实施例提供的基于设备监理的电站设备质量数据处理方法，通过计算第一判断矩阵对应的权重以及各个隶属度矩阵，确定出目标电站设备的质量等级，可以保证确定出的质量等级的客观性。
结合第一方面，或第一方面第一实施方式至第四实施方式中任一项，在第一方面第五实施方式中，所述确定各个所述第二判断矩阵对应的权重，包括：
判断所述第二判断矩阵中元素的数量是否大于预设值；
当所述第二判断矩阵中元素的数量小于或等于所述预设值时，确定所述第二判断矩阵对应的权重为主观权重。
本申请实施例提供的基于设备监理的电站设备质量数据处理方法，在第二判断矩阵中元素的数量较少时，可以直接利用主观方式确定第二判断矩阵的权重，可以提高该质量数据处理的效率。
结合第一方面第五实施方式，在第一方面第六实施方式中，所述确定各个所述第二判断矩阵对应的权重，包括：
当所述第二判断矩阵中元素的数量大于所述预设值时，基于所述第二判断矩阵计算所述第二判断矩阵对应的权重。
本申请实施例提供的基于设备监理的电站设备质量数据处理方法，在第二判断矩阵中元素的数量较多时，在第二判断矩阵的基础上计算第二判断矩阵对应的权重，保证了第二判断矩阵对应的权重的准确性。
根据第二方面，本申请实施例还提供了一种基于设备监理的电站设备质量数据处理装置，包括：
第一获取模块，用于获取目标电站设备的质量模型，所述质量模型包括所述目标电站设备的质与其对应的第一质量指标的第一判断矩阵，以及各个第一质量指标与其对应的第二质量指标的第二判断矩阵；
确定模块，用于确定各个所述第二判断矩阵对应的权重；
第二获取模块，用于获取所述目标电站设备中对应于各个所述第二质量指标的设备数 量；
第一确定模块，用于基于对应于各个所述第二质量指标的设备数量，得到各个第一质量指标对应的隶属度矩阵；
第二确定模块，用于根据各个所述第二判断矩阵对应的权重与相应的隶属度矩阵进行数值计算，确定所述目标电站设备的质量问题对应的第一质量指标，以对所述目标电站设备的制造参数进行调整。
根据第三方面，本申请实施例提供了一种电子设备，包括：存储器和处理器，所述存储器和所述处理器之间互相通信连接，所述存储器中存储有计算机指令，所述处理器通过执行所述计算机指令，从而执行第一方面或者第一方面的任意一种实施方式中所述的基于设备监理的电站设备质量数据处理方法。
根据第四方面，本申请实施例提供了一种计算机可读存储介质，所述计算机可读存储介质存储计算机指令，所述计算机指令用于使所述计算机执行第一方面或者第一方面的任意一种实施方式中所述的基于设备监理的电站设备质量数据处理方法。
附图说明
为了更清楚地说明本申请具体实施方式或现有技术中的技术方案，下面将对具体实施方式或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图是本申请的一些实施方式，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是根据本申请实施例的基于设备监理的电站设备质量数据处理方法的流程图；
图2是根据本申请实施例的基于设备监理的电站设备质量数据处理方法的流程图；
图3是根据本申请实施例的基于设备监理的电站设备质量数据处理方法的流程图；
图4是根据本申请实施例的基于设备监理的电站设备质量数据处理装置的结构框图；
图5是本申请实施例提供的电子设备的硬件结构示意图。
具体实施方式
为使本申请实施例的目的、技术方案和优点更加清楚，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
根据本申请实施例，提供了一种基于设备监理的电站设备质量数据处理方法实施例，需要说明的是，在附图的流程图示出的步骤可以在诸如一组计算机可执行指令的计算机系统中执行，并且，虽然在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于此处的顺序执行所示出或描述的步骤。
在本实施例中提供了一种基于设备监理的电站设备质量数据处理方法，可用于电子设备，如电脑、手机、平板电脑等，图1是根据本申请实施例的基于设备监理的电站设备质量数据处理方法的流程图，如图1所示，该流程包括如下步骤：
S11，获取目标电站设备的质量模型。
其中，所述质量模型包括所述目标电站设备的质量与其对应的第一质量指标的第一判断矩阵，以及各个第一质量指标与其对应的第二质量指标的第二判断矩阵。
具体地，可以将质量模型划分为三层，分别为目标层、准则层以及指标层。所述的目标层为电站设备制造质量，在下文中以A表示；所述的准则层包括至少一个第一质量指标，在下文中以Bi表示，i表示第一质量指标的数量。指标层包括至少一个第二质量指标，在下文中以Cj表示，j表示第二质量指标的数量。
其中，目标层为电站设备制造质量；准则层的第一质量指标包括：原材料质量(B 1)、焊接质量(B 2)、外观质量(B 3)、装配质量(B 4)、性能试验(B 5)、防腐包装(B 6)；指标层的第二质量指标包括质量证明文件(C 1)、外观质量(C 2)、入厂性能复验(C 3)、成型质量(C 4)、探伤 质量(C 5)、理化性能检验(C 6)、设计审查(C 7)、尺寸检查(C 8)、变形划伤(C 9)、清洁度(C 10)、工艺审查(C 11)、叶装配过程及结果(C 12)、试验设备(C 13)、试验过程及结果(C 14)、防腐质量(C 15)、标识质量(C 16)以及包装质量(C 17)。详细请参见表1所示：
表1目标电站设备的质量模型
Figure PCTCN2020138969-appb-000001
此处需要说明的是，上述第一质量指标以及第二质量指标仅仅是作为一种示例性描述，本申请的保护范围并不限于此，具体可以根据实际情况进行相应的设置。
如表1所示，目标电站设备的质量模型分为三个层次，各层次中的质量指标如下所示：
第一层次：A＝(B 1、B 2、B 3、B 4、B 5、B 6)；
第二层次：B 1＝(C 1，C 2，C 3)；
B 2＝(C 4，C 5，C 6)；
B 3＝(C 7，C 8，C 9，C 10)；
B 4＝(C 11，C 12)；
B 5＝(C 13，C 14)；
B 6＝(C 15，C 16，C 17)。
基于上文所述的层级关系，构建反映第t级层中的一组评价指标对其对应的第t-1级层中的一项评价指标的影响程度的若干个判断矩阵。采用“1-9标度法”构造判断矩阵，标度法的含义如下表2所示：
表2标度法含义说明
标度 含义
1 表示两个因素相比，具有相同重要性
3 表示两个因素相比，前者比后者稍重要
5 表示两个因素相比，前者比后者明显重要
7 表示两个因素相比，前者比后者强烈重要
9 表示两个因素相比，前者比后者极端重要
2,4,6,8 表示上述相邻判断的中间值
倒数 若因素i与因素i的重要性之比为a ij，那么因素j与因素i重要性之
  比为a ji＝1/a ij。
采用“1-9标度法”对电站设备制造质量及其对应的6项评价指标构造判断矩阵A，如表3所示：
表3电站设备制造质量判断矩阵A
  原材料质量 焊接质量 外观质量 装配质量 性能试验 防腐包装
原材料质量 a 11 a 12 a 13 a 14 a 15 a 16
焊接质量 a 21 a 22 a 23 a 24 a 25 a 26
外观质量 a 31 a 32 a 33 a 34 a 35 a 36
装配质量 a 41 a 42 a 43 a 44 a 45 a 46
性能试验 a 51 a 52 a 53 a 54 a 55 a 56
防腐包装 a 61 a 62 a 63 a 64 a 65 a 66
采用“1-9标度法”对原材料质量指标及其对应的3项评价指标构造判断矩阵B 1，如表4所示：
表4原材料质量判断矩阵B 1
  质量证明文件 外观质量 入厂性能复验
质量证明文件 a 11 a 12 a 13
外观质量 a 21 a 22 a 23
入厂性能复验 a 31 a 32 a 33
采用“1-9标度法”对焊接质量指标及其对应的3项评价指标构造判断矩阵B 2，如表5所示：
表5焊接质量判断矩阵B 2
  成型质量 探伤质量 理化性能检验
成型质量 a 11 a 12 a 13
探伤质量 a 21 a 22 a 23
理化性能检验 a 31 a 32 a 33
采用“1-9标度法”对外观质量指标及其对应的4项评价指标构造判断矩阵B 3，如表5所示：
表5外观质量判断矩阵B 3
  设计审查 尺寸检查 变形划伤 清洁度
设计审查 a 11 a 12 a 13 a 14
尺寸检查 a 21 a 22 a 23 a 24
变形划伤 a 31 a 32 a 33 a 34
清洁度 a 41 a 42 a 43 a 44
采用“1-9标度法”对防腐包装及其对应的3项评价指标构造判断矩阵B 6，如下表6所示：
表6防腐包装判断矩阵B 6
  防腐质量 标识质量 包装质量
防腐质量 a 11 a 12 a 13
标识质量 a 21 a 22 a 23
包装质量 a 31 a 32 a 33
由以上处理，就可以得到第一判断矩阵A，以及对应于各个第一质量指标的第二判断矩阵B 1-B 6。具体地，第二判断矩阵B 1与第一质量指标-原材料质量对应，第二判断矩阵B 2与第一质量指标-焊接质量对应，第二判断矩阵B 3与第一质量指标-外观质量对应，第二判断矩阵B 4与第一质量指标-装配质量对应，第二判断矩阵B 5与第一质量指标-性能试验对应，第二 判断矩阵B 6与第一质量指标-焊接质量防腐包装对应。
所述质量模型可以是电子设备从外界获取到的，也可以是用户通过人机交互的方式输入电子设备中的，在此对电子设备获取目标电站设备的质量模型的方式并不做任何限定。
S12，确定各个第二判断矩阵对应的权重。
电子设备在获取到各个第二判断矩阵之后，可以采用主观权重赋值的方式，确定各个第二判断矩阵对应的权重，也可以是利用第二判断矩阵计算得到的。在此对第二判断矩阵对应的权重的确定方式并不做任何限制，具体可以根据实际情况进行相应的设置。
关于该步骤具体将在下文中进行详细描述。
S13，获取目标电站设备中对应于各个第二质量指标的设备数量。
目标电站设备中对应于各个第二质量指标的设备数量，可以是人工对多个目标电站设备进行分析，得到存在各个第二质量指标问题的设备，对其数量进行统计，就可以得到目标电站设备中对应于各个第二质量指标的设备数量。
所述的设备数量可以是实现存储在电子设备中的，也可以是电子设备从外界获取到的等等。
S14，基于对应于各个第二质量指标的设备数量，得到各个第一质量指标对应的隶属度矩阵。
其中，可以事先对评价指标中的多层次指标使用模糊评价，构建评价指标评价等级集L，L＝(L1，L2，L3，L4)，分别对应于优、良、中以及差4种等级，来表征设备质量状况。
在电子设备中可以事先存储有各个目标电站设备中分别对应于第二质量指标的设备数量，得到属于各个评价等级的设备数量，从而就可以得到各个第一质量指标对应的隶属度矩阵。
具体将在下文中对该步骤进行详细描述。
S15，根据各个第二判断矩阵对应的权重与相应的隶属度矩阵进行数值计算，确定目标电站设备的质量问题对应的第一质量指标，以对目标电站设备的制造参数进行调整。
电子设备可以依次将各个第二判断矩阵对应的权重与相应的隶属度矩阵进行相乘，得到目标电站设备的质量对应的第一质量指标。在确定出存在质量问题的第一质量指标之后，就可以针对该第一质量指标对应的目标电站设备的制造参数进行调整。
例如，若经过上述处理，确定出目标电站设备中存在质量问题的第一质量指标为焊接质量，则需要对焊接过程的制造参数进行调整；若确定出目标电站设备中存在质量问题的第一质量指标为装配质量，则需要对装配过程的制造参数进行调整。
本实施例提供的基于设备监理的电站设备质量数据处理方法，利用模板设备的质量模型进行相应权重的计算处理，在此基础上结合各个第二质量指标的设备数量进行各个第一质量指标对应的隶属度矩阵计算，使得计算得到的隶属度能够准确反映目标电站设备的实际情况，提高了质量数据确定的准确性，最后基于确定出的质量问题对应的第一质量指标对相应的制造参数进行调整，可以保证制造参数的调整是针对实际问题进行的，保证了后续生产出的电站设备的质量。
在本实施例中提供了一种基于设备监理的电站设备质量数据处理方法，可用于电子设备，如电脑、手机、平板电脑等，图2是根据本申请实施例的基于设备监理的电站设备质量数据处理方法的流程图，如图2所示，该流程包括如下步骤：
S21，获取目标电站设备的质量模型。
其中，所述质量模型包括所述目标电站设备的质与其对应的第一质量指标的第一判断矩阵，以及各个第一质量指标与其对应的第二质量指标的第二判断矩阵。
详细请参见图1所示实施例的S11，在此不再赘述。
S22，确定各个第二判断矩阵对应的权重。
详细请参见图2所示实施例的S12，在此不再赘述。
S23，获取目标电站设备中对应于各个第二质量指标的设备数量。
详细请参见图2所示实施例的S13，在此不再赘述。
S24，基于对应于各个第二质量指标的设备数量，得到各个第一质量指标对应的隶属度矩阵。
具体地，上述S24可以包括如下步骤：
S241，计算对应于所有第二质量指标的设备数量的设备总和。
如上文所述，构建有评价指标评价等级集L，L＝(L1，L2，L3，L4)。进一步地，可以基于设备制造过程中发现的质量问题，根据质量问题程度进行分级，分为一级缺陷、二级缺陷和三级缺陷。此处的缺陷与电站设备质量质量评价等级相对应，构建如表7所示电站设备制造质量评价等级。
表7电站设备制造质量评价等级
Figure PCTCN2020138969-appb-000002
在上述表7所示的电站设备制造质量评价等级的指导下，就可以统计各个第二质量指标下存在质量问题的设备数量。
S242，分别计算各个第二质量指标的设备数量与设备总和的比值，得到各个第一质量指标对应的隶属度矩阵。
具体地，对第二质量指标Ci进行评价，采用百分比统计法，将质量问题等级评价结果进行百分比统计，作为等级隶属度。例如，对应于第二质量指标Ci存在质量问题的设备数量总数为y件，其中，特征为Lm等级的质量问题的设备总和为x件，由此可知，第二质量指标Ci的Lm隶属度为：
r im＝x/y，(i＝1,2,....18；m＝1,2...4)。
进一步地，就可以得到第一质量指标对应的等级隶属度矩阵
Figure PCTCN2020138969-appb-000003
经过上述方式的处理，就可以得到上述准则层的各个第一质量指标对应的隶属度矩阵，
Figure PCTCN2020138969-appb-000004
其中，隶属度矩阵中的各个元素的大小是与评价指标评价等级Lm对应的。
S25，根据各个第二判断矩阵对应的权重与相应的隶属度矩阵进行数值计算，确定目标电站设备的质量问题对应的第一质量指标，以对目标电站设备的制造参数进行调整。
具体地，上述S25可以包括如下步骤：
S251，分别计算各个第二判断矩阵对应的权重与相应的隶属度矩阵的乘积，得到与各个第一质量指标对应的质量评估矩阵。
电子设备在经过上述S22的处理后，得到各个第二判断矩阵对应的权重；在经过上述S23-S24的处理后，得到各个第二判断矩阵对应的隶属度矩阵。进一步地，电子设备通过计 算各个第二判断矩阵对应的权重(W B1-W B6)以及相应的隶属度矩阵(M B1-M B6)的乘积，就可以得到与各个第一质量指标对应的质量评估矩阵。
具体可以采用如下方式表示：
Figure PCTCN2020138969-appb-000005
Figure PCTCN2020138969-appb-000006
Figure PCTCN2020138969-appb-000007
Figure PCTCN2020138969-appb-000008
Figure PCTCN2020138969-appb-000009
Figure PCTCN2020138969-appb-000010
S252，基于各个第一质量指标对应的质量评估矩阵，确定目标电站设备的质量问题对应的第一质量指标。
由于隶属度矩阵中各个元素的大小是与评价指标评价等级Lm对应的，相应地，经过上述S251处理之后得到的质量评估矩中各个元素的大小也是与评价指标评价等级Lm对应。因此，电子设备在计算得到各个第一质量指标对应的质量评估矩阵之后，通过各个元素的大小就可以确定该第一质量指标所隶属的评价等级；再通过比较各个第一质量指标所隶属的评价等级，就可以确定出目标电站设备的质量问题对应的第一质量指标。
作为本实施例的一种可选实施方式，上述S252可以包括如下步骤：
(1)针对各个第一质量指标对应的质量评估矩阵，比较质量评估矩阵中各个元素的大小，确定质量评估矩阵对应的评估等级。
如上文所述，各个第一质量指标对应的质量评估矩阵是与各个评价指标评价等级对应的，依据最大隶属度原则，通过比较同一质量评估矩阵中各个元素的大小，就可以确定出该质量评估矩阵对应的评估等级。
(2)利用各个第一质量指标对应的评估等级，确定目标电站设备的质量问题对应的第一质量指标。
电子设备通过比较各个第一质量指标对应的评估等级就可以确定哪些第一质量指标是存在质量问题的，从而就可以确定出目标电站设备的质量问题对应的第一质量指标。
S253，利用质量问题对应的第一质量指标，确定第一质量指标的制造参数，以对制造参数进行调整。
如上文所述，电子设备再确定出目标电站设备中存在质量问题的第一质量指标之后，就可以确定第一质量指标对应的制造参数，从而可以确定出需要对哪些制造参数进行调整。
本实施例提供的基于设备监理的电站设备质量数据处理方法，在隶属度计算中利用各个第二质量指标的设备数量与设备总和的比值，使得计算得到的隶属度矩阵的准确性；利用质量评估矩阵确定质量问题对应的第二质量指标，以便有针对性地对制造参数进行调整，保证了后续电站设备的制造质量。
在本实施例中提供了一种基于设备监理的电站设备质量数据处理方法，可用于电子设备，如电脑、手机、平板电脑等，图3是根据本申请实施例的基于设备监理的电站设备质量数据处理方法的流程图，如图3所示，该流程包括如下步骤：
S31，获取目标电站设备的质量模型。
其中，所述质量模型包括所述目标电站设备的质与其对应的第一质量指标的第一判断矩阵，以及各个第一质量指标与其对应的第二质量指标的第二判断矩阵。
详细请参见图2所示实施例的S21，在此不再赘述。
S32，确定各个第二判断矩阵对应的权重。
具体地，上述S32可以包括如下步骤：
S321，判断第二判断矩阵中元素的数量是否大于预设值。
第二判断矩阵中元素的数量表示各个第一质量指标对应的第二质量指标的数量，数量越小可以认为该第一质量指标的评价较为简单，再进行权重计算时可以直接采用主观赋值的方式确定该第二判断矩阵对应的权重。
当所述第二判断矩阵中元素的数量小于或等于所述预设值时，执行S322；否则，执行S323。
S322，确定第二判断矩阵对应的权重为主观权重。
所述的主观权重可以通过专家打分法赋予相应的权重，例如，对应于上述的装配质量以及性能试验对应的第二判断矩阵，得到相应的权重
Figure PCTCN2020138969-appb-000011
S323，基于第二判断矩阵计算第二判断矩阵对应的权重。
电子设备可以从采用层次分析法计算各个第二判断矩阵对应的权重，
Figure PCTCN2020138969-appb-000012
在初次确定出各个第二判断矩阵对应的权重之后，对其进行一致性校验，计算各个第二判断矩阵对应的校验系数CR，
Figure PCTCN2020138969-appb-000013
其中，
Figure PCTCN2020138969-appb-000014
一致性校验指标，式中：λ max为最大特征根，n为构造矩阵维数，
Figure PCTCN2020138969-appb-000015
平均随机一致性指标RI通过查下表获得：
表8平均随机一致性指标RI
n 3 4 5 6 7 8 9
RI 0.58 0.90 1.12 1.24 1.32 1.41 1.45
判断矩阵的校验系数CR值越小，说明判断矩阵的一致性程度越好，若校验系数CR<0.1，判断矩阵通过一致性校验，若CR>0.1，则需重新构建判断矩阵。
S33，获取目标电站设备中对应于各个第二质量指标的设备数量。
详细请参见图2所示实施例的S23，在此不再赘述。
S34，基于对应于各个第二质量指标的设备数量，得到各个第一质量指标对应的隶属度矩阵。
详细请参见图2所示实施例的S24，在此不再赘述。
S35，根据各个第二判断矩阵对应的权重与相应的隶属度矩阵进行数值计算，确定目标电站设备的质量问题对应的第一质量指标，以对目标电站设备的制造参数进行调整。
详细请参见图2所示实施例的S25，在此不再赘述。
S36，确定第一判断矩阵对应的权重。
其中，电子设备还通过计算第一判断矩阵对应的权重W A，以及相应的隶属度矩阵，确定目标电站设备的质量等级。其中，关于第一判断矩阵的权重确定方式可以参见上述第二判断矩阵对应的权重确定方式，在此不再赘述。
S37，计算第一判断矩阵对应的权重与各个第一质量指标对应的隶属度矩阵，得到目标电站设备对应的隶属度矩阵。
具体地，目标电站设备对应的隶属度矩阵可以采用下述公式计算得到：
Figure PCTCN2020138969-appb-000016
S38，基于目标电站设备对应的隶属度矩阵中各个元素的大小，确定目标电站设备的质量等级。
电子设备在计算得到目标电站设备对应的隶属度矩阵之后，就可以比较该隶属度矩阵中 各个元素的大小，确定目标电站设备的质量等级，所述的质量等级为上述L1-L4中的一种。
本实施例提供的基于设备监理的电站设备质量数据处理方法，在第二判断矩阵中元素的数量较少时，可以直接利用主观方式确定第二判断矩阵的权重，可以提高该质量数据处理的效率；在第二判断矩阵中元素的数量较多时，在第二判断矩阵的基础上计算第二判断矩阵对应的权重，保证了第二判断矩阵对应的权重的准确性。
作为本实施例的一个具体应用实施例，上述的基于设备监理的电站设备质量数据处理方法可以包括如下步骤：
步骤一：获取目标电站设备的质量模型，所得到的第一判断矩阵以及第二判断矩阵如下所示：
目标层A的第一判断矩阵为：
Figure PCTCN2020138969-appb-000017
同理，标准层的各个第二判断矩阵为：
Figure PCTCN2020138969-appb-000018
Figure PCTCN2020138969-appb-000019
Figure PCTCN2020138969-appb-000020
Figure PCTCN2020138969-appb-000021
步骤二：计算指标权重。
通过计算矩阵权向量得到目标层A对准则层的各指标权重，即为矩阵W A：
W A＝[0.148 0.222 0.464 0.068 0.030 0.068]
步骤三：一致性检验。
计算判断矩阵的校验系数CR，
Figure PCTCN2020138969-appb-000022
其中：
一致性检验指标
Figure PCTCN2020138969-appb-000023
式中：λ max为最大特征根，n为构造矩阵维数，
Figure PCTCN2020138969-appb-000024
其中，
Figure PCTCN2020138969-appb-000025
Figure PCTCN2020138969-appb-000026
计算一致性指标CI：
Figure PCTCN2020138969-appb-000027
查表8确定相应的平均随机一致性指标RI，对于6阶的判断矩阵，查表得到RI＝1.24。
求出一致性比例CR，判断矩阵的校验系数CR值越小，说明判断矩阵的一致性程度越好，
Figure PCTCN2020138969-appb-000028
可知CR<0.1，判断矩阵通过一致性检验，权重计算结果合理。若CR>0.1，则需重新构建判断矩阵。
同理可得准则层B 1、B 2、B 3、B 4、B 5经归一化计算权向量和一致性检验后的结果如下：
Figure PCTCN2020138969-appb-000029
Figure PCTCN2020138969-appb-000030
Figure PCTCN2020138969-appb-000031
Figure PCTCN2020138969-appb-000032
Figure PCTCN2020138969-appb-000033
Figure PCTCN2020138969-appb-000034
步骤四：对评价指标体系中的多层次指标使用模糊评价，构建评价指标评价等级集L，L＝(L1，L2，L3，L4)对应优、良、中、差4种等级。
步骤五：计算各评价指标等级隶属度。以原材料质量B 1为例，其各指标层等级评价结果隶属度如下表9所示。
表9原材料质量各指标等级隶属度
评价指标 L 1优 L 2良 L 3中 L 4差
C 1 1 0 0 0
C 2 0 0.9 0.1 0
C 3 1 0 0 0
由上表可以得到，原材料质量对应的等级隶属度评价矩阵为
Figure PCTCN2020138969-appb-000035
Figure PCTCN2020138969-appb-000036
同理可得，B 2，B 3，B 4，B 5，B 6指标等级隶属度评价矩阵为
Figure PCTCN2020138969-appb-000037
Figure PCTCN2020138969-appb-000038
Figure PCTCN2020138969-appb-000039
Figure PCTCN2020138969-appb-000040
Figure PCTCN2020138969-appb-000041
Figure PCTCN2020138969-appb-000042
步骤六：准则层评价结果计算与分析。
将B 1的指标权重系数
Figure PCTCN2020138969-appb-000043
与等级隶属度评价矩阵
Figure PCTCN2020138969-appb-000044
相乘即得到B 1指标评价集
Figure PCTCN2020138969-appb-000045
Figure PCTCN2020138969-appb-000046
根据最大隶属度原则，可知原材料的质量情况为优，等级隶属度为57.1％。
同理，可得准则层其他指标的评价集：
Figure PCTCN2020138969-appb-000047
可知焊接情况为良，等级隶属度为42.2％。
Figure PCTCN2020138969-appb-000048
可知外观质量情况为良，等级隶属度为69.1％。
Figure PCTCN2020138969-appb-000049
可知装配质量情况为良，等级隶属度为48％。
Figure PCTCN2020138969-appb-000050
可知性能试验情况为优，等级隶属度为100％。
Figure PCTCN2020138969-appb-000051
可知防腐包装情况为优，等级隶属度为91.1％。
步骤七：计算目标层综合评价结果并分析。
Figure PCTCN2020138969-appb-000052
设备制造质量评价等级的模糊子集为：
Figure PCTCN2020138969-appb-000053
依据最大隶属度原则，确定某电站设备质量对L2等级的隶属度最高，等级隶属度为50.9％，设备制造总体质量评价为良。
在本实施例中还提供了一种基于设备监理的电站设备质量数据处理装置，该装置用于实现上述实施例及优选实施方式，已经进行过说明的不再赘述。如以下所使用的，术语“模块”可以实现预定功能的软件和/或硬件的组合。尽管以下实施例所描述的装置较佳地以软件来实现，但是硬件，或者软件和硬件的组合的实现也是可能并被构想的。
本实施例提供一种基于设备监理的电站设备质量数据处理装置，如图4所示，包括：
第一获取模块41，用于获取目标电站设备的质量模型，所述质量模型包括所述目标电站设备的质与其对应的第一质量指标的第一判断矩阵，以及各个第一质量指标与其对应的第二质量指标的第二判断矩阵；
确定模块42，用于确定各个所述第二判断矩阵对应的权重；
第二获取模块43，用于获取所述目标电站设备中对应于各个所述第二质量指标的设备数量；
第一确定模块44，用于基于对应于各个所述第二质量指标的设备数量，得到各个第一质量指标对应的隶属度矩阵；
第二确定模块45，用于根据各个所述第二判断矩阵对应的权重与相应的隶属度矩阵进行数值计算，确定所述目标电站设备的质量问题对应的第二质量指标，以对所述目标电站设备的制造参数进行调整。
本实施例中的基于设备监理的电站设备质量数据处理装置是以功能单元的形式来呈现，这里的单元是指ASIC电路，执行一个或多个软件或固定程序的处理器和存储器，和/或其他可以提供上述功能的器件。
上述各个模块的更进一步的功能描述与上述对应实施例相同，在此不再赘述。
本申请实施例还提供一种电子设备，具有上述图4所示的基于设备监理的电站设备质量数据处理装置。
请参阅图5，图5是本申请可选实施例提供的一种电子设备的结构示意图，如图5所示，该电子设备可以包括：至少一个处理器51，例如CPU(Central Processing Unit，中央处理器)，至少一个通信接口53，存储器54，至少一个通信总线52。其中，通信总线52用于实现这些组件之间的连接通信。其中，通信接口53可以包括显示屏(Display)、键盘 (Keyboard)，可选通信接口53还可以包括标准的有线接口、无线接口。存储器54可以是高速RAM存储器(Random Access Memory，易挥发性随机存取存储器)，也可以是非不稳定的存储器(non-volatile memory)，例如至少一个磁盘存储器。存储器54可选的还可以是至少一个位于远离前述处理器51的存储装置。其中处理器51可以结合图4所描述的装置，存储器54中存储应用程序，且处理器51调用存储器54中存储的程序代码，以用于执行上述任一方法步骤。
其中，通信总线52可以是外设部件互连标准(peripheral component interconnect，简称PCI)总线或扩展工业标准结构(extended industry standard architecture，简称EISA)总线等。通信总线52可以分为地址总线、数据总线、控制总线等。为便于表示，图5中仅用一条粗线表示，但并不表示仅有一根总线或一种类型的总线。
其中，存储器54可以包括易失性存储器(英文：volatile memory)，例如随机存取存储器(英文：random-access memory，缩写：RAM)；存储器也可以包括非易失性存储器(英文：non-volatile memory)，例如快闪存储器(英文：flash memory)，硬盘(英文：hard disk drive，缩写：HDD)或固态硬盘(英文：solid-state drive，缩写：SSD)；存储器54还可以包括上述种类的存储器的组合。
其中，处理器51可以是中央处理器(英文：central processing unit，缩写：CPU)，网络处理器(英文：network processor，缩写：NP)或者CPU和NP的组合。
其中，处理器51还可以进一步包括硬件芯片。上述硬件芯片可以是专用集成电路(英文：application-specific integrated circuit，缩写：ASIC)，可编程逻辑器件(英文：programmable logic device，缩写：PLD)或其组合。上述PLD可以是复杂可编程逻辑器件(英文：complex programmable logic device，缩写：CPLD)，现场可编程逻辑门阵列(英文：field-programmable gate array，缩写：FPGA)，通用阵列逻辑(英文：generic array logic,缩写：GAL)或其任意组合。
可选地，存储器54还用于存储程序指令。处理器51可以调用程序指令，实现如本申请图1至3实施例中所示的基于设备监理的电站设备质量数据处理方法。
本申请实施例还提供了一种非暂态计算机存储介质，所述计算机存储介质存储有计算机可执行指令，该计算机可执行指令可执行上述任意方法实施例中的基于设备监理的电站设备质量数据处理方法。其中，所述存储介质可为磁碟、光盘、只读存储记忆体(Read-Only Memory，ROM)、随机存储记忆体(Random Access Memory，RAM)、快闪存储器(Flash Memory)、硬盘(Hard Disk Drive，缩写：HDD)或固态硬盘(Solid-State Drive，SSD)等；所述存储介质还可以包括上述种类的存储器的组合。
虽然结合附图描述了本申请的实施例，但是本领域技术人员可以在不脱离本申请的精神和范围的情况下做出各种修改和变型，这样的修改和变型均落入由所附权利要求所限定的范围之内。

Claims (10)

  1. 一种基于设备监理的电站设备质量数据处理方法，其特征在于，包括：
    获取目标电站设备的质量模型，所述质量模型包括所述目标电站设备的质与其对应的第一质量指标的第一判断矩阵，以及各个第一质量指标与其对应的第二质量指标的第二判断矩阵；
    确定各个所述第二判断矩阵对应的权重；
    获取所述目标电站设备中对应于各个所述第二质量指标的设备数量；
    基于对应于各个所述第二质量指标的设备数量，得到各个第一质量指标对应的隶属度矩阵；
    根据各个所述第二判断矩阵对应的权重与相应的隶属度矩阵进行数值计算，确定所述目标电站设备的质量问题对应的第一质量指标，以对所述目标电站设备的制造参数进行调整。
  2. 根据权利要求1所述的方法，其特征在于，所述基于对应于各个所述第二质量指标的设备数量，确定各个第一质量指标对应的隶属度矩阵，包括：
    计算对应于所有所述第二质量指标的设备数量的设备总和；
    分别计算各个所述第二质量指标的设备数量与所述设备总和的比值，得到各个所述第一质量指标对应的隶属度矩阵。
  3. 根据权利要求2所述的方法，其特征在于，所述根据各个所述第二判断矩阵对应的权重与相应的隶属度矩阵进行数值计算，确定所述目标电站设备的质量问题对应的第一质量指标，以对所述目标电站设备的制造参数进行调整，包括：
    分别计算各个所述第二判断矩阵对应的权重与相应的隶属度矩阵的乘积，得到与各个所述第一质量指标对应的质量评估矩阵；
    基于各个所述第一质量指标对应的质量评估矩阵，确定所述目标电站设备的质量问题对应的第一质量指标；
    利用所述质量问题对应的第一质量指标，确定所述第一质量指标的制造参数，以对所述制造参数进行调整。
  4. 根据权利要求3所述的方法，其特征在于，所述基于各个所述第一质量指标对应的质量评估矩阵，确定所述目标电站设备的质量问题对应的第一质量指标，包括：
    针对各个所述第一质量指标对应的质量评估矩阵，比较所述质量评估矩阵中各个元素的大小，确定所述质量评估矩阵对应的评估等级；
    利用各个所述第一质量指标对应的评估等级，确定所述目标电站设备的质量问题对应的第一质量指标。
  5. 根据权利要求1所述的方法，其特征在于，所述方法还包括：
    确定所述第一判断矩阵对应的权重；
    计算所述第一判断矩阵对应的权重与各个所述第一质量指标对应的隶属度矩阵，得到所述目标电站设备对应的隶属度矩阵；
    基于所述目标电站设备对应的隶属度矩阵中各个元素的大小，确定所述目标电站设备的质量等级。
  6. 根据权利要求1-5中任一项所述的方法，其特征在于，所述确定各个所述第二判断矩阵对应的权重，包括：
    判断所述第二判断矩阵中元素的数量是否大于预设值；
    当所述第二判断矩阵中元素的数量小于或等于所述预设值时，确定所述第二判断矩阵对应的权重为主观权重。
  7. 根据权利要求6所述的方法，其特征在于，所述确定各个所述第二判断矩阵对应的权重，包括：
    当所述第二判断矩阵中元素的数量大于所述预设值时，基于所述第二判断矩阵计算所述第二判断矩阵对应的权重。
  8. 一种基于设备监理的电站设备质量数据处理装置，其特征在于，包括：
    第一获取模块，用于获取目标电站设备的质量模型，所述质量模型包括所述目标电站设备的质与其对应的第一质量指标的第一判断矩阵，以及各个第一质量指标与其对应的第二质量指标的第二判断矩阵；
    确定模块，用于确定各个所述第二判断矩阵对应的权重；
    第二获取模块，用于获取所述目标电站设备中对应于各个所述第二质量指标的设备数量；
    第一确定模块，用于基于对应于各个所述第二质量指标的设备数量，得到各个第一质量指标对应的隶属度矩阵；
    第二确定模块，用于根据各个所述第二判断矩阵对应的权重与相应的隶属度矩阵进行数值计算，确定所述目标电站设备的质量问题对应的第一质量指标，以对所述目标电站设备的制造参数进行调整。
  9. 一种电子设备，其特征在于，包括：
    存储器和处理器，所述存储器和所述处理器之间互相通信连接，所述存储器中存储有计算机指令，所述处理器通过执行所述计算机指令，从而执行权利要求1-7中任一项所述的基于设备监理的电站设备质量数据处理方法。
  10. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质存储有计算机指令，所述计算机指令用于使所述计算机执行权利要求1-7中任一项所述的基于设备监理的电站设备质量数据处理方法。
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116204515A (zh) * 2022-12-21 2023-06-02 广州城市规划技术开发服务部有限公司 一种基于数据质量定义的数据质量量化计算方法及装置

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160145994A1 (en) * 2014-11-20 2016-05-26 Petrochina Company Limited Evaluation Method and Evaluation Device for Water Breakthrough Risk of Production Wells in Aquifer Drive Gas Reservoirs
CN105938609A (zh) * 2016-04-28 2016-09-14 国家电网公司 一种实现多层次指标体系的电网运行评估方法
CN107862442A (zh) * 2017-10-19 2018-03-30 国网重庆市电力公司璧山供电分公司 一种10kv及以下电力客户受电工程建设质量综合评价方法
CN108985649A (zh) * 2018-08-01 2018-12-11 国电联合动力技术有限公司 基于风电设备异常分析数据的时序模糊层次分析评估方法
CN109034538A (zh) * 2018-06-26 2018-12-18 广东电网有限责任公司 一种面向变电站自动化设备运行质量的评价分析方法
CN111523080A (zh) * 2020-04-10 2020-08-11 广东电网有限责任公司电力科学研究院 一种电力系统的pas系统综合评估方法及相关装置

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160145994A1 (en) * 2014-11-20 2016-05-26 Petrochina Company Limited Evaluation Method and Evaluation Device for Water Breakthrough Risk of Production Wells in Aquifer Drive Gas Reservoirs
CN105938609A (zh) * 2016-04-28 2016-09-14 国家电网公司 一种实现多层次指标体系的电网运行评估方法
CN107862442A (zh) * 2017-10-19 2018-03-30 国网重庆市电力公司璧山供电分公司 一种10kv及以下电力客户受电工程建设质量综合评价方法
CN109034538A (zh) * 2018-06-26 2018-12-18 广东电网有限责任公司 一种面向变电站自动化设备运行质量的评价分析方法
CN108985649A (zh) * 2018-08-01 2018-12-11 国电联合动力技术有限公司 基于风电设备异常分析数据的时序模糊层次分析评估方法
CN111523080A (zh) * 2020-04-10 2020-08-11 广东电网有限责任公司电力科学研究院 一种电力系统的pas系统综合评估方法及相关装置

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116204515A (zh) * 2022-12-21 2023-06-02 广州城市规划技术开发服务部有限公司 一种基于数据质量定义的数据质量量化计算方法及装置

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