CN112508378A - Processing method and device for screening power equipment production manufacturers - Google Patents
Processing method and device for screening power equipment production manufacturers Download PDFInfo
- Publication number
- CN112508378A CN112508378A CN202011381113.2A CN202011381113A CN112508378A CN 112508378 A CN112508378 A CN 112508378A CN 202011381113 A CN202011381113 A CN 202011381113A CN 112508378 A CN112508378 A CN 112508378A
- Authority
- CN
- China
- Prior art keywords
- power equipment
- manufacturer
- equipment production
- data
- evaluation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 164
- 238000012216 screening Methods 0.000 title claims abstract description 56
- 238000003672 processing method Methods 0.000 title claims abstract description 14
- 238000012549 training Methods 0.000 claims abstract description 71
- 238000013210 evaluation model Methods 0.000 claims abstract description 61
- 238000000034 method Methods 0.000 claims abstract description 40
- 238000012545 processing Methods 0.000 claims abstract description 15
- 238000010801 machine learning Methods 0.000 claims abstract description 12
- 238000011156 evaluation Methods 0.000 claims description 43
- 238000012360 testing method Methods 0.000 claims description 26
- 238000003860 storage Methods 0.000 claims description 14
- 238000013528 artificial neural network Methods 0.000 claims description 11
- 239000002245 particle Substances 0.000 claims description 4
- 238000001514 detection method Methods 0.000 description 11
- 238000013461 design Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 7
- 238000011161 development Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 6
- 238000012797 qualification Methods 0.000 description 6
- 239000000047 product Substances 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000007689 inspection Methods 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 3
- 238000010606 normalization Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000005034 decoration Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 210000002569 neuron Anatomy 0.000 description 2
- 238000012827 research and development Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 238000012356 Product development Methods 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012886 linear function Methods 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 238000011017 operating method Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000013441 quality evaluation Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 239000011265 semifinished product Substances 0.000 description 1
Images
Classifications
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Theoretical Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a processing method and a processing device for screening production manufacturers of electric power equipment. Wherein, the method comprises the following steps: acquiring equipment index data of a plurality of power equipment production manufacturers; inputting the equipment index data into an evaluation model, and determining the score of each power equipment manufacturer by the evaluation model, wherein the evaluation model is obtained by using multiple groups of training data through machine learning training, and each group of data in the multiple groups of training data comprises: the device index data of the power equipment production manufacturer and the score of the power equipment production manufacturer corresponding to the index data; and screening out the optimal power equipment production manufacturer from the plurality of power equipment production manufacturers according to the scores. The invention solves the technical problem of poor reliability in screening of power equipment manufacturers in the related art.
Description
Technical Field
The invention relates to the field of power equipment, in particular to a processing method and a processing device for screening production manufacturers of power equipment.
Background
At present, when a national power grid company selects a production manufacturer, the national power grid company mainly verifies the qualification capability, the qualification condition of the production manufacturer and the manufacturing capability according to the verification and judgment, and the production manufacturer should meet the requirements of the standard and also meet the regulations of the current national relevant standards. The evaluation method mainly passes document auditing and field verification. The method mainly verifies the conditions of qualification information, design development capability, production and manufacturing capability, test detection capability, raw material component management, after-sales service, capacity and the like of a production manufacturer. The method mainly aims to reduce the repetitive labor of a production manufacturer in the process of making the bid document and improve the bid evaluation working efficiency. Not as a prerequisite for bidding. And qualification prequalification is required in the bidding link. Therefore, it is necessary to enhance the management of the production manufacturer and to improve the quality of the equipment from the source.
However, the prior art has the following disadvantages:
(1) the selection of the power grid equipment production manufacturer mainly depends on various levels of unit material departments, so that a large number of production manufacturers who falsify and report equipment information and have inconsistent actual production capacity are not fed back to the bidding purchasing process, the credit consciousness of the production manufacturers is weak, the failure rate of the equipment subjected to successful bidding is increased frequently, and the reliability of a power grid system is reduced and a large amount of expenses are lost in later operation and maintenance inspection of a company.
(2) The evaluation and selection of the production manufacturers of the power grid enterprises mainly depend on the subjective opinions of equipment experts for scoring and selection, particularly, materials are submitted by a large number of equipment production manufacturers every year, however, the experience of the experts is limited, the evaluation engineering is easy to neglect, the conditions of the production manufacturers cannot be completely reflected, and the evaluation and selection link is relatively fair and insufficient.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a processing method and a processing device for screening power equipment production manufacturers, which are used for at least solving the technical problem of poor reliability in screening the power equipment production manufacturers in the related art.
According to an aspect of an embodiment of the present invention, there is provided a processing method for screening power equipment production manufacturers, including: acquiring equipment index data of a plurality of power equipment production manufacturers; inputting the equipment index data into an evaluation model, and determining the score of each power equipment manufacturer by the evaluation model, wherein the evaluation model is obtained by machine learning training by using multiple groups of training data, and each group of data in the multiple groups of training data comprises: the method comprises the steps that equipment index data of an electric power equipment production manufacturer and scores of the electric power equipment production manufacturer corresponding to the index data are obtained; and screening out the optimal power equipment production manufacturer from the plurality of power equipment production manufacturers according to the scores.
Optionally, before obtaining the equipment indicator data of the plurality of electric power equipment production manufacturers, the method further comprises: establishing an evaluation index system of a power equipment production manufacturer, wherein the evaluation index system of the power equipment production manufacturer at least comprises the following steps: a plurality of evaluation dimensions and different levels of metrics corresponding to the plurality of evaluation dimensions.
Optionally, before obtaining the equipment indicator data of the plurality of electric power equipment production manufacturers, the method further comprises: and establishing an evaluation model based on a neural network algorithm of the extreme learning machine.
Optionally, the establishing an evaluation model based on the neural network algorithm of the extreme learning machine includes: acquiring a plurality of groups of training data, wherein the plurality of groups of training data are divided into a training sample set and a test sample set; and respectively training the training sample set and the testing sample set by using an extreme learning machine to obtain the evaluation model.
Optionally, after the training sample set and the testing sample set are respectively trained by using an extreme learning machine to obtain the evaluation model, the method further includes: and optimizing the evaluation model by utilizing a particle swarm algorithm to obtain the optimized evaluation model.
Optionally, the step of selecting an optimal power equipment production manufacturer from the plurality of power equipment production manufacturers according to the score comprises: ranking the plurality of electric power equipment production manufacturers according to the scores to obtain ranking results; and screening out the optimal power equipment manufacturer according to the ranking result.
According to another aspect of the embodiments of the present invention, there is also provided a processing apparatus for screening power equipment manufacturers, including: the acquisition module is used for acquiring equipment index data of a plurality of power equipment manufacturers; a determining module, configured to input the device indicator data into an evaluation model, and determine a score of each power device manufacturer according to the evaluation model, where the evaluation model is obtained by machine learning training using multiple sets of training data, and each set of data in the multiple sets of training data includes: the method comprises the steps that equipment index data of an electric power equipment production manufacturer and scores of the electric power equipment production manufacturer corresponding to the index data are obtained; and the screening module is used for screening out the optimal power equipment production manufacturer from the plurality of power equipment production manufacturers according to the scores.
Optionally, the apparatus further comprises: the first establishing module is used for establishing an electric power equipment production manufacturer evaluation index system before acquiring equipment index data of a plurality of electric power equipment production manufacturers, wherein the electric power equipment production manufacturer evaluation index system at least comprises: a plurality of evaluation dimensions and different levels of metrics corresponding to the plurality of evaluation dimensions.
According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, where the computer-readable storage medium includes a stored program, where the program, when executed, controls a device in which the computer-readable storage medium is located to perform the processing method for electric power device manufacturer screening described in any one of the above.
According to another aspect of the embodiments of the present invention, there is also provided a processor, configured to execute a program, where the program executes the processing method for electric power equipment production manufacturer screening described in any one of the above.
In the embodiment of the invention, equipment index data of a plurality of power equipment production manufacturers are acquired; inputting the equipment index data into an evaluation model, and determining the score of each power equipment manufacturer by the evaluation model, wherein the evaluation model is obtained by machine learning training by using multiple groups of training data, and each group of data in the multiple groups of training data comprises: the method comprises the steps that equipment index data of an electric power equipment production manufacturer and scores of the electric power equipment production manufacturer corresponding to the index data are obtained; according to the scores, the optimal power equipment production manufacturer is screened out from the multiple power equipment production manufacturers, and the purpose of screening out the excellent equipment production manufacturers can be achieved through the implementation mode of the invention, so that the selection efficiency of the production manufacturers is improved, the screening result is more accurate and fair, the technical effect of providing scientific and credible preferred basis for power grid bidding purchase is achieved, and the technical problem of poor reliability in screening of the power equipment production manufacturers in the related technology is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a process for power equipment production manufacturer screening according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a processing device for power equipment production manufacturer screening according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
In accordance with an embodiment of the present invention, there is provided an embodiment of a processing method for power equipment production manufacturer screening, it is noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that presented herein.
Fig. 1 is a flowchart of a processing method of electric power equipment production manufacturer screening according to an embodiment of the present invention, as shown in fig. 1, the method including the steps of:
step S102, acquiring equipment index data of a plurality of power equipment manufacturers;
the equipment index data includes, but is not limited to, equipment characteristic attributes of multiple dimensions such as test report certificate, product certification, supply performance, design, research and development, production and manufacture, test detection, security management, production site environment, equipment and facility management, and warehouse management.
Step S104, inputting the equipment index data into an evaluation model, and determining the score of each power equipment manufacturer by the evaluation model, wherein the evaluation model is obtained by using multiple groups of training data through machine learning training, and each group of data in the multiple groups of training data comprises: the device index data of the power equipment production manufacturer and the score of the power equipment production manufacturer corresponding to the index data;
and step S106, screening out the optimal power equipment production manufacturer from the plurality of power equipment production manufacturers according to the scores.
It should be noted that, the neural network technology can be utilized, the neural network algorithm of the extreme learning machine is adopted to establish the evaluation model of the production manufacturer, the unified standard for selecting high-quality equipment is established, the selection efficiency of the production manufacturer is improved, the real situation can be reflected better, and excellent equipment production manufacturers can be screened out according to the scoring ranking. Based on the fact that a manufacturer of power grid equipment is in operation, the evaluation accuracy and fairness of an evaluation system are emphasized, and scientific and credible optimal selection basis is provided for power grid bidding purchase.
Through the steps, the equipment index data of a plurality of power equipment production manufacturers can be acquired; inputting the equipment index data into an evaluation model, and determining the score of each power equipment manufacturer by the evaluation model, wherein the evaluation model is obtained by using multiple groups of training data through machine learning training, and each group of data in the multiple groups of training data comprises: the device index data of the power equipment production manufacturer and the score of the power equipment production manufacturer corresponding to the index data; the method and the device have the advantages that the optimal power equipment production manufacturer is screened out from the multiple power equipment production manufacturers according to the scores, the purpose of screening out the excellent equipment production manufacturers can be achieved through the implementation mode of the method and the device, so that the selection efficiency of the production manufacturers is improved, the screening result is more accurate and fair, the scientific and credible preferred basis is provided for power grid bidding purchase, and the technical problem of poor reliability in screening of the power equipment production manufacturers in the related technology is solved.
Optionally, before obtaining the device indicator data of the plurality of electrical device production manufacturers, the method further includes: establishing an evaluation index system of the power equipment production manufacturer, wherein the evaluation index system of the power equipment production manufacturer at least comprises the following steps: the multiple evaluation dimensions and the indexes of different levels corresponding to the multiple evaluation dimensions.
As an alternative embodiment, a candidate library of evaluation indexes of the power equipment manufacturer may be established. On the basis of the basic evaluation criteria of 23 production manufacturer selection processes, in consideration of the characteristics of the power equipment, 170 kinds of alternative indexes related to the quality of the production manufacturers are provided and distributed to a plurality of power experts in the form of questionnaires, and further, the final evaluation indexes influencing the selection of the production manufacturers are obtained. The questionnaire includes the primary index and the score of the primary index, the secondary index corresponding to the primary index, and the score of each secondary index. For example, the primary indicator may be: quality guarantee and compensation, wherein the secondary indexes corresponding to the primary indexes can be quality systems, equipment quality, financial capacity and the like, and the index connotations of the quality systems include but are not limited to whether the quality systems pass ISO9000, QS9000 or TL9000, evidence that the quality systems are being executed and the like; the index connotation of the quality of the equipment comprises but is not limited to the qualification rate of the equipment, the defect type of the equipment and the like; the indicators of financial ability include, but are not limited to, total assets, registered capital, etc.
As an alternative example, the index may be made to the manufacturer of the power equipment based on the importance of the index (1 being very important, 2 being unimportant, 3 being general, 4 being more important, 5 being very important)And (4) grading the equipment indexes. To make the selection of the index more reliable, the statistical result is madeαThe reliability analysis of the reliability coefficient method is shown as the following formula:
wherein,nis the total number of subject items in the scale,
is as followsiThe intra-topic variance of the topic score,
the variance of the total score for all the questions. As can be seen from the above formula,αthe coefficient evaluation is the consistency among the scores of the items in the scale, and belongs to the intrinsic consistency coefficient. The method is applicable to reliability analysis of attitudes and opinion questionnaires (tables).
Further, the quality evaluation index of the power equipment manufacturer is obtained through the analysis, so as to establish an evaluation index system of the power equipment manufacturer, wherein the evaluation index system of the power equipment manufacturer at least comprises: the multiple evaluation dimensions and the indexes of different levels corresponding to the multiple evaluation dimensions. It should be noted that the above-mentioned multiple evaluation dimensions include, but are not limited to, basic factors, core factors, influencing factors, etc.; the first-level indexes corresponding to the basic factors at least comprise detection reports, identification certificates, product authentication and supply performance; the first-level indexes corresponding to the core factors at least comprise design, research, development, production, manufacture and test detection; the secondary indexes corresponding to the design and development include, but are not limited to, technical sources and support, design and development contents, design and development personnel, design and development tools, patent acquisition conditions, participation standard making conditions, new product development, informatization level, product winning conditions and the like; the secondary indexes corresponding to the production and manufacturing include, but are not limited to, production plants, production processes, production equipment, production, technology, quality management personnel and the like; the secondary indexes corresponding to the test detection comprise but are not limited to test sites, test detection management, test detection equipment, test detection personnel, field sampling and the like; the first-level indexes corresponding to the influence factors at least comprise safety management, production field environment, equipment and facility management and warehousing management; wherein, the second-level indexes corresponding to the safety management include but are not limited to a safety quality production management system, a safety quality inspection system, a safety quality reward punishment assessment system and the like; the secondary indexes corresponding to the production site environment include but are not limited to plant environment, safety signs and the like; the secondary indexes corresponding to the equipment and facility management include, but are not limited to, machine account files, operating procedures, instructions, personnel qualifications, test detection records, inspection and maintenance records, and the like; the second level indexes corresponding to the warehousing management include, but are not limited to, management system, semi-finished product warehousing management, and the like.
Through the implementation mode, the equipment index data of multiple dimensions such as the detection report certificate, the product authentication, the supply performance, the design research and development, the production and manufacture, the test detection, the safety management, the production site environment, the equipment facility management, the storage management and the like can be comprehensively considered, and the problem that the selection index system of the current production manufacturer is single is solved.
Optionally, before obtaining the device indicator data of the plurality of electrical device production manufacturers, the method further includes: and establishing an evaluation model based on a neural network algorithm of the extreme learning machine.
The evaluation model has the advantages of high training speed, good generalization, high classification precision and the like, can ensure the objectivity and rationality of selection of production manufacturers of power enterprises, and greatly improves the working efficiency.
Optionally, the establishing an evaluation model based on the neural network algorithm of the extreme learning machine includes: acquiring a plurality of groups of training data, wherein the plurality of groups of training data are divided into a training sample set and a test sample set; and respectively training the training sample set and the testing sample set by using an extreme learning machine to obtain an evaluation model.
Optionally, after the training sample set and the testing sample set are respectively trained by using an extreme learning machine to obtain an evaluation model, the method further includes: and optimizing the evaluation model by using a particle swarm algorithm to obtain the optimized evaluation model.
As an alternative embodiment, the production manufacturer evaluation index normalization process may be performed. In order to prevent problems such as network training time increase and network convergence failure which may be caused by the existence of singular sample data, the sample data set is normalized before training. Normalization can be done with a linear function, i.e.:
wherein, X is the original data,Xmax，Xminsample data maximum and minimum values.
As an optional embodiment, after preprocessing such as characterization, digitization and normalization is carried out on the indexes influencing the selection of the production manufacturer, a training set is formed together with the scores of the existing production manufacturer (the scores are comprehensively scored according to historical experience and expert experience and are used as the basis of model training), and the evaluation index values of the historical production manufacturer are selected as training input data XTrainingSelecting historical production manufacturer score as training output data YTraining. The tested manufacturer index value is XTestingThe manufacturer of the test predicts a score of Y to be evaluatedTesting. The history data is trained by using an extreme learning machine, an optimal model is established by adjusting algorithm parameters, and the global optimal output weight of the extreme learning machine can be written as follows:
wherein H+＝(HTH)-1 H TMoore-Penrose generalized inverse of the hidden layer output matrix H;
is an outputA weight matrix;Tis the output matrix of the network.
Wherein, ω isiThe vector is a dimension vector, and n multiplied by 1 represents the connection weight of the neuron of the input layer and the hidden layer; biIs the threshold value of the hidden layer neuron.
Linear regression in high-dimensional space adopts the principle of minimizing structural risk to reduce the complexity of the model, and then H' is:
continuously reading data to obtain an output result y:
optionally, the step of screening out the optimal power equipment production manufacturer from the plurality of power equipment production manufacturers according to the scores comprises: ranking the plurality of power equipment production manufacturers according to the scores to obtain ranking results; and screening out the optimal power equipment manufacturer according to the ranking result.
As an alternative embodiment, the production manufacturer equipment index data is input into the input layer of the neural network, and the output of the output layer of the neural network is the score of the production manufacturer, so as to select the optimal production manufacturer. And respectively establishing limit learning machine models for different equipment for evaluation to obtain the final score of the selected production manufacturer, and optimizing according to the final score of the production manufacturer.
Example 2
According to another aspect of the embodiments of the present invention, there is also provided a processing apparatus for electric power equipment production manufacturer screening, fig. 2 is a schematic diagram of the processing apparatus for electric power equipment production manufacturer screening according to the embodiments of the present invention, and as shown in fig. 2, the processing apparatus for electric power equipment production manufacturer screening includes: an acquisition module 22, a determination module 24, and a screening module 26. The processing device screened by the power equipment manufacturer will be described in detail below.
An obtaining module 22, configured to obtain device index data of multiple power device manufacturers; a determining module 24, connected to the obtaining module 22, configured to input the device indicator data into an evaluation model, and determine a score of each power device manufacturer according to the evaluation model, where the evaluation model is obtained by machine learning training using multiple sets of training data, and each set of data in the multiple sets of training data includes: the device index data of the power equipment production manufacturer and the score of the power equipment production manufacturer corresponding to the index data; and a screening module 26 connected to the determining module 24 for screening an optimal power equipment manufacturer from the plurality of power equipment manufacturers according to the scores.
It should be noted that the above modules may be implemented by software or hardware, for example, for the latter, the following may be implemented: the modules can be located in the same processor; alternatively, the modules may be located in different processors in any combination.
It should be noted here that the above-mentioned obtaining module 22, determining module 24 and screening module 26 correspond to steps S102 to S106 in embodiment 1, and the above-mentioned modules are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to what is disclosed in embodiment 1 above. It should be noted that the modules described above as part of an apparatus may be implemented in a computer system such as a set of computer-executable instructions.
The method and the device can achieve the aim of screening excellent equipment production manufacturers, thereby improving the selection efficiency of the production manufacturers, enabling the screening result to be more accurate and fair, providing the scientific and credible technical effect of the optimal basis for power grid bidding purchase, and further solving the technical problem of poor reliability in screening of the power equipment production manufacturers in the related technology.
Optionally, the apparatus further comprises: the first establishing module is used for establishing an electric power equipment production manufacturer evaluation index system before acquiring equipment index data of a plurality of electric power equipment production manufacturers, wherein the electric power equipment production manufacturer evaluation index system at least comprises: the multiple evaluation dimensions and the indexes of different levels corresponding to the multiple evaluation dimensions.
Optionally, before obtaining the device index data of the plurality of power device manufacturers, the apparatus further includes: and the second establishing module is used for establishing an evaluation model based on a neural network algorithm of the extreme learning machine.
Optionally, the second establishing module includes: the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a plurality of groups of training data, and the plurality of groups of training data are divided into a training sample set and a test sample set; and the training unit is used for respectively training the training sample set and the testing sample set by using the extreme learning machine to obtain an evaluation model.
Optionally, the second establishing module further includes: and the optimization unit is used for training the training sample set and the testing sample set respectively by using an extreme learning machine to obtain an evaluation model, and then optimizing the evaluation model by using a particle swarm algorithm to obtain an optimized evaluation model.
Optionally, the screening module 26 includes: the ranking unit is used for ranking the plurality of electric power equipment production manufacturers according to the scores to obtain ranking results; and the screening unit is used for screening out the optimal power equipment production manufacturer according to the ranking result.
Example 3
According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium including a stored program, wherein when the program runs, a device in which the computer-readable storage medium is located is controlled to execute the processing method for electric power device manufacturer screening of any one of the above.
Optionally, in this embodiment, the computer-readable storage medium may be located in any one of a group of computer terminals in a computer network or in any one of a group of mobile terminals, and the computer-readable storage medium includes a stored program.
Optionally, the program when executed controls an apparatus in which the computer-readable storage medium is located to perform the following functions: acquiring equipment index data of a plurality of power equipment production manufacturers; inputting the equipment index data into an evaluation model, and determining the score of each power equipment manufacturer by the evaluation model, wherein the evaluation model is obtained by using multiple groups of training data through machine learning training, and each group of data in the multiple groups of training data comprises: the device index data of the power equipment production manufacturer and the score of the power equipment production manufacturer corresponding to the index data; and screening out the optimal power equipment production manufacturer from the plurality of power equipment production manufacturers according to the scores.
Example 4
According to another aspect of the embodiments of the present invention, there is also provided a processor for executing a program, where the program executes a processing method of the electric power equipment production manufacturer screening.
The embodiment of the invention provides equipment, which comprises a processor, a memory and a program which is stored on the memory and can run on the processor, wherein the processor executes the program and realizes the following steps: acquiring equipment index data of a plurality of power equipment production manufacturers; inputting the equipment index data into an evaluation model, and determining the score of each power equipment manufacturer by the evaluation model, wherein the evaluation model is obtained by using multiple groups of training data through machine learning training, and each group of data in the multiple groups of training data comprises: the device index data of the power equipment production manufacturer and the score of the power equipment production manufacturer corresponding to the index data; and screening out the optimal power equipment production manufacturer from the plurality of power equipment production manufacturers according to the scores.
The invention also provides a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device: acquiring equipment index data of a plurality of power equipment production manufacturers; inputting the equipment index data into an evaluation model, and determining the score of each power equipment manufacturer by the evaluation model, wherein the evaluation model is obtained by using multiple groups of training data through machine learning training, and each group of data in the multiple groups of training data comprises: the device index data of the power equipment production manufacturer and the score of the power equipment production manufacturer corresponding to the index data; and screening out the optimal power equipment production manufacturer from the plurality of power equipment production manufacturers according to the scores.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A method for processing a screening of a manufacturer of an electric power equipment, comprising:
acquiring equipment index data of a plurality of power equipment production manufacturers;
inputting the equipment index data into an evaluation model, and determining the score of each power equipment manufacturer by the evaluation model, wherein the evaluation model is obtained by machine learning training by using multiple groups of training data, and each group of data in the multiple groups of training data comprises: the method comprises the steps that equipment index data of an electric power equipment production manufacturer and scores of the electric power equipment production manufacturer corresponding to the index data are obtained;
and screening out the optimal power equipment production manufacturer from the plurality of power equipment production manufacturers according to the scores.
2. The method of claim 1, wherein prior to obtaining equipment indicator data for a plurality of electrical equipment production manufacturers, the method further comprises:
establishing an evaluation index system of a power equipment production manufacturer, wherein the evaluation index system of the power equipment production manufacturer at least comprises the following steps: a plurality of evaluation dimensions and different levels of metrics corresponding to the plurality of evaluation dimensions.
3. The method of claim 1, wherein prior to obtaining equipment indicator data for a plurality of electrical equipment production manufacturers, the method further comprises:
and establishing an evaluation model based on a neural network algorithm of the extreme learning machine.
4. The method of claim 3, wherein the establishing an evaluation model based on an extreme learning machine neural network algorithm comprises:
acquiring a plurality of groups of training data, wherein the plurality of groups of training data are divided into a training sample set and a test sample set;
and respectively training the training sample set and the testing sample set by using an extreme learning machine to obtain the evaluation model.
5. The method of claim 4, wherein after the training sample set and the testing sample set are respectively trained by using an extreme learning machine to obtain the evaluation model, the method further comprises:
and optimizing the evaluation model by utilizing a particle swarm algorithm to obtain the optimized evaluation model.
6. The method of any of claims 1 to 5, wherein screening the plurality of electrical equipment production manufacturers for an optimal electrical equipment production manufacturer based on the score comprises:
ranking the plurality of electric power equipment production manufacturers according to the scores to obtain ranking results;
and screening out the optimal power equipment manufacturer according to the ranking result.
7. A processing apparatus for screening of power equipment manufacturer, comprising:
the acquisition module is used for acquiring equipment index data of a plurality of power equipment manufacturers;
a determining module, configured to input the device indicator data into an evaluation model, and determine a score of each power device manufacturer according to the evaluation model, where the evaluation model is obtained by machine learning training using multiple sets of training data, and each set of data in the multiple sets of training data includes: the method comprises the steps that equipment index data of an electric power equipment production manufacturer and scores of the electric power equipment production manufacturer corresponding to the index data are obtained;
and the screening module is used for screening out the optimal power equipment production manufacturer from the plurality of power equipment production manufacturers according to the scores.
8. The apparatus of claim 7, further comprising:
the first establishing module is used for establishing an electric power equipment production manufacturer evaluation index system before acquiring equipment index data of a plurality of electric power equipment production manufacturers, wherein the electric power equipment production manufacturer evaluation index system at least comprises: a plurality of evaluation dimensions and different levels of metrics corresponding to the plurality of evaluation dimensions.
9. A computer-readable storage medium, comprising a stored program, wherein when the program runs, the apparatus where the computer-readable storage medium is located is controlled to execute the processing method of electric power equipment production manufacturer screening according to any one of claims 1 to 6.
10. A processor, characterized in that the processor is configured to run a program, wherein the program is configured to execute the processing method of the electric power equipment production manufacturer screening according to any one of claims 1 to 6 when running.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011381113.2A CN112508378A (en) | 2020-11-30 | 2020-11-30 | Processing method and device for screening power equipment production manufacturers |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011381113.2A CN112508378A (en) | 2020-11-30 | 2020-11-30 | Processing method and device for screening power equipment production manufacturers |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112508378A true CN112508378A (en) | 2021-03-16 |
Family
ID=74968562
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011381113.2A Pending CN112508378A (en) | 2020-11-30 | 2020-11-30 | Processing method and device for screening power equipment production manufacturers |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112508378A (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017152532A1 (en) * | 2016-03-09 | 2017-09-14 | 深圳大学 | Cognitive model-based computational thinking training method and device |
| CN109118122A (en) * | 2018-09-17 | 2019-01-01 | 武汉理工大学 | Outsourcing supplier's evaluation method based on mixing PSO-Adam neural network |
| CN109460825A (en) * | 2018-10-24 | 2019-03-12 | 阿里巴巴集团控股有限公司 | For constructing the Feature Selection Algorithms, device and equipment of machine learning model |
| CN109543939A (en) * | 2018-10-11 | 2019-03-29 | 北京信息科技大学 | A kind of method of green building productions certification risk evaluation model building |
| CN110443364A (en) * | 2019-06-21 | 2019-11-12 | 深圳大学 | A kind of deep neural network multitask hyperparameter optimization method and device |
| WO2020000248A1 (en) * | 2018-06-27 | 2020-01-02 | 大连理工大学 | Space reconstruction based method for predicting key performance parameters of transition state acceleration process of aircraft engine |
| CN111242430A (en) * | 2019-12-31 | 2020-06-05 | 国网北京市电力公司 | Power equipment supplier evaluation method and device |
| CN111639843A (en) * | 2020-05-21 | 2020-09-08 | 中国工商银行股份有限公司 | Provider selection method and device based on residual error neural network |
-
2020
- 2020-11-30 CN CN202011381113.2A patent/CN112508378A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017152532A1 (en) * | 2016-03-09 | 2017-09-14 | 深圳大学 | Cognitive model-based computational thinking training method and device |
| WO2020000248A1 (en) * | 2018-06-27 | 2020-01-02 | 大连理工大学 | Space reconstruction based method for predicting key performance parameters of transition state acceleration process of aircraft engine |
| CN109118122A (en) * | 2018-09-17 | 2019-01-01 | 武汉理工大学 | Outsourcing supplier's evaluation method based on mixing PSO-Adam neural network |
| CN109543939A (en) * | 2018-10-11 | 2019-03-29 | 北京信息科技大学 | A kind of method of green building productions certification risk evaluation model building |
| CN109460825A (en) * | 2018-10-24 | 2019-03-12 | 阿里巴巴集团控股有限公司 | For constructing the Feature Selection Algorithms, device and equipment of machine learning model |
| CN110443364A (en) * | 2019-06-21 | 2019-11-12 | 深圳大学 | A kind of deep neural network multitask hyperparameter optimization method and device |
| CN111242430A (en) * | 2019-12-31 | 2020-06-05 | 国网北京市电力公司 | Power equipment supplier evaluation method and device |
| CN111639843A (en) * | 2020-05-21 | 2020-09-08 | 中国工商银行股份有限公司 | Provider selection method and device based on residual error neural network |
Non-Patent Citations (2)
| Title |
|---|
| 吕锡坪: "《高等学校管理学》", 31 May 1993, 山东教育出版社, pages: 468 * |
| 李五四;靳慧丽;: "制造商评价与选择供应商的模糊神经网络决策模型", 农村经济与科技, no. 07, pages 82 - 84 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108564286B (en) | Artificial intelligent financial wind-control credit assessment method and system based on big data credit investigation | |
| Zizlavsky | Innovation performance measurement: research into Czech business practice | |
| Liao et al. | Does corporate social performance pay back quickly? A longitudinal content analysis on international contractors | |
| Venturini | Intelligent technologies and productivity spillovers: Evidence from the Fourth Industrial Revolution | |
| Maksimovic et al. | Do conglomerate firms allocate resources inefficiently across industries? Theory and evidence | |
| Christmann et al. | The relative influence of country conditions, industry structure, and business strategy on multinational corporation subsidiary performance | |
| Tang et al. | The determinants of ESG ratings: Rater ownership matters | |
| McCord et al. | Understanding delays in housing construction: evidence from Northern Ireland | |
| KR100811307B1 (en) | Prediction method for earning rate in construction | |
| WO2016139666A1 (en) | Predictive strategic outcomes by combining human crowdsourcing | |
| CN113537807B (en) | Intelligent wind control method and equipment for enterprises | |
| CN113807747A (en) | Enterprise budget management maturity evaluation system | |
| Rajabalizadeh et al. | Managerial ability and intellectual capital disclosure | |
| US11995667B2 (en) | Systems and methods for business analytics model scoring and selection | |
| CN112488581A (en) | Intelligent manufacturing capability maturity assessment model | |
| CN111861234A (en) | Method for evaluating comprehensive service capability of electric energy meter supplier | |
| CN114881515A (en) | Supplier selection method, supplier selection device, storage medium and electronic equipment | |
| Dragomir et al. | Does corporate governance improve integrated reporting quality? A meta-analytical investigation | |
| Zhang et al. | Mining customer requirement from helpful online reviews | |
| Lin et al. | The relative efficiency of quality management practices: A comparison study on American‐, Japanese‐, and Taiwanese‐owned firms in Taiwan | |
| Itan et al. | Corporate Governance and Environmental Disclosure: Assessing The Role of Environmental Performance | |
| Shnorr | Integral assessment of retail digitalization | |
| CN114048925A (en) | Power grid comprehensive operation early warning method and device and terminal equipment | |
| Gleue et al. | Decision support for the automotive industry: Forecasting residual values using artificial neural networks | |
| CN112381440A (en) | Evaluation method, device, processor and evaluation system for power operator environment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |