CN113222438A - Method, apparatus, device, medium and product for determining productivity of machine - Google Patents

Method, apparatus, device, medium and product for determining productivity of machine Download PDF

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CN113222438A
CN113222438A CN202110565335.8A CN202110565335A CN113222438A CN 113222438 A CN113222438 A CN 113222438A CN 202110565335 A CN202110565335 A CN 202110565335A CN 113222438 A CN113222438 A CN 113222438A
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machine
productivity
data
preset
determining
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CN113222438B (en
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李艺伟
胡雪晴
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Agricultural Bank of China
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    • 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
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Abstract

The embodiment of the invention provides a productivity determination method, a productivity determination device, equipment, a productivity determination medium and a productivity determination product of a machine, wherein the method comprises the following steps: the method comprises the steps of obtaining production completion data, machine performance related data and running time data of a plurality of preset machines in the product production process; aiming at each preset machine, determining productivity scores corresponding to production completion data, machine performance related data and running time data respectively; and determining productivity data corresponding to each preset machine according to each productivity value and a preset hierarchical weight decision analysis model. The productivity of the predetermined machine that can make the determination is more accurate.

Description

Method, apparatus, device, medium and product for determining productivity of machine
Technical Field
The embodiment of the invention relates to the technical field of data processing, in particular to a method, a device, equipment, a medium and a product for determining the productivity of a machine.
Background
With the continuous development of artificial intelligence technology, the production of products is continuously oriented to an automatic process. I.e. the production line formed by the machine completes the production of the product. To better perform operations such as assignment of production tasks or procurement of machines, it is necessary to determine the production performance of each machine, i.e., to determine the productivity of each machine.
In the prior art, the productivity of the machine is generally determined according to fixed indexes marked in a machine use specification, such as power, model and the like of the machine. Such an index does not truly reflect the productivity of the machine in the actual production process, resulting in a low accuracy of the determined productivity of the machine.
Disclosure of Invention
The embodiment of the invention provides a method, a device, equipment, a medium and a product for determining the productivity of a machine, which are used for solving the technical problem that the productivity of the machine determined in the prior art is low in accuracy.
In a first aspect, an embodiment of the present invention provides a productivity determining method for a machine, including:
the method comprises the steps of obtaining production completion data, machine performance related data and running time data of a plurality of preset machines in the product production process;
determining productivity scores corresponding to the production completion data, the machine performance related data and the running time data respectively for each preset machine;
and determining productivity data corresponding to each preset machine according to each productivity score and a preset hierarchical weight decision analysis model.
In a second aspect, an embodiment of the present invention provides a productivity determining apparatus for a machine, including:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring production completion data, machine performance related data and running time data of a plurality of preset machines in the production process of products;
the first determining module is used for determining productivity scores corresponding to the production completion data, the machine performance related data and the running time data respectively aiming at each preset machine;
and the second determining module is used for determining the productivity data corresponding to each preset machine according to each productivity score and a preset hierarchical weight decision analysis model.
In a third aspect, the present invention provides an electronic device comprising: at least one processor, a memory, and a transceiver;
the processor, the memory and the transceiver are interconnected through a circuit;
the memory stores computer-executable instructions; the transceiver is used for receiving and transmitting data with the machine maintenance system server;
the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the method of the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon computer-executable instructions for implementing the method according to any one of the first aspect when executed by a processor.
In a fifth aspect, the invention provides a computer program product comprising a computer program which, when executed by a processor, implements the method of the first aspect.
According to the productivity determining method, device, equipment, medium and product of the machine provided by the embodiment of the invention, production completion data, machine performance related data and running time data of a plurality of preset machines in the product production process are obtained; aiming at each preset machine, determining productivity scores corresponding to production completion data, machine performance related data and running time data respectively; and determining productivity data corresponding to each preset machine according to each productivity value and a preset hierarchical weight decision analysis model. The production completion data, the machine performance related data and the running time data are closely related to the performance of each preset machine in the product production process, so that the productivity of the preset machine can be accurately determined by adopting the production completion data, the machine performance related data and the running time data. And after determining the productivity scores respectively corresponding to the associated factors on the basis of each preset machine, the total productivity score is determined according to the productivity scores in a pertinence manner, so that different preset machines can be considered to have different responsibilities, the associated factors of the total productivity score are determined to have different considering tendencies, and the productivity of the determined preset machine is more accurate.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
FIG. 1 is a network architecture diagram of a productivity determination method of a machine in which embodiments of the present invention may be implemented;
FIG. 2 is a flow chart illustrating a method for determining productivity of a machine according to an embodiment of the present invention;
FIG. 3 is a flow chart illustrating a method for determining productivity of a machine according to another embodiment of the present disclosure;
FIG. 4 is a flow chart illustrating a method for determining productivity of a machine according to another embodiment of the present disclosure;
FIG. 5 is a diagram of a client operating interface in a method for determining productivity of a machine according to an embodiment of the present invention;
FIG. 6 is a flow chart illustrating a method for determining productivity of a machine according to yet another embodiment of the present disclosure;
FIG. 7 is a schematic diagram of a productivity determination apparatus of a machine according to an embodiment of the present invention;
fig. 8 is a block diagram of an electronic device for implementing a productivity determination method of a machine of an embodiment of the present invention.
With the foregoing drawings in mind, certain embodiments of the disclosure have been shown and described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the concepts of the disclosure to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
For a clear understanding of the technical solutions of the present application, a detailed description of the prior art solutions is first provided.
In the prior art, the productivity of the machine is generally determined according to fixed indexes marked in a machine use specification, such as power, model and the like of the machine. Or a professional technician can check the production running condition of the machine to determine the productivity of the machine. The productivity of the machine is determined inaccurately because the fixed index does not truly reflect the productivity of the machine in the actual production process. The productivity of the machine determined by the skilled technician is highly dependent on the technician and is also a rough estimate, leading to inaccuracies in the determined productivity of the machine.
Therefore, in the face of the technical problems in the prior art, the inventor finds that the production performance of the machine is closely related to the performance of the machine in the production process of the product through creative research. The most closely related factors include the production completion of the machine during the production of the product, the performance of the machine, and the duration of the machine capable of operating continuously. Moreover, the responsibility of each preset machine in the production task is different, so that the contribution of each relevant factor for determining the productivity of each preset machine is different. It is necessary to accurately determine the weight of each associated factor. Specifically, production completion data, machine performance related data, and run time data of each preset machine may be obtained from a production task that each preset machine has completed. And determining productivity scores corresponding to the association factors for each preset machine, determining weights corresponding to the productivity scores according to the productivity scores corresponding to the preset machines and a preset hierarchical weight decision analysis model, and determining total productivity scores corresponding to the preset machines according to the productivity scores and the corresponding weights.
Therefore, the inventor proposes a technical scheme of the embodiment of the invention based on the above creative discovery. The following describes a network architecture of a method for determining productivity of a machine according to an embodiment of the present invention.
Fig. 1 is a network architecture diagram of a productivity determination method of a machine according to an embodiment of the present invention, and as shown in fig. 1, the network architecture of the productivity determination method of a machine according to the embodiment includes: an electronic device 1, a production task management system 2, and a machine maintenance system server 3. The electronic device 1 is connected to the production task management system 2 and the machine maintenance system server 3 in a communication manner. The electronic device 1 captures production task data on a production task management system 2 web page by adopting a crawler technology, and acquires machine performance related data and running time data of each preset machine from a machine maintenance system server by accessing a machine maintenance system server 3. After the electronic equipment 1 acquires production completion data, machine performance related data and running time data of a plurality of preset machines in the product production process, determining productivity scores corresponding to the production completion data, the machine performance related data and the running time data respectively for each preset machine; and determining productivity data corresponding to each preset machine according to each productivity value and a preset hierarchical weight decision analysis model.
The productivity determining method of the machine provided by the embodiment of the invention can be applied to a scene of distributing production tasks and a scene of purchasing the machine. If the user acquires the production task or the purchasing task, the client corresponding to the productivity determining method of the loading machine triggers a machine productivity determining request on an operation interface of the client, and the electronic equipment queries the adjusted weight, productivity score and total productivity score of the target machine corresponding to the production completion data, the machine performance related data and the running time data respectively according to the machine productivity determining request. And then the distribution of production tasks or the purchase of machines is carried out according to the adjusted weight, the productivity score and the total productivity score.
It should be noted that the method for determining the productivity of the machine according to the embodiment of the present invention may also be applied to other application scenarios related to the productivity of the machine, and the method is not limited in this embodiment.
The following describes the technical solutions of the present invention and how to solve the above technical problems with specific embodiments. The following specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
Example one
Fig. 2 is a flowchart illustrating a method for determining productivity of a machine according to an embodiment of the present invention, where as shown in fig. 2, an executing entity of the method for determining productivity of a machine is a productivity determining apparatus of the machine, and the productivity determining apparatus of the machine is located in an electronic device, the method for determining productivity of a machine according to the embodiment includes the following steps:
step 101, production completion data, machine performance related data and running time data of a plurality of preset machines in a product production process are obtained.
In this embodiment, the preset machine is each machine on a production line for producing a product.
In this embodiment, the production completion data, the machine performance related data, and the runtime data corresponding to each preset machine are factors associated with the productivity of the corresponding preset machine. Each relevant factor is obtained from the production tasks that the preset machine has completed.
In this embodiment, production completion data, data related to machine performance, and runtime data of each preset machine during production of a product in a completed production task may be stored locally. Or the third party system and the server are accessed to obtain the production completion data, the data related to the machine performance and the running time data of each preset machine in the process of producing the product, which is not limited in this embodiment.
The production completion data may include the number of produced products, the number of unqualified products, and the product reject ratio in the production task, and may further include other production completion data, which is not limited in this embodiment.
Wherein the machine performance related data may include: machine complexity. The frequency of occurrence of a fault in a production task and the repair duration after the fault occur may further include other machine performance related data, which is not limited in this embodiment.
The runtime data may include, among other things: the method comprises the steps of presetting the running time of a machine for completing a production task in the production task according to the standard time length, presetting the fault time when the machine runs according to the standard time length, presetting the excess working time beyond the standard time length, and the like.
Step 102, determining productivity scores corresponding to production completion data, machine performance related data and running time data respectively for each preset machine.
In this embodiment, for each preset machine, the corresponding productivity score can be determined according to the association factor and the preset productivity score determination algorithm.
Wherein, the corresponding preset productivity score determination algorithms may also be different according to the difference of the related factors.
Step 103, determining productivity data corresponding to each preset machine according to each productivity score and a preset hierarchical weight decision analysis model.
In this embodiment, the responsibility of each preset machine in the production task is different, so the contribution of the related factors determining the productivity is also different. Therefore, for each preset machine, a preset hierarchical weight decision analysis model is adopted to determine the weight corresponding to the productivity score of each associated factor, and the preset hierarchical weight decision analysis model determines the corresponding productivity data according to the productivity score and the corresponding weight.
Wherein the productivity data may be a total productivity score.
According to the productivity determining method of the machine, production completion data, machine performance related data and running time data of a plurality of preset machines in the production process of products are obtained; aiming at each preset machine, determining productivity scores corresponding to production completion data, machine performance related data and running time data respectively; and determining productivity data corresponding to each preset machine according to each productivity value and a preset hierarchical weight decision analysis model. The production completion data, the machine performance related data and the running time data are closely related to the performance of each preset machine in the product production process, so that the productivity of the preset machine can be accurately determined by adopting the production completion data, the machine performance related data and the running time data. And after determining the productivity scores respectively corresponding to the associated factors on the basis of each preset machine, the total productivity score is determined according to the productivity scores in a pertinence manner, so that different preset machines can be considered to have different responsibilities, the associated factors of the total productivity score are determined to have different considering tendencies, and the productivity of the determined preset machine is more accurate.
Example two
Fig. 3 is a schematic flowchart of a method for determining productivity of a machine according to another embodiment of the present invention, and as shown in fig. 3, the method for determining productivity of a machine according to the present embodiment further refines steps 101 to 103 based on the method for determining productivity of a machine according to the first embodiment of the present invention, and then the method for determining productivity of a machine according to the present embodiment includes the following steps:
step 201, capturing production completion data on a production task management system webpage by adopting a crawler technology.
In this embodiment, the production management information included in the production task management system may be stored in a table and provide a service in the form of a dynamic web page. As in table 1, the production management information may include: a machine information table, a production task detailed information table, a production task machine information registration table, a production task organization information registration table, a production task machine configuration information table, a machine working time registration table, and the like.
In this embodiment, after logging in the production task management system by using the user name and the password, the electronic device crawls a web page through a crawler technology and url of the web page, stores production management information into a page database, and then analyzes the production management information to extract production completion data.
Wherein the production completion data of each preset machine comprises: the number of produced products, the number of unqualified products and the product reject ratio.
Table 1: production management information schematic table
Figure BDA0003080491800000071
Step 202, accessing the machine maintenance system server to obtain the machine performance related data and the running time data of each preset machine from the machine maintenance system server.
In this embodiment, the machine maintenance system server stores, in advance, machine performance related data and runtime data of each preset machine in the process of producing a product. Therefore, the electronic device can send a data acquisition request to the machine maintenance system server, and the machine maintenance system server acquires the machine performance related data and the running time data of each preset machine according to the data acquisition request and sends the machine performance related data and the running time data to the electronic device.
Wherein the machine performance related data comprises: machine complexity, fault repair duration, and fault frequency. The runtime data may include: and in the production task, the operation time of the production task is completed according to the standard time length, the fault time is generated according to the standard time length, and the standard time length is over the extra working time length.
Step 203, determining productivity scores corresponding to the production completion data, the machine performance related data and the running time data for each preset machine.
As an optional implementation manner, in this embodiment, the determining the productivity score corresponding to the production completion data for each preset machine in step 203 includes the following steps:
step 2031, constructing a first initial matrix by using the number of the produced products, the number of unqualified products and the unqualified rate of the products corresponding to each preset machine.
Specifically, in this embodiment, first, a first initial matrix is constructed by the number of produced products, the number of rejected products, and the reject ratio of the products corresponding to each preset machine. M1 production completion data corresponding to n preset machines are included in the first initial matrix. The m1 production completion data corresponding to each preset machine is a row vector. And carrying out homotrending, normalization and non-dimensionalization processing on the first initial matrix to form a first normalized matrix.
Wherein, the m1 production completion data are the number of produced products, the number of unqualified products and the product failure rate. n is a number greater than 1.
Step 2032, determining productivity scores corresponding to the predetermined machines according to the first initial matrix and the first TOPSIS algorithm model.
The first TOPSIS algorithm model is a model for determining the productivity scores corresponding to the production completion data in each preset machine.
Specifically, in this embodiment, the optimal value and the worst value of the data corresponding to each preset machine are determined according to the first normalization matrix. Typically the maximum value of each column in the first normalized matrix
Figure BDA0003080491800000081
And minimum value
Figure BDA0003080491800000082
Respectively calculating the distance between each row index in the standardized matrix and the optimal value:
Figure BDA0003080491800000083
may be represented by formula 1. Respectively calculating the distance between each row index in the standardized matrix and the worst value:
Figure BDA0003080491800000084
may be represented by formula 2.
Figure BDA0003080491800000085
Figure BDA0003080491800000086
Wherein, aijProduction completion number for ith row and jth columnAccordingly.
In this embodiment, the ratio of the distance from the worst value to the sum of the distances from the worst value and the best value is used as the relative closeness: miAnd can be represented by formula 3.
Figure BDA0003080491800000091
In this embodiment, a first mapping relationship between the value range of M and the corresponding productivity score is pre-stored. Then determining M corresponding to the production completion data of each preset machineiAnd then, determining the corresponding productivity score according to the first mapping relation.
In addition, M isiThe larger the value of (A) is, the closer to the optimum value is, and the farther from the worst value is.
In this embodiment, the machine performance related data includes: machine complexity, fault repair duration, and fault frequency.
As an optional implementation manner, in this embodiment, the determining, for each preset machine, the productivity score corresponding to the machine performance related data in step 203 includes the following steps:
step 2033, a second initial matrix is constructed by using the machine complexity, the fault repairing duration and the fault frequency corresponding to each preset machine.
Specifically, in this embodiment, a second initial matrix is first constructed according to the machine complexity, the fault repairing duration, and the fault frequency corresponding to each preset machine. M2 machine performance related data corresponding to n preset machines are included in the second initial matrix. The m2 pieces of machine performance related data corresponding to each preset machine is a row vector. And carrying out homotrending, normalization and non-dimensionalization processing on the second initial matrix to form a second normalized matrix.
Wherein, the m2 pieces of machine performance related data are the complexity of the machine, the fault repairing time length and the fault frequency.
Step 2034, determining productivity scores corresponding to the predetermined machines according to the second initial matrix and the second TOPSIS algorithm model.
The second TOPSIS algorithm model is a model for determining the productivity score corresponding to the machine performance related data in each preset machine.
In this embodiment, the implementation manner of step 2034 is similar to the implementation manner of step 2032 in this embodiment of the present invention, and details are not repeated here.
In this embodiment, the step 203 of determining the productivity score corresponding to the runtime data for each preset machine includes the following steps:
step 2035, inputting the running time data corresponding to each preset machine into the K-means clustering algorithm.
Step 2036, classifying the running time data by a K-means clustering algorithm to obtain a clustering result.
Specifically, in this embodiment, first, K cluster centers (m) are initialized in the K-means clustering algorithm1,m2,....,mk) And forming a vector by using the running time data corresponding to each preset machine, and inputting the vector into the K-means clustering algorithm. And distributing each vector to the class with the minimum distance between the cluster center and the vector, searching the cluster center again, and distributing each vector to the class with the minimum distance between the cluster center and the vector. And continuously performing loop iteration until the change of all the cluster centers is small or the set maximum loop times are reached.
Step 2037, determining the clustering center of the clustering result corresponding to each runtime data.
Step 2038, determine the productivity score corresponding to the cluster center to which each runtime data belongs as the productivity score corresponding to the runtime data.
In this embodiment, the productivity score corresponding to the running time data represented by the cluster center is determined, and then the cluster center to which the running time data corresponding to each preset machine belongs is determined, so that the productivity score of the running time data corresponding to each preset machine is determined according to the productivity score of the cluster center to which the running time data corresponds.
For example, the cluster center represents runtime data with four productivity scores, class a 100, class B90, class C80, and class D70. The productivity score range of the runtime data corresponding to each preset machine is determined to be [70, 100 ].
Step 204, determining productivity data corresponding to each preset machine according to each productivity score and a preset hierarchical weight decision analysis model.
As an alternative implementation, in this embodiment, step 204 includes the following steps:
step 2041, obtain initial weights corresponding to the productivity scores.
In this embodiment, for each preset machine, the initial weights corresponding to the productivity scores of the production completion data, the machine performance related data, and the runtime data are determined. For example, the initial weights are 0.3, 0.4, and 0.3, respectively.
Step 2042, each productivity score and corresponding initial weight are input into the AHP model.
The AHP model is a hierarchical weight decision analysis model, quantitative analysis and qualitative analysis are combined, relative importance degree among various productivity scores is determined according to initial weight, and adjusted weight is given.
Step 2043, determining the adjusted weight and total productivity score corresponding to each productivity score through the AHP model, and outputting the total productivity score corresponding to each preset machine.
Specifically, in this embodiment, each productivity score and the corresponding initial weight are input into the AHP model, and the AHP model adjusts the initial weight corresponding to each productivity score according to a plurality of preset criteria to obtain the adjusted weight corresponding to each productivity score, and then calculates the total productivity score according to the productivity scores and the adjusted weight. And outputting the total productivity scores corresponding to the preset machines.
According to the productivity determining method of the machine, when the production completion data, the machine performance related data and the running time data of the multiple preset machines in the product production process are obtained, the production completion data are captured on the production task management system webpage by adopting a crawler technology, the machine maintenance system server is accessed, the machine performance related data and the running time data of the preset machines are obtained from the machine maintenance system server, and the more accurate and comprehensive production completion data, machine performance related data and running time data can be obtained.
In the productivity determining method of the machine according to the embodiment, when determining the productivity scores corresponding to the production completion data and the machine performance related data respectively for each preset machine, the TOPSIS algorithm model is adopted to determine the corresponding productivity scores. Therefore, the productivity scores corresponding to the production completion data and the machine performance related data in each preset machine can be accurately determined.
According to the productivity determining method of the machine, when the productivity score corresponding to the running time data is determined for each preset machine, the running time data corresponding to each preset machine is input into a K-means clustering algorithm; classifying the running time data through a K-means clustering algorithm to obtain a clustering result; determining a clustering center of a clustering result corresponding to each running time data; and determining the productivity score corresponding to the clustering center to which each operation time data belongs as the productivity score corresponding to the operation time data. The K-means clustering algorithm can accurately classify the data, so that the productivity score corresponding to the running time data in each preset machine can be accurately determined.
EXAMPLE III
Fig. 4 is a flowchart illustrating a method for determining productivity of a machine according to another embodiment of the present invention, and as shown in fig. 4, the method for determining productivity of a machine according to the present embodiment further includes other steps based on the method for determining productivity of a machine according to the first embodiment or the second embodiment, and the method for determining productivity of a machine according to the present embodiment includes the following steps:
step 301, receiving a machine productivity determination request triggered by a user, where the machine productivity determination request includes: identification information of the target machine.
In this embodiment, after determining the productivity data corresponding to each preset machine, the electronic device associates and stores the adjusted weight, the productivity score, and the total productivity score corresponding to the preset machine identification information, the production completion data, the machine performance related data, and the running time data, respectively, in the database, and provides the query client to the user.
In this embodiment, as shown in fig. 5, in the operation interface of the client, the user may input the identification information of the preset machine, and trigger the machine productivity determination request by clicking the "confirm" icon. The electronic equipment analyzes the machine productivity determination request to obtain the identification information of the target machine.
The identification information of the target machine may be a serial number of the target machine or other information uniquely representing the target machine.
Step 302, according to the machine productivity determination request, the adjusted weight, productivity score and total productivity score of the target machine corresponding to the identification information of the target machine are queried.
Step 303, displaying the productivity scores and the total productivity score in text form.
Step 304, displaying each adjusted weight in a form of a graph.
In this embodiment, the database queries, according to the identification information of the target machine, the adjusted weight, productivity score and total productivity score of the target machine after the production completion data, the machine performance related data and the runtime data are respectively corresponding.
In this embodiment, as shown in fig. 5, display areas for adjusted weights, productivity scores and total productivity scores of the target machine after the production completion data, the machine performance related data and the runtime data are respectively defined in advance, wherein the productivity scores and the total productivity scores corresponding to the production completion data, the machine performance related data and the runtime data can be displayed on the left side of the operation interface in text form. The adjusted weights of the target machine after the production completion data, the machine performance related data and the running time data are respectively and correspondingly displayed on the right side of the operation interface in a graphic form.
The productivity determining method of a machine provided by the embodiment receives a machine productivity determining request triggered by a user, wherein the machine productivity determining request comprises: identification information of the target machine; according to the machine productivity determination request, inquiring the adjusted weight, productivity score and total productivity score of the target machine corresponding to the identification information of the target machine respectively in the production completion data, the machine performance related data and the running time data; displaying each productivity score and the total productivity score in a text form; the adjusted weights are displayed in a graphic form, so that when a user has a query requirement on the productivity of the preset machine, the user can query through the operation interface of the client and display the weights on the operation interface in more detail, and the experience of the user in determining the productivity of the machine is improved.
Example four
Fig. 6 is a flowchart illustrating a method for determining productivity of a machine according to a further embodiment of the present invention, and as shown in fig. 6, the method for determining productivity of a machine according to the present embodiment further includes other steps based on the method for determining productivity of a machine according to the first embodiment or the second embodiment, and the method for determining productivity of a machine according to the present embodiment includes the following steps:
step 401, a new production task is obtained.
In this embodiment, after determining the total productivity score corresponding to each preset machine, a new production task is obtained, where the new production task may include: detailed information of production tasks, participating mechanisms and participating preset equipment.
Step 402, allocating production tasks according to the productivity data corresponding to each preset machine.
In the embodiment, the productivity data corresponding to each participated preset machine can be put into the detailed information, the production tasks are distributed according to the preset distribution strategy, and the productivity data corresponding to each participated preset machine is considered when the production tasks are distributed according to the preset distribution strategy, so that the production efficiency of the production tasks and the quality of the produced products can be effectively improved.
The preset allocation policy is not limited in this embodiment.
EXAMPLE five
Fig. 7 is a schematic structural diagram of a productivity determining apparatus of a machine according to an embodiment of the present invention, and as shown in fig. 7, a productivity determining apparatus 50 of a machine according to the embodiment includes: an obtaining module 51, a first determining module 52 and a second determining module 53.
The obtaining module 51 is configured to obtain production completion data, machine performance related data, and running time data of a plurality of preset machines in a product production process. The first determining module 52 is configured to determine, for each preset machine, productivity scores corresponding to the production completion data, the machine performance related data, and the runtime data, respectively. The second determining module 53 is configured to determine productivity data corresponding to each predetermined machine according to each productivity score and the predetermined hierarchical weight decision analysis model.
Optionally, the obtaining module 51 is specifically configured to:
capturing production completion data on a production task management system webpage by adopting a crawler technology; and accessing the machine maintenance system server to acquire the machine performance related data and the running time data of each preset machine from the machine maintenance system server.
Optionally, the production completion data comprises: the number of produced products, the number of unqualified products and the product reject ratio are reduced;
accordingly, the first determining module 52, when determining the productivity score corresponding to the production completion data for each preset machine, is specifically configured to:
constructing a first initial matrix by adopting the number of produced products, the number of unqualified products and the unqualified rate of the products corresponding to each preset machine; and determining the productivity value corresponding to each preset machine according to the first initial matrix and the first TOPSIS algorithm model.
Optionally, the machine performance related data comprises: machine complexity, fault repair duration, and fault frequency;
accordingly, the first determining module 52, when determining the productivity score corresponding to the machine performance related data for each preset machine, is specifically configured to:
constructing a second initial matrix by adopting the machine complexity, the fault repairing duration and the fault frequency corresponding to each preset machine; and determining the productivity value corresponding to each preset machine according to the second initial matrix and the second TOPSIS algorithm model.
Optionally, the first determining module 52, when determining the productivity score corresponding to the runtime data for each preset machine, is specifically configured to:
inputting the running time data corresponding to each preset machine into a K-means clustering algorithm; classifying the running time data through a K-means clustering algorithm to obtain a clustering result; determining a clustering center of a clustering result corresponding to each running time data; and determining the productivity score corresponding to the clustering center to which each operation time data belongs as the productivity score corresponding to the operation time data.
Optionally, the preset hierarchical weight decision analysis model is an AHP model; the productivity data is a total productivity score;
the second determining module 53 is specifically configured to: acquiring initial weights corresponding to the productivity scores; inputting each productivity score and the corresponding initial weight into an AHP model; and determining the adjusted weight and the total productivity value corresponding to each productivity value through an AHP model, and outputting the total productivity value corresponding to each preset machine.
Optionally, the productivity determining apparatus of a machine provided in this embodiment further includes: the device comprises a receiving module, an inquiry module and a display module.
The receiving module is used for receiving a machine productivity determining request triggered by a user, wherein the machine productivity determining request comprises: identification information of the target machine; the query module is used for determining a request according to the productivity of the machine and querying the adjusted weight, the productivity value and the total productivity value of the target machine corresponding to the identification information of the target machine respectively after the production completion data, the machine performance related data and the running time data are correspondingly adjusted; the display module is used for displaying the productivity scores and the total productivity score in a text form; each adjusted weight is displayed in graphical form.
Optionally, the productivity determining apparatus of a machine provided in this embodiment further includes: and a distribution module.
The obtaining module 51 is further configured to obtain a new production task; and the distribution module is also used for distributing the production tasks according to the productivity data corresponding to each preset machine.
The productivity determining apparatus of the machine provided in this embodiment may execute the technical solutions of the method embodiments shown in fig. 2-4 and fig. 6, and the implementation principle and technical effects thereof are similar to those of the method embodiments shown in fig. 2-4 and fig. 6, and are not described in detail herein.
EXAMPLE six
Fig. 8 is a block diagram of an electronic device for implementing the method for determining the productivity of a machine according to an embodiment of the present invention, and as shown in fig. 8, an electronic device 60 according to the present embodiment includes: at least one processor 61, a memory 62 and a transceiver 63.
The processor 61, the memory 62 and the transceiver 63 are electrically interconnected.
In the present embodiment, memory 62 stores computer-executable instructions; and a transceiver 62 for transceiving data with the machine maintenance system server.
The at least one processor 61 executes the computer-executable instructions stored by the memory 62, so that the at least one processor 61 executes the method provided by any one of the first to fourth embodiments.
EXAMPLE seven
The seventh embodiment of the present invention provides a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are executed by a processor, the computer-readable storage medium is used to implement the method provided in any one of the first to fourth embodiments.
In an exemplary embodiment, a computer program product is also provided, which includes a computer program that is executed by a processor to perform the method provided by any one of the first to fourth embodiments.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims (12)

1. A productivity determination method for a machine, comprising:
the method comprises the steps of obtaining production completion data, machine performance related data and running time data of a plurality of preset machines in the product production process;
determining productivity scores corresponding to the production completion data, the machine performance related data and the running time data respectively for each preset machine;
and determining productivity data corresponding to each preset machine according to each productivity score and a preset hierarchical weight decision analysis model.
2. The method of claim 1, wherein the obtaining production completion data, machine performance related data, and run-time data of the plurality of pre-set machines during production of the product comprises:
capturing production completion data on a production task management system webpage by adopting a crawler technology;
and accessing a machine maintenance system server to acquire the machine performance related data and the running time data of each preset machine from the machine maintenance system server.
3. The method of claim 1 or 2, wherein the production completion data comprises: the number of produced products, the number of unqualified products and the product reject ratio are reduced;
determining, for each preset machine, a productivity score corresponding to the production completion data, including:
constructing a first initial matrix by adopting the number of produced products, the number of unqualified products and the unqualified rate of the products corresponding to each preset machine;
and determining the productivity value corresponding to each preset machine according to the first initial matrix and the first TOPSIS algorithm model.
4. The method of claim 1 or 2, wherein the machine performance related data comprises: machine complexity, fault repair duration, and fault frequency;
for each preset machine, determining a productivity score corresponding to the machine performance related data, including:
constructing a second initial matrix by adopting the machine complexity, the fault repairing duration and the fault frequency corresponding to each preset machine;
and determining the productivity value corresponding to each preset machine according to the second initial matrix and the second TOPSIS algorithm model.
5. The method of claim 1 or 2, wherein determining, for each preset machine, a productivity score corresponding to the runtime data comprises:
inputting the running time data corresponding to each preset machine into a K-means clustering algorithm;
classifying the running time data through the K-means clustering algorithm to obtain a clustering result;
determining a clustering center of a clustering result corresponding to each running time data;
and determining the productivity score corresponding to the clustering center to which each operation time data belongs as the productivity score corresponding to the operation time data.
6. The method according to claim 1 or 2, wherein the predetermined hierarchical weight decision analysis model is an AHP model; the productivity data is a total productivity score;
the determining productivity data corresponding to each preset machine according to each productivity score and a preset hierarchical weight decision analysis model comprises:
acquiring initial weights corresponding to the productivity scores;
inputting each productivity score and the corresponding initial weight into an AHP model;
and determining the adjusted weight and the total productivity value corresponding to each productivity value through the AHP model, and outputting the total productivity value corresponding to each preset machine.
7. The method of claim 6, further comprising:
receiving a user-triggered machine productivity determination request, the machine productivity determination request including: identification information of the target machine;
according to the machine productivity determination request, inquiring the adjusted weight, productivity score and total productivity score of the target machine corresponding to the identification information of the target machine respectively in the production completion data, the machine performance related data and the running time data;
displaying each productivity score and the total productivity score in a text form;
each adjusted weight is displayed in graphical form.
8. The method of claim 1 or 2, wherein determining productivity data for each predetermined machine based on each of the productivity scores and a predetermined hierarchical weight decision analysis model further comprises:
acquiring a new production task;
and distributing the production tasks according to the productivity data corresponding to each preset machine.
9. A productivity determination apparatus for a machine, comprising:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring production completion data, machine performance related data and running time data of a plurality of preset machines in the production process of products;
the first determining module is used for determining productivity scores corresponding to the production completion data, the machine performance related data and the running time data respectively aiming at each preset machine;
and the second determining module is used for determining the productivity data corresponding to each preset machine according to each productivity score and a preset hierarchical weight decision analysis model.
10. An electronic device, comprising: at least one processor, a memory, and a transceiver;
the processor, the memory and the transceiver are interconnected through a circuit;
the memory stores computer-executable instructions; the transceiver is used for receiving and transmitting data with the machine maintenance system server;
the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the method of any one of claims 1-8.
11. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, perform the method of any one of claims 1-8.
12. A computer program product comprising a computer program, characterized in that the computer program realizes the method of any of claims 1-8 when executed by a processor.
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