CN115809795B - Method and device for evaluating bearing capacity of production team based on digitalization - Google Patents
Method and device for evaluating bearing capacity of production team based on digitalization Download PDFInfo
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Abstract
The invention relates to a production evaluation technology, and discloses a method and a device for evaluating bearing capacity of a production team based on digitalization, wherein the method comprises the following steps: performing data cleaning on the historical production data to obtain standard production data, and splitting the standard production data into a plurality of weight-class production data; extracting a vacation data sequence from the weight level production data, generating a vacation analysis model corresponding to the target weight level type by using the vacation data sequence, extracting tool production data from the standard production data, and extracting a target production parameter set from the tool production data; generating a team production model by using the target production parameter set, and converging all team production models into a team model set; and acquiring production information of the teams to be analyzed, and analyzing the bearing capacity of the teams to be analyzed by using the teams model and the holiday model. The invention can improve the accuracy of the bearing capacity evaluation of the production team.
Description
Technical Field
The invention relates to the technical field of production evaluation, in particular to a method and a device for evaluating bearing capacity of a production team based on digitalization.
Background
With the progress of modernization, more and more institutions and organizations start to expand production, which also greatly increases the number of production activities, and a production team is a main production unit in the production activities, and one production team comprises a plurality of production staff with different proficiency, so that in order to improve the production efficiency, the production team needs to be evaluated for bearing capacity;
the existing evaluation methods of the bearing capacity of the production teams are mostly based on big data, for example, by inquiring the historical production data of each production teams, a production model is built for each production teams, and then the bearing capacity of the production teams on the production tasks is analyzed, in practical application, the types of the production teams are various, the evaluation methods based on the big data cannot evaluate the bearing capacity of the randomly constituted production teams, and therefore the accuracy in the process of evaluating the bearing capacity of the production teams is possibly lower.
Disclosure of Invention
The invention provides a method and a device for evaluating bearing capacity of a production team based on digitalization, which mainly aim to solve the problem of lower accuracy in carrying out bearing capacity evaluation of the production team.
In order to achieve the above object, the present invention provides a method for evaluating the carrying capacity of a production team based on digitization, comprising:
acquiring historical production data of a target institution, performing data cleaning on the historical production data to obtain standard production data, and splitting the standard production data into a plurality of weight-level production data according to weight-level categories of production personnel;
selecting the class of the weight as a target class of the weight one by one, extracting a vacation data sequence corresponding to the target class of the weight from the class of the weight production data, iteratively updating a preset primary vacation model by using the vacation data sequence to obtain a vacation analysis model corresponding to the target class of the weight, and converging all the vacation analysis models into a vacation model set, wherein the iteratively updating the preset primary vacation model by using the vacation data sequence to obtain the vacation analysis model corresponding to the target class of the weight comprises the steps of: extracting the length time sequence characteristic and the self-attention time sequence characteristic of the vacation data sequence by using a preset initial vacation model; generating an analysis vacation data sequence according to the long and short time sequence characteristics and the self-attention time sequence characteristics, and extracting a partial sequence corresponding to the analysis vacation data sequence from the vacation data sequence as a target vacation data sequence; calculating a deviation value between the target vacation data sequence and the analyzed vacation data sequence using a vacation deviation algorithm as follows:
wherein ,
means that the deviation value,/->
Refers to the analysis of the vacation data sequenceAnd the total number of data of said analyzed vacation data sequence is equal to the total number of data of said target vacation data sequence,/for>
Refers to->
Data of->
Means +.f in the analysis of the vacation data sequence>
Data of->
Refers to the +.sup.th in the target vacation data sequence>
Data of->
Is a preset reference coefficient, < >>
Is a preset balance coefficient; carrying out iterative updating on model parameters of the initial vacation model according to the deviation value to obtain a vacation analysis model corresponding to the target weight class;
selecting production tools one by one as target production tools, extracting tool production data corresponding to the target production tools from the standard production data, and splitting the tool production data into a tool period sequence according to a production period;
generating a target production parameter set according to the tool period sequence, establishing a primary team production model according to the target production parameter set, carrying out iterative updating on the primary team production model by utilizing the target production parameter set to obtain a team production model corresponding to the target production tool, and collecting all the team production models into a team model set;
Acquiring production information of a group to be analyzed, extracting configuration information and expected production duration from the production information, updating the expected production duration into standard production duration by using the vacation model set, and analyzing the bearing capacity of the group to be analyzed by using the group model set, the standard production duration and the configuration information.
Optionally, the data cleaning is performed on the historical production data to obtain standard production data, including:
screening out messy code data and offside data from the historical production data to obtain screening production data;
vectorizing the screening production data to obtain a screening production vector set, and adding position vectors to each screening production vector in the screening production vector set to obtain a position production vector set;
performing feature clustering on the position production vector sets to obtain a plurality of production vector classes, and collecting the clustering centers of the production vector classes into a production center vector set;
mapping the production center vector set into the screening production data to obtain a clustering center data set;
and filling each null value data in the screening production data according to the clustering center data set to obtain standard production data.
Optionally, the filling the null data in the screening production data according to the clustering center data set to obtain standard production data includes:
selecting null data in the screening production data one by one as target null data;
calculating Euclidean distance between the target null data and each cluster center data in the cluster center data set;
selecting cluster center data with the minimum Euclidean distance as target cluster data;
and filling the target null data by using the target cluster data until the target null data is the last null data in the screening production data, and taking the filled screening production data as standard production data.
Optionally, the extracting the vacation data sequence corresponding to the target class of the rights class from the production data of the rights class includes:
arranging the weight production data according to time sequence to obtain time sequence production data;
extracting a production data sequence from the time sequence production data by using a preset time domain window;
selecting production data in the production data sequence one by one as target production data, and extracting primary vacation data of each production person from the target production data;
Calculating the vacation data corresponding to the target production data according to all the primary vacation data, and arranging all the vacation data into a vacation data sequence.
Optionally, the extracting the long-short time sequence feature and the self-attention time sequence feature of the vacation data sequence by using a preset initial vacation model includes:
screening a memory characteristic sequence from the vacation data sequence by using a forgetting gate in a long and short memory layer of a preset initial vacation model;
performing feature updating on the memory feature sequence and the vacation data sequence by using a feature state gate in the long and short memory layers to obtain updated state features;
performing feature fusion on the updated state features and the memory feature sequence by using an output gate in the long and short memory layers to obtain long and short time sequence features;
extracting self-attention time sequence characteristics from the vacation data sequence by utilizing the self-attention layer of the initial vacation model.
Optionally, the generating the target production parameter set according to the tool cycle sequence includes:
selecting tool period data in the tool period sequence one by one as target tool period data;
Taking the production team data in the target tool period data as target production team data, extracting yield parameters from the target production team data, and splitting the target production team data into a plurality of personnel arrays according to production personnel;
the personnel arrays are selected one by one to serve as target personnel arrays, weight level parameters and time length parameters are extracted from the target personnel arrays, and all weight level parameters and all time length parameters of the target production team data are collected to form standard parameter sets;
and integrating the yield parameter and the standard parameter set into a standard production parameter set of the target production team data, and integrating all the standard production parameter sets into a target production parameter set.
Optionally, the establishing a primary team production model according to the target production parameter set includes:
counting the total number of categories of the weight level parameters in the target production parameter set, and establishing a primary team production model as follows according to the total number of categories:
wherein ,
is the total amount of analytical production corresponding to the primary team production model,/->
Refers to->
The class of rights class->
Refers to the total number of said species,/- >
Means that +.about.in the target production parameter set>
The +.f. of the class of rights>
Personnel group->
Means that +.about.in the target production parameter set>
The total number of personal arrays of said class of rights, -/->
Refers to->
Production weight of said class of rights, < ->
Means that +.about.in the target production parameter set>
The +.f. of the class of rights>
The corresponding level parameter of the personnel array, +.>
Means that +.about.in the target production parameter set>
The +.f. of the class of rights>
And a duration parameter corresponding to the personnel array.
Optionally, the iteratively updating the primary team production model by using the target production parameter set to obtain a team production model corresponding to the target production tool, including:
selecting standard production parameter sets in the target production parameter set one by one as target standard production parameter sets, taking yield parameters in the target standard production parameter sets as real yield parameters, and taking standard parameter sets in the target standard production parameter sets as target standard parameter sets;
substituting the target standard parameter set into the primary team production model to obtain an analysis production parameter;
and iteratively updating model parameters of the primary team production model according to the loss value between the analysis production parameter and the real yield parameter until the loss value is smaller than a preset loss threshold value, and taking the updated primary team production model as a team production model corresponding to the target production tool.
Optionally, the updating the expected production duration to a standard production duration using the vacation model set includes:
calculating a weight class vacation data sequence corresponding to each weight class by using the vacation model set;
selecting the weight-class vacation data sequences one by one as target vacation data sequences, and generating target working data sequences according to the target vacation data sequences;
calculating a working coefficient according to the target working data sequence, multiplying the working coefficient by the expected production duration to obtain standard weight production duration corresponding to each weight class, and collecting all the standard weight production durations into standard production duration.
In order to solve the above problems, the present invention also provides a load-bearing capacity evaluation apparatus for a production team based on digitization, the apparatus comprising:
the data cleaning module is used for acquiring historical production data of a target mechanism, performing data cleaning on the historical production data to obtain standard production data, and splitting the standard production data into a plurality of weight-level production data according to weight-level types of production personnel;
the vacation model module is used for selecting the weight class type as a target weight class type one by one, extracting a vacation data sequence corresponding to the target weight class type from the weight class production data, iteratively updating a preset primary vacation model by using the vacation data sequence to obtain a vacation analysis model corresponding to the target weight class type, and converging all the vacation analysis models into a vacation model set, wherein the iteratively updating the preset primary vacation model by using the vacation data sequence to obtain the vacation analysis model corresponding to the target weight class type comprises the following steps: extracting the length time sequence characteristic and the self-attention time sequence characteristic of the vacation data sequence by using a preset initial vacation model; generating an analysis vacation data sequence according to the long and short time sequence characteristics and the self-attention time sequence characteristics, and extracting a partial sequence corresponding to the analysis vacation data sequence from the vacation data sequence as a target vacation data sequence; calculating a deviation value between the target vacation data sequence and the analyzed vacation data sequence using a vacation deviation algorithm as follows:
wherein ,
means that the deviation value,/->
Means that the total number of data of said analyzed vacation data sequence is equal to the total number of data of said target vacation data sequence,/->
Refers to->
Data of->
Means +.f in the analysis of the vacation data sequence>
Data of->
Refers to the +.sup.th in the target vacation data sequence>
Data of->
Is a preset reference coefficient, < >>
Is a preset balance coefficient; carrying out iterative updating on model parameters of the initial vacation model according to the deviation value to obtain a vacation analysis model corresponding to the target weight class;
the tool period module is used for selecting production tools one by one as target production tools, extracting tool production data corresponding to the target production tools from the standard production data, and splitting the tool production data into a tool period sequence according to the production period;
the team model module is used for generating a target production parameter set according to the tool cycle sequence, establishing a primary team production model according to the target production parameter set, carrying out iterative updating on the primary team production model by utilizing the target production parameter set to obtain a team production model corresponding to the target production tool, and collecting all the team production models into a team model set;
The bearing evaluation module is used for acquiring production information of the teams to be analyzed, extracting configuration information and expected production duration from the production information, updating the expected production duration into standard production duration by using the vacation model set, and analyzing the bearing capacity of the teams to be analyzed by using the teams model set, the standard production duration and the configuration information.
According to the embodiment of the invention, the historical production data of a target mechanism is obtained, the historical production data is subjected to data cleaning to obtain standard production data, the accuracy of historical production data can be improved, the accuracy of a subsequent data set is improved, the standard production data is split into a plurality of weight-class production data according to weight-class classes of production personnel, the vacation models of production personnel of different weight-class classes can be conveniently extracted subsequently, the vacation time sequence of each weight-class can be extracted by extracting a vacation data sequence corresponding to the target weight-class from the weight-class production data, the vacation analysis model corresponding to the target weight-class is obtained by iteratively updating a preset primary vacation model by utilizing the vacation data sequence, all the vacation analysis models are collected into a vacation model set, the vacation trend of the production personnel of each weight-class can be analyzed, the accuracy of the carrying capacity of a group is improved, the production data corresponding to the target production tool can be extracted from the standard production data, the production data can be split according to the production cycle parameters of the tool, and the subsequent production cycle data can be conveniently extracted according to the tool cycle;
By means of the target production parameter set, iterative updating is conducted on the primary team production model to obtain a team production model corresponding to the target production tool, the relation between the production personnel configuration and the production time and the production total amount can be established for each production tool, the accuracy of bearing capacity assessment is improved, configuration information and expected production duration are extracted from production information of a team to be analyzed, the expected production duration is updated into standard production duration by means of the vacation model set, and the bearing capacity of the team to be analyzed is analyzed by means of the team model set, the standard production duration and the configuration information. Therefore, the method and the device for evaluating the bearing capacity of the production team based on the digitalization can solve the problem of lower accuracy in the process of evaluating the bearing capacity of the production team.
Drawings
FIG. 1 is a flow chart of a method for evaluating load-bearing capacity of a production team based on digitization according to an embodiment of the present invention;
FIG. 2 is a flow chart of generating standard production data according to an embodiment of the present invention;
FIG. 3 is a flow chart of generating a vacation data sequence according to an embodiment of the present invention;
FIG. 4 is a functional block diagram of a load-bearing capacity assessment device based on a digitalized production team according to an embodiment of the present invention;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a method for evaluating the bearing capacity of a production team based on digitalization. The execution subject of the method for evaluating the carrying capacity of the production team based on the digitalization comprises, but is not limited to, at least one of a server, a terminal and the like which can be configured to execute the method provided by the embodiment of the application. In other words, the method for evaluating the carrying capacity of the production team based on the digitization may be performed by software or hardware installed in the terminal device or the server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of a method for evaluating load-bearing capacity of a production team based on digitization according to an embodiment of the invention is shown. In this embodiment, the method for evaluating the carrying capacity of the production team based on the digitization includes:
s1, acquiring historical production data of a target mechanism, performing data cleaning on the historical production data to obtain standard production data, and splitting the standard production data into a plurality of weight-class production data according to weight class of production personnel.
In the embodiment of the invention, the target mechanism refers to a mechanism needing to analyze the carrying capacity of the production team, the mechanism can be a factory or a company, and the historical production data refers to the personnel configuration, the machine configuration, the production time period, the production quantity and the like of each production team in the past historical time of the target mechanism.
In the embodiment of the present invention, the step of performing data cleaning on the historical production data to obtain standard production data includes:
screening out messy code data and offside data from the historical production data to obtain screening production data;
vectorizing the screening production data to obtain a screening production vector set, and adding position vectors to each screening production vector in the screening production vector set to obtain a position production vector set;
Performing feature clustering on the position production vector sets to obtain a plurality of production vector classes, and collecting the clustering centers of the production vector classes into a production center vector set;
mapping the production center vector set into the screening production data to obtain a clustering center data set;
and filling each null value data in the screening production data according to the clustering center data set to obtain standard production data.
In detail, the scrambled data refers to data without practical meaning such as @, #, and the offside data refers to data beyond the data value range, for example, the throughput is-50; and vectorizing the screening production data by utilizing a single thermal coding or word2vec algorithm to obtain a screening production vector set.
Specifically, a transducer model may be used to add a position vector to each of the set of screening production vectors to obtain a set of position production vectors; and carrying out feature clustering on the position production vector set by using a k nearest neighbor algorithm to obtain a plurality of production vector classes, wherein the clustering center refers to a vector corresponding to a center point of the production vector classes, and the null data refers to data on positions corresponding to the screened out messy code data and offside data in the screened production data.
In detail, referring to fig. 2, the filling each null data in the screening production data according to the cluster center data set to obtain standard production data includes:
s21, selecting null value data in the screening production data one by one to serve as target null value data;
s22, calculating Euclidean distance between the target null data and each cluster center data in the cluster center data set;
s23, selecting cluster center data with the minimum Euclidean distance as target cluster data;
and S24, filling the target null data by using the target cluster data until the target null data is the last null data in the screening production data, and taking the filled screening production data as standard production data.
In detail, the producer refers to a worker in a production team of the target institution, and the authority level refers to a level of the producer, such as a skilled producer, a general producer, a novice producer, and the like.
In detail, the splitting the standard production data into a plurality of weight production data according to the weight class of the producer includes: and selecting the weight class of one producer as a target weight class one by one, and extracting weight class production data corresponding to the target weight class from the standard production data.
In the embodiment of the invention, the historical production data of the target mechanism is obtained, and the historical production data is subjected to data cleaning to obtain the standard production data, so that the accuracy of the historical production data can be improved, the accuracy of a subsequent data set can be improved, and the standard production data is split into the plurality of weight-class production data according to the weight class of the production personnel, so that the vacation models of the production personnel with different weight class classes can be conveniently and subsequently extracted.
S2, selecting the weight class type one by one as a target weight class type, extracting a vacation data sequence corresponding to the target weight class type from the weight class production data, iteratively updating a preset primary vacation model by using the vacation data sequence to obtain a vacation analysis model corresponding to the target weight class type, and converging all the vacation analysis models into a vacation model set.
In the embodiment of the present invention, the vacation data sequence refers to a sequence formed by the vacation proportion in the time domain, and each vacation data in the vacation data sequence is the vacation proportion of all producers of the target class in a fixed time period.
In an embodiment of the present invention, referring to fig. 3, the extracting a vacation data sequence corresponding to the target class of rights from the rights class production data includes:
S31, arranging the weight production data according to a time sequence to obtain time sequence production data;
s32, extracting a production data sequence from the time sequence production data by utilizing a preset time domain window;
s33, selecting production data in the production data sequence one by one as target production data, and extracting primary vacation data of each producer from the target production data;
s34, calculating the vacation data corresponding to the target production data according to all the primary vacation data, and arranging all the vacation data into a vacation data sequence.
In detail, the window length of the time domain window may be one day, and the extracting the production data sequence from the time-series production data by using the preset time domain window means that the time-series production data is slid by using the preset time domain window, and the production data corresponding to the time domain window at each moment is arranged into the production data sequence.
Specifically, the primary vacation data refers to data of whether each producer is vacated, for example, the primary vacation data of 1 represents that the corresponding producer in the target production data is not vacated, and the primary vacation data of 0 represents that the corresponding producer in the target production data is vacated.
In detail, calculating the vacation data corresponding to the target production data according to all the primary vacation data means calculating a vacation sum of all the primary vacation data, and dividing the vacation sum by the total number of the primary vacation data, thereby obtaining the vacation data corresponding to the target production data.
In the embodiment of the present invention, the iterative updating of the preset primary vacation model by using the vacation data sequence to obtain the vacation analysis model corresponding to the target weight class type includes:
extracting the length time sequence characteristic and the self-attention time sequence characteristic of the vacation data sequence by using a preset initial vacation model;
generating an analysis vacation data sequence according to the long and short time sequence characteristics and the self-attention time sequence characteristics, and extracting a partial sequence corresponding to the analysis vacation data sequence from the vacation data sequence as a target vacation data sequence;
calculating a deviation value between the target vacation data sequence and the analyzed vacation data sequence using a vacation deviation algorithm as follows:
wherein ,
means that the deviation value,/->
Means that the total number of data of said analyzed vacation data sequence is equal to the total number of data of said target vacation data sequence,/- >
Refers to->
Data of->
Means +.f in the analysis of the vacation data sequence>
Data of->
Refers to the +.sup.th in the target vacation data sequence>
Data of->
Is a preset reference coefficient, < >>
Is a preset balance coefficient;
and carrying out iterative updating on the model parameters of the initial vacation model according to the deviation value to obtain a vacation analysis model corresponding to the target weight class.
In detail, by calculating the deviation value between the target vacation data sequence and the analysis vacation data sequence by using the vacation deviation algorithm, the characterization range of the deviation value can be improved according to the difference between the average number of the target vacation data sequence and the average number of the analysis vacation data sequence.
In detail, the initial holiday model includes a long-short memory layer and a self-attention layer, the forget gate may be a forget gate of the long-short memory network, the characteristic state gate may be a cell state of the long-short memory network, and the output gate may be an output gate of the long-short memory network; the autonomous force layer may be an encoder of a transducer model.
In detail, the extracting the long-short time sequence characteristic and the self-attention time sequence characteristic of the vacation data sequence by using a preset initial vacation model comprises the following steps:
Screening a memory characteristic sequence from the vacation data sequence by using a forgetting gate in a long and short memory layer of a preset initial vacation model;
performing feature updating on the memory feature sequence and the vacation data sequence by using a feature state gate in the long and short memory layers to obtain updated state features;
performing feature fusion on the updated state features and the memory feature sequence by using an output gate in the long and short memory layers to obtain long and short time sequence features;
extracting self-attention time sequence characteristics from the vacation data sequence by utilizing the self-attention layer of the initial vacation model.
In detail, the generating the analysis vacation data sequence according to the long-short time sequence feature and the self-attention time sequence feature refers to performing full-connection operation on the long-short time sequence feature to obtain long-short time sequence data, performing transcoding operation on the self-attention time sequence feature to obtain self-attention time sequence data, and adding the long-short time sequence data and the self-attention time sequence data into the analysis vacation data sequence, wherein a decoder of a transducer model can be used for performing transcoding operation on the self-attention time sequence feature to obtain self-attention time sequence data.
In the embodiment of the invention, the vacation time sequence characteristics of each class can be extracted by extracting the vacation data sequence corresponding to the target class from the class production data, the vacation analysis model corresponding to the target class is obtained by iteratively updating the preset primary vacation model by utilizing the vacation data sequence, and all the vacation analysis models are collected into a vacation model set, so that the vacation trend of the producer of each class can be analyzed, and the accuracy of the class bearing capacity is improved.
S3, selecting production tools one by one as target production tools, extracting tool production data corresponding to the target production tools from the standard production data, and splitting the tool production data into a tool period sequence according to the production period.
In the embodiment of the invention, the production tools are production machines of the target mechanism, the production tools can be robots or flow tables of one type, and the step of selecting one production tool as the target production tool one by one refers to the step of selecting one production tool as the target production tool one by one.
In detail, the extracting the tool production data corresponding to the target production tool from the standard production data refers to integrating the production team data, the production cycle data, the throughput, and other data of the standard production data, which are utilized in the target production tool, into tool production data.
Specifically, the production cycle refers to a time period between the start of one production and the end of the production of each production team, and the splitting of the tool production data into a tool cycle sequence according to the production cycle refers to extracting tool cycle data from the tool production data according to time sequence and arranging all the tool cycle data into a tool cycle sequence.
In the embodiment of the invention, the tool production data corresponding to the target production tool is extracted from the standard production data, and is split into the tool periodic sequence according to the production period, so that the standard production data can be split according to the production tool and the production period, and the subsequent extraction of the target production parameter set is convenient.
S4, generating a target production parameter set according to the tool period sequence, establishing a primary team production model according to the target production parameter set, carrying out iterative updating on the primary team production model by utilizing the target production parameter set to obtain a team production model corresponding to the target production tool, and collecting all the team production models into a team model set.
In the embodiment of the invention, the target production parameter set includes personnel configuration data, production duration data and yield data of each production period.
In an embodiment of the present invention, the generating the target production parameter set according to the tool period sequence includes:
selecting tool period data in the tool period sequence one by one as target tool period data;
taking the production team data in the target tool period data as target production team data, extracting yield parameters from the target production team data, and splitting the target production team data into a plurality of personnel arrays according to production personnel;
the personnel arrays are selected one by one to serve as target personnel arrays, weight level parameters and time length parameters are extracted from the target personnel arrays, and all weight level parameters and all time length parameters of the target production team data are collected to form standard parameter sets;
and integrating the yield parameter and the standard parameter set into a standard production parameter set of the target production team data, and integrating all the standard production parameter sets into a target production parameter set.
In detail, the production team data refers to the relevant data of the production team corresponding to the target tool period data in the current production period, the weight level parameter refers to the weight level type of the producer corresponding to the target personnel array, the duration parameter refers to the working duration corresponding to the target personnel array, and the yield parameter refers to the yield of the production period corresponding to the production team data.
In detail, the establishing a primary team production model according to the target production parameter set includes:
counting the total number of categories of the weight level parameters in the target production parameter set, and establishing a primary team production model as follows according to the total number of categories:
wherein ,
is the total amount of analytical production corresponding to the primary team production model,/->
Refers to->
The class of rights class->
Refers to the total number of said species,/->
Means that +.about.in the target production parameter set>
The +.f. of the class of rights>
Personnel group->
Means that +.about.in the target production parameter set>
The total number of personal arrays of said class of rights, -/->
Refers to->
Production weight of said class of rights, < ->
Means that +.about.in the target production parameter set>
The +.f. of the class of rights>
The corresponding level parameter of the personnel array, +.>
Means that +.about.in the target production parameter set>
The +.f. of the class of rights>
And a duration parameter corresponding to the personnel array.
In detail, the production weight is a model parameter in the primary team production model, and is used for representing the production efficiency of the production personnel corresponding to each class of weight.
In the embodiment of the present invention, the step of iteratively updating the primary team production model by using the target production parameter set to obtain a team production model corresponding to the target production tool includes:
Selecting standard production parameter sets in the target production parameter set one by one as target standard production parameter sets, taking yield parameters in the target standard production parameter sets as real yield parameters, and taking standard parameter sets in the target standard production parameter sets as target standard parameter sets;
substituting the target standard parameter set into the primary team production model to obtain an analysis production parameter;
and iteratively updating model parameters of the primary team production model according to the loss value between the analysis production parameter and the real yield parameter until the loss value is smaller than a preset loss threshold value, and taking the updated primary team production model as a team production model corresponding to the target production tool.
In the embodiment of the invention, the primary team production model is iteratively updated by utilizing the target production parameter set to obtain the team production model corresponding to the target production tool, so that the relation between the production personnel configuration and the production time and the total production quantity can be established for each production tool, and the accuracy of the bearing capacity assessment is improved.
S5, acquiring production information of the teams to be analyzed, extracting configuration information and expected production duration from the production information, updating the expected production duration into standard production duration by using the vacation model set, and analyzing the bearing capacity of the teams to be analyzed by using the teams model set, the standard production duration and the configuration information.
In the embodiment of the invention, the production information includes the number of production staff of each authority class, the expected production duration of the current production cycle and the types of production tools, wherein the configuration information includes the number of production staff of each authority class, the types of production tools and the expected total production amount.
In the embodiment of the present invention, the updating the expected production duration to the standard production duration by using the vacation model set includes:
calculating a weight class vacation data sequence corresponding to each weight class by using the vacation model set;
selecting the weight-class vacation data sequences one by one as target vacation data sequences, and generating target working data sequences according to the target vacation data sequences;
calculating a working coefficient according to the target working data sequence, multiplying the working coefficient by the expected production duration to obtain standard weight production duration corresponding to each weight class, and collecting all the standard weight production durations into standard production duration.
In detail, the analyzing the bearing capacity of the to-be-analyzed group by using the group model set, the standard production duration and the configuration information refers to selecting a corresponding group production model from the group model set according to the production tool types in the configuration information, calculating the analysis production total amount of the to-be-analyzed group by using the standard weight production duration of each weight class in the group production model and the standard production duration and the number of production staff of each weight class in the configuration information, and evaluating the bearing capacity of the to-be-analyzed group according to the analysis production total amount and the expected production total amount in the configuration information.
In detail, the generating the target working data sequence according to the target working data sequence refers to replacing the number with an absolute value of a numerical value obtained by subtracting 1 from each data in the target working data sequence, so as to obtain the target working data sequence, and the calculating the working coefficient according to the target working data sequence refers to taking an average value of the target working data sequence as the working coefficient.
In the embodiment of the invention, the configuration information and the expected production time length are extracted from the production information by acquiring the production information of the group to be analyzed, the expected production time length is updated into the standard production time length by using the vacation model set, the bearing capacity of the group to be analyzed is analyzed by using the group model set, the standard production time length and the configuration information, the bearing capacity can be analyzed according to the weight class of each producer in the group to be analyzed, the class of the used production tool and the effective production time length, the bearing capacity is analyzed according to the bearing capacity, and the accuracy of the bearing capacity analysis is improved.
According to the embodiment of the invention, the historical production data of a target mechanism is obtained, the historical production data is subjected to data cleaning to obtain standard production data, the accuracy of historical production data can be improved, the accuracy of a subsequent data set is improved, the standard production data is split into a plurality of weight-class production data according to weight-class classes of production personnel, the vacation models of production personnel of different weight-class classes can be conveniently extracted subsequently, the vacation time sequence of each weight-class can be extracted by extracting a vacation data sequence corresponding to the target weight-class from the weight-class production data, the vacation analysis model corresponding to the target weight-class is obtained by iteratively updating a preset primary vacation model by utilizing the vacation data sequence, all the vacation analysis models are collected into a vacation model set, the vacation trend of the production personnel of each weight-class can be analyzed, the accuracy of the carrying capacity of a group is improved, the production data corresponding to the target production tool can be extracted from the standard production data, the production data can be split according to the production cycle parameters of the tool, and the subsequent production cycle data can be conveniently extracted according to the tool cycle;
By means of the target production parameter set, iterative updating is conducted on the primary team production model to obtain a team production model corresponding to the target production tool, the relation between the production personnel configuration and the production time and the production total amount can be established for each production tool, the accuracy of bearing capacity assessment is improved, configuration information and expected production duration are extracted from production information of a team to be analyzed, the expected production duration is updated into standard production duration by means of the vacation model set, and the bearing capacity of the team to be analyzed is analyzed by means of the team model set, the standard production duration and the configuration information. Therefore, the method for evaluating the bearing capacity of the production team based on the digitalization can solve the problem of lower accuracy in the process of evaluating the bearing capacity of the production team.
FIG. 4 is a functional block diagram of a load-bearing capacity assessment device based on a digitized production team according to an embodiment of the present invention.
The carrying capacity evaluation device 100 based on the digital production team according to the present invention may be installed in an electronic device. Depending on the functions implemented, the digital production team based loadability assessment apparatus 100 may include a data cleansing module 101, a vacation model module 102, a tool period module 103, a team model module 104, and a loadability assessment module 105. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the data cleaning module 101 is configured to obtain historical production data of a target mechanism, perform data cleaning on the historical production data to obtain standard production data, and split the standard production data into a plurality of weight production data according to weight class of a producer;
the vacation model module 102 is configured to select the class type as a target class type one by one, extract a vacation data sequence corresponding to the target class type from the class production data, iteratively update a preset primary vacation model by using the vacation data sequence to obtain a vacation analysis model corresponding to the target class type, and aggregate all the vacation analysis models into a vacation model set, where iteratively update the preset primary vacation model by using the vacation data sequence to obtain a vacation analysis model corresponding to the target class type, and include: extracting the length time sequence characteristic and the self-attention time sequence characteristic of the vacation data sequence by using a preset initial vacation model; generating an analysis vacation data sequence according to the long and short time sequence characteristics and the self-attention time sequence characteristics, and extracting a partial sequence corresponding to the analysis vacation data sequence from the vacation data sequence as a target vacation data sequence; calculating a deviation value between the target vacation data sequence and the analyzed vacation data sequence using a vacation deviation algorithm as follows:
wherein ,
means that the deviation value,/->
Means that the total number of data of said analyzed vacation data sequence is equal to the total number of data of said target vacation data sequence,/->
Refers to->
Data of->
Means +.f in the analysis of the vacation data sequence>
Data of->
Refers to the +.sup.th in the target vacation data sequence>
Data of->
Is a preset reference coefficient, < >>
Is a preset balance coefficient; carrying out iterative updating on model parameters of the initial vacation model according to the deviation value to obtain a vacation analysis model corresponding to the target weight class;
the tool period module 103 is configured to select production tools one by one as a target production tool, extract tool production data corresponding to the target production tool from the standard production data, and split the tool production data into a tool period sequence according to a production period;
the team model module 104 is configured to generate a target production parameter set according to the tool cycle sequence, establish a primary team production model according to the target production parameter set, iteratively update the primary team production model by using the target production parameter set to obtain a team production model corresponding to the target production tool, and collect all the team production models into a team model set;
The load-bearing evaluation module 105 is configured to obtain production information of a group to be analyzed, extract configuration information and expected production duration from the production information, update the expected production duration to a standard production duration by using the vacation model set, and analyze the load-bearing capacity of the group to be analyzed by using the group model set, the standard production duration and the configuration information.
In detail, the modules in the carrying capacity evaluation device 100 based on a digitalized production team in the embodiment of the present invention use the same technical means as the carrying capacity evaluation method based on a digitalized production team described in fig. 1 to 3, and can produce the same technical effects, which are not described herein.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. Multiple units or means stated in the system may also be implemented by one unit or means, either by software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A method for evaluating load-bearing capacity of a production team based on digitization, the method comprising:
s1: acquiring historical production data of a target institution, performing data cleaning on the historical production data to obtain standard production data, and splitting the standard production data into a plurality of weight-level production data according to weight-level categories of production personnel;
s2: selecting the class of the weight as a target class of the weight one by one, extracting a vacation data sequence corresponding to the target class of the weight from the class of the weight production data, iteratively updating a preset primary vacation model by using the vacation data sequence to obtain a vacation analysis model corresponding to the target class of the weight, and converging all the vacation analysis models into a vacation model set, wherein the iteratively updating the preset primary vacation model by using the vacation data sequence to obtain the vacation analysis model corresponding to the target class of the weight comprises the steps of:
S21: extracting the length time sequence characteristic and the self-attention time sequence characteristic of the vacation data sequence by using a preset initial vacation model;
s23: generating an analysis vacation data sequence according to the long and short time sequence characteristics and the self-attention time sequence characteristics, and extracting a partial sequence corresponding to the analysis vacation data sequence from the vacation data sequence as a target vacation data sequence;
s23: calculating a deviation value between the target vacation data sequence and the analyzed vacation data sequence using a vacation deviation algorithm as follows:
wherein ,
means that the deviation value,/->
Means that the total number of data of said analyzed vacation data sequence is equal to the total number of data of said target vacation data sequence,/->
Refers to->
Data of->
Means +.f in the analysis of the vacation data sequence>
Data of->
Refers to the +.sup.th in the target vacation data sequence>
Data of->
Is a preset reference coefficient, < >>
Is a preset balance coefficient;
s24: carrying out iterative updating on model parameters of the initial vacation model according to the deviation value to obtain a vacation analysis model corresponding to the target weight class;
S3: selecting production tools one by one as target production tools, extracting tool production data corresponding to the target production tools from the standard production data, and splitting the tool production data into a tool period sequence according to a production period;
s4: generating a target production parameter set according to the tool period sequence, establishing a primary team production model according to the target production parameter set, carrying out iterative updating on the primary team production model by utilizing the target production parameter set to obtain a team production model corresponding to the target production tool, and collecting all the team production models into a team model set;
s5: acquiring production information of a group to be analyzed, extracting configuration information and expected production duration from the production information, updating the expected production duration into standard production duration by using the vacation model set, and analyzing the bearing capacity of the group to be analyzed by using the group model set, the standard production duration and the configuration information.
2. The method for evaluating the carrying capacity of a production team based on digitalization as claimed in claim 1, wherein said performing data cleansing on said historical production data to obtain standard production data comprises:
Screening out messy code data and offside data from the historical production data to obtain screening production data;
vectorizing the screening production data to obtain a screening production vector set, and adding position vectors to each screening production vector in the screening production vector set to obtain a position production vector set;
performing feature clustering on the position production vector sets to obtain a plurality of production vector classes, and collecting the clustering centers of the production vector classes into a production center vector set;
mapping the production center vector set into the screening production data to obtain a clustering center data set;
and filling each null value data in the screening production data according to the clustering center data set to obtain standard production data.
3. The method for evaluating the carrying capacity of a production team based on digitalization as claimed in claim 2, wherein said filling each null data in said screening production data according to said cluster center data set to obtain standard production data comprises:
selecting null data in the screening production data one by one as target null data;
calculating Euclidean distance between the target null data and each cluster center data in the cluster center data set;
Selecting cluster center data with the minimum Euclidean distance as target cluster data;
and filling the target null data by using the target cluster data until the target null data is the last null data in the screening production data, and taking the filled screening production data as standard production data.
4. The method for evaluating the carrying capacity of a production team based on digitalization of claim 1, wherein the extracting the vacation data sequence corresponding to the target class of rights from the class of rights production data comprises:
arranging the weight production data according to time sequence to obtain time sequence production data;
extracting a production data sequence from the time sequence production data by using a preset time domain window;
selecting production data in the production data sequence one by one as target production data, and extracting primary vacation data of each production person from the target production data;
calculating the vacation data corresponding to the target production data according to all the primary vacation data, and arranging all the vacation data into a vacation data sequence.
5. The method for evaluating the carrying capacity of a production team based on digitalization of claim 1, wherein the extracting the long and short time series features and the self-attention time series features of the vacation data sequence by using a preset initial vacation model comprises:
Screening a memory characteristic sequence from the vacation data sequence by using a forgetting gate in a long and short memory layer of a preset initial vacation model;
performing feature updating on the memory feature sequence and the vacation data sequence by using a feature state gate in the long and short memory layers to obtain updated state features;
performing feature fusion on the updated state features and the memory feature sequence by using an output gate in the long and short memory layers to obtain long and short time sequence features;
extracting self-attention time sequence characteristics from the vacation data sequence by utilizing the self-attention layer of the initial vacation model.
6. The method for digital production team based load bearing capacity assessment of claim 1, wherein said generating a set of target production parameters from said tool cycle sequence comprises:
selecting tool period data in the tool period sequence one by one as target tool period data;
taking the production team data in the target tool period data as target production team data, extracting yield parameters from the target production team data, and splitting the target production team data into a plurality of personnel arrays according to production personnel;
The personnel arrays are selected one by one to serve as target personnel arrays, weight level parameters and time length parameters are extracted from the target personnel arrays, and all weight level parameters and all time length parameters of the target production team data are collected to form standard parameter sets;
and integrating the yield parameter and the standard parameter set into a standard production parameter set of the target production team data, and integrating all the standard production parameter sets into a target production parameter set.
7. The method for digital production team based load bearing capacity assessment of claim 6, wherein said building a primary team production model from said target production parameter set comprises:
counting the total number of categories of the weight level parameters in the target production parameter set, and establishing a primary team production model as follows according to the total number of categories:
wherein , 
is the total amount of analytical production corresponding to the primary team production model,/->
Refers to->
The class of rights class->
Refers to the total number of said species,/->
Means that +.about.in the target production parameter set>
The +.f. of the class of rights>
An array of personnel's staff members,
means that +.about.in the target production parameter set >
The total number of personal arrays of said class of rights, -/->
Refers to->
Production weight of said class of rights, < ->
Means that +.about.in the target production parameter set>
The +.f. of the class of rights>
The corresponding level parameter of the personnel array, +.>
Refers to the target production parameter setFirst->
The +.f. of the class of rights>
And a duration parameter corresponding to the personnel array.
8. The method for evaluating the carrying capacity of a production team based on digitalization as claimed in claim 1, wherein said iteratively updating the primary team production model by using the target production parameter set to obtain a team production model corresponding to the target production tool comprises:
selecting standard production parameter sets in the target production parameter set one by one as target standard production parameter sets, taking yield parameters in the target standard production parameter sets as real yield parameters, and taking standard parameter sets in the target standard production parameter sets as target standard parameter sets;
substituting the target standard parameter set into the primary team production model to obtain an analysis production parameter;
and iteratively updating model parameters of the primary team production model according to the loss value between the analysis production parameter and the real yield parameter until the loss value is smaller than a preset loss threshold value, and taking the updated primary team production model as a team production model corresponding to the target production tool.
9. The method for digital production team based load bearing capacity assessment of claim 1, wherein said updating said expected production time period to a standard production time period using said set of holiday models comprises:
calculating a weight class vacation data sequence corresponding to each weight class by using the vacation model set;
selecting the weight-class vacation data sequences one by one as target vacation data sequences, and generating target working data sequences according to the target vacation data sequences;
calculating a working coefficient according to the target working data sequence, multiplying the working coefficient by the expected production duration to obtain standard weight production duration corresponding to each weight class, and collecting all the standard weight production durations into standard production duration.
10. A digital production team based load bearing capacity assessment device, the device comprising:
the data cleaning module is used for acquiring historical production data of a target mechanism, performing data cleaning on the historical production data to obtain standard production data, and splitting the standard production data into a plurality of weight-level production data according to weight-level types of production personnel;
The vacation model module is used for selecting the weight class type as a target weight class type one by one, extracting a vacation data sequence corresponding to the target weight class type from the weight class production data, iteratively updating a preset primary vacation model by using the vacation data sequence to obtain a vacation analysis model corresponding to the target weight class type, and converging all the vacation analysis models into a vacation model set, wherein the iteratively updating the preset primary vacation model by using the vacation data sequence to obtain the vacation analysis model corresponding to the target weight class type comprises the following steps: extracting the length time sequence characteristic and the self-attention time sequence characteristic of the vacation data sequence by using a preset initial vacation model; generating an analysis vacation data sequence according to the long and short time sequence characteristics and the self-attention time sequence characteristics, and extracting a partial sequence corresponding to the analysis vacation data sequence from the vacation data sequence as a target vacation data sequence; calculating a deviation value between the target vacation data sequence and the analyzed vacation data sequence using a vacation deviation algorithm as follows:
wherein ,
means that the deviation value,/- >
Means that the total number of data of said analyzed vacation data sequence is equal to the total number of data of said target vacation data sequence,/->
Refers to->
Data of->
Means +.f in the analysis of the vacation data sequence>
Data of->
Refers to the +.sup.th in the target vacation data sequence>
Data of->
Is a preset reference coefficient, < >>
Is a preset balance coefficient; carrying out iterative updating on model parameters of the initial vacation model according to the deviation value to obtain a vacation analysis model corresponding to the target weight class;
the tool period module is used for selecting production tools one by one as target production tools, extracting tool production data corresponding to the target production tools from the standard production data, and splitting the tool production data into a tool period sequence according to the production period;
the team model module is used for generating a target production parameter set according to the tool cycle sequence, establishing a primary team production model according to the target production parameter set, carrying out iterative updating on the primary team production model by utilizing the target production parameter set to obtain a team production model corresponding to the target production tool, and collecting all the team production models into a team model set;
The bearing evaluation module is used for acquiring production information of the teams to be analyzed, extracting configuration information and expected production duration from the production information, updating the expected production duration into standard production duration by using the vacation model set, and analyzing the bearing capacity of the teams to be analyzed by using the teams model set, the standard production duration and the configuration information.
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