Production line resource regulation and control method and device, electronic equipment and storage medium

Abstract

The invention provides a production line resource regulation and control method and device, electronic equipment and a storage medium. The method comprises the steps that when a production task is received, the production task is disassembled into sub-tasks executed in different time periods; obtaining a first evaluation index of each resource feature of each resource; obtaining a second evaluation index of a preset production line feature corresponding to each resource feature, and calculating a matching degree between the production line feature and each corresponding resource by using the second evaluation index and each corresponding first evaluation index; according to the preset importance degree of each resource feature in each time period, weighting the matching degree of each resource feature of each resource to obtain an adaptation degree matrix representing the adaptation degree of each resource in each time period; and inputting the adaptation degree matrix, the plurality of time periods and each resource into a planning model to obtain a configuration matrix for regulating and controlling a configuration relationship between each resource and the time period. According to the method, the intelligent level and the production efficiency of production scheduling are remarkably improved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of production line scheduling, and in particular to a production line resource regulation method and device, an electronic device, and a storage medium. BACKGROUND

[0002] The configuration of resources generally focuses on a single resource feature to evaluate the resource, or only uses a simple match between a single resource feature and a production line or a production task to measure whether the resource is suitable for the current production line and / or production task, ignoring the differences of each resource in multiple aspects and lacking comprehensive evaluation of the multi-dimensional features of the resource. In fact, it is difficult to accurately reflect the characteristics of each resource with only a single indicator, resulting in a final configuration scheme that cannot maximize the effectiveness of each resource.

[0003] At the same time, in the current resource configuration method, different work periods are generally regarded as homogeneous variables, and the differences between different time points, different task contents and task requirements of each work period are not reflected, making it difficult to achieve high-quality resource configuration.

[0004] Therefore, there is a need for a configuration scheme that can maximize the value of each resource. SUMMARY

[0005] Therefore, the purpose of the present application is to provide a production line resource regulation method and device, an electronic device, and a storage medium, which determines the matching degree of each resource and the production line based on the resource features of each resource, so that the matching degree can be used to obtain an adaptation matrix of each resource and sub-tasks in different periods, so that the planning model can effectively output a configuration matrix between each resource and the period.

[0006] To achieve the above purpose, the present application provides a production line resource regulation method, comprising: In response to receiving a production task, the production task is decomposed into a plurality of sub-tasks respectively executed in different periods according to the total task quantity and the production load coefficient of the production task, and a preset coefficient threshold value; Obtain a first evaluation index of each resource feature of each resource, a plurality of resources are used to jointly complete the production task of the production line, and each first evaluation index represents the level of the corresponding resource feature in the form of a fuzzy interval number; Obtain a second evaluation index corresponding to each resource feature of a preset production line feature, and calculate the matching degree between the production line feature and each corresponding resource using the second evaluation index and each corresponding first evaluation index; According to the importance of each resource feature to each sub-task, the matching degree of each resource feature of each resource is weighted to obtain an adaptation matrix representing the adaptation of each resource to each sub-task in each period; The fitness matrix, the plurality of time periods and the respective resources are input into the planning model to obtain a configuration matrix for scheduling a configuration relationship between the respective resources and the time periods, so that when the respective resources are scheduled according to the configuration matrix, the total fitness sum is maximum.

[0007] Further, after the production task is decomposed into a plurality of sub-tasks respectively performed in different time periods, the method comprises: The number of sub-tasks in each time period is determined by using the ratio of the total task amount to the estimated task duration; The production load coefficient is determined by using the ratio of the number of sub-tasks to the preset production capacity of the production line; The size relationship between the production load coefficient and the preset coefficient threshold is determined; In the case where the production load coefficient is greater than the coefficient threshold, the number of time periods is increased, and the production load coefficient is determined again; In the case where the production load coefficient is equal to the coefficient threshold, the number of time periods is not adjusted; In the case where the production load coefficient is less than the coefficient threshold, the number of time periods is reduced, and the production load coefficient is determined again.

[0008] Further, the first evaluation index of each resource characteristic of each resource is obtained, comprising: One or more score values of each resource characteristic of each resource are obtained, and each score value of each resource characteristic is normalized into a score set; The score set is converted into a fuzzy interval number, which is used as the corresponding first evaluation index.

[0009] The fuzzy interval number is a numerical interval with an upper bound and a lower bound; In the case where any resource characteristic has a single score value, the score value in the score set is respectively used as the upper bound and the lower bound of the fuzzy interval number; In the case where the resource characteristic has a plurality of score values and the number of score values is less than a preset number threshold, the maximum value of the plurality of score values in the score set is used as the upper bound of the fuzzy interval number, and the minimum value is used as the lower bound of the fuzzy interval number; In the case where the resource characteristic has a plurality of score values and the number of score values is greater than or equal to the number threshold, the center value, entropy and hyperentropy of the score set are determined; The sum of the center value, entropy and hyperentropy of the score set is determined as the upper bound of the fuzzy interval number, and the difference between the result of superimposing the entropy and the hyperentropy and the center value is determined as the lower bound of the fuzzy interval number.

[0010] Further, the matching degree between the production line characteristic and each corresponding resource is calculated by using the second evaluation index and each corresponding first evaluation index, comprising: In a case where the resource feature has multiple score values and the number of score values is greater than or equal to the number threshold, a matching probability between the first evaluation index and the corresponding second evaluation index is calculated and used as a matching degree between the first evaluation index and the corresponding second evaluation index.

[0011] Further, the matching degrees between the production line feature and each corresponding resource are calculated respectively by using the second evaluation index and each corresponding first evaluation index, and the matching degrees comprise: In a case where the number of score values possessed by the resource feature is less than the number threshold, a preset feature weight matrix is obtained, and the feature weight matrix is used to represent a demand degree of the production line for each resource feature at a corresponding time period; For each time period, the differences between each first evaluation index and the corresponding second evaluation index are weighted by using the feature weight matrix, so as to obtain the matching degrees between the production line and each resource at the corresponding time period.

[0012] The subtask further comprises multiple demand indexes, and each demand index represents a level requirement of the subtask for a corresponding resource feature. Further, after the adaptation degree matrix, the multiple time periods and the resources are input into the planning model, the method further comprises: resources in which any first evaluation index does not meet the demand indexes of any subtask are selected from the resources, and each subtask and the resources that do not meet the corresponding demand indexes are combined to form a first conflict matrix; multiple resources that are incompatible with each other are selected from the historical configuration information of all resources, and a second conflict matrix is formed; The first conflict matrix and the second conflict matrix are input into the planning model to obtain a configuration matrix.

[0013] Based on the same inventive concept, the application further provides a production line resource regulation device, comprising: a work time period configuration module, a production resource feature evaluation module, a configuration problem modeling module and a scheduling scheme generation module. The work time period configuration module is configured to, in response to receiving a production task, decompose the production task into multiple subtasks respectively executed at different time periods according to a total task amount and a production load coefficient of the production task and a preset coefficient threshold. The production resource feature evaluation module is configured to obtain a first evaluation index of each resource feature of each resource, and multiple resources are used to jointly complete a production task of a production line, and each first evaluation index represents a level of a corresponding resource feature in the form of a fuzzy interval number. The configuration problem modeling module is configured to obtain second evaluation indexes corresponding to respective resource features and preset production line features, calculate matching degrees between the production line features and each corresponding resource by using the second evaluation indexes and each corresponding first evaluation index, weight the matching degrees of each resource feature of each resource according to the importance of the resource features for the sub-tasks, and obtain an adaptation degree matrix representing adaptation degrees of the resources and the sub-tasks in the time periods. The scheduling scheme generation is configured to input the adaptation degree matrix, the time periods and the resources into a planning model to obtain a configuration matrix for scheduling the configuration relationship between the resources and the time periods, so that the total adaptation degree is maximum when the resources are scheduled according to the configuration matrix.

[0014] Based on the same inventive concept, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the production line resource control method according to any one of the above.

[0015] Based on the same inventive concept, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions for causing the computer to execute the production line resource control method.

[0016] Based on the same inventive concept, the present application also provides a computer program product, including computer program instructions, when the computer program instructions are executed on a computer, the computer executes the production line resource control method according to any one of the above.

[0017] As can be seen from the above, the production line resource control method, device, storage medium, electronic device and program provided by the present application can realize the optimal configuration of resources and time periods by decomposing production tasks into multiple sub-tasks, constructing an adaptation degree matrix based on the matching degrees of resource features and production line features, and finally generating a configuration matrix by using a planning model. It can be seen that the method comprehensively considers the multi-dimensional features of resources and the dynamic demand of production lines, can significantly improve resource utilization and reduce resource waste, while ensuring efficient completion of production tasks. In addition, by maximizing the total adaptation degree, the overall production efficiency is optimized and the production cost is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present application or related art, the following will briefly introduce the drawings needed to be used in the embodiments or related art descriptions. Obviously, the drawings in the following description are only embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0019] Figure 1 a flow chart of the production line resource regulation method of the embodiment of the present application; Figure 2 a flow chart of the time period adjustment method of the embodiment of the present application; Figure 3 a flow chart of the method for determining the first evaluation index of the embodiment of the present application; Figure 4 a structural schematic diagram of the production line resource regulation device of the embodiment of the present application; Figure 5 a structural schematic diagram of the electronic device of the embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the embodiments and the accompanying drawings.

[0021] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be understood as the common meanings understood by those skilled in the art to which the present application belongs. The terms "first", "second", and similar terms used in the embodiments of the present application do not represent any order, number, or importance, but are only used to distinguish different components. The terms "include", "contain", and similar terms mean that the components or objects before the terms encompass the components or objects listed after the terms and their equivalents, and do not exclude other components or objects. The terms "connect" or "connected" and similar terms do not mean physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms "up", "down", "left", "right", and the like only represent relative positional relationships, and when the absolute positions of the described objects change, the relative positional relationships can also change accordingly.

[0022] As described in the background section, the related production line resource regulation methods are still difficult to meet the needs in actual production.

[0023] The applicant found in the process of implementing the present application that the main problem of the related production line resource regulation methods is that the configuration of resources generally focuses on a single resource characteristic to evaluate the resource, or only uses a simple matching of a single resource characteristic with a production line or a production task to measure whether the resource is suitable for the current production line and / or production task, ignoring the differences of each resource in multiple aspects and lacking comprehensive evaluation of the multi-dimensional characteristics of the resource. In fact, it is difficult to accurately reflect the characteristics of each resource by relying on a single index, resulting in that the final configuration scheme cannot maximize the effect of each resource.

[0024] Meanwhile, in the current resource configuration mode, different work periods are generally regarded as homogeneous variables, and the differences of work periods at different time points, different task contents and task demands cannot be reflected, so that it is difficult to achieve high-quality resource configuration.

[0025] Based on this, one or more embodiments of the present application provide a production line resource regulation method, which determines the matching degree of each resource and the production line based on the resource characteristics of each resource, so that the matching degree can be used to obtain an adaptation degree matrix of each resource and a time period sub-task, so that the planning model can effectively output a configuration matrix between each resource and the time period.

[0026] The production line resource regulation method of the present application can be executed by a computer device, which is any electronic device with data calculation, processing and storage capabilities, such as mobile phones, PCs (Personal Computers), tablet computers and other terminal devices, and the embodiments of the present application do not limit this.

[0027] The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0028] Reference Figure 1 The production line resource regulation method of one embodiment of the present application includes the following steps: Step S101, in response to receiving a production task, the production task is disassembled into a plurality of sub-tasks respectively executed in different time periods according to the total task amount of the production task and the production load coefficient, and a preset coefficient threshold.

[0029] In this embodiment, when the production task is received, the production task can be disassembled into a plurality of sub-tasks according to a plurality of time periods set in advance, so that the corresponding sub-tasks can be executed in each time period.

[0030] For example, for a preset 10 equal time periods, the total production amount of the production task can be divided into 10 sub-tasks with the same number of time periods, and one corresponding sub-task can be executed in each time period.

[0031] The number of time periods can be set according to historical experience or actual situation.

[0032] Step S102, obtaining a first evaluation index of each resource characteristic of each resource, a plurality of resources are used to cooperate with the production line to complete the production task, and each first evaluation index represents the level of the corresponding resource characteristic in the form of fuzzy interval number.

[0033] In the embodiment, based on the preset multiple resources, multiple resource features are set for the resources in advance, so that when each resource is measured, the same multiple resource features are used for measurement, and each resource feature can be regarded as reflecting the characteristics of the resource from one aspect.

[0034] In the process of performing the above production task by the production line, the multiple resources are used to cooperate with the production line to complete the production task.

[0035] The purpose of the method is to configure multiple suitable resources for the production line, the corresponding subtask and the corresponding time period from all resources before the corresponding subtask is completed by the production line in each time period.

[0036] Based on this, each resource feature of each resource can be determined, and a first evaluation index of each resource feature can be obtained.

[0037] The first evaluation index can be used to evaluate the level of the corresponding resource feature, and for each resource, the first evaluation index of each resource feature can reflect the characteristics of the resource from multiple aspects.

[0038] In step S103, a second evaluation index of a preset production line feature corresponding to each resource feature is obtained, and the matching degree between the production line feature and each corresponding resource is calculated using the second evaluation index and each corresponding first evaluation index.

[0039] Based on the resource features of each resource determined in the foregoing steps, multiple production line features corresponding to the resource features can be set for the production line, wherein each production line feature corresponds to a resource feature, and each production line feature can be regarded as reflecting the demand of the production line for a resource feature from one aspect.

[0040] Based on this, a second evaluation index of each production line feature can be obtained.

[0041] The third evaluation index can be used to evaluate the level of the corresponding production line feature.

[0042] Further, for each resource feature of each resource, the matching degree between the first evaluation index and the corresponding second evaluation index of the resource feature can be calculated, and after comprehensively considering the matching degrees between each first evaluation index and the corresponding second evaluation index of the resource, the matching degree between the resource and the production line can be determined.

[0043] In step S104, the matching degrees of the resource features of each resource are weighted according to the importance of each resource feature for each subtask, and a matching degree matrix representing the matching degrees of the resources and the subtasks in each time period is obtained.

[0044] Based on the time periods divided in the foregoing steps, since the execution process of the entire production task is a process in which the corresponding subtasks are completed by the production lines in each time period, for the subtask of each time period, the matching degree of each resource feature of each resource can be weighted to obtain the adaptation degree of each resource to the subtask in the time period.

[0045] Based on this, for the production line executing the corresponding subtask in each time period, the adaptation degree matrix can be constructed by using the adaptation degrees between each resource and the production line.

[0046] In this step, before weighting the matching degrees of each resource feature of each resource, an index weight of the first evaluation index of each resource feature can be set in advance, which specifically represents the contribution size of the corresponding resource feature to the matching degree when measuring the matching degree between the production line and the single resource, that is, the importance of the feature resource to the time period. For example, for a preset production line, the resource feature S10 of the resource is more important than the resource feature S9 of the resource, so the resource feature S10 is more important than the resource feature S9 when measuring the matching degree.

[0047] Based on this, for the production line used in each time period, a corresponding index weight vector containing the index weights of the resource features can be obtained, and accordingly, the matching degrees of the resource features can be weighted by using the index weight vector, so as to obtain the adaptation degrees between the time period and the corresponding resource. Further, the adaptation degrees of each resource in each time period can be assembled into an adaptation degree matrix.

[0048] Step S105 inputs the adaptation degree matrix, the plurality of time periods, and the resources into the planning model to obtain a configuration matrix for scheduling the configuration relationship between the resources and the time periods, so that when the resources are scheduled according to the configuration matrix, the adaptation degree sum is maximum.

[0049] Based on the adaptation degree matrix determined in the foregoing steps, the adaptation degree matrix, the divided time periods, and the preset resources can be input into the planning model, and the configuration matrix can be output.

[0050] The configuration matrix specifically includes the time periods and the resources, and specifically represents the configuration relationship between the resources and the time periods.

[0051] In this embodiment, when the resources are configured for each time period by using the configuration matrix, the adaptation degree sum of each adaptation degree of each time period can be maximum.

[0052] Wherein, for the matching degree between each time period and each resource, after summing up, the single time period matching degree sum corresponding to the time period is obtained, and after summing up each single time period matching degree sum, the matching degree sum can be obtained.

[0053] Specifically, after inputting the matching degree matrix of each time period and each resource into the planning model, a target function can be set in the planning model, and the target function takes the maximum matching degree sum as the solving target.

[0054] Wherein, the target function can be represented as formula (1) as follows: (1) Wherein, ρ The matching degree sum is represented as q i,j The matching degree between the i th resource and the j th time period is represented as j T i,j The i th resource is allocated to the j th time period j p ij The preset applicable degree of the i th resource in the j th time period is represented as cost j The cost of the j th resource is represented as

[0055] Based on this, the planning model can be used to solve the target function, so as to obtain the configuration matrix.

[0056] It can be seen that, by decomposing the production task into multiple subtasks, and constructing the matching degree matrix based on the matching degree of the resource characteristics and the production line characteristics, and finally generating the configuration matrix by using the planning model, the optimal configuration of the resource and the time period is realized. It can be seen that, the method comprehensively considers the multi-dimensional characteristics of the resource and the dynamic demand of the production line, can significantly improve the resource utilization rate, reduce resource waste, and at the same time ensure the efficient completion of the production task. In addition, by maximizing the matching degree sum, the overall production efficiency is optimized, and the production cost is reduced.

[0057] In another embodiment of the present application, before receiving the production task, the resource information of each resource can be obtained in advance, and the production line information of the production line can be obtained, and when the production task is received, the production task information of the production task can be obtained.

[0058] Wherein, the resource cooperating with the production line can be, for example, the power resource for the production line or the production tool for the production line, etc., and in some cases, the resource can also be human resource, and each resource represents a production personnel.

[0059] ​​​​Further, the resource information of each resource obtained can be specifically each resource feature.

[0060] Further, the production line information obtained can be specifically each production line feature.

[0061] The production line feature can be, for example, complexity of the production line, operation characteristics, and technical update frequency, etc.

[0062] Based on the obtained each resource feature and each production line feature, they can be divided into features represented by specific values, such as the technical update frequency of the production line, and features not represented by specific values, such as the complexity of the production line.

[0063] For the features not represented by specific values, expert scoring can be used to represent the values of the features.

[0064] Based on this, the values of each resource feature and each production line feature can be normalized to facilitate subsequent operation.

[0065] For example, taking a resource with 10 resource features as an example, the resource features can be represented as a set S ={ S 1, S 2, …, S 10}, and the corresponding production line features can be represented as a set P ={ P 1, P 2, …, P 10}.

[0066] The value of the resource feature of the first S k resource can be represented as , and the value of the production line feature P k of the production line can be represented as .

[0067] Further, the production line information also includes production capacity , which is used to represent the maximum amount of tasks that the production line can complete per unit time.

[0068] In this embodiment, the received production task can be specifically embodied in the form of production task information, and the production task information can specifically include the total task amount O of the production task T dand the demand for each resource feature, such as the demand for resources S j . D j .

[0069] In an embodiment of the present application, after the production task is split into multiple sub-tasks respectively executed in different time periods, the pre-divided time periods can also be adjusted according to specific circumstances.

[0070] Reference Figure 2 , Figure 2 A flowchart of a time period adjustment method of an embodiment of the present application is shown, including the following steps: Step S201, determining the sub-task quantity of each time period by using the ratio of the total task quantity to the estimated task duration.

[0071] In the embodiment, based on the determined production task information, the task quantity of each time period can be determined according to the ratio between the total task quantity and the task duration when splitting the total task quantity.

[0072] Specifically, the task quantity of each time period can be calculated according to formula (2) as shown below W d : (2) Step S202, determining the production load coefficient by using the ratio of the sub-task quantity to the production line preset production capacity.

[0073] Based on the task quantity of each time period determined in the foregoing step S201, the production load coefficient can be further determined.

[0074] The production load coefficient can reflect whether the currently divided time period is sufficient or insufficient relative to the total task quantity.

[0075] In the embodiment, the ratio between the sub-task quantity of each time period and the production capacity of the production line can be determined as the production load coefficient.

[0076] Specifically, the production load coefficient can be calculated according to formula (3) as shown below : (3) Step S203, determining the size relationship between the production load coefficient and the preset coefficient threshold value.

[0077] In this step, based on the production load coefficient determined in the foregoing step S202, the currently divided time period can be determined to be sufficient or insufficient by using the pre-set coefficient threshold value, where the coefficient threshold value can be set to 1, for example.

[0078] Specifically, the size relationship between the production load coefficient and the coefficient threshold value can be determined, for example, whether the production load coefficient is greater than 1, less than 1 or equal to 1.

[0079] In step S204, in the case that the production load coefficient is greater than the coefficient threshold value, the number of time periods is increased, and the subtask amount of each time period is determined again.

[0080] In this step, based on the comparison in the foregoing step S203, in the case that the production load coefficient is greater than the coefficient threshold value, for example, the production load coefficient is greater than 1, it can be considered that the current time period is insufficient, and the number of time periods can be further increased, and the production load coefficient is calculated again after the subtask amount of each time period is determined again.

[0081] In step S205, in the case that the production load coefficient is equal to the coefficient threshold value, the number of time periods is not adjusted.

[0082] In this step, based on the comparison in the foregoing step S203, in the case that the production load coefficient is equal to the coefficient threshold value, for example, the production load coefficient is equal to 1, it can be considered that the current time period is sufficient and exactly suitable for the production task, and the number of time periods does not need to be adjusted.

[0083] In step S206, in the case that the production load coefficient is less than the coefficient threshold value, the number of time periods is reduced, and the production load coefficient is determined again.

[0084] In this step, based on the comparison in the foregoing step S203, in the case that the production load coefficient is less than the coefficient threshold value, for example, the production load coefficient is less than 1, it can be considered that the current time period is sufficient and excessive, and the number of time periods can be further reduced, and the production load coefficient is calculated again after the subtask amount of each time period is determined again.

[0085] It can be seen that, by dynamically adjusting the number of time periods and the production load coefficient, the embodiment ensures that the subtask amount of each time period matches the production capacity of the production line, thereby avoiding the problems of overloading or idling of the production line caused by unreasonable division of time periods, and improving the flexibility and adaptability of production scheduling. By adjusting the number of time periods in real time, the changes in production tasks can be better responded to, and the production efficiency and resource utilization rate can be improved.

[0086] In an embodiment of the present application, in the process of obtaining the first evaluation index of each resource feature of each resource, for the value of each resource feature, when the value itself is a specific value, the value is taken as the first evaluation index of the resource feature, and when the value is one or more expert score values, the first evaluation index of the resource feature can be determined according to the specific situation of expert scoring.

[0087] Reference Figure 3 , Figure 3 A flow chart of a method for determining a first evaluation index according to an embodiment of the present application is shown, comprising the following steps: Step S301, obtaining one or more score values of each resource feature of each resource, and normalizing each score value of each resource feature into a score set.

[0088] In this step, for each resource, one or more score values of each resource feature of the resource can be obtained first, and in order to facilitate subsequent calculation, each resource feature of a single score value or multiple score values can be normalized into a score set.

[0089] In a specific example, for each resource feature, in the process of normalizing its score value into a score set, the normalization can be performed respectively according to the number of score values of the resource feature, so as to obtain a score set , wherein k is the number of score values.

[0090] Among them, the number of score values can be divided into three cases, the resource feature has a single score value, the resource feature has 2 to 5 score values, and the score value of the resource feature is greater than or equal to 6.

[0091] Step S302, in the case that any resource feature has a single score value, the score value in the score set is taken as the upper bound and the lower bound of the fuzzy interval number respectively, and as the corresponding first evaluation index.

[0092] Specifically, taking the fourth resource feature of the i-th resource as an example, when it has only a single score value v, the score value v in the score set can be taken as the upper bound and the lower bound of the fuzzy interval respectively, that is, the upper bound and the lower bound are the same at this time, and is expressed as .

[0093] Further, the fuzzy interval is taken as the corresponding first evaluation index.

[0094] Step S303, in the case that the resource feature has multiple score values and the number of score values is less than a preset number threshold, the maximum value of the multiple score values in the score set is taken as the upper bound of the fuzzy interval number, and the minimum value is taken as the lower bound of the fuzzy interval number.

[0095] Among them, the number threshold can be preset, and the number threshold can be, for example, 6 score values.

[0096] In this step, when the score set has multiple score values and the number of score values is less than or equal to 5, the score set can be expressed as a numerical interval, that is, a fuzzy interval number , wherein Vmindenotes the minimum value in the plurality of score values, Vmax denotes the maximum value in the plurality of score values.

[0097] Accordingly, the maximum value Vmax is taken as the upper bound of the fuzzy interval number, and the minimum value Vmin is taken as the lower bound of the fuzzy interval number.

[0098] Further, the fuzzy interval is taken as the corresponding first evaluation index.

[0099] In step S304, in the case that the resource feature has a plurality of score values and the number of score values is greater than or equal to the number threshold, the center value, the entropy and the hyper entropy of the score set are determined, the sum of the center value, the entropy and the hyper entropy of the score set is determined as the upper bound of the fuzzy interval number, and the difference between the result of the superposition of the entropy and the hyper entropy and the center value is determined as the lower bound of the fuzzy interval number.

[0100] In this step, the number threshold is taken as 6 as a specific example, when it has a plurality of score values and the number of score values is greater than or equal to 6, the score set can be converted into a fuzzy interval number , and the score set is taken as the first evaluation index, wherein denotes the upper bound of the fuzzy interval number, denotes the lower bound of the fuzzy interval number.

[0101] Specifically, for each resource feature, based on the score set determined in the foregoing step, a plurality of digital features of the score set can be determined for it.

[0102] Specifically, the plurality of digital features can be, for example, the center value, the entropy, and the hyper entropy of the score set.

[0103] The center value of the score set specifically reflects the average value of each score value of the resource feature; the entropy reflects the dispersion degree of the expert score, that is, reflects the uncertainty of the score value of the resource feature; and the hyper entropy reflects the fluctuation of the entropy.

[0104] In a specific example, taking the i-th resource feature of the 4th resource as an example, the center value can be determined according to the formula (4) shown as follows : (4) wherein V denotes the normalized score set of the resource feature, k denotes the total number of score values in the score set, and the subscripts S4 and i denote the i-th resource feature of the 4th resource.

[0105] Further, taking the i-th resource feature of the 4th resource as an example, the entropy of the expert score can be determined according to the formula (5) shown as follows : (5) Further, taking the i th resource feature of the 4 th resource as an example, the super-entropy of the expert score can be determined according to formula (6) shown as follows : (6) Based on the determined center value, entropy and super-entropy, the upper bound and the lower bound of the fuzzy interval number can be further determined by using the center value, entropy and super-entropy.

[0106] Specifically, taking the i th resource feature of the 4 th resource as an example, the upper bound of the first fuzzy interval number can be calculated according to formula (7) shown as follows : (7) Wherein, is the influence coefficient of the super-entropy, which can be a constant in the embodiment.

[0107] Further, taking the i th resource feature of the 4 th resource as an example, the lower bound of the fuzzy interval number can be calculated according to formula (8) shown as follows : (8) Based on this, the fuzzy interval number composed of the upper bound determined by formula (7) and the lower bound determined by formula (8) can be determined, and the fuzzy interval is taken as the corresponding first evaluation index.

[0108] It can be seen that, by normalizing the score value and calculating the center value, entropy and super-entropy, the embodiment provides an accurate and reasonable first evaluation index for the resource feature, which can effectively avoid or reduce the fuzziness and uncertainty of the expert score, ensure the accuracy and reliability of the evaluation index, and through the interval representation of the evaluation index, the actual level of the resource feature can be more comprehensively reflected, thereby providing a reliable basis for subsequent matching degree calculation.

[0109] In an embodiment of the present application, in the process of calculating the matching degree between the production line feature and each corresponding resource by using the second evaluation index and each corresponding first evaluation index, the first evaluation index can be divided into: a first case where the resource feature has one or more score values and the number of score values is less than a number threshold, and a second case where the resource feature has multiple score values and the number of score values is greater than or equal to the number threshold, and the matching degree is determined in different ways for the two cases.

[0110] In the embodiment, for the above-mentioned first case, a pre-set feature weight matrix can be obtained first.

[0111] Among them, the feature weight matrix contains multiple feature weight vectors, each feature weight vector is used to represent the degree of demand of the production line for a corresponding resource feature when the production line performs the subtask in each time period. Furthermore, the feature weight matrix represents the degree of demand of the production line for each resource feature in each time period.

[0112] Based on this, for each resource feature and its corresponding production line feature, the feature weight vector can be used to weight the difference between the first evaluation indicator corresponding to the resource feature and the second evaluation indicator corresponding to the production line feature, so as to obtain the matching degree between the resource feature and its corresponding production line feature.

[0113] Specifically, the matching degree can be calculated according to the following formula (9): (9) in, Indicates the resources and j The matching degree between the first resource feature and the corresponding first production line feature between the production lines that execute the subtask in the time period, represents the feature weight vector, Indicates the j The second evaluation index of the first production line characteristic of the production line that performs the subtask in the time period, Indicates the The first evaluation indicator of the first resource characteristic of a resource.

[0114] In this embodiment, for the second case mentioned above, for the resource characteristics and the corresponding production line characteristics, the matching probability between the first evaluation indicator of the resource characteristic and the second evaluation indicator of the production line characteristic can be determined, and used as the matching degree between the first evaluation indicator and the corresponding second evaluation indicator, that is, the matching probability between the resource characteristic and the production line characteristic, and used as the matching degree between the two.

[0115] Specifically, the matching degree between the two can be determined according to the following formula (10): (10) in, Indicates the resources and j The production lines that execute subtasks in the time period k The resource characteristics and the corresponding k The matching degree between the characteristics of the production lines, Indicates that for j The production line that performs the subtask in the time period kThe lower bound of the second evaluation index of a production line characteristic represents the minimum requirement for the production line characteristic. Indicates that for The first resource k The upper bound of the first evaluation index of a resource characteristic represents the highest level that the resource characteristic can provide, among which, , , accordingly, It represents the probability that the resource characteristics meet the production line characteristics, that is, the probability that the two match.

[0116] Based on this, the set of each feature to the production line features of each time period can be used as a matching matrix.

[0117] As can be seen, this embodiment uses both matching probability and weighted difference methods to calculate matching degrees for different types of scoring sets, ensuring the scientific and adaptable nature of the matching degree calculation. By introducing a feature weight matrix, the importance of different resource features can be dynamically adjusted, thereby more accurately reflecting the matching relationship between production lines and resources and improving the rationality of scheduling solutions.

[0118] In another embodiment of the present application, for each resource, based on the determined matching matrix, the degree of compatibility between the resource and the corresponding time period can be determined by weighting the matching degrees according to the obtained weights of the various indicators and in combination with the corresponding matching degrees.

[0119] Specifically, the degree of fitness can be calculated according to the following formula (11): (11) in, q i,j Indicates the i Resources and j The adaptability between time periods, w k Indicates the The first resource k The indicator weight of each resource characteristic, 10 means The number of resource features preset for each resource is 10.

[0120] Based on this, when determining the degree of compatibility between each resource and each time period, the respective degrees of compatibility may be combined into a degree of compatibility matrix, and the configuration matrix may be determined using the degree of compatibility matrix.

[0121] In some cases, after the adaptability matrix, multiple time periods and various resources are input into the planning model, there may be incompatibilities between resources and subtasks. For example, a first evaluation indicator of the resource cannot meet the demand indicator corresponding to a subtask. There may also be incompatibilities between different resources. Therefore, it is possible to further determine a first conflict matrix for representing the incompatibility between resources and production lines, and a second conflict matrix for representing the incompatibility between resources.

[0122] Among them, a subtask has multiple demand indicators, and each demand indicator specifically represents the demand of the corresponding subtask for various resource characteristics.

[0123] Specifically, for the first conflict matrix, we can first construct a matrix with the dimension m × n The initial matrix, where m Indicates the number of resources, n Represents the number of time periods, that is, the number of subtasks. All elements in the initial matrix are 0.

[0124] Furthermore, by traversing various resources, for each resource, it is determined whether the first evaluation index of each resource characteristic satisfies the requirement index of the subtask in each time period.

[0125] Specifically, for resources , among its various resource characteristics, any one or more resource characteristics have the first evaluation index that does not meet the subtask j demand indicator, then the resource Subtasks that are incompatible with their counterparts j The element pointed to S ij Set to 1.

[0126] Furthermore, for resources i , the first evaluation index of each resource feature satisfies the subtask j The various demand indicators of the resource Subtasks that are incompatible with their counterparts j The element pointed to S ij Set to 0.

[0127] Accordingly, after traversing various resources, a first conflict matrix can be obtained.

[0128] Furthermore, for the second conflict matrix, we can first construct a matrix with the dimension m × n The initial matrix of m Represents the number of resources. All elements in this initial matrix are 0.

[0129] Further, by traversing each resource, it is determined whether each resource is incompatible with other resources.

[0130] Specifically, for a resource , if it is incompatible with a resource j , the resource is pointed to the element j pointed by the corresponding incompatible resource S ij is set to 1.

[0131] Further, for a resource , if it is compatible with a resource j , the resource is pointed to the element j pointed by the corresponding incompatible resource S ij is set to 0.

[0132] Accordingly, after traversing each resource, the second conflict matrix can be obtained.

[0133] Based on this, after inputting the adaptation degree matrix, multiple time periods, and each resource into the planning model, the first conflict matrix and the second conflict matrix are also inputted into the planning model, so that the model can effectively avoid the incompatible conditions between resources and subtasks and between resources when planning the configuration matrix.

[0134] The first conflict matrix and the second conflict matrix are constructed in this embodiment, which effectively avoids the incompatible problems between resources and subtasks and between resources. This method can significantly reduce production interruptions or efficiency declines caused by resource conflicts, ensuring the feasibility and stability of the scheduling scheme. Through the introduction of the conflict matrix, the output of the planning model is further optimized, and the reliability of resource configuration is improved.

[0135] In some other cases, other constraint conditions can also be considered in the process of solving the objective function by using the planning model, so that the configuration matrix solved by the planning is more reasonable.

[0136] Specifically, the planning model can also be set with one or more constraint conditions of the formulas (12) to (15) as shown below: (12) (13) (14) (15) wherein, D(d) represents the i-th resource da set of time periods of a day, line i indicates a time period The production line performing subtasks, formula (12) is used to constrain the length of continuous use of resources to be less than or equal to 5 days, formula (13) is used to indicate that any resource can only cooperate to perform a time period of subtasks in a day, formula (14) is used to indicate that the resource cannot continuously cooperate with multiple time period subtasks, and formula (15) is used to indicate that for a production line, the resource characteristics of a resource must be able to meet the requirements of the production line characteristics.

[0137] It can be seen that in this embodiment, based on various constraint conditions, the planning model can obtain a more reasonable configuration matrix when solving the objective function.

[0138] It should be noted that the method of the embodiments of the present application can be executed by a single device, such as a computer or a server, etc. The method of the embodiments of the present application can also be applied to a distributed scenario, and completed by multiple devices cooperating with each other. In this distributed scenario, one of the multiple devices can only execute one or more steps in the method of the embodiments of the present application, and the multiple devices can interact with each other to complete the method.

[0139] It should be noted that the above describes some embodiments of the present application. In some cases, the actions or steps described in the present application can be executed in an order different from the above-described embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or can be advantageous.

[0140] Based on the same inventive concept, the embodiments of the present application also provide a production line resource regulation device corresponding to any of the above-mentioned embodiment methods.

[0141] Reference Figure 4 , the production line resource regulation device comprises a production resource management module 401, a job time period configuration module 402, a historical data analysis module 403, a production resource characteristic evaluation module 404, a configuration problem modeling module 405, a scheduling scheme generation module 406, and a dynamic monitoring and adaptive adjustment module 407. The production resource management module 401 is configured to, before receiving a production task, can pre-acquire resource information of each resource.

[0142] Further, the acquired resource information of each resource can be each resource characteristic; and the acquired production line information can be each production line characteristic.

[0143] ​Based on the obtained resource features and production line features, the resource features and production line features can be divided into features represented by specific values.

[0144] Based on this, the values of the resource features and production line features can be normalized to facilitate subsequent operation.

[0145] The operation period configuration module 402 is configured to, in response to receiving a production task, decompose the production task into a plurality of sub-tasks respectively executed in different time periods according to the total task amount of the production task and the production load coefficient, and a preset coefficient threshold. The historical data analysis module 403 is configured to collect and store relevant data of each resource and its resource features in historical work, and collect and store relevant data of the production line and its production line features in historical work.

[0146] Further, by recording the historical matching degree between each resource and the production line in different historical periods for different historical production tasks.

[0147] Based on this, by analyzing the production history data, the compatibility between each resource and the production line and the time period in the production history is determined, based on which each historical matching degree and its corresponding resource features and production line features can be composed into production history data, so that in subsequent operations, when determining the evaluation index based on each time period and its corresponding sub-task, and when determining the adaptation degree matrix, the production history data and other current relevant data can be introduced together to determine the evaluation index and the adaptation degree matrix.

[0148] The production resource feature evaluation module 404 is configured to obtain a first evaluation index of each resource feature of each resource, and a plurality of resources are used to jointly cooperate with the production line to complete the production task, and each first evaluation index represents the level of the corresponding resource feature in the form of fuzzy interval number. The configuration problem modeling module 405 is configured to obtain a second evaluation index corresponding to each resource feature and a preset production line feature, calculate the matching degree between the production line feature and each corresponding resource using the second evaluation index and each corresponding first evaluation index, and weight the matching degree of each resource feature of each resource according to the importance of each resource feature for each sub-task to obtain an adaptation degree matrix representing the adaptation degree of each resource and each sub-task in each time period. The scheduling scheme generation module 406 is configured to input the adaptation degree matrix, a plurality of time periods and each resource into a planning model to obtain a configuration matrix for scheduling the configuration relationship between each resource and time period, so that when each resource is scheduled according to the configuration matrix, the total adaptation degree is maximum.

[0149] The dynamic monitoring and adaptive adjustment module 407 is configured to perform scheduling of resources according to the configuration matrix determined above, and to perform dynamic monitoring during the scheduling process, thereby ensuring smooth completion of production tasks.

[0150] During the dynamic monitoring process, the operation status of the production line, such as equipment failure and production progress, can be dynamically monitored. In the event of abnormal conditions, such as missing key resources and / or machine failure, which cause production to be blocked, a corresponding alarm can be generated, and the current production task-related production data can be stored as next production history data, i.e., transmitted to the historical data analysis module.

[0151] As an optional embodiment, the job period configuration module 402 is specifically configured to: determine the sub-task quantity of each period using the ratio of the total task quantity to the estimated task duration; determine the production load coefficient using the ratio of the sub-task quantity to the production line's preset production capacity; determine the production load coefficient using the ratio of the sub-task quantity to the production line's preset production capacity; in the case where the production load coefficient is greater than the coefficient threshold, increase the number of periods and determine the production load coefficient again; in the case where the production load coefficient is equal to the coefficient threshold, do not adjust the number of periods; in the case where the production load coefficient is less than the coefficient threshold, reduce the number of periods and determine the production load coefficient again.

[0152] As an optional embodiment, the first evaluation index is represented as an interval with an upper bound and a lower bound, and correspondingly, the production resource feature evaluation module 404 is specifically configured to: obtain one or more score values pre-set for each resource feature of each resource, and normalize each score value of each resource feature into a score set; convert the score set into a fuzzy interval number, and use it as the corresponding first evaluation index.

[0153] The fuzzy interval number is a numerical interval with an upper bound and a lower bound; Converting the score set into a fuzzy interval number includes: in the case where any resource feature has a single score value, the score values in the score set are respectively used as the upper bound and the lower bound of the fuzzy interval number; in the case where a resource feature has multiple score values and the number of score values is less than a preset number threshold, the maximum value of the multiple score values in the score set is used as the upper bound of the fuzzy interval number, and the minimum value is used as the lower bound of the fuzzy interval number; In a case where the resource feature has multiple score values and the number of score values is greater than or equal to the number threshold, a center value, an entropy and a hyper entropy of the score set are determined; A sum of the center value, the entropy and the hyper entropy of the score set is determined as an upper bound of the fuzzy interval number, and a difference between a result of superimposition of the entropy and the hyper entropy and the center value is determined as a lower bound of the fuzzy interval number.

[0154] As an optional embodiment, the configuration problem modeling module 405 is specifically configured to: In a case where the resource feature has multiple score values and the number of score values is greater than or equal to the number threshold, a matching probability between the first evaluation index and the corresponding second evaluation index is calculated and used as the matching degree between the first evaluation index and the corresponding second evaluation index.

[0155] In a case where the number of score values of the resource feature is less than the number threshold, a preset feature weight matrix is obtained, and the feature weight matrix is used to represent a demand degree of the production line to each resource feature at a corresponding time period. For each time period, the feature weight matrix is used to weight a difference between each first evaluation index and a corresponding second evaluation index, to obtain a matching degree between the production line and each resource at the corresponding time period.

[0156] As an optional embodiment, the subtask further includes multiple demand indexes, each demand index representing a level requirement of the subtask to a corresponding resource feature, and accordingly, the scheduling scheme generation module 406 is further specifically configured to: Selecting, from the resources, a resource in which any first evaluation index does not meet a demand index of any subtask, and grouping the subtasks and the resource not meeting the corresponding demand index to form a first conflict matrix; Selecting, from historical configuration information of all resources, multiple resources that are incompatible with each other, and grouping the multiple resources to form a second conflict matrix; Inputting the first conflict matrix and the second conflict matrix into the planning model to obtain a configuration matrix.

[0157] For the convenience of description, the above apparatus is described in various modules respectively according to functions. Of course, in the implementation of the embodiments of the present application, the functions of the modules can be implemented in one or more software and / or hardware.

[0158] The apparatuses in the above embodiments are used to implement the corresponding production line resource regulation method in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be described herein.

[0159] Based on the same inventive concept, embodiments of the present application also provide an electronic device corresponding to the production line resource regulation method of any of the above embodiments, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the production line resource regulation method of any of the above embodiments.

[0160] Figure 5 A more specific hardware structure of an electronic device is shown in the embodiment, which can include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 for internal communication within the device.

[0161] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, etc., for executing related programs to implement the technical solutions provided by the embodiments of the present application.

[0162] The memory 1020 can be implemented by a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1020 can store an operating system and other application programs, and when the technical solutions provided by the embodiments of the present application are implemented by software or firmware, the related program codes are stored in the memory 1020 and executed by the processor 1010.

[0163] The input / output interface 1030 is used to connect input / output modules to realize information input and output. The input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. The input device can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device can include a display, a speaker, a vibrator, an indicator light, etc.

[0164] The communication interface 1040 is used to connect a communication module (not shown in the figure) to realize the communication interaction between the device and other devices. The communication module can realize communication through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.).

[0165] Bus 1050 includes a path for transferring information between the various components (e.g., processor 1010, memory 1020, input / output interface 1030, and communication interface 1040) of the device.

[0166] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device can also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device can also only contain the components necessary to implement the embodiments of the present application, and does not have to contain all the components shown in the figure.

[0167] The device of the above embodiment is used to implement the corresponding production line resource regulation method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which are not repeated here.

[0168] Based on the same inventive concept, corresponding to any of the above embodiment methods, the present application also provides a non-transitory computer readable storage medium, which stores computer instructions for causing the computer to execute the production line resource regulation method according to any of the above embodiments.

[0169] The computer readable medium of the present embodiment includes permanent and non-permanent, removable and non-removable media, which can be realized by any method or technology to store information. The information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0170] The computer instructions stored in the storage medium of the above embodiment are used to cause the computer to execute the production line resource regulation method according to any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which are not repeated here.

[0171] Based on the same concept, the application also provides a computer program product corresponding to the method of any of the above embodiments, comprising computer program instructions, when the computer program instructions run on a computer, make the computer execute the production line resource regulation method as any of the above embodiments, has the beneficial effect of the corresponding method embodiment, here is no more tedious.

[0172] Those skilled in the art should understand that the discussion of any of the above embodiments is only exemplary, and is not intended to imply that the scope of the application is limited to these examples; under the idea of the application, the above embodiments or technical features in different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes of different aspects of the embodiments of the application as described above, for the sake of brevity, they are not provided in detail.

[0173] In addition, in order to simplify the description and discussion, and so as not to make the embodiments of the application difficult to understand, the known power / ground connections of integrated circuit (IC) chips and other components can or can not be shown in the provided drawings. In addition, the devices can be shown in the form of block diagrams in order to avoid making the embodiments of the application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the application are to be implemented (i.e. these details should be entirely within the understanding of those skilled in the art). Where specific details (e.g. circuits) are set forth in order to describe the exemplary embodiments of the application, it will be apparent to those skilled in the art that the embodiments of the application can be practiced without these specific details or with variations of these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0174] Although the application has been described in conjunction with specific embodiments thereof, many alternatives, modifications and variations will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g. dynamic RAM (DRAM)) can use the embodiments discussed.

[0175] Embodiments of the application are intended to cover all such alternatives, modifications and variations as falling within the broad scope of the application. Accordingly, any omission, modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the application shall be included in the protection scope of the application.

Claims

1. A production line resource control method, characterized in that: include: In response to a received production task, the production task is decomposed into a plurality of subtasks to be executed in different time periods according to the total task volume and production load coefficient of the production task and a preset coefficient threshold; Obtaining a first evaluation index of each resource characteristic of each resource, wherein the multiple resources are used to cooperate with the production line to complete the production task, and each first evaluation index represents the level of the corresponding resource characteristic in the form of a fuzzy interval number; Obtaining second evaluation indicators of preset production line characteristics corresponding to each resource characteristic, and calculating the matching degree between the production line characteristics and each corresponding resource using the second evaluation indicators and each corresponding first evaluation indicator; According to the importance of each resource feature to each subtask, the matching degree of each resource feature is weighted to obtain the fitness matrix representing the fitness of each resource and the subtask in each time period; The adaptability matrix, multiple time periods and various resources are input into a planning model to obtain a configuration matrix for scheduling the configuration relationship between various resources and time periods, so that when various resources are scheduled according to the configuration matrix, the total adaptability is maximized.

2. The production line resource control method according to claim 1, characterized in that: After breaking down the production task into a plurality of subtasks to be executed at different time periods, the method includes: Determine the subtask amount for each time period using the ratio of the total task amount to the estimated task duration; Determining a production load factor using a ratio of the subtask volume to a preset productivity of the production line; Determining the magnitude relationship between the production load coefficient and a preset coefficient threshold; In the case where the production load coefficient is greater than the coefficient threshold, increasing the number of the time periods and determining the production load coefficient again; When the production load factor is equal to the factor threshold, the number of the time periods is not adjusted; In a case where the production load coefficient is less than the coefficient threshold, the number of the time periods is reduced and the production load coefficient is determined again.

3. The production line resource control method according to claim 1, characterized in that: The first evaluation index of each resource characteristic of each resource is obtained, including: Obtain one or more pre-set scoring values ​​for each resource feature of each resource, and normalize the individual scoring values ​​of each resource feature into a scoring set; The rating set is converted into a fuzzy interval number and used as the corresponding first evaluation indicator.

4. The production line resource control method according to claim 3, characterized in that: The fuzzy interval number is a numerical interval with an upper bound and a lower bound; The step of converting the score set into a fuzzy interval number includes: In the case where any resource feature has a single score value, the score values ​​in the score set are used as the upper and lower bounds of the fuzzy interval number respectively; If a resource feature has multiple scoring values ​​and the number of scoring values ​​is less than a preset threshold, the maximum value of the multiple scoring values ​​in the scoring set is used as the upper bound of the fuzzy interval number, and the minimum value is used as the lower bound of the fuzzy interval number; When a resource feature has multiple scoring values ​​and the number of scoring values ​​is greater than or equal to the quantity threshold, the central value, entropy and super entropy of the scoring set are determined, and the sum of the central value, entropy and super entropy of the scoring set is determined as the upper limit of the fuzzy interval number, and the difference between the result of the superposition of entropy and super entropy and the central value is determined as the lower limit of the fuzzy interval number.

5. The production line resource control method according to claim 4, characterized in that: The using the second evaluation index and each corresponding first evaluation index to respectively calculate the matching degree between the production line characteristics and each corresponding resource includes: When the resource feature has multiple scoring values ​​and the number of scoring values ​​is greater than or equal to the quantity threshold, the matching probability between the first evaluation indicator and the corresponding second evaluation indicator is calculated and used as the matching degree between the first evaluation indicator and the corresponding second evaluation indicator.

6. The production line resource control method according to claim 4, characterized in that: The using the second evaluation index and each corresponding first evaluation index to respectively calculate the matching degree between the production line characteristics and each corresponding resource includes: When the number of scoring values ​​of the resource characteristics is less than the quantity threshold, a preset feature weight matrix is ​​obtained, wherein the feature weight matrix is ​​used to represent the degree of demand for each resource characteristic of the production line in the corresponding time period; For each time period, the feature weight matrix is ​​used to weight the differences between each first evaluation indicator and the corresponding second evaluation indicator to obtain the matching degree between the production line and each resource in the corresponding time period.

7. The production line resource control method according to claim 1, characterized in that: The subtask also includes a plurality of demand indicators, each demand indicator represents the level of requirement of the subtask on the corresponding resource characteristics; After inputting the adaptability matrix, the multiple time periods, and the resources into the planning model, the method further includes: Select any resource whose first evaluation index does not meet the requirement index of any subtask from each resource, and form a first conflict matrix with each subtask and the resource that does not meet the corresponding requirement index; Selecting multiple incompatible resources from the historical configuration information of all resources and forming a second conflict matrix; The first conflict matrix and the second conflict matrix are input into the planning model to obtain the configuration matrix.

8. A production line resource control device, characterized in that: include: Operation period configuration module, production resource characteristic evaluation module, configuration problem modeling module and scheduling solution generation module; The operation period configuration module is configured to, in response to receiving a production task, decompose the production task into a plurality of subtasks to be executed in different time periods according to the total task volume and production load coefficient of the production task and a preset coefficient threshold; The production resource characteristic evaluation module is configured to obtain a first evaluation index of each resource characteristic of each resource, wherein the plurality of resources are used to cooperate with the production line to complete the production task, and each first evaluation index represents the level of the corresponding resource characteristic in the form of a fuzzy interval number; The configuration problem modeling module is configured to obtain a second evaluation index corresponding to a preset production line characteristic of each resource characteristic, calculate the degree of matching between the production line characteristic and each corresponding resource using the second evaluation index and each corresponding first evaluation index, and weight the matching degree of each resource characteristic according to the importance of each resource characteristic to each subtask to obtain a fitness matrix representing the fitness of each resource and the subtask within each time period; The scheduling scheme generation module is configured to input the adaptability matrix, multiple time periods and each resource into a planning model to obtain a configuration matrix for scheduling the configuration relationship between each resource and the time period, so that when each resource is scheduled according to the configuration matrix, the total adaptability is maximized.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executed by the processor, wherein: When the processor executes the computer program, the production line resource control method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the production line resource control method according to any one of claims 1 to 7.

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