CN114240061A - Task matching method and device for manufacturing workshop - Google Patents
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Abstract
本发明实施例公开了一种用于制造车间的任务匹配方法及装置，涉及智能制造技术领域，能够按照人员能力合理分配任务，提高人员执行任务效率。本发明包括：根据车间ID，从人员信息数据库中，提取与车间ID关联的人员ID，并提取人员ID对应的技能信息，所述技能信息包括技能类型信息和技能权重信息；根据任务信息，查询任务需求模板，并确定执行任务所需的技能类型和技能权重信息；确定匹配度满足任务需求模板的人员信息，并确定参与本次任务的人员；当任务执行完毕后，根据历史任务数据对本次任务结果进行打分，得到参与本次任务的人员的积分值；根据所得到的积分值，更新参与本次任务的人员的技能权重信息。
The embodiment of the present invention discloses a task matching method and device for a manufacturing workshop, which relate to the technical field of intelligent manufacturing, and can reasonably allocate tasks according to personnel capabilities and improve the efficiency of personnel performing tasks. The invention includes: extracting the staff ID associated with the workshop ID from the staff information database according to the workshop ID, and extracting the skill information corresponding to the staff ID, where the skill information includes skill type information and skill weight information; according to the task information, querying Task requirement template, and determine the skill type and skill weight information required to perform the task; determine the personnel information whose matching degree meets the task requirement template, and determine the personnel participating in this task; when the task is completed, according to the historical task data Scoring the results of this task to obtain the integral value of the personnel participating in this mission; according to the obtained integral value, update the skill weight information of the personnel participating in this mission.
Description
Technical Field
The invention relates to the technical field of intelligent manufacturing, in particular to a task matching method and device for a manufacturing workshop.
Background
Technicians, as an essential part of the manufacturing process, have a tremendous impact on the manufacturing system. Meanwhile, due to the lack of automation capacity, many production devices cannot complete a full-automatic production process, manual intervention mode operation or auxiliary equipment is still needed, but the stability of the self capacity level can be influenced by the problems of physical and mental factors, fatigue and the like of the personnel.
Although technologies such as intelligent manufacturing workshops have been developed, it is not practical to realize unmanned whole process for all manufacturing departments, and technicians such as technicians and engineers with certain experience and skills in almost all manufacturing workshops are still an essential part for improving manufacturing efficiency and improving manufacturing capability.
However, the current problem is very real, that is, in the existing manufacturing system, no objective uniform description mode exists for personnel relative to the performance parameters of the machine equipment. This results in tasks that are often performed by personnel with task difficulties that do not match the ability of the personnel. Most traditional way is to adopt manual management to alleviate the problem, but this causes cost and redundancy in personnel management, and these personnel management posts often need personnel who are both knowledgeable and technical to participate, and such people are often experienced "teachers". On one hand, the number of people is rare, and the intelligent manufacturing workshop with larger and larger scale is difficult to meet; on the other hand, such persons have accumulated a lot of technical skills and experience themselves, and changing their job implies a lack of use and waste of their work skills. However, some current solutions, such as using a call terminal and a dispatch controller to implement a personnel dispatch function, do not substantially improve the problem that task difficulty is not matched with personnel ability, and only do articles in terms of facilitating personnel communication and operation. Therefore, the dilemma of task assignment at present cannot be solved by adopting the traditional manual management mode, and intelligent and digital means need to be developed to solve the problem.
Disclosure of Invention
The embodiment of the invention provides a task matching method and device for a manufacturing workshop, which can reasonably distribute tasks according to the capability of personnel and improve the efficiency of the personnel to execute the tasks.
In order to achieve the above purpose, the embodiment of the invention adopts the following technical scheme:
in a first aspect, an embodiment of the present invention provides a method, including:
s1, extracting a personnel ID related to the workshop ID from a personnel information database according to the workshop ID, and extracting skill information corresponding to the personnel ID, wherein the skill information comprises skill type information and skill weight information;
s2, inquiring a task requirement template according to the task information, and determining a skill type and skill weight information required by task execution;
s3, determining personnel information with matching degree meeting the task requirement template according to the results obtained by executing S1 and S2, and determining personnel participating in the task;
s4, after the task is executed, scoring the task result according to the historical task data to obtain the integral value of the personnel participating in the task;
and S5, updating the skill weight information of the personnel participating in the task according to the integral value obtained by executing the S4.
In a second aspect, an embodiment of the present invention provides an apparatus, including:
the skill information preprocessing module is used for extracting a personnel ID related to the workshop ID from a personnel information database according to the workshop ID and extracting skill information corresponding to the personnel ID, wherein the skill information comprises skill type information and skill weight information;
the task information preprocessing module is used for inquiring the task requirement template according to the task information and determining the skill type and the skill weight information required by the task execution;
the task matching degree analysis module is used for determining personnel information of a template with matching degree meeting the task requirement and determining personnel participating in the task;
the task result analysis module is used for scoring the task result according to historical task data after the task is executed, so as to obtain the integral value of the personnel participating in the task;
and the skill weight updating module is used for updating the skill weight information of the personnel participating in the task according to the obtained integral value.
The scheme in the prior art which does not fully consider the matching degree of the personnel and the current task is adopted. The task matching method and device for the manufacturing workshop, provided by the embodiment of the invention, can describe a scheme of matching calculation of the personnel capacity system and the task, so that the task is reasonably distributed according to the personnel capacity, and the task execution efficiency of the personnel is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a diagram illustrating one possible example of a hierarchy tree of human skills, according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a possible example of a task matching degree calculation process according to an embodiment of the present invention.
FIG. 3 is a diagram illustrating one possible example of a skill weight update distribution scheme provided by an embodiment of the present invention;
FIG. 4 is a schematic flow chart of a method provided by an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention. As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items. It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The scheme of the embodiment can be mainly applied to a scene based on matching of personnel and tasks in a discrete manufacturing workshop, and has the main design purposes that: compared with the man-machine task scheduling mode of the existing manufacturing system, the method can objectively describe the skill level of the personnel, reflect the skill bias of different personnel, and simultaneously carry out matching calculation according to the skill of the personnel and the task content to obtain the task matching degree of objective evaluation, so that more appropriate personnel can be selected to execute the task according to the task matching degree.
An embodiment of the present invention provides a task matching method for a manufacturing shop, as shown in fig. 4, including:
and S1, extracting the personnel ID related to the workshop ID from the personnel information database according to the workshop ID, and extracting the skill information corresponding to the personnel ID.
Wherein the skill information comprises skill type information and skill weight information.
And S2, inquiring the task requirement template according to the task information, and determining the skill type and the skill weight information required by the task.
And S3, determining the personnel information of which the matching degree meets the task requirement template according to the results obtained by executing S1 and S2, and determining the personnel participating in the task.
And S4, after the task is executed, scoring the task result according to the historical task data to obtain the integral value of the personnel participating in the task.
For example: according to parameters such as error rate and success rate when personnel perform tasks, completion time of personnel and the like, corresponding functions can be set to calculate the integral value, such as: the integral value score is success rate a + (completion time/historical average completion time) b + (1-error rate) c, where a is 20, b is 60, and c is 20.
And S5, updating the skill weight information of the personnel participating in the task according to the integral value obtained by executing the S4.
In this embodiment, the skill information is recorded as a skill level tree of the person. The personnel skill level tree comprises at least one layer of skill information established on a root node, and the skill information on each layer consists of the skill type information of the layer and the skill weight information of the layer.
The personnel skill level tree is mainly used for representing skill deviation of personnel, and the visualization degree of the personnel skill level tree is high, so that the display is facilitated. And the personnel skill level tree is also convenient for database storage, and the relation among all layers in the personnel skill level tree, the skill type information and the skill weight information in all layers are only required to be stored through a database table.
And the weight value of the personnel skill level tree is recorded in the root node. Each layer established on the root node comprises at least one node, one type of skill is recorded in one node, and the weight value of the one skill is also recorded. And the weight value recorded in one father node is the sum of the weight values of all the child nodes of the father node. If one node in the first layer corresponds to at least one node in the second layer, the at least one node corresponding to the second layer is used as a child node of one node in the first layer. For example: as shown in FIG. 1, each node of the staff skill level tree is composed of skills and weights, representing the proficiency level of the corresponding skill. The node weight value of the personnel skill level tree is the sum of all child node weight values of the node, and the time complexity of inquiring the skill weight is effectively reduced. The root node in the skill level tree of the person does not represent any skill, and the weight value of the root node represents the overall skill level situation of the person.
Specifically, in the task requirement template, a task ID, and skill type information and skill weight information corresponding to the task ID are recorded. The skill type information and the skill weight information corresponding to the task ID are divided into at least two groups, each group corresponding to an execution level of a task.
Wherein the execution level of the task at least comprises: good and excellent. Or the execution level of the task is positively correlated with the processing index, and the processing index comprises the magnitude of product precision and/or the yield of the product.
Specifically, the task requirement template records the skill type and the skill mastering level required for executing the task. For different execution levels of tasks, there may be some differences between the setting of the skill type and the skill mastering level, for example, if the execution level of the task is required to be passed, the requirement for the skill mastering level may be reduced (the weight value requirement is lower than the weight average value), or for a product requiring high processing precision or a product requiring high processing yield, the requirement for the skill mastering level may be increased (the weight value requirement is higher than the weight average value), and the weight average value may be calculated by a statistical means. For example: as shown in fig. 1, each node may include a plurality of child nodes, the skills of the child nodes are categorized into parent node skill categories, and if the parent node skills are machine maintenance skills, the child nodes may be machine maintenance, milling machine maintenance, and the like, and the personnel skill level tree model may be designed according to actual needs of the workshop.
In this embodiment, step S3 includes:
and screening the personnel according to the skill weight information corresponding to the task ID. Wherein, people with irrelevant skills can be screened in advance according to the skill requirement of the task and the skill level of the people. And determining task requirement skills according to the skill type information corresponding to the task ID, and determining the requirement proportion of each kind of skill corresponding to the task ID after normalization processing. Multiplying the demand proportion of each type of skill corresponding to the task ID by the weight value of each type of skill corresponding to the task ID, and then summing all multiplication results to obtain a first matching degree. For example: as shown in fig. 2, each person task includes task details and skill requirements, the person is screened according to the weight of the skill requirements, the skill requirements are normalized, the requirement proportion of each skill is calculated, the screened person calculates the matching degree according to the two parts of the task requirement skills and the non-task requirement skills, and the total matching degree is the sum of the two parts. And multiplying the task requirement skill part by the skill ratio and the corresponding skill weight and summing the result to obtain the matching degree of the task requirement skill part. For example: in this embodiment, a quality matching formula may be adopted, and the following formula is given mainly by referring to the person level skill tree model:
m(wi，tj)＝δ1(wi，tj)+δ2(wi，tj) Wherein, delta1(wi,tj) Represents a person wiFor task tjThe value of the influence of the degree of skill in the requirement on the task match, δ2(wi,tj) Represents a person wiFor non-task tjThe influence value of the skill mastery degree in the requirement on the task matching, wherein sigma (0)<σ<1) Is a reduction factor which indicates that the further the distance between two skills is, the weaker the degree of association between them, for the interpretation of some other of them: s ═ S, [ S [1 ]],s[2],…,s[k]]Where S is the root node, S1]，s[2]，…，s[k]Is a subtree under the root node. Each node S belongs to S and corresponds to a key value pair<k(s),d(s)>Where k(s) represents a skill and d(s) represents a weight of the skill. For each node S ∈ S, we denote its depth in S, fast, (S)(n)(s) (0. ltoreq. n.ltoreq. depth (s)) represents a parent node n levels higher than s, child(s) represents a set of child nodes of s. For a certain skill s of a worker wi in HST, his weight is the sum of his sub-skill weights, i.e.:
in the formula
Representing the weight of the person wi with respect to the skill s,
representing the sub-skill weights of the person wi with respect to the skill s.
For each task tjE.g. T, we define a set of task requirements
Wherein s isiRepresenting the skills of the non-root nodes. All task requirements are defined by the task publisher in advanceTo make the description of the task clearer, we require that stricter constraints be added to the skill set of the task requirements. In task demand collection
In (3), there is no parent-child or ancestor relationship between elements, i.e.:
m(wi,tj) To describe a person wiAnd task tjThe degree of match between them is called the match quality.
Theoretically, each node in the HST has a path to any skill, but most of the skills are far away, and the influence of the skills is reduced to be extremely small or even negligible. So in practice it can be simply assumed that all effects originate from a common ancestor node.
Further, the method also comprises the following steps: and determining the skill required by the non-task according to the skill information corresponding to the personnel ID of the screened personnel and the determined task required skill. And calculating the attenuation coefficient according to the distance relation of skills in the staff skill level tree model. And acquiring a second matching degree by using the attenuation coefficient, the demand ratio of each type of technology corresponding to the task ID and the weight value of each type of technology corresponding to the task ID. And taking the sum of the first matching degree and the second matching degree as the matching degree of the personnel relative to the task.
For the non-task requirement skill part, calculating an attenuation coefficient according to the distance relation of skills in the personnel skill level tree model, and performing task matching on the non-task requirement skill part by using the attenuation coefficient, the skill proportion and the skill weight. For example:
as shown in fig. 2, each person task includes task details and skill requirements, the person is screened according to the weight of the skill requirements, the skill requirements are normalized, the requirement proportion of each skill is calculated, the screened person calculates the matching degree according to the two parts of the task requirement skills and the non-task requirement skills, and the total matching degree is the sum of the two parts. And multiplying the task requirement skill part by the skill ratio and the corresponding skill weight and summing the result to obtain the matching degree of the task requirement skill part. And for the non-task requirement skill part, calculating attenuation coefficients according to the distance relation of skills in the personnel skill level tree model, and performing task matching on the non-task requirement skill part by using the attenuation coefficients, the skill proportion and the skill weight.
As shown in fig. 3, the personnel executes the completed task according to the requirement, and the result of the task is evaluated according to the historical completion condition of the personnel in the task workshop. And returning a point value to the personnel according to the quality of the result, wherein the point value can be positive or negative, a positive value represents the skill reward of the personnel, and a negative value represents the skill punishment of the personnel. And distributing the integral value according to the proportion condition of each skill in the task requirement skills, and directly updating the integral value into the weight value of the corresponding skill node. For each updated skill node, since its weight value is the sum of all child node weight values, the weight needs to be updated recursively for the child nodes, and all child nodes are equally assigned the integral value of the parent node.
The scheme in the prior art which does not fully consider the matching degree of the personnel and the current task is adopted. In the embodiment, a scheme capable of describing matching calculation of the personnel capacity system and the tasks is designed, so that the tasks are reasonably distributed according to the personnel capacity, and the efficiency of executing the tasks by the personnel is improved.
The present embodiment also provides a task matching device for a manufacturing plant, as shown in fig. 5, including:
and the skill information preprocessing module is used for extracting the personnel ID related to the workshop ID from the personnel information database according to the workshop ID and extracting the skill information corresponding to the personnel ID.
Wherein the skill information comprises skill type information and skill weight information. And the skill information preprocessing module is used for describing the skill deviation of the personnel in the aspect of manufacturing, comprises the establishment of a workshop skill set and the association between skills, and sends the personnel skill deviation to a workshop personnel scheduling system as a calculation data source of a scheduling result.
And the task information preprocessing module is used for inquiring the task requirement template according to the task information and determining the skill type and the skill weight information required by the task execution.
The task information preprocessing module is used for describing detailed contents of tasks, skill contents of personnel required for executing the tasks and skill mastering levels. Plant personnel have the opportunity to perform the task only if they meet the minimum requirements for task skills.
And the task matching degree analysis module is used for determining the personnel information of the template with matching degree meeting the task requirement and determining the personnel participating in the task.
The task matching degree analysis module is used for calculating the matching degree between the workshop staff and the task and calculating the matching degree of related skills of the staff according to the skill requirement proportion of the task content. Meanwhile, the relevance of skills is considered, and the skills required by non-tasks are also added into the matching degree calculation, so that the influence caused by the skill level of the personnel can be more objectively described.
And the task result analysis module is used for scoring the task result according to the historical task data after the task is executed, so as to obtain the integral value of the personnel participating in the task.
The task result analysis module is used for analyzing the task execution result, evaluating and scoring the task result according to historical task data of all staff in the workshop, and feeding back an integral value to the staff according to the scoring result.
And the skill weight updating module is used for updating the skill weight information of the personnel participating in the task according to the obtained integral value.
The skill weight updating module has the function that after the personnel complete the task, the evaluation feedback points are obtained, the skill weight updating module evenly distributes the points to the corresponding skills according to the task skill requirements, and the mastering weight of the personnel in the skill aspect is improved.
Specifically, the skill information is recorded as a personnel skill level tree, the personnel skill level tree includes at least one layer of skill information established on a root node, and the skill information on each layer is composed of skill type information of the layer and skill weight information of the layer. And the weight value of the personnel skill level tree is recorded in the root node. Each layer established on the root node comprises at least one node, one type of skill is recorded in one node, and the weight value of the one skill is also recorded. And the weight value recorded in one father node is the sum of the weight values of all the child nodes of the father node.
In the embodiment, the skill mastering degree of the personnel is objectively described through the skill information preprocessing module, objective evaluation indexes are provided instead of the traditional responsibility distribution system when the task is executed, and the task execution efficiency can be improved. Through the skill information preprocessing module, the skill deviation of the personnel in the vehicle can be seen visually, the personnel can grasp the self ability characteristics, and the short skill board can be compensated more easily. Through the task information preprocessing module, the difficulty degree of the task can be simply seen; the number of tasks executed by the personnel and the execution result can be seen through the skill weight updating module, and both are beneficial to the performance evaluation of the workshop.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus embodiment, since it is substantially similar to the method embodiment, it is relatively simple to describe, and reference may be made to some descriptions of the method embodiment for relevant points. The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
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| CN115660318A (en) * | 2022-09-30 | 2023-01-31 | 南京航空航天大学 | A Human-Machine Collaborative Task Allocation Method for IoT Manufacturing Workshop |
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