Intelligent production scheduling control method and system for door body production
By using intelligent production scheduling control methods, the production tasks of doors are broken down, personnel scheduling and material management are optimized, and anomaly detection is carried out in combination with environmental disturbance index. This solves the problems of insufficient personnel skill matching and inaccurate material management in traditional management, and achieves efficient and reliable production management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HUIZHOU HOLAK INTEGRATED HOME FURNISHING CO LTD
- Filing Date
- 2026-03-24
- Publication Date
- 2026-06-12
AI Technical Summary
Traditional door production management methods have failed to effectively match task characteristics with personnel skills, resulting in reduced efficiency and quality; inaccurate material management has led to production line stagnation; and untimely anomaly detection has affected production quality and efficiency.
By using intelligent production scheduling control methods, the production tasks of the doors are decomposed, the matching degree and urgency of personnel and tasks are analyzed, material management is optimized, and anomaly detection and handling are carried out using production line simulation models and environmental disturbance indices.
It has achieved reliable personnel scheduling, avoided material shortages, improved production quality and efficiency, and realized refined, flexible and highly reliable production management.
Smart Images
Figure CN122198516A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data analysis technology, and in particular to an intelligent scheduling control method and system for door production. Background Technology
[0002] As the manufacturing industry transforms towards digitalization and intelligence, the production of customized and mass-produced products, such as doors, faces increasingly complex challenges. Traditional production management methods, particularly in human resource scheduling, largely rely on fixed shifts or subjective assignments by team leaders, resulting in simplistic scheduling logic. This approach only considers whether personnel are available, neglecting the dynamic matching between task characteristics and personnel skills. It also fails to optimize the urgency of tasks in relation to personnel priorities, leading to situations where critical tasks are performed by inexperienced personnel, resulting in a decline in both efficiency and quality. Furthermore, in terms of material and production coordination, current methods rely on rough material preparation based on the master plan, with manual inventory checks conducted before production begins. This can easily lead to production line shutdowns or frequent production changes due to shortages of individual materials. Moreover, in terms of anomaly control during production, existing technologies largely depend on fixed threshold alarms or post-production manual inspections to detect anomalies. This approach often suffers from response delays and, due to a lack of comprehensive analysis of real-time production environment data, results in inaccurate anomaly detection. Handling measures are often based on experience and are not timely, impacting the quality and efficiency of door production. Summary of the Invention
[0003] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides an intelligent scheduling control method and system for door production, which realizes refined, flexible and highly reliable management of door production operations.
[0004] To address the aforementioned technical problems, this invention provides an intelligent scheduling control method for door production, the method comprising: Obtain the door body production task, decompose the door body production task into several sub-tasks, and extract the task characteristics of each sub-task. Based on the characteristics of the work tasks, a matching degree analysis is performed between the operators and the work task requirements to obtain the matching degree analysis results. Based on the matching degree analysis results, a personnel priority analysis is performed to obtain the personnel priority. Based on the personnel priority and the urgency of the task, a personnel scheduling analysis is performed to obtain the personnel scheduling information. Based on the door production operation task, material availability analysis is performed to obtain material availability information, and based on the material availability information, the production line simulation model is used to determine the start-up material input resources. Determine the capacity constraints of the door production line, and carry out door production operations based on the capacity constraints, personnel scheduling information, and start-up material resources; During the door production process, real-time data of the door production process is collected, and anomaly detection of the production process is performed based on the real-time data using the environmental disturbance index to obtain anomaly detection information. Based on the anomaly detection information, the processing measures are analyzed to obtain processing measure information.
[0005] Optionally, the step of decomposing the door production task into several sub-tasks and extracting the task characteristics of each sub-task includes: The production execution system decomposes the door production task into several sub-tasks. Keyword extraction is performed on each subtask to obtain the keyword information corresponding to each subtask, and the repetitive execution requirement analysis is performed based on the keyword information to obtain the repetitive execution requirement information. Based on the keyword information and the process flow diagram, a complex operation requirement analysis is performed to obtain complex operation requirement information. Based on the keyword information, the operation accuracy requirement analysis is performed to obtain the operation accuracy requirement information. Based on the operation repetitive execution requirement information, operation complexity requirement information and operation accuracy requirement information, the operation task characteristics of each sub-task are determined.
[0006] Optionally, the step of performing a matching degree analysis between operators and task requirements based on the characteristics of the task to obtain a matching degree analysis result, performing personnel priority analysis based on the matching degree analysis result to obtain personnel priority, and performing personnel scheduling analysis based on the personnel priority and task urgency to obtain personnel scheduling information includes: Based on the characteristics of the job tasks, determine the job skill requirements information; Based on the personnel database, the personnel skill information of each operator is determined, and based on the job skill requirement information and personnel skill information, the matching degree analysis between the operators and job task requirements is performed to obtain the matching degree analysis results. Based on the matching degree analysis results, the matching relationship between the work tasks and the operators is determined, and based on the matching relationship, personnel priority analysis is performed to obtain personnel priority; Obtain the task deadline for each subtask, and perform a task urgency analysis based on the task deadline to obtain task urgency information; Based on the characteristics of the tasks, a first list of tasks to be completed independently by humans and a second list of tasks to be completed collaboratively by humans and machines are determined. Based on the matching degree analysis results, combined with task urgency information and personnel priority, personnel scheduling analysis is performed using the first task list and the second task list to obtain personnel scheduling information.
[0007] Optionally, the step of performing a matching degree analysis between operators and job task requirements based on the job skill requirement information and personnel skill information to obtain the matching degree analysis results includes: Based on the job skill requirements information and personnel skill information, intersecting skill information is determined; Based on the intersecting skill information, the skill difference information between the operator and the job requirements is determined, and the matching degree analysis between the operator and the job requirements is performed based on the skill difference information to obtain the matching degree analysis results.
[0008] Optionally, the step of performing material kitting analysis based on the door production task to obtain material kitting information, and determining the start-up material input resources based on the material kitting information using a production line simulation model, includes: Based on the door production operation task, material demand data and material supply data are determined, and material kitting analysis is performed based on the material demand data and material supply data to obtain material kitting information. A production line simulation model is constructed based on production line equipment information, and material substitution information is determined based on material kitting information. Based on the material availability information and material substitution information, the production line simulation model is used to determine the start-up material input resources.
[0009] Optionally, the step of performing material kitting analysis based on the material demand data and material supply data to obtain material kitting information includes: Material allocation priority analysis is performed based on material demand data and material supply data to obtain material allocation priority information; Based on the material allocation priority information, a process material shortage analysis is performed to obtain process material shortage information. Based on the process material shortage information and the material allocation priority information, a material availability analysis is performed to obtain material availability information.
[0010] Optionally, determining the capacity constraint of the door production line and carrying out door production operations based on the capacity constraint, personnel scheduling information, and material input resources includes: The capacity constraints of the door production line are determined based on the production plan and equipment capacity. Based on the aforementioned capacity constraints, personnel scheduling information, and start-up and material input resources, production control instructions for regional coordination in the production line are determined, and the production control instructions are converted into command formats to obtain production control instructions after command format conversion. The production control instructions, converted from command format, control the equipment on the production line to perform door production operations.
[0011] Optionally, the step of using the environmental disturbance index based on the real-time data to detect anomalies in the production process and obtain anomaly detection information includes: Environmental data is extracted based on the real-time data, and feature analysis is performed based on the environmental data to obtain instantaneous fluctuation characteristics and long-term trend characteristics. The environmental disturbance index is determined based on the instantaneous fluctuation characteristics and long-term trend characteristics; Based on the real-time data, the environmental disturbance index is combined with machine learning algorithms to detect anomalies in the production process and obtain anomaly detection information.
[0012] Optionally, the step of analyzing processing measures based on the anomaly detection information to obtain processing measure information includes: Based on the anomaly detection information, anomaly cause tracing processing is performed to obtain anomaly cause tracing information; Based on the anomaly cause tracing information, several candidate measures are identified using the measures information database, and the implementation effect of each candidate measure is predicted to obtain implementation effect prediction information. Based on the implementation effect prediction information of each candidate measure, the treatment measure information is selected from all candidate measure information.
[0013] In addition, the present invention also provides an intelligent production scheduling control system for door body production, the system comprising: Task feature extraction module: used to obtain the door body production operation task, decompose the door body production operation task to obtain several sub-tasks, and extract the operation task features of each sub-task; Personnel scheduling analysis module: used to perform matching degree analysis between operators and task requirements based on the characteristics of the task, obtain matching degree analysis results, perform personnel priority analysis based on the matching degree analysis results, obtain personnel priority, and perform personnel scheduling analysis based on the personnel priority and task urgency to obtain personnel scheduling information; Material supply resource determination module: used to perform material availability analysis based on the door production operation task, obtain material availability information, and determine the start-up material supply resources based on the material availability information using the production line simulation model; Door production module: used to determine the capacity constraints of the door production line, and to carry out door production operations based on the capacity constraints, personnel scheduling information and start-up material resources; Anomaly detection module: used to collect real-time data of the door production process during the door production process, and to perform anomaly detection in the production process based on the real-time data using the environmental disturbance index, so as to obtain anomaly detection information; Anomaly handling module: used to analyze the handling measures based on the anomaly detection information and obtain handling measure information.
[0014] In this embodiment of the invention, the door production task is decomposed into several sub-tasks, and the task characteristics of each sub-task are extracted. Based on these task characteristics, a matching degree analysis is performed between operators and task requirements. The matching degree analysis results, combined with task urgency and personnel priority, are used for personnel scheduling analysis, which improves the reliability of personnel scheduling analysis and avoids quality risks and efficiency losses caused by mismatched personnel skills. Material availability analysis is performed based on the door production task to obtain material availability information. Based on this information, a production line simulation model is used to determine the starting material input resources, avoiding production stoppages due to material shortages. The capacity constraints of the door production line are determined. Door production operations are then conducted based on these constraints, personnel scheduling information, and starting material input resources, enabling higher-quality door production. During the door production process, real-time data is collected. Based on the real-time data, anomalies in the production process are detected using an environmental disturbance index. Based on the anomaly detection information, processing measures are analyzed. By incorporating the environmental disturbance index, more reliable and comprehensive anomaly detection is achieved, avoiding untimely handling of anomalies in the door production process. This enables refined, flexible, and highly reliable management of door production operations. Attached Figure Description
[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0016] Figure 1 This is a flowchart illustrating the intelligent scheduling control method for door production in an embodiment of the present invention. Figure 2 This is a flowchart illustrating an intelligent production scheduling control method for door production according to another embodiment of the present invention. Figure 3 This is a schematic diagram of the structural composition of the intelligent production scheduling control system for door production in an embodiment of the present invention. Detailed Implementation
[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0018] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating an intelligent scheduling control method for door production according to an embodiment of the present invention. The method includes: S11: Obtain the door body production operation task, decompose the door body production operation task into several sub-tasks, and extract the operation task characteristics of each sub-task. In the specific implementation of this invention, the door body production task is obtained, and the door body production task is decomposed into several sub-tasks based on the production execution system; keywords are extracted from each sub-task to obtain keyword information corresponding to each sub-task, and the repetitive execution requirement analysis is performed based on the keyword information to obtain repetitive execution requirement information; the complexity requirement analysis is performed based on the keyword information combined with the process flow diagram to obtain complexity requirement information; the accuracy requirement analysis is performed based on the keyword information to obtain accuracy requirement information, and the task characteristics of each sub-task are determined based on the repetitive execution requirement information, complexity requirement information, and accuracy requirement information, thus obtaining more comprehensive task characteristics.
[0019] S12: Based on the characteristics of the work task, perform a matching degree analysis between the operators and the work task requirements to obtain the matching degree analysis results. Based on the matching degree analysis results, perform personnel priority analysis to obtain personnel priority. Based on the personnel priority and the urgency of the task, perform personnel scheduling analysis to obtain personnel scheduling information. In the specific implementation of this invention, the following steps are taken: First, the skill requirements for the assigned tasks are determined based on the task characteristics. Second, the skill information of each operator is determined based on a personnel database. Third, a matching degree analysis is performed between the operator and the task requirements based on the skill requirements and the skill information, yielding a matching degree analysis result. Fourth, the matching relationship between the task and the operator is determined based on the matching relationship, and a personnel priority analysis is performed based on the matching relationship, yielding a personnel priority. Fifth, the deadline for each sub-task is obtained, and the urgency of each sub-task is analyzed based on the deadline, yielding a task urgency information. Sixth, a first list of tasks to be completed independently and a second list of tasks to be completed collaboratively by humans and machines are determined based on the task characteristics. Finally, based on the matching degree analysis result, the task urgency information, and the personnel priority, the first and second task lists are used for personnel scheduling analysis, achieving more reliable personnel scheduling analysis and avoiding the problem of reduced efficiency and quality due to unskilled personnel operating critical tasks.
[0020] S13: Perform material availability analysis based on the door production operation task to obtain material availability information, and determine the start-up material input resources based on the material availability information using the production line simulation model. In the specific implementation of this invention, material demand data and material supply data are determined based on the door production operation task. Material availability analysis is performed based on the material demand data and material supply data to obtain material availability information. A production line simulation model is constructed based on the production line equipment information. Material substitution information is determined based on the material availability information and material substitution information. The production line simulation model is used to determine the start-up material resources, which can avoid production stoppages caused by material shortages.
[0021] S14: Determine the capacity constraints of the door production line, and carry out door production operations based on the capacity constraints, personnel scheduling information, and start-up material resources; In the specific implementation of this invention, the capacity constraint of the door production line is determined based on the production plan and equipment capacity; based on the capacity constraint, personnel scheduling information and start-up material resources, the regional collaborative production control instructions in the production line are determined, and the production control instructions are converted into command formats to obtain the production control instructions after command format conversion; based on the production control instructions after command format conversion, the equipment of the production line is controlled to perform door production operations, so as to achieve more reliable door production operations and improve the quality and efficiency of door production.
[0022] S15: During the door production process, real-time data of the door production process is collected, and anomaly detection of the production process is performed based on the real-time data using the environmental disturbance index to obtain anomaly detection information. In the specific implementation of this invention, during the door production process, real-time data of the door production process is collected. Environmental data is extracted based on the real-time data, and feature analysis is performed on the environmental data to obtain instantaneous fluctuation characteristics and long-term trend characteristics. An environmental disturbance index is determined based on the instantaneous fluctuation characteristics and long-term trend characteristics. Anomaly detection in the production process is performed using the environmental disturbance index combined with machine learning algorithms based on the real-time data. Considering the impact of the environmental disturbance index on the door production process, the anomaly detection in the production process is made more accurate.
[0023] S16: Analyze the processing measures based on the anomaly detection information to obtain processing measure information.
[0024] In the specific implementation of this invention, the abnormality detection information is used to trace the cause of the abnormality to obtain the cause of the abnormality traceability information; based on the cause of the abnormality traceability information, a number of candidate measure information are determined using a measure information database, and the implementation effect of each candidate measure information is predicted to obtain the implementation effect prediction information; based on the implementation effect prediction information of each candidate measure information, the treatment measure information is selected from all candidate measure information, which can improve the reliability of the treatment measure analysis, better solve the abnormalities in the door body production process, and improve the quality of door body production.
[0025] In this embodiment of the invention, the door production task is decomposed into several sub-tasks, and the task characteristics of each sub-task are extracted. Based on these task characteristics, a matching degree analysis is performed between operators and task requirements. The matching degree analysis results, combined with task urgency and personnel priority, are used for personnel scheduling analysis, which improves the reliability of personnel scheduling analysis and avoids quality risks and efficiency losses caused by mismatched personnel skills. Material availability analysis is performed based on the door production task to obtain material availability information. Based on this information, a production line simulation model is used to determine the starting material input resources, avoiding production stoppages due to material shortages. The capacity constraints of the door production line are determined. Door production operations are then conducted based on these constraints, personnel scheduling information, and starting material input resources, enabling higher-quality door production. During the door production process, real-time data is collected. Based on the real-time data, anomalies in the production process are detected using an environmental disturbance index. Based on the anomaly detection information, processing measures are analyzed. By incorporating the environmental disturbance index, more reliable and comprehensive anomaly detection is achieved, avoiding untimely handling of anomalies in the door production process. This enables refined, flexible, and highly reliable management of door production operations.
[0026] Example 2 Please see Figure 2 , Figure 2 This is a flowchart illustrating an intelligent scheduling control method for door production according to another embodiment of the present invention, the method comprising: S201: Obtain the door body production operation task, decompose the door body production operation task into several sub-tasks, and extract the operation task characteristics of each sub-task. In the specific implementation of this invention, the step of decomposing the door production task into several sub-tasks and extracting the task characteristics of each sub-task includes: decomposing the door production task into several sub-tasks based on the production execution system; extracting keywords from each sub-task to obtain keyword information corresponding to each sub-task, and performing a repetitive execution requirement analysis based on the keyword information to obtain repetitive execution requirement information; performing a complex requirement analysis based on the keyword information and the process flow diagram to obtain complex requirement information; performing a precision requirement analysis based on the keyword information to obtain precision requirement information, and determining the task characteristics of each sub-task based on the repetitive execution requirement information, complex requirement information, and precision requirement information.
[0027] Specifically, the door production task is obtained from the enterprise's resource planning orders or production execution system (MES), and is input by relevant personnel. The door production task includes the door production order, specifications, quantity, delivery date, and priority. Based on the MES, the door production task is decomposed into several sub-tasks. The MES has a built-in standard process route or bill of materials (BOM) structure tree for each door, which can break down the door production task into a series of sequential or parallel sub-tasks that can be assigned to specific workstations or workers. Each sub-task contains specific information such as task description, required resources, and expected completion time.
[0028] Keyword extraction is performed on each subtask to obtain corresponding keyword information. Each subtask is then segmented into several words. Based on a pre-defined keyword dictionary, keywords are extracted from all segments to obtain corresponding keywords. For example, subtask a, which involves cutting and shaping wooden frames, might extract keywords such as: CNC sawing, mortise and tenon processing, dimensional tolerance ±0.5mm, solid wood material, and cycle. Based on this keyword information, a repetitive execution requirement analysis is performed to obtain repetitive execution requirement information. This analysis determines whether the subtask requires highly repetitive operations in the production of a batch of door orders. Keywords are matched with weighted scores using a pre-defined weighted dictionary. This dictionary predefines high-repetition keywords and low-repetition keywords along with their weighted scores. High-repetition keywords include batch, CNC, and cycle; low-repetition keywords include depending on the situation, adjustment, and diagnosis. All weighted scores are summed to obtain the final target weighted score. This target weighted score is then matched with a pre-defined level threshold to obtain the corresponding requirement level. Finally, repetitive execution requirement information containing the target weighted score, level, and key judgment criteria is generated for each subtask. For example, if the keywords are CNC (+0.8), fixed program (+0.7), standard size (+0.6), and batch (+0.9), the target weight score is 0.8 + 0.7 + 0.6 + 0.9 = 3.0. The repetitive requirement level matched by the target weight score is high. The repetitive execution requirement information is: score: 3.0, repetitive requirement level: high, key criteria: CNC, fixed program, standard size, batch.
[0029] Based on the keyword information and the process flow diagram, a complexity requirement analysis is performed to obtain the complexity requirement information. The keyword information is associated with a predefined complexity dimension table, and the corresponding complexity score of the keyword information in the complexity dimension table is matched. The process nodes and paths that the subtasks collaborate with and depend on in the process flow diagram are analyzed. The number of process nodes and path lengths are counted. The counted number, path length and complexity score are weighted and summed to obtain the final complexity score of the operation. The complexity score of the operation, process nodes, path and other information are integrated to obtain the complexity requirement information of the operation.
[0030] Based on the keyword information, an analysis of the job accuracy requirements is performed to obtain job accuracy requirement information. Accuracy requirements related to dimensions, time, and physical characteristics can be directly extracted from the keywords. The accuracy requirement level is matched according to the extracted keywords. For example, if the keywords contain dimensional tolerance ±0.5mm, it indicates a clear and high accuracy requirement. Based on the job repetitive execution requirement information, job complexity requirement information, and job accuracy requirement information, the job task characteristics of each sub-task are determined. That is, the job task characteristics of a sub-task are composed of the job repetitive execution requirement information, job complexity requirement information, and job accuracy requirement information. For example, sub-task number: b, name: wooden frame cutting and forming, repeatability requirement: 0.95 (high), complexity requirement: 0.6 (medium), accuracy requirement: 0.9 (high, corresponding to ±0.5mm).
[0031] S202: Based on the characteristics of the work task, perform a matching degree analysis between the operators and the work task requirements to obtain the matching degree analysis results, and perform personnel priority analysis based on the matching degree analysis results to obtain personnel priority, and perform personnel scheduling analysis based on the personnel priority and the urgency of the task to obtain personnel scheduling information; In the specific implementation of this invention, the following steps include: analyzing the matching degree between operators and task requirements based on the characteristics of the task to obtain matching degree analysis results; performing personnel priority analysis based on the matching degree analysis results to obtain personnel priority; and performing personnel scheduling analysis based on the personnel priority and task urgency to obtain personnel scheduling information. These steps include: determining task skill requirement information based on the task characteristics; determining the personnel skill information of each operator based on a personnel database; performing a matching degree analysis between operators and task requirements based on the task skill requirement information and personnel skill information to obtain matching degree analysis results; determining the matching relationship between the task and the operator based on the matching degree analysis results; performing personnel priority analysis based on the matching relationship to obtain personnel priority; obtaining the task deadline for each sub-task; performing a task urgency analysis based on the task deadline to obtain task urgency information; determining a first list of tasks to be completed independently by humans and a second list of tasks to be completed collaboratively by humans and machines based on the task characteristics; and performing personnel scheduling analysis using the first and second task lists based on the matching degree analysis results, task urgency information, and personnel priority to obtain personnel scheduling information.
[0032] Specifically, based on the characteristics of the task, the skill requirements are determined, that is, the characteristics of the task are transformed into specific skill tags and level requirements. For example, a highly complex and high-precision CNC carving task of a door may require the following skill levels: CNC programming: advanced, drawing recognition: intermediate, precision measurement: advanced, and tool compensation: intermediate.
[0033] Based on the personnel database, the skill information of each operator is determined. The personnel database contains the skill information of all operators, thus allowing the extraction of individual operator skill information from the database. A matching degree analysis is performed on the matching degree between the operators and the job requirements based on the job skill requirement information and the personnel skill information. The matching degree can be determined by analyzing the intersecting skills and skill differences between the job skill requirement information and the personnel skill information.
[0034] Based on the matching degree analysis results, the matching relationship between the task and the operator is determined. The matching relationship is that multiple personnel have a similar degree of matching with the same task. Based on this matching relationship, personnel priority analysis is performed to obtain personnel priority. When multiple personnel have a similar degree of matching with the same task, the system determines priority according to preset rules, including: skill level principle: those with higher matching degrees are given priority; load balancing principle: those with lighter current task loads are given priority. This determines the operator priority.
[0035] Obtain the deadline for each subtask, and perform a task urgency analysis based on the deadline to obtain task urgency information. Different deadlines correspond to different task urgency levels. Match the corresponding task urgency information in the urgency list according to the deadline.
[0036] Based on the characteristics of the work tasks, a first list of tasks to be completed independently by humans and a second list of tasks to be completed collaboratively by humans and machines are determined. According to the preset classification rules and the characteristics of the work tasks, the corresponding task lists are determined. The preset classification rules clearly define the task lists classified according to different task characteristics. The first task list (completed independently by humans): tasks with high repetition, low complexity, or requiring flexible manual operation, such as material handling, simple assembly, and packaging; the second task list (completed collaboratively by humans and machines): tasks with medium to high complexity, high precision requirements, or involving automated equipment, such as operating a machining center, welding in collaboration with robots, and final assembly assisted by visual quality inspection.
[0037] Based on the matching degree analysis results, combined with task urgency information and personnel priority, personnel scheduling analysis is performed using the first and second task lists to obtain personnel scheduling information. According to the matching degree analysis results, qualified operators are selected from all operators, such as those with a matching degree greater than or equal to a preset threshold. After selecting qualified operators, based on task urgency information, operators with the highest matching degree are selected from the second task list for tasks with higher urgency. Then, personnel scheduling is performed on the first task list based on personnel priority. This allows for the use of the matching degree analysis results as a foundation, combined with the optimization factors of task urgency and personnel priority. Under the constraints of the first and second task lists, a scheduling algorithm (such as a rule engine, optimization algorithm, or reinforcement learning model) is run to obtain the final personnel scheduling information. For example, if a task belongs to the second task list and has high urgency, it is assigned from personnel with the highest matching degree and highest personnel priority (e.g., currently available); if a task belongs to the first task list and has medium urgency, personnel with medium matching degree but requiring skills training can be appropriately considered.
[0038] Furthermore, the step of performing a matching degree analysis between operators and job task requirements based on the job skill requirement information and personnel skill information to obtain a matching degree analysis result includes: determining intersecting skill information based on the job skill requirement information and personnel skill information; determining the skill difference information between operators and job task requirements based on the intersecting skill information; and performing a matching degree analysis between operators and job task requirements based on the skill difference information to obtain a matching degree analysis result.
[0039] Specifically, based on the job skill requirement information and personnel skill information, intersecting skill information is determined, that is, skills that have an intersection between the job skill requirement information and personnel skill information are identified as intersecting skill information.
[0040] Based on the intersecting skill information, the skill difference information between the operator and the job requirements is determined. The operator's mastery of the skills in the intersecting skill information is determined from the personnel database. The mastery of the skills required by the job requirements is determined. The two mastery levels are subtracted to obtain the skill difference information. Based on the skill difference information, the matching degree analysis between the operator and the job requirements is performed. The weights corresponding to the skill difference information are matched. The matching degree between each operator and the job requirements is obtained according to the skill difference information and its weights. This is the matching degree analysis result. For example, the task requirements are: {Skill A: Intermediate (2), Skill B: Elementary (1)}, weights wA=0.7, wB=0.3, personnel a: {Skill A: Advanced (3), Skill B: Elementary (1)}, intersecting skills: {A, B}. Skill difference: DA=3-2=1; DB=1-1=0, matching degree M_score_a = 0.7 0.9 +0.3 1.0 = 0.63 + 0.3 = 0.93.
[0041] S203: Determine material demand data and material supply data based on the door body production operation task, and perform material kitting analysis based on the material demand data and material supply data to obtain material kitting information; In the specific implementation of this invention, the step of performing material kitting analysis based on the material demand data and material supply data to obtain material kitting information includes: performing material allocation priority analysis based on the material demand data and material supply data to obtain material allocation priority information; performing process material shortage analysis based on the material allocation priority information to obtain process material shortage information; and performing material kitting analysis based on the process material shortage information and material allocation priority information to obtain material kitting information.
[0042] Specifically, material demand data and material supply data are determined based on the door production operation task. Material demand data and material supply data are determined in the inventory database and warehouse management system according to the door production operation task. Material demand data includes all required material types, specifications, quantities, and demand time points, etc. Material supply data includes current available inventory, in-transit inventory, and detailed information on materials that have been planned to be supplied.
[0043] Material allocation priority analysis is performed based on material demand data and material supply data to obtain material allocation priority information. The material demand data is compared to the material supply data. If the material demand data is greater than the material supply data, the task urgency and criticality of each required material are obtained. The task urgency and criticality are obtained from the door production operation task information input by the user. The priority of each material is calculated based on the task urgency and criticality, and the material with the higher priority is allocated first.
[0044] Based on the material allocation priority information, process material shortage analysis is performed to obtain process material shortage information. A virtual warehouse is constructed, with its inventory equal to the total available supply of the material. Material pre-deduction and verification are performed based on the material allocation priority information. This process essentially rehearses the future material issuance and consumption process in a virtual environment. The types and quantities of materials in short supply in each process are determined based on the material pre-deduction and verification results, which constitute the process material shortage information. Based on the process material shortage information and the material allocation priority information, material kitting analysis is performed to obtain material kitting information. The severity of the material shortage is determined based on the quantity of materials in the process material shortage information. The degree of shortage for high-priority materials is determined based on the material allocation priority information. If high-priority materials are in short supply, they are judged as incomplete kitting, and the quantity, type, and substitute materials are recorded. If low-priority materials are in short supply, the allowable time to resolve the shortage problem and the substitute materials are determined. All of the above information is integrated to obtain the material kitting information.
[0045] S204: Construct a production line simulation model based on production line equipment information, and determine material substitution information based on material kitting information; In the specific implementation of this invention, production line equipment information is input into the simulation system backend to construct a production line simulation model. This simulation model is a digital twin that can simulate the flow, processing, queuing, and congestion of materials on the production line. When a shortage of the originally planned material is determined based on the material availability information, the system will automatically query the material substitution rule library. If an allowed substitute material exists, material substitution information will be generated.
[0046] S205: Based on the material availability information and material substitution information, determine the start-up material input resources using the production line simulation model; In the specific implementation of this invention, the production line simulation model is used to determine the start-up material resources based on the material completeness information and material substitution information. The material completeness information and material substitution information are input into the production line simulation model to simulate the production of the door body. According to the simulation scenario, a comprehensive balance is made among multiple objectives such as delivery guarantee, production efficiency, cost control, and risk exposure, and the final start-up material resources are automatically identified. For example, using substitute materials (scenario C) may lead to a decrease in efficiency and an increase in quality costs, but compared with the empty capacity caused by waiting for materials (scenario D), the total cost of scenario C is lower. Therefore, the material simulation information of scenario C is selected as the start-up material resources.
[0047] S206: Determine the capacity constraints of the door production line, and carry out door production operations based on the capacity constraints, personnel scheduling information, and start-up material resources; In the specific implementation of this invention, determining the capacity constraint of the door production line and performing door production operations based on the capacity constraint, personnel scheduling information, and material input resources includes: determining the capacity constraint of the door production line based on the production plan and equipment capacity; determining regional collaborative production control instructions in the production line based on the capacity constraint, personnel scheduling information, and material input resources, and converting the production control instructions into command formats to obtain production control instructions after command format conversion; and controlling the equipment of the production line to perform door production operations based on the production control instructions after command format conversion.
[0048] Specifically, the capacity constraints of the door production line are determined based on the production plan and equipment capacity. The production plan is the total amount of work to be completed in a certain period of time, and the equipment capacity is the maximum output rate of each key piece of equipment under ideal conditions. The overall equipment efficiency is obtained from the database. The effective available capacity = theoretical maximum capacity × average overall equipment efficiency. The capacity constraints of the door production line are composed of the overall equipment efficiency, effective available capacity, production plan, and equipment capacity.
[0049] Based on the aforementioned capacity constraints, personnel scheduling information, and material input resources, production control instructions for regional coordination within the production line are determined. These instructions are then converted into a command format that is recognizable by the production equipment. The converted production control instructions are used to control the production line equipment for door production operations. These instructions are then distributed to the corresponding operators and equipment, enabling them to perform the door production tasks.
[0050] S207: During the door production process, real-time data of the door production process is collected, and anomaly detection of the production process is performed based on the real-time data using the environmental disturbance index to obtain anomaly detection information. In a specific implementation of this invention, the step of detecting anomalies in the production process using the environmental disturbance index based on the real-time data to obtain anomaly detection information includes: extracting environmental data based on the real-time data, and performing feature analysis based on the environmental data to obtain instantaneous fluctuation characteristics and long-term trend characteristics; determining the environmental disturbance index based on the instantaneous fluctuation characteristics and long-term trend characteristics; and using the environmental disturbance index combined with a machine learning algorithm based on the real-time data to detect anomalies in the production process to obtain anomaly detection information.
[0051] Specifically, during the door production process, real-time data is collected. This real-time data mainly includes: equipment status data (spindle current, motor vibration, temperature, pressure, servo drive parameters); process data (temperature and humidity of the spray booth, paint flow rate and atomization pressure, actual temperature of each zone in the curing oven); product quality data (real-time image feature values and online measured dimensions of the visual inspection system); and personnel and environmental data (operation time of key workstations, material consumption rate, environmental dust concentration, temperature, and humidity). Environmental data, such as ambient temperature and humidity, is extracted based on the real-time data. Feature analysis is then performed on this environmental data to obtain instantaneous fluctuation characteristics and long-term trend characteristics. Instantaneous fluctuation characteristics can calculate statistics such as the coefficient of variation, kurtosis, and range within a short time window, capturing sudden disturbances such as a sudden drop in air pressure or a sudden spike in voltage. Long-term trend characteristics can calculate the slope of the moving average and seasonal / periodic residuals within a long time window, revealing slow drifts such as a slow rise in room temperature due to the decline in air conditioning system efficiency or a slow drop in air pressure due to gradual filter clogging.
[0052] The environmental disturbance index is determined based on the instantaneous fluctuation characteristics and long-term trend characteristics. The environmental disturbance index = f(instantaneous fluctuation characteristics, long-term trend characteristics), where f can be a weighted fusion model. For example, the environmental disturbance index EPI_t = α. Normalize(fluctuation characteristic_t) +β |Normalize(trend_feature_t)|, where α and β are weight coefficients, and Normalize is the normalization function.
[0053] Based on the real-time data, the environmental disturbance index is combined with machine learning algorithms to detect anomalies in the production process, thereby obtaining anomaly detection information. The machine learning model is trained using historical normal production data (including real-time process data, quality data and their corresponding environmental disturbance indices, etc.). The real-time data and environmental disturbance index are input into the trained machine learning model to detect anomalies, thereby obtaining anomaly detection information, such as equipment anomalies or process anomalies. By associating environmental factors, the anomaly detection in the production process can be made more accurate, providing a stronger guarantee for the stability and optimization of the production process.
[0054] S208: Analyze the processing measures based on the anomaly detection information to obtain processing measure information.
[0055] In a specific implementation of the present invention, the step of analyzing processing measures based on the anomaly detection information to obtain processing measure information includes: performing anomaly cause tracing processing based on the anomaly detection information to obtain anomaly cause tracing information; determining several candidate measure information using a measure information database based on the anomaly cause tracing information, and predicting the implementation effect of each candidate measure information to obtain implementation effect prediction information; and selecting processing measure information from all candidate measure information based on the implementation effect prediction information of each candidate measure information.
[0056] Specifically, based on the anomaly detection information, anomaly cause tracing processing is performed to obtain anomaly cause tracing information. The system maintains a production causal knowledge graph, where nodes represent equipment, materials, process parameters, and environmental factors, and edges represent the influence relationships between them (e.g., air pressure drop → leading to → poor spray atomization). Starting from the indicators in the anomaly information, multi-step backtracking is performed in the graph. Combining real-time data (e.g., if the air pressure did indeed drop sharply at that time), the confidence level of each possible causal path is automatically calculated, and the most likely root cause node is identified as the anomaly cause tracing information.
[0057] Based on the anomaly cause tracing information, the system uses a measure information database to identify several candidate measures. The system then filters all possible intervention measures applicable to the current risk situation from the measure information database, forming a candidate measure set. These candidate measures may include various types such as adjusting equipment parameters, replacing components, optimizing environmental conditions, and suspending production. The system then predicts the implementation effect of each candidate measure, obtaining implementation effect prediction information. This process can be based on historical data or process mechanism models. The prediction process fully considers the potential effects of each candidate measure, such as its impact on production efficiency, product accuracy, and equipment lifespan. The implementation effect prediction information can be a quantitative indicator, such as the percentage of risk reduction, the increase in production efficiency, or the increase in cost.
[0058] The system selects the best intervention measure from all candidate measures by predicting the implementation effects of each candidate measure. This means that after predicting the implementation effects of all candidate measures, the system will comprehensively evaluate and compare these predictions according to the preset optimization goals, and select the best one or a group of intervention measures as the target intervention measure information. This ensures that the generated intervention measures are highly customized and optimized, thereby significantly improving the accuracy and effectiveness of abnormal response. The abnormal handling of the door production process is carried out based on this intervention measure information.
[0059] For example, several candidate measures are selected from the measure information database, such as: 1. Increase the cooling fan speed by 20%; 2. Suspend the current production line for equipment inspection; 3. Adjust the ambient temperature to 22℃. Next, the system predicts the implementation effect of each candidate measure. For example, it predicts that "increasing the cooling fan speed by 20%" may lead to a 2% increase in energy consumption but reduces the risk of overheating by 70% and has a small impact on production efficiency; it predicts that "suspending the production line" can completely eliminate the risk but will cause huge production losses; and it predicts that "adjusting the ambient temperature" may take a long time to take effect.
[0060] Finally, based on this predicted implementation effect information and combined with preset optimization objectives (e.g., minimizing the impact on production efficiency while ensuring effective risk control), the system selects the optimal solution from all candidate measures. In this example, the system might choose to increase the cooling fan speed by 20% as the treatment measure because it effectively reduces risk while minimizing the impact on production efficiency.
[0061] In this embodiment of the invention, the door production task is decomposed into several sub-tasks, and the task characteristics of each sub-task are extracted. Based on these task characteristics, a matching degree analysis is performed between operators and task requirements. The matching degree analysis results, combined with task urgency and personnel priority, are used for personnel scheduling analysis, which improves the reliability of personnel scheduling analysis and avoids quality risks and efficiency losses caused by mismatched personnel skills. Material availability analysis is performed based on the door production task to obtain material availability information. Based on this information, a production line simulation model is used to determine the starting material input resources, avoiding production stoppages due to material shortages. The capacity constraints of the door production line are determined. Door production operations are then conducted based on these constraints, personnel scheduling information, and starting material input resources, enabling higher-quality door production. During the door production process, real-time data is collected. Based on the real-time data, anomalies in the production process are detected using an environmental disturbance index. Based on the anomaly detection information, processing measures are analyzed. By incorporating the environmental disturbance index, more reliable and comprehensive anomaly detection is achieved, avoiding untimely handling of anomalies in the door production process. This enables refined, flexible, and highly reliable management of door production operations.
[0062] Example 3 Please see Figure 3 , Figure 3 This is a schematic diagram of the structural composition of an intelligent production scheduling control system for door production according to an embodiment of the present invention. The system includes: Task feature extraction module 31: used to obtain the door body production operation task, decompose the door body production operation task to obtain several sub-tasks, and extract the operation task features of each sub-task; Personnel scheduling analysis module 32: is used to perform matching degree analysis between operators and task requirements based on the characteristics of the task, obtain matching degree analysis results, perform personnel priority analysis based on the matching degree analysis results, obtain personnel priority, and perform personnel scheduling analysis based on the personnel priority and task urgency to obtain personnel scheduling information. Material supply resource determination module 33: is used to perform material availability analysis based on the door body production operation task, obtain material availability information, and determine the start-up material supply resources based on the material availability information using the production line simulation model; Door production module 34: used to determine the capacity constraints of the door production line, and to carry out door production operations based on the capacity constraints, personnel scheduling information and start-up material resources; Anomaly detection module 35: used to collect real-time data of the door production process during the door production process, and to perform anomaly detection of the production process based on the real-time data using the environmental disturbance index, and obtain anomaly detection information. Anomaly handling module 36: used to analyze the handling measures based on the anomaly detection information and obtain handling measure information.
[0063] In the specific implementation of this invention, the specific implementation methods of the system items can be referred to the implementation methods of the above-mentioned method items, and will not be repeated here.
[0064] In this embodiment of the invention, the door production task is decomposed into several sub-tasks, and the task characteristics of each sub-task are extracted. Based on these task characteristics, a matching degree analysis is performed between operators and task requirements. The matching degree analysis results, combined with task urgency and personnel priority, are used for personnel scheduling analysis, which improves the reliability of personnel scheduling analysis and avoids quality risks and efficiency losses caused by mismatched personnel skills. Material availability analysis is performed based on the door production task to obtain material availability information. Based on this information, a production line simulation model is used to determine the starting material input resources, avoiding production stoppages due to material shortages. The capacity constraints of the door production line are determined. Door production operations are then conducted based on these constraints, personnel scheduling information, and starting material input resources, enabling higher-quality door production. During the door production process, real-time data is collected. Based on the real-time data, anomalies in the production process are detected using an environmental disturbance index. Based on the anomaly detection information, processing measures are analyzed. By incorporating the environmental disturbance index, more reliable and comprehensive anomaly detection is achieved, avoiding untimely handling of anomalies in the door production process. This enables refined, flexible, and highly reliable management of door production operations.
[0065] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.
[0066] Furthermore, the above provides a detailed description of the intelligent scheduling control method and system for door production provided by the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.
Claims
1. A smart scheduling control method for door production, characterized in that, The method includes: Obtain the door body production task, decompose the door body production task into several sub-tasks, and extract the task characteristics of each sub-task. Based on the characteristics of the work tasks, a matching degree analysis is performed between the operators and the work task requirements to obtain the matching degree analysis results. Based on the matching degree analysis results, a personnel priority analysis is performed to obtain the personnel priority. Based on the personnel priority and the urgency of the task, a personnel scheduling analysis is performed to obtain the personnel scheduling information. Based on the door production operation task, material availability analysis is performed to obtain material availability information, and based on the material availability information, the production line simulation model is used to determine the start-up material input resources. Determine the capacity constraints of the door production line, and carry out door production operations based on the capacity constraints, personnel scheduling information, and start-up material resources; During the door production process, real-time data of the door production process is collected, and anomaly detection of the production process is performed based on the real-time data using the environmental disturbance index to obtain anomaly detection information. Based on the anomaly detection information, the processing measures are analyzed to obtain processing measure information.
2. The intelligent scheduling control method for door production according to claim 1, characterized in that, The process of decomposing the door production task into several sub-tasks and extracting the task characteristics of each sub-task includes: The production execution system decomposes the door production task into several sub-tasks. Keyword extraction is performed on each subtask to obtain the keyword information corresponding to each subtask, and the repetitive execution requirement analysis is performed based on the keyword information to obtain the repetitive execution requirement information. Based on the keyword information and the process flow diagram, a complex operation requirement analysis is performed to obtain complex operation requirement information. Based on the keyword information, the operation accuracy requirement analysis is performed to obtain the operation accuracy requirement information. Based on the operation repetitive execution requirement information, operation complexity requirement information and operation accuracy requirement information, the operation task characteristics of each sub-task are determined.
3. The intelligent production scheduling control method for door production according to claim 1, characterized in that, The process involves performing a matching degree analysis between operators and task requirements based on the characteristics of the task, obtaining matching degree analysis results, performing personnel priority analysis based on the matching degree analysis results, obtaining personnel priorities, and performing personnel scheduling analysis based on the personnel priorities and task urgency to obtain personnel scheduling information, including: Based on the characteristics of the job tasks, determine the job skill requirements information; Based on the personnel database, the personnel skill information of each operator is determined, and based on the job skill requirement information and personnel skill information, the matching degree analysis between the operators and job task requirements is performed to obtain the matching degree analysis results. Based on the matching degree analysis results, the matching relationship between the work tasks and the operators is determined, and based on the matching relationship, personnel priority analysis is performed to obtain personnel priority; Obtain the task deadline for each subtask, and perform a task urgency analysis based on the task deadline to obtain task urgency information; Based on the characteristics of the tasks, a first list of tasks to be completed independently by humans and a second list of tasks to be completed collaboratively by humans and machines are determined. Based on the matching degree analysis results, combined with task urgency information and personnel priority, personnel scheduling analysis is performed using the first task list and the second task list to obtain personnel scheduling information.
4. The intelligent scheduling control method for door production according to claim 3, characterized in that, The matching degree analysis between operators and job task requirements based on the job skill requirement information and personnel skill information, and the matching degree analysis results, include: Based on the job skill requirements information and personnel skill information, intersecting skill information is determined; Based on the intersecting skill information, the skill difference information between the operator and the job requirements is determined, and the matching degree analysis between the operator and the job requirements is performed based on the skill difference information to obtain the matching degree analysis results.
5. The intelligent production scheduling control method for door body production according to claim 1, characterized in that, The step of performing material kitting analysis based on the door production operation task to obtain material kitting information, and determining the start-up material input resources based on the material kitting information using a production line simulation model, includes: Based on the door production operation task, material demand data and material supply data are determined, and material kitting analysis is performed based on the material demand data and material supply data to obtain material kitting information. A production line simulation model is constructed based on production line equipment information, and material substitution information is determined based on material kitting information. Based on the material availability information and material substitution information, the production line simulation model is used to determine the start-up material input resources.
6. The intelligent production scheduling control method for door body production according to claim 5, characterized in that, The process of performing material kitting analysis based on the material demand data and material supply data to obtain material kitting information includes: Material allocation priority analysis is performed based on material demand data and material supply data to obtain material allocation priority information; Based on the material allocation priority information, a process material shortage analysis is performed to obtain process material shortage information. Based on the process material shortage information and the material allocation priority information, a material availability analysis is performed to obtain material availability information.
7. The intelligent scheduling control method for door production according to claim 1, characterized in that, The process of determining the capacity constraint of the door production line and carrying out door production operations based on the capacity constraint, personnel scheduling information, and material input resources includes: The capacity constraints of the door production line are determined based on the production plan and equipment capacity. Based on the aforementioned capacity constraints, personnel scheduling information, and start-up and material input resources, production control instructions for regional coordination in the production line are determined, and the production control instructions are converted into command formats to obtain production control instructions after command format conversion. The production control instructions, converted from command format, control the equipment on the production line to perform door production operations.
8. The intelligent production scheduling control method for door body production according to claim 1, characterized in that, The method of detecting anomalies in the production process based on the real-time data using the environmental disturbance index to obtain anomaly detection information includes: Environmental data is extracted based on the real-time data, and feature analysis is performed based on the environmental data to obtain instantaneous fluctuation characteristics and long-term trend characteristics. The environmental disturbance index is determined based on the instantaneous fluctuation characteristics and long-term trend characteristics; Based on the real-time data, the environmental disturbance index is combined with machine learning algorithms to detect anomalies in the production process and obtain anomaly detection information.
9. The intelligent production scheduling control method for door production according to claim 1, characterized in that, The analysis of processing measures based on the anomaly detection information to obtain processing measure information includes: Based on the anomaly detection information, anomaly cause tracing processing is performed to obtain anomaly cause tracing information; Based on the anomaly cause tracing information, several candidate measures are identified using the measures information database, and the implementation effect of each candidate measure is predicted to obtain implementation effect prediction information. Based on the implementation effect prediction information of each candidate measure, the treatment measure information is selected from all candidate measure information.
10. An intelligent production scheduling control system for door manufacturing, characterized in that, The system includes: Task feature extraction module: used to obtain the door body production operation task, decompose the door body production operation task to obtain several sub-tasks, and extract the operation task features of each sub-task; Personnel scheduling analysis module: used to perform matching degree analysis between operators and task requirements based on the characteristics of the task, obtain matching degree analysis results, perform personnel priority analysis based on the matching degree analysis results, obtain personnel priority, and perform personnel scheduling analysis based on the personnel priority and task urgency to obtain personnel scheduling information; Material supply resource determination module: used to perform material availability analysis based on the door production operation task, obtain material availability information, and determine the start-up material supply resources based on the material availability information using the production line simulation model; Door production module: used to determine the capacity constraints of the door production line, and to carry out door production operations based on the capacity constraints, personnel scheduling information and start-up material resources; Anomaly detection module: used to collect real-time data of the door production process during the door production process, and to perform anomaly detection in the production process based on the real-time data using the environmental disturbance index, so as to obtain anomaly detection information; Anomaly handling module: used to analyze the handling measures based on the anomaly detection information and obtain handling measure information.