A resource dynamic allocation system for mold manufacturing

Abstract

This invention discloses a dynamic resource allocation system for mold manufacturing. The system includes a data acquisition device, a production resource database, a dynamic resource allocation server, a load balancing analysis device, an anomaly early warning device, a data fusion analysis platform, and a decision support terminal. This application uses the data acquisition device to acquire the real-time operating status of various production resources, achieving comprehensive resource information perception; the dynamic resource allocation server dynamically adjusts the allocation scheme based on real-time status, effectively solving the problem of manual scheduling being unable to handle abnormal situations; the load balancing analysis device comprehensively analyzes and optimizes equipment load, avoiding the phenomenon of critical equipment becoming a bottleneck while other equipment has low utilization; and the anomaly early warning device monitors and warns of abnormal situations in the production process in real time, ensuring that problems are handled promptly. Overall, this improves the production management level and operational efficiency of mold manufacturing enterprises.

Description

Technical Field

[0001] This invention relates to the field of resource management, and in particular to a dynamic resource allocation system for mold manufacturing. Background Technology

[0002] Mold manufacturing, as a crucial foundation of the manufacturing industry, is characterized by its complex processes, numerous procedures, and diverse equipment, involving multiple technological stages such as CNC machining, EDM, wire cutting, and injection molding. The mold production process requires comprehensive coordination of production equipment, cutting tools, fixtures, raw materials, and human resources to ensure the smooth execution of production plans. With increasingly diversified market demands, mold manufacturing enterprises face challenges such as small order batches, diverse product types, and tight delivery cycles. Therefore, achieving efficient utilization and dynamic optimization of production resources has become a key factor in enhancing enterprise competitiveness.

[0003] However, traditional mold manufacturing companies rely heavily on manual experience for resource allocation and scheduling, making it difficult to dynamically adjust based on real-time production status. When abnormal situations arise during production, such as equipment failures, order changes, material shortages, or urgent orders, manual reassessment of resource requirements for each process and adjustments to allocation plans are necessary. This process is time-consuming and can easily lead to production delays or idle equipment. Furthermore, mold manufacturing workshops have a wide variety of equipment with varying processing capabilities, resulting in significant differences in capacity. Traditional methods lack comprehensive consideration of equipment load balancing, causing some key equipment to become production bottlenecks while other equipment has low utilization rates, leading to inefficient resource allocation. In addition, the large amount of process data, quality data, and equipment operation data generated during mold production are stored in a scattered manner, lacking effective data fusion and analysis methods, making it difficult to support precise and scientific management decisions. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic resource allocation system for mold manufacturing, which solves the problems mentioned in the background art.

[0005] This invention is implemented as follows: a dynamic resource allocation system for mold manufacturing, comprising a data acquisition device, a production resource database, a dynamic resource allocation server, a load balancing analysis device, an anomaly early warning device, a data fusion analysis platform, and a decision support terminal, wherein: The data acquisition device is configured to collect various production data in the mold manufacturing workshop in real time, including equipment operating status data, tool usage status data, fixture scheduling status data, raw material inventory data, human resource attendance data, and production process parameter data. The collected data is preprocessed and then transmitted to the production resource database. The production resource database is configured to store various resource data generated during the mold manufacturing process and establish a resource information association model to achieve comprehensive management and rapid retrieval of equipment information, tool information, fixture information, raw material information and personnel information, providing data support for dynamic resource allocation. The resource dynamic allocation server is configured to obtain real-time status information of various resources from the production resource database according to the needs of production plan orders, match resource needs with process requirements, generate resource allocation schemes, and dynamically adjust the allocation schemes according to real-time status changes during production to adapt to abnormal situations such as equipment failure, order changes, material shortages or emergency orders. The load balancing analysis device is configured to acquire the processing capacity parameters and current load status of each production equipment, analyze the capacity differences of different equipment, identify bottleneck and non-bottleneck equipment, and calculate the optimal task allocation ratio of each equipment through optimization algorithms to achieve balanced distribution of equipment load and improve overall equipment utilization. The abnormality early warning device is configured to monitor the status of production resources in real time. When abnormalities are detected, such as abnormal equipment operating parameters, tool wear exceeding a preset threshold, raw material inventory below safety stock, or insufficient human resources, an early warning signal is automatically triggered and the abnormal information is pushed to the decision support terminal. At the same time, the resource dynamic allocation server is notified to adjust the plan. The data fusion and analysis platform is configured to fuse and process the collected process data, quality data, and equipment operation data, and to use data mining technology to analyze the regularities and characteristics of the production process, extract key quality influencing factors and equipment performance indicators, and provide data support for management decisions. The decision support terminal is configured to display the real-time status of production resources, load balancing analysis results, anomaly warning information, and data fusion analysis reports to managers, and provides an interactive interface for managers to adjust resource allocation strategies, thereby achieving precise management through human-machine collaboration.

[0006] Preferably, the data acquisition device further includes an equipment operation status acquisition unit, a tool status acquisition unit, and a material status acquisition unit, which are used to acquire the operating parameters of various production equipment such as CNC machining equipment, EDM equipment, wire cutting equipment, and injection molding equipment, as well as tool life data and raw material inventory data.

[0007] Preferably, the resource dynamic allocation server further includes a production plan parsing module, a process resource requirement calculation module, and a dynamic scheduling optimization module. The production plan parsing module is used to parse the process route and delivery time requirements of the order. The process resource requirement calculation module is used to calculate the equipment type, tool specifications, fixture type, and personnel skill requirements required for each process. The dynamic scheduling optimization module is used to perform re-optimization scheduling based on changes in resource status during real-time production.

[0008] Preferably, the load balancing analysis device adopts a load balancing algorithm based on process flow relationship, comprehensively considers the connection time and equipment switching time between adjacent processes, calculates the globally optimal task allocation scheme, and avoids key equipment from becoming a production bottleneck.

[0009] Preferably, the anomaly warning device supports a multi-level warning mechanism, which is divided into prompt level, warning level and emergency level according to the severity of the anomaly. Different levels correspond to different processing procedures and notification targets to ensure that the anomaly is handled in a timely and effective manner.

[0010] Preferably, the data fusion analysis platform further includes a process parameter optimization analysis function, which identifies the optimal range of process parameters by analyzing the correlation between process data and quality data, and guides the parameter optimization of the production process.

[0011] Furthermore, the decision support terminal supports mobile access, allowing managers to view the status of production resources and receive abnormal warning information in real time via mobile devices, thereby improving management response speed.

[0012] Compared with the prior art, the present invention has the following beneficial effects: The resource dynamic allocation system for mold manufacturing provided by this invention acquires the real-time operating status of various production resources through a data acquisition device, achieving comprehensive perception of resource information. The resource dynamic allocation server dynamically adjusts the allocation scheme based on real-time status, effectively solving the problem of traditional manual scheduling being unable to handle abnormal situations. A load balancing analysis device comprehensively analyzes and optimizes equipment load, avoiding the phenomenon of some equipment becoming bottlenecks while other equipment has low utilization, thus improving resource allocation efficiency. A data fusion analysis platform integrates and analyzes scattered process data, quality data, and equipment operation data, supporting more precise and scientific management decisions. An anomaly early warning device monitors and warns of abnormal situations in the production process in real time, ensuring timely handling of problems and overall improving the production management level and operational efficiency of mold manufacturing enterprises. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the overall technical solution architecture of the dynamic resource allocation system for mold manufacturing proposed in this invention; Figure 2 This is a schematic diagram of the core scheduling principle and load balancing collaborative framework of the resource dynamic allocation server in this invention. Detailed Implementation

[0014] Example 1 To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0015] A dynamic resource allocation system for mold manufacturing includes a data acquisition device, a production resource database, a dynamic resource allocation server, a load balancing analysis device, an anomaly early warning device, a data fusion analysis platform, and a decision support terminal. The data acquisition device is used to collect various production data in the mold manufacturing workshop in real time, including equipment operation status data, tool usage status data, fixture scheduling status data, raw material inventory data, human resource attendance data, and production process parameter data, and transmits the collected data to the production resource database after preprocessing. The production resource database is used to store various resource data generated during the mold manufacturing process and to establish a resource information association model to achieve comprehensive management and rapid retrieval of equipment information, tool information, fixture information, raw material information and personnel information, providing data support for dynamic resource allocation. The resource dynamic allocation server is used to obtain real-time status information of various resources from the production resource database according to the needs of production plan orders, match resource needs with process requirements, generate resource allocation schemes, and dynamically adjust the allocation schemes according to real-time status changes during production to adapt to abnormal situations such as equipment failure, order changes, material shortages or emergency orders. The load balancing analysis device is used to obtain the processing capacity parameters and current load status of each production equipment, analyze the capacity differences of different equipment, identify bottleneck and non-bottleneck equipment, and calculate the optimal task allocation ratio of each equipment through optimization algorithms to achieve balanced distribution of equipment load and improve overall equipment utilization. The abnormality early warning device is used to monitor the status of production resources in real time. When abnormalities are detected, such as abnormal equipment operating parameters, tool wear exceeding a preset threshold, raw material inventory below safety stock, or insufficient human resources, an early warning signal is automatically triggered and the abnormal information is pushed to the decision support terminal. At the same time, the resource dynamic allocation server is notified to adjust the plan. The data fusion and analysis platform is used to fuse and process the collected process data, quality data, and equipment operation data. It uses data mining technology to analyze the regularity and characteristics of the production process, extract key quality influencing factors and equipment efficiency indicators, and provide data support for management decisions. The decision support terminal is used to display the real-time status of production resources, load balancing analysis results, anomaly warning information, and data fusion analysis reports to managers, and provides an interactive interface for managers to adjust resource allocation strategies, thereby achieving precise management through human-machine collaboration.

[0016] Furthermore, the data acquisition device includes an equipment operation status acquisition unit, a tool status acquisition unit, and a material status acquisition unit; The equipment operation status acquisition unit is used to collect the operating parameters of various production equipment such as CNC machining equipment, EDM equipment, wire cutting equipment and injection molding equipment, including equipment start-up status, running time, processing progress, spindle speed, cutting force parameters, coolant temperature and equipment fault code information, and sends the collected equipment operation parameters to the production resource database after standardizing the format conversion. The tool status acquisition unit is used to collect service life data of various tools used in the mold manufacturing process, including cumulative tool usage time, number of parts processed by the tool, tool wear degree, tool replacement records and tool inventory information, and to perform full life cycle tracking and management of tool status through the tool management database. The material status acquisition unit is used to collect raw material inventory data, including raw material type, inventory quantity, warehousing date, shelf life, safety stock threshold, and procurement plan information. It also collects fixture scheduling status data, including fixture current position, fixture usage status, fixture maintenance records, and fixture availability information.

[0017] Furthermore, the resource dynamic allocation server includes a production plan parsing module, a process resource requirement calculation module, and a dynamic scheduling optimization module; The production planning parsing module is used to parse the process route and delivery time requirements of orders, decompose the order information into specific production task units, establish the mapping relationship between orders and processes, and calculate the weight coefficient of each order according to the order priority and the urgency of the delivery date, so as to provide a sorting basis for subsequent resource allocation. The process resource requirement calculation module is used to calculate the equipment type, tool specifications, fixture type and personnel skill requirements required for each process. Based on the parameter information in the process card, combined with the equipment capacity database and personnel skill matrix, it matches available resources that meet the processing requirements, calculates the required quantity and time window of each resource, and generates a process resource requirement list. The dynamic scheduling optimization module is used to re-optimize the scheduling based on changes in resource status during real-time production. When a change in the status of a resource in the resource allocation scheme is detected, the rescheduling algorithm is automatically triggered. Under the premise of meeting the process constraints and delivery date constraints, a new resource allocation scheme is generated, and the updated allocation instructions are sent to the corresponding execution units.

[0018] Furthermore, the load balancing analysis device adopts a load balancing algorithm based on process flow relationship, comprehensively considers the connection time and equipment switching time between adjacent processes, calculates the globally optimal task allocation scheme, and avoids key equipment from becoming a production bottleneck. The load balancing analysis device further includes a device capacity assessment unit, a load status monitoring unit, and a task allocation optimization unit; The equipment capability assessment unit is used to conduct a comprehensive capability assessment of various production equipment in the mold manufacturing workshop. It collects parameters such as the processing accuracy range, maximum stroke, rated power, maximum spindle speed and types of materials that can be processed, establishes equipment capability files, and calculates the actual processing efficiency coefficient of the equipment based on historical processing data, providing basic data support for load balancing analysis. The load status monitoring unit is used to collect the current load status of each device in real time, including the current processing task of the device, the estimated completion time, the idle time period of the device, the fault maintenance status of the device, and the device utilization rate index. The load distribution of each device is displayed through a visual load matrix to help managers identify areas of unbalanced load. The task allocation optimization unit is used to calculate the optimal task allocation ratio for each device based on the equipment capacity assessment results and load status monitoring data using a load balancing algorithm. Under the premise of ensuring that the production plan is completed on time, tasks are preferentially allocated to devices with lower loads, while avoiding the allocation of consecutive processes to devices with large capacity differences, so as to reduce the waiting time between processes and improve the overall production efficiency.

[0019] Furthermore, the anomaly warning device supports a multi-level warning mechanism, which is divided into alert level, warning level and emergency level according to the severity of the anomaly. Different levels correspond to different processing procedures and notification targets to ensure that the anomaly is handled in a timely and effective manner. The anomaly early warning device includes an equipment anomaly monitoring unit, a cutting tool anomaly monitoring unit, a material anomaly monitoring unit, and a human resource anomaly monitoring unit; The equipment anomaly monitoring unit is used to monitor the operating parameters of CNC machining equipment, EDM equipment, wire cutting equipment and injection molding equipment in real time. When the equipment operating parameters are detected to exceed the preset normal range, an equipment anomaly warning is triggered, and the warning level is determined according to the degree of parameter deviation. Temperature anomaly, vibration anomaly and machining accuracy anomaly are set as emergency warning, motor overload and cooling system anomaly are set as warning warning, and equipment operating efficiency decline is set as prompt warning. The tool abnormality monitoring unit is used to monitor the usage status of the tool. When the tool wear exceeds the preset threshold, the number of parts processed by the tool reaches the replacement warning line, or the tool experiences abnormal conditions such as chipping, a tool abnormality warning is triggered to remind the management personnel to replace the tool in time to avoid a decline in processing quality or equipment damage due to tool problems. The material anomaly monitoring unit is used to monitor the raw material inventory status. When the raw material inventory is lower than the safety stock threshold, the procurement plan is not delivered on time, or the raw material quality inspection is unqualified, a material anomaly warning is triggered to ensure the continuity of material supply during the production process. The human resources anomaly monitoring unit is used to monitor the attendance status of human resources. When abnormal situations such as absence of key personnel, mismatch between personnel skills and process requirements, or excessive fatigue occur, a human resources anomaly warning is triggered, prompting managers to adjust personnel allocation or arrange overtime support in a timely manner.

[0020] Furthermore, the data fusion analysis platform further includes a process parameter optimization analysis function, which identifies the optimal range of process parameters by analyzing the correlation between process data and quality data, and guides the parameter optimization of the production process; The data fusion and analysis platform includes a data cleaning unit, a data association unit, a pattern mining unit, and a decision support unit. The data cleaning unit is used to perform quality cleaning processing on the collected raw data, identify and process missing values, outliers and duplicate values ​​in the data, and ensure the integrity and consistency of the data through data verification algorithms, so as to provide a reliable data foundation for subsequent data analysis. The data association unit is used to establish the relationship between process data, quality data and equipment operation data. It integrates data scattered in different systems through key fields such as mold number, process number, equipment number and timestamp to form a complete product life cycle data chain. The pattern mining unit is used to analyze the regular characteristics in the production process using data mining technology, including the correlation between process parameters and product quality, the influence relationship between equipment operating parameters and processing efficiency, the correlation factors between tool life and processing parameters, and the matching relationship between personnel allocation and production capacity. It extracts key quality influencing factors and equipment efficiency indicators and generates a data mining analysis report. The decision support unit is used to present the results of data fusion analysis to managers in the form of visual reports, including suggestions for process parameter optimization, equipment maintenance plan optimization, personnel allocation optimization, and inventory management optimization, providing data support and reference for management decisions.

[0021] Furthermore, the decision support terminal supports mobile access, allowing managers to view the status of production resources and receive abnormal warning information in real time via mobile devices, thereby improving management response speed. The decision support terminal includes a real-time status display unit, an early warning information push unit, a strategy adjustment interaction unit, and a data report query unit. The real-time status display unit is used to display the real-time status of production resources in a chart and graphical manner, including equipment operation status distribution map, tool inventory status list, raw material inventory status dashboard, personnel attendance status dashboard, and current production plan progress Gantt chart, to help managers quickly grasp the overall situation of the production site. The warning information push unit is used to send the warning information triggered by the abnormal warning device to the terminal device of the administrator through the push service. It supports multiple push methods such as SMS, instant messaging application and mobile application push, and selects different push strategies according to the warning level to ensure that important warning information can be delivered in a timely manner. The strategy adjustment interaction unit is used to provide a graphical resource allocation strategy adjustment interface. Managers can view the current resource allocation plan, modify resource allocation rules, adjust task priorities, and manually intervene in task allocation on the interface. The results of the interaction will be fed back to the resource dynamic allocation server in real time, triggering the plan update process. The data report query unit is used to provide historical data query and statistical analysis report functions. Managers can query historical production data according to conditions such as time range, equipment type, process type and product model, and generate capacity analysis reports, quality analysis reports, equipment utilization rate reports and personnel efficiency reports, providing data basis for management improvement.

[0022] Furthermore, in this embodiment, the data acquisition device establishes a data connection with the production resource database through an industrial IoT gateway, uses the MQTT IoT communication protocol to achieve reliable data transmission, and employs the TLS encryption algorithm to ensure data security during data transmission. The production resource database adopts a distributed database architecture, including a master database and multiple slave databases. The master database is responsible for data writing and core business processing, while the slave databases are responsible for data reading and query services. High availability and read-write separation of data are achieved through the database cluster. The resource dynamic allocation server is deployed on a cloud computing platform, using a containerized deployment method and equipped with automatic scaling up and down functions. It can dynamically adjust computing resources according to business load to ensure that the system can still run stably during peak production periods. The load balancing analysis device uses a high-performance computing server equipped with a GPU graphics processor acceleration card to accelerate the calculation process of the load balancing algorithm and shorten the generation time of the task allocation scheme. The anomaly warning device is equipped with an independent alarm channel, which is securely isolated from the production control system to ensure that the sending of alarm information will not affect the normal production control process. The data fusion and analysis platform adopts a big data processing architecture, is equipped with a distributed computing engine and a distributed storage system, and is capable of handling the storage and analysis needs of massive production data. The decision support terminal supports both web browser access and mobile application access. The web version adopts a responsive design layout, which can adapt to display devices with different resolutions. The mobile application supports iOS and Android operating systems.

[0023] Example 2 To meet the application needs of mold manufacturing enterprises of different sizes, this invention also provides a variant embodiment of a dynamic resource allocation system for mold manufacturing. This embodiment adopts a hybrid architecture that combines edge computing and cloud computing, and is suitable for application scenarios where production equipment is widely distributed and data transmission requires high real-time performance.

[0024] A dynamic resource allocation system for mold manufacturing includes an edge data acquisition unit, an edge computing node, a production resource database, a cloud-based dynamic resource allocation server, a cloud-based load balancing analysis device, a cloud-based anomaly early warning device, a data fusion analysis platform, and a decision support terminal. The edge data acquisition unit is deployed on-site in the mold manufacturing workshop to collect various production data in real time, including equipment operation status data, tool usage status data, fixture scheduling status data, raw material inventory data, and human resource attendance data. After preprocessing and compressing the collected data, it is transmitted to the edge computing node through the 5G industrial private network. The edge computing node is deployed on a local server in the workshop. It is used to perform preliminary processing and real-time analysis on the data collected by the edge data acquisition unit, so as to realize the immediate response to the equipment operating status and abnormal situation in the workshop. At the same time, the processed data is transmitted to the cloud server for in-depth analysis and long-term storage. The production resource database adopts a distributed database system deployed in the cloud to store various resource data generated during the mold manufacturing process, and establishes a resource information association model to realize comprehensive management and rapid retrieval of equipment information, tool information, fixture information, raw material information and personnel information, providing data support for dynamic resource allocation; The cloud-based dynamic resource allocation server is deployed on a cloud computing platform. It is used to obtain real-time status information of various resources from the production resource database according to the needs of production plan orders, match resource needs with process requirements, generate resource allocation schemes, and dynamically adjust the allocation schemes according to real-time status changes during production to adapt to abnormal situations such as equipment failure, order changes, material shortages, or emergency orders. The cloud-based load balancing analysis device is deployed on a cloud computing platform to obtain the processing capacity parameters and current load status of each production equipment, analyze the capacity differences of different equipment, identify bottleneck and non-bottleneck equipment, and calculate the optimal task allocation ratio of each equipment through optimization algorithms to achieve balanced distribution of equipment load and improve overall equipment utilization. The cloud-based anomaly early warning device is deployed on a cloud computing platform to monitor the status of production resources in real time. When abnormal situations such as abnormal equipment operating parameters, tool wear exceeding a preset threshold, raw material inventory below safety stock, or insufficient human resources are detected, an early warning signal is automatically triggered and the abnormal information is pushed to the decision support terminal. At the same time, the cloud-based resource dynamic allocation server is notified to adjust the plan. The data fusion and analysis platform, deployed on a cloud computing platform, is used to fuse and process collected process data, quality data, and equipment operation data. It employs data mining techniques to analyze patterns and characteristics in the production process, extract key quality influencing factors and equipment performance indicators, and provide data support for management decisions. The decision support terminal is used to display the real-time status of production resources, load balancing analysis results, anomaly warning information, and data fusion analysis reports to managers, and provides an interactive interface for managers to adjust resource allocation strategies, thereby achieving precise management through human-machine collaboration.

[0025] Furthermore, the edge computing node includes an edge data processing module, an edge anomaly detection module, and an edge caching module; The edge data processing module is used to clean, transform, and aggregate the raw data collected by the edge data acquisition unit, extract key feature data, remove redundant information, and reduce the amount of data transmission while retaining core data features. The edge anomaly detection module is used to realize real-time detection of emergency anomalies of the equipment at the edge. When the equipment operating parameters are detected to deviate significantly from the normal range, a local alarm is triggered immediately without waiting for cloud judgment, and the equipment is protected by emergency shutdown through the industrial control system to prevent the anomaly from escalating and causing equipment damage or safety accidents. The edge caching module is used to cache the production data of the most recent period on the edge side. When the network connection is interrupted or the cloud server is unavailable, the edge computing node can independently maintain basic data collection and monitoring functions. After the network is restored, the cached data is automatically synchronized to the cloud to ensure data integrity.

[0026] Furthermore, the cloud-based load balancing analysis device adopts a layered load balancing strategy, including a global load balancing layer and a local load balancing layer. The global load balancing layer is used to perform load balancing analysis among multiple production workshops or production lines. It comprehensively considers the capacity status and order distribution of each workshop and production line, calculates task allocation schemes across workshops or production lines, and realizes optimized resource allocation at the enterprise level. The local load balancing layer is used to perform load balancing analysis within a single workshop or production line. It comprehensively considers the capacity differences of each piece of equipment and the process flow relationship within the workshop, calculates the task allocation scheme at the equipment level, and realizes the optimal allocation of resources at the workshop level.

[0027] Furthermore, the edge data acquisition unit in this embodiment adopts a modular design, and is configured with corresponding data acquisition modules according to different types of production equipment, including CNC equipment data acquisition modules, EDM equipment data acquisition modules, wire cutting equipment data acquisition modules and injection molding equipment data acquisition modules. Each acquisition module is connected to the edge computing node through a unified industrial interface to achieve standardization and scalability of data acquisition. The edge computing node uses a high-performance industrial server, equipped with a multi-core central processing unit and a large capacity of memory, which can simultaneously handle high-speed acquisition and real-time analysis tasks of multiple data streams. The cloud-based dynamic resource allocation server adopts a microservice architecture, which breaks down functions such as production plan parsing, process resource requirement calculation and dynamic scheduling optimization into independent microservices, and achieves efficient communication and elastic scaling between services through a service mesh. The decision support terminal adopts a web application architecture, allowing administrators to access all system functions through a browser without installing dedicated client software, thus reducing the complexity of system deployment and maintenance.

[0028] The present invention also provides a data interaction method for the above-mentioned system, comprising the following steps: Step S1: The data acquisition device collects various production data from the mold manufacturing workshop in real time, and transmits the collected data to the production resource database after preprocessing. Step S2: The resource dynamic allocation server obtains real-time resource status information from the production resource database, matches resource requirements with process requirements, and generates a resource allocation plan. Step S3: The load balancing analysis device obtains the processing capacity parameters and current load status of each production equipment, and calculates the optimal task allocation ratio for each equipment; Step S4: The anomaly warning device monitors the status of production resources in real time. When an anomaly is detected, it triggers an early warning signal and pushes the anomaly information to the decision support terminal. Step S5: The data fusion analysis platform integrates and processes the collected process data, quality data, and equipment operation data to extract key influencing factors; Step S6: The decision support terminal displays the status of production resources and early warning information to the management personnel, and provides an interactive interface for strategy adjustment.

[0029] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dynamic resource allocation system for mold manufacturing, characterized in that, include: The data acquisition device is used to collect various production data in the mold manufacturing workshop in real time, including equipment operation status data, tool usage status data, fixture scheduling status data, raw material inventory data, human resource attendance data, and production process parameter data. After preprocessing the collected data, it is transmitted to the production resource database. The production resource database is used to store various resource data generated during the mold manufacturing process and to establish a resource information association model to achieve comprehensive management and rapid retrieval of equipment information, tool information, fixture information, raw material information and personnel information; A resource dynamic allocation server is connected to the production resource database. It is used to obtain real-time status information of various resources from the production resource database according to the needs of production plan orders, match resource needs with process requirements, generate resource allocation schemes, and dynamically adjust the allocation schemes according to real-time status changes during the production process. The load balancing analysis device is connected to the resource dynamic allocation server to obtain the processing capacity parameters and current load status of each production equipment, analyze the capacity differences of different equipment, identify bottleneck equipment and non-bottleneck equipment, and calculate the optimal task allocation ratio of each equipment through optimization algorithms. An abnormality warning device is used to monitor the status of production resources in real time. When abnormalities are detected, such as abnormal equipment operating parameters, tool wear exceeding a preset threshold, raw material inventory below safety stock, or insufficient human resources, an early warning signal is automatically triggered and the abnormal information is pushed to the decision support terminal. At the same time, the resource dynamic allocation server is notified. The data fusion and analysis platform is used to integrate and process collected process data, quality data, and equipment operation data. It uses data mining technology to analyze the regularity and characteristics of the production process and extract key quality influencing factors and equipment efficiency indicators. The decision support terminal is connected to the data fusion analysis platform, the anomaly warning device, and the load balancing analysis device. It is used to display the real-time status of production resources, load balancing analysis results, anomaly warning information, and data fusion analysis reports to managers, and provides an interactive interface for managers to adjust resource allocation strategies.

2. The resource dynamic allocation system for mold manufacturing as described in claim 1, characterized in that, The data acquisition device includes an equipment operation status acquisition unit, a tool status acquisition unit, and a material status acquisition unit; The equipment operation status acquisition unit is used to collect the operating parameters of CNC machining equipment, EDM equipment, wire cutting equipment and injection molding equipment, including equipment start-up status, running time, processing progress, spindle speed, cutting force parameters, coolant temperature and equipment fault code information; The tool status acquisition unit is used to collect service life data of various tools, including cumulative tool usage time, number of parts processed by the tool, tool wear degree, tool replacement records, and tool inventory information. The material status acquisition unit is used to collect raw material inventory data, including raw material type, inventory quantity, warehousing date, shelf life, safety stock threshold, and procurement plan information, and at the same time collect fixture scheduling status data.

3. The resource dynamic allocation system for mold manufacturing as described in claim 1, characterized in that, The resource dynamic allocation server includes a production plan parsing module, a process resource requirement calculation module, and a dynamic scheduling optimization module; The production planning parsing module is used to parse the process route and delivery time requirements of orders, decompose the order information into specific production task units, establish the mapping relationship between orders and processes, and calculate the weight coefficient of each order according to the order priority and the urgency of the delivery period. The process resource requirement calculation module is used to calculate the equipment type, tool specifications, fixture type and personnel skill requirements required for each process. Based on the parameter information in the process card, it matches available resources that meet the processing requirements and calculates the required quantity and time window for each resource. The dynamic scheduling optimization module is used to re-optimize the scheduling based on changes in resource status during real-time production. When a change in the status of a resource in the resource allocation scheme is detected, the rescheduling algorithm is automatically triggered to generate a new resource allocation scheme under the premise of meeting the process constraints and delivery date constraints.

4. The resource dynamic allocation system for mold manufacturing as described in claim 1, characterized in that, The load balancing analysis device includes a device capacity assessment unit, a load status monitoring unit, and a task allocation optimization unit; The equipment capability assessment unit is used to conduct a comprehensive capability assessment of various production equipment in the mold manufacturing workshop, collect parameters such as the processing accuracy range, maximum stroke, rated power, maximum spindle speed and types of materials that can be processed, and establish an equipment capability file. The load status monitoring unit is used to collect the current load status of each device in real time, including the current processing task of the device, the estimated completion time, the idle time period of the device, the fault maintenance status of the device, and the device utilization rate index. The task allocation optimization unit is used to calculate the optimal task allocation ratio for each device based on the device capacity assessment results and load status monitoring data, and to prioritize the allocation of tasks to devices with lower loads while ensuring that the production plan is completed on time.

5. The resource dynamic allocation system for mold manufacturing as described in claim 1, characterized in that, The anomaly early warning device includes an equipment anomaly monitoring unit, a cutting tool anomaly monitoring unit, a material anomaly monitoring unit, and a human resource anomaly monitoring unit; The equipment anomaly monitoring unit is used to monitor the operating parameters of CNC machining equipment, EDM equipment, wire cutting equipment and injection molding equipment in real time. When the monitored equipment operating parameters exceed the preset normal range, an equipment anomaly warning is triggered, and the warning level is determined according to the degree of parameter deviation. The tool abnormality monitoring unit is used to monitor the usage status of the tool. When the tool wear exceeds a preset threshold, the number of parts processed by the tool reaches the replacement warning line, or the tool experiences abnormal conditions such as chipping, a tool abnormality warning is triggered. The material anomaly monitoring unit is used to monitor the raw material inventory status. When the raw material inventory is lower than the safety stock threshold, the procurement plan is not delivered on time, or the raw material quality inspection is unqualified, a material anomaly warning is triggered. The human resources anomaly monitoring unit is used to monitor the attendance status of human resources. When abnormal situations such as absence of key personnel, mismatch between personnel skills and process requirements, or excessive fatigue occur, a human resources anomaly warning is triggered.

6. The resource dynamic allocation system for mold manufacturing as described in claim 1, characterized in that, The data fusion and analysis platform includes a data cleaning unit, a data association unit, a pattern mining unit, and a decision support unit. The data cleaning unit is used to perform quality cleaning processing on the collected raw data, identify and process missing values, outliers and duplicate values ​​in the data, and ensure the integrity and consistency of the data through data verification algorithms. The data association unit is used to establish the association between process data, quality data and equipment operation data, and to associate and integrate data scattered in different systems through key fields such as mold number, process number, equipment number and timestamp; The pattern mining unit is used to analyze the pattern characteristics in the production process using data mining technology, extract key quality influencing factors and equipment performance indicators, and generate a data mining analysis report. The decision support unit is used to present the data fusion and analysis results to managers in the form of visual reports, including suggestions for process parameter optimization, equipment maintenance plans, and inventory management optimization.

7. The resource dynamic allocation system for mold manufacturing as described in claim 1, characterized in that, The decision support terminal includes a real-time status display unit, an early warning information push unit, a strategy adjustment interaction unit, and a data report query unit. The real-time status display unit is used to display the real-time status of production resources in a chart and graphical manner, including a distribution map of equipment operation status, a list of tool inventory status, a dashboard of raw material inventory status, and a Gantt chart of the current production plan progress. The warning information push unit is used to send the warning information triggered by the abnormal warning device to the terminal device of the management personnel through the push service, and supports push via SMS, instant messaging application and mobile application. The strategy adjustment interaction unit is used to provide a graphical resource allocation strategy adjustment interface. Administrators can view the current resource allocation scheme, modify resource allocation rules, and adjust task priorities on the interface. The results of the interaction will be fed back to the resource dynamic allocation server in real time. The data report query unit is used to provide historical data query and statistical analysis report functions. Managers can query historical production data based on conditions such as time range, equipment type, process type and product model.

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