A multi-factory tire production collaborative scheduling and resource allocation system

Abstract

This invention relates to the technical field of industrial manufacturing and production management, and in particular to a multi-factory tire production collaborative scheduling and resource allocation system. The system includes a unified order management center, a data acquisition unit, a data processing and analysis unit, a factory capability twin model unit, a collaborative scheduling and resource allocation unit, an operation interface unit, and a report generation and query unit. The unified order management center is used to collect, parse, and manage all customer orders, forming a unified global order pool. The data acquisition unit includes equipment sensors, logistics sensors, and a manual input terminal. Equipment sensors are installed on production equipment in each factory to collect real-time information on equipment operating status, production parameters, and production progress. Logistics sensors are installed in warehouses and transport vehicles. This system reduces equipment idle time and production cycles, improves the efficiency of tire production, and optimizes resource allocation and production task distribution.

Description

Technical Field

[0001] This invention relates to the technical field of industrial manufacturing and production management, and in particular to a multi-factory tire production collaborative scheduling and resource allocation system. Background Technology

[0002] Tire manufacturing is a complex industrial process that blends process and discrete steps, involving multiple stages such as mixing, calendering, cutting, molding, vulcanization, and testing, and producing a wide variety of product specifications. Large tire companies typically own multiple factories located in different geographical locations, each with its own specific equipment capabilities, product expertise, raw material inventory, and cost structure.

[0003] Currently, production scheduling in each factory is mostly conducted independently, with only coarse-grained order allocation and financial accounting done through enterprise resource planning (ERP) systems. This model suffers from overall inefficiency and rigid configuration, resulting in poor optimization of logistics costs.

[0004] As exemplified by the existing patent with publication number CN119090175A, this invention provides a factory production intelligent management system based on a large language model, applicable to the field of factory production management systems. This invention includes a gateway module, a system management module, an ERP integration module, a production management module, a task scheduling module, a file management module, an authentication module, a logistics management module, and a multi-factory management module.

[0005] It was found that the existing system is ineffective in coordinating and scheduling resources among multiple factories, which will affect the effectiveness of multi-factory production coordination scheduling and resource allocation. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a multi-factory tire production collaborative scheduling and resource allocation system that reduces equipment idle time and production cycle, improves the efficiency of enterprise tire production, and optimizes resource allocation and production task assignment.

[0007] The present invention provides a multi-factory tire production collaborative scheduling and resource allocation system, comprising a unified order management center, a data acquisition unit, a data processing and analysis unit, a factory capacity twin model unit, a collaborative scheduling and resource allocation unit, an operation interface unit, and a report generation and query unit; Unified Order Management Center: Used to collect, parse, and manage all customer orders, forming a unified global order pool; The data acquisition unit includes equipment sensors, logistics sensors, and a manual input terminal; Equipment sensors: Sensors are installed on production equipment in various factories to collect real-time information on equipment operating status, production parameters, and production progress. Logistics sensors: Sensors are installed in warehouses and transport vehicles to collect real-time information on raw material inventory, work-in-process inventory, finished product inventory, as well as the location and transportation status of transport vehicles. By utilizing efficient transport vehicles, the rapid and safe transfer of raw materials can be achieved. Manual input terminal: Set up a manual input terminal to facilitate operators to manually input information that cannot be collected by sensors; The data processing and analysis unit includes a data cleaning and preprocessing module, a data storage and management module, and a data analysis and mining module; Data cleaning and preprocessing module: It is connected to the data acquisition layer and is used to clean and preprocess the acquired raw data, remove noisy and erroneous data, fill in missing data, and convert the data into a unified format. Data storage and management module: This module stores the cleaned and preprocessed data in the database. The database uses a distributed architecture to ensure data security and reliability. At the same time, it categorizes and manages the data for easy querying and retrieval. Data Analysis and Mining Module: Utilizing data mining algorithms and machine learning techniques, this module analyzes and mines data in the database to extract valuable information and patterns. By analyzing factors influencing production efficiency, bottlenecks in resource utilization, and changing trends in market demand, it provides decision support for production scheduling and resource allocation. Factory Capability Twin Model Unit: Connected to the data acquisition layer, it establishes a high-fidelity digital twin model for each physical factory based on the data acquired from the data acquisition layer, and updates the dynamic capacity data, resource inventory data, work-in-process status, and cost parameters of each factory in real time. The collaborative scheduling and resource allocation unit includes a production task collaborative allocation module, a production scheduling optimization module, and a resource allocation optimization module. Production task collaborative allocation module: Based on market demand, order information, production progress of each factory, and equipment status, the collaborative scheduling algorithm is used to rationally allocate production tasks. By considering the semi-finished product supply and circulation production collaboration relationships between factories, the rational allocation of production tasks is achieved. Production scheduling optimization module: For the production process within each factory, a collaborative scheduling algorithm is used to optimize the production sequence and production time arrangement, reduce equipment idle time and production cycle. At the same time, in a multi-factory environment, the transportation time and transportation cost between factories are considered, and production scheduling is combined with transportation scheduling to achieve collaborative optimization of production and transportation. Resource allocation optimization module: Utilizes a dynamic resource allocation model to comprehensively evaluate and analyze the human, material, and financial resources of multiple factories. Based on the needs of production tasks and the actual situation of each factory, it rationally allocates resources by relocating idle equipment to factories with busy production tasks, thereby maximizing resource utilization. User interface unit: Provides users with an intuitive and convenient user interface, making it easy for users to perform operations such as production task allocation, production scheduling adjustment, and resource configuration management. The user interface adopts a graphical design to reduce the difficulty of operation for users. Report generation and query unit: Generates production progress reports, equipment utilization reports, and resource consumption reports according to user needs. Users can view historical production data and statistical information at any time through the report query function, providing a reference for production decisions. Through multi-factory collaborative scheduling and production scheduling optimization, it reduces equipment idle time and production cycle, improves the efficiency of tire production, optimizes resource allocation and production task assignment, and reduces raw material procurement costs, equipment idle costs, and transportation costs. Through data analysis and mining, it provides a scientific basis for enterprise production decisions, helps enterprises optimize production management processes, and improves management level and the scientific nature of decision-making.

[0008] Preferably, the collaborative scheduling algorithm establishes a multi-objective optimization model by comprehensively considering the production capacity, equipment status, transportation costs, and order priority of each factory. The model aims to minimize the production cycle, reduce transportation costs, and improve the order fulfillment rate. The algorithm is solved using a genetic algorithm, and through continuous iteration and optimization, the optimal allocation scheme of production tasks among multiple factories is obtained. The algorithm steps are as follows: S1. Initialize the population: Randomly generate a set of production task allocation schemes as the initial population; S2. Fitness Assessment: Calculate the fitness value of each individual based on the multi-objective optimization model. The higher the fitness value, the better the solution. S3. Selection operation: Use roulette wheel selection to select individuals with higher fitness to enter the next generation of the population; S4. Crossover operation: Perform a crossover operation on the selected individuals to generate new individuals and increase the diversity of the population; S5. Mutation operation: Perform mutation operation on the new individual to prevent the algorithm from getting trapped in a local optimum; S6. Termination Condition Judgment: When the preset number of iterations or the fitness value meets the requirements, the algorithm terminates and outputs the optimal solution. The genetic algorithm is used to solve the problem. By initializing the population, multiple production task allocation schemes are generated, which provides a rich foundation for optimization, effectively shortens the production cycle, improves production efficiency, reduces transportation costs, saves enterprise expenses, improves order fulfillment rate, enhances customer satisfaction, and thus enhances the enterprise's competitiveness in the market.

[0009] Preferably, the resource dynamic allocation model is based on real-time production data and market demand information to establish a resource demand prediction model to predict the resource demand of each factory in different time periods. At the same time, considering the availability and sharing of resources, a resource allocation model is established to realize the dynamic allocation of resources. The specific implementation of the model is as follows: S1. Resource Demand Forecasting: Using time series analysis methods, historical production data is analyzed and mined to establish a resource demand forecasting model. Based on the forecast results, the resource demand of each factory in the future period is determined. S2. Resource Availability Assessment: Real-time monitoring of resource usage in each factory, including equipment operating status and personnel attendance, to assess resource availability; S3. Resource Allocation Optimization: Based on resource demand forecasts and resource availability assessments, a resource allocation model is established using linear programming to minimize resource idle costs and meet production demands, thereby finding the optimal resource allocation scheme. This effectively reduces resource waste, lowers enterprise operating costs, ensures timely fulfillment of production needs, guarantees smooth production processes, improves overall production efficiency, and enhances the enterprise's ability to achieve sustainable development and maximize benefits.

[0010] Preferably, it also includes a production monitoring interface module and an early warning mechanism module; Production monitoring interface module: used to display the production status, equipment operation status and production progress information of each factory in real time. Managers can view the production dynamics at any time through the monitoring interface, identify problems in a timely manner and take measures to solve them. Early warning mechanism module: By setting early warning thresholds, when abnormal situations occur during the production process, the system will promptly issue early warning information and provide corresponding solutions and suggestions. The early warning information will be sent to relevant managers via SMS and interface pop-ups.

[0011] Preferably, it also includes a wired network module, a wireless network module, and an Internet module; Wired network module: Establish a wired local area network within each factory to transmit data collected by equipment sensors and logistics sensors to the factory's data server via the wired network; Wireless network module: Used for data transmission via wireless network for some mobile devices and areas where cabling is difficult; Internet module: Used to connect the data servers of each factory to the data center of the corporate headquarters, enabling data sharing and interaction among multiple factories. At the same time, the corporate headquarters can communicate with suppliers and customers through the Internet to obtain raw material supply information and market demand information in a timely manner.

[0012] Preferably, the transport vehicle includes a load-bearing device, a shell, a top cover, a storage rack, guide wheels, a support pad, and sensors; An opening is provided at the top of the casing; The bottom of the top cover connects to the top of the storage rack; The storage rack is divided into multiple compartments; Multiple sets of guide wheels are rotatably installed on the lower part of the outer side wall of the storage rack; Multiple sets of support pads are set at the bottom of the inner side wall of the shell; Multiple sets of sensors are installed in multiple compartments of the storage rack; The carrying device is located at the bottom of the housing. It is used to move the housing and fix the bottom of the storage rack. Different goods to be transferred are placed into multiple compartments of the storage rack, improving the convenience of separate storage and transfer of different types of goods. Multiple sets of sensors detect the goods stored in different compartments, improving the convenience of collecting information on the retrieval and placement of goods. After the goods are stored on the storage rack, the storage rack is placed inside the housing, and the top opening of the housing is closed by the top cover, thereby sealing the goods. The bottom of the storage rack is fixed by the carrying device, and at the same time, the carrying device moves the housing, thereby improving the safety, efficiency and convenience of goods transfer.

[0013] Preferably, the bearing device includes a drive device, a positioning box, a strip-shaped through hole, a connecting column, a strip-shaped positioning component, a base, a traveling wheel, and a swivel wheel; The positioning box is rotated and mounted on the base; A strip-shaped through-hole is located at the top of the positioning box; The connecting column is located at the bottom of the storage rack; The strip positioning element is set at the bottom end of the connecting column, and the shape of the strip positioning element matches the shape of the strip through hole; The drive unit is mounted on the positioning box and the base, and is used to provide power for the rotation of the positioning box; The top of the base is connected to the bottom of the housing; Both the traveling wheels and the casters are mounted rotatably at the bottom of the base. When the storage rack is placed inside the housing, the strip-shaped positioning component moves downwards through the strip-shaped through-hole into the positioning box. Then, the driving device drives the positioning box to rotate, so that the angle of the strip-shaped through-hole and the strip-shaped positioning component form an intersection angle, thereby locking the strip-shaped positioning component inside the positioning box and preventing it from detaching, thus positioning the storage rack. When it is necessary to remove the storage rack upwards, the driving device drives the positioning box to rotate and reset. At this time, the angle of the strip-shaped through-hole and the angle of the strip-shaped positioning component are aligned, making it easy to move the strip-shaped positioning component upwards out of the positioning box, improving the convenience and reliability of fixing the storage rack. The traveling wheels and casters work together to support the movement of the base, improving the convenience of transferring goods.

[0014] Preferably, the drive device includes a bearing, a worm gear, a motor, and a worm wheel; The bearing seat is mounted on the outer wall of the base; The worm gear is rotatably mounted on the bearing seat; The motor is mounted on the outer wall of the shaft seat, and the motor output end is connected to the worm gear. The worm gear is installed on the outer wall of the positioning box and meshes with the worm. The motor drives the worm to rotate, which in turn drives the positioning box to rotate through the meshing of the worm gear, thereby adjusting the angle of the strip-shaped through hole.

[0015] Preferably, it also includes a connector and a traction arm; The connector is installed on the outer wall of the base; The end of the traction arm is rotatably mounted on the connector; by setting the connector and traction arm, the convenience of external equipment to traction and move the base is improved.

[0016] Preferably, it also includes a crane boom; The boom is installed at the top of the roof; by installing the boom, the ease of lifting and moving the roof upwards is improved.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: by optimizing multi-factory collaborative scheduling and production scheduling, it reduces equipment idle time and production cycle, improves the efficiency of tire production, optimizes resource allocation and production task allocation, reduces raw material procurement costs, equipment idle costs and transportation costs, and provides scientific basis for enterprise production decisions through data analysis and mining, helps enterprises optimize production management processes, and improves management level and the scientific nature of decision-making. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the isometric structure of the present invention; Figure 2 This is a schematic diagram of the data acquisition unit. Figure 3 This is a structural diagram of units such as the factory capacity twin model unit and the collaborative scheduling and resource allocation unit; Figure 4 This is an isometric structural diagram of the connection between the top cover and the boom, etc. Figure 5 This is an isometric structural diagram of the connection between the top cover and the storage rack, etc. Figure 6 This is a partial isometric structural diagram showing the connection between the storage rack and guide wheels, etc. Figure 7 This is an isometric partial structural diagram of the connection between the storage rack and the connecting columns, etc. Figure 8 This is a partial isometric structural diagram of the connection between the base and connecting parts, etc. Figure 9 This is a partial isometric structural diagram of the connection between the shell and the support pad, etc. Figure 10 This is a partial isometric structural diagram of the connection between the positioning box and the worm gear, etc.

[0019] The following labels are used in the attached diagram: 101, housing; 102, top cover; 103, storage rack; 104, guide wheel; 105, support pad; 106, sensor; 201, positioning box; 202, strip-shaped through hole; 203, connecting column; 204, strip-shaped positioning component; 205, base; 206, traveling wheel; 207, universal wheel; 301, axle seat; 302, worm gear; 303, motor; 304, worm wheel; 401, connecting component; 402, traction arm; 501, boom. Detailed Implementation

[0020] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. The present invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0021] Example 1 like Figures 1 to 3 As shown, the present invention provides a multi-factory tire production collaborative scheduling and resource allocation system, which includes a unified order management center, a data acquisition unit, a data processing and analysis unit, a factory capacity twin model unit, a collaborative scheduling and resource allocation unit, an operation interface unit, and a report generation and query unit. Unified Order Management Center: Used to collect, parse, and manage all customer orders, forming a unified global order pool; The data acquisition unit includes equipment sensors, logistics sensors, and a manual input terminal; Equipment sensors: Sensors are installed on production equipment in various factories to collect real-time information on equipment operating status, production parameters, and production progress. Logistics sensors: Sensors are installed in warehouses and transport vehicles to collect real-time information on raw material inventory, work-in-process inventory, finished product inventory, as well as the location and transportation status of transport vehicles. By utilizing efficient transport vehicles, the rapid and safe transfer of raw materials can be achieved. Manual input terminal: Set up a manual input terminal to facilitate operators to manually input information that cannot be collected by sensors; The data processing and analysis unit includes a data cleaning and preprocessing module, a data storage and management module, and a data analysis and mining module; Data cleaning and preprocessing module: It is connected to the data acquisition layer and is used to clean and preprocess the acquired raw data, remove noisy and erroneous data, fill in missing data, and convert the data into a unified format. Data storage and management module: This module stores the cleaned and preprocessed data in the database. The database uses a distributed architecture to ensure data security and reliability. At the same time, it categorizes and manages the data for easy querying and retrieval. Data Analysis and Mining Module: Utilizing data mining algorithms and machine learning techniques, this module analyzes and mines data in the database to extract valuable information and patterns. By analyzing factors influencing production efficiency, bottlenecks in resource utilization, and changing trends in market demand, it provides decision support for production scheduling and resource allocation. Factory Capability Twin Model Unit: Connected to the data acquisition layer, it establishes a high-fidelity digital twin model for each physical factory based on the data acquired from the data acquisition layer, and updates the dynamic capacity data, resource inventory data, work-in-process status, and cost parameters of each factory in real time. The collaborative scheduling and resource allocation unit includes a production task collaborative allocation module, a production scheduling optimization module, and a resource allocation optimization module. Production task collaborative allocation module: Based on market demand, order information, production progress of each factory, and equipment status, the collaborative scheduling algorithm is used to rationally allocate production tasks. By considering the semi-finished product supply and circulation production collaboration relationships between factories, the rational allocation of production tasks is achieved. Production scheduling optimization module: For the production process within each factory, a collaborative scheduling algorithm is used to optimize the production sequence and production time arrangement, reduce equipment idle time and production cycle. At the same time, in a multi-factory environment, the transportation time and transportation cost between factories are considered, and production scheduling is combined with transportation scheduling to achieve collaborative optimization of production and transportation. Resource allocation optimization module: Utilizes a dynamic resource allocation model to comprehensively evaluate and analyze the human, material, and financial resources of multiple factories. Based on the needs of production tasks and the actual situation of each factory, it rationally allocates resources by relocating idle equipment to factories with busy production tasks, thereby maximizing resource utilization. User interface unit: Provides users with an intuitive and convenient user interface, making it easy for users to perform operations such as production task allocation, production scheduling adjustment, and resource configuration management. The user interface adopts a graphical design to reduce the difficulty of operation for users. Report generation and query unit: Generates production progress reports, equipment utilization reports, and resource consumption reports according to user needs. Users can view historical production data and statistical information at any time through the report query function to provide reference for production decisions. The collaborative scheduling algorithm establishes a multi-objective optimization model by comprehensively considering factors such as the production capacity, equipment status, transportation costs, and order priority of each factory. The model aims to minimize the production cycle, reduce transportation costs, and improve the order fulfillment rate. It uses a genetic algorithm to solve the problem and obtains the optimal allocation scheme of production tasks among multiple factories through continuous iteration and optimization. The algorithm steps are as follows: S1. Initialize the population: Randomly generate a set of production task allocation schemes as the initial population; S2. Fitness Assessment: Calculate the fitness value of each individual based on the multi-objective optimization model. The higher the fitness value, the better the solution. S3. Selection operation: Use roulette wheel selection to select individuals with higher fitness to enter the next generation of the population; S4. Crossover operation: Perform a crossover operation on the selected individuals to generate new individuals and increase the diversity of the population; S5. Mutation operation: Perform mutation operation on the new individual to prevent the algorithm from getting trapped in a local optimum; S6. Termination condition judgment: When the preset number of iterations or the fitness value meets the requirements, the algorithm terminates and outputs the optimal solution; In this embodiment, by optimizing multi-factory collaborative scheduling and production scheduling, equipment idle time and production cycle are reduced, the efficiency of tire production is improved, resource allocation and production task allocation are optimized, and raw material procurement costs, equipment idle costs and transportation costs are reduced. Through data analysis and mining, scientific basis is provided for the company's production decisions, helping the company optimize production management processes and improve management level and the scientific nature of decision-making.

[0022] Example 2 Based on Example 1, such as Figures 1 to 3 As shown, the present invention provides a multi-factory tire production collaborative scheduling and resource allocation system. The resource dynamic allocation model is based on real-time production data and market demand information to establish a resource demand prediction model to predict the resource demand of each factory in different time periods. At the same time, considering the availability and sharing of resources, a resource allocation model is established to realize the dynamic allocation of resources. The specific implementation of the model is as follows: S1. Resource Demand Forecasting: Using time series analysis methods, historical production data is analyzed and mined to establish a resource demand forecasting model. Based on the forecast results, the resource demand of each factory in the future period is determined. S2. Resource Availability Assessment: Real-time monitoring of resource usage in each factory, including equipment operating status and personnel attendance, to assess resource availability; S3. Resource Allocation Optimization: Based on the resource demand forecast and resource availability assessment results, a resource allocation model is established using linear programming to minimize resource idle costs and meet production needs, and the optimal resource allocation scheme is solved. It also includes a production monitoring interface module and an early warning mechanism module; Production monitoring interface module: used to display the production status, equipment operation status and production progress information of each factory in real time. Managers can view the production dynamics at any time through the monitoring interface, identify problems in a timely manner and take measures to solve them. Early warning mechanism module: By setting early warning thresholds, when an abnormal situation occurs during the production process, the system will promptly issue early warning information and provide corresponding solutions and suggestions. The early warning information will be sent to relevant managers via SMS and interface pop-up. It also includes a wired network module, a wireless network module, and an internet module; Wired network module: Establish a wired local area network within each factory to transmit data collected by equipment sensors and logistics sensors to the factory's data server via the wired network; Wireless network module: Used for data transmission via wireless network for some mobile devices and areas where cabling is difficult; Internet module: Used to connect the data servers of each factory to the data center of the corporate headquarters, enabling data sharing and interaction among multiple factories. At the same time, the corporate headquarters can communicate with suppliers and customers through the Internet to obtain raw material supply information and market demand information in a timely manner. In this embodiment, a genetic algorithm is used to solve the problem. By initializing the population, multiple production task allocation schemes are generated, providing a rich foundation for optimization. This effectively shortens the production cycle, improves production efficiency, reduces transportation costs, saves enterprise expenses, increases order fulfillment rate, enhances customer satisfaction, thereby improving the enterprise's competitiveness in the market, effectively reduces resource idleness and waste, lowers enterprise operating costs, ensures that production needs are met in a timely manner, guarantees the smooth operation of the production process, improves overall production efficiency, and helps the enterprise achieve sustainable development and maximize benefits.

[0023] Example 3 Based on Example 1, such as Figures 4 to 10 As shown, the present invention provides a multi-factory tire production collaborative scheduling and resource allocation system, wherein the transport vehicle includes a carrying device, a shell 101, a top cover 102, a storage rack 103, guide wheels 104, a support pad 105, and a sensor 106. An opening is provided at the top of the housing 101; The bottom of the top cover 102 is connected to the top of the storage rack 103; The storage rack 103 is divided into multiple compartments; Multiple sets of guide wheels 104 are rotatably installed on the lower part of the outer side wall of the storage rack 103; Multiple sets of support pads 105 are all set at the bottom of the inner side wall of the housing 101; Multiple sets of sensors 106 are respectively installed in multiple compartments of the storage rack 103; The support device is located at the bottom of the housing 101. The support device is used to move the housing 101 and to fix the bottom of the storage rack 103. The supporting device includes a driving device, a positioning box 201, a strip-shaped through hole 202, a connecting column 203, a strip-shaped positioning component 204, a base 205, a traveling wheel 206, and a universal wheel 207; The positioning box 201 is rotatably mounted on the base 205; The strip-shaped through hole 202 is provided at the top of the positioning box 201; The connecting post 203 is located at the bottom of the storage rack 103; The strip positioning element 204 is provided at the bottom end of the connecting post 203, and the shape of the strip positioning element 204 matches the shape of the strip through hole 202; The drive unit is mounted on the positioning box 201 and the base 205, and the drive unit is used to provide power for the rotation of the positioning box 201. The top of the base 205 is connected to the bottom of the housing 101; Both the traveling wheel 206 and the swivel wheel 207 are rotatably mounted on the bottom of the base 205; The drive device includes a bearing 301, a worm gear 302, a motor 303, and a worm wheel 304; The bearing seat 301 is mounted on the outer side wall of the base 205; The worm gear 302 is rotatably mounted on the bearing 301; Motor 303 is mounted on the outer wall of bearing 301, and the output end of motor 303 is connected to worm gear 302; The worm gear 304 is installed on the outer wall of the positioning box 201, and the worm gear 304 meshes with the worm 302. It also includes connector 401 and traction arm 402; Connector 401 is mounted on the outer wall of base 205; The end of the traction arm 402 is rotatably mounted on the connector 401; It also includes boom 501; The boom 501 is installed at the top of the top cover 102; different goods to be transferred are placed into multiple compartments of the storage rack 103, improving the convenience of separate storage and transfer of different types of goods. Multiple sets of sensors 106 sense the goods stored in different compartments, improving the convenience of collecting information on goods retrieval and placement. After the goods are stored on the storage rack 103, the storage rack 103 is placed inside the housing 101, and the top opening of the housing 101 is closed by the top cover 102, thereby sealing the goods. The bottom of the storage rack 103 is fixed by the bearing device, and at the same time, the bearing device moves the housing 101, thereby improving the safety, efficiency and convenience of goods transfer; after the storage rack 103 is placed inside the housing 101, the strip positioning member 204 points downwards. The strip-shaped through-hole 202 is inserted into the positioning box 201. Then, the positioning box 201 is rotated by the drive device, so that the angle of the strip-shaped through-hole 202 and the strip-shaped positioning element 204 form an intersection angle, thereby locking the strip-shaped positioning element 204 inside the positioning box 201 and preventing it from detaching, thus achieving the positioning of the storage rack 103. When it is necessary to remove the storage rack 103 upwards, the positioning box 201 is rotated and reset by the drive device. At this time, the angle of the strip-shaped through-hole 202 and the angle of the strip-shaped positioning element 204 are aligned, which makes it easy to move the strip-shaped positioning element 204 upwards out of the positioning box 201, improving the convenience and reliability of fixing the storage rack 103. The movement is improved by the cooperation of the traveling wheels 206 and the universal wheels 207 with the support base 205, which improves the convenience of transferring goods.

[0024] The main functions achieved by this invention are: 1. By optimizing multi-factory collaborative scheduling and production scheduling, reduce equipment idle time and production cycle, improve the efficiency of tire production, optimize resource allocation and production task allocation, and reduce raw material procurement costs, equipment idle costs and transportation costs. 2. The genetic algorithm is used to solve the problem. By initializing the population, multiple production task allocation schemes are generated, which provides a rich foundation for optimization, effectively shortens the production cycle and improves production efficiency. 3. Improve the safety, efficiency, and convenience of cargo transshipment.

[0025] The motor 303 of the multi-factory tire production collaborative scheduling and resource allocation system of the present invention is commercially available. Technical personnel in this industry only need to install and operate it according to the accompanying instruction manual, without requiring any creative work from those skilled in the art.

[0026] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A multi-factory tire production collaborative scheduling and resource allocation system, characterized in that, It includes a unified order management center, a data acquisition unit, a data processing and analysis unit, a factory capability twin model unit, a collaborative scheduling and resource allocation unit, an operation interface unit, and a report generation and query unit; Unified Order Management Center: Used to collect, parse, and manage all customer orders, forming a unified global order pool; The data acquisition unit includes equipment sensors, logistics sensors, and a manual input terminal; Equipment sensors: Sensors are installed on production equipment in various factories to collect real-time information on equipment operating status, production parameters, and production progress. Logistics sensors: Sensors are installed in warehouses and transport vehicles to collect real-time information on raw material inventory, work-in-process inventory, finished product inventory, as well as the location and transportation status of transport vehicles; Manual input terminal: Used for operators to manually input information that cannot be collected by sensors; The data processing and analysis unit includes a data cleaning and preprocessing module, a data storage and management module, and a data analysis and mining module; Data cleaning and preprocessing module: It is connected to the data acquisition layer and is used to clean and preprocess the acquired raw data, remove noisy and erroneous data, fill in missing data, and convert the data into a unified format. Data storage and management module: This module stores the cleaned and preprocessed data in the database, which uses a distributed architecture. It also categorizes, stores, and manages the data. Data Analysis and Mining Module: Utilizing data mining algorithms and machine learning techniques, this module analyzes and mines data in the database to extract valuable information and patterns. It analyzes factors influencing production efficiency, bottlenecks in resource utilization, and changing trends in market demand. Factory Capability Twin Model Unit: Connected to the data acquisition layer, it establishes a high-fidelity digital twin model for each physical factory based on the data acquired from the data acquisition layer, and updates the dynamic capacity data, resource inventory data, work-in-process status, and cost parameters of each factory in real time. The collaborative scheduling and resource allocation unit includes a production task collaborative allocation module, a production scheduling optimization module, and a resource allocation optimization module. Production task collaborative allocation module: Based on market demand, order information, production progress of each factory, and equipment status, the collaborative scheduling algorithm is used to rationally allocate production tasks. By considering the semi-finished product supply and circulation production collaboration relationships between factories, the rational allocation of production tasks is achieved. Production scheduling optimization module: For the production process within each factory, a collaborative scheduling algorithm is used to optimize the production sequence and production time arrangement, reduce equipment idle time and production cycle. At the same time, in a multi-factory environment, the transportation time and transportation cost between factories are considered, and production scheduling is combined with transportation scheduling. Resource allocation optimization module: Utilizes a dynamic resource allocation model to comprehensively evaluate and analyze the human, material, and financial resources of multiple factories. Based on the needs of production tasks and the actual situation of each factory, it rationally allocates resources by relocating idle equipment to factories with busy production tasks. User interface unit: Provides users with an intuitive and convenient user interface; Report generation and query unit: Generates production progress reports, equipment utilization reports, and resource consumption reports according to user needs.

2. The multi-factory tire production collaborative scheduling and resource allocation system as described in claim 1, characterized in that, The collaborative scheduling algorithm establishes a multi-objective optimization model by comprehensively considering factors such as the production capacity, equipment status, transportation costs, and order priority of each factory. The model aims to minimize the production cycle, reduce transportation costs, and improve the order fulfillment rate. It uses a genetic algorithm to solve the problem and obtains the optimal allocation scheme of production tasks among multiple factories through continuous iteration and optimization. The algorithm steps are as follows: S1. Initialize the population: Randomly generate a set of production task allocation schemes as the initial population; S2. Fitness Assessment: Calculate the fitness value of each individual based on the multi-objective optimization model. The higher the fitness value, the better the solution. S3. Selection operation: Use roulette wheel selection to select individuals with higher fitness to enter the next generation of the population; S4. Crossover operation: Perform a crossover operation on the selected individuals to generate new individuals and increase the diversity of the population; S5. Mutation operation: Perform mutation operation on the new individual to prevent the algorithm from getting trapped in a local optimum; S6. Termination condition judgment: When the preset number of iterations or the fitness value meets the requirements, the algorithm terminates and outputs the optimal solution.

3. The multi-factory tire production collaborative scheduling and resource allocation system as described in claim 1, characterized in that, The resource dynamic allocation model is based on real-time production data and market demand information. It establishes a resource demand prediction model to predict the resource demand of each factory in different time periods. At the same time, considering the availability and sharing of resources, it establishes a resource allocation model to realize the dynamic allocation of resources. The specific implementation of the model is as follows: S1. Resource Demand Forecasting: Using time series analysis methods, historical production data is analyzed and mined to establish a resource demand forecasting model. Based on the forecast results, the resource demand of each factory in the future period is determined. S2. Resource Availability Assessment: Real-time monitoring of resource usage in each factory, including equipment operating status and personnel attendance, to assess resource availability; S3. Resource Allocation Optimization: Based on the resource demand forecast and resource availability assessment results, a resource allocation model is established using linear programming methods. The goal is to minimize the cost of idle resources and meet production needs, and to find the optimal resource allocation scheme.

4. The multi-factory tire production collaborative scheduling and resource allocation system as described in claim 1, characterized in that, It also includes a production monitoring interface module and an early warning mechanism module; Production monitoring interface module: used to display the production status, equipment operation status and production progress information of each factory in real time. Managers can view the production dynamics at any time through the monitoring interface, identify problems in a timely manner and take measures to solve them. Early warning mechanism module: By setting early warning thresholds, when abnormal situations occur during the production process, the system will promptly issue early warning information and provide corresponding solutions and suggestions. The early warning information will be sent to relevant managers via SMS and interface pop-ups.

5. The multi-factory tire production collaborative scheduling and resource allocation system as described in claim 1, characterized in that, It also includes a wired network module, a wireless network module, and an internet module; Wired network module: Establish a wired local area network within each factory to transmit data collected by equipment sensors and logistics sensors to the factory's data server via the wired network; Wireless network module: Used for data transmission via wireless network for some mobile devices and areas where cabling is difficult; Internet module: Used to connect the data servers of each factory to the data center of the corporate headquarters, enabling data sharing and interaction among multiple factories. At the same time, the corporate headquarters can communicate with suppliers and customers through the Internet to obtain raw material supply information and market demand information in a timely manner.

6. The multi-factory tire production collaborative scheduling and resource allocation system as described in claim 1, characterized in that, The transport vehicle includes a load-bearing device, a shell (101), a top cover (102), a storage rack (103), guide wheels (104), a support pad (105), and a sensor (106). An opening is provided at the top of the housing (101); The bottom of the top cover (102) is connected to the top of the storage rack (103); The storage rack (103) is divided into multiple compartments; Multiple sets of guide wheels (104) are rotatably installed on the lower part of the outer side wall of the storage rack (103); Multiple sets of support pads (105) are all set at the bottom of the inner side wall of the housing (101); Multiple sets of sensors (106) are respectively installed in multiple compartments of the storage rack (103); The support device is located at the bottom of the housing (101). The support device is used to move the housing (101) and to fix the bottom of the storage rack (103).

7. The multi-factory tire production collaborative scheduling and resource allocation system as described in claim 6, characterized in that, The bearing device includes a drive device, a positioning box (201), a strip-shaped through hole (202), a connecting column (203), a strip-shaped positioning component (204), a base (205), a traveling wheel (206), and a universal wheel (207). The positioning box (201) is rotatably mounted on the base (205); A strip-shaped through hole (202) is provided at the top of the positioning box (201); The connecting post (203) is located at the bottom of the storage rack (103); A strip positioning element (204) is provided at the bottom end of the connecting post (203), and the shape of the strip positioning element (204) matches the shape of the strip through hole (202); The drive unit is installed on the positioning box (201) and the base (205), and the drive unit is used to provide power for the rotation of the positioning box (201); The top of the base (205) is connected to the bottom of the housing (101); The traveling wheels (206) and the casters (207) are both mounted on the bottom of the base (205) in a rotating manner.

8. The multi-factory tire production collaborative scheduling and resource allocation system as described in claim 7, characterized in that, The drive device includes a bearing (301), a worm (302), a motor (303), and a worm wheel (304). The bearing seat (301) is mounted on the outer wall of the base (205); The worm (302) is rotatably mounted on the bearing (301); The motor (303) is mounted on the outer wall of the bearing seat (301), and the output end of the motor (303) is connected to the worm (302); The worm gear (304) is installed on the outer wall of the positioning box (201), and the worm gear (304) meshes with the worm (302).

9. A multi-factory tire production collaborative scheduling and resource allocation system as described in claim 7, characterized in that, It also includes a connector (401) and a traction arm (402); The connector (401) is mounted on the outer wall of the base (205); The end of the traction arm (402) is rotatably mounted on the connector (401).

10. A multi-factory tire production collaborative scheduling and resource allocation system as described in claim 6, characterized in that, It also includes the boom (501); The boom (501) is located at the top of the top cover (102).

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