Multi-variety and multi-frequency automatic weft yarn distribution method and device in home textile industry

Abstract

The embodiment of the invention discloses an automatic distribution method and device for multi-variety and multi-frequency weft yarns in the home textile industry. The method comprises the steps of obtaining a weft yarn production order; performing analysis and demand prediction on the weft yarn production order to obtain a prediction result; analyzing the inventory of the weft yarns to obtain an analysis result; determining a distribution scheme according to the prediction result and the analysis result; and a distribution path is optimized according to the distribution scheme, and automatic distribution is carried out. By implementing the method provided by the embodiment of the invention, the delivery efficiency can be improved, human errors can be reduced, and the production response speed and flexibility can be improved.

Description

Technical Field

[0001] The invention relates to an automation algorithm for textile machinery, and more specifically to an automatic delivery method and device for multi-variety and multi-frequency weft yarns in the home textile industry. Background Art

[0002] In the home textile industry, a wide variety of products, different order volumes and frequent changes are common characteristics. The traditional manual or semi-automatic weft yarn delivery method has shown some obvious limitations and is difficult to meet the current efficient and accurate production needs. However, the manual delivery method requires a lot of manpower due to manual operation. Especially in large-scale production or multi-variety orders, workers need to frequently carry and distribute weft yarns, which is a test of physical strength and energy; manual operation is prone to errors and confusion, such as wrong weft yarn specifications, quantities or staggered delivery targets, which will directly affect the production quality and delivery time of the product, and increase the subsequent correction and remediation costs; due to the unpredictability of manual operation, the production progress is often restricted by the speed and accuracy of delivery. This limitation is particularly significant when facing urgent orders or production plans that need to be adjusted quickly, affecting the company's sensitivity and response speed to market changes; traditional delivery methods are usually batch operations, which are difficult to flexibly adjust according to real-time needs. This leads to low resource utilization of the production line and insufficient production capacity, especially in a market environment with increasing customization needs.

[0003] Therefore, it is necessary to design a new method to improve distribution efficiency, reduce human errors, and increase production response speed and flexibility. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects of the prior art and provide a method and device for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry.

[0005] To achieve the above purpose, the present invention adopts the following technical solution: a method for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry, comprising:

[0006] Get weft yarn production order;

[0007] Analyzing and forecasting the demand of the weft yarn production order to obtain a forecast result;

[0008] Analyze the inventory of weft yarn to obtain analysis results;

[0009] Determine a distribution plan according to the prediction result and the analysis result;

[0010] The delivery route is optimized according to the delivery plan, and the delivery is automated.

[0011] A further technical solution is: parsing and forecasting the demand of the weft yarn production order to obtain a forecast result includes:

[0012] parsing key information in the weft yarn production order, wherein the key information includes product type, yarn type, quantity, production date and delivery date;

[0013] The demand for weft yarn of each loom at different time nodes is calculated based on the key information, and combined with the corresponding time nodes to obtain a prediction result.

[0014] A further technical solution is: the analysis of the weft yarn inventory to obtain the analysis result includes:

[0015] Use ABC classification method to classify and manage the inventory of weft yarn;

[0016] For each weft yarn, mark and record it according to its storage time to ensure that the first weft yarn stored in the warehouse is sent out first. Set up a clear storage area in the warehouse, store different batches of weft yarn in different locations, and mark the storage time and weft yarn information;

[0017] Use modern warehouse management systems or ERP systems to monitor and manage inventory data in real time, and regularly collect statistics and analyze inventory data to generate inventory reports and first-in-first-out implementation reports for various types of weft yarns, and collect statistics on data that can be distributed at any time to obtain analysis results.

[0018] A further technical solution is: determining the distribution plan according to the prediction result and the analysis result includes:

[0019] Setting basic delivery goals based on the prediction results and the analysis results;

[0020] Use optimization algorithms to design the best delivery routes based on customer location, demand, and traffic conditions combined with basic goals;

[0021] According to the prediction results and the optimized delivery routes, a specific delivery plan is formulated to obtain a definite delivery solution.

[0022] A further technical solution is: optimizing the delivery path according to the delivery plan and performing automated delivery, including:

[0023] A heuristic algorithm is used to generate a preliminary path solution according to the preset optimization goal;

[0024] Use linear programming models to optimize the initial path solution and consider multiple constraints to achieve the optimal delivery path;

[0025] Automated delivery is performed by automated guided vehicles according to dispatch instructions.

[0026] A further technical solution is: the automatic distribution by the automatic guided vehicle according to the dispatching instruction includes:

[0027] Design and implement automated delivery dispatch systems;

[0028] Use GPS and IoT technologies to monitor the location and operation of automated guided vehicles in real time;

[0029] Design an automated delivery process and use automated guided vehicles for automated delivery.

[0030] Its further technical solution is: after optimizing the delivery path according to the delivery plan and performing automated delivery, it also includes:

[0031] Obtain feedback information collected by the automated guided vehicle;

[0032] The feedback information is processed and analyzed, and adjustments to inventory and distribution plans are updated.

[0033] The present invention also provides a multi-variety and multi-frequency weft yarn automatic delivery device for the home textile industry, comprising:

[0034] An order acquisition unit, used for acquiring weft yarn production orders;

[0035] A prediction unit, used for analyzing and predicting the demand of the weft yarn production order to obtain a prediction result;

[0036] An analysis unit, used for analyzing the inventory of the weft yarn to obtain an analysis result;

[0037] A distribution plan determination unit, used to determine a distribution plan according to the prediction result and the analysis result;

[0038] The distribution optimization unit is used to optimize the distribution route according to the distribution plan and perform automated distribution.

[0039] The present invention further provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor implements the above method when executing the computer program.

[0040] The present invention also provides a storage medium, wherein the storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0041] The beneficial effects of the present invention compared with the prior art are as follows: the present invention obtains weft yarn production orders and performs demand forecasting and inventory analysis to formulate effective distribution plans, and improves distribution efficiency, reduces human errors, and improves production response speed and flexibility by optimizing routes and automating distribution.

[0042] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0044] Figure 1 A schematic diagram of an application scenario of a method for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry provided by an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of a process flow of a method for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry provided by an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of a sub-process of a method for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry provided by an embodiment of the present invention;

[0047] Figure 4 A schematic diagram of a sub-process of a method for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry provided by an embodiment of the present invention;

[0048] Figure 5 A schematic diagram of a sub-process of a method for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry provided by an embodiment of the present invention;

[0049] Figure 6 A schematic diagram of a sub-process of a method for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry provided by an embodiment of the present invention;

[0050] Figure 7 A schematic diagram of a sub-process of a method for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry provided by an embodiment of the present invention;

[0051] Figure 8 A schematic diagram of a process flow of a method for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry provided by another embodiment of the present invention;

[0052] Fig. 9 A schematic block diagram of a multi-variety and multi-frequency weft yarn automatic delivery device for the home textile industry provided by an embodiment of the present invention;

[0053] Fig.10 A schematic block diagram of a multi-variety and multi-frequency weft yarn automatic delivery device for the home textile industry provided by another embodiment of the present invention;

[0054] Fig.11A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0057] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0058] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0059] See also Figure 1 and Figure 2 , Figure 1 A schematic diagram of an application scenario of the method for automatic delivery of multiple varieties and multiple frequencies of weft yarns in the home textile industry provided by an embodiment of the present invention. Figure 2 A schematic flow chart of a method for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry provided by an embodiment of the present invention. The method for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry is applied to a server. The server interacts with an automatic guided vehicle and a terminal to automatically distribute weft yarns based on artificial intelligence and big data technology, thereby realizing automatic classification, prediction and distribution of weft yarns and improving distribution efficiency and accuracy.

[0060] The method of this embodiment can be embedded into the production management system of home textile enterprises through software development, and seamlessly connected with existing automation equipment (such as intelligent robots, AGVs, IoT sensors, etc.). In specific implementation, customized development can be carried out according to the actual situation of the enterprise to meet the needs of different scales and different production modes.

[0061] Figure 21 is a flow chart of a method for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry provided by an embodiment of the present invention. Figure 2 As shown, the method includes the following steps S110 to S150.

[0062] S110, obtaining a weft yarn production order.

[0063] In this example, it is a production order in the textile industry, specifically involving the production and processing of weft yarn. Weft yarn is a fiber form in the spinning process, which refers to the fiber bundles straightened along the weft direction of the textile. In textile production, a weft yarn production order refers to an instruction or document containing detailed production requirements and specifications issued by a company or factory in order to produce a specific fabric or product based on market demand or internal planning.

[0064] Specifically, a weft yarn production order usually includes the following:

[0065] Product specifications and requirements: including specific product specifications such as the fiber composition of the weft yarn, yarn thickness, strength requirements, color, etc.

[0066] Production quantity and delivery time: Specify the production quantity and the time required to complete production and delivery.

[0067] Production process and procedures: including detailed production process information such as production lines, process steps, machine equipment usage, and quality control standards.

[0068] Cost and expense budget: includes estimated production costs, raw material costs, labor costs and other economic indicators.

[0069] Quality requirements and inspection standards: Specify product quality standards and inspection requirements to ensure that the final product meets customer or market needs.

[0070] These orders are processed and tracked through the supply chain management system or the company's internal production management system to ensure that the production process proceeds smoothly as planned and ultimately completes products that meet customer needs.

[0071] S120: Analyze and forecast the demand of the weft yarn production order to obtain a forecast result.

[0072] In this embodiment, the prediction result refers to the demand quantity of each loom for weft yarn and the predicted value of the time node.

[0073] In this embodiment, the system is used to monitor the inventory status of weft yarns in the warehouse, and combined with historical sales data and current order status, the demand for various types of weft yarns in the future is predicted to provide data support for procurement and distribution.

[0074] In one embodiment, see Figure 3, the above-mentioned step S120 may include steps S121 to S122.

[0075] S121. Analyze key information in the weft yarn production order, wherein the key information includes product type, yarn type, quantity, production date and delivery date.

[0076] In this embodiment, step S121 mainly extracts key information from the weft yarn production order, including but not limited to:

[0077] Product Category: Specifies the specific type of product to be produced for an order, which may include different fiber compositions, yarn specifications, etc.

[0078] Yarn type: including the material, thickness, strength and other characteristics of the yarn.

[0079] Quantity: The number of weft yarns required for the order, usually given in weight or length.

[0080] Production Dates and Delivery Periods: Determine the start date and final delivery date of a production order, which are critical to production planning and inventory management.

[0081] Automated data extraction can automatically extract order information through the enterprise resource planning (ERP) system or production management system.

[0082] Manual review and confirmation ensures that the information extracted from the order is accurate.

[0083] By accurately extracting order information, you can ensure the accuracy and timeliness of production planning and supply chain management. Knowing order quantities and delivery dates helps optimize inventory management and avoid inventory backlogs or supply disruptions.

[0084] In addition, daily order information is collected, including order number, customer name, product specifications, quantity, delivery date, etc. Time feature data and product data are grouped and merged by sales region and product code to ensure data integrity and consistency.

[0085] A demand forecasting model can be built using an integrated algorithm based on LightGBM. This method uses grid exploration to tune parameters and uses information from historical order data for training.

[0086] The specific steps include:

[0087] Data cleaning: Delete redundant data and outliers, and use mean interpolation to fill missing values ​​to ensure sequence continuity;

[0088] Feature engineering: Introduce time series features such as holidays, seasonal changes, time periods, and major category features such as product categories and regions;

[0089] Model training: Input the cleaned data into the LightGBM model for training, and optimize the model parameters through cross-validation and grid search;

[0090] Use the trained model to predict future order demand. For example, you can use the product demand data from the previous three months to predict demand for the next month.

[0091] The forecast results should take into account the mutual constraints of different products and the correlation between different models of products;

[0092] According to the predicted demand, formulate a reasonable production plan and schedule. The specific steps include:

[0093] Determine the quantity of materials needed, including the amount of yarn, number of weaving machines, and time requirements for dyeing and finishing machines;

[0094] Prepare detailed production orders, specifying production quantity and delivery date.

[0095] Use ERP system or similar software for material requirements planning (MRP) to generate detailed production plans.

[0096] During the production process, weft yarn sensors and other equipment are used to monitor yarn tension and movement in real time to ensure the stability of the production process and product quality;

[0097] According to the actual production situation and changes in market demand, timely adjust production plans and schedules to cope with emergencies and market changes.

[0098] Through the above steps, the demand and time nodes of each loom for weft yarn can be accurately predicted, thereby optimizing supply chain management and improving production efficiency and customer satisfaction.

[0099] S122. Calculate the demand for weft yarn of each loom at different time nodes according to the key information, and combine it with the corresponding time nodes to obtain a prediction result.

[0100] In this embodiment, based on the order information obtained through analysis, the demand for weft yarn of each loom at different time points is calculated and predicted to determine a suitable purchase and distribution plan.

[0101] Demand calculation: Combined with product type, yarn type and order quantity, calculate the amount of weft yarn required for each loom at each time point.

[0102] Time node forecasting: Use historical sales data and current order status, combined with forecasting models (such as time series analysis, machine learning models, etc.) to predict the demand for weft yarn at each time node in the future.

[0103] Determining the demand at each time point helps to develop more effective production plans and avoid surpluses or shortages. Knowing the demand in advance can optimize the procurement and distribution of raw materials, reduce costs and ensure the continuity and stability of production.

[0104] In summary, through the above steps, companies can achieve more accurate production and supply chain management based on customer orders and forecasted demand, improve production efficiency and customer satisfaction, while reducing inventory and operating costs.

[0105] S130: Analyze the inventory of weft yarn to obtain analysis results.

[0106] In this embodiment, the analysis result refers to the classification of each weft yarn and each box weight, and then the statistics of the obtained data can be distributed at any time in combination with the first-in-first-out rule.

[0107] In one embodiment, see Figure 4 , the above-mentioned step S130 may include steps S131 to S133.

[0108] S131. Use the ABC classification method to classify and manage the inventory of weft yarn;

[0109] S132. For each weft yarn, mark and record it according to its storage time to ensure that the weft yarn that is first stored is sent out first. Set up a clear storage area in the warehouse, store different batches of weft yarn in different locations, and mark the storage time and weft yarn information.

[0110] S133. Use modern warehouse management system or ERP system to monitor and manage inventory data in real time, and regularly collect statistics and analyze inventory data, generate inventory reports and first-in-first-out implementation reports for various types of weft yarns, and collect statistics of data that can be distributed at any time to obtain analysis results.

[0111] Specifically, the inventory data of all weft yarns is collected, including the type, quantity, weight of each box of each weft yarn, etc.; these data are classified by the type of weft yarn and the weight of each box. For example, weft yarns can be divided into three categories: light, medium and heavy, and each category is further subdivided into different weight levels.

[0112] Weft yarns are classified using the ABC classification method. Based on sales, sales volume, product characteristics, profit contribution rate, and inventory value, weft yarns are divided into Class A, Class B, and Class C.

[0113] Category A: Weft yarns with high sales or high profits but small quantities.

[0114] Category B: Weft yarns with medium sales or medium profits but large quantities.

[0115] Category C: Weft yarns with low sales or low profits and the largest quantity.

[0116] For each weft yarn, mark and record it according to its storage time to ensure that the weft yarn that is first stored is the first to be sent out; set up clear storage areas in the warehouse, store different batches of weft yarn in different locations, and mark the storage time and weft yarn information.

[0117] Use a modern warehouse management system (WMS) or ERP system to monitor and manage inventory data in real time. The system automatically updates the inventory status and location of each weft yarn; the system should be able to display the inventory quantity, batch information and corresponding first-in-first-out order of each weft yarn in real time.

[0118] Regularly check the accuracy of inventory data to ensure that all records are accurate; adjust classification standards and first-in-first-out strategies in a timely manner according to actual demand and market changes to maintain the flexibility and effectiveness of inventory management.

[0119] Regularly collect statistics and analyze inventory data to generate inventory reports and first-in-first-out implementation reports for various types of weft yarns; analysis reports can help management understand which weft yarns need to be processed first and which weft yarns may need to increase or reduce inventory.

[0120] Through the above steps, it is possible to effectively classify each type of weft yarn and each box weight in inventory, and combine it with the first-in-first-out rule to calculate the data that can be allocated at any time, thereby improving inventory management efficiency, reducing inventory costs, and enhancing the company's operational capabilities.

[0121] S140: Determine a distribution plan based on the prediction result and the analysis result.

[0122] In this embodiment, the distribution plan refers to the distribution quantity required for each order, each loom, and each weft yarn.

[0123] In one embodiment, see Figure 5 , the above-mentioned step S140 may include steps S141 to S143.

[0124] S141. Set basic distribution goals based on the prediction results and the analysis results.

[0125] In this embodiment, the basic goals of distribution are set according to the predicted demand and analysis results. These goals usually include the following aspects:

[0126] Service level requirements: Determine customer requirements for delivery times, such as just-in-time delivery or fixed delivery dates.

[0127] Cost-effectiveness considerations: Consider the optimization of distribution costs, including costs of manpower, fuel, vehicles, etc.

[0128] Environmental impact: Consider the impact of reducing carbon emissions or traffic congestion on the environment.

[0129] Determining clear delivery targets helps maintain consistency and efficiency in the subsequent optimization process; setting targets based on customer needs ensures that customer expectations are met in terms of time or cost, thereby enhancing customer satisfaction.

[0130] S142. Use optimization algorithms to design optimal delivery routes based on customer location, demand, and traffic conditions combined with basic objectives.

[0131] In this embodiment, an ant colony algorithm, a genetic algorithm, a simulated annealing algorithm, etc. may be used to find the best path through computer simulation and optimization. Real-time traffic data is used, and factors such as traffic congestion and road conditions are considered.

[0132] By optimizing routes, reducing mileage and time, fuel consumption and labor costs are reduced; ensuring that delivery is completed in the shortest time possible, and improving the timeliness and reliability of delivery.

[0133] Of course, you can also use the following algorithm to design the optimal delivery route.

[0134] A* algorithm: A* algorithm is a global path planning method that combines the advantages of Dijkstra algorithm and best-first search. The algorithm finds the shortest path by combining the actual cost from the current node to the target node and the heuristic estimated cost.

[0135] Dijkstra algorithm: Dijkstra algorithm is used for path search in weighted graphs to find the shortest path from one node to all other nodes. It is suitable for static path planning and can plan the transportation path of an automated guided vehicle in a predetermined environment.

[0136] Genetic Algorithm: Genetic algorithm simulates the biological evolution process and optimizes the solution through operations such as selection, crossover and mutation. It is suitable for dynamic path planning and adjusts the transportation path according to real-time logistics demand and environmental changes.

[0137] Time Window Algorithm: The time window algorithm solves the path conflict and deadlock problems in multiple automated guided vehicle systems. By setting time windows to coordinate the paths of different automated guided vehicles to avoid conflicts, it is usually used in combination with path planning methods such as the A* algorithm to achieve global conflict-free path planning.

[0138] S143. Formulate a specific distribution plan based on the prediction results and the optimized distribution route to obtain a confirmed distribution solution.

[0139] In this embodiment, the specific delivery time of each order is determined, and a delivery schedule is formulated; appropriate vehicles and personnel resources are allocated to ensure that the delivery task can be carried out smoothly.

[0140] Planning ahead can help prevent potential delivery problems, such as traffic jams or lack of manpower.

[0141] Able to adjust according to real-time situation to adapt to different customer needs and transportation conditions.

[0142] In summary, these three steps together constitute a complete distribution optimization process. By setting clear goals, using optimization algorithms to design optimal routes, and formulating detailed distribution plans, it can significantly improve distribution efficiency, reduce costs, and increase customer satisfaction.

[0143] S150: Optimize the delivery route according to the delivery plan and perform automated delivery.

[0144] In one embodiment, see Figure 6 , the above-mentioned step S150 may include steps S151 to S153.

[0145] S151. Generate a preliminary path solution using a heuristic algorithm according to a preset optimization goal.

[0146] In this embodiment, a heuristic algorithm is used to generate a preliminary delivery path solution to serve as a basis for subsequent optimization. A heuristic algorithm is a method for finding solutions, which is usually used to solve complex combinatorial optimization problems.

[0147] Specifically, you can choose the most appropriate algorithm such as simulated annealing algorithm, genetic algorithm, ant colony algorithm, etc. according to the characteristics of the specific problem. The algorithm generates preliminary distribution path solutions based on the preset optimization goals and constraints. These paths may be generated based on empirical rules or simple rules.

[0148] Heuristic algorithms can generate feasible preliminary solutions in a short time, providing a starting point for subsequent optimization. They can handle the diversity and uncertainty in actual distribution problems, such as different distribution locations, traffic conditions, etc.

[0149] Specifically, in the path planning process of AGV (automatic guided vehicle), using heuristic algorithms to generate preliminary path solutions is an efficient and commonly used method. Heuristic algorithms can provide a relatively high-quality driving path for AGV based on preset optimization goals, such as the shortest path, minimum energy consumption, and avoiding obstacles.

[0150] Specifically, the heuristic algorithm considers a series of factors to evaluate the pros and cons of different paths, such as path length, number of turns, distance to obstacles, etc. Through continuous iteration and optimization, the algorithm can gradually approach the optimal solution, thereby generating a preliminary path that meets the optimization goal.

[0151] In this process, AGV can flexibly adjust the optimization objectives and heuristic algorithm parameters according to actual needs and environmental changes. For example, in scenarios that require rapid response, path length can be given priority; while in scenarios that require energy conservation and emission reduction, energy consumption factors can be given more consideration.

[0152] In practical applications, it is also necessary to combine other methods and technologies, such as genetic algorithms, particle swarm optimization, etc., to further improve the quality and efficiency of path planning.

[0153] S152. Use the linear programming model to optimize the preliminary path solution and consider multiple constraints to achieve the optimal delivery path.

[0154] In this embodiment, a linear programming model is used to optimize the preliminary path solution, taking into account various constraints to achieve the optimal delivery path.

[0155] Specifically, the distribution problem is formalized as a linear programming problem, and the objective function and constraints are defined; mathematical optimization techniques, such as linear programming solvers, are used to optimize the preliminary path solution to achieve the goal of minimizing cost or maximizing efficiency.

[0156] The linear programming model ensures that the optimal delivery path is found, taking into account various constraints (such as time windows, vehicle capacity, etc.), thereby reducing costs and improving efficiency. The path can be precisely adjusted to adapt to real-time changing demand and environmental conditions.

[0157] S153. Automated delivery is performed by an automated guided vehicle according to dispatch instructions.

[0158] Specifically, the AGV uses sensors such as laser, infrared, and ultrasonic waves combined with automation and navigation technology to sense the surrounding environment and achieve autonomous navigation and obstacle avoidance. It uses laser or visual sensors and landmark QR codes to scan the surrounding environment, obtain landmarks or feature points, and then locate and build maps. These maps not only help the AGV determine its own position, but also provide a basis for path planning and navigation decisions, realizing an automated delivery process.

[0159] In one embodiment, see Figure 7 , the above-mentioned step S153 may include steps S1531 to S1533.

[0160] S1531. Design and implement an automated distribution scheduling system.

[0161] In this embodiment, an integrated delivery scheduling system is designed to receive optimized delivery path information and schedule automated guided vehicles; a suitable scheduling algorithm is developed to assign tasks to available automated guided vehicles to ensure efficiency and balanced load.

[0162] The automated system can reduce human scheduling errors and improve the accuracy and reliability of delivery tasks; the system can quickly respond to real-time changes in delivery demand, making the delivery process more flexible and efficient.

[0163] S1532. Use GPS and IoT technologies to monitor the location and operation of automated guided vehicles in real time.

[0164] In this embodiment, GPS and IoT technologies are installed on the AGV to collect real-time data such as vehicle location, speed, and operating status. A monitoring system is developed to track and manage the location and operating status of all AGVs in real time.

[0165] It can monitor the vehicle location in real time and adjust the delivery plan in time to deal with unexpected factors such as traffic and weather. Through real-time monitoring, it ensures the safe driving of vehicles and safe delivery of goods, and improves the reliability of services.

[0166] S1533. Design an automated delivery process and use automated guided vehicles for automated delivery.

[0167] In this embodiment, an automated delivery process is designed, including complete path planning and operation process from the starting point to the end point. The automated guided vehicle, dispatching system and monitoring system are integrated together to realize unmanned fully automated delivery.

[0168] The automated delivery process reduces human intervention, lowers operating costs and management costs. Automated guided vehicles can deliver seamlessly according to optimized routes and schedules, improving the efficiency and speed of delivery.

[0169] Based on the allocation calculation results, a dispatch instruction is issued, and the automatic guided vehicle (AGV) goes to the designated location and then transports it to the designated loom rack along the predetermined path.

[0170] In summary, these steps constitute a complete automated distribution optimization process, which generates preliminary path solutions through heuristic algorithms, optimizes path solutions through linear programming, and uses automated guided vehicles to achieve automated distribution, which can significantly improve distribution efficiency, reduce costs, and ensure high-quality distribution services.

[0171] The above method has the following benefits:

[0172] Improve production efficiency: Automated delivery reduces manual operations, speeds up delivery, and thus improves overall production efficiency.

[0173] Reduce error rate: Intelligent identification and scheduling systems reduce errors caused by human factors and improve the accuracy and consistency of product production.

[0174] Optimize inventory management: Real-time monitoring and forecasting of inventory status help companies arrange procurement plans reasonably and avoid problems such as excess or shortage of inventory.

[0175] Improve production line flexibility: The automation system can adapt to the production needs of multiple varieties and multiple frequencies, improving the flexibility and response speed of the production line.

[0176] Reduce operating costs: Automated distribution reduces labor costs, optimizes resource utilization, and effectively reduces the overall operating costs of the enterprise.

[0177] The above-mentioned method for automatic delivery of multi-variety and multi-frequency weft yarns in the home textile industry obtains weft yarn production orders and conducts demand forecasting and inventory analysis to formulate effective delivery plans, and improves delivery efficiency, reduces human errors, and increases production response speed and flexibility through optimized routes and automated delivery.

[0178] Figure 8 FIG. 1 is a flow chart of a method for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry provided by another embodiment of the present invention. Figure 8 As shown, a method for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry in this embodiment includes steps S210-S270. Steps S210-S250 are similar to steps S110-S150 in the above embodiment and are not described in detail. Steps S260-S270 added in this embodiment are described in detail below.

[0179] S260. Obtain feedback information collected by the automatic guided vehicle.

[0180] In this embodiment, the automated guided vehicle (AGV) collects various feedback information about the weft yarn (or other materials) transportation and distribution process. This information includes:

[0181] Location information: The current location of the AGV and its movement trajectory in the production site.

[0182] Transportation Status: The real-time status of material transportation, such as in transit, waiting, or completed.

[0183] Abnormal conditions: such as reports of equipment failures, route blockages, or other unexpected events.

[0184] The location and status of materials in the production process can be instantly understood, ensuring real-time controllability of the production and distribution process. It provides detailed monitoring of each stage in the production process, which helps to make timely adjustments and optimizations. The collected historical data can be used to analyze and optimize future distribution plans and processes.

[0185] S270: Process and analyze the feedback information, and update the inventory and distribution plan.

[0186] In this embodiment, the feedback information collected from the AGV is processed, analyzed and utilized to adjust the inventory management and distribution plan. Specifically, the following operations may be performed:

[0187] Information processing and analysis: Aggregate, parse and analyze the various types of feedback collected. This may involve the use of data analysis tools and algorithms to identify patterns, anomalies or potential opportunities for improvement.

[0188] Inventory Update: Update inventory information in the system based on real-time collected data. This helps avoid excess or shortage of inventory and optimizes the flow of materials in the supply chain.

[0189] Distribution plan adjustment: Adjust and optimize the material distribution plan based on the latest inventory status and real-time demand forecast. This includes rearranging AGV routes and task allocation to maximize efficiency and responsiveness.

[0190] Real-time updated inventory information and accurate demand forecasts help reduce inventory costs while ensuring efficient supply chain operations. Adjustments based on real-time data can minimize transportation time and costs and improve distribution efficiency. Automated data processing and update processes reduce human intervention, thereby reducing potential errors and operational risks.

[0191] In summary, steps S260 and S270 combine real-time data collection, intelligent analysis and automated adjustment, which significantly improves the efficiency, accuracy and flexibility of the production and distribution processes.

[0192] Fig. 9 1 is a schematic block diagram of a multi-variety and multi-frequency weft yarn automatic delivery device for the home textile industry provided by an embodiment of the present invention. Fig. 9 As shown, corresponding to the above multi-variety and multi-frequency weft automatic delivery method for the home textile industry, the present invention also provides a multi-variety and multi-frequency weft automatic delivery device 300 for the home textile industry. The multi-variety and multi-frequency weft automatic delivery device 300 for the home textile industry includes a unit for executing the above multi-variety and multi-frequency weft automatic delivery method for the home textile industry, and the device can be configured in a server. For details, please refer to Fig. 9 The multi-variety and multi-frequency weft yarn automatic distribution device 300 for the home textile industry includes an order acquisition unit 301, a prediction unit 302, an analysis unit 303, a distribution plan determination unit 304 and a distribution optimization unit 305.

[0193] The order acquisition unit 301 is used to acquire the weft production order; the prediction unit 302 is used to parse and predict the demand of the weft production order to obtain the prediction result; the analysis unit 303 is used to analyze the inventory of the weft to obtain the analysis result; the distribution plan determination unit 304 is used to determine the distribution plan according to the prediction result and the analysis result; the distribution optimization unit 305 is used to optimize the distribution path according to the distribution plan and perform automated distribution.

[0194] In one embodiment, the prediction unit 302 includes:

[0195] The parsing subunit is used to parse the key information in the weft yarn production order, wherein the key information includes product type, yarn type, quantity, production date and delivery period; the calculation subunit is used to calculate the demand for weft yarn of each loom at different time nodes based on the key information, and combine it with the corresponding time nodes to obtain a prediction result.

[0196] In one embodiment, the analysis unit 303 includes:

[0197] The classification subunit is used to classify and manage the inventory of weft yarn using the ABC classification method;

[0198] Set up a sub-unit to mark and record each weft yarn according to its storage time, to ensure that the first stored weft yarn is sent out first, and set up a clear storage area in the warehouse to store different batches of weft yarn in different locations, and mark the storage time and weft yarn information;

[0199] The management subunit is used to monitor and manage inventory data in real time using a modern warehouse management system or ERP system, and to regularly collect statistics and analyze inventory data, generate inventory reports and first-in-first-out implementation reports for various types of weft yarns, and collect statistics on data that can be distributed at any time to obtain analysis results.

[0200] In one embodiment, the delivery plan determination unit 304 includes:

[0201] A target setting subunit, used for setting a basic target of distribution according to the prediction result and the analysis result;

[0202] The route design subunit is used to design the optimal delivery route based on the customer location, demand and traffic conditions in combination with the basic objectives using an optimization algorithm;

[0203] The plan formulation subunit is used to formulate a specific distribution plan based on the prediction results and the optimized distribution route to obtain a definite distribution solution.

[0204] In one embodiment, the distribution optimization unit 305 includes:

[0205] A path generation subunit, used to generate a preliminary path solution using a heuristic algorithm according to a preset optimization target;

[0206] The optimization subunit is used to optimize the preliminary path solution using a linear programming model and to achieve the optimal delivery path by considering various constraints.

[0207] The distribution subunit is used to perform automated distribution by an automatic guided vehicle according to dispatch instructions.

[0208] In one embodiment, the delivery subunit comprises:

[0209] The system design subunit is used to design and implement automated distribution scheduling systems;

[0210] A monitoring subunit for real-time monitoring of the position and operation of the AGV using GPS and IoT technologies;

[0211] The distribution design subunit is used to design automated distribution processes and to perform automated distribution by automated guided vehicles.

[0212] Fig.11 FIG. 3 is a schematic block diagram of an automatic weft yarn delivery device 300 for the home textile industry with multiple varieties and multiple frequencies provided by another embodiment of the present invention. Fig.11 As shown, the multi-variety and multi-frequency weft yarn automatic delivery device 300 for the home textile industry of this embodiment is based on the above embodiment and adds an information acquisition unit and an update unit.

[0213] An information acquisition unit 306 is used to acquire feedback information collected by the automatic guided vehicle;

[0214] The updating unit 307 is used to process and analyze the feedback information and update the inventory and distribution plan adjustments.

[0215] It should be noted that technicians in the relevant field can clearly understand that the specific implementation process of the above-mentioned multi-variety and multi-frequency weft yarn automatic delivery device 300 for the home textile industry and each unit can refer to the corresponding description in the aforementioned method embodiment, and for the convenience and conciseness of the description, it will not be repeated here.

[0216] The above-mentioned multi-variety and multi-frequency weft yarn automatic delivery device 300 for the home textile industry can be implemented in the form of a computer program. The computer program can be used in Fig.11 Runs on the computer device shown.

[0217] See also Fig.11 , Fig.115 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 500 may be a server, wherein the server may be an independent server or a server cluster composed of multiple servers.

[0218] See also Fig.11 The computer device 500 includes a processor 502 , a memory and a network interface 505 connected via a system bus 501 , wherein the memory may include a non-volatile storage medium 503 and an internal memory 504 .

[0219] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions, and when the program instructions are executed, the processor 502 can execute a method for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry.

[0220] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500 .

[0221] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a method for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry.

[0222] The network interface 505 is used to communicate with other devices over the network. Fig.11 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0223] The processor 502 is used to run the computer program 5032 stored in the memory to implement the following steps:

[0224] Obtaining a weft yarn production order; parsing the weft yarn production order and forecasting demand to obtain forecast results; analyzing the weft yarn inventory to obtain analysis results; determining a distribution plan based on the forecast results and the analysis results; optimizing a distribution path based on the distribution plan, and performing automated distribution.

[0225] In one embodiment, when the processor 502 implements the step of parsing the weft yarn production order and predicting the demand to obtain the prediction result, the processor 502 specifically implements the following steps:

[0226] Analyze the key information in the weft yarn production order, wherein the key information includes product type, yarn type, quantity, production date and delivery period; calculate the demand for weft yarn of each loom at different time nodes based on the key information, and combine it with the corresponding time nodes to obtain a prediction result.

[0227] In one embodiment, when the processor 502 implements the step of analyzing the inventory of the weft yarn to obtain the analysis result, the processor 502 specifically implements the following steps:

[0228] Use the ABC classification method to classify and manage the inventory of weft yarns; for each type of weft yarn, mark and record it according to its storage time to ensure that the weft yarn that is first stored in the warehouse is the first to be issued, and set up clear storage areas in the warehouse, store different batches of weft yarns in different locations, and mark the storage time and weft yarn information; use a modern warehouse management system or ERP system to monitor and manage inventory data in real time, and regularly count and analyze inventory data, generate inventory reports and first-in-first-out execution reports for various types of weft yarns, and count data that can be allocated at any time to obtain analysis results.

[0229] In one embodiment, when the processor 502 implements the step of determining the delivery plan according to the prediction result and the analysis result, the processor 502 specifically implements the following steps:

[0230] The basic distribution objectives are set according to the prediction results and the analysis results; the optimal distribution route is designed according to the basic objectives based on the customer location, demand and traffic conditions using an optimization algorithm; and a specific distribution plan is formulated according to the prediction results and the optimized distribution route to obtain a definite distribution solution.

[0231] In one embodiment, the processor 502 implements the following steps when optimizing the delivery path according to the delivery plan and performing the automated delivery step:

[0232] A heuristic algorithm is used to generate a preliminary path solution based on the preset optimization goal. A linear programming model is used to optimize the preliminary path solution, and a variety of constraints are considered to achieve the optimal delivery path. Automated guided vehicles perform automated delivery according to dispatch instructions.

[0233] In one embodiment, when the processor 502 implements the step of performing automated delivery by the automated guided vehicle according to the dispatch instruction, the processor 502 specifically implements the following steps:

[0234] Design and implement an automated delivery dispatch system; use GPS and IoT technology to monitor the location and operation of automated guided vehicles in real time; design an automated delivery process and use automated guided vehicles for automated delivery.

[0235] In one embodiment, after optimizing the delivery path according to the delivery plan and performing the automated delivery step, the processor 502 further implements the following steps:

[0236] Obtaining feedback information collected by the automated guided vehicle; processing and analyzing the feedback information, and updating adjustments to inventory and delivery plans.

[0237] It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0238] It can be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiment of the above method.

[0239] Therefore, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein when the computer program is executed by a processor, the processor executes the following steps:

[0240] Obtaining a weft yarn production order; parsing the weft yarn production order and forecasting demand to obtain forecast results; analyzing the weft yarn inventory to obtain analysis results; determining a distribution plan based on the forecast results and the analysis results; optimizing a distribution path based on the distribution plan, and performing automated distribution.

[0241] In one embodiment, when the processor executes the computer program to implement the step of parsing the weft yarn production order and forecasting the demand to obtain the forecast result, the processor specifically implements the following steps:

[0242] Analyze the key information in the weft yarn production order, wherein the key information includes product type, yarn type, quantity, production date and delivery period; calculate the demand for weft yarn of each loom at different time nodes based on the key information, and combine it with the corresponding time nodes to obtain a prediction result.

[0243] In one embodiment, when the processor executes the computer program to implement the step of analyzing the inventory of weft yarns to obtain the analysis result, the processor specifically implements the following steps:

[0244] Use the ABC classification method to classify and manage the inventory of weft yarns; for each type of weft yarn, mark and record it according to its storage time to ensure that the weft yarn that is first stored in the warehouse is the first to be issued, and set up clear storage areas in the warehouse, store different batches of weft yarns in different locations, and mark the storage time and weft yarn information; use a modern warehouse management system or ERP system to monitor and manage inventory data in real time, and regularly count and analyze inventory data, generate inventory reports and first-in-first-out execution reports for various types of weft yarns, and count data that can be allocated at any time to obtain analysis results.

[0245] In one embodiment, when the processor executes the computer program to implement the step of determining the delivery plan according to the prediction result and the analysis result, the processor specifically implements the following steps:

[0246] The basic distribution objectives are set according to the prediction results and the analysis results; the optimal distribution route is designed according to the basic objectives based on the customer location, demand and traffic conditions using an optimization algorithm; and a specific distribution plan is formulated according to the prediction results and the optimized distribution route to obtain a definite distribution solution.

[0247] In one embodiment, when the processor executes the computer program to optimize the delivery path according to the delivery plan and perform the automated delivery step, the processor specifically implements the following steps:

[0248] A heuristic algorithm is used to generate a preliminary path solution based on the preset optimization goal. A linear programming model is used to optimize the preliminary path solution, and a variety of constraints are considered to achieve the optimal delivery path. Automated guided vehicles perform automated delivery according to dispatch instructions.

[0249] In one embodiment, when the processor executes the computer program to implement the step of performing automated delivery by an automated guided vehicle according to the dispatch instruction, the processor specifically implements the following steps:

[0250] Design and implement an automated delivery dispatch system; use GPS and IoT technology to monitor the location and operation of automated guided vehicles in real time; design an automated delivery process and use automated guided vehicles for automated delivery.

[0251] In one embodiment, after executing the computer program to optimize the delivery path according to the delivery plan and perform the automated delivery step, the processor further implements the following steps:

[0252] Obtaining feedback information collected by the automated guided vehicle; processing and analyzing the feedback information, and updating adjustments to inventory and delivery plans.

[0253] The storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage media that can store program codes.

[0254] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0255] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0256] The steps in the method of the embodiment of the present invention can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present invention can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0257] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0258] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry, characterized in that: include: Get weft yarn production order; Analyzing and forecasting the demand of the weft yarn production order to obtain a forecast result; Analyze the inventory of weft yarn to obtain analysis results; Determine a distribution plan according to the prediction result and the analysis result; The delivery route is optimized according to the delivery plan, and the delivery is automated.

2. The method for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry according to claim 1, characterized in that: The parsing and demand forecasting of the weft yarn production order to obtain a forecast result includes: parsing key information in the weft yarn production order, wherein the key information includes product type, yarn type, quantity, production date and delivery date; The demand for weft yarn of each loom at different time nodes is calculated based on the key information, and combined with the corresponding time nodes to obtain a prediction result.

3. The method for automatic delivery of multiple varieties and multiple frequencies of weft yarns in the home textile industry according to claim 1, characterized in that: The analyzing of the weft yarn inventory to obtain the analysis result includes: Use ABC classification method to classify and manage the inventory of weft yarn; For each weft yarn, mark and record it according to its storage time to ensure that the first weft yarn stored in the warehouse is sent out first. Set up a clear storage area in the warehouse, store different batches of weft yarn in different locations, and mark the storage time and weft yarn information; Use modern warehouse management systems or ERP systems to monitor and manage inventory data in real time, and regularly collect statistics and analyze inventory data to generate inventory reports and first-in-first-out implementation reports for various types of weft yarns, and collect statistics on data that can be distributed at any time to obtain analysis results.

4. The method for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry according to claim 1, characterized in that: Determining the distribution plan according to the prediction result and the analysis result includes: Setting basic delivery goals based on the prediction results and the analysis results; Use optimization algorithms to design the best delivery routes based on customer location, demand, and traffic conditions combined with basic goals; According to the prediction results and the optimized delivery routes, a specific delivery plan is formulated to obtain a definite delivery solution.

5. The method for automatic delivery of multiple varieties and multiple frequencies of weft yarns in the home textile industry according to claim 4, characterized in that: The optimizing the delivery path according to the delivery plan and performing automated delivery includes: A heuristic algorithm is used to generate a preliminary path solution according to the preset optimization goal; Use linear programming models to optimize the initial path solution and consider multiple constraints to achieve the optimal delivery path; Automated delivery is performed by automated guided vehicles according to dispatch instructions.

6. The method for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry according to claim 5, characterized in that: The automated delivery by the automated guided vehicle according to the dispatching instruction includes: Design and implement automated delivery dispatch systems; Use GPS and IoT technologies to monitor the location and operation of automated guided vehicles in real time; Design an automated delivery process and use automated guided vehicles for automated delivery.

7. The method for automatically distributing multiple varieties and multiple frequencies of weft yarns in the home textile industry according to claim 1, characterized in that: After optimizing the delivery route according to the delivery plan and performing automated delivery, the method further includes: Obtain feedback information collected by the automated guided vehicle; The feedback information is processed and analyzed, and adjustments to inventory and distribution plans are updated.

8. An automatic weft yarn delivery device for multiple varieties and frequencies in the home textile industry, characterized in that: include: An order acquisition unit, used for acquiring weft yarn production orders; A prediction unit, used for analyzing and predicting the demand of the weft yarn production order to obtain a prediction result; An analysis unit, used for analyzing the inventory of the weft yarn to obtain an analysis result; A distribution plan determination unit, used to determine a distribution plan according to the prediction result and the analysis result; The distribution optimization unit is used to optimize the distribution route according to the distribution plan and perform automated distribution.

9. A computer device, characterized in that: The computer device comprises a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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