Automatic picking and dynamic sorting processing method for food processing industry

Abstract

The invention provides an automatic weight picking and dynamic sorting processing method in the food processing industry, and relates to the technical field of computer application, and the method comprises the steps: receiving packaged food data from a food processing line, the data comprising a food ID, a production date, a shelf life and a packaging weight; configuring a weight picking rule for the packaged food, wherein the weight picking rule comprises an allowable weight error range, a minimum packaging weight and a maximum packaging weight; according to the weight picking rule, automatic weight picking calculation is conducted on the packaged food, whether the packaged food meets the weight requirement or not is judged, and the packaged food which does not meet the requirement is marked; according to the order information and the warehouse model, a sorting path is planned based on a dynamic optimization sorting path algorithm, and the dynamic optimization sorting path algorithm comprehensively considers the food shelf life, the packaging weight and the order priority; and sending a sorting task to an execution system, wherein the sorting task comprises the sorting path.

Description

Technical Field

[0001] This application belongs to the field of computer application technology, specifically relating to an automatic weighing and dynamic picking method for the food processing industry. Background Technology

[0002] In the food processing industry, accurate product weight and shelf-life management are key factors in ensuring product quality and customer satisfaction. Traditional picking and weighing processes rely heavily on manual operation, which is not only inefficient but also prone to errors due to subjective judgment, such as inaccurate weight checks or missed shelf-life monitoring. As businesses grow and order volumes increase, existing warehouse management systems (such as ERP systems), while capable of recording basic data, suffer from significant shortcomings in automation and intelligence. Specifically, current technologies lack automatic verification mechanisms for packaged food weights and cannot dynamically optimize picking routes based on real-time order demands and warehouse conditions. This leads to low picking efficiency, high labor costs, significant food waste (especially for products nearing their expiration date), and increased overall operating costs. Furthermore, existing route planning methods are largely based on static warehouse layouts and do not comprehensively consider food characteristics (such as shelf life and weight), making them ill-suited to the food industry's demands for efficient and precise management. Summary of the Invention

[0003] This application provides an automatic weighing and dynamic picking method for the food processing industry to solve one of the aforementioned technical problems.

[0004] The technical solution adopted in this application is as follows: This application provides an automatic weighing and dynamic picking method for the food processing industry, including: Receive packaged food data from the food processing line, the data including food ID, production date, shelf life, and packaging weight; Configure weighing rules for the packaged food, the weighing rules including the allowable weight error range, minimum package weight and maximum package weight; According to the weighing rules, the packaged food is automatically weighed to determine whether it meets the weight requirements, and packaged food that does not meet the requirements is marked. Based on order information and warehouse model, picking routes are planned using a dynamic optimization picking route algorithm, which comprehensively considers food shelf life, packaging weight, and order priority. Send a picking task to the execution system, wherein the picking task includes the picking path.

[0005] According to one embodiment of this application, configuring the weighing rules for the packaged food includes: The ERP management system provides a rule configuration interface to receive user-inputted weighing rule parameters; Based on the weighing rule parameters, the weighing rule is generated and stored.

[0006] According to one embodiment of this application, the step of planning the picking route based on the order information and warehouse model using a dynamic optimization picking route algorithm includes: Construct a weighted graph model of the warehouse, where nodes represent shelves, workstations, or shipping areas, edges represent movement paths, and the weights of the edges are set based on movement distance, time, or cost. The food shelf life factor is introduced into the weighted graph model to assign higher picking priority to foods that are about to expire. The optimal picking route is dynamically calculated using a path search algorithm based on real-time order data and warehouse inventory status.

[0007] According to one embodiment of this application, the path search algorithm includes the A* algorithm or the Dijkstra algorithm.

[0008] According to one embodiment of this application, the method further includes: During the picking process, the warehouse model is updated in real time to record the status of the picked goods; The subsequent picking route is dynamically adjusted based on order changes or picking progress.

[0009] According to one embodiment of this application, the method further includes: After picking is completed, compare the actual picking path with the planned path to evaluate picking efficiency; Optimization suggestions are generated based on the evaluation results.

[0010] A second aspect of this application provides an automated weighing and dynamic picking system for the food processing industry, characterized in that it includes: The first transceiver is used to receive packaged food data and order information from the food processing line; The first processor is used for: Configure weighing rules for the packaged food and perform automatic weighing calculations based on the weighing rules; Based on order information and warehouse model, a picking route is planned using a dynamic optimization picking route algorithm; The first transceiver is also used to send a picking task, including the picking path, to the execution system.

[0011] According to one embodiment of this application, the first processor is further configured to: Construct a weighted graph model of the warehouse and incorporate food shelf life factors into the model; The picking path is dynamically adjusted based on real-time data.

[0012] A third aspect of this application provides a computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the steps described in the method.

[0013] A fourth aspect of this application provides an electronic device including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method as described.

[0014] Due to the adoption of the above technical solution, the beneficial effects achieved by this application are as follows: This application, by "receiving packaged food data from the food processing line" and "configuring weighing rules for the packaged food," allows the system to automatically perform real-time calculations and judgments based on preset rules (such as weight error range, minimum and maximum packaging weight), replacing traditional manual inspection, reducing subjective errors, and improving inspection speed and consistency. Simultaneously, "marking non-compliant packaged food" ensures that substandard products are identified and handled promptly, guaranteeing product quality from the source.

[0015] By "planning picking routes based on order information and warehouse models using a dynamic optimization picking route algorithm," and comprehensively considering food shelf life, packaging weight, and order priority, the system can dynamically generate optimal routes, prioritizing the handling of near-expiry foods and heavier items. This shortens picking time, reduces food waste, and balances workstation load. This directly improves warehouse operation efficiency and reduces labor and time costs.

[0016] This solution is entirely based on software algorithms, requiring no additional hardware. It seamlessly integrates with existing ERP systems by "sending picking tasks to the execution system," achieving real-time data synchronization and process automation. This not only reduces system deployment and maintenance costs but also improves the enterprise's information management level, enabling the system to flexibly adapt to food processing companies of different sizes and needs. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart illustrating an automatic weighing and dynamic picking method for the food processing industry provided in this application embodiment; Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0018] Figure label: 810, Processor; 820, Communication interface; 830, Memory; 840, Communication bus. Detailed Implementation

[0019] To more clearly illustrate the overall concept of this application, a detailed explanation is provided below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application may also be implemented in other ways different from those described herein. Therefore, the scope of protection of this application is not limited to the specific embodiments disclosed below. It should be noted that, unless otherwise specified, the embodiments of this application and the features thereof can be combined with each other.

[0021] In this application, unless otherwise expressly specified and limited, the "above" or "below" of the second feature can mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediate medium. In the description of this specification, references to terms such as "an embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more embodiments or examples.

[0022] Example 1 like Figure 1 As shown, an automated weighing and dynamic picking method for the food processing industry includes: Receive packaged food data from the food processing line, including food ID, production date, shelf life, and packaging weight.

[0023] As described above, this step describes the process by which the system automatically acquires key data on packaged food from the food processing line. This data serves as the foundational input for subsequent automated weighing and dynamic picking processes. Specifically, "receiving" refers to establishing a connection with the data source (such as sensors, scanning equipment, or production management system) of the food processing line through a software interface or communication module, enabling real-time or timed data acquisition and transmission. "From the food processing line" emphasizes that the data source is directly linked to the production process, ensuring the timeliness and accuracy of the information. "Packaged food data" encompasses food ID (used to uniquely identify product type or batch), production date (recording the time point of food manufacturing), shelf life (defining the period during which the food can be safely used), and packaging weight (reflecting the actual weight value of the product). The core purpose of this step is to provide the system with structured, processable raw data to support subsequent rule-based weighing calculations and path optimization decisions, thereby replacing the outdated methods that rely on manual recording or scattered data sources, and improving the overall automation level and data consistency of the process.

[0024] For example, consider a typical food processing scenario: After packaged food is sealed on the production line, weight sensors automatically measure the weight of each package (e.g., 500g). Simultaneously, RFID readers or QR code scanners collect the attached food ID (e.g., "F001"), production date (e.g., "2025-03-30"), and shelf life (e.g., "30 days"). This data is transmitted in real-time to the integrated ERP system via an IoT gateway or dedicated communication protocol (e.g., HTTP / REST or MQTT). Upon receiving the data, the system immediately parses and stores it, forming a complete record of the packaged food. For instance, for a batch of biscuit products, the data might include the food ID "BISCUIT_A", production date "2025-03-30", shelf life "90 days", and packaging weight "250g". Through this seamless data flow, the system can acquire key parameters without human intervention, providing immediate basis for subsequent automatic weighing (such as determining whether the weight is within the allowable error range of 200-300g) and dynamic picking (such as prioritizing products nearing their expiration date).

[0025] Configure weighing rules for the packaged food, the weighing rules including the allowable weight error range, minimum package weight and maximum package weight.

[0026] As described above, this step involves setting personalized weight detection standards for different types of packaged foods in the system, which form the core basis for subsequent automatic weight checking and judgment. Specifically, "configuring weight checking rules" means that through the rule configuration interface provided by the system, the user sets various threshold parameters related to weight according to product characteristics and quality requirements; the "allowed weight error range" defines the acceptable positive and negative deviation intervals between the actual measured weight and the nominal weight, which is used to identify the boundary conditions for qualified weights; the "minimum package weight" sets the minimum weight standard that a single package must reach to prevent short-weight products; the "maximum package weight" limits the upper weight limit that a single package should not exceed to avoid the risks of over-packaging or equipment overload. This configuration process realizes the parameterization and customization of weight detection standards, enabling the same system to adapt to the different quality requirements of different categories of foods and providing a clear decision benchmark for subsequent automated weight judgment.

[0027] For example, taking the canned food production line as an example, quality management personnel set weight checking rules for the "tomato can" product in the system configuration interface: the allowed weight error range is set to ±10 grams, that is, for a can with a nominal weight of 500 grams, the actual weight between 490 grams and 510 grams is considered qualified; the minimum package weight is set to 485 grams to ensure that the net content of the product is not lower than this threshold; the maximum package weight is set to 520 grams to prevent overfilling or foreign matter mixing. When the cans on the production line pass through the weighing station, the system automatically calls these preset rules for real-time judgment - for example, a can with a measured weight of 488 grams, although lower than the nominal value but still above the minimum package weight and within the error range, is judged to be qualified; while a can with a measured weight of 478 grams is marked as a non-conforming product because it is lower than the minimum package weight. Through this rule-based configuration method, the system can accurately perform weight detection and ensure the consistency of product quality.

[0028] According to the described weight checking rules, perform automatic weight checking calculations on the packaged foods, judge whether they meet the weight requirements, and mark the packaged foods that do not meet the requirements.

[0029] As described above, this step outlines the entire process of automated weight detection and classification of packaged foods based on preset weighing rules. Specifically, "automatic weighing calculation" refers to the system comparing and analyzing the received real-time package weight data with the threshold parameters in the configured rules, and using a built-in algorithm to determine weight compliance. "Determining whether it meets weight requirements" encompasses multi-dimensional verification of minimum packaging weight, maximum packaging weight, and allowable error range, forming a comprehensive pass / fail conclusion. "Marking non-compliant packaged foods" means that when a food is determined to be non-compliant, the system automatically adds a specific identifier to the food record. This identifier can be reflected in database status field updates, visual warning signal triggering, or instruction generation linked to downstream equipment. The entire process achieves full automation of weight detection, replacing traditional manual sampling methods, ensuring the objectivity and consistency of weight judgment, and providing a clear basis for the subsequent processing of non-compliant products.

[0030] For example, taking a bagged flour production line, the system has been configured with weighing rules for "5kg extra-grade flour": the allowable error range is ±20g, the minimum weight is 4980g, and the maximum weight is 5020g. When a bag of flour passes through the automatic weighing station, the system obtains its measured weight as 4975g and immediately initiates automatic weighing calculation—comparing this value with the rule parameters, it finds that 4975g is both lower than the minimum weight of 4980g and exceeds the negative error range (4980g) of the nominal value of 5000g. Therefore, the system determines that the product does not meet the weight requirements. After the determination, the system immediately marks the corresponding product record as "weight non-compliant" and sends a command to the production line control terminal, causing the bag of flour to be automatically guided to the defective product recycling area at the diversion device, while qualified products continue to flow to the normal process of the packaging terminal. Through this continuous automated processing, real-time identification and physical separation of products with non-compliant weights are achieved. Based on order information and warehouse model, picking routes are planned using a dynamic optimization picking route algorithm, which comprehensively considers food shelf life, packaging weight, and order priority.

[0031] As described above, this step describes the intelligent planning process by which the system generates the optimal picking route based on multi-dimensional decision factors. Specifically, "order information and warehouse model" constitute the basic data environment for route planning. The order information includes demand elements such as the required product types, quantities, and delivery deadlines, while the warehouse model digitally reproduces spatial relationships such as shelf layout, workstation locations, and aisle structure. The "dynamically optimized picking route algorithm" is an intelligent decision-making mechanism that can respond to environmental changes in real time. It quantifies key factors such as food shelf life (using remaining expiration date as an indicator of urgency), packaging weight (affecting handling efficiency and stacking safety), and order priority (based on customer level or business importance) into algorithm parameters by establishing a weighted evaluation system. "Planning the picking route" is the process of iteratively calculating the algorithm to output a movement sequence with the lowest overall cost and highest efficiency under given constraints. This route not only ensures the orderly execution of picking operations but also achieves the best balance between time, resources, and quality requirements through multi-objective optimization.

[0032] For example, consider the daily operation of a large food storage center. The system simultaneously receives three orders: Order A is an urgent supermarket replenishment order (high priority), containing yogurt with a remaining shelf life of 3 days; Order B is a regular e-commerce order (medium priority), containing various snack foods; and Order C is a wholesale order (low priority), containing heavy items such as rice and flour. Based on a dynamic optimization algorithm, the system first constructs a warehouse topology model containing the locations of all items to be picked. Then, it initiates multi-factor path planning—the algorithm assigns a higher timeliness weight to yogurt nearing its expiration date, a shortest path weight to heavy items, and a priority weight to high-priority orders. Through weighted calculation, the final picking path might look like this: prioritize picking from the yogurt shelf for Order A, then sequentially retrieve lighter items from Order B using the shortest path, and finally process the heavy items from Order C, automatically avoiding currently congested passages along the way. The entire path planning ensures that multiple objectives—prioritizing perishable foods, optimizing the handling path for heavy items, and ensuring timely delivery of high-priority orders—are achieved simultaneously.

[0033] Send a picking task to the execution system, wherein the picking task includes the picking path.

[0034] As described above, this step describes the task distribution process by which the system transforms the planning results into executable instructions and distributes them to downstream equipment. Specifically, "sending picking tasks" means that the system transmits a set of instructions containing complete operational elements to the execution terminal through a preset communication interface and protocol; the "execution system" includes, but is not limited to, automated guided vehicle control systems, picker handheld terminals, robotic arm controllers, and other implementation units that can directly drive physical operations; the "picking task," as a structured data packet, not only contains the core "picking path," a spatial movement sequence, but also integrates the corresponding execution parameters, such as the dwell time standards at each station in the path, the specific specifications for picking and placing items, and contingency plans for handling abnormal situations; this step realizes the final link from system decision-making to on-site execution, ensuring that the theoretical path calculated through optimization can be accurately transformed into actual warehousing operations, forming a closed-loop management of decision-making, transmission, and execution.

[0035] For example, in the actual operation of an intelligent food storage center, after the system completes path planning, it generates a standardized picking task instruction package. This task package is sent via the warehouse's Wi-Fi network to the smart glasses and handheld terminals worn by the pickers. The smart glasses display a 3D navigation path using augmented reality, guiding them sequentially to shelf A-05 (to retrieve dairy products with two days remaining on their shelf life), shelf B-12 (to retrieve heavier grains and oils), and shelf C-03 (to retrieve general snacks). Simultaneously, the handheld terminal receives a detailed product list, quantity, and placement requirements. If the execution system is an AGV fleet, the task package will contain precise coordinate sequences, speed control parameters, and docking instructions, guiding multiple AGVs to collaboratively complete the picking operation with a "heavy goods priority, perishable goods priority" approach. The entire task distribution process ensures that the execution units can accurately understand and implement the optimized path planned by the system.

[0036] According to one embodiment of this application, configuring the weighing rules for the packaged food includes: The ERP management system provides a rule configuration interface to receive user-inputted weighing rule parameters; Based on the weighing rule parameters, the weighing rule is generated and stored.

[0037] As mentioned above, the ERP management system provides a rule configuration interface to receive user-inputted weighing rule parameters. This configuration interface serves as a human-computer interaction interface, enabling users to set various thresholds and conditions related to weight detection according to actual business needs. Specific parameters input by the user through this interface include, but are not limited to, the allowable weight error range, minimum packaging weight, and maximum packaging weight.

[0038] Based on the received deduplication rule parameters, the system generates deduplication rules with executable logic and stores these rules in the system database. This process transforms the user-inputted condition parameters into standardized rule forms that the system can recognize and invoke, providing a basis for subsequent automatic deduplication calculations.

[0039] According to one embodiment of this application, the step of planning the picking route based on the order information and warehouse model using a dynamic optimization picking route algorithm includes: Construct a weighted graph model of the warehouse, where nodes represent shelves, workstations, or shipping areas, edges represent movement paths, and the weights of the edges are set based on movement distance, time, or cost. The food shelf life factor is introduced into the weighted graph model to assign higher picking priority to foods that are about to expire. The optimal picking route is dynamically calculated using a path search algorithm based on real-time order data and warehouse inventory status.

[0040] As described above, firstly, a weighted graph model of the warehouse is constructed. In this model, nodes represent key locations in the warehouse, including shelf locations, workstations, and shipping areas; edges represent feasible movement paths between nodes; and each edge is assigned a corresponding weight value, which is set based on one or more factors such as movement distance, required time, or transportation cost.

[0041] Secondly, the food shelf-life factor is introduced into the constructed weighted graph model. By calculating the remaining shelf life of food, the system assigns higher picking priority to foods nearing their expiration date, ensuring that perishable products are handled preferentially.

[0042] Finally, based on real-time order data and warehouse inventory status, a path search algorithm is used for dynamic calculation. This algorithm calculates the optimal picking path under current conditions in real time within a weighted graph model, based on continuously updated warehouse status information and order requirements, thereby achieving dynamic optimization of path planning.

[0043] According to one embodiment of this application, the path search algorithm includes the A* algorithm or the Dijkstra algorithm.

[0044] As mentioned above, the A* algorithm efficiently calculates the approximate optimal path from the starting point to the target node by comprehensively evaluating the actual path cost and the estimated heuristic cost during the search process. This algorithm is suitable for picking scenarios that require rapid response to dynamic order changes.

[0045] Dijkstra's algorithm systematically traverses all reachable nodes in a graph and updates the shortest path information step by step, ultimately determining the globally optimal path from the source node to all other nodes in the graph. This algorithm is suitable for warehouse layouts that require ensuring the optimal overall path cost.

[0046] Both algorithms are based on a pre-built weighted graph model for path calculation. They generate optimized movement sequences by evaluating the weight values ​​of each edge in the graph, thereby enabling automatic planning of picking paths.

[0047] According to one embodiment of this application, the method further includes: During the picking process, the warehouse model is updated in real time to record the status of the picked goods; The subsequent picking route is dynamically adjusted based on order changes or picking progress.

[0048] As mentioned above, during the picking task execution, the system continuously monitors the operation progress and updates the warehouse model in real time. This update operation specifically records the current location status of the picked goods, changes in inventory quantity, and equipment occupancy.

[0049] Based on the updated warehouse status data, the system dynamically adjusts subsequent picking routes according to two trigger conditions: first, in response to temporary changes in order content, including adding or canceling orders; and second, to adapt to deviations in actual picking progress, including delays or early completion of operations. The system generates an optimized route that matches the current warehouse status and order requirements by re-executing the route search algorithm, and then sends the adjusted route to the execution system in real time.

[0050] According to one embodiment of this application, the method further includes: After picking is completed, compare the actual picking path with the planned path to evaluate picking efficiency; Optimization suggestions are generated based on the evaluation results.

[0051] As mentioned above, after the picking operation is completed, the system automatically collects the path data generated during the actual execution process and compares and analyzes it with the original planned path. This comparative analysis includes the following quantitative indicators: the difference between the actual walking distance and the planned distance, the deviation between the dwell time at each node and the estimated time, and the comparison between the total task completion time and the expected time.

[0052] Based on the comparative analysis results, the system evaluates picking efficiency from the following dimensions: path adherence, time utilization, and resource consumption rate. According to the evaluation results, the system generates optimization suggestions, including: adjusting the weight parameters of the picking path algorithm, optimizing the node distribution structure of the warehouse model, and improving the task allocation strategy. These suggestions provide data support for subsequent rule optimization and algorithm adjustments in picking operations.

[0053] A second aspect of this application provides an automated weighing and dynamic picking system for the food processing industry, characterized in that it includes: The first transceiver is used to receive packaged food data and order information from the food processing line; The first processor is used for: Configure weighing rules for the packaged food and perform automatic weighing calculations based on the weighing rules; Based on order information and warehouse model, a picking route is planned using a dynamic optimization picking route algorithm; The first transceiver is also used to send a picking task, including the picking path, to the execution system.

[0054] According to one embodiment of this application, the first processor is further configured to: Construct a weighted graph model of the warehouse and incorporate food shelf life factors into the model; The picking path is dynamically adjusted based on real-time data.

[0055] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in any of the first aspects above.

[0056] Figure 2 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 2 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call logical instructions in the memory 830 to execute the method in any of the embodiments of the first aspect described above, the method including: Receive packaged food data from the food processing line, the data including food ID, production date, shelf life, and packaging weight; Configure weighing rules for the packaged food, the weighing rules including the allowable weight error range, minimum package weight and maximum package weight; According to the weighing rules, the packaged food is automatically weighed to determine whether it meets the weight requirements, and packaged food that does not meet the requirements is marked. Based on order information and warehouse model, picking routes are planned using a dynamic optimization picking route algorithm, which comprehensively considers food shelf life, packaging weight, and order priority. Send a picking task to the execution system, wherein the picking task includes the picking path.

[0057] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0058] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program, the computer program being able to be stored on a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer being able to perform the methods provided by the above methods, the method comprising: Receive packaged food data from the food processing line, the data including food ID, production date, shelf life, and packaging weight; Configure weighing rules for the packaged food, the weighing rules including the allowable weight error range, minimum package weight and maximum package weight; According to the weighing rules, the packaged food is automatically weighed to determine whether it meets the weight requirements, and packaged food that does not meet the requirements is marked. Based on order information and warehouse model, picking routes are planned using a dynamic optimization picking route algorithm, which comprehensively considers food shelf life, packaging weight, and order priority. Send a picking task to the execution system, wherein the picking task includes the picking path.

[0059] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the methods provided by the above methods, the method comprising: Receive packaged food data from the food processing line, the data including food ID, production date, shelf life, and packaging weight; Configure weighing rules for the packaged food, the weighing rules including the allowable weight error range, minimum package weight and maximum package weight; According to the weighing rules, the packaged food is automatically weighed to determine whether it meets the weight requirements, and packaged food that does not meet the requirements is marked. Based on order information and warehouse model, picking routes are planned using a dynamic optimization picking route algorithm, which comprehensively considers food shelf life, packaging weight, and order priority. Send a picking task to the execution system, wherein the picking task includes the picking path.

[0060] For any parts not mentioned in this application, existing technologies may be used or referenced.

[0061] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0062] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. An automated weighing and dynamic picking method for the food processing industry, characterized in that, include: Receive packaged food data from the food processing line, the data including food ID, production date, shelf life, and packaging weight; Configure weighing rules for the packaged food, the weighing rules including the allowable weight error range, minimum package weight and maximum package weight; According to the weighing rules, the packaged food is automatically weighed to determine whether it meets the weight requirements, and packaged food that does not meet the requirements is marked. Based on order information and warehouse model, picking routes are planned using a dynamic optimization picking route algorithm, which comprehensively considers food shelf life, packaging weight, and order priority. Send a picking task to the execution system, wherein the picking task includes the picking path.

2. The method according to claim 1, characterized in that, The configuration of the weighing rules for the packaged food includes: The ERP management system provides a rule configuration interface to receive user-inputted weighing rule parameters; Based on the weighing rule parameters, the weighing rule is generated and stored.

3. The method according to claim 1, characterized in that, The step of planning the picking route based on order information and warehouse model using a dynamic optimization picking route algorithm includes: Construct a weighted graph model of the warehouse, where nodes represent shelves, workstations, or shipping areas, edges represent movement paths, and the weights of the edges are set based on movement distance, time, or cost. The food shelf life factor is introduced into the weighted graph model to assign higher picking priority to foods that are about to expire. The optimal picking route is dynamically calculated using a path search algorithm based on real-time order data and warehouse inventory status.

4. The method according to claim 3, characterized in that, The path search algorithm includes either the A* algorithm or the Dijkstra algorithm.

5. The method according to claim 1, characterized in that, The method further includes: During the picking process, the warehouse model is updated in real time to record the status of the picked goods; The subsequent picking route is dynamically adjusted based on order changes or picking progress.

6. The method according to claim 1, characterized in that, The method further includes: After picking is completed, compare the actual picking path with the planned path to evaluate picking efficiency; Optimization suggestions are generated based on the evaluation results.

7. An automated weighing and dynamic sorting system for the food processing industry, characterized in that, include: The first transceiver is used to receive packaged food data and order information from the food processing line; The first processor is used for: Configure weighing rules for the packaged food and perform automatic weighing calculations based on the weighing rules; Based on order information and warehouse model, a picking route is planned using a dynamic optimization picking route algorithm; The first transceiver is also used to send a picking task, including the picking path, to the execution system.

8. The system according to claim 7, characterized in that, The first processor is also used for: Construct a weighted graph model of the warehouse and incorporate food shelf life factors into the model; The picking path is dynamically adjusted based on real-time data.

9. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1-6.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1-6.

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