Warehouse logistics intelligent management method and system based on RFID technology

Abstract

The invention relates to a warehouse logistics intelligent management method and system based on an RFID technology. The method comprises the following steps: obtaining warehouse logistics information through an RFID radio frequency tag and inputting the warehouse logistics information to a prediction model; when the prediction information is received, obtaining warehouse location information associated with the warehouse logistics information; inputting the prediction information, the warehouse logistics information and the warehouse location information into a spatial simulation model; and when the storage simulation information is received, warehouse logistics scheduling management is carried out. The warehouse logistics information is read through the RFID technology, the inventory management accuracy is improved, meanwhile, inventory-based supplementary prediction is carried out through the prediction model, supplementary prediction information-based storage simulation is carried out through the space simulation model, an optimal storage scheme is found out, and the inventory management efficiency is improved. The method has the effects of improving the flexibility and accuracy of warehouse logistics article inventory supplementary scheduling, improving the utilization rate of inventory free space and reducing the storage cost.

Description

Technical Field

[0001] The present application relates to the technical field of warehousing logistics management, and in particular to an intelligent management method and system for warehousing logistics based on RFID technology. Background Art

[0002] In the field of warehousing and logistics management, with the development of global economic integration and the rise of the Internet, the logistics service industry has expanded rapidly and competition among enterprises has become increasingly fierce. Traditional warehousing and logistics management methods mainly rely on manual operations and paper records, and there are many problems, such as inventory backlogs, excessive use of working capital, difficulty in cargo positioning, low warehouse efficiency, complex data statistics, and low space utilization. These problems have seriously restricted the operating efficiency and management level of logistics companies, increased the operating costs of enterprises, and affected the market competitiveness of enterprises.

[0003] In order to improve this situation, many companies have begun to introduce information management methods. Among them, RFID (Radio Frequency Identification) technology has been widely used in the field of warehousing and logistics management due to its advantages of non-contact identification, long identification distance, fast speed, non-damage, and large capacity. RFID technology uses radio signals to identify specific targets and read and write related data. There is no need to establish mechanical or optical contact between the identification system and the specific target, which greatly simplifies the operation process and improves management efficiency and accuracy.

[0004] However, existing technologies often use fixed inventory thresholds to replenish inventory of items in warehousing logistics. This method lacks flexibility and accuracy and is difficult to respond promptly to real-time inventory changes. At the same time, since the free inventory space between different items is not fully considered, the inventory space is not fully utilized, which further affects the overall efficiency of logistics management. Summary of the invention

[0005] In order to improve the flexibility and accuracy of inventory replenishment scheduling for warehousing and logistics items, and at the same time improve the utilization rate of inventory free space, the present application provides a warehousing and logistics intelligent management method and system based on RFID technology.

[0006] The above-mentioned invention objective of the present application is achieved through the following technical solutions:

[0007] A warehousing logistics intelligent management method based on RFID technology, comprising the steps of:

[0008] Obtain warehousing logistics information through RFID radio frequency tags;

[0009] Input warehouse logistics information into the pre-trained forecasting model to perform inventory-based replenishment forecasting;

[0010] When receiving the prediction information output by the prediction model, obtaining the storage location information corresponding to the associated storage logistics information, wherein the storage location information includes storage area information and storage inventory information;

[0011] Input the prediction information output by the prediction model, the storage logistics information associated with the prediction information, and the corresponding storage location information into the pre-trained space simulation model to perform space storage simulation;

[0012] When storage simulation information output by the space simulation model is received, warehouse logistics scheduling management is performed based on the storage simulation information.

[0013] By adopting the above technical solution, using RFID technology, the warehousing and logistics information is quickly read through the radio frequency tag, and the obtained warehousing and logistics information is input into the pre-trained prediction model, so that the model can predict the replenishment demand situation based on inventory based on historical data and current information. After the prediction model outputs the prediction information, the storage location information corresponding to the warehousing and logistics information associated with the prediction information is obtained, that is, the relevant information of the storage location that needs to replenish inventory, including storage area information and warehouse inventory information, and the prediction information, the warehousing and logistics information associated with the prediction information and the corresponding storage location information are input into the pre-trained space The simulation model performs spatial storage simulation to optimize the space utilization and logistics efficiency of the warehouse, and realizes reasonable scheduling management of warehousing and logistics according to the storage simulation information output by the spatial simulation model; the present application uses RFID technology to read warehousing and logistics information, reduces human errors, and improves the accuracy of inventory management. At the same time, the prediction model is used to perform inventory-based replenishment prediction, and the spatial simulation model is used to perform storage simulation based on the replenishment prediction information to find the optimal storage solution, which has the effect of improving the flexibility and accuracy of inventory replenishment scheduling of warehousing and logistics items, while improving the utilization rate of inventory idle space and reducing storage costs.

[0014] In a preferred example, the present application can be further configured as follows: the prediction model includes an information matching layer, a feature extraction layer, a threshold comparison layer, and a prediction output layer, and the step of inputting the warehousing logistics information into the pre-trained prediction model to perform replenishment prediction based on inventory includes the steps of:

[0015] The information matching layer matches the preset corresponding inventory change data and promotion activity data based on the input warehousing and logistics information;

[0016] The feature extraction layer extracts corresponding trend features based on the matched inventory change data and promotion activity data;

[0017] The threshold comparison layer sets dynamic thresholds based on trend characteristics and compares the warehouse logistics information with them;

[0018] When the comparison result determines that inventory needs to be replenished, the prediction output layer outputs prediction information based on the comparison result and trend characteristics.

[0019] By adopting the above technical solution, the prediction model includes an information matching layer, a feature extraction layer, a threshold comparison layer and a prediction output layer. The information matching layer is the starting layer of the prediction model, which matches the corresponding inventory change data and promotion activity data based on the input warehousing and logistics information. On the basis of the information matching layer, the feature extraction layer extracts the corresponding trend features from the matched inventory change data and promotion activity data. The threshold comparison layer sets a dynamic threshold adjusted according to the current inventory status, sales trends, promotion activities and other factors based on the trend features extracted by the feature extraction layer, and compares the warehousing and logistics information with the dynamic threshold to determine whether the inventory needs to be replenished. When the comparison result determines that the inventory needs to be replenished, the prediction output layer outputs the prediction information based on the comparison result and the trend features. The present application uses a prediction model including an information matching layer, a feature extraction layer, a threshold comparison layer and a prediction output layer to perform inventory-based replenishment prediction on the input warehousing and logistics information, thereby realizing in-depth mining and analysis of warehousing and logistics information, and providing accurate, timely and flexible inventory replenishment prediction information.

[0020] In a preferred example, the present application can be further configured as follows: the space simulation model includes a space layout layer and a cargo distribution layer, and the step of inputting the prediction information output by the prediction model, the storage logistics information associated with the prediction information, and the corresponding storage location information into the pre-trained space simulation model to perform space storage simulation includes the steps of:

[0021] The space layout layer constructs a virtual storage space scene based on the input storage location information;

[0022] The cargo distribution layer performs spatial storage modeling and outputs storage simulation information based on preset distribution rules, input forecast information and warehousing logistics information, and virtual warehousing space scenarios.

[0023] By adopting the above-mentioned technical scheme, the spatial simulation model includes a spatial layout layer and a cargo distribution layer. The spatial layout layer receives the input storage location information and constructs a virtual storage space scene based on it. The cargo distribution layer simulates spatial storage based on the preset allocation rules combined with the input prediction information, storage logistics information and the virtual storage space scene constructed by the spatial layout layer to find the optimal cargo storage plan; the present application constructs a spatial simulation model and inputs the prediction information, storage logistics information and storage location information into the model for spatial storage simulation, which has the effect of improving the utilization rate of storage space and logistics efficiency. The virtual storage space scene constructed by the spatial layout layer can more intuitively understand the actual layout and capacity of the warehouse, and provide an accurate basis for cargo distribution. The cargo distribution layer can find the optimal storage plan based on the received information and the preset allocation rules, thereby reducing the transportation distance and time of goods and improving the overall efficiency of warehousing and logistics.

[0024] In a preferred example, the present application can be further configured as follows: the step of constructing a virtual storage space scene based on the input storage location information at the space layout layer includes the following steps:

[0025] The spatial layout layer analyzes the warehouse inventory information within the preset historical time period and obtains the warehouse space change information in combination with the warehouse area information;

[0026] The space layout layer constructs a virtual storage space scene based on storage space change information and storage area information.

[0027] By adopting the above technical scheme, the spatial layout layer first analyzes the warehouse inventory information within a preset historical time period, so as to understand the storage history of the warehouse and the flow of goods, and combines the storage area information (such as the layout of the warehouse, the location and size of the shelves, etc.) to further obtain the change information of the storage space, and construct a virtual storage space scene based on the storage space change information and storage area information obtained from the above analysis; the present application analyzes the warehouse inventory information within a preset historical time period through the spatial layout layer, and obtains the storage space change information in combination with the storage area information, and then constructs a virtual storage space scene, which has the effect of improving the accuracy and efficiency of warehouse management. By analyzing historical data, the storage history of the warehouse and the flow of goods can be more accurately understood, providing a basis for formulating reasonable storage strategies; at the same time, the constructed virtual storage space scene can intuitively display the current status and changes of the warehouse, helping managers to better grasp the actual situation of the warehouse, thereby improving the accuracy and efficiency of warehouse management.

[0028] In a preferred example, the present application can be further configured as follows: the preset allocation rules include storage requirement rules, space utilization evaluation rules and score allocation rules, and the cargo allocation layer performs a spatial storage model and outputs storage simulation information based on the preset allocation rules, input forecast information and storage logistics information and virtual storage space scenarios, including the steps of:

[0029] The cargo distribution layer identifies the storage requirement information in the warehousing logistics information, and identifies the corresponding virtual sub-scene in the virtual warehousing space scene through the storage requirement information and storage requirement rules;

[0030] The cargo distribution layer obtains several space utilization schemes based on virtual sub-scenes through space utilization evaluation rules;

[0031] The cargo allocation layer calculates a comprehensive score for each space utilization plan through the score allocation rules, and outputs storage simulation information based on the comprehensive score calculation results.

[0032] By adopting the above technical solution, the cargo distribution layer identifies the storage requirement information in the warehousing logistics information, which includes the physical characteristics of the cargo (such as size, weight, shape, etc.), storage conditions (such as temperature, humidity, etc.) and safety requirements, and uses the storage requirement information and storage requirement rules to identify virtual sub-scenes that meet the storage requirements in the virtual warehousing space scene, that is, areas in the warehouse that can meet the storage conditions of specific cargo. The cargo distribution layer applies the preset space utilization evaluation rules to generate several space utilization plans for the identified virtual sub-scenes. Finally, the cargo distribution layer uses the preset scoring allocation rules to evaluate the space utilization of each virtual sub-scene. The utilization plan is comprehensively scored and calculated, and storage simulation information, i.e., the optimal space storage plan, is output based on the comprehensive scoring results; the present application constructs a space storage model through the cargo allocation layer and outputs storage simulation information, which has the effect of improving the utilization rate of storage space and storage efficiency. By comprehensively considering storage requirements, space utilization rate and multiple scoring factors, the cargo allocation layer generates the optimal space storage plan, thereby maximizing the storage capacity of the warehouse, reducing space waste, and improving the storage and retrieval efficiency of goods; at the same time, the automated allocation process based on the calculation results also reduces manual intervention and errors, and improves the accuracy and reliability of warehouse management.

[0033] In a preferred example, the present application can be further configured as follows: the virtual sub-scene includes a plurality of virtual shelves, and the steps of the cargo distribution layer obtaining a plurality of space utilization solutions based on the virtual sub-scenes through the space utilization evaluation rule include:

[0034] The cargo distribution layer evaluates the space utilization of several virtual shelves through a preset space utilization formula, which is calculated as follows:

[0035] Among them, U space is the space utilization of the virtual shelf, V used is the space volume currently used by the virtual shelf, V prestored V is the volume of pre-storage cargo included in the storage requirement information, total is the total space volume of the virtual shelf;

[0036] The goods allocation layer determines the virtual shelf whose space utilization is less than a preset value as a space utilization solution based on the virtual sub-scene.

[0037] By adopting the above technical solution, the cargo distribution layer applies a preset space utilization formula to evaluate the space utilization of each virtual shelf in the virtual sub-scene, and the formula takes into account the space volume currently used by the virtual shelf, the pre-stored cargo volume contained in the storage requirement information, and the total space volume of the virtual shelf; the cargo distribution layer screens out virtual shelves whose space utilization is less than a preset value, and the screened out virtual shelves are determined as space utilization solutions based on virtual sub-scenes; the present application evaluates the space utilization of virtual shelves through the cargo distribution layer, and screens out virtual shelves whose space utilization is less than a preset value as space utilization solutions, which has the effect of accurately evaluating and optimizing the utilization of storage space. At the same time, it screens out shelves suitable for loading as storage solutions, which can more effectively utilize storage space and improve storage efficiency and cargo access speed.

[0038] In a preferred example, the present application can be further configured as follows: the cargo distribution layer performs comprehensive score calculation on each space utilization scheme according to the score distribution rule, and outputs the storage simulation information based on the comprehensive score calculation result, including:

[0039] The cargo allocation layer evaluates the comprehensive score of each space utilization scheme through a preset scoring calculation formula, which is calculated as follows:

[0040] Among them, S is the comprehensive score of the space utilization plan, α is the weight coefficient of the space utilization of the virtual shelf, and U space is the space utilization rate of the virtual shelf, β is the weight coefficient of the entrance and exit proximity coefficient, D max is the maximum distance between the virtual sub-scene and the entrance and exit, D exit is the distance between the virtual shelf and the entrance and exit of the space utilization plan, is the entrance and exit proximity coefficient, γ is the weight coefficient of the satisfaction of special storage requirements, S special The degree of satisfaction of preset special storage requirements;

[0041] The storage simulation information is outputted in descending order according to the comprehensive score calculation results.

[0042] By adopting the above technical solution, the cargo distribution layer evaluates the comprehensive score of each space utilization plan based on a preset scoring calculation formula, which takes into account multiple factors, including the space utilization of the virtual shelf, the entrance and exit proximity coefficient, and the satisfaction of special storage requirements, and assigns a corresponding weight coefficient to each factor; the comprehensive score of each space utilization plan is obtained by calculation, and according to the comprehensive score calculation result, the storage simulation information is output in descending order of the score; the present application evaluates the comprehensive score of each space utilization plan through the cargo distribution layer, and outputs the storage simulation information in descending order of the comprehensive score, which has the effect of comprehensively considering multiple factors to optimize the cargo storage location, not only considering the space utilization, but also combining the entrance and exit proximity and the satisfaction of special storage requirements, so as to more comprehensively evaluate the pros and cons of each storage plan; by giving priority to recommending storage locations with high comprehensive scores, the storage and retrieval efficiency of goods can be improved, special storage needs can be met, and the overall layout of the warehouse can be optimized.

[0043] The second object of the invention is achieved by the following technical solutions:

[0044] An intelligent warehouse logistics management system based on RFID technology, comprising:

[0045] The first information acquisition module is used to obtain storage logistics information through RFID radio frequency tags;

[0046] The first input module is used to input the warehouse logistics information into the pre-trained prediction model to perform inventory-based replenishment prediction;

[0047] A second information acquisition module is used to, when receiving the prediction information output by the prediction model, acquire the storage location information corresponding to the associated storage logistics information, wherein the storage location information includes storage area information and storage inventory information;

[0048] The second input module is used to input the prediction information output by the prediction model, the storage logistics information associated with the prediction information and the corresponding storage location information into the pre-trained space simulation model to perform space storage simulation;

[0049] The scheduling management module is used to perform warehousing logistics scheduling management based on the storage simulation information when receiving the storage simulation information output by the space simulation model.

[0050] By adopting the above technical solution, the first information acquisition module is used to obtain warehousing and logistics information through RFID radio frequency tags; the first input module is used to input the warehousing and logistics information into a pre-trained prediction model for inventory-based replenishment prediction; the second information acquisition module is used to, when receiving the prediction information output by the prediction model, obtain the storage location information corresponding to the associated warehousing and logistics information, the storage location information includes storage area information and warehouse inventory information; the second input module is used to input the prediction information output by the prediction model, the warehousing and logistics information associated with the prediction information and the corresponding storage location information into a pre-trained space simulation model for space storage simulation; the scheduling management module is used to perform warehousing and logistics scheduling management based on the storage simulation information when receiving the storage simulation information output by the space simulation model.

[0051] In summary, the present application includes at least one of the following beneficial technical effects:

[0052] 1. This application uses RFID technology to read warehouse logistics information, reduce human errors, and improve the accuracy of inventory management. At the same time, the prediction model is used to make replenishment predictions based on inventory, and the storage simulation based on the replenishment prediction information is performed through the space simulation model to find the optimal storage plan, which has the effect of improving the flexibility and accuracy of inventory replenishment scheduling for warehouse logistics items, while improving the utilization rate of inventory idle space and reducing storage costs;

[0053] 2. This application uses a prediction model including an information matching layer, a feature extraction layer, a threshold comparison layer, and a prediction output layer to perform inventory-based replenishment prediction on the input warehousing and logistics information, thereby achieving in-depth mining and analysis of warehousing and logistics information and providing accurate, timely, and flexible inventory replenishment prediction information;

[0054] 3. This application constructs a spatial simulation model and inputs the forecast information, warehousing logistics information and warehousing location information into the model for spatial storage simulation, which has the effect of improving the utilization rate of warehousing space and logistics efficiency. The virtual warehousing space scene constructed by the spatial layout layer can more intuitively understand the actual layout and capacity of the warehouse, providing an accurate basis for cargo distribution, and the cargo distribution layer can find the optimal storage plan based on the received information and the preset distribution rules, thereby reducing the transportation distance and time of the cargo and improving the overall efficiency of warehousing and logistics;

[0055] 4. This application analyzes the warehouse storage information within a preset historical time period through the spatial layout layer, and obtains the storage space change information in combination with the storage area information, and then constructs a virtual storage space scene, which has the effect of improving the accuracy and efficiency of warehouse management. By analyzing historical data, the storage history of the warehouse and the flow of goods can be more accurately understood, providing a basis for formulating reasonable storage strategies; at the same time, the constructed virtual storage space scene can intuitively display the current status and changes of the warehouse, helping managers to better understand the actual situation of the warehouse, thereby improving the accuracy and efficiency of warehouse management;

[0056] 5. This application constructs a spatial storage model through the cargo allocation layer and outputs storage simulation information, which has the effect of improving storage space utilization and storage efficiency. By comprehensively considering storage requirements, space utilization and multiple scoring factors, the cargo allocation layer generates the optimal spatial storage plan, thereby maximizing the storage capacity of the warehouse, reducing space waste, and improving the storage and access efficiency of goods; at the same time, the automated allocation process based on the calculation results also reduces manual intervention and errors, and improves the accuracy and reliability of warehouse management;

[0057] 6. The present application evaluates the space utilization of virtual shelves through the cargo allocation layer, and selects virtual shelves with space utilization less than a preset value as a space utilization solution, which has the effect of accurately evaluating and optimizing the utilization of storage space. At the same time, it selects shelves suitable for loading as storage solutions, which can more effectively utilize storage space and improve storage efficiency and cargo access speed; and evaluates the comprehensive score of each space utilization solution through the cargo allocation layer, and outputs storage simulation information in order from high to low according to the comprehensive score, which has the effect of comprehensively considering multiple factors to optimize the cargo storage location, not only considering the space utilization, but also combining the proximity of entrances and exits and the satisfaction of special storage requirements, so as to more comprehensively evaluate the pros and cons of each storage solution; by giving priority to recommending storage locations with high comprehensive scores, it can improve the access efficiency of goods, meet special storage needs, and optimize the overall layout of the warehouse. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a flow chart of an embodiment of a warehousing logistics intelligent management method based on RFID technology in the present application;

[0059] Figure 2 This is a flowchart of the implementation of step S20 in an embodiment of a warehousing logistics intelligent management method based on RFID technology in the present application;

[0060] Figure 3 This is a flowchart for implementing step S42 in an embodiment of a warehousing logistics intelligent management method based on RFID technology in the present application. DETAILED DESCRIPTION

[0061] The following is combined with Figure 1-3 This application is described in further detail.

[0062] In one embodiment, if Figure 1 As shown, the present application discloses a warehousing logistics intelligent management method based on RFID technology, which specifically includes the following steps:

[0063] S10: Obtaining warehousing logistics information through RFID radio frequency tags;

[0064] In this embodiment, RFID (Radio Frequency Identification) is a technology that uses radio signals to identify specific targets and read related data. RFID tags (also called RFID tags) are usually attached to items and contain information about the items. Warehousing and logistics information refers to data and information related to warehouse storage and logistics transportation, including the inventory, storage location, in and out records, transportation status, goods attributes (such as weight, volume, category, etc.) and related warehousing and logistics operation information.

[0065] Specifically, RFID technology is used to quickly read relevant data information of warehousing and logistics goods through radio frequency tags;

[0066] S20: Input the warehouse logistics information into the pre-trained prediction model to perform replenishment prediction based on inventory;

[0067] In this embodiment, the prediction model is a mathematical model established based on historical data and statistical methods, and is used to predict the value or trend at a certain point in the future, specifically, to predict inventory demand, goods sales volume or replenishment time, etc., to help formulate inventory management and procurement plans;

[0068] Specifically, the acquired warehousing logistics information is input into a pre-trained forecasting model, so that the model can forecast the replenishment demand situation based on inventory based on historical data and current information;

[0069] S30: when receiving the prediction information output by the prediction model, obtaining the storage location information corresponding to the associated storage logistics information, wherein the storage location information includes storage area information and storage inventory information;

[0070] In this embodiment, the forecast information is information related to the replenishment management forecast of the goods inventory, including the future sales volume of the goods, the inventory replenishment time and the corresponding inventory replenishment demand; the storage location information is specific information about the geographical location of the warehouse, the division of the storage area of ​​the goods in the warehouse and the distribution of the inventory, including the storage area information (including the floor, storage area, shelf information of the warehouse, etc.) and the warehouse storage information (including the storage location, quantity, type, occupied space information of the goods, etc.);

[0071] Specifically, after the prediction model outputs the prediction information, the storage location information corresponding to the storage logistics information associated with the prediction information is obtained, that is, the relevant information of the storage location that needs to replenish inventory, including storage area information and warehouse inventory information;

[0072] S40: inputting the prediction information output by the prediction model, the storage logistics information associated with the prediction information and the corresponding storage location information into the pre-trained space simulation model to perform space storage simulation;

[0073] In this embodiment, the space simulation model is a computer model used to simulate and optimize the space layout, specifically, to generate different storage plans based on the input forecast information, storage logistics information and storage location information, and to evaluate the pros and cons of each plan, so as to formulate the best storage layout and cargo storage strategy;

[0074] Specifically, the prediction information, the warehousing logistics information associated with the prediction information, and the storage location information associated with the warehousing logistics information are input into a pre-trained space simulation model so as to perform a space storage simulation to optimize the space utilization and logistics efficiency of the warehouse;

[0075] S50: When receiving the storage simulation information output by the space simulation model, performing warehouse logistics scheduling management based on the storage simulation information;

[0076] In this embodiment, the storage simulation information is specific information about the cargo storage plan generated by the space simulation model, including the storage location, storage method, shelf utilization rate, etc. of the cargo; the warehouse logistics scheduling management is the process of planning, organizing and controlling the storage, transportation and distribution of cargo in the warehouse, specifically storing the cargo to be replenished in the corresponding location based on the storage simulation information, and also includes moving the other cargo stored on the shelf to the corresponding location during the replenishment of cargo in the warehouse based on the storage simulation information;

[0077] Specifically, reasonable scheduling management of warehousing logistics is achieved based on the storage simulation information output by the space simulation model.

[0078] In one embodiment, if Figure 2 As shown, the prediction model includes an information matching layer, a feature extraction layer, a threshold comparison layer and a prediction output layer, and step S20 includes:

[0079] S21: The information matching layer matches the preset corresponding inventory change data and promotion activity data based on the input warehousing and logistics information;

[0080] S22: The feature extraction layer extracts corresponding trend features based on the matched inventory change data and promotion activity data;

[0081] S23: The threshold comparison layer sets a dynamic threshold based on trend characteristics and compares the warehousing logistics information with it;

[0082] S24: When the comparison result determines that inventory needs to be replenished, the prediction output layer outputs prediction information based on the comparison result and trend characteristics;

[0083] In this embodiment, the information matching layer is the first layer in the prediction model, and its main function is to match the inventory change data and promotion activity data of the corresponding goods based on the input warehousing and logistics information; the inventory change data is the data that records the historical in and out of the goods; the promotion activity data is the historical promotion activity data of the goods and the future promotion activity information, wherein the historical promotion activity data includes the duration, sales volume and sales volume change of the promotion activity; the feature extraction layer is the second layer in the prediction model, and its main function is to extract the corresponding trend features based on the matched inventory change data and promotion activity data, including the change trend of the inventory volume and the impact trend of the promotion activity, for subsequent analysis and prediction; the threshold comparison layer is the third layer in the prediction model, and its main function is to set the dynamic threshold based on the extracted trend feature and compare the warehousing and logistics information with it; the prediction output layer is the fourth layer in the prediction model, when the threshold comparison layer determines that the inventory needs to be replenished, the prediction output layer will output the prediction information based on the comparison result and the trend feature, and the prediction information includes the inventory volume to be replenished, the time point of replenishment and the specific storage location, etc., for guiding the subsequent warehousing and logistics operations;

[0084] Specifically, the prediction model includes an information matching layer, a feature extraction layer, a threshold comparison layer and a prediction output layer. The information matching layer is the starting layer of the prediction model, which matches the corresponding inventory change data and promotion activity data based on the input warehousing and logistics information. On the basis of the information matching layer, the feature extraction layer extracts the corresponding trend features from the matched inventory change data and promotion activity data. The threshold comparison layer sets a dynamic threshold value adjusted according to factors such as the current inventory status, sales trends and promotion activities based on the trend features extracted by the feature extraction layer, and compares the warehousing and logistics information with the dynamic threshold value to determine whether the inventory needs to be replenished. When the comparison result determines that the inventory needs to be replenished, the prediction output layer will output the prediction information based on the comparison result and trend features.

[0085] In one embodiment, the space simulation model includes a space layout layer and a cargo distribution layer, and step S40 includes the steps of:

[0086] S41: The space layout layer constructs a virtual storage space scene based on the input storage location information;

[0087] S42: The cargo distribution layer performs a spatial storage model and outputs storage simulation information based on preset distribution rules, input forecast information and storage logistics information, and virtual storage space scenarios;

[0088] In this embodiment, the spatial layout layer is the first layer in the spatial simulation model, and its main function is to construct a virtual storage space scene based on the input storage location information (including storage area information and storage inventory information), that is, to create a virtual environment similar to the actual warehouse layout, so as to carry out subsequent cargo allocation and spatial storage simulation; the cargo allocation layer is the second layer in the spatial simulation model, and its main function is to perform spatial storage simulation and output storage simulation information based on the preset allocation rules, the input forecast information and storage logistics information, and the virtual storage space scene constructed by the spatial layout layer; the storage simulation information is the specific information about the cargo storage plan generated by the spatial simulation model, including the storage location, storage method, shelf utilization rate, etc. of the cargo;

[0089] Specifically, the spatial simulation model includes a spatial layout layer and a cargo distribution layer. The spatial layout layer receives the input storage location information and constructs a virtual storage space scene based on it. The cargo distribution layer simulates spatial storage based on preset allocation rules combined with the input prediction information, storage logistics information and the virtual storage space scene constructed by the spatial layout layer to find the optimal cargo storage solution.

[0090] In one embodiment, step S41 includes the steps of:

[0091] S411: The spatial layout layer analyzes the warehouse storage information within a preset historical time period, and obtains the warehouse space change information in combination with the warehouse area information;

[0092] S412: The space layout layer constructs a virtual storage space scene based on the storage space change information and storage area information;

[0093] In this embodiment, the warehouse storage information is the detailed information of the goods stored in the warehouse, including the type, quantity, storage location, storage time, etc. of the goods; the storage area information is the division and layout information of each area inside the warehouse, including the name, location, size, function, etc. of the area; the storage space change information is the information on the changes in the storage location and quantity of the goods in the warehouse within a preset historical time period, which is used to reflect the utilization of the warehouse space and the flow of goods;

[0094] Specifically, the spatial layout layer first analyzes the warehouse inventory information within a preset historical time period to understand the historical storage situation of the warehouse and the flow changes of goods, and combines the storage area information (such as the layout of the warehouse, the location and size of the shelves, etc.) to further obtain the change information of the storage space, and construct a virtual storage space scene based on the storage space change information and storage area information obtained from the above analysis.

[0095] In one embodiment, the preset allocation rules include storage requirement rules, space utilization evaluation rules, and score allocation rules, such as Figure 3 As shown, step S42 includes the steps of:

[0096] S421: The cargo distribution layer identifies storage requirement information in the warehousing logistics information, and identifies the corresponding virtual sub-scene in the virtual warehousing space scene through the storage requirement information and the storage requirement rules;

[0097] S422: The cargo distribution layer obtains a number of space utilization solutions based on virtual sub-scenes through space utilization evaluation rules;

[0098] S423: The cargo distribution layer performs comprehensive score calculation on each space utilization scheme according to the score distribution rule, and outputs storage simulation information based on the comprehensive score calculation result;

[0099] In this embodiment, the preset allocation rules are the rules and conditions followed when allocating goods, which ensure that the goods can be reasonably allocated to various locations in the warehouse, while meeting storage requirements, optimizing space utilization and improving storage efficiency; storage requirement rules are specific conditions that the goods need to meet during storage, including temperature, humidity, light, ventilation, etc.; virtual sub-scenes are a component of virtual storage space scenes; space utilization evaluation rules are rules for evaluating warehouse space utilization efficiency, including shelf heights, number of layers, and goods stacking methods and stacking heights; scoring allocation rules are rules for comprehensively evaluating and comparing different space utilization schemes, including indicators in multiple aspects such as space utilization, goods accessibility, handling efficiency, and safety, and by scoring each scheme, the optimal storage scheme is selected;

[0100] Specifically, the cargo distribution layer identifies storage requirement information in the warehousing logistics information, which includes the physical characteristics of the cargo (such as size, weight, shape, etc.), storage conditions (such as temperature, humidity, etc.) and safety requirements, and uses the storage requirement information and storage requirement rules to identify virtual sub-scenes that meet the storage requirements in the virtual warehousing space scene, that is, areas in the warehouse that can meet specific cargo storage conditions. The cargo distribution layer applies preset space utilization evaluation rules to generate several space utilization plans for the identified virtual sub-scenes. Finally, the cargo distribution layer uses preset score allocation rules to perform comprehensive score calculations on each space utilization plan, and outputs storage simulation information based on the comprehensive score results, that is, the optimal space storage plan.

[0101] In one embodiment, the virtual sub-scene includes a plurality of virtual shelves, and step S422 includes the steps of:

[0102] S4221: The cargo distribution layer evaluates the space utilization of several virtual shelves through a preset space utilization formula, and the calculation method is as follows:

[0103] Among them, U space is the space utilization of the virtual shelf, V used is the space volume currently used by the virtual shelf, V prestored V is the volume of pre-storage cargo included in the storage requirement information, total is the total space volume of the virtual shelf;

[0104] S4222: The goods distribution layer determines the virtual shelf with a space utilization rate less than a preset value as a space utilization rate solution based on the virtual sub-scenario;

[0105] In this embodiment, the virtual sub-scene is a component of the virtual storage space scene, which includes a number of virtual shelves. The virtual shelf is a model that simulates a shelf in an actual warehouse and is used to represent the storage location of goods in a virtual environment. At the same time, the virtual shelf is a basic unit in the virtual sub-scene, which represents a storage area in the warehouse. Each virtual shelf has specific space volume information and its bearing capacity information, which is used to reasonably simulate the storage of goods. In the goods distribution layer, the virtual shelf is the basis for evaluating space utilization and formulating storage plans; the space utilization formula is a mathematical expression for calculating the space utilization of the virtual shelf, which helps the goods distribution layer to evaluate the space utilization efficiency of each virtual shelf, so as to formulate a more reasonable storage plan; the space utilization is the ratio of the used space to the total space in the virtual shelf, which reflects the utilization degree of the virtual shelf space. By calculating the space utilization, the storage efficiency of the virtual shelf can be understood, and the storage plan can be adjusted accordingly to optimize the space utilization; the preset value is a preset threshold value, which is used to determine whether the space utilization of the virtual shelf meets the requirements. If the space utilization is less than the preset value, it means that the shelf has enough space to store more goods, so it is determined as part of the space utilization plan based on the virtual sub-scene;

[0106] Furthermore, the cargo distribution layer determines the virtual shelf whose space utilization is less than a preset value and whose load-bearing capacity meets the cargo storage requirements as a space utilization solution based on the virtual sub-scenario;

[0107] Specifically, the cargo distribution layer applies a preset space utilization formula to evaluate the space utilization of each virtual shelf in the virtual sub-scene. The formula takes into account the space volume currently used by the virtual shelf, the pre-stored cargo volume contained in the storage requirement information, and the total space volume of the virtual shelf. The cargo distribution layer screens out virtual shelves whose space utilization is less than a preset value, and the screened out virtual shelves are determined as the space utilization plan based on the virtual sub-scene.

[0108] In one embodiment, step S423 includes the steps of:

[0109] S4231: The cargo distribution layer evaluates the comprehensive score of each space utilization scheme through a preset scoring calculation formula, which is calculated as follows:

[0110] Among them, S is the comprehensive score of the space utilization plan, α is the weight coefficient of the space utilization of the virtual shelf, and U space is the space utilization rate of the virtual shelf, β is the weight coefficient of the entrance and exit proximity coefficient, D max is the maximum distance between the virtual sub-scene and the entrance and exit, D exit is the distance between the virtual shelf and the entrance and exit of the space utilization plan, is the entrance and exit proximity coefficient, γ is the weight coefficient of the satisfaction of special storage requirements, S special The degree of satisfaction of preset special storage requirements;

[0111] S4232: Outputting and storing simulation information in descending order according to the comprehensive score calculation results;

[0112] In this embodiment, the scoring calculation formula is a mathematical expression for evaluating the comprehensive score of each space utilization plan, and the resulting comprehensive score is the result after considering multiple factors, including the space utilization of the virtual shelf, the entrance and exit proximity coefficient, and the satisfaction of special storage requirements; the comprehensive score is a quantitative indicator for evaluating the pros and cons of the space utilization plan. By calculating the comprehensive score of each plan, the pros and cons of different plans can be compared to select the best storage plan; the weight coefficient of the space utilization of the virtual shelf is a coefficient used to adjust the proportion of the space utilization of the virtual shelf in the comprehensive score. By setting the weight coefficient, the importance of space utilization in the storage plan is reflected; the weight coefficient of the entrance and exit proximity coefficient is used to adjust the entrance and exit proximity coefficient. The coefficient of proportion in the comprehensive score, the entrance and exit proximity coefficient considers the distance between the goods and the entrance and exit when they are stored and retrieved, for example: the closer the distance, the more convenient the storage and access, and the more suitable it is for the storage of goods with a large flow; the entrance and exit proximity coefficient is an indicator to evaluate the distance between the virtual shelf and the entrance and exit, for example: the closer the shelf is to the entrance and exit, the higher its proximity coefficient, indicating that it is more convenient to store and retrieve goods; the weight coefficient of the satisfaction of special storage requirements is a coefficient used to adjust the proportion of the satisfaction of special storage requirements in the comprehensive score. Special storage requirements include temperature, humidity, light, ventilation, fire prevention and other conditions. Meeting the corresponding requirements is crucial to ensuring the quality and safety of specific goods; the preset special storage requirements satisfaction is a satisfaction standard preset according to the special storage requirements of the goods;

[0113] Specifically, the cargo distribution layer evaluates the comprehensive score of each space utilization plan based on a preset scoring calculation formula. The formula takes into account multiple factors, including the space utilization of virtual shelves, the entrance and exit proximity coefficient, and the satisfaction of special storage requirements, and assigns a corresponding weight coefficient to each factor; the comprehensive score of each space utilization plan is obtained by calculation, and according to the comprehensive score calculation result, the storage simulation information is output in descending order of score.

[0114] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0115] In one embodiment, a warehousing logistics intelligent management system based on RFID technology is provided, and the warehousing logistics intelligent management system based on RFID technology corresponds one-to-one to the warehousing logistics intelligent management method based on RFID technology in the above embodiment. An RFID-based warehousing logistics intelligent management system includes:

[0116] The first information acquisition module is used to obtain storage logistics information through RFID radio frequency tags;

[0117] The first input module is used to input the warehouse logistics information into the pre-trained prediction model to perform inventory-based replenishment prediction;

[0118] A second information acquisition module is used to, when receiving the prediction information output by the prediction model, acquire the storage location information corresponding to the associated storage logistics information, wherein the storage location information includes storage area information and storage inventory information;

[0119] The second input module is used to input the prediction information output by the prediction model, the storage logistics information associated with the prediction information and the corresponding storage location information into the pre-trained space simulation model to perform space storage simulation;

[0120] A scheduling management module, which is used to perform storage logistics scheduling management based on the storage simulation information output by the space simulation model when receiving the storage simulation information;

[0121] Optionally, also include:

[0122] The information matching layer module is used to match the preset corresponding inventory change data and promotion activity data based on the input warehousing and logistics information;

[0123] The feature extraction layer module is used to extract corresponding trend features based on the matched inventory change data and promotion activity data;

[0124] The threshold comparison layer module is used to set dynamic thresholds based on trend characteristics and compare the warehouse logistics information with them; when the comparison result determines that inventory replenishment is needed, the forecast information is output based on the comparison result and trend characteristics;

[0125] Optionally, also include:

[0126] The space layout layer module is used to construct a virtual storage space scene based on the input storage location information;

[0127] The cargo distribution layer module is used to perform spatial storage modeling and output storage simulation information based on preset distribution rules, input forecast information and warehousing logistics information, and virtual warehousing space scenarios;

[0128] Optionally, the spatial layout layer module includes:

[0129] The spatial information acquisition submodule is used to analyze the warehouse inventory information within a preset historical time period and obtain the storage space change information in combination with the storage area information;

[0130] The space scene construction submodule is used to construct a virtual storage space scene based on storage space change information and storage area information;

[0131] Optionally, the cargo distribution layer module includes:

[0132] An identification submodule, used to identify storage requirement information in the warehousing logistics information, and identify the corresponding virtual sub-scene in the virtual storage space scene through the storage requirement information and storage requirement rules;

[0133] A utilization scheme acquisition submodule is used to acquire several space utilization schemes based on virtual sub-scenes through space utilization evaluation rules;

[0134] A comprehensive scoring submodule is used to calculate the comprehensive score of each space utilization scheme according to the scoring allocation rule, and output storage simulation information based on the comprehensive scoring calculation result;

[0135] Optionally, the utilization plan acquisition submodule includes:

[0136] The first calculation module is used for evaluating the space utilization of several virtual shelves at the cargo distribution layer through a preset space utilization formula, and the calculation method is as follows:

[0137] Among them, U space is the space utilization of the virtual shelf, V used is the space volume currently used by the virtual shelf, V prestored V is the volume of pre-storage cargo included in the storage requirement information, total is the total space volume of the virtual shelf;

[0138] A scheme determination submodule, used for determining a virtual shelf whose space utilization is less than a preset value as a space utilization scheme based on a virtual sub-scenario;

[0139] Optionally, the comprehensive scoring submodule includes:

[0140] The second calculation submodule is used to evaluate the comprehensive score of each space utilization scheme through a preset score calculation formula, and the calculation method is as follows:

[0141] Among them, S is the comprehensive score of the space utilization plan, α is the weight coefficient of the space utilization of the virtual shelf, and U spaceis the space utilization rate of the virtual shelf, β is the weight coefficient of the entrance and exit proximity coefficient, D max is the maximum distance between the virtual sub-scene and the entrance and exit, D exit is the distance between the virtual shelf and the entrance and exit of the space utilization plan, is the entrance and exit proximity coefficient, γ is the weight coefficient of the satisfaction of special storage requirements, S special The degree of satisfaction of preset special storage requirements;

[0142] The sorting submodule is used to output and store simulation information in descending order according to the comprehensive score calculation results.

[0143] For the specific definition of a warehousing logistics intelligent management system based on RFID technology, please refer to the definition of a warehousing logistics intelligent management method based on RFID technology above, which will not be repeated here. Each module in the above-mentioned warehousing logistics intelligent management system based on RFID technology can be implemented in whole or in part through software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0144] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A warehousing logistics intelligent management method based on RFID technology, characterized by: The method comprises the following steps: obtaining storage logistics information through RFID radio frequency tags; Input warehouse logistics information into the pre-trained forecasting model to perform inventory-based replenishment forecasting; When receiving the prediction information output by the prediction model, obtaining the storage location information corresponding to the associated storage logistics information, wherein the storage location information includes storage area information and storage inventory information; Input the prediction information output by the prediction model, the warehousing logistics information associated with the prediction information, and the corresponding warehousing location information into the pre-trained space simulation model to perform space storage simulation; When storage simulation information output by the space simulation model is received, warehouse logistics scheduling management is performed based on the storage simulation information.

2. According to claim 1, a warehousing logistics intelligent management method based on RFID technology is characterized by: The prediction model includes an information matching layer, a feature extraction layer, a threshold comparison layer and a prediction output layer. The step of inputting the warehousing logistics information into the pre-trained prediction model to perform replenishment prediction based on inventory includes the steps of: The information matching layer matches the preset corresponding inventory change data and promotion activity data based on the input warehousing and logistics information; The feature extraction layer extracts corresponding trend features based on the matched inventory change data and promotion activity data; The threshold comparison layer sets dynamic thresholds based on trend characteristics and compares the warehouse logistics information with them; When the comparison result determines that inventory needs to be replenished, the prediction output layer outputs prediction information based on the comparison result and trend characteristics.

3. The intelligent management method for warehousing logistics based on RFID technology according to claim 1 is characterized by: The spatial simulation model includes a spatial layout layer and a cargo distribution layer. The step of inputting the prediction information output by the prediction model, the storage logistics information associated with the prediction information and the corresponding storage location information into the pre-trained spatial simulation model for spatial storage simulation includes the following steps: The space layout layer constructs a virtual storage space scene based on the input storage location information; The cargo distribution layer performs spatial storage modeling and outputs storage simulation information based on preset distribution rules, input forecast information and warehousing logistics information, and virtual warehousing space scenarios.

4. The intelligent management method for warehousing logistics based on RFID technology according to claim 3 is characterized by: The step of constructing a virtual storage space scene based on the input storage location information in the space layout layer includes the following steps: The spatial layout layer analyzes the warehouse inventory information within the preset historical time period and obtains the warehouse space change information in combination with the warehouse area information; The space layout layer constructs a virtual storage space scene based on storage space change information and storage area information.

5. The intelligent management method for warehousing logistics based on RFID technology according to claim 3 is characterized by: The preset allocation rules include storage requirement rules, space utilization rate evaluation rules and scoring allocation rules. The cargo allocation layer performs a spatial storage model and outputs storage simulation information based on the preset allocation rules, input forecast information and storage logistics information and virtual storage space scenarios, including the following steps: The cargo distribution layer identifies the storage requirement information in the warehousing logistics information, and identifies the corresponding virtual sub-scene in the virtual warehousing space scene through the storage requirement information and storage requirement rules; The cargo distribution layer obtains several space utilization schemes based on virtual sub-scenes through space utilization evaluation rules; The cargo allocation layer calculates a comprehensive score for each space utilization plan through the score allocation rules, and outputs storage simulation information based on the comprehensive score calculation results.

6. The intelligent management method for warehousing logistics based on RFID technology according to claim 5 is characterized by: The virtual sub-scene includes a plurality of virtual shelves, and the steps of obtaining a plurality of space utilization schemes based on the virtual sub-scenes by the cargo distribution layer through the space utilization evaluation rule include: The cargo distribution layer evaluates the space utilization of several virtual shelves through a preset space utilization formula, which is calculated as follows: Among them, U space is the space utilization of the virtual shelf, V used is the space volume currently used by the virtual shelf, V prestored V is the volume of pre-storage cargo included in the storage requirement information, total is the total space volume of the virtual shelf; The goods allocation layer determines the virtual shelf whose space utilization is less than a preset value as a space utilization solution based on the virtual sub-scene.

7. The intelligent management method for warehousing logistics based on RFID technology according to claim 5 is characterized by: The cargo distribution layer performs comprehensive score calculation on each space utilization scheme according to the score distribution rule, and outputs storage simulation information based on the comprehensive score calculation result, including: The cargo allocation layer evaluates the comprehensive score of each space utilization scheme through a preset scoring calculation formula, which is calculated as follows: Among them, S is the comprehensive score of the space utilization plan, α is the weight coefficient of the space utilization of the virtual shelf, and U space is the space utilization rate of the virtual shelf, β is the weight coefficient of the entrance and exit proximity coefficient, D max is the maximum distance between the virtual sub-scene and the entrance and exit, D exit is the distance between the virtual shelf and the entrance and exit of the space utilization plan, is the entrance and exit proximity coefficient, γ is the weight coefficient of the satisfaction of special storage requirements, S special The degree of satisfaction of preset special storage requirements; The storage simulation information is outputted in descending order according to the comprehensive score calculation results.

8. An intelligent management system for warehousing logistics based on RFID technology, characterized by: include: The first information acquisition module is used to obtain storage logistics information through RFID radio frequency tags; The first input module is used to input the warehouse logistics information into the pre-trained prediction model to perform inventory-based replenishment prediction; A second information acquisition module is used to, when receiving the prediction information output by the prediction model, acquire the storage location information corresponding to the associated storage logistics information, wherein the storage location information includes storage area information and storage inventory information; The second input module is used to input the prediction information output by the prediction model, the storage logistics information associated with the prediction information and the corresponding storage location information into the pre-trained space simulation model to perform space storage simulation; The scheduling management module is used to perform warehousing logistics scheduling management based on the storage simulation information when receiving the storage simulation information output by the space simulation model.

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