Intelligent warehouse management method, system and device and storage medium

Abstract

The invention provides an intelligent warehouse management method, system and device and a storage medium, and relates to the technical field of warehouse management, and the method comprises the steps: obtaining the basic information of target goods to be stored in a warehouse center and the remaining storage area of the warehouse center; according to the basic information, determining a storable area of the target cargo from the remaining storage areas; according to the historical order data of the target cargo, predicting the total order number of the target cargo and the order number of each order in a preset time length, calculating a first heat index of the target cargo, and determining a storable position of the target cargo in the storable area according to the first heat index; obtaining a first storage position of an associated cargo associated with the target cargo and a second popularity index of the associated cargo; and determining a target storage position of the target cargo in the storable positions in combination with the first storage position and the second popularity index. The method has the technical effects that the goods taking efficiency is improved, and the labor cost is reduced.

Description

Technical Field

[0001] The present application relates to the field of warehouse management technology, and specifically to an intelligent warehouse management method, system, equipment and storage medium. Background Art

[0002] With the rapid development of e-commerce, the storage management of goods in storage centers has become increasingly complex. Reasonable allocation of storage locations is not only related to the utilization efficiency of storage space, but also directly affects the efficiency of subsequent pickup. How to scientifically plan the storage location of goods and improve the efficiency of storage management has become a technical problem that needs to be solved in the industry.

[0003] The current warehouse management method is usually based on the basic information of the goods, such as allocating storage locations based on attributes such as volume and weight and the remaining storage space in the warehouse center. Although this method solves the basic storage needs of goods, it only stays at the static matching level and does not fully consider the actual needs of the pickup process. In the actual pickup process, there may be problems such as long pickup routes and frequent round trips, which reduces the efficiency of pickup and increases labor costs. Summary of the invention

[0004] The present application provides an intelligent warehouse management method, system, device and storage medium for improving picking efficiency and reducing labor costs.

[0005] In a first aspect, the present application provides an intelligent warehousing management method, the method comprising: obtaining basic information of target goods to be stored in a storage center, and the remaining storage area of ​​the storage center; determining the storable area of ​​the target goods from the remaining storage area based on the basic information; obtaining historical order data of the target goods, and predicting the total number of orders of the target goods and the order quantity of each order within a preset time period based on the historical order data; calculating a first heat index of the target goods in combination with the total number of orders and the order quantity of each order, and determining the storable location of the target goods in the storable area based on the first heat index; obtaining a first storage location of associated goods associated with the target goods, and a second heat index of the associated goods; and determining the target storage location of the target goods in the storable location in combination with the first storage location and the second heat index.

[0006] By adopting the above technical solution, by obtaining the basic information of the target goods and the remaining storage area of ​​the storage center, the basic storage area is first determined, and then the future order situation of the target goods is predicted based on the historical order data and the first heat index is calculated, and then the final target storage location is determined in combination with the storage location of the related goods and the second heat index. This multi-dimensional storage location determination method not only guarantees the basic storage needs, but also realizes the prediction of the pickup frequency through the heat index, and takes into account the storage location relationship of related goods, so that the goods with higher popularity and strong correlation can be concentrated in an area that is convenient for pickup, effectively improving the pickup efficiency and reducing labor costs.

[0007] Optionally, the cargo information includes volume, weight and storage conditions, and determining the storable area for the target cargo from the remaining storage areas based on the cargo information includes: determining the storage area in the remaining storage area that meets the volume, weight and storage conditions as the storable area for the target cargo.

[0008] By adopting the above technical solution, by matching the volume, weight and storage conditions of the target goods with the remaining storage area, it is ensured that the selected storable area meets the basic storage needs of the goods, the accuracy and rationality of the storage location allocation are achieved, and the problem of damage to goods or waste of storage space due to mismatched storage conditions is avoided.

[0009] Optionally, the calculating the first popularity index of the target product in combination with the total number of orders and the order quantity of each order includes: obtaining an order quantity benchmark value of each order of the target product, substituting the total number of orders, the order quantity of each order and the order quantity benchmark value into a preset formula, and calculating the first popularity index of the target product; wherein: The preset formula is: ; In the formula, is the first heat index, and are weight coefficients, is the total number of orders, is the preset duration, For the The order quantity of the order, is the base value of the order quantity.

[0010] By adopting the above technical solution, the first heat index is calculated by substituting the total number of orders, the order quantity of each order and the order quantity benchmark value into a preset formula, wherein the formula weights the order frequency and order size by assigning weight coefficients respectively, and introduces a logarithmic function to balance the influence of the total number of orders, thereby achieving accurate quantification of the heat of the target goods, taking into account both the influence of the order frequency and the difference in order size, so that the heat index can more accurately reflect the actual pickup demand of the goods.

[0011] Optionally, determining the storable location of the target goods in the storable area based on the first heat index includes: obtaining a third heat index of each storage location in the storable area; calculating the difference between the first heat index and the third heat index, and determining the storage location where the difference is less than a preset threshold as the storable location of the target goods.

[0012] By adopting the above technical solution, by calculating the difference between the first heat index of the target goods and the third heat index of each storage location in the storable area, and determining the storage location with a difference less than a preset threshold as the storable location, the storage location screening of heat matching is realized, so that goods with similar heat can be stored in adjacent areas, thereby optimizing the pickup route and avoiding the problem of low pickup efficiency caused by the mixing of goods with large temperature differences.

[0013] Optionally, combining the first storage location and the second heat index to determine the target storage location of the target goods in the storable locations includes: searching for a target shipping frequency corresponding to the second heat index in a relational database, and using the target shipping frequency as the shipping frequency of the associated goods; if the shipping frequency is higher than a preset frequency, using the storage location in the storable locations that is closest to the first storage location as the target storage location of the target goods; if the shipping frequency is not higher than the preset frequency, using the storage location in the storable locations that is closest to the shipping port as the target storage location of the target goods.

[0014] By adopting the above technical solution, by obtaining the shipping frequency corresponding to the second heat index of the related goods, and taking different storage strategies according to the comparison result between the shipping frequency and the preset frequency: when the shipping frequency is high, priority is given to the principle of the closest distance to the first storage location of the related goods; when the shipping frequency is low, priority is given to the principle of the closest distance to the shipping port. This differentiated storage location selection method not only ensures the centralized storage of high-frequency related goods to improve the efficiency of batch picking, but also ensures that low-frequency goods are easy to pick up individually, thereby realizing dynamic optimization of storage location allocation.

[0015] Optionally, after determining the target storage location of the target goods in the storable location, the method further includes: obtaining first RFID tag information corresponding to the target goods, and second RFID tag information of the pallet corresponding to the target storage location; binding the first RFID tag information with the second RFID tag information to generate binding information; storing the binding information, basic information of the target goods and location information of the target storage location into a data processing module, so that the data processing module performs full life cycle traceability management of the target goods.

[0016] By adopting the above technical solution, by binding the first RFID tag information of the target goods with the second RFID tag information of the target storage position pallet, and storing the binding information, the basic information of the target goods and the target storage position information into the data processing module, the accurate positioning of the storage position of the target goods and the associated storage of the goods information are achieved, and the data processing module is provided with complete traceability data support, thereby ensuring the traceability of the target goods from warehousing to delivery, and improving the accuracy and reliability of warehouse management.

[0017] Optionally, after determining the target storage location of the target goods in the storable locations, the method further includes: obtaining first goods information of other stored items stored within a preset range of the target storage location, and second goods information of the target goods; combining the first goods information and the second goods information, determining whether there are incompatible attributes between the target goods and the other stored items; if the incompatible attributes exist, re-determining the final storage location of the target goods from the storable locations.

[0018] By adopting the above technical solution, by obtaining the first cargo information of other stored items within the preset range of the target storage location and the second cargo information of the target cargo, and adjusting the storage location according to the incompatible attribute judgment results of the two, the final storage location is re-determined when incompatible attributes are found, effectively avoiding the potential safety hazards or quality impacts caused by the storage of goods with incompatible attributes in adjacent areas, thereby improving the safety of warehouse management and the rationality of cargo storage.

[0019] In a second aspect, the present application provides an intelligent warehouse management system, the system comprising: a first acquisition module, a determination module, a prediction module, a first combination module, a second acquisition module and a second combination module; wherein: The first acquisition module is used to acquire basic information of target goods to be stored in a storage center and the remaining storage area of ​​the storage center; the determination module is used to determine the storable area of ​​the target goods from the remaining storage area based on the basic information; the prediction module is used to acquire historical order data of the target goods, and predict the total number of orders of the target goods and the order quantity of each order within a preset time period based on the historical order data; the first combination module is used to calculate the first heat index of the target goods by combining the total number of orders and the order quantity of each order, and determine the storable location of the target goods in the storable area based on the first heat index; the second acquisition module is used to acquire the first storage location of associated goods associated with the target goods and the second heat index of the associated goods; the second combination module is used to determine the target storage location of the target goods in the storable location by combining the first storage location and the second heat index.

[0020] In the third aspect, the present application provides an electronic device, which adopts the following technical solution: it includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a computer program such as any one of the above-mentioned intelligent warehouse management methods.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: storing a computer program that can be loaded by a processor and execute any of the above-mentioned intelligent warehousing management methods.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: By obtaining the basic information of the target goods and the remaining storage area of ​​the warehousing center, the basic storage area is first determined. Then, based on the historical order data, the future order situation of the target goods is predicted and the first heat index is calculated. The final target storage location is determined in combination with the storage location of the related goods and the second heat index. This multi-dimensional storage location determination method not only guarantees the basic storage needs, but also predicts the frequency of picking up goods through the heat index. At the same time, it takes into account the storage location relationship of related goods, so that goods with higher popularity and strong correlation can be concentrated in areas that are convenient for picking up, effectively improving the efficiency of picking up goods and reducing labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flowchart of an intelligent warehouse management method provided by an embodiment of the present application; Figure 2It is a structural diagram of an intelligent warehouse management system provided by an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0024] Description of the reference numerals: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0026] In the description of the embodiments of the present application, words such as "illustrative", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "illustrative", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "illustrative", "for example" or "for example" is intended to present related concepts in a concrete way.

[0027] Figure 1 Schematic diagram of a flow chart of an intelligent warehouse management method provided by an embodiment of the present application. Figure 1 As shown, the method includes S101-S106: S101, obtaining basic information of target goods to be stored in a storage center and the remaining storage area of ​​the storage center.

[0028] In the specific implementation of the present invention, the basic information of the target goods to be stored in the storage center is first obtained through the data acquisition module. The basic information includes the volume, weight, storage conditions and other attribute information of the goods. Among them, the volume information is used to determine the space occupied by the target goods, the weight information is used to evaluate whether the load-bearing capacity of the storage area meets the requirements, and the storage conditions include environmental parameters such as temperature requirements, humidity requirements, and anti-corrosion requirements. These basic information can be obtained by scanning the barcode or QR code on the outer packaging of the goods, or by manual entry, so as to ensure the accuracy of subsequent storage location allocation.

[0029] At the same time, the warehouse management system obtains the remaining storage area information of the warehouse center in real time. Specifically, the warehouse management system collects the occupancy status of the cargo space through sensors installed at each cargo space in the warehouse center, and uploads the collected status information to the central control system. The central control system determines the occupancy status of each cargo space based on the received status information, thereby obtaining the remaining storage area of ​​the current warehouse center. The information of the remaining storage area includes the three-dimensional coordinates of the storage location, load-bearing capacity, environmental parameters, etc.

[0030] By obtaining the basic information of the target goods and the remaining storage area information of the storage center, the necessary data support can be provided for the subsequent storage location allocation. On the one hand, the basic information of the target goods can be used to screen the storage locations that meet the storage conditions to avoid damage to the goods caused by storing them in inappropriate locations; on the other hand, the information of the remaining storage area can help the system quickly locate the available storage space and improve storage efficiency. In addition, this information can also be used for the system's inventory management and goods traceability. When you need to query the storage location of a batch of goods or retrieve a batch of goods, you can quickly locate the target goods based on this basic information.

[0031] For example, when a batch of medicines that need to be kept at a constant temperature needs to be stored, the system first obtains the volume and weight information of the batch of medicines, as well as the required storage temperature and other environmental parameters. At the same time, the system queries the remaining storage areas in the storage center and selects the storage areas with constant temperature functions. In this way, the system can ensure that the batch of medicines is stored in a suitable environment, which not only guarantees the quality of the medicines, but also improves the efficiency of storage management.

[0032] S102: Determine a storage area for the target goods from the remaining storage areas according to the basic information.

[0033] After obtaining the basic information of the target goods and the remaining storage area information of the storage center, the system needs to determine the storage area suitable for storing the target goods from the remaining storage area. The system will first select the storage locations in the remaining storage area whose space size is greater than or equal to the volume of the target goods based on the volume information of the target goods to ensure that the goods can be completely placed in the storage space. Then, based on the weight information of the target goods, the system will further select the storage locations whose load-bearing capacity meets the requirements from the selected storage locations to avoid safety hazards caused by overweight.

[0034] After completing the basic space and load-bearing screening, the system will further accurately match the target goods according to their storage conditions. For example, for cold chain goods that need to be stored at low temperatures, the system will give priority to storage areas with refrigeration functions and a temperature range that meets the requirements; for flammable and explosive items, the system will select dedicated storage areas equipped with corresponding safety facilities; for goods that are sensitive to humidity, the system will select storage areas with dehumidification or moisture-proof functions.

[0035] When determining the storage area, the system will also consider the geographical location of the storage area. For example, for heavy goods, the system will give priority to storage areas close to the ground to reduce the difficulty of handling; for goods that need to be frequently in and out of the warehouse, the system will give priority to storage areas close to the cargo channel to improve work efficiency; for goods with special storage requirements, the system will select storage areas equipped with corresponding monitoring equipment to ensure storage safety.

[0036] Through this multi-dimensional screening mechanism, the system can quickly determine the area most suitable for storing the target goods. For example, when a batch of fragile electronic products needs to be stored, the system will not only consider the size and load-bearing capacity of the storage space, but also select a storage area with shockproof functions and controllable temperature and humidity, while giving priority to locations away from frequent operation areas to reduce the risk of damage to the goods.

[0037] Based on the above embodiment, as an optional implementation, in S102, the cargo information includes volume, weight and storage conditions. According to the cargo information, determining the storable area of ​​the target cargo from the remaining storage areas specifically includes: determining the storage area in the remaining storage area that meets the volume, weight and storage conditions as the storable area of ​​the target cargo.

[0038] Specifically, the system first obtains the volume information of the target goods, including data in three dimensions: length, width, and height. By comparing these data with the spatial dimensions of the remaining storage areas, the system selects the storage space that can accommodate the target goods. For example, if the volume of a batch of goods is 1.2 cubic meters, the system will exclude all storage areas with available space less than 1.2 cubic meters to ensure that the goods can be placed in the storage location intact.

[0039] At the same time, the system will take into account the weight information of the goods. Different storage areas may have different load-bearing capacities, which are related to factors such as the structural strength of the shelf and the load-bearing capacity of the ground. For example, for goods weighing 500 kg, the system will select a storage area with a load-bearing capacity of not less than 500 kg to ensure storage safety. In addition, some special goods may need to consider the load-bearing requirements per unit area, and the system will also include this factor in the screening conditions.

[0040] Storage conditions are another important screening dimension, which include special requirements such as temperature, humidity, dustproof, shockproof, and lightproof. For example, for medicines that need to be stored at a constant temperature, the system will give priority to storage areas with temperature control functions; for fragile items, the system will choose storage areas equipped with shockproof facilities; for goods that are afraid of moisture, it will choose storage areas with dehumidification functions.

[0041] S103, obtaining historical order data of the target goods, and predicting the total number of orders for the target goods and the order quantity of each order within a preset period of time based on the historical order data.

[0042] After determining the storage area for the target goods, the system needs to predict the future outbound demand for the goods by analyzing historical order data, thereby optimizing the allocation of storage locations.

[0043] Specifically, the system first extracts the historical order data of the target goods from the order management database. These data include information such as the order occurrence time, order frequency, specific quantity of each order, and seasonal fluctuation characteristics within a certain period of time in the past.

[0044] The system uses machine learning algorithms to analyze and process historical order data. First, it uses time series analysis methods to identify cyclical patterns and seasonal characteristics in order data. For example, certain goods may see a surge in demand during holidays or experience cyclical fluctuations in certain seasons. Second, the system combines forecasting methods such as moving average and exponential smoothing to perform trend analysis on order data and identify long-term trends. At the same time, the system also considers the impact of external factors such as market environment and promotional activities on order demand.

[0045] Based on these analysis results, the system can predict the total number of orders for the target goods within a preset period of time (such as the next month or quarter). This prediction process mainly considers the frequency of historical orders and the distribution characteristics of orders in the time dimension. At the same time, the system will also predict the order quantity of each order. This prediction is mainly based on the quantity distribution characteristics in historical orders and the change pattern of order volume.

[0046] For example, for a food product with strong seasonality, the system will focus on analyzing its order distribution characteristics in different seasons. Assuming that historical data shows that the order frequency and order volume of this product in summer are significantly higher than those in other seasons, the system will appropriately increase the predicted value when predicting summer order demand based on this feature. At the same time, if it is found that the product has a recent growth trend, the system will also take this trend factor into account when predicting.

[0047] S104, calculating a first heat index of the target goods based on the total number of orders and the order quantity of each order, and determining a storable location of the target goods in the storable area according to the first heat index.

[0048] After obtaining the order forecast data of the target goods, the system needs to calculate the first heat index of the target goods. This index reflects the turnover frequency and storage and access requirements of the goods and is an important basis for determining the final storage location.

[0049] Specifically, the system first obtains the total number of orders and the order quantity of each order within a preset time period, and at the same time obtains the order quantity benchmark value of the goods of this type, wherein the order quantity benchmark value is the standard order quantity obtained based on the historical order data of the goods of this type.

[0050] The system uses a preset formula , calculate the first heat index. In this formula, α and γ are weight coefficients used to balance the impact of order frequency and order quantity on the heat index. Among them, Indicates the frequency of orders per unit time. It represents the ratio of actual order quantity to benchmark order quantity. It is used to logarithmize the total number of orders, making the growth of the heat index more gradual.

[0051] Specifically, It is the first heat index of the target goods, which is used to quantify the turnover activity of the goods. The higher the index, the more frequent the storage and retrieval needs of the goods, and the more they need to be stored in a convenient location.

[0052] F represents the total number of orders within the preset time, reflecting the frequency of goods being taken. The more orders there are, the more frequent the goods are circulated, the more storage and retrieval operations there are, and therefore a more convenient storage location is needed.

[0053] T represents the preset duration, which can be in days, weeks or months. The frequency of orders per unit time is obtained by calculating F / T. This standardized processing makes the popularity index under different preset durations comparable.

[0054] represents the order quantity of the i-th order, It indicates the total number of orders within a preset time period. The order quantity is considered because the handling of large quantities of goods requires more operating space and more convenient locations.

[0055] Indicates the base value of order quantity, which is the standard order quantity for this type of goods. The ratio of the actual order quantity to the benchmark order quantity can be used to determine whether the current order quantity of the goods exceeds the normal level. It is a reference value of the standard order quantity set for a certain type of goods, which reflects the typical order size of this type of goods under normal circumstances. The setting of this benchmark value is based on the following considerations: First, the benchmark value is usually determined based on the physical characteristics of the goods. For example, for goods stored on standard pallets, one pallet may hold exactly 100 boxes, so 100 boxes can be used as a natural order quantity benchmark value because it conforms to the actual operating unit; for goods that need to be shipped in full boxes, the standard packaging quantity of a box can also be used as a reference for the benchmark value.

[0056] Secondly, the benchmark value will take into account the industry characteristics of the goods. For example, some industrial raw materials may be calculated by tonnage, and their benchmark value may be 5 tons or 10 tons; while for daily consumer goods, they may be calculated by box or piece, and their benchmark value may be 50 boxes or 100 pieces. The setting of such benchmark values ​​needs to fully consider industry practices and customers' general purchasing habits.

[0057] Thirdly, the determination of the benchmark value also needs to take into account actual transportation and operating conditions. If standard trucks are usually used for transportation, the benchmark value can refer to the standard load of a truck; if forklifts are often used, the benchmark value can refer to the standard load capacity of the forklift. This allows the benchmark value to be better matched with the actual operation. For example, if the benchmark value of a certain product is 100 boxes and the actual order averages 120 boxes, the ratio is 1.2, indicating that the order volume is higher than the standard level and it is necessary to consider allocating a more convenient storage location. By using benchmark values, the system can more objectively evaluate the storage needs of different goods, making the storage location allocation more reasonable.

[0058] α and γ are weight coefficients, and α+γ=1. α is used to adjust the weight of the order frequency, and γ is used to adjust the weight of the order quantity. By adjusting these two coefficients, the influence of frequency and quantity on the heat index can be balanced according to actual needs. The determination of α and γ is based on the actual operation characteristics and business needs of the storage center, mainly considering the following aspects: First, it is necessary to statistically analyze the operating characteristics of the storage center. By collecting and analyzing daily operating data, evaluate the impact of order frequency and order quantity on operating efficiency. For example, for a storage center that is mainly operated manually, frequent pickups will significantly increase manpower consumption and operating time. At this time, the value of α should be increased to highlight the impact of frequency; while for a storage center with a high degree of automation, the processing of large batches of orders may occupy more system resources. At this time, the value of γ should be increased to emphasize the impact of quantity.

[0059] Secondly, the characteristics of different types of goods need to be considered. For small and easy-to-carry goods, order frequency may be more important than order quantity, because the burden of each pickup operation is relatively small, and α can be set larger at this time; for large and difficult-to-carry goods, the impact of order quantity is more significant, because the transportation of a large number of goods requires more operating space and time, and γ should be set larger at this time.

[0060] Thirdly, it is necessary to analyze the layout characteristics of the storage center. If the warehouse has many aisles and the storage location is more accessible, then the impact of order frequency may be relatively small, and α can be appropriately reduced; if the warehouse space is tight and the storage location needs to be adjusted frequently, then the impact of order quantity may be greater, and γ can be appropriately increased.

[0061] As a correction term, the purpose of using the logarithmic function is to reduce the excessive impact of the heat index when the number of orders is too large. grows faster; when F is larger, The growth has slowed down, which is in line with the demand characteristics of actual warehousing operations.

[0062] The formula combines the frequency term, quantity term and correction term in a multiplicative form, mainly based on the following considerations: and The weighted sum of , taking into account the two core factors of order frequency and order quantity; As a correction item, it avoids the problem of excessive growth of the heat index when the number of orders is too large.

[0063] For example, suppose a batch of goods has 20 orders within 30 days, each order quantity is 90 pieces, the benchmark order quantity is 100 pieces, α=0.6, γ=0.4, then: F / T=20 / 30≈0.667, which means an average of about 0.667 orders per day; =(90×20) / (20×100)=0.9, indicating that the order volume is slightly lower than the benchmark value, and the benchmark value is set to 1; ≈1.322, which plays a moderate regulatory role; the final heat index =1.023.

[0064] After obtaining the first heat index, the system will obtain the third heat index of each storage location in the storable area. The calculation method of the third heat index is the same as that of the first heat index. The third heat index is an inherent attribute of each storage location, reflecting the convenience and operating efficiency of the location. The system calculates the difference between the first heat index and the third heat index, and determines the storage location with a difference less than a preset threshold as the storable location of the target goods. This matching mechanism ensures that goods with higher heat are stored in a convenient location, while goods with lower heat can be stored in a relatively distant or inaccessible location.

[0065] This storage location allocation method based on heat index can achieve reasonable utilization of storage space. For example, when the first heat index of a batch of goods is high, the system will give priority to storing it in a location close to the goods channel and convenient for forklift operation to improve storage and retrieval efficiency; while for goods with lower heat index, they can be arranged in a farther storage area, which can reduce the occupation of high-frequency storage and retrieval areas.

[0066] Based on the above embodiment, as an optional implementation, in S104, determining the storable location of the target goods in the storable area according to the first heat index specifically includes S41-S42: S41, obtaining a third heat index of each storage location in the storable area.

[0067] S42, calculating the difference between the first heat index and the third heat index, and determining the storage location where the difference is less than a preset threshold as a storage location for the target goods.

[0068] After determining the storage area for the target goods, the system needs to further select a specific storage location. In this process, the system first obtains the third heat index of each storage location in the storage area. The third heat index reflects the usage characteristics of the storage location itself, including factors such as the convenience of operation, historical storage records, and turnover efficiency of the location. The calculation of this index takes into account multiple dimensions such as the distance of the storage location from the access channel, the adequacy of the operating space, and the turnover of the historical storage goods at the location.

[0069] The system evaluates the matching degree between the storage location and the demand for goods by calculating the difference between the first heat index of the target goods and the third heat index of each storage location. The idea of ​​this difference calculation is based on the principle of "matching the heat of the goods with the heat of the storage location", that is, goods with higher heat should be stored in higher heat locations, and goods with lower heat can be stored in lower heat locations.

[0070] For example, if the first heat index of a batch of goods is 0.85, the system will calculate the difference between it and the third heat index of each available storage location. If the third heat index of a storage location is 0.82, then the difference is |0.85-0.82|=0.03. If the preset threshold is set to 0.1, since 0.03<0.1, this storage location will be determined as one of the available storage locations for the target goods.

[0071] S105, obtaining a first storage location of an associated item associated with the target item and a second heat index of the associated item.

[0072] After determining the storage locations of the target goods, the system needs to consider the associations between the goods to optimize the selection of storage locations. The system first obtains the information of the associated goods that have an association relationship with the target goods from the goods association database. These associations are usually manifested as goods that are often shipped together or belong to the same order, such as certain product combinations, supporting products, or complete sets of tools.

[0073] The system obtains the current first storage location of these related goods, that is, the location where the related goods are actually stored. At the same time, the system also needs to obtain the second heat index of these related goods. The second heat index is calculated based on the order data of the related goods, and the calculation method is similar to the first heat index, reflecting the access activity of the related goods. The purpose of obtaining this information is to take into account the location relationship with the related goods when determining the final storage location of the target goods, thereby improving the overall access efficiency.

[0074] For example, when the system finds that the target product has a high overlap in orders with a related product, it needs to consider storing the target product close to the related product. Assuming that product A and product B are often shipped together, the system will obtain the storage location of product B and its second heat index, which will be used to calculate the final storage location of target product A.

[0075] S106: Determine a target storage location of the target goods in the storable locations by combining the first storage location and the second heat index.

[0076] After obtaining the first storage location and the second heat index of the associated goods, the system needs to determine the final target storage location of the target goods. The system first queries the relational database to obtain the target shipment frequency corresponding to the second heat index of the associated goods. This target shipment frequency reflects the actual outbound pattern of the associated goods and can be used as an important indicator to evaluate the activity level of the associated goods.

[0077] The system uses the obtained target shipping frequency as the shipping frequency of the associated goods and compares it with the preset frequency. This preset frequency is a threshold set based on the actual operating experience of the storage center to determine the activeness of the goods' outbound shipment. When the shipping frequency of the associated goods is higher than the preset frequency, it indicates that these associated goods often need to be shipped together. At this time, the system will select the storage location closest to the first storage location of the associated goods from the available storage locations as the target storage location of the target goods.

[0078] For example, suppose the shipping frequency corresponding to the second heat index of the associated product B is 3 times a day, and the preset frequency is 2 times a day. Since 3>2, the system will store the target product A in the available location closest to the current storage location of product B. The advantage of this is that it can reduce the moving distance when picking up goods and improve work efficiency.

[0079] On the contrary, when the shipment frequency of the associated goods is not higher than the preset frequency, it indicates that the outbound demand of the associated goods is relatively low. At this time, the system will give priority to the access convenience of the target goods themselves. In this case, the system will use the storage location closest to the shipping port among the available storage locations as the target storage location of the target goods. This strategy ensures that even if the outbound frequency of the associated goods is not high, the target goods can obtain a relatively convenient storage location.

[0080] Based on the above embodiment, as an optional implementation, in S106, combining the first storage location and the second heat index, determining the target storage location of the target goods in the storable locations specifically includes S61-S63: S61, searching a relational database for a target shipping frequency corresponding to the second popularity index, and using the target shipping frequency as the shipping frequency of the associated goods.

[0081] When determining the storage location of the target goods, the system needs to fully consider the associations and outbound characteristics between the goods. First, the system obtains the corresponding target shipping frequency by querying the relational database based on the acquired second heat index of the associated goods. This target shipping frequency represents the actual outbound frequency of the associated goods and can intuitively reflect the access activity of the associated goods. For example, when the second heat index is 0.8, querying the relational database may obtain a target shipping frequency of 4 times a day, and the system uses this frequency value as the shipping frequency of the associated goods.

[0082] S62: If the delivery frequency is higher than the preset frequency, the storage location closest to the first storage location among the available storage locations is used as the target storage location of the target goods.

[0083] S63: If the delivery frequency is not higher than the preset frequency, the storage location closest to the delivery port among the available storage locations is used as the target storage location of the target goods.

[0084] The system then compares the obtained shipment frequency with the preset frequency, which is a benchmark value set based on the warehouse center's operating experience. Taking 2 times a day as the preset frequency, when the shipment frequency of the associated goods is 4 times a day, since 4>2, the system determines that this is a high-frequency shipment situation. In this case, the system will choose to store the target goods in the storable location closest to the first storage location of the associated goods. The advantage of this storage strategy is that when two goods that are often shipped together are stored in a close location, the moving distance and time when picking up the goods can be significantly reduced.

[0085] On the contrary, if the shipping frequency of the associated goods is lower than or equal to the preset frequency, for example, once a day, the system will adopt a different storage strategy because 1≤2. In this case, the system will give priority to the convenience of the storage location itself and select the storage location closest to the shipping port as the storage location of the target goods. The reason for this is that when the correlation between goods is not strong, placing the target goods near the shipping port can improve the efficiency of individual storage and retrieval.

[0086] After determining the target storage location of the target goods in the storable locations, the following steps are also included: Obtain the first RFID tag information corresponding to the target goods and the second RFID tag information of the pallet corresponding to the target storage location; bind the first RFID tag information with the second RFID tag information to generate binding information; store the binding information, the basic information of the target goods and the location information of the target storage location into the data processing module, so that the data processing module can perform full life cycle traceability management on the target goods.

[0087] An RFID tag (Radio Frequency Identification) is an identification device that uses radio frequency signals for contactless data exchange. In this embodiment, the RFID tag is mainly composed of a chip and an antenna. The chip is used to store and process data, and the antenna is used to interact with the RFID reader. Each RFID tag has a unique electronic code that can read and write information. For example, the first RFID tag attached to the goods has a built-in storage chip that can record the basic information of the goods (such as the goods code, specification model, production date, etc.) and storage requirements (such as temperature range, humidity requirements, etc.); while the second RFID tag installed on the pallet records the basic information of the pallet (such as pallet number, load-bearing information, etc.) and location information (such as the area, shelf number, etc.). When the RFID reader transmits a radio frequency signal, the tag will send the stored information back to the reader after receiving the signal, thereby realizing rapid identification and automatic collection of goods information.

[0088] After determining the target storage location of the target goods, the system needs to establish a complete goods traceability system to achieve full life cycle management of the goods. This process first involves the acquisition and binding of RFID tag information. The system obtains the first RFID tag information on the target goods, which contains basic information such as the unique identification code, production date, batch number, etc. At the same time, the system also obtains the second RFID tag information on the pallet corresponding to the target storage location, which records the pallet number, location information, and carrying capacity.

[0089] The system binds the two RFID tag information to generate unique binding information. This binding information establishes the correspondence between the goods and the storage location, ensuring that the system can accurately track the location and status of the goods. For example, when a batch of goods is placed on a specific pallet, the system will bind the RFID tag of the goods to the RFID tag of the pallet, forming a binding relationship of "goods A-pallet B".

[0090] After the binding is completed, the system will store the binding information, the basic information of the target goods (such as the name, specifications, quantity, etc. of the goods), and the location information of the target storage location (such as shelf number, number of layers, area code, etc.) into the data processing module. The integration of this information enables the system to achieve all-round monitoring and management of goods. The data processing module is the core component of the intelligent warehouse management system. It is responsible for processing, storing and analyzing all data information related to the goods. This module can be understood as a comprehensive data management and analysis platform, which not only stores data, but also has the functions of data processing, analysis and decision support.

[0091] After determining the target storage location of the target cargo in the storable location, the method further includes: Obtain first cargo information of other stored items stored within a preset range of the target storage location, and second cargo information of the target cargo; determine whether there are incompatible attributes between the target cargo and other stored items by combining the first cargo information and the second cargo information; if there are incompatible attributes, re-determine the final storage location of the target cargo from the storable locations.

[0092] After determining the target storage location, the system needs to perform a mutual exclusivity check on the goods to ensure the safety of the storage environment and the stability of the goods quality. This process first involves information acquisition. The system will obtain the first cargo information of all stored items within the preset range of the target storage location (usually the surrounding 1-3 meters area). This information includes key parameters such as the physical and chemical properties of the items, storage requirements, temperature and humidity conditions. At the same time, the system will also obtain the second cargo information of the target goods, including its physical characteristics, chemical properties, storage environment requirements and other data.

[0093] The system uses a professional cargo characteristic matching algorithm to conduct a comprehensive analysis and mutual exclusion judgment on this information. The basis for mutual exclusion judgment comes from the preset mutual exclusion relationship database of items, which covers common mutual exclusion types, such as chemical reactivity mutual exclusion, odor impact mutual exclusion, temperature and humidity requirement mutual exclusion, etc. For example, when the system detects that the target cargo is a flammable chemical and there is a strong oxidizer within the preset range, it will immediately identify this dangerous mutual exclusion combination. When the system finds incompatible attributes, it will trigger the storage location reselection mechanism. This process will reselect the appropriate storage location in the remaining storable locations based on the principle of safety first.

[0094] For example, after the system selects a target storage location for a batch of coffee beans, it obtains the first cargo information within the preset range and finds that nearby storage locations contain daily chemicals with strong odors, such as laundry detergent and perfume. After comparing the second cargo information of coffee beans (easy to absorb odors, need to be moisture-proof, etc.), the system immediately identifies the risk of odor mutual exclusion.

[0095] Based on the above method, the present application also discloses an intelligent warehouse management system, such as Figure 2 As shown, Figure 2 : is a structural diagram of an intelligent warehouse management system provided by an embodiment of the present application, the system includes: a first acquisition module, a determination module, a prediction module, a first combination module, a second acquisition module and a second combination module; wherein: The first acquisition module is used to acquire the basic information of the target goods to be stored in the storage center and the remaining storage area of ​​the storage center; the determination module is used to determine the storable area of ​​the target goods from the remaining storage area based on the basic information; the prediction module is used to acquire the historical order data of the target goods, and predict the total number of orders of the target goods and the order quantity of each order within a preset time period based on the historical order data; the first combination module is used to calculate the first heat index of the target goods based on the total number of orders and the order quantity of each order, and determine the storable location of the target goods in the storable area based on the first heat index; the second acquisition module is used to acquire the first storage location of the associated goods associated with the target goods, and the second heat index of the associated goods; the second combination module is used to determine the target storage location of the target goods in the storable location based on the first storage location and the second heat index.

[0096] It should be noted that: when the system provided in the above embodiment realizes its functions, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0097] See also Figure 3 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0098] The communication bus 1002 is used to realize the connection and communication between these components.

[0099] The user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0100] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0101] Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

[0102] Among them, the memory 1005 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may optionally also be at least one storage device located away from the aforementioned processor 1001. As Figure 3 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of an intelligent warehouse management method.

[0103] exist Figure 3 In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 1001 can be used to call an application program for an intelligent warehousing management method stored in the memory 1005. When executed by one or more processors, the electronic device executes one or more methods described in the above embodiments.

[0104] An electronic device readable storage medium stores instructions, which, when executed by one or more processors, enable the electronic device to execute one or more of the methods described in the above embodiments.

[0105] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0106] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0107] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0108] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0109] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0110] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0111] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An intelligent warehouse management method, characterized in that: The method comprises: Obtaining basic information of target goods to be stored in a storage center and remaining storage area of ​​the storage center; Determining a storage area for the target goods from the remaining storage areas according to the basic information; Obtaining historical order data of the target goods, and predicting the total number of orders for the target goods and the order quantity of each order within a preset period of time based on the historical order data; Calculating a first heat index of the target goods in combination with the total number of orders and the order quantity of each order, and determining a storable location of the target goods in the storable area according to the first heat index; Acquire a first storage location of an associated item associated with the target item, and a second heat index of the associated item; In combination with the first storage location and the second heat index, a target storage location of the target cargo is determined among the storable locations.

2. The intelligent warehouse management method according to claim 1, characterized in that: The basic information includes volume, weight and storage conditions, and determining the storage area of ​​the target goods from the remaining storage area according to the basic information includes: The storage area in the remaining storage area that meets the volume, the weight and the storage condition is determined as the storable area for the target goods.

3. The intelligent warehouse management method according to claim 1, characterized in that: The calculating the first popularity index of the target product by combining the total number of orders and the order quantity of each order includes: Obtaining the order quantity benchmark value of each order of the target goods, substituting the total number of orders, the order quantity of each order and the order quantity benchmark value into a preset formula, and calculating the first heat index of the target goods; wherein: The preset formula is: ; In the formula, is the first heat index, and are weight coefficients, is the total number of orders, is the preset duration, For the The order quantity of the order, is the base value of the order quantity.

4. The intelligent warehouse management method according to claim 1, characterized in that: The determining, according to the first heat index, a storable location of the target cargo in the storable area includes: Obtaining a third heat index of each storage location in the storable area; The difference between the first heat index and the third heat index is calculated, and a storage location where the difference is less than a preset threshold is determined as a storage location for the target cargo.

5. The intelligent warehouse management method according to claim 1, characterized in that: The step of determining a target storage location of the target cargo in the storable locations by combining the first storage location and the second heat index comprises: searching a relational database for a target shipping frequency corresponding to the second popularity index, and using the target shipping frequency as the shipping frequency of the associated goods; If the delivery frequency is higher than the preset frequency, the storage location closest to the first storage location among the available storage locations is used as the target storage location of the target goods; If the delivery frequency is not higher than the preset frequency, the storage location closest to the delivery port among the storable locations is used as the target storage location of the target goods.

6. The intelligent warehouse management method according to claim 1, characterized in that: After determining the target storage location of the target goods in the storable location, the method further includes: Acquire the first RFID tag information corresponding to the target goods and the second RFID tag information of the pallet corresponding to the target storage location; Binding the first RFID tag information with the second RFID tag information to generate binding information; The binding information, the basic information of the target goods and the location information of the target storage location are stored in a data processing module, so that the data processing module can perform full life cycle traceability management on the target goods.

7. The intelligent warehouse management method according to claim 1, characterized in that: After determining the target storage location of the target goods in the storable location, the method further includes: Acquire first cargo information of other stored items stored within a preset range of the target storage location, and second cargo information of the target cargo; Combining the first cargo information and the second cargo information, determining whether the target cargo has incompatible attributes with the other stored items; If the incompatible attributes exist, the final storage location of the target goods is re-determined from the storable locations.

8. An intelligent warehouse management system, characterized in that: The system comprises: a first acquisition module, a determination module, a prediction module, a first combination module, a second acquisition module and a second combination module; wherein: The first acquisition module is used to acquire basic information of target goods to be stored in the storage center and the remaining storage area of ​​the storage center; The determining module is used to determine the storable area of ​​the target goods from the remaining storage area according to the basic information; The prediction module is used to obtain the historical order data of the target goods, and predict the total number of orders for the target goods and the order quantity of each order within a preset time period based on the historical order data; The first combining module is used to combine the total number of orders and the order quantity of each order to calculate a first heat index of the target goods, and determine a storable location of the target goods in the storable area according to the first heat index; The second acquisition module is used to acquire a first storage location of an associated item associated with the target item, and a second heat index of the associated item; The second combining module is used to combine the first storage location and the second heat index to determine a target storage location of the target cargo in the storable locations.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 7.

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