Storage goods location management method and system
Optimizing the cargo space allocation strategy through sensor network and cargo space evaluation model, solving the problems of manual errors and low space utilization in traditional warehousing management, and achieving efficient and intelligent warehousing management.
Patent Information
- Application Number
- CN202510599036.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-10
- Publication Date
- 2025-07-25
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Traditional warehouse storage space management relies on manual experience, resulting in high error rates and the inability to make rational use of warehouse space, affecting operational efficiency and increasing logistics costs.
Cargo data is collected through sensor networks, a cargo space evaluation model is constructed and suitability scores are calculated, a scientific cargo space allocation strategy is generated, and a warehouse management strategy is optimized in combination with the warehouse management objective function.
It realizes efficient, accurate and dynamic management of warehouse cargo spaces, improves space utilization and cargo entry and exit efficiency, and reduces operating costs.
Smart Images
Figure CN120373569A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of logistics warehousing, and particularly to a warehousing location management method and system. Background Art
[0002] Traditional warehousing location management methods often rely on manual experience to plan locations. This method not only consumes a lot of manpower and time, but also is extremely error-prone. For example, due to memory deviation or work negligence of staff, goods may be placed in inappropriate locations, resulting in difficulties in subsequent goods search and prolonging the goods in and out time.
[0003] In addition, existing technologies use fixed rules to allocate locations, that is, arrange storage locations according to the category of goods or the order of warehousing. However, these rules do not comprehensively consider the complex dynamic factors in the actual operation of the warehouse, such as the differences in the frequency of goods in and out at different time periods, the space limitations and load-bearing capacities of different areas in the warehouse, and the shelf life, volume, weight, etc. of the goods themselves. As a result, the warehouse space cannot be reasonably utilized, leading to phenomena such as goods accumulation and location idleness, seriously affecting the warehousing operation efficiency, increasing logistics costs, and being difficult to meet the requirements of efficient logistics warehousing.
[0004] Therefore, it is necessary to provide a warehousing location management method and system to solve the above technical problems. Summary of the Invention
[0005] To solve the above technical problems, the present invention provides a warehousing location management method and system, which are used to solve the problems in the prior art that it is difficult to achieve efficient, accurate, and dynamic management of warehousing locations, the utilization rate of warehousing space and the efficiency of goods in and out are low, and the operation cost is relatively high.
[0006] A warehousing location management method provided by the present invention, the management method includes: Collecting current goods data of warehousing goods based on a sensor network and performing preprocessing operations; Obtaining current location data of warehousing locations, and generating a location allocation strategy in combination with the current goods data; Constructing a location evaluation model and calculating the suitability score of the location allocation strategy; Constructing a warehousing management objective function, and determining a warehousing management strategy according to the suitability score.
[0007] Preferably, the collecting current goods data of warehousing goods based on a sensor network and performing preprocessing operations specifically includes: The sensor network includes a weight sensor, a volume sensor, and a positioning sensor, which are respectively used to collect the current weight data, current volume data, and current location data of the warehousing goods; Summarize the current weight data, the current volume data, and the current location data to generate the current cargo data, and perform a preprocessing operation on the current cargo data.
[0008] Preferably, the construction process of the goods location evaluation model is as follows: Generate a goods location evaluation data set based on the current goods location data and the current cargo data ; Calculate the Gini impurity of the goods location evaluation data set as follows: In the formula, represents the Gini impurity of the goods location evaluation data set ; represents the total number of data categories in the goods location evaluation data set ; represents the proportion of the data of the th category in the goods location evaluation data set ; Select the feature and the splitting point , and divide the goods location evaluation data set into subsets. The Gini impurity of the divided goods location evaluation data set is as follows: In the formula, represents the Gini impurity of the divided goods location evaluation data set ; represents the number of subsets obtained by the division; represents the th subset obtained by the division; and respectively represent the total amount of data in the goods location evaluation data set and the subset ; When the Gini impurity of the divided goods location evaluation data set is the smallest, the corresponding feature and the splitting point are the decision tree splitting basis corresponding to the goods location evaluation model, and the subset number is the number of decision trees corresponding to the goods location evaluation model; Construct the goods location evaluation model based on the random forest algorithm based on the decision tree splitting basis and the number of decision trees.
[0009] Preferably, set the goods location allocation strategy To allocate the th storage goods to the th storage location, the location allocation strategy corresponding location evaluation data set and the suitability score are respectively and ; Based on the location evaluation model, calculate the suitability score as follows: In the formula, represents the location evaluation data set corresponding to the location allocation strategy ; represents the prediction result of the random forest algorithm on the location evaluation data set , that is, the suitability score corresponding to the location allocation strategy ; represents the number of decision trees corresponding to the location evaluation model; represents the th decision tree's prediction result on the location evaluation data set .
[0010] Preferably, the expression of the warehouse management objective function is as follows: In the formula, represents the operation of taking the maximum value; represents the quantity of storage goods; represents the quantity of storage locations; represents the suitability score corresponding to the location allocation strategy ; represents the decision variable corresponding to the location allocation strategy . If the th storage good is normally allocated to the th storage location, then . If the th storage good is not normally allocated to the th storage location, then .
[0011] Preferably, the expression of the constraint conditions of the warehouse management objective function is as follows: In the formula, represents the quantity of storage goods; represents the quantity of storage locations; represents the decision variable corresponding to the location allocation strategy . If the The normal allocation of a storage item to the th storage location, then . If the th storage item is not normally allocated to the th storage location, then ; represents the current weight of the th storage item; represents the maximum load-bearing capacity of the th storage location; represents the current volume of the th storage item; represents the available space volume of the th storage location; Summarize all the storage location allocation strategies that make the storage management objective function and the constraint conditions hold to obtain the storage management strategy.
[0012] A storage location management system, the management system includes: A data acquisition and preprocessing module, used to collect the current item data of the storage items based on the sensor network and perform preprocessing operations; A storage location allocation strategy generation module, used to obtain the current storage location data of the storage locations, and combine the current item data to generate a storage location allocation strategy; A suitability score calculation module, used to construct a storage location evaluation model and calculate the suitability score of the storage location allocation strategy; A storage management strategy determination module, used to construct a storage management objective function and determine the storage management strategy according to the suitability score.
[0013] An electronic device, including a memory and a processor, wherein a computer program is stored in the memory. The feature is that when the processor runs the computer program stored in the memory, the processor executes the steps of a storage location management method as described in any one of the above.
[0014] A readable storage medium, wherein a computer program is stored in the readable storage medium. The feature is that when the computer program is executed by a processor, it is used to implement the steps of a storage location management method as described in any one of the above.
[0015] Compared with the related technology, a storage location management method and system provided by the present invention have the following beneficial effects: The present invention can collect the current cargo data of the storage goods based on the sensor network and perform preprocessing operations; obtain the current location data of the storage locations, and generate a location allocation strategy in combination with the current cargo data; construct a location evaluation model and calculate the suitability score of the location allocation strategy; construct a warehouse management objective function, and determine the warehouse management strategy according to the suitability score, so as to realize the efficient, accurate and dynamic management of the storage locations, improve the utilization rate of the storage space and the efficiency of goods inbound and outbound, and reduce the operation cost.
[0016] The present invention collects the current data of the storage goods through the sensor network and performs preprocessing operations on it, so as to effectively filter out noise and outliers, obtain more accurate cargo data, and reduce decision-making errors caused by data errors. The present invention can fully consider the characteristics of the goods and the conditions of the storage locations, and generate a location allocation strategy by combining the cargo data and the location data, so as to reasonably allocate the goods to the appropriate locations, improve the utilization rate of the storage space, reduce space waste, and reduce the storage cost. The present invention can construct a location evaluation model to calculate the suitability score of the location allocation strategy, and then can evaluate the advantages and disadvantages of the location allocation strategy through a scientific quantification method, which is convenient for timely discovering and adjusting unreasonable allocation schemes, and making the location allocation result more in line with the actual needs. The present invention can determine the warehouse management strategy based on the suitability score, realize the intelligentization and scientification of the warehouse management, and can dynamically adjust the management plan according to different situations, improve the efficiency of goods inbound and outbound, and enhance the flexibility and adaptability of the warehouse management. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] Figure 1 It is a flowchart of a storage location management method provided by an embodiment of the present invention; Figure 2 It is a flowchart of generating the current cargo data provided by an embodiment of the present invention; Figure 3 It is a system block diagram of a storage location management system provided by an embodiment of the present invention; Figure 4 It is a schematic hardware structure diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0018] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.
[0019] Such as Figure 1As shown below is a flowchart of a warehouse location management method provided by an embodiment of the present invention. Figure 1 The execution subject of the method shown can be a software and / or hardware device. The execution subject of this application can include but is not limited to at least one of the following: user equipment, network equipment, etc. Among them, user equipment can include but is not limited to computers, smartphones, personal digital assistants (Personal Digital Assistant, abbreviated as: PDA), and the above-mentioned electronic devices, etc. Network equipment can include but is not limited to a single network server, a server group composed of multiple network servers, or a cloud composed of a large number of computers or network servers based on cloud computing. Among them, cloud computing is a type of distributed computing, which is composed of a group of loosely coupled computers to form a super virtual computer. This embodiment does not make any restrictions on this. It includes steps S1 to S4, specifically as follows: S1, collect the current cargo data of the warehouse cargo based on the sensor network and perform preprocessing operations; Among them, the sensor network refers to a network composed of multiple different types of sensors, specifically including weight sensors, volume sensors, and positioning sensors.
[0020] Specifically, the weight sensor can sense the weight information of the cargo in real time; the volume sensor can accurately measure the volume information of the cargo; the positioning sensor can continuously collect the position information of the cargo. Combining these weight, volume, and position information, the current cargo data can be obtained.
[0021] In practical applications, since the data collected by the sensors may be affected by various interferences and there are noises, errors, or outliers, therefore, it is necessary to perform preprocessing operations on these data. The preprocessing operations specifically include data cleaning, that is, removing obviously incorrect or invalid data, such as unreasonable maximum or minimum values collected by the weight sensor; data standardization, that is, unifying data of different types and different magnitudes to a suitable scale, for example, converting the unit of volume data to cubic meters uniformly.
[0022] S2, obtain the current location data of the warehouse location, and combine the current cargo data to generate a location allocation strategy; Among them, the current location data of the warehouse location can be obtained, such as the spatial position coordinates of the location, which are used to determine the specific orientation of the location in the warehouse; the size of the location, which is used to determine the volume range that the location can accommodate; the load-bearing capacity of the location, which is used to limit the upper limit of the weight of the cargo that can be placed on the location, etc.
[0023] Then, these location data can be combined with the preprocessed cargo data to generate a location allocation strategy, so that the most suitable location can be accurately matched for the cargo, and efficient utilization of the warehouse space can be achieved.
[0024] S3. Construct a storage location evaluation model and calculate the suitability score of the storage location allocation strategy; It can be understood that a storage location evaluation model can be constructed, comprehensively considering various complex factors such as the inbound and outbound frequency of goods, the correlation between goods, and the distance between the storage location and the warehouse entrance and exit, to calculate the suitability score of the storage location allocation strategy. The higher the suitability score, the more the storage location allocation strategy can meet the actual needs of warehouse management.
[0025] S4. Construct a warehouse management objective function and determine the warehouse management strategy according to the suitability score.
[0026] Finally, a warehouse management objective function can be constructed, and according to the suitability score of the storage location allocation strategy, the objective function can be optimized and solved, so as to determine a scientific and reasonable warehouse management strategy, ensure the intelligent and efficient operation of warehouse management, improve the utilization rate of warehouse space, enhance the inbound and outbound efficiency of goods, and reduce the operation cost.
[0027] In the specific implementation process, as Figure 2 shown, the step of collecting the current goods data of the warehouse goods based on the sensor network and performing preprocessing operations specifically includes: The sensor network includes a weight sensor, a volume sensor, and a positioning sensor, which are respectively used to collect the current weight data, current volume data, and current position data of the warehouse goods; Summarize the current weight data, the current volume data, and the current position data to generate the current goods data, and perform preprocessing operations on the current goods data.
[0028] In practical applications, data can be collected through weight, volume, and positioning sensors to accurately obtain the weight, volume, and position information of goods, thereby ensuring data quality, improving the reliability and availability of data, providing strong support for subsequent decisions such as storage location allocation and inventory management, and effectively improving the efficiency and accuracy of warehouse management.
[0029] The construction process of the storage location evaluation model is as follows: Generate a storage location evaluation data set based on the current storage location data and the current goods data ; Calculate the Gini impurity of the storage location evaluation data set as follows: In the formula, represents the Gini impurity of the storage location evaluation data set ; represents the total number of data categories in the storage location evaluation data set ; Represents the proportion of the data of the type in the location evaluation data set; In the category of data; Select features and splitting points , and divide the location evaluation data set into subsets. The Gini impurity of the location evaluation data set after division is as follows: wherein, represents the Gini impurity of the location evaluation data set after division; represents the number of subsets obtained by division; represents the th subset obtained by division; and respectively represent the total amounts of data in the location evaluation data set and the subset ; When the Gini impurity of the location evaluation data set after division is the smallest, the corresponding feature and splitting point are the decision tree division basis corresponding to the location evaluation model, and the number of subsets is the number of decision trees corresponding to the location evaluation model; Based on the decision tree division basis and the number of decision trees, construct the location evaluation model based on the random forest algorithm. Among them, a location evaluation data set can be generated based on the current location and goods data, and this data set comprehensively covers key information related to warehousing.
[0030] It should be noted that when selecting features and splitting points, using the Gini impurity to divide the data set can make the division result more reasonable, reduce the data mixing degree to the greatest extent, and improve the discrimination ability of the model for different location and goods combination situations.
[0031] When the Gini impurity of the location evaluation data set after division is the smallest, it can be determined that the feature and splitting point at this time are the decision tree division basis, the number of subsets at this time is the number of decision trees, and then a location evaluation model based on the random forest algorithm can be constructed. This model can accurately evaluate the suitability of the location allocation strategy, provide scientific and reliable decision support for warehousing management, and help improve the utilization rate of warehousing space and the efficiency of goods inbound and outbound.
[0032]
[0033] Set the location allocation strategy To allocate the th storage goods to the th storage location, the location evaluation data set and the suitability score corresponding to the location allocation strategy are respectively and ; Based on the location evaluation model, calculate the suitability score as follows: In the formula, represents the location evaluation data set corresponding to the location allocation strategy ; represents the prediction result of the random forest algorithm on the location evaluation data set , that is, the suitability score corresponding to the location allocation strategy ; represents the number of decision trees corresponding to the location evaluation model; represents the th decision tree's prediction result on the location evaluation data set .
[0034] Among them, the suitability score can be calculated using the random forest algorithm based on the location evaluation model. The random forest algorithm integrates the prediction results of multiple decision trees, and each decision tree analyzes and judges the location evaluation data set from different perspectives. And the comprehensive judgment results of multiple decision trees can effectively avoid the one-sidedness of a single decision, greatly improving the comprehensiveness and accuracy of strategy evaluation.
[0035] In practical applications, warehouse managers can intuitively determine the advantages and disadvantages of different location allocation strategies through the suitability score, thereby improving the utilization efficiency of warehouse space, optimizing the storage layout of goods, and further enhancing the overall level and operational efficiency of warehouse management.
[0036] The expression of the warehouse management objective function is as follows: In the formula, represents the maximum value operation; represents the quantity of storage goods; represents the quantity of storage locations; represents the suitability score corresponding to the location allocation strategy ; represents the decision variable corresponding to the location allocation strategy , if the th storage goods are normally allocated to the th storage location, then , if the If the storage goods are not properly allocated to the th storage location, then .
[0037] The expression of the constraint conditions of the storage management objective function is as follows: In the formula, represents the quantity of storage goods; represents the number of storage locations; represents the decision variable corresponding to the storage location allocation strategy . If the th storage good is properly allocated to the th storage location, then . If the th storage good is not properly allocated to the th storage location, then ; represents the current weight of the th storage good; represents the maximum load-bearing capacity of the th storage location; represents the current volume of the th storage good; represents the available space volume of the th storage location; Summarize all the storage location allocation strategies that make the storage management objective function and the constraint conditions hold to obtain the storage management strategy.
[0038] When planning the storage location allocation, the storage management objective function can maximize the total suitability score of the storage location allocation strategy, achieve the optimal allocation of storage resources, and thus improve the utilization rate of storage space and the rationality of goods storage.
[0039] By strictly restricting the decision variables, the weight of goods and the load-bearing capacity of storage locations, and the volume of goods and the available space of storage locations, the safety and rationality of storage location allocation can be ensured, preventing the damage of storage locations due to overweight or over-volume use, ensuring that the incoming goods have appropriate storage space, avoiding the situation of goods being crushed and damaged, and guaranteeing the safety of warehouse facilities.
[0040] In addition, by summarizing the storage location allocation strategies that meet the objective function and constraint conditions, the storage management strategy can be obtained, and then various storage location allocation strategies can be effectively integrated, providing a comprehensive and scientific decision-making basis for warehouse managers, and ultimately realizing the efficient, safe and intelligent operation of storage management and reducing the operation cost of storage management.
[0041] For example Figure 3As shown in the figure, it is a system block diagram of a warehousing location management system provided by an embodiment of the present invention. The management system includes: A data acquisition and preprocessing module, configured to collect current cargo data of warehoused goods based on a sensor network and perform preprocessing operations; A location allocation strategy generation module, configured to obtain current location data of warehousing locations, and generate a location allocation strategy in combination with the current cargo data; A suitability score calculation module, configured to construct a location evaluation model and calculate the suitability score of the location allocation strategy; A warehousing management strategy determination module, configured to construct a warehousing management objective function and determine a warehousing management strategy according to the suitability score.
[0042] Figure 3 The device in the shown embodiment can correspondingly be used to execute Figure 1 the steps in the method embodiment shown. The implementation principle and technical effects are similar and will not be elaborated here.
[0043] An electronic device includes a memory and a processor. A computer program is stored in the memory. It is characterized in that when the processor runs the computer program stored in the memory, the processor executes the steps of a warehousing location management method as described in any one of the above.
[0044] As Figure 4 shown, it is a schematic hardware structure diagram of an electronic device provided by an embodiment of the present invention. The electronic device 40 includes: a processor 41, a memory 42, and a computer program; where The memory 42 is used to store the computer program. The memory can also be a flash memory. The computer program is, for example, an application program or a functional module for implementing the above method.
[0045] The processor 41 is used to execute the computer program stored in the memory to implement each step performed by the device in the above method. For details, reference can be made to the relevant descriptions in the previous method embodiment.
[0046] Optionally, the memory 42 can be either independent or integrated with the processor 41.
[0047] When the memory 42 is a device independent of the processor 41, the device can further include: A bus 43, used to connect the memory 42 and the processor 41.
[0048] A readable storage medium stores a computer program. It is characterized in that when the computer program is executed by a processor, it is used to implement the steps of a warehousing location management method as described in any one of the above.
[0049] Among them, the readable storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium can be any available medium accessible by a general-purpose or special-purpose computer. For example, the readable storage medium is coupled to the processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). In addition, the ASIC can be located in a user device. Of course, the processor and the readable storage medium can also exist as discrete components in a communication device. The readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.
[0050] The present invention also provides a program product, which includes execution instructions stored in a readable storage medium. At least one processor of the device can read the execution instructions from the readable storage medium, and the execution of the execution instructions by at least one processor causes the device to implement the methods provided by the above various embodiments.
[0051] In the above embodiments of the device, it should be understood that the processor can be a central processing unit (CPU for short), or other general-purpose processors, digital signal processors (DSP for short), application specific integrated circuits (ASIC for short), etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in combination with the present invention can be directly embodied as being completed by the execution of a hardware processor, or by a combination of hardware and software modules in the processor.
[0052] Through the introduction of the above embodiments, the present invention can, through the warehousing location management method and system, collect the current goods data of the warehousing goods based on the sensor network and perform preprocessing operations; obtain the current location data of the warehousing locations, combine the current goods data, and generate a location allocation strategy; construct a location evaluation model and calculate the suitability score of the location allocation strategy; construct a warehousing management objective function, and determine the warehousing management strategy according to the suitability score, so as to realize the efficient, accurate and dynamic management of the warehousing locations, improve the utilization rate of the warehousing space and the goods inbound and outbound efficiency, and reduce the operation cost.
[0053] The present invention collects the current data of the warehoused goods through a sensor network and performs preprocessing operations on it, so as to effectively filter out noise and outliers, obtain more accurate goods data, and reduce decision-making errors caused by data errors. The present invention can fully consider the characteristics of goods and the conditions of storage locations, combine the goods data and the storage location data to generate a storage location allocation strategy, so that the goods can be reasonably allocated to appropriate storage locations, improve the utilization rate of the warehousing space, reduce space waste, and lower the warehousing cost. The present invention can construct a storage location evaluation model to calculate the suitability score of the storage location allocation strategy, and then can evaluate the advantages and disadvantages of the storage location allocation strategy in a scientific and quantitative manner, facilitating the timely discovery and adjustment of unreasonable allocation plans, making the storage location allocation result more in line with the actual needs. The present invention can determine the warehousing management strategy based on the suitability score, realize the intelligentization and scientification of warehousing management, and can dynamically adjust the management plan according to different situations, improve the efficiency of goods inbound and outbound, and enhance the flexibility and adaptability of warehousing management.
[0054] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.
Claims
1. A storage location management method, characterized in that, The management method includes: Collecting current goods data of the warehoused goods based on a sensor network and performing preprocessing operations; Obtaining current location data of the storage locations, and generating a location allocation strategy in combination with the current goods data; Constructing a location evaluation model and calculating the suitability score of the location allocation strategy; Constructing a warehousing management objective function and determining a warehousing management strategy according to the suitability score.
2. The warehousing location management method according to claim 1, wherein The step of collecting current goods data of the warehoused goods based on a sensor network and performing preprocessing operations specifically includes: The sensor network includes a weight sensor, a volume sensor, and a positioning sensor, which are respectively used to collect the current weight data, current volume data, and current location data of the warehoused goods; Summarizing the current weight data, the current volume data, and the current location data to generate the current goods data, and performing preprocessing operations on the current goods data.
3. The warehousing location management method according to claim 2, characterized in that, The construction process of the location evaluation model is as follows: Generate a location evaluation data set based on the current location data and the current cargo data ; Calculate the bin evaluation data set for the following Gini impurity: In the formula, represents the Gini impurity of the location evaluation data set ; represents the total number of data categories in the location evaluation data set ; represents the proportion of the th type of data in the location evaluation data set ; Select features and split points , and divide the storage location evaluation data set into subsets. The Gini impurity of the divided storage location evaluation data set is as follows: In the formula, represents the bin evaluation data set after partitioning 's Gini impurity; represents the number of subsets obtained by partitioning; represents the -th subset obtained by partitioning; and respectively represent the total amount of data in the bin evaluation data set and the subset ; When the Gini impurity of the divided goods location evaluation data set is the smallest, the corresponding feature and the division point are the decision tree division basis corresponding to the goods location evaluation model, and the number of subsets is the number of decision trees corresponding to the goods location evaluation model; Based on the decision tree division basis and the number of decision trees, constructing the location evaluation model based on the random forest algorithm.
4. A warehousing location management method according to claim 3, characterized in that, Set the storage location allocation strategy For allocating the th storage good to the th storage location, the corresponding storage location evaluation data set and the suitability score of the storage location allocation strategy are respectively and ; Calculate the suitability score based on the storage location evaluation model as follows: In the formula, represents the storage location allocation strategy corresponding storage location evaluation data set; represents the prediction result of the random forest algorithm on the storage location evaluation data set i.e., the suitability score corresponding to the storage location allocation strategy corresponding; represents the number of decision trees corresponding to the storage location evaluation model; represents the th decision tree's prediction result on the storage location evaluation data set ; 5. A warehousing location management method according to claim 4, characterized in that The expression of the warehousing management objective function is as follows: In the formula, represents the maximum value operation; represents the quantity of stored goods; represents the quantity of storage locations; represents the storage location allocation strategy corresponding suitability score; represents the storage location allocation strategy corresponding decision variable. If the th stored good is normally allocated to the th storage location, then , if the th stored good is not normally allocated to the th storage location, then .
6. A warehousing location management method according to claim 5, characterized in that, The expression of the constraint conditions of the warehousing management objective function is as follows: Wherein, represents the quantity of the stored goods; represents the quantity of the storage locations; represents the location allocation strategy corresponding decision variable. If the -th stored good is normally allocated to the -th storage location, then . If the -th stored good is not normally allocated to the -th storage location, then ; represents the current weight of the -th stored good; represents the maximum load-bearing capacity of the -th storage location; represents the current volume of the -th stored good; represents the available space volume of the -th storage location; All the storage location allocation strategies that satisfy the storage management objective function and the constraint conditions are summarized to obtain the storage management strategy.
7. A storage location management system, which is applied to a storage location management method according to any one of claims 1-6, and the management system includes: A data collection and preprocessing module, which is used to collect current goods data of the warehoused goods based on a sensor network and perform preprocessing operations; A location allocation strategy generation module, which is used to obtain current location data of the storage locations, and generate a location allocation strategy in combination with the current goods data; A suitability score calculation module, which is used to construct a location evaluation model and calculate the suitability score of the location allocation strategy; A warehousing management strategy determination module, which is used to construct a warehousing management objective function and determine a warehousing management strategy according to the suitability score.
8. An electronic device, comprising a memory and a processor, wherein a computer program is stored in the memory, characterized in that, When the processor runs the computer program stored in the memory, the processor executes the steps of a storage location management method according to any one of claims 1-6.
9. A readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it is used to implement the steps of a storage location management method according to any one of claims 1-6.