Warehouse management method and device

Abstract

The invention provides a warehouse management method and device, and the method comprises the steps: obtaining a work data table based on pre-collected multi-source data; according to the working data table, index data under the preset working index are determined, and whether a preset early warning condition is triggered or not is judged based on the index data; and triggering a preset early warning condition in response to the index data, generating early warning information and a deployment decision, and adjusting work deployment in the warehouse based on the deployment decision. According to the embodiment, data sharing between independent systems can be realized, the working condition of warehouse personnel and the personnel deployment condition in the warehouse are monitored, the warehouse resource configuration is optimized, and the warehouse operation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of logistics and warehousing, and in particular to a warehouse management method and device. Background Art

[0002] With the rapid development of global e-commerce and international logistics, warehouses of all types are expanding in size, their layouts becoming increasingly complex, and the number of employees within them is also increasing. Warehouse operational efficiency directly impacts the responsiveness and service quality of the entire supply chain. Traditional warehouse management relies on manual record-keeping and independent system management. In recent years, the application of information systems such as human resource management platforms and warehouse management platforms has significantly improved the level of automation in warehouse management. However, these independent operations lead to data fragmentation, making it impossible to obtain real-time information on employee work status and regional load, making it difficult to optimize warehouse resource allocation and reducing warehouse operational efficiency. Summary of the Invention

[0003] In view of this, the embodiments of the present invention at least provide a warehouse management method, device, electronic device and storage medium, which can realize data sharing between independent systems, monitor the work status of warehouse personnel and the deployment of personnel in the warehouse, optimize warehouse resource allocation, and improve warehouse operation efficiency.

[0004] In a first aspect, an embodiment of the present invention provides a warehouse management method, comprising:

[0005] Based on the multi-source data collected in advance, a working data table is obtained;

[0006] According to the work data table, determine the indicator data under the preset work indicators, and judge whether the preset warning conditions are triggered based on the indicator data;

[0007] In response to the preset warning conditions triggered by the indicator data, warning information and deployment decisions are generated to adjust the work deployment in the warehouse based on the deployment decisions.

[0008] Optionally, the multi-source data includes attendance data, task data, and area entry and exit data; before obtaining the work data table based on the pre-collected multi-source data, the following is also included:

[0009] Based on the pre-developed data interface, the warehouse staff attendance data is obtained from the human resources management platform, and the task data is obtained from the warehouse management platform;

[0010] Area entry and exit data is determined based on the near-field communication devices set up in various areas of the warehouse and the scanning records of warehouse personnel on the near-field communication devices.

[0011] Optionally, the preset work indicator includes work efficiency; according to the work data table, determining indicator data under the preset work indicator includes:

[0012] Based on the data in the task start time, task end time, and task status fields in the work data table, determine the indicator data corresponding to the work efficiency.

[0013] Optionally, the preset work indicator includes personnel distribution; determining indicator data under the preset work indicator according to the work data table, and judging whether to trigger a preset warning condition based on the indicator data, including:

[0014] Based on the entry and exit data of different areas in the warehouse recorded in the work data table, determine the indicator data corresponding to the personnel distribution in each area;

[0015] Based on the indicator data and the pre-set threshold range, determine whether the indicator data triggers the preset warning conditions.

[0016] Optionally, in response to the indicator data triggering a preset warning condition, generating warning information and a deployment decision to adjust the work deployment in the warehouse based on the deployment decision, including:

[0017] In response to the indicator data corresponding to the personnel distribution being less than a lower limit of a threshold range, determining that the warehouse in the area corresponding to the indicator data is short of warehouse personnel, generating an early warning message corresponding to the shortage of warehouse personnel, and making a deployment decision based on the number of warehouse personnel corresponding to each area in the warehouse;

[0018] In response to the indicator data corresponding to the personnel distribution being greater than the upper limit of a preset range, it is determined that there is an excess of warehouse personnel in the area corresponding to the indicator data, early warning information corresponding to the excess warehouse personnel is generated, and a deployment decision is generated based on the number of warehouse personnel corresponding to each area in the warehouse.

[0019] Optionally, the preset work indicator includes personnel behavior; determining indicator data under the preset work indicator according to the work data table, and judging whether to trigger a preset warning condition based on the indicator data, including:

[0020] Based on the area entry and exit data recorded in the work data table, determine the residence time of warehouse personnel in each area, and use the residence time as the indicator data corresponding to the personnel behavior;

[0021] Determine the estimated completion time of the task based on the task type;

[0022] In response to the estimated completion time being less than the stay time and the task status being incomplete, it is determined that the indicator data triggers a preset warning condition.

[0023] In a second aspect, an embodiment of the present invention provides a warehouse management device, comprising:

[0024] An acquisition module, for obtaining a working data table based on pre-acquired multi-source data;

[0025] The determination module is used to determine the indicator data under the preset work indicators according to the work data table, and judge whether the preset warning conditions are triggered based on the indicator data;

[0026] The generation module is used to generate warning information and deployment decisions in response to preset warning conditions triggered by indicator data, so as to adjust the work deployment in the warehouse based on the deployment decisions.

[0027] In a third aspect, an embodiment of the present invention further provides an electronic device comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the steps in the above-mentioned first aspect or any optional implementation of the first aspect are performed.

[0028] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned first aspect or any optional implementation of the first aspect are executed.

[0029] In a fifth aspect, an embodiment of the present invention further provides a computer program product, including a computer program, which implements the method of any of the above embodiments when executed by a processor.

[0030] Any of the above aspects or any implementations of any of these aspects achieves data sharing and collaboration between different independent systems through multi-source data collection and the construction of a work data table. First, the execution entity of this embodiment of the present invention can obtain relevant warehouse data from multiple sources, including but not limited to personnel attendance data, task data, and area entry and exit data. After preprocessing and aggregation, this data can be generated into a work data table. Based on the work data table, data that meets preset work indicators can be further extracted and used for real-time monitoring. Specifically, the execution entity of this embodiment of the present invention can determine whether to trigger an alert based on preset alert conditions, such as excessive workload, insufficient or excessive staffing in a certain area, etc., through dynamic analysis of indicator data. Once an alert condition is triggered, corresponding alert information can be generated, and deployment decisions can be made based on this information, instructing management to take effective measures to adjust personnel deployment and resource allocation within the warehouse. This allows for effective monitoring of the work status of warehouse personnel and their deployment. Real-time analysis and alerts provide timely decision support for warehouse management, avoiding inefficiencies caused by uneven distribution of personnel resources or idle equipment. Furthermore, personnel arrangements and work tasks can be dynamically adjusted based on deployment decisions to ensure the rational use of warehouse resources.

[0031] The beneficial effects of the above-mentioned warehouse management device, electronic device and storage medium can be found in the description of the above-mentioned warehouse management method and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. The drawings herein are incorporated into and constitute a part of the specification. These drawings illustrate embodiments consistent with the present invention and, together with the specification, are used to illustrate the technical solutions of the present invention. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those skilled in the art, other relevant drawings can be obtained based on these drawings without inventive effort.

[0033] Figure 1 A flow chart of a warehouse management method provided by an embodiment of the present invention is shown;

[0034] Figure 2 A schematic diagram showing a flow chart of a warehouse management method provided by an embodiment of the present invention is shown;

[0035] Figure 3 A schematic diagram of a warehouse management device provided by an embodiment of the present invention is shown;

[0036] Figure 4 An exemplary system architecture is shown in which embodiments of the present invention may be applied;

[0037] Figure 5 A schematic structural diagram of a computer system of a terminal device or server for implementing an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0038] In order to make the purpose, 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 in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0039] It should be noted that in the technical solution of the present invention, the collection, use, storage, sharing and transfer of user personal information involved are in compliance with the provisions of relevant laws and regulations, and it is necessary to inform the user and obtain the user's consent or authorization. When applicable, the user's personal information is de-identified and / or anonymized and / or encrypted.

[0040] The raising of the above problems and the solutions are the results obtained by the inventor after practice and careful research. The process of discovering the above problems and the solutions proposed for the above problems should be the contributions made by the inventor to the present invention during the process of the invention.

[0041] The technical solutions of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The components of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0042] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0043] To facilitate understanding of this embodiment, a detailed introduction to a warehouse management method disclosed in an embodiment of the present invention is first provided. The warehouse management method provided in this embodiment of the present invention is generally executed by a computer device with certain computing capabilities. This computer device may include, for example, a terminal device, a server, or other processing device. The terminal device may be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, an in-vehicle device, a wearable device, or the like. In some possible implementations, the warehouse management method may be implemented by a processor invoking computer-readable instructions stored in a memory.

[0044] See also Figure 1 FIG. 1 is a flowchart of a warehouse management method according to an embodiment of the present invention, wherein the method includes steps S101 to S103, wherein:

[0045] S101: Obtain a work data table based on pre-collected multi-source data.

[0046] In an embodiment of the present invention, multi-source data includes attendance data, task data and area entry and exit data; before obtaining the work data table based on the pre-collected multi-source data, it also includes: based on a pre-developed data interface, obtaining the attendance data of warehouse personnel from the human resources management platform, and obtaining task data from the warehouse management platform; based on the near-field communication devices set in each area of ​​the warehouse and the scanning records of the warehouse personnel on the near-field communication devices, determining the area entry and exit data.

[0047] In specific implementation, Figure 2 As shown, the human resources management platform only records warehouse personnel's arrival and departure times and cannot track their specific work content or time spent in specific areas within the warehouse. Warehouse personnel's specific operations can only be recorded via the warehouse management platform, but this data cannot be combined with the human resources management platform's attendance data, making it impossible to comprehensively assess the work deployment within the warehouse. Therefore, embodiments of the present invention can connect the execution entity of this embodiment with the human resources management platform and the warehouse management platform based on pre-developed data interfaces to obtain warehouse personnel's attendance data and task data. Attendance data primarily records warehouse personnel's attendance, including work hours, rest periods, and leave records, while task data relates to the scheduling of various tasks within the warehouse, such as the type of task assigned to different employees, task deadlines, and task priority. This data provides a clear understanding of each warehouse personnel's work hours, workload, and the execution status of their current tasks, providing a basis for subsequent work scheduling. Furthermore, the execution entity of this embodiment of the present invention can monitor entry and exit of warehouse areas using near-field communication (NFC) devices. Specifically, near-field communication (NFC) devices can be installed in key warehouse areas, such as shelving, loading and unloading areas, and packaging areas, to record information about personnel entering and exiting these areas. When warehouse personnel, carrying a linked identification device, such as a badge or ID card, pass through the NFC device, the device automatically scans and records their entry and exit time and location. These scanned records provide real-time insights into personnel movements throughout the warehouse, including their work areas and duration of stay, effectively tracking each worker's progress.

[0048] In this embodiment of the present invention, all multi-source data can be aggregated into a work data table. Based on this work data table, the execution entity of this embodiment of the present invention can conduct a comprehensive analysis to assess each warehouse employee's task completion, workload, work efficiency, and personnel deployment within the warehouse, providing a basis for triggering warning conditions. For example, if an employee is taking too long on a task, the task data can be used to determine whether work progress is lagging, combined with their attendance data to analyze whether there is any work progress lag, and then generate corresponding warning information.

[0049] In addition to attendance data, task data, and area entry and exit data, multi-source data can also include other types of data information, such as equipment usage data, inventory data, transportation data, etc. Equipment usage data can help monitor the operating status and usage frequency of equipment in the warehouse; inventory data reflects the storage conditions, circulation speed, and inventory volume of items in the warehouse; transportation data provides the transportation status of goods from the warehouse to other destinations to ensure the smoothness of the logistics process. It should be noted that the above examples of multi-source data are only used as examples of feasible implementation methods in the embodiments of the present invention, and do not constitute an improper interpretation of the present invention. In actual applications, it can be selected and set according to actual conditions. The present invention does not make specific limitations on this, and is based on the ability to achieve its functions.

[0050] S102: According to the work data table, determine the indicator data under the preset work indicator, and judge whether the preset warning condition is triggered based on the indicator data.

[0051] In an embodiment of the present invention, the preset work indicators include work efficiency; according to the work data table, the indicator data under the preset work indicators are determined, including: based on the data under the task start time, task end time and task status fields in the work data table, determining the indicator data corresponding to the work efficiency.

[0052] In specific implementations, task start and end times can be provided by the warehouse management platform to record task scheduling and completion milestones. Task status can indicate the current status of a task, such as "pending," "in progress," or "completed." In addition to obtaining data from the work data table, these fields can also be combined with near-field communication device entry and exit records to further confirm the status. For example, when an employee's ID card is recognized as "entered" in a task area, the task is automatically determined to have started. When the employee leaves the area and is marked as "task completed" in the system, the task ends. To analyze work efficiency, each task record in the work data table can be traversed, extracting the task start and end times, and calculating the time spent on each task. By combining the task status field, efficiency statistics are only calculated for completed tasks to ensure data accuracy. Work efficiency metrics can be expressed as "number of tasks completed per unit time" or "average processing time per task." For example, if the system finds that an employee completed 10 packaging tasks on a given day, each taking an average of 15 minutes, their average work efficiency for that day would be 4 tasks / hour. In contrast, if other employees only complete 6 tasks on average, the former's efficiency is significantly higher. After confirming the operating efficiency index data, the executive body of the embodiment of the present invention can initiate corresponding management measures accordingly. For example, if the system finds that an employee's efficiency is lower than the average level when performing an outbound task, it can push an early warning message to the management personnel, and at the same time, combine the employee's task history records to determine whether it is due to unreasonable task allocation, excessive workload, or low personal ability matching. Then, the task allocation rules can be adjusted to assign the employee to the type of task he is better at, or more personnel can be assigned to high-intensity tasks to improve overall efficiency.

[0053] In an embodiment of the present invention, the preset work indicators include personnel distribution; according to the work data table, the indicator data under the preset work indicators is determined, and based on the indicator data, it is judged whether the preset warning conditions are triggered, including: based on the area entry and exit data of different areas in the warehouse recorded in the work data table, the indicator data corresponding to the personnel distribution in each area is determined; based on the indicator data and the pre-set threshold range, it is judged whether the indicator data triggers the preset warning conditions.

[0054] In specific implementations, the execution entity of embodiments of the present invention can perform analysis based on a pre-constructed work data table containing consolidated area entry and exit data. Whenever a warehouse employee enters or exits an area, their identity information and timestamp are automatically recorded. Based on this information, the execution entity of embodiments of the present invention can accurately locate each employee's specific location during various time periods. For example, a large warehouse is divided into multiple functional areas, including incoming, outgoing, sorting, packaging, and equipment maintenance. The execution entity of embodiments of the present invention can collect entry and exit records for each area from a near-field communication device, match them with employee information, and aggregate the number of employees in each area at a specific point in time or time period, thereby determining personnel distribution indicators. For example, if 15 employees are detected in the outgoing area during a certain time period, while only 3 are in the sorting area, and the task data table shows that the sorting area currently has the highest workload, potential issues with uneven personnel distribution can be immediately identified. To implement early warning functions, reasonable personnel thresholds can be pre-set for each area based on business rules, such as requiring a minimum of 5 and no more than 10 employees to handle normal tasks in a certain area. When the actual data on personnel distribution exceeds or falls below the preset range, it can be automatically determined that the preset warning condition has been triggered, and the corresponding warning information can be generated. For example, if there is only one person in the warehousing area, but the system sets a minimum configuration of three people, an "insufficient staffing" alarm can be issued; or, if a large number of people gather in the packaging area within a short period of time, exceeding the maximum threshold, an "overcrowded" alarm may be triggered to prevent operational confusion or safety accidents. The steps after the warning condition is triggered will be explained in detail in S103 and will not be repeated here.

[0055] In an embodiment of the present invention, the preset work indicators include personnel behavior; according to the work data table, the indicator data under the preset work indicators is determined, and based on the indicator data, it is judged whether the preset warning conditions are triggered, including: based on the area entry and exit data recorded in the work data table, the residence time of warehouse personnel in each area is determined, and the residence time is used as the indicator data corresponding to the personnel behavior; based on the task type of the task, the expected completion time of the task is determined; in response to the expected completion time being less than the residence time and the task status is incomplete, it is determined that the indicator data triggers the preset warning conditions.

[0056] In specific implementations, based on the area entry and exit data recorded in the work data table, the length of time warehouse personnel spend in each work area can be counted. By analyzing the entry and exit timestamps of warehouse personnel in specific areas, their actual length of stay in that area can be calculated, and this stay time can be used as key data reflecting personnel behavior. At the same time, the expected completion time for each task can be determined in combination with the task type in the task data. The determination of the expected completion time can be based on the general completion time of similar tasks obtained from historical task data statistics, such as an average of 10 minutes for "sorting tasks" and 8 minutes for "packing tasks"; or it can be combined with factors such as the complexity of the current task, the workload, and the personnel's ability level, and the algorithm can dynamically evaluate and derive a personalized estimated time. In actual application scenarios, for example, an employee is assigned a sorting task in the sorting area. According to the system's historical records, the task is estimated to be completed within 10 minutes. However, the system detects that the employee has stayed in the sorting area for more than 15 minutes, and the task status is still displayed as "incomplete". This indicates that the employee's behavior does not match the task progress, thereby triggering the "abnormal personnel behavior" warning condition. After the corresponding warning information is generated, the warning information can be pushed to warehouse managers or data recording platforms, etc., indicating possible emergencies such as inefficient operations, operational deviations or equipment failures, and employee leave.

[0057] In the embodiment of the present invention, Figure 2 As shown, in addition to personnel distribution, personnel behavior, and work efficiency, other preset work indicators can also be set, such as task completion rate, equipment utilization rate, abnormal event frequency, and degree of collaborative work. The task completion rate can be calculated by using the task status field recorded in the work data table to calculate the ratio of completed tasks to the total number of tasks within a certain timeframe, thereby determining overall execution progress and task fulfillment. Equipment utilization rate combines work records with equipment sensor data to calculate the ratio of accumulated equipment operating time to available time, which can be used to assess whether critical equipment is idle or overloaded. Abnormal event frequency can be calculated based on the number of alarms, faults, and misoperations recorded in the system, per unit time or per unit task, to identify potential risk concentration points or process weaknesses within the operation. The degree of collaborative work can be modeled and analyzed based on the degree of overlap in the time spent by multiple employees on the same task or the degree of task correlation, reflecting team collaboration efficiency and multi-position synergy. It should be noted that the above examples of preset work indicators are only used as feasible implementation methods in the embodiments of the present invention, and do not constitute improper limitations on the present invention. In actual applications, corresponding preset work indicators can be set according to actual conditions and needs. The embodiments of the present invention do not make specific limitations on this, and the ability to achieve its functions shall prevail.

[0058] S103: In response to the indicator data triggering the preset warning condition, generate warning information and deployment decision to adjust the work deployment in the warehouse based on the deployment decision.

[0059] In an embodiment of the present invention, in response to the indicator data triggering a preset warning condition, warning information and allocation decisions are generated to adjust the work deployment in the warehouse based on the allocation decision, including: in response to the indicator data corresponding to the personnel distribution being less than the lower limit of the threshold range, determining that there is a shortage of warehouse personnel in the area corresponding to the indicator data, generating warning information corresponding to the shortage of warehouse personnel, and generating an allocation decision based on the number of warehouse personnel corresponding to each area in the warehouse; in response to the indicator data corresponding to the personnel distribution being greater than the upper limit of the preset range, determining that there is a surplus of warehouse personnel in the area corresponding to the indicator data, generating warning information corresponding to the surplus of warehouse personnel, and generating an allocation decision based on the number of warehouse personnel corresponding to each area in the warehouse.

[0060] Continuing with the above example, Figure 2As shown, when the personnel distribution indicator triggers a warning condition, corresponding warning information and deployment decisions can be generated. For example, during peak operating hours at a large e-commerce warehouse, analysis of area entry and exit data in the work data table reveals that the packing area currently has only two people. However, based on historical experience and a pre-set threshold range, the packing area requires at least five people to ensure efficient package processing. In this case, the personnel distribution indicator data for the packing area is considered to be below the lower threshold, indicating a staff shortage. An "Insufficient staff in the packing area" warning message is immediately generated and sent to warehouse management or the intelligent scheduling platform. The personnel distribution in other areas of the warehouse can then be analyzed and deployment decisions made accordingly. Suppose, for example, that the sorting area currently has nine people, while the corresponding upper threshold is only six, indicating a clear overstaffing situation. Furthermore, the sorting workload is not currently high. Based on this, the personnel distribution indicator data for the sorting area is considered to be above the upper threshold, automatically generating an "Overstaffing in the sorting area" warning message. Based on the two warning messages generated, a specific redeployment decision can be made: a recommendation to temporarily redeploy three employees from the sorting area to the packaging area to alleviate the shortage and improve overall task processing efficiency. Redeployment decisions can be made based not only on the current number of staff in each area, but also on factors such as the changing trends in task volume, task urgency, and the personnel's past adaptability to tasks in each area, ensuring that the redeployment is targeted and feasible. Furthermore, task adjustment notifications can be sent to the redeployed warehouse personnel, guiding them to their new work areas. The redeployment process and results are recorded to provide feedback data for subsequent optimization of scheduling strategies. This effectively avoids waste of personnel resources and regional staff shortages, and improves the efficient use of human resources within the warehouse. For example, during peak logistics periods such as "Double 11" (Singles' Day) or "New Year's Shopping Festival," the execution entity of this embodiment of the present invention can automatically balance the dynamic allocation of human resources within the warehouse, reducing waiting times and avoiding bottlenecks, thereby improving overall operational efficiency and the timeliness of customer order fulfillment, ensuring the stability and efficiency of warehouse operations.

[0061] According to the second aspect of the embodiment of the present invention, Figure 3 As shown, a warehouse management device 300 is provided, comprising:

[0062] The acquisition module 301 is used to obtain a working data table based on pre-acquired multi-source data;

[0063] The determination module 302 is used to determine the indicator data under the preset work indicator according to the work data table, and determine whether the preset warning condition is triggered based on the indicator data;

[0064] The generation module 303 is used to generate warning information and deployment decisions in response to the indicator data triggering the preset warning conditions, so as to adjust the work deployment in the warehouse based on the deployment decision.

[0065] Optionally, the multi-source data includes attendance data, task data, and area entry and exit data; the acquisition module 301 is further used to:

[0066] Based on the pre-developed data interface, the warehouse staff attendance data is obtained from the human resources management platform, and the task data is obtained from the warehouse management platform;

[0067] Area entry and exit data is determined based on the near-field communication devices set up in various areas of the warehouse and the scanning records of warehouse personnel on the near-field communication devices.

[0068] Optionally, the preset work indicator includes work efficiency; the determination module 302 is specifically configured to:

[0069] Based on the data in the task start time, task end time, and task status fields in the work data table, determine the indicator data corresponding to the work efficiency.

[0070] Optionally, the preset work indicator includes personnel distribution; the determination module 302 is specifically configured to:

[0071] Based on the entry and exit data of different areas in the warehouse recorded in the work data table, determine the indicator data corresponding to the personnel distribution in each area;

[0072] Based on the indicator data and the pre-set threshold range, determine whether the indicator data triggers the preset warning conditions.

[0073] Optionally, the generating module 303 is specifically configured to:

[0074] In response to the indicator data corresponding to the personnel distribution being less than a lower limit of a threshold range, determining that the warehouse in the area corresponding to the indicator data is short of warehouse personnel, generating an early warning message corresponding to the shortage of warehouse personnel, and making a deployment decision based on the number of warehouse personnel corresponding to each area in the warehouse;

[0075] In response to the indicator data corresponding to the personnel distribution being greater than the upper limit of a preset range, it is determined that there is an excess of warehouse personnel in the area corresponding to the indicator data, early warning information corresponding to the excess warehouse personnel is generated, and a deployment decision is generated based on the number of warehouse personnel corresponding to each area in the warehouse.

[0076] Optionally, the preset work indicator includes personnel behavior; the determination module 302 is specifically configured to:

[0077] Based on the area entry and exit data recorded in the work data table, determine the residence time of warehouse personnel in each area, and use the residence time as the indicator data corresponding to the personnel behavior;

[0078] Determine the estimated completion time of the task based on the task type;

[0079] In response to the estimated completion time being less than the stay time and the task status being incomplete, it is determined that the indicator data triggers a preset warning condition.

[0080] According to the third aspect of an embodiment of the present invention, an electronic device for warehouse management is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors implement the method provided by the first aspect of the embodiment of the present invention.

[0081] According to a fourth aspect of an embodiment of the present invention, a computer-readable medium is provided, on which a computer program is stored. When the program is executed by a processor, the method provided by the first aspect of the embodiment of the present invention is implemented.

[0082] According to a fifth aspect of an embodiment of the present invention, a computer program product is provided, comprising a computer program, which implements the method of any of the above embodiments when executed by a processor.

[0083] Figure 4 An exemplary system architecture 400 is shown to which the warehouse management method or warehouse management apparatus according to the present invention can be applied.

[0084] like Figure 4 As shown, system architecture 400 may include terminal devices 401, 402, 403, a network 404, and a server 405. Network 404 is used to provide a medium for communication links between terminal devices 401, 402, 403 and server 405. Network 404 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0085] Users can use terminal devices 401, 402, and 403 to interact with server 405 via network 404 to receive or send messages, etc. Terminal devices 401, 402, and 403 can be installed with various communication client applications, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0086] The terminal devices 401 , 402 , and 403 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0087] Server 405 may be a server that provides various services, such as a backend management server (for example only) that supports shopping websites browsed by users using terminal devices 401, 402, and 403. The backend management server may process received warehouse management requests and provide feedback (for example only) on the processing results to the terminal devices.

[0088] It should be noted that the warehouse management method provided in the embodiment of the present invention is generally executed by the server 405, and accordingly, the warehouse management device is generally installed in the server 405. The warehouse management method provided in the embodiment of the present invention can also be executed by the terminal devices 401, 402, and 403, and accordingly, the warehouse management device can be installed in the terminal devices 401, 402, and 403.

[0089] It should be understood that Figure 4 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0090] Reference below Figure 5 , which shows a schematic structural diagram of a computer system 500 of a terminal device suitable for implementing an embodiment of the present invention. Figure 5 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0091] like Figure 5 As shown, computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage unit 508 into a random access memory (RAM) 503. Various programs and data required for the operation of system 500 are also stored in RAM 503. CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to bus 504.

[0092] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, mouse, and the like; an output section 507 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and speakers; a storage section 508 including devices such as a hard disk; and a communication section 509 including a network interface card such as a LAN card or a modem. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. Removable media 511, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 510 as needed, so that computer programs read from the removable media can be installed in the storage section 508 as needed.

[0093] In particular, according to embodiments disclosed herein, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed herein include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program containing program code for executing the methods illustrated in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509 and / or installed from removable media 511. When executed by central processing unit (CPU) 501, the computer program performs the aforementioned functions defined in the system of the present invention.

[0094] It should be noted that the computer-readable medium described in the present invention may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable storage media may include, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present invention, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such a propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, or any suitable combination thereof.

[0095] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0096] The modules described in the embodiments of the present invention may be implemented in software or hardware. The modules described may also be provided in a processor. For example, a processor may include an acquisition module, a determination module, and a generation module. The names of these modules do not, in some cases, limit the modules themselves. For example, the acquisition module may also be described as a "module for obtaining a working data table based on pre-acquired multi-source data."

[0097] As another aspect, the present invention further provides a computer-readable medium, which may be included in the device described in the above embodiment; or may exist independently and not be incorporated into the device. The computer-readable medium carries one or more programs, and when the one or more programs are executed by a device, the device implements the following method: obtaining a work data table based on pre-collected multi-source data; determining indicator data under preset work indicators based on the work data table, and determining whether to trigger a preset warning condition based on the indicator data; generating warning information and a deployment decision in response to the indicator data triggering the preset warning condition, so as to adjust the work deployment within the warehouse based on the deployment decision.

[0098] Finally, it should be noted that the above embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A warehouse management method, characterized in that: include: Based on the multi-source data collected in advance, a working data table is obtained; Determine the indicator data under the preset work indicator according to the work data table, and judge whether the preset warning condition is triggered based on the indicator data; In response to the indicator data triggering a preset warning condition, warning information and deployment decisions are generated to adjust the work deployment in the warehouse based on the deployment decisions.

2. The method according to claim 1, characterized in that The multi-source data includes attendance data, task data and area entry and exit data; based on the pre-collected multi-source data, before obtaining the work data table, it also includes: Based on the pre-developed data interface, the warehouse staff attendance data is obtained from the human resources management platform, and the task data is obtained from the warehouse management platform; The area entry and exit data are determined based on the near field communication devices set in each area of ​​the warehouse and the scanning records of the warehouse personnel on the near field communication devices.

3. The method according to claim 1, characterized in that The preset work indicators include work efficiency; according to the work data table, the indicator data under the preset work indicators are determined, including: Based on the data in the task start time, task end time and task status fields in the work data table, indicator data corresponding to the work efficiency is determined.

4. The method according to claim 1, wherein The preset work indicators include personnel distribution; according to the work data table, determining indicator data under the preset work indicators, and judging whether to trigger the preset warning conditions based on the indicator data, including: Based on the entry and exit data of different areas in the warehouse recorded in the work data table, determine the indicator data corresponding to the personnel distribution in each area; Based on the indicator data and a preset threshold range, it is determined whether the indicator data triggers a preset warning condition.

5. The method according to claim 4, characterized in that In response to the indicator data triggering a preset warning condition, generating warning information and a deployment decision, and adjusting the work deployment in the warehouse based on the deployment decision, including: In response to the indicator data corresponding to the personnel distribution being less than a lower limit of a threshold range, determining that the warehouse in the area corresponding to the indicator data is short of warehouse personnel, generating warning information corresponding to the shortage of warehouse personnel, and making a deployment decision based on the number of warehouse personnel corresponding to each area in the warehouse; In response to the indicator data corresponding to the personnel distribution being greater than the upper limit of a preset range, it is determined that there is an excess of warehouse personnel in the area corresponding to the indicator data, early warning information corresponding to the excess warehouse personnel is generated, and a deployment decision is generated based on the number of warehouse personnel corresponding to each area in the warehouse.

6. The method according to claim 1, characterized in that The preset work indicators include personnel behavior; according to the work data table, determining indicator data under the preset work indicators, and judging whether to trigger preset warning conditions based on the indicator data, including: Determine the residence time of warehouse personnel in each area based on the area entry and exit data recorded in the work data table, and use the residence time as indicator data corresponding to personnel behavior; Determining an estimated completion time of the task based on the task type; In response to the estimated completion time being less than the stay time and the task status being unfinished, it is determined that the indicator data triggers a preset warning condition.

7. A warehouse management device, characterized in that: include: An acquisition module is used to obtain a work data table based on pre-collected multi-source data; the multi-source data includes attendance data, task data, and area entry and exit data; A determination module, configured to determine indicator data under a preset work indicator according to the work data table, and determine whether a preset warning condition is triggered based on the indicator data; A generation module is used to generate warning information and deployment decisions in response to the indicator data triggering the preset warning conditions, so as to adjust the work deployment in the warehouse based on the deployment decision.

8. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer-readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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