Task allocation method and device based on face recognition, equipment and medium

Abstract

The invention provides a task allocation method and device based on face recognition, equipment and a medium, is used for the technical field of warehouse task allocation, and can solve the problems of low allocation efficiency and accuracy of existing warehouse tasks. Comprising the following steps: determining target warehouse personnel and to-be-processed warehouse tasks, and determining an emergency value of each to-be-processed warehouse task; according to the emergency value and a preset emergency threshold value, whether an emergency warehouse task exists in the to-be-processed warehouse tasks or not is judged; if not, according to multiple task types corresponding to the to-be-processed warehouse task, determining a completion efficiency value of the target warehouse personnel for each task type, and according to each completion efficiency value, determining a target completion efficiency value with the maximum value and a target task type corresponding to the target completion efficiency value; determining a target path distance and a target warehouse task according to the path distance between each target to-be-processed warehouse task and the target warehouse personnel; in this way, the efficiency and accuracy of warehouse task allocation are improved.

Description

Technical Field

[0001] This application relates to the field of warehouse task allocation technology, and in particular to a task allocation method, apparatus, device and medium based on face recognition. Background Technology

[0002] Smart warehousing refers to a modern logistics operation environment that utilizes advanced technologies such as the Internet of Things, artificial intelligence, big data, and robotics to digitize, automate, and intelligently transform all aspects of traditional warehouses, including storage, picking, distribution, and management. In smart warehousing, warehouse tasks specifically include work directly related to the flow of goods, such as receiving and inspection, picking route optimization, and outbound verification, carried out through a warehouse management system (WMS).

[0003] Existing warehouse task allocation methods generally involve manual button triggering or fixed process node broadcasting, which means that corresponding warehouse tasks are assigned to warehouse personnel according to a certain task sequence. Furthermore, the voice commands used for task allocation may be disconnected from real-time operations, resulting in poor information reach and data interoperability, and an inability to collaborate.

[0004] Therefore, existing warehouse task allocation methods suffer from low allocation efficiency and accuracy. Summary of the Invention

[0005] This application provides a task allocation method, apparatus, device, and medium based on face recognition to solve the problems of low allocation efficiency and accuracy in existing warehouse task allocation methods.

[0006] Firstly, this application provides a task allocation method based on face recognition, the method comprising: Based on facial recognition images, the target warehouse personnel and pending warehouse tasks are identified, and the urgency value of each pending warehouse task is determined according to the corresponding task deadline, customer level, and inventory value. Based on the urgency value and the preset urgency threshold, determine whether there are any urgent warehouse tasks among the pending warehouse tasks; If not, then based on the multiple task types corresponding to the warehouse tasks to be processed, determine the completion efficiency value of the target warehouse personnel for each task type, and based on each completion efficiency value, determine the target completion efficiency value with the largest value and its corresponding target task type; The target path distance is determined based on the path distance between each target pending warehouse task and the target warehouse personnel, and the corresponding target warehouse task is determined based on the target path distance; the target pending warehouse task is the pending warehouse task corresponding to the target task type.

[0007] In some embodiments of this application, determining target warehouse personnel and warehouse tasks to be processed based on facial recognition images includes: Based on the preset detection range of the sensor, the person to be identified is determined; the person to be identified is the person who enters the detection range. The face recognition of the person to be identified is performed to obtain the corresponding face image, and the face image is compared with each target face image to obtain the corresponding comparison result; If the comparison result is that the face image matches any target face image, then the person to be identified is determined to be the target warehouse personnel, and the warehouse task to be processed is determined. If the comparison results show that the face image does not match any of the target face images, then it is determined that the person to be identified is not a staff member of the target warehouse.

[0008] In some embodiments of this application, the urgency value of each pending warehouse task is determined based on the corresponding task deadline, customer level, and inventory value, including: Determine the task duration based on the current time and the task deadline, and calculate the time urgency value based on the difference between the preset duration threshold and the task duration. Based on the preset level correspondence rules, the urgency value corresponding to the customer level is determined; the level correspondence rules are used to determine the preset score corresponding to different customer levels. The inventory urgency value is calculated based on the difference between the preset inventory threshold and the inventory value. Then, the duration urgency value, the level urgency value, and the inventory urgency value are weighted according to the preset weight values ​​to obtain the urgency value.

[0009] In some embodiments of this application, determining whether there are urgent warehouse tasks among the pending warehouse tasks based on an urgency value and a preset urgency threshold includes: Based on the emergency value and the preset emergency threshold, determine whether the emergency value is greater than the preset emergency threshold; If so, then it is determined that there is an urgent warehouse task among the pending warehouse tasks; If not, then it is determined that there are no urgent warehouse tasks among the pending warehouse tasks.

[0010] In some embodiments of this application, the target path distance is determined based on the path distance between each target warehouse task to be processed and the target warehouse personnel, including: Based on multiple preset path points in the target warehouse map, the path points corresponding to the personnel in the target warehouse and the warehouse path points corresponding to each target warehouse task to be processed are traversed to obtain at least one path corresponding to each warehouse path point. Based on the path length of each path, determine the shortest path corresponding to each warehouse path point, and compare the path lengths of each shortest path to obtain the target path distance with the shortest path length.

[0011] In some embodiments of this application, determining the corresponding target warehouse task based on the target path distance includes: Based on the target path distance, determine the corresponding target warehouse path point, and generate the target warehouse task based on the target warehouse path point and the target path distance; Based on the target warehouse task, generate corresponding prompt messages; these prompt messages are used to assign the target warehouse task to the target warehouse personnel.

[0012] In some embodiments of this application, after determining whether there are any urgent warehouse tasks among the warehouse tasks to be processed based on the urgency value and a preset urgency threshold, the method further includes: If so, then generate the target warehouse task based on the warehouse path point corresponding to the emergency warehouse task.

[0013] Secondly, this application provides a task allocation device based on face recognition, the device comprising: The urgency value determination module is used to identify target warehouse personnel and pending warehouse tasks based on facial recognition images, and to determine the urgency value of each pending warehouse task according to the corresponding task deadline, customer level and inventory value. The judgment module is used to determine whether there are any urgent warehouse tasks among the pending warehouse tasks based on the urgency value and the preset urgency threshold. The type determination module is used to determine the completion efficiency value of the target warehouse personnel for each task type based on the multiple task types corresponding to the warehouse task to be processed, if not, and to determine the target completion efficiency value with the largest value and its corresponding target task type based on each completion efficiency value. The task determination module is used to determine the target path distance based on the path distance between each target warehouse task to be processed and the target warehouse personnel, and to determine the corresponding target warehouse task based on the target path distance; the target warehouse task to be processed is the warehouse task to be processed corresponding to the target task type.

[0014] Thirdly, this application provides a computer device, including: a processor, and a memory communicatively connected to the processor; The memory stores the instructions that the computer executes; The processor executes computer execution instructions stored in memory to implement the method of this application.

[0015] Fourthly, this application provides a computer-readable storage medium storing program code, which, when executed by a processor, is used to implement the method of this application.

[0016] This application provides a task allocation method, apparatus, device, and medium based on facial recognition. It identifies target warehouse personnel and pending warehouse tasks based on facial recognition images, and determines the urgency value of each pending warehouse task according to the corresponding task deadline, customer level, and inventory value. Based on the urgency value and a preset urgency threshold, it determines whether any urgent warehouse tasks exist. If not, it determines the completion efficiency value of the target warehouse personnel for each task type based on multiple task types corresponding to the pending warehouse tasks, and determines the target completion efficiency value with the highest value and its corresponding target task type. It also determines the target path distance based on the path distance between each target pending warehouse task and the target warehouse personnel, and determines the corresponding target warehouse task based on the target path distance. The target pending warehouse task is the pending warehouse task corresponding to the target task type.

[0017] Thus, by using facial recognition images to identify target warehouse personnel and pending warehouse tasks, and combining task deadlines, customer levels, and inventory values, the urgency of each task is quantified. This not only enables precise binding of personnel identity and tasks based on facial recognition, thus avoiding misassignment due to mismatched personnel, but also allows for task priority ranking through multi-dimensional data, solving the problems of ambiguous task matching and difficulty in identifying urgent tasks in traditional allocation methods. By comparing the urgency value with preset thresholds, it can determine whether there are urgent tasks, quickly filtering out urgent tasks that need to be prioritized, such as tasks nearing deadlines, high-level tasks, or tasks with critical inventory levels, to ensure timely response to urgent tasks and avoid risks such as order delays and inventory imbalances caused by task delays. When there are no urgent tasks, analysis... The system prioritizes tasks with the highest completion efficiency for different task types, achieving personalized allocation that matches personnel to their roles. This fully leverages the operational strengths of warehouse staff, reduces inefficiencies caused by mismatches between tasks and personnel capabilities, and improves the quality and quantity of warehouse tasks completed per unit time. For pending tasks under the target task type, the system combines the path distance between personnel and task locations to determine the final target task. This effectively shortens personnel travel time during task execution, reduces ineffective travel costs, further optimizes warehouse operation processes, improves the accuracy and efficiency of warehouse task allocation, reduces the subjectivity and randomness of traditional manual allocation, and aligns with the intelligent and refined management needs of smart warehousing, thereby reducing operating costs and ensuring order fulfillment quality. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] Figure 1A flowchart illustrating a task allocation method based on face recognition provided in an embodiment of this application; Figure 2 A schematic diagram of another task allocation method based on face recognition provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a task allocation device based on face recognition provided in an embodiment of this application; Figure 4 This is a structural block diagram of a device for executing a face recognition-based task allocation method according to an embodiment of this application. Detailed Implementation

[0020] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0021] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0022] Figure 1 This is a flowchart illustrating a task allocation method based on face recognition, provided as an embodiment of this application. Figure 1 As shown, this task allocation method based on face recognition may include the following steps: S110. Based on facial recognition images, identify the personnel in the target warehouse and the warehouse tasks to be processed, and determine the urgency value of each warehouse task to be processed according to the corresponding task deadline, customer level and inventory value.

[0023] Among them, facial recognition images refer to the facial image data of warehouse personnel obtained through warehouse recognition equipment. Facial data is biometric information, which is unique and unchangeable. Facial images obtained through facial recognition can be matched to determine the identity information of the current personnel, so as to determine whether they are warehouse personnel who need to perform warehouse operations.

[0024] The target warehouse personnel are those identified through facial recognition image matching, who possess the corresponding warehousing operation permissions, such as the specific personnel responsible for receiving, picking, or verifying outbound warehouse tasks.

[0025] The pending warehouse tasks are the actual warehousing operations that need to be performed by the target warehouse personnel, such as inbound inspection, which specifically involves checking the quantity and specifications of goods, and outbound verification, which specifically involves checking the consistency between orders and goods.

[0026] The task deadline refers to the final time node that the pending warehouse task must be completed. For example, if the outbound document needs to be reviewed by 6 PM on day X, then "6 PM on day X" is the task deadline for this review task. The closer the current time is to the task deadline, the higher the time urgency of this task, and it needs to be completed as soon as possible.

[0027] Customer level refers to the preset level corresponding to the customer for each pending warehouse task, thus representing the preset hierarchical division between different customers. For example, if customer 1 of pending warehouse task 1 is at level A, and customer 2 of pending warehouse task 2 is at level B, then customer 1's level is higher than customer 2's. The higher the level, the greater the customer's influence, and the corresponding task needs to be processed first.

[0028] The inventory value is the current actual inventory quantity of the goods associated with the pending warehouse task. For example, if there are only 5 units of a certain item in stock, the corresponding inventory value is 5. When the inventory value is lower than the preset threshold, or the inventory value cannot meet the order requirements, the urgency of the task is significantly increased, which means that the corresponding task needs to be processed first.

[0029] The urgency value is a numerical value that quantifies the urgency of a warehouse task by combining three dimensions: task deadline, task level, and inventory value. A higher value indicates that the task needs to be processed with priority. Task deadline, task level, and inventory value can reflect the urgency of the current warehouse task from three different dimensions: time, customer, and inventory. By quantifying and combining the values ​​of these three dimensions, the urgency value corresponding to the warehouse task is obtained, so as to determine the execution priority of each warehouse task to be processed.

[0030] Based on this, facial recognition is performed on the current personnel to determine whether they are qualified personnel for the target warehouse. If so, the pending warehouse tasks that need to be processed are further determined. In order to determine the execution priority of each task, it can be quantified through three dimensions: task deadline, customer level, and inventory value, to obtain the corresponding urgency value, so as to determine the target task that needs to be executed at the moment based on the urgency value.

[0031] S120. Based on the urgency value and the preset urgency threshold, determine whether there are any urgent warehouse tasks among the pending warehouse tasks.

[0032] The preset emergency threshold is a pre-set emergency value used to determine whether there are any urgent warehouse tasks in the current pending warehouse tasks. If the corresponding emergency value is higher than the preset emergency threshold, it is an urgent warehouse task. For example, the preset emergency threshold can be 50. If the emergency value of a pending warehouse task is 60, which is higher than the preset emergency threshold of 50, then the task is an urgent warehouse task.

[0033] An urgent warehouse task represents the warehouse task with the highest urgency among the current pending warehouse tasks, which needs to be executed first. If a warehouse task is an urgent warehouse task, it means that the target warehouse personnel need to execute this urgent warehouse task immediately to avoid causing serious impact.

[0034] Based on this, by comparing the corresponding urgency value with the preset urgency threshold, it can be determined whether there are any urgent warehouse tasks that must be executed immediately among the various pending warehouse tasks. Thus, based on the quantification of urgency value, it can achieve accurate identification and timely response to urgent tasks.

[0035] S130. If not, then based on the multiple task types corresponding to the warehouse task to be processed, determine the completion efficiency value of the target warehouse personnel for each task type, and based on each completion efficiency value, determine the target completion efficiency value with the largest value and its corresponding target task type.

[0036] The task type refers to the actual type of each pending warehouse task. In the actual execution of warehouse tasks, pending warehouse tasks may correspond to multiple different actual task types, such as receiving, picking, and outbound verification. Different warehouse personnel may be good at different task types. By determining the task types that warehouse personnel are best at, the warehouse tasks can be accurately allocated, thereby improving the efficiency of task allocation and task execution.

[0037] The completion efficiency value is the execution efficiency value of the target warehouse personnel for different types of warehouse tasks. It can be an efficiency index value that is quantitatively calculated based on the historical work data of the target warehouse personnel for a certain type of task. The completion efficiency value is the execution efficiency of warehouse personnel for different types of tasks, and can reflect the types of tasks that warehouse personnel are good at.

[0038] The target completion efficiency value refers to the highest efficiency value among the completion efficiency values ​​of personnel in the target warehouse for each task type. It represents the optimal efficiency performance index under the current working ability of the personnel. The target task type is the task type corresponding to the target completion efficiency value. For example, if the efficiency value of personnel for receiving and inspecting goods is 70% and the efficiency value for outbound verification is 90%, then the target completion efficiency value is 90%, and the corresponding target task type is outbound verification.

[0039] Based on this, if there are no urgent warehouse tasks that need to be executed immediately, targeted warehouse tasks can be assigned according to the actual operational capabilities of warehouse personnel, thereby improving task execution efficiency and quality. By determining the historical efficiency values ​​of personnel for various tasks, the target task type corresponding to the target completion efficiency value can be determined, so that personnel can be further assigned warehouse tasks of this type in the future.

[0040] S140. Determine the target path distance based on the path distance between each target warehouse task to be processed and the target warehouse personnel, and determine the corresponding target warehouse task based on the target path distance; the target warehouse task to be processed is the warehouse task to be processed corresponding to the target task type.

[0041] The target path distance refers to the shortest movement path length among the path distances between the work locations of each target warehouse task and the current location of the warehouse personnel. In practical applications, after determining the task types that the current warehouse personnel are proficient in, to further determine which specific task under that type should be assigned, the path distance between the work points of each warehouse task under that type and the location of the warehouse personnel can be calculated. This allows the selection of the shortest target path distance and its corresponding warehouse task, thus avoiding the problem of excessively long personnel movement time and reduced work efficiency due to task locations being too far away. For example, if the warehouse personnel are proficient in task type A and there are three actual tasks to be executed: Task 1, Task 2, and Task 3, and the distance between Task 1 and the warehouse personnel is 10 meters, Task 2 is 5 meters, and Task 3 is 14 meters, then the 5-meter distance for Task 2 is the target path distance.

[0042] The target warehouse task is the actual task corresponding to the target path distance. For example, if 5 meters is the target path distance for task 2, then task 2 is the target warehouse task, which is the warehouse task that the warehouse personnel need to perform at the moment.

[0043] Based on this, after determining the types of tasks that warehouse personnel are good at, it is also necessary to further combine the path distance to determine the actual task corresponding to the shortest target path distance, and obtain the target warehouse task that the warehouse personnel need to perform.

[0044] Based on the feasible implementation of S110 described above, this application further provides a method for determining target warehouse personnel and warehouse tasks to be processed based on facial recognition images, including: Based on the preset detection range of the sensor, the person to be identified is determined; the person to be identified is the person who enters the detection range. The face recognition of the person to be identified is performed to obtain the corresponding face image, and the face image is compared with each target face image to obtain the corresponding comparison result; If the comparison result is that the face image matches any target face image, then the person to be identified is determined to be the target warehouse personnel, and the warehouse task to be processed is determined. If the comparison results show that the face image does not match any of the target face images, then it is determined that the person to be identified is not a staff member of the target warehouse.

[0045] Among them, the pre-set sensors refer to devices that are pre-deployed in key areas of the warehouse, such as entrances and exits, shelf partitions, and workstations, and have the function of sensing the presence of people. The pre-set sensors can accurately define specific detection ranges, such as within 5 meters of a certain shelf area or warehouse entrance passage, so as to capture the signals of people entering the range in real time for subsequent identification.

[0046] The person to be identified refers to someone who enters the detection range defined by the preset sensor and whose identity needs to be confirmed through the subsequent facial recognition process. This person may be a warehouse worker, a visitor, or an unrelated person such as a non-worker.

[0047] The target face image refers to the standard facial feature image of the authorized warehouse operator, which is pre-stored in the system database. This image is then used for subsequent face comparison and is associated with the identity information of the target warehouse personnel (such as name, work authority, and type of task).

[0048] Based on this, if a person enters the detection range of the preset sensor, facial recognition can be performed through the preset recognition device. The identity can be quickly verified by comparing the facial image of the person to be identified with the target facial image one by one. If the comparison matches, the information of the person corresponding to the target facial image can be directly associated so as to achieve accurate allocation of warehouse tasks to be processed. If the comparison does not match, irrelevant personnel are excluded.

[0049] Based on the feasible implementation of S110 described above, this application further provides a method for determining the urgency value of each pending warehouse task according to the corresponding task deadline, customer level, and inventory value, including: Determine the task duration based on the current time and the task deadline, and calculate the time urgency value based on the difference between the preset duration threshold and the task duration. Based on the preset level correspondence rules, the urgency value corresponding to the customer level is determined; the level correspondence rules are used to determine the preset score corresponding to different customer levels. The inventory urgency value is calculated based on the difference between the preset inventory threshold and the inventory value. Then, the duration urgency value, the level urgency value, and the inventory urgency value are weighted according to the preset weight values ​​to obtain the urgency value.

[0050] The current time refers to the current point in time when the target warehouse personnel are identified and tasks need to be assigned. The task duration is the time between the identification of the target warehouse personnel and the actual deadline of the task. For example, if the target warehouse personnel are identified at 14:00 and the deadline for the pending warehouse task is 24:00, then the task duration for this pending warehouse task is 10 hours.

[0051] The preset duration threshold refers to a pre-set time standard used to quantify task duration. The duration urgency value is the urgency value obtained by the difference between the preset duration threshold and the task duration. For example, if the preset duration threshold is 20 hours and the task duration is 10 hours, the corresponding duration urgency value is 10. In practical applications, the closer a warehouse task is to its deadline, the shorter the remaining task time, the more urgent the task is, and the higher the corresponding duration urgency value should be. By calculating the difference between the preset duration threshold and the task duration, the corresponding duration urgency value is obtained, so as to quantify the urgency of the task from the perspective of the task deadline.

[0052] The preset level correspondence rules refer to the pre-determined mapping rules used to convert customer levels into level urgency values. By pre-setting specific level urgency values ​​corresponding to different customer levels, for example, the rule sets the level urgency value for VIP customers to be 10 and the level urgency value for ordinary customers to be 5. The formulation of level correspondence rules needs to be combined with business needs such as customer cooperation priority and order amount size, so as to accurately measure the actual level corresponding to different customers.

[0053] The urgency level is a numerical value that reflects the priority of a customer's needs, which is obtained by matching the customer's level with the preset level correspondence rules. For example, if the customer level is VIP, the corresponding urgency level is 10 according to the rules.

[0054] The preset inventory threshold refers to a pre-determined quantity standard used to determine whether the inventory of goods associated with a task is tight; for example, if the preset inventory threshold is 100, then if the actual inventory value corresponding to a certain task is less than 100, it indicates that the inventory of that task is insufficient and needs to be replenished as soon as possible.

[0055] The inventory urgency value is a numerical value that reflects the degree of inventory shortage, obtained by the difference between the preset inventory threshold and the inventory value. For example, if the inventory value corresponding to a certain task is 20 and the preset inventory threshold is 100, then the corresponding inventory urgency value is 80. This value is relatively large, which means that the task's inventory is seriously insufficient and needs to be replenished as soon as possible.

[0056] The preset weight value refers to the pre-set coefficient used to adjust the proportion of the duration urgency value, the level urgency value, and the inventory urgency value in the final urgency value. For example, if the preset duration urgency value is A with a weight of 0.4, the level urgency value is B with a weight of 0.3, and the inventory urgency value is C with a weight of 0.3, then the final urgency value is 0.4*A + 0.3*B + 0.3*C.

[0057] Based on this, the time urgency value and inventory urgency value are obtained by calculating the difference between the task duration and inventory value and their respective thresholds. Then, according to the preset level correspondence rules, the level urgency value corresponding to the customer level is determined. In order to obtain the urgency value by weighting the time urgency value, level urgency value and inventory urgency value according to the weight value.

[0058] Based on the feasible implementation of S120 described above, this application further provides a method for determining whether there are urgent warehouse tasks among the pending warehouse tasks based on an urgency value and a preset urgency threshold, including: Based on the emergency value and the preset emergency threshold, determine whether the emergency value is greater than the preset emergency threshold; If so, then it is determined that there is an urgent warehouse task among the pending warehouse tasks; If not, then it is determined that there are no urgent warehouse tasks among the pending warehouse tasks.

[0059] The preset emergency threshold is a pre-determined value used to determine whether an emergency warehouse task exists based on the value of the emergency value. That is, if the emergency value is greater than the preset emergency threshold, it indicates that an emergency warehouse task exists, and otherwise it does not exist.

[0060] Based on this, by comparing the value of the urgency value with the preset urgency threshold, it can be determined whether there are any urgent warehouse tasks among the pending warehouse tasks.

[0061] Based on the feasible implementation of S140 described above, this application further provides a method for determining the target path distance based on the path distance between each target warehouse task to be processed and the target warehouse personnel, including: Based on multiple preset path points in the target warehouse map, the path points corresponding to the personnel in the target warehouse and the warehouse path points corresponding to each target warehouse task to be processed are traversed to obtain at least one path corresponding to each warehouse path point. Based on the path length of each path, determine the shortest path corresponding to each warehouse path point, and compare the path lengths of each shortest path to obtain the target path distance with the shortest path length.

[0062] The target warehouse map is a digital spatial model that perfectly matches the physical layout of the actual warehouse. It includes key spatial information such as warehouse racking zones, workstations, aisle directions, and obstacle locations, thereby generating a coordinate system of preset path points for path planning and spatial analysis. This provides a unified coordinate mapping environment for subsequent path determination, avoiding path calculation deviations caused by ambiguous spatial information. At the same time, the information on aisles and obstacles in the map ensures that the path generated by traversing the path points conforms to the actual passage rules of the warehouse.

[0063] Preset waypoints are fixed coordinate points marked in advance on the target warehouse map, covering key access nodes of the warehouse. They are the basic nodes for building a standardized path network for the warehouse, facilitating subsequent path calculations and avoiding route confusion caused by the lack of fixed nodes in path planning. At the same time, by using preset waypoints, the spatial locations of personnel and tasks are connected, ensuring that the paths generated during the traversal of waypoints are all valid routes that conform to the actual passage logic, reducing the generation of invalid paths.

[0064] Personnel waypoints refer to the precise coordinates of the current physical location of personnel in the target warehouse. These coordinates can be directly mapped to the preset waypoint network on the target warehouse map, thereby locating the real-time spatial position of personnel in the target warehouse.

[0065] Warehouse path points are fixed coordinate points that correspond one-to-one with the work locations of each target warehouse task to be processed. These coordinate points have been pre-entered into the target warehouse map and are associated with the preset path point network. They are the spatial mapping of the target warehouse tasks to be processed in the digital map and are directly related to the physical execution location of the tasks.

[0066] Path point traversal refers to using a preset path point network on the target warehouse map as a basis, starting with a "personnel path point" and ending with a single warehouse path point, and trying different combinations of preset path points one by one to generate all valid passage paths from the start point to the end point. The traversal logic must avoid obstacles and restricted areas in the map and only generate paths that conform to the actual passage rules of the warehouse. For example, if shelf 05 in area A corresponds to warehouse path point A and the personnel in the target warehouse correspond to personnel path point B, then by determining all paths from path point B to path point A, multiple feasible paths are obtained.

[0067] Path length refers to the total physical length of a complete path calculated based on the calibrated physical distances between preset path points in the target warehouse map. The calculation process can ensure that the data is consistent with the actual travel distance by accurately accumulating the distances between each preset path point in the path.

[0068] Based on this, the task type corresponding to warehouse personnel may include multiple actual tasks to be executed. There may be multiple feasible paths between the task operation point corresponding to each task and the current location point of the personnel. By calculating the multiple feasible paths between each warehouse path point and the personnel path point, and determining the corresponding path length, the length comparison is performed to obtain the shortest path between each warehouse path point and the personnel path point. In order to further compare the shortest path length between different warehouse path points, the target path length can be obtained.

[0069] Based on the feasible implementation of S140 described above, this application further provides a method for determining the corresponding target warehouse task based on the target path distance, including: Based on the target path distance, determine the corresponding target warehouse path point, and generate the target warehouse task based on the target warehouse path point and the target path distance; Based on the target warehouse task, generate corresponding prompt messages; these prompt messages are used to assign the target warehouse task to the target warehouse personnel.

[0070] The target warehouse path point refers to a specific warehouse path point in the preset path point network on the target warehouse map that corresponds to the target path distance. For example, the warehouse path point corresponding to shelf 05 in zone 1 is A, the warehouse path point corresponding to shelf 08 in zone 3 is B, and the personnel path point corresponding to the target warehouse personnel is C. Then the path distance between A and C is 10 meters, and the path distance between B and C is 20 meters. 20 is the target path distance, and the corresponding warehouse path point B is the target warehouse path point.

[0071] The prompts are generated based on a defined target warehouse task and are used to convey task instructions to personnel in the target warehouse. These prompts can take the form of dynamic voice instructions, touchscreen visual prompts, or terminal vibration prompts, and include the task type, specific task location, and travel route. In practical applications, the prompts can be voice prompts; high-fidelity local speakers are used in low-noise areas to provide clear guidance, while bone conduction headphones or AR glasses are used in high-noise and long-distance areas to ensure information privacy and accuracy.

[0072] Based on this, by determining the path points of the target warehouse, the corresponding task operation points are determined. Furthermore, based on task information such as task type, specific task operation location, and travel route, prompt information is generated to assign the target warehouse task to the target warehouse personnel.

[0073] Based on the feasible implementation of S140 described above, this application further provides a method that, after determining whether there are any urgent warehouse tasks among the warehouse tasks to be processed based on the urgency value and a preset urgency threshold, includes: If so, then generate the target warehouse task based on the warehouse path point corresponding to the emergency warehouse task.

[0074] Based on this, if the urgency value of the task is higher than the preset urgency threshold, that is, if there is an urgent warehouse task among the pending warehouse tasks, the shortest path can be determined directly based on the path point corresponding to the urgent warehouse task and the current path point of the warehouse personnel, so as to generate the target warehouse task, thereby realizing a rapid response and processing of urgent tasks.

[0075] Please refer to Figure 2 , Figure 2 A schematic diagram of another task allocation method based on face recognition provided in this application embodiment; as shown Figure 2 As shown, through multi-module collaboration, voice commands and dynamic services are integrated, eliminating interaction delays and process fragmentation. In practical applications, the command terminal equipment can intelligently switch between silent and full-function working states, achieving zero-latency service response and on-demand energy allocation.

[0076] In some embodiments of this application, based on facial recognition images, target warehouse personnel and pending warehouse tasks are identified, and the urgency value of each pending warehouse task is determined according to the corresponding task deadline, customer level, and inventory value. Based on the urgency value and a preset urgency threshold, it is determined whether there are any urgent warehouse tasks among the pending warehouse tasks. If not, the completion efficiency value of the target warehouse personnel for each task type is determined according to multiple task types corresponding to the pending warehouse tasks, and the target completion efficiency value with the largest value and its corresponding target task type are determined based on each completion efficiency value. The target path distance is determined based on the path distance between each target pending warehouse task and the target warehouse personnel, and the corresponding target warehouse task is determined based on the target path distance. The target pending warehouse task is the pending warehouse task corresponding to the target task type.

[0077] Thus, by using facial recognition images to identify target warehouse personnel and pending warehouse tasks, and combining task deadlines, customer levels, and inventory values, the urgency of each task is quantified. This not only enables precise binding of personnel identity and tasks based on facial recognition, thus avoiding misassignment due to mismatched personnel, but also allows for task priority ranking through multi-dimensional data, solving the problems of ambiguous task matching and difficulty in identifying urgent tasks in traditional allocation methods. By comparing the urgency value with preset thresholds, it can determine whether there are urgent tasks, quickly filtering out urgent tasks that need to be prioritized, such as tasks nearing deadlines, high-level tasks, or tasks with critical inventory levels, to ensure timely response to urgent tasks and avoid risks such as order delays and inventory imbalances caused by task delays. When there are no urgent tasks, analysis... The system prioritizes tasks with the highest completion efficiency for different task types, achieving personalized allocation that matches personnel to their roles. This fully leverages the operational strengths of warehouse staff, reduces inefficiencies caused by mismatches between tasks and personnel capabilities, and improves the quality and quantity of warehouse tasks completed per unit time. For pending tasks under the target task type, the system combines the path distance between personnel and task locations to determine the final target task. This effectively shortens personnel travel time during task execution, reduces ineffective travel costs, further optimizes warehouse operation processes, improves the accuracy and efficiency of warehouse task allocation, reduces the subjectivity and randomness of traditional manual allocation, and aligns with the intelligent and refined management needs of smart warehousing, thereby reducing operating costs and ensuring order fulfillment quality.

[0078] Figure 3 This is a schematic diagram of the structure of a task allocation device 300 based on face recognition, provided in an embodiment of this application. Figure 3 As shown, the task allocation device 300 based on face recognition includes: an urgency value determination module 310, a judgment module 320, a type determination module 330, and a task determination module 340; wherein: The urgency value determination module 310 is used to determine the target warehouse personnel and pending warehouse tasks based on facial recognition images, and to determine the urgency value of each pending warehouse task according to the corresponding task deadline, customer level and inventory value. The judgment module 320 is used to determine whether there are any urgent warehouse tasks among the pending warehouse tasks based on the urgency value and the preset urgency threshold. The type determination module 330 is used to determine the completion efficiency value of the target warehouse personnel for each task type based on the multiple task types corresponding to the warehouse task to be processed, and to determine the target completion efficiency value with the largest value and its corresponding target task type based on each completion efficiency value. The task determination module 340 is used to determine the target path distance based on the path distance between each target warehouse task to be processed and the target warehouse personnel, and to determine the corresponding target warehouse task based on the target path distance; the target warehouse task to be processed is the warehouse task to be processed corresponding to the target task type.

[0079] In this embodiment of the application, the emergency value determination module 310 can also be specifically used for: Based on the preset detection range of the sensor, the person to be identified is determined; the person to be identified is the person who enters the detection range. The face recognition of the person to be identified is performed to obtain the corresponding face image, and the face image is compared with each target face image to obtain the corresponding comparison result; If the comparison result is that the face image matches any target face image, then the person to be identified is determined to be the target warehouse personnel, and the warehouse task to be processed is determined. If the comparison results show that the face image does not match any of the target face images, then it is determined that the person to be identified is not a staff member of the target warehouse.

[0080] In this embodiment of the application, the emergency value determination module 310 can also be specifically used for: Determine the task duration based on the current time and the task deadline, and calculate the time urgency value based on the difference between the preset duration threshold and the task duration. Based on the preset level correspondence rules, the urgency value corresponding to the customer level is determined; the level correspondence rules are used to determine the preset score corresponding to different customer levels. The inventory urgency value is calculated based on the difference between the preset inventory threshold and the inventory value. Then, the duration urgency value, the level urgency value, and the inventory urgency value are weighted according to the preset weight values ​​to obtain the urgency value.

[0081] In this embodiment of the application, the determination module 320 can also be specifically used for: Based on the emergency value and the preset emergency threshold, determine whether the emergency value is greater than the preset emergency threshold; If so, then it is determined that there is an urgent warehouse task among the pending warehouse tasks; If not, then it is determined that there are no urgent warehouse tasks among the pending warehouse tasks.

[0082] In this embodiment of the application, the task determination module 340 can also be specifically used for: Based on multiple preset path points in the target warehouse map, the path points corresponding to the personnel in the target warehouse and the warehouse path points corresponding to each target warehouse task to be processed are traversed to obtain at least one path corresponding to each warehouse path point. Based on the path length of each path, determine the shortest path corresponding to each warehouse path point, and compare the path lengths of each shortest path to obtain the target path distance with the shortest path length.

[0083] In this embodiment of the application, the task determination module 340 can also be specifically used for: Based on the target path distance, determine the corresponding target warehouse path point, and generate the target warehouse task based on the target warehouse path point and the target path distance; Based on the target warehouse task, generate corresponding prompt messages; these prompt messages are used to assign the target warehouse task to the target warehouse personnel.

[0084] In this embodiment of the application, other modules in a face recognition-based task allocation device 300 may also be specifically used for: If so, then generate the target warehouse task based on the warehouse path point corresponding to the emergency warehouse task.

[0085] Figure 4 This is a schematic diagram of the structure of a device for executing a task allocation method based on face recognition according to an embodiment of this application. Figure 4 As shown, the device 400 includes: The device 400 may include a processor 401 with one or more processing cores, a memory 402 with one or more computer-readable storage media, a communication component 403, and other components. The processor 401, memory 402, and communication component 403 are connected via a bus 404.

[0086] In the specific implementation process, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to execute the above-mentioned task allocation method based on face recognition.

[0087] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0088] Furthermore, the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0089] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0090] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0091] In some embodiments, a computer program product is also provided, including a computer program or instructions that, when executed by a processor, implement the steps in any of the above-described face recognition-based task allocation methods.

[0092] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0093] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware, and these instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0094] Therefore, embodiments of this application provide a computer-readable storage medium storing multiple lines of program code that can be loaded by a processor to execute steps in any of the face recognition-based task allocation methods provided in embodiments of this application.

[0095] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0096] According to one aspect of this application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium.

[0097] Since the instructions stored in the storage medium can execute the steps in any of the face recognition-based task allocation methods provided in the embodiments of this application, the beneficial effects that any of the face recognition-based task allocation methods provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0098] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the appended claims.

[0099] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A face recognition based task allocation method, characterized in that, The method comprises: determining a target warehouse staff and a to-be-processed warehouse task based on a face recognition image, and determining an emergency value of each of the to-be-processed warehouse tasks according to a corresponding task deadline, a customer level and an inventory value; determining whether there is an emergency warehouse task in the to-be-processed warehouse tasks according to the emergency value and a preset emergency threshold value; if not, determining a completion efficiency value of each of the task types for the target warehouse staff according to a plurality of task types corresponding to the to-be-processed warehouse tasks, and determining a target completion efficiency value with a maximum value and a target task type corresponding thereto according to each of the completion efficiency values; determining a target path distance according to a path distance between each of the target to-be-processed warehouse tasks and the target warehouse staff, and determining a corresponding target warehouse task according to the target path distance; the target to-be-processed warehouse task is the to-be-processed warehouse task corresponding to the target task type.

2. The method of claim 1, wherein, The method comprises: determining a to-be-identified staff based on a detection range of a preset sensor; the to-be-identified staff is a staff entering the detection range; performing face recognition on the to-be-identified staff to obtain a corresponding face image, and comparing the face image with each of target face images to obtain a corresponding comparison result; if the comparison result is that the face image is consistent with any one of the target face images, determining that the to-be-identified staff is the target warehouse staff, and determining the to-be-processed warehouse task; if the comparison result is that the face image is inconsistent with each of the target face images, determining that the to-be-identified staff is not the target warehouse staff.

3. The method of claim 1, wherein, The method comprises: determining a task duration according to a current time and the task deadline, and calculating a duration emergency value according to a difference between a preset duration threshold value and the task duration; determining a level emergency value corresponding to the customer level according to a preset level corresponding rule; the level corresponding rule is used to determine a preset score value corresponding to different customer levels; calculating an inventory emergency value according to a difference between a preset inventory threshold value and the inventory value, and performing weighted processing on the duration emergency value, the level emergency value and the inventory emergency value according to a preset weight value to obtain the emergency value.

4. The method of claim 1, wherein, The method comprises: determining whether the emergency value is greater than the preset emergency threshold value according to the emergency value and the preset emergency threshold value; if yes, determining that the to-be-processed warehouse task has the emergency warehouse task; if not, determining that the to-be-processed warehouse task does not have the emergency warehouse task.

5. The method of claim 1, wherein, The method comprises: According to a plurality of preset path points in a target warehouse map, path point traversal is performed on personnel path points corresponding to the target warehouse personnel and warehouse path points corresponding to each target to-be-processed warehouse task, to obtain at least one path corresponding to each warehouse path point; According to path lengths of each path, a shortest path corresponding to each warehouse path point is determined, and path lengths of each shortest path are compared to obtain the target path distance with the shortest path length.

6. The method of claim 1, wherein, The determining of the corresponding target warehouse task according to the target path distance comprises: The corresponding target warehouse path point is determined according to the target path distance, and the target warehouse task is generated according to the target warehouse path point and the target path distance; According to the target warehouse task, corresponding prompt information is generated; the prompt information is used to assign the target warehouse task to the target warehouse personnel.

7. The method of claim 1, wherein, After the judging of whether there is an urgent warehouse task in the to-be-processed warehouse task according to the emergency value and a preset emergency threshold, the method further comprises: If yes, the target warehouse task is generated according to a warehouse path point corresponding to the urgent warehouse task.

8. A task assignment apparatus based on face recognition, characterized by, The device comprises: An emergency value determination module is configured to determine a target warehouse personnel and a to-be-processed warehouse task based on a face recognition image, and determine an emergency value of each to-be-processed warehouse task according to a corresponding task deadline, a customer level and an inventory value; A judging module is configured to judge whether there is an urgent warehouse task in the to-be-processed warehouse task according to the emergency value and a preset emergency threshold; A type determination module is configured to, if no, determine a completion efficiency value of each task type for the target warehouse personnel according to a plurality of task types corresponding to the to-be-processed warehouse task, and determine a target completion efficiency value with the largest value and a target task type corresponding to the target completion efficiency value according to each completion efficiency value. A task determination module is configured to determine a target path distance according to a path distance between each target to-be-processed warehouse task and the target warehouse personnel, and determine a corresponding target warehouse task according to the target path distance; the target to-be-processed warehouse task is a to-be-processed warehouse task corresponding to the target task type.

9. A computer device, comprising: Comprise: One or more processors; Memory; One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are configured to execute the method according to any one of claims 1 to 7.

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

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