Distributed storage checking robot collaborative operation method and device and medium

Abstract

The invention discloses a distributed storage checking robot collaborative operation method and device and a medium, and the method comprises the steps: obtaining a distributed network architecture, and carrying out the real-time state synchronization of the distributed network architecture, so as to obtain node state data; on the basis of the node state data, migration task metadata are determined through dynamic task migration; obtaining a current task demand, and determining task allocation data through an improved contract network protocol according to the current task demand and the task metadata; performing task constraint processing on the task distribution data to obtain a task priority execution list, and obtaining path space-time reserved parameters through a space-time corridor mechanism based on the task priority execution list; and according to the task allocation data and the path space-time reserved parameters, through dynamic path obstacle avoidance planning, determining a collaborative execution path of the warehouse checking robot. According to the method, the technical problem that space-time conflicts and energy consumption imbalance exist in cooperation of the storage checking robots is solved.

Description

Technical Field

[0001] The present application relates to the technical field, and in particular to a method, equipment and medium for collaborative operation of distributed warehouse inventory robots. Background Art

[0002] With the growing demand for automated warehouse logistics, robot-based intelligent inventory systems are gradually replacing traditional manual operations. Generally, warehouse robots utilize a centralized control architecture, relying on high-bandwidth communication networks like WiFi or ZigBee. In large, complex warehouses, this centralized architecture is prone to task interruptions due to network latency or single points of failure, and struggles to support cross-node task migration. Traditional communication protocols also experience severe signal attenuation in environments densely packed with metal shelves, making them difficult to meet wide-area coverage requirements.

[0003] In recent years, low-power wide area network (LPWAN) technology has been introduced into the warehouse IoT. However, its low bandwidth and high latency make traditional task allocation algorithms unsuitable for dynamic inventory changes, and multi-robot collaborative path planning is prone to communication congestion and motion conflicts. On the one hand, while existing technologies have proposed blockchain-based task state synchronization methods, they fail to address the conflict between lightweight data compression and real-time task migration in LoRa networks. On the other hand, existing technologies use improved A* algorithms for path optimization, but fail to consider the issues of spatial and temporal conflict resolution and energy balance when multiple robots collaborate. Summary of the Invention

[0004] The embodiments of the present application provide a method, device, and medium for collaborative operation of distributed warehouse inventory robots, which solve the technical problems of spatiotemporal conflicts and unbalanced energy consumption in the collaboration of warehouse inventory robots.

[0005] In the first aspect, an embodiment of the present application provides a distributed warehouse inventory robot collaborative operation method, characterized in that the method includes: obtaining a distributed network architecture, and synchronizing the status of the distributed network architecture in real time to obtain node status data; determining migration task metadata based on the node status data through dynamic task migration; obtaining current task requirements, and determining task allocation data through an improved contract network protocol based on the current task requirements and task metadata; performing task constraint processing on the task allocation data to obtain a task priority execution list, and based on the task priority execution list, obtaining path space-time reservation parameters through a space-time corridor mechanism; determining the collaborative execution path of the warehouse inventory robot through dynamic path obstacle avoidance planning based on the task allocation data and the path space-time reservation parameters.

[0006] In one implementation of the present application, the status of the distributed network architecture is synchronized in real time to obtain node status data, specifically including: based on the distributed network architecture, obtaining heartbeat packet data through offline decision execution; performing dynamic cluster head node election on the heartbeat packet data to determine the local task arbitration result; according to the local task arbitration result, obtaining node status data through periodic aggregation of the entire network status.

[0007] In one implementation of the present application, based on the node status data, the migration task metadata is determined through dynamic task migration, specifically including: determining the node load coefficient based on the node status data, and performing node status analysis on the node load coefficient to obtain the node monitoring status; performing node abnormality status judgment on the node monitoring status to obtain the node abnormality status, and determining the migration task metadata based on the node abnormality status.

[0008] In one implementation of the present application, task allocation data is determined through an improved contract network protocol based on current task requirements and task metadata, specifically including: synchronizing the task metadata to the domain node, and performing a comprehensive parameter evaluation on the task metadata synchronized to the domain node to obtain a cost function of the contract network protocol; based on the cost function, determining the task allocation data through protocol cost analysis.

[0009] In one implementation of the present application, based on the task priority execution list, the path space-time reservation parameters are obtained through the space-time corridor mechanism, specifically including: based on the task priority execution list, the execution path is discretized to obtain space cube units; distributed reservation of space cube units to determine space occupancy rights; reservation configuration of the determined space occupancy rights to obtain path space-time reservation parameters.

[0010] In one implementation of the present application, the collaborative execution path of the warehouse inventory robot is determined based on the task allocation data and the path time and space reservation parameters through dynamic path obstacle avoidance planning, specifically including: based on the task allocation data, through the coarse-grained topology analysis of the warehouse environment, determining the key nodes of the task path; performing real-time adjustment of node conflicts on the key nodes of the task path to obtain the node obstacle position; according to the node obstacle position, determining the path energy consumption data through the warehouse inventory robot motion energy consumption prediction; performing path selection weight analysis on the path energy consumption data to determine the collaborative execution path of the warehouse inventory robot.

[0011] In one implementation of the present application, the path energy consumption data is determined based on the node obstacle position through the warehouse inventory robot's motion energy consumption prediction, specifically including: based on the node obstacle position, determining the node waiting time of the warehouse inventory robot through task execution priority judgment; obtaining the key parameters of the task path of the warehouse inventory robot, and determining the path energy consumption data through calibration coefficient configuration based on the node waiting time and the key parameters of the task path; wherein the key parameters of the task path include: elevation change, steering angle.

[0012] In one implementation of the present application, after determining the collaborative execution path of the warehouse inventory robot through dynamic path obstacle avoidance planning based on the task allocation data and the path time and space reservation parameters, the method also includes: performing robot proximity cross-verification on the collaborative execution path to determine the warehouse goods distribution status; based on the warehouse goods distribution status, obtaining the robot task node execution data through warehouse goods inventory, and uploading the robot task node execution data to the warehouse cloud.

[0013] In the second aspect, an embodiment of the present application also provides a distributed warehouse inventory robot collaborative operation device, characterized in that the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor so that the at least one processor can: obtain a distributed network architecture, and synchronize the status of the distributed network architecture in real time to obtain node status data; based on the node status data, determine the migration task metadata through dynamic task migration; obtain current task requirements, and determine task allocation data through an improved contract network protocol based on the current task requirements and task metadata; perform task constraint processing on the task allocation data to obtain a task priority execution list, and based on the task priority execution list, obtain path space-time reservation parameters through a space-time corridor mechanism; determine the collaborative execution path of the warehouse inventory robot through dynamic path obstacle avoidance planning based on the task allocation data and the path space-time reservation parameters.

[0014] On the third aspect, the embodiment of the present application also provides a non-volatile computer storage medium for the collaborative operation of distributed warehouse inventory robots, which stores computer executable instructions, and is characterized in that the computer executable instructions are set to: obtain a distributed network architecture, and synchronize the status of the distributed network architecture in real time to obtain node status data; based on the node status data, determine the migration task metadata through dynamic task migration; obtain current task requirements, and determine task allocation data through an improved contract network protocol based on the current task requirements and task metadata; perform task constraint processing on the task allocation data to obtain a task priority execution list, and based on the task priority execution list, obtain path space-time reservation parameters through a space-time corridor mechanism; determine the collaborative execution path of the warehouse inventory robot through dynamic path obstacle avoidance planning based on the task allocation data and the path space-time reservation parameters.

[0015] The embodiments of the present application provide a method, device and medium for the collaborative operation of distributed warehouse inventory robots. Through cross-node task migration, dynamic task allocation and collaborative path planning under a layered hybrid network architecture, the technical problems of spatiotemporal conflicts and uneven energy consumption in the collaboration of warehouse inventory robots are solved, and seamless cross-node task migration, dynamic resource allocation of robots and improvement of path planning reliability are achieved under low communication overhead, thereby improving the robustness and collaborative efficiency of the automated collaboration of inventory robots in distributed warehouses. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 A flow chart of a distributed warehouse inventory robot collaborative operation method provided in an embodiment of the present application;

[0018] Figure 2 A schematic diagram of the internal structure of a distributed warehouse inventory robot collaborative operation device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0020] The embodiments of the present application provide a method, device and medium for the collaborative operation of distributed warehouse inventory robots. Through cross-node task migration, dynamic task allocation and collaborative path planning under a layered hybrid network architecture, the technical problems of spatiotemporal conflicts and uneven energy consumption in the collaboration of warehouse inventory robots are solved, and seamless cross-node task migration, dynamic resource allocation of robots and improvement of path planning reliability are achieved under low communication overhead, thereby improving the robustness and collaborative efficiency of the automated collaboration of inventory robots in distributed warehouses.

[0021] The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0022] Figure 1 This is a flow chart of a distributed warehouse inventory robot collaborative operation method provided by the embodiment of this application. Figure 1 As shown, the embodiment of the present application provides a distributed warehouse inventory robot collaborative operation method, which specifically includes the following steps:

[0023] Step 101: Obtain a distributed network architecture and synchronize the state of the distributed network architecture in real time to obtain node state data.

[0024] Exemplarily, the distributed network architecture adopts a layered hybrid architecture, consisting of a local decision-making layer, a regional coordination layer, and a global synchronization layer. The distributed network architecture is synchronized in real time to obtain node status data, thereby realizing real-time recording of task changes and improving data consistency.

[0025] Specifically, the state of the distributed network architecture is synchronized in real time to obtain node status data, including: obtaining heartbeat packet data through offline decision execution based on the distributed network architecture; performing dynamic cluster head node election on the heartbeat packet data to determine the local task arbitration result; and obtaining node status data through periodic aggregation of the entire network status based on the local task arbitration result.

[0026] In one embodiment, the distributed network architecture adopts a hierarchical hybrid architecture, consisting of a local decision-making layer, a regional coordination layer, and a global synchronization layer. In the local decision-making layer, each robot has a built-in lightweight intelligent agent that supports offline autonomous decision-making and broadcasts heartbeat packets via LoRa. The heartbeat packets include: location, battery level, and task queue status. The regional coordination layer dynamically elects cluster head nodes based on signal strength and remaining battery level (RSSI>-90dBm and remaining battery level>40%), responsible for local task arbitration and conflict detection, reducing global communication pressure. The global synchronization layer aggregates key network status every 5 minutes through the LoRaWAN gateway and uses the Tangle architecture (DAG data structure) to store task change records to ensure state consistency.

[0027] To address the low-bandwidth characteristics of LoRa, an adaptive data compression strategy is designed: Delta encoding and Varint compression are used for path coordinates, Protocol Buffers Nano serialization is used for task status, and LZ4 fast mode is enabled for text data to improve the compression rate.

[0028] Furthermore, channel contention is controlled through logical clock time slot allocation. Nodes send requests with Lamport timestamps, and the receiver processes them in timestamp order. For emergency messages, a 125kHz bandwidth and SF12 spreading factor are used to improve receiving sensitivity.

[0029] Step 102: Determine migration task metadata based on node status data through dynamic task migration.

[0030] For example, dynamic task migration is to migrate tasks across abnormal nodes so that the tasks of the warehouse inventory robot can be dynamically migrated to reduce node pressure and prevent task stagnation at task nodes.

[0031] Specifically, based on the node status data, the migration task metadata is determined through dynamic task migration, including: determining the node load coefficient based on the node status data, and performing node status analysis on the node load coefficient to obtain the node monitoring status; performing node abnormality status judgment on the node monitoring status to obtain the node abnormality status, and determining the migration task metadata based on the node abnormality status.

[0032] In one embodiment, node status data is collected by periodically broadcasting heartbeat packets, which are supplemented with the node's load factor. If a node's heartbeat is not received three times in a row, or if the load factor exceeds 80%, the task migration process is triggered. The cluster head node broadcasts the task metadata (including task ID, priority, completed path points, and the last scanned RFID tag) of the failed node to neighboring nodes.

[0033] Furthermore, through lightweight blockchain technology, task status changes are recorded as transaction packages, including the predecessor transaction hash, Ed25519 signature, and incremental status. When a new node joins, historical task records are quickly synchronized by verifying the hash chain.

[0034] Step 103: Obtain current task requirements, and determine task allocation data based on the current task requirements and task metadata through the improved contract network protocol.

[0035] Exemplarily, the improved contract network protocol is divided into three stages: bidding, tendering, and bid evaluation. Through the improved contract network protocol, the task cost is evaluated and tasks are allocated according to the task cost.

[0036] Specifically, according to the current task requirements and task metadata, the task allocation data is determined through the improved contract network protocol, including: synchronizing the task metadata to the domain node, and performing a comprehensive parameter evaluation on the task metadata synchronized to the domain node to obtain the cost function of the contract network protocol; based on the cost function, the task allocation data is determined through protocol cost analysis.

[0037] In one embodiment, the improved contract network protocol is divided into three stages: bidding, tendering, and bid evaluation. First, the bidding party broadcasts the task constraints, including deadlines, required sensor types, etc.

[0038] The bidder then conducts a task cost assessment, which is explained by the following formula.

[0039]

[0040] Among them, Cost is the protocol cost, α, β, and γ are the weight coefficients supporting online learning adjustment;

[0041] D is the remaining distance from the current node to the mission target point;

[0042] V is the average moving speed of the node;

[0043] E is the estimated energy consumption, which can be estimated based on historical energy consumption data;

[0044] Q is the path congestion index, which is dynamically adjusted based on the recent node density in the area.

[0045] Based on the cost function, the TOPSIS algorithm is used to comprehensively evaluate the bidder's credibility, signal stability and cost value, and the optimal execution node is selected.

[0046] Step 104: Perform task constraint processing on the task allocation data to obtain a task priority execution list, and based on the task priority execution list, obtain the path space-time reservation parameters through the space-time corridor mechanism.

[0047] For example, by combining the space-time corridor mechanism with the speed barrier method to adjust the path in real time, collaborative path planning for warehouse inventory robots is achieved, improving the reliability of path planning.

[0048] Specifically, based on the task priority execution list, the path space-time reservation parameters are obtained through the space-time corridor mechanism, including: based on the task priority execution list, the execution path is discretized to obtain space cube units; distributed reservation of space cube units to determine space occupancy rights; reservation configuration of the determined space occupancy rights to obtain path space-time reservation parameters.

[0049] In one embodiment, a coarse-grained topology is generated based on a three-dimensional warehouse map. The A* algorithm is used to calculate the initial path. Key nodes include charging stations, RFID calibration points, and emergency exits. Path weighting takes shelf height into account. The path is discretized into a 0.5m×0.5m×10s space-time cube. Nodes apply for cube occupancy rights through the Distributed Reservation Protocol (DRP) to obtain the path's space-time reservation parameters.

[0050] Step 105: Determine the collaborative execution path of the warehouse inventory robot through dynamic path obstacle avoidance planning based on the task allocation data and the path time and space reservation parameters.

[0051] Exemplarily, when a conflict state is detected, the speed barrier method is triggered to adjust the path in real time, and the obstacle location is broadcast to neighboring nodes.

[0052] Specifically, according to the task allocation data and the spatiotemporal reservation parameters of the path, the collaborative execution path of the warehouse inventory robot is determined through dynamic path obstacle avoidance planning, including: based on the task allocation data, through the coarse-grained topology analysis of the warehouse environment, determining the key nodes of the task path; performing real-time adjustment of node conflicts on the key nodes of the task path to obtain the node obstacle position; according to the node obstacle position, determining the path energy consumption data through the warehouse inventory robot motion energy consumption prediction; performing path selection weight analysis on the path energy consumption data to determine the collaborative execution path of the warehouse inventory robot.

[0053] Furthermore, based on the node obstacle position, the path energy consumption data is determined through the warehouse inventory robot's motion energy consumption prediction, specifically including: based on the node obstacle position, determining the node waiting time of the warehouse inventory robot through task execution priority judgment; obtaining the key parameters of the warehouse inventory robot's task path, and determining the path energy consumption data through calibration coefficient configuration based on the node waiting time and the key parameters of the task path; among which, the key parameters of the task path include: elevation change and steering angle.

[0054] By judging the task execution priority, the node waiting time of the warehouse inventory robot is determined, and the robot motion energy consumption prediction formula is constructed based on the key parameters of the warehouse inventory robot's task path, which is explained by the following formula.

[0055] E=k1·∑Δh+k2·∑θ turn +k3·t idle (2)

[0056] Among them, k1, k2, and k3 are calibration coefficients;

[0057] Δh is the absolute value of elevation change;

[0058] θ turn is the steering angle, which is 0 when driving straight;

[0059] t idle Waiting time for the robot.

[0060] The ground friction coefficient is learned online through motor current feedback, and the calibration coefficient k1 is dynamically updated.

[0061] Furthermore, after determining the collaborative execution path of the warehouse inventory robot through dynamic path obstacle avoidance planning based on the task allocation data and the path time and space reservation parameters, the method also includes: performing robot proximity cross-verification on the collaborative execution path to determine the warehouse cargo distribution status; based on the warehouse cargo distribution status, obtaining the robot task node execution data through warehouse cargo inventory, and uploading the robot task node execution data to the warehouse cloud.

[0062] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, this application embodiment also provides a distributed warehouse inventory robot collaborative operation device, the structure of which is as follows: Figure 2 shown.

[0063] Figure 2 This is a schematic diagram of the internal structure of a distributed warehouse inventory robot collaborative operation device provided in the embodiment of this application. Figure 2 As shown, the equipment includes:

[0064] at least one processor 201;

[0065] and, a memory 202 communicatively coupled to the at least one processor;

[0066] The memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by the at least one processor 201 to enable the at least one processor 201 to:

[0067] Obtain a distributed network architecture and synchronize its status in real time to obtain node status data; determine migration task metadata based on node status data through dynamic task migration; obtain current task requirements and determine task allocation data through an improved contract network protocol based on current task requirements and task metadata; perform task constraint processing on task allocation data to obtain a task priority execution list, and based on the task priority execution list, obtain path space-time reservation parameters through a space-time corridor mechanism; determine the collaborative execution path of the warehouse inventory robot through dynamic path obstacle avoidance planning based on the task allocation data and path space-time reservation parameters.

[0068] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium for collaborative operation of distributed warehouse inventory counting robots stores computer executable instructions, wherein the computer executable instructions are set as follows:

[0069] Obtain a distributed network architecture and synchronize its status in real time to obtain node status data; determine migration task metadata based on node status data through dynamic task migration; obtain current task requirements and determine task allocation data through an improved contract network protocol based on current task requirements and task metadata; perform task constraint processing on task allocation data to obtain a task priority execution list, and based on the task priority execution list, obtain path space-time reservation parameters through a space-time corridor mechanism; determine the collaborative execution path of the warehouse inventory robot through dynamic path obstacle avoidance planning based on the task allocation data and path space-time reservation parameters.

[0070] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from the other embodiments. In particular, the IoT device and media embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.

[0071] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0072] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0073] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0074] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0075] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0076] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0077] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0078] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0079] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0080] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A distributed warehouse inventory robot collaborative operation method, characterized in that: The method comprises: Acquire a distributed network architecture and synchronize the state of the distributed network architecture in real time to obtain node state data; Determining migration task metadata based on the node status data through dynamic task migration; Obtaining current task requirements, and determining task allocation data based on the current task requirements and the task metadata through an improved contract network protocol; Performing task constraint processing on the task allocation data to obtain a task priority execution list, and obtaining a path space-time reservation parameter based on the task priority execution list through a space-time corridor mechanism; According to the task allocation data and the path time and space reservation parameters, the collaborative execution path of the warehouse inventory robot is determined through dynamic path obstacle avoidance planning.

2. A distributed warehouse inventory robot collaborative operation method according to claim 1, characterized in that: The distributed network architecture is synchronized in real time to obtain node status data, specifically including: Based on the distributed network architecture, heartbeat packet data is obtained through offline decision execution; Performing dynamic cluster head node election on the heartbeat packet data to determine the local task arbitration result; According to the local task arbitration result, the node status data is obtained by periodically aggregating the entire network status.

3. A distributed warehouse inventory robot collaborative operation method according to claim 1, characterized in that: Based on the node status data, the migration task metadata is determined through dynamic task migration, specifically including: Determining a node load factor based on the node status data, and performing a node status analysis on the node load factor to obtain a node monitoring status; A node abnormality state determination is performed on the node monitoring state to obtain the node abnormality state, and the migration task metadata is determined according to the node abnormality state.

4. A distributed warehouse inventory robot collaborative operation method according to claim 1, characterized in that: According to the current task requirements and the task metadata, task allocation data is determined through the improved contract network protocol, specifically including: Synchronizing the task metadata to the domain node, and performing a comprehensive parameter evaluation on the task metadata synchronized to the domain node to obtain a cost function of the contract network protocol; The task allocation data is determined based on the cost function through protocol cost analysis.

5. A distributed warehouse inventory robot collaborative operation method according to claim 1, characterized in that: Based on the task priority execution list, the space-time reserved parameters of the path are obtained through the space-time corridor mechanism, including: Based on the task priority execution list, a space cube unit is obtained by discretizing the execution path; Distributed reservation of the space cube units to determine space occupancy rights; The determined space occupancy right is reserved and configured to obtain the path time and space reservation parameter.

6. A distributed warehouse inventory robot collaborative operation method according to claim 1, characterized in that: According to the task allocation data and the path time and space reservation parameters, the collaborative execution path of the warehouse inventory robot is determined through dynamic path obstacle avoidance planning, specifically including: Based on the task allocation data, determining the key nodes of the task path through coarse-grained topological analysis of the warehouse environment; Performing real-time node conflict adjustments on key nodes of the task path to obtain node obstacle locations; According to the node obstacle position, the path energy consumption data is determined by predicting the motion energy consumption of the warehouse inventory robot; A path selection weight analysis is performed on the path energy consumption data to determine the collaborative execution path of the warehouse inventory counting robot.

7. A distributed warehouse inventory robot collaborative operation method according to claim 6, characterized in that: According to the node obstacle position, the path energy consumption data is determined by predicting the motion energy consumption of the warehouse inventory robot, specifically including: Based on the node obstacle position, the node waiting time of the warehouse inventory counting robot is determined by task execution priority judgment; The key parameters of the task path of the warehouse inventory robot are obtained, and the path energy consumption data is determined by configuring the calibration coefficient according to the node waiting time and the key parameters of the task path; wherein the key parameters of the task path include: elevation change and steering angle.

8. A distributed warehouse inventory robot collaborative operation method according to claim 1, characterized in that: After determining the collaborative execution path of the warehouse inventory counting robot through dynamic path obstacle avoidance planning based on the task allocation data and the path time and space reservation parameters, the method further includes: Performing robot proximity cross-verification on the collaborative execution path to determine the warehouse goods allocation status; Based on the warehouse goods distribution status, the robot task node execution data is obtained through warehouse goods inventory, and the robot task node execution data is uploaded to the warehouse cloud.

9. A distributed warehouse inventory robot collaborative operation equipment, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: Acquire a distributed network architecture and synchronize the state of the distributed network architecture in real time to obtain node state data; Determining migration task metadata based on the node status data through dynamic task migration; Obtaining current task requirements, and determining task allocation data based on the current task requirements and the task metadata through an improved contract network protocol; Performing task constraint processing on the task allocation data to obtain a task priority execution list, and obtaining a path space-time reservation parameter based on the task priority execution list through a space-time corridor mechanism; According to the task allocation data and the path time and space reservation parameters, the collaborative execution path of the warehouse inventory robot is determined through dynamic path obstacle avoidance planning.

10. A non-volatile computer storage medium for collaborative operation of distributed warehouse inventory counting robots, storing computer-executable instructions, characterized in that: The computer executable instructions are configured to: Acquire a distributed network architecture and synchronize the state of the distributed network architecture in real time to obtain node state data; Determining migration task metadata based on the node status data through dynamic task migration; Obtaining current task requirements, and determining task allocation data based on the current task requirements and the task metadata through an improved contract network protocol; Performing task constraint processing on the task allocation data to obtain a task priority execution list, and obtaining a path space-time reservation parameter based on the task priority execution list through a space-time corridor mechanism; According to the task allocation data and the path time and space reservation parameters, the collaborative execution path of the warehouse inventory robot is determined through dynamic path obstacle avoidance planning.

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