A composite robot management system and method applied to a smart laboratory

Abstract

The application relates to the technical field of robot management, in particular to a composite robot management system and method applied to a smart laboratory, which comprises an upstream business layer, a central scheduling layer and an execution layer; the upstream business layer comprises a laboratory information management system, a manufacturing execution system and a warehouse management system and is used for issuing at least one of material transfer or test task instructions. The application can be reliably connected with the upstream business system and the downstream test equipment through a standardized interface, can make multi-target optimization decisions by comprehensively considering task characteristics and real-time states of mobile robots with the aid of a multi-dimensional dynamic scheduling engine, can realize dynamic optimal allocation of robot resources, can automatically respond to task instructions, can complete material transfer, equipment connection and result feedback, can significantly reduce manual intervention, and can improve consistency and efficiency of laboratory operation and overall resource utilization efficiency.

Description

Technical Field

[0001] This invention relates to the field of robot management technology, and in particular to a composite robot management system and method for use in smart laboratories. Background Technology

[0002] In the fields of smart laboratories and unmanned operations, the application of automated mobile robots (AMRs) is gradually expanding to scientific research scenarios such as biomedicine, environmental monitoring, and food safety. Through automated handling and transfer, they demonstrate significant potential in improving experimental efficiency and reducing human error. However, most current laboratory handling robots remain in a stage of single-function, isolated systems, and have not yet achieved deep integration with Laboratory Information Management Systems (LIMS), Manufacturing Execution Systems (MES), and various automated testing equipment. Tasks often rely on manual triggering and scheduling, failing to automatically form a closed-loop task based on sample generation, testing requirements, or result feedback. This makes it difficult to achieve fully automated "order placement—transfer—testing—feedback" processes, limiting their in-depth application in laboratory automation.

[0003] Most existing scheduling systems employ static or rule-based task allocation mechanisms, lacking the ability to comprehensively assess the multi-dimensional attributes of tasks and the real-time status of AMRs. Key factors such as task priority, deadlines, material characteristics (e.g., temperature control requirements, fragility), and the AMR's real-time battery level, location, and carrier compatibility are not incorporated into a dynamic evaluation system. This results in scheduling decisions being unable to adapt to complex and ever-changing experimental scenarios, easily leading to uneven resource utilization, delays in critical tasks, and limited overall operational efficiency. Furthermore, information generated by robots during transportation tasks, such as path trajectories, duration records, environmental data (temperature and humidity), and docking status, is often not collected and integrated in a structured manner, failing to achieve data interoperability with laboratory quality management systems or information platforms. This not only results in a lack of auditable and traceable digital documentation for the transportation process but also hinders data-driven process optimization and root cause analysis of anomalies.

[0004] Therefore, there is an urgent need to build a highly integrated, intelligently scheduled, and data-closed-loop robot management system to promote the upgrading of laboratory automation. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a composite robot management system and method for use in smart laboratories.

[0006] In a first aspect, embodiments of the present invention provide a composite robot management system for smart laboratories, comprising: an upstream business layer, a central scheduling layer, and an execution layer; The upstream business layer includes a laboratory information management system, a manufacturing execution system, and a warehouse management system, which are used to issue at least one of the following instructions: material transfer or testing tasks. The central scheduling layer includes: a task access and parsing module, a multi-dimensional dynamic scheduling engine, and a data closed-loop management module. The task access and parsing module receives and parses task instructions from the upstream business layer or human terminals through standardized interfaces, generating structured task objects containing task type, start and end points, material attributes, and priorities. The multi-dimensional dynamic scheduling engine dynamically calculates and allocates target mobile robots based on the characteristics of the structured task objects and the real-time status of mobile robots in the execution layer. The data closed-loop management module records the entire task process data in a structured manner and synchronizes the status and results with the upstream business layer. The execution layer includes at least one composite robot and at least one testing device, which are used to receive and execute action instructions issued by the central scheduling layer, complete material handling, automatic docking with the testing device, and reporting of test results.

[0007] In conjunction with the first aspect, the multidimensional dynamic task scheduling strategy library in the multidimensional dynamic scheduling engine includes: The task feature abstraction unit is used to parse structured task objects into feature vectors containing task type, priority, deadline, material attributes, start and end coordinates, and required vehicle type. The composite robot capability and status management unit is used to maintain the capability tag set and real-time status snapshot of each composite robot. The capability tag set includes the supported vehicle types and load capacity, and the real-time status snapshot includes the current position, real-time battery level, and health status. A configurable policy template library stores multiple scheduling policy templates, including the shortest path first policy, the earliest deadline first policy, the highest capacity matching first policy, and the optimal overall energy consumption policy. The multi-objective strategy scoring engine is connected to the task feature abstraction unit, the composite robot capability and state management unit, and the configurable strategy template library, respectively. It is used to perform multi-dimensional weighted scoring on candidate composite robots based on the selected strategy template and output the optimal allocation decision.

[0008] In conjunction with the first aspect, the multi-dimensional dynamic scheduling engine also includes: The strategy selector automatically matches and activates the corresponding scheduling strategy template from the configurable strategy template library based on the task type attribute in the structured task object.

[0009] In conjunction with the first aspect, the central dispatch layer also includes: The traffic coordination and path planning module connects with the multi-dimensional dynamic scheduling engine and all composite robots. It is used to perform collaborative path planning based on the global map and the real-time location of all composite robots, reserve passage windows for composite robots, and realize early warning and dynamic avoidance of collisions.

[0010] In conjunction with the first aspect, the composite robot in the execution layer is equipped with a modular docking mechanism; the modular docking mechanism includes a quick-change carrier adapter component and / or a robotic arm component for performing loading and unloading actions; The composite robot is also equipped with a standard equipment communication interface, which is used for two-way communication with the test equipment to perform control handshake, status confirmation and action coordination signals when it arrives at the target test equipment.

[0011] In conjunction with the first aspect, the data closed-loop management module includes: The full-process data acquisition device is used to collect task execution data from the composite robot and testing equipment in real time. The task execution data includes timestamps, location trajectories, ambient temperature and humidity, vehicle barcode recognition results, equipment docking logs, and test result data. The structured data encapsulation unit, connected to the end-to-end data collector, is used to encapsulate task execution data according to a predefined format to generate traceable digital transportation and testing credentials. The upstream system synchronization interface connects to the structured data encapsulation unit and is used to transmit digital transportation and testing credentials and task status back to the corresponding system in the upstream business layer in real time.

[0012] In conjunction with the first aspect, the system also includes: The visual monitoring and interactive terminal communicates with the central dispatch layer to display the real-time location of all composite robots in the system, task queues, task execution progress, and test equipment status; and provides interactive interfaces for manual task initiation, task cancellation, emergency pause, and status query.

[0013] In conjunction with the first aspect, the task access and parsing module connects to the upstream business layer through a standardized application programming interface and / or event message bus to achieve automatic triggering and parsing of task instructions and reverse synchronization of task status.

[0014] Secondly, this application provides a method for managing composite robots in a smart laboratory. This method is applied to the system described above and includes: Receive task instructions, which are either automatically triggered by the upstream business layer or actively initiated by a manual terminal; Parse the task instructions, extract task features, and generate a structured task object containing task type, start and end coordinates, material attributes, priority, and deadline. Obtain real-time status snapshots of all available composite robots in the execution layer. The status snapshots should include at least position, battery level, vehicle compatibility, and health status. The structured task objects and real-time status snapshots are input into the multi-dimensional dynamic scheduling engine, which performs multi-objective evaluation based on the pre-set strategy library and dynamically selects the target composite robot. A sequence of composite instructions is issued to the selected target composite robot. The sequence of composite instructions controls the target composite robot to perform the following actions in sequence: going to the material picking point, verifying and loading materials, navigating to the target testing equipment, performing automatic docking, waiting for the test to be completed, unloading materials, and returning to the standby point. Throughout the entire task execution cycle, the status data and environmental data reported by the target composite robot are continuously collected and structured. At key nodes and at the end of the task, the data is synchronized to the upstream business layer to update the material status and close the task process.

[0015] In conjunction with the second aspect, the steps of inputting structured task objects and real-time state snapshots into a multi-dimensional dynamic scheduling engine, performing multi-objective evaluation based on a pre-set strategy library, and dynamically selecting the target composite robot include: Based on the task priority and deadline in the structured task object, the available composite robots are filtered by urgency to obtain an initial set of robots. Based on the vehicle or environmental requirements required by the material attributes in the structured task object, the matching degree is calculated with the capability tags of each composite robot in the initial robot set, and composite robots with matching degrees higher than a preset threshold are selected to form a candidate robot set. For the candidate robot set, a score is calculated based on at least one optimization objective among path efficiency, composite robot energy consumption, and global task load balancing. The final target composite robot is determined based on the score results.

[0016] Thirdly, this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor runs the computer program to cause the electronic device to perform the above-described method.

[0017] Fourthly, this application provides a storage medium storing computer program instructions, which are read and executed by a processor to perform the above-described method.

[0018] The embodiments of this invention bring the following beneficial effects: This application provides a composite robot management system and method applied to a smart laboratory. The system includes: an upstream business layer, a central scheduling layer, and an execution layer; the upstream business layer includes a laboratory information management system, a manufacturing execution system, and a warehouse management system, used to issue at least one of material transfer or testing task instructions; the central scheduling layer includes: a task access and parsing module, a multi-dimensional dynamic scheduling engine, and a data closed-loop management module; the task access and parsing module is used to receive and parse task instructions from the upstream business layer or a human terminal through a standardized interface, generating a structured task object containing task type, start and end point, material attributes, and priority; the multi-dimensional dynamic scheduling engine is used to dynamically calculate and allocate target mobile robots based on the characteristics of the structured task object and the real-time status of the mobile robots in the execution layer; the data closed-loop management module is used to structurally record the entire task process data and synchronize the status and results to the upstream business layer; the execution layer includes at least one composite robot and at least one testing device, used to receive and execute action instructions issued by the central scheduling layer, complete material handling, automatic docking with the testing device, and reporting of test results.

[0019] In this application, the task access and parsing module and standardized interface achieve deep automatic integration with upstream business systems, enabling tasks to be automatically triggered along with business flows, establishing an end-to-end automated link and reducing manual intervention. Secondly, the multi-dimensional dynamic scheduling engine makes dynamic optimization decisions by comprehensively considering the multi-dimensional attributes of tasks and the real-time status of robots, achieving efficient utilization of global resources while ensuring critical tasks. In addition, the data closed-loop management module performs structured collection and encapsulation of data throughout the entire process, generating traceable digital credentials and transmitting them back to the business system, achieving process transparency and quality control.

[0020] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0022] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 A schematic diagram of a composite robot management system architecture applied to a smart laboratory is provided as an embodiment of the present invention; Figure 2 A flowchart illustrating a composite robot management method applied to a smart laboratory, provided as an embodiment of the present invention; Figure 3 This is a schematic diagram of the electronic device structure provided in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the entire process of material distribution and testing tasks performed by the system provided in this application for AMR.

[0024] Figure label: 10 - Upstream Business Layer, 20 - Central Dispatch Layer, 30 - Execution Layer; 130 - Processor, 131 - Memory, 132 - Bus, 133 - Communication interface. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] To facilitate understanding of this embodiment, the technical terms used in this application will be briefly introduced below.

[0027] Autonomous Mobile Robots (AMRs) are typically equipped with multi-sensor fusion systems, including LiDAR, depth vision cameras, ultrasonic sensors, and inertial measurement units (IMUs). They can move in unknown environments while simultaneously building high-precision maps in real time and determining their exact location within the map. They can not only identify static objects like walls and shelves but also detect and classify dynamic obstacles in real time, such as walking employees, other moving equipment, and temporarily placed goods.

[0028] After introducing the technical terms used in this application, the application scenarios and design concepts of the embodiments of this application will be briefly described below.

[0029] Currently, the laboratory's AMR (Automatic Mobile Robot) has limited functionality and is not deeply integrated with systems such as LIMS (Limited Information Management System), relying on manual scheduling. The existing system cannot make intelligent decisions based on multi-dimensional task attributes and robot status, and process data is not collected and utilized, hindering the unmanned operation and optimization of the entire process. There is an urgent need to build an intelligent scheduling and data-closed-loop management system.

[0030] Based on this, this application provides a composite robot management system and method for use in smart laboratories.

[0031] Example 1 This application provides a composite robot management system for use in smart laboratories, combining... Figure 1 As shown, the system includes: an upstream business layer 10, a central scheduling layer 20, and an execution layer 30.

[0032] The upstream business layer 10 includes a laboratory information management system, a manufacturing execution system, and a warehouse management system, which are used to issue at least one of the following instructions: material transfer or testing task instructions.

[0033] The central scheduling layer 20 includes: a task access and parsing module, a multi-dimensional dynamic scheduling engine, and a data closed-loop management module. The task access and parsing module is used to receive and parse task instructions from the upstream business layer or manual terminals through a standardized interface, generating structured task objects containing task type, start and end points, material attributes, and priorities. The multi-dimensional dynamic scheduling engine is used to dynamically calculate and allocate target mobile robots based on the characteristics of the structured task objects and the real-time status of mobile robots in the execution layer. The data closed-loop management module is used to structurally record the entire task process data and synchronize the status and results with the upstream business layer.

[0034] The execution layer 30 includes at least one composite robot and at least one testing device, which is used to receive and execute action instructions issued by the central scheduling layer, complete material handling, automatic docking with the testing device, and reporting of test results.

[0035] This application reliably connects with upstream business systems and downstream testing equipment through standardized interfaces. Relying on a multi-dimensional dynamic scheduling engine, it can make multi-objective optimization decisions by comprehensively considering task characteristics and the real-time status of mobile robots, and realize the dynamic optimal allocation of robot resources. It can automatically respond to task instructions, complete material transfer, equipment docking and result feedback, significantly reduce manual intervention, and improve the consistency and efficiency of laboratory operation and the overall resource utilization efficiency.

[0036] like Figure 1 As shown, the composite robot management system applied in the smart laboratory is logically clearly divided into three collaborative layers: the upstream business layer 10, the central scheduling layer 20, and the execution layer 30. These layers communicate through standardized interfaces, forming a complete closed loop from instruction to execution and then to feedback.

[0037] Among them, the upstream business layer 10 is the initiation point for various business needs of the laboratory, constituting the source of task instructions and the destination of data for this system. It mainly includes: Laboratory Information Management System (LIMS): Manages sample information, testing items, standard methods, and final reports. It can automatically trigger task instructions such as "Sample SAMPLE-2024-001 has completed pretreatment and needs to be sent to ICP-MS for heavy metal detection."

[0038] Manufacturing Execution System (MES): Manages production orders and work-in-process processes. It can issue instructions closely related to production batches, such as "send the third intermediate product of batch BATCH-A to the stability test chamber in the clean area".

[0039] Warehouse Management System (WMS): Manages the inventory status and storage location information of materials. It can issue material transfer instructions, such as "Retrieve 5 standard samples from low-temperature storage location C-02 and move them to the sample pretreatment area".

[0040] The central control layer 20, also known as the SAGE central control system, is the intelligent control hub of the entire system. It contains three key functional modules: Task Access and Parsing Module: As the system entry point, this module receives raw task requests from upstream business layer 10 or manual terminals (such as tablets, PADs, and physical call button boxes). This module performs syntax parsing and semantic understanding on the requests, transforming them into a unified, structured task object within the system. This object is a data entity containing rich attributes, at least encapsulating: task type (e.g., "constant temperature sample delivery," "empty pallet recycling," "equipment loading and unloading"), start and end point coordinates (based on a laboratory digital map), material attributes (e.g., "4℃ constant temperature," "light-protected," "fragile"), business priority (e.g., "urgent," "high," "normal"), expected / latest completion time, and required carrier type (e.g., "microplate carrier," "test tube rack carrier").

[0041] The multi-dimensional dynamic scheduling engine is the core embodiment of the system's intelligence. It continuously retrieves structured task objects from the task access module and obtains real-time status snapshots of all AMRs from the execution layer 30. The engine embeds a multi-dimensional dynamic task scheduling strategy library, capable of performing complex multi-objective optimization calculations based on the multi-dimensional characteristics of tasks and the real-time capability status of AMRs, dynamically assigning the most suitable AMR execution unit to each task. Its scheduling goal is not only to complete transportation but also to pursue optimal global efficiency, ensure critical mission success, and minimize energy consumption.

[0042] The data closed-loop management module is responsible for data flow management throughout the task lifecycle. It subscribes to and collects all data generated during AMR execution in real time, including: timestamp sequences accurate to the second, high-frequency location trajectory points, integrated environmental sensor data (temperature and humidity), docking operation logs with testing equipment (such as "robotic arm extended," "gripper closed," and "signal handshake successful"), and task status transition records. The module cleanses, correlates, and encapsulates this heterogeneous data into structured digital transport credentials, and automatically pushes them back to the corresponding upstream business systems via interfaces (e.g., updating test results and transport environment records in the LIMS sample tracking chain), thereby achieving end-to-end data closure.

[0043] Execution layer 30 is the physical execution terminal of the system, consisting of automated mobile robots (AMRs) and laboratory automation equipment. Mobile Robot (AMR): As a mobile execution unit, it receives and executes precise sequences of instructions from the central scheduling layer 20. Instructions may include target locations, travel speed, picking and placing goods, and docking procedures with equipment. The AMR possesses autonomous navigation capabilities and can operate safely in dynamic laboratory environments.

[0044] Testing equipment refers to various types of automated laboratory equipment, such as biochemical analyzers, chromatographs, incubators, and integrity testers. These devices work in conjunction with the AMR (Automatic MR System) through an intelligent interface. When the AMR delivers materials, the two interact via communication protocols (such as proprietary protocols based on TCP / IP or standard industrial protocols) to automatically load the samples. The equipment then executes preset testing or processing procedures.

[0045] like Figure 1 As shown in the diagram, the upstream business layer 10 represents the source of instructions and the destination of business data for the laboratory, signifying the laboratory's information management environment. The Laboratory Information Management System (LIMS), Manufacturing Execution System (MES), and Warehouse Management System (WMS) are the core business management systems of the laboratory. LIMS is responsible for the lifecycle management of samples, testing orders, and reports; MES manages production work orders and work-in-process processes; and WMS manages material inventory and storage location status. Together, they constitute the business requirements side of the task. Task Issuance: This arrow indicates a business action. When any of the above systems meets specific business conditions (such as generating an inspection form in LIMS, completing a process in MES, or initiating a data transfer instruction in WMS), they will automatically issue structured task instructions to the central scheduling layer through standardized interfaces (API / message bus). This replaces the traditional manual triggering mode and automates task source generation. The standardized interface (e.g., using an API design based on RESTful specifications) exposes a set of predefined API endpoints (e.g., / api / v1 / task) through the task access and parsing module. Upstream business systems send task instructions in JSON format to this endpoint via HTTP POST requests, thus achieving automatic task instruction issuance.

[0046] The central dispatch layer 20 is responsible for receiving, parsing, scheduling, and monitoring. The task access and parsing module corresponds to "task management" in the diagram; the multi-dimensional dynamic scheduling engine corresponds to "task scheduling strategy" and "AMR operation management" in the diagram; the traffic coordination and route planning module corresponds to "route planning" and "traffic control" in the diagram; and the data closed-loop management module corresponds to "data reporting" pointing to the upper business layer and the data aggregation function of the central dispatch layer 20 in the diagram.

[0047] In the execution layer 30, the test equipment refers to various automated analytical instruments, testing machines, or processing equipment in the laboratory (such as chromatographs, incubators, and performance testing benches); the program management is located on the test equipment side or integrated with its control system, and is responsible for managing the test programs or processing methods that the equipment can execute; test data refers to the result data generated after the test equipment executes the test, which is reported through the interface.

[0048] Specifically, the task management module is responsible for receiving raw task instructions from upstream, parsing and verifying them, generating a unified structured task object within the system, and tracking its status throughout its entire lifecycle (such as creation, queuing, execution, and completion).

[0049] Task scheduling strategy: This is the core rule base of the multi-dimensional dynamic scheduling engine. It stores various configurable scheduling algorithm templates (such as "shortest path first", "earliest deadline first", and "highest capability match first"). The strategy selector automatically matches and activates the optimal strategy combination based on the task type and attributes.

[0050] AMR Management: This module maintains the digital twin profiles of all composite robots. Its "management" content includes: static registration information of robots (ID, model, capabilities), dynamic status monitoring (health status, current task), and maintenance of the overall resource pool of the robot cluster.

[0051] AMR Job Management: This module works closely with the scheduling engine to translate scheduling decisions into specific job instruction sequences for individual robots. It manages the job queue for each AMR and coordinates the action flow (material picking, transportation, docking, etc.) during its execution.

[0052] Map data: The system maintains a high-precision global digital map of the laboratory, which includes static environmental information such as passageways, workstations, equipment locations, and restricted areas. This is the foundation for all spatial calculations (such as path planning and positioning).

[0053] Path planning: Based on map data and mission objectives, calculates the collision-free, optimal driving path from the origin to the destination for AMR. In advanced mode, this planning works in conjunction with the traffic control module.

[0054] Traffic Control: This module enables spatiotemporal management of multi-robot collaborative operation. Its core functions include: reserving passage windows for robots on key road sections to avoid conflicts; real-time monitoring of all robot positions to provide collision warnings and dynamic avoidance; and managing right-of-way at intersections and narrow passages to ensure smooth overall logistics.

[0055] Communication Interface: This serves as the bridge for control and data exchange between the central scheduling layer and the execution layer (AMR and test equipment). It defines and implements standard communication protocols to ensure reliable command issuance and real-time status data transmission.

[0056] Data reporting (pointing to the central dispatch layer): This arrow indicates that real-time operational data (such as location, power, sensor readings, equipment status, and test results) from the execution layer (AMR and test equipment) are continuously reported to the central dispatch layer through the communication interface.

[0057] Data Reporting (pointing to the business layer): This arrow represents the function of the data closed-loop management module. It synchronously transmits the processed task data (structured and encapsulated as digital credentials) back to the upstream business layer (LIMS / MES / WMS) through an interface, thereby updating the business status and realizing information closure.

[0058] Combination Figure 1As shown, the forward task instruction flow constitutes a complete workflow from virtual business requirements to automated execution of physical entities. This process begins with task instructions automatically generated by upstream business systems (such as LIMS and MES) based on business logic, or proactively initiated by operators via terminals. These instructions are first received by the task access and parsing module, and after standardized parsing and semantic understanding, are transformed into structured task objects that the system can process internally. Subsequently, the multi-dimensional dynamic scheduling engine performs multi-objective optimization calculations based on the multi-dimensional features of this task object (such as priority and material attributes) and the real-time status of all robots, completing the intelligent decision of "who should execute." After the decision is issued, the traffic coordination and path planning module intervenes, planning a collision-free, high-efficiency path for the selected robot based on the global map and real-time traffic conditions, and reserving the right-of-way for key road sections. Finally, a complete instruction package integrating task objectives, action sequences, and path information is sent to the execution layer through a unified communication interface. At this point, the autonomous mobile robot (AMR) begins autonomous navigation and transport. Upon arrival at the target, it collaborates with the testing equipment through a standardized interface to automatically load materials and initiate testing, accurately translating digital instructions into physical world operations.

[0059] The reverse data feedback stream enables a closed-loop data flow from the physical world to the information system, which is crucial for ensuring process transparency and result reliability. Throughout the robot's execution and equipment testing process, various real-time data are continuously generated, including the robot's precise positioning, driving status, environmental sensor readings (such as temperature and humidity), and raw results generated by the testing equipment. This multi-source, heterogeneous raw data is reported to the central scheduling system in real time via a communication interface. The data closed-loop management module, acting as the data processing hub, collects and cleans the reported data stream in real time, accurately associates it with the task ID, and then encapsulates it into a structured digital transport and testing credential with tamper-proof verification according to predetermined specifications. Finally, this module automatically and in real-time synchronizes the task completion status and this complete digital credential back to the upstream business system (such as LIMS or MES) that initiated the task through a standardized interface. This process not only drives the automatic updating of work order and sample statuses in the business system, forming a closed loop in the business flow, but more importantly, it provides complete, reliable, and auditable electronic traceability evidence for every laboratory operation.

[0060] Example 2 The difference between this embodiment and the composite robot management system for smart laboratories provided in Embodiment 1 is that the multi-dimensional dynamic task scheduling strategy library in the multi-dimensional dynamic scheduling engine includes: a task feature abstraction unit, a composite robot capability and state management unit, a configurable strategy template library, and a multi-objective strategy scoring engine.

[0061] The task feature abstraction unit is used to parse structured task objects into feature vectors containing task type, priority, deadline, material attributes, start and end coordinates, and required vehicle type.

[0062] Understandably, this task feature abstraction unit is responsible for transforming the structured task object output by the task input and parsing module into a feature vector that the scheduling engine can directly compute. The extracted dimensions include: Task types: Different types can trigger different scheduling strategies, such as "ordinary feeding", "constant temperature transportation", "dangerous goods transportation", "empty vehicle recovery" and "equipment docking assistance".

[0063] Priority and Deadline: "Priority" is a qualitative or quantitative indicator of urgency (e.g., level 1-5); "Deadline" is the latest time when a task must be completed. Together, they constitute the time constraint for a task.

[0064] Material properties: These describe the physical and logical requirements of the transported object, such as: "must be kept at a constant temperature of 2-8℃", "must be protected from light", "is fragile", or "has biological hazards". These properties determine the required AMR's vehicle capacity and environmental control capabilities.

[0065] Space and vehicle requirements: "Start and end coordinates" define the geographical route of transportation; "Required vehicle type" specifies the specific fixtures or containers that the AMR must carry to perform this task (such as 96-well plate carriers, centrifuge racks).

[0066] The composite robot capability and status management unit is used to maintain the capability tag set and real-time status snapshot of each composite robot. The capability tag set includes the supported vehicle types and load capacity, and the real-time status snapshot includes the current location, real-time battery level, and health status.

[0067] Understandably, this unit maintains a dynamically updated digital twin profile for each AMR in the system, including: Static capability tags: such as AMR unique number, maximum load capacity, size, list of supported vehicle types, whether it integrates a robotic arm, maximum travel speed and other inherent attributes.

[0068] Dynamic Status Snapshot: Real-time data acquired from the AMR itself or onboard sensors, including: current battery level (SoC), real-time map location (x, y, θ), current task status (idle, running, charging, faulty), currently mounted vehicle ID, and device health status code (e.g., everything is normal, LiDAR is dirty, drive wheels are abnormal). This snapshot is updated at a high frequency (e.g., every second) and is the core input for dynamic scheduling.

[0069] A configurable policy template library stores multiple scheduling policy templates, including the shortest path first policy, the earliest deadline first policy, the highest capacity matching first policy, and the overall energy consumption optimal policy.

[0070] Understandably, this configurable policy template library pre-constructs various basic and composite scheduling policy algorithms for the scheduling engine to call or combine according to different scenarios. Common policy templates include: Shortest path priority: Select the available AMR with the shortest expected travel path for the task, aiming to reduce the time spent on a single task and the total travel distance.

[0071] Earliest Deadline First (EDD): Prioritize tasks with the earliest deadlines and assign them the earliest available Mobile Task (AMR) that can complete them the fastest (it may not be the closest in distance, but the one with the earliest estimated completion time after considering speed and path availability).

[0072] Prioritize the highest capability match: For tasks with special requirements (such as constant temperature), prioritize AMRs with perfect matching status (such as those using a constant temperature vehicle and the temperature is within the set range) to reduce the preparation work for changing vehicles.

[0073] Optimal system energy consumption: Under the premise of meeting basic requirements, select a scheduling scheme to minimize the total energy consumption of all AMRs, or prioritize the use of AMRs with high power to balance battery consumption.

[0074] Load balancing: Prefers to assign tasks to AMRs with shorter current task queues to avoid some AMRs being overloaded while others are idle.

[0075] The multi-objective strategy scoring engine is connected to the task feature abstraction unit, the composite robot capability and state management unit, and the configurable strategy template library, respectively. It is used to perform multi-dimensional weighted scoring on candidate composite robots based on the selected strategy template (for example, after configuring weights for the scores corresponding to each indicator, a weighted sum is performed), and output the optimal allocation decision.

[0076] Understandably, when a new task to be scheduled appears, the scoring engine will: Candidate set generation: First, based on the basic requirements of the mission (such as vehicle type) and the real-time status of the AMR (such as whether the battery level is below the safety threshold or whether it is healthy), a candidate set that meets the conditions is selected from all AMRs.

[0077] Multi-dimensional scoring: For each AMR in the candidate set, its score is calculated across various dimensions based on the currently activated policy template combination. For example, when using both "priority-first" and "path optimization" policies, the engine assigns a very high base score to high-priority tasks, then deducts corresponding distance points based on the estimated distance from each AMR to the task's starting point, ultimately obtaining a comprehensive score. The calculation model can be a weighted summation, fuzzy comprehensive evaluation, or a more complex reinforcement learning model.

[0078] Optimal decision: Select the AMR with the highest overall score as the final executor of the task, and issue the allocation decision to that AMR.

[0079] Suppose the system receives 5 new tasks (#1 to #5) simultaneously. Currently, there are 3 idle AMRs (A, B, C), 1 charging (D), and 1 executing a task (E). Among them, task #1 has a priority of "urgent", task #2 has a deadline of "half an hour later", and the remaining tasks have normal priority and no explicit deadline.

[0080] Task feature abstraction: The system parses out that task #1 has the "urgent" attribute and task #2 has the "time limit" attribute.

[0081] AMR Status Acquisition: Confirm that AMRs A, B, and C are in an idle and available state.

[0082] Strategy matching and scoring: The scheduling engine activates the "highest priority first" and "earliest deadline first" strategy templates.

[0083] First, urgent task #1 is prioritized. The engine calculations show that AMR A is closest to its starting point and has sufficient power, so task #1 is assigned to AMR A.

[0084] Next, handle the time-limited task #2. Of the remaining idle AMRs B and C, select the one expected to complete earliest (possibly taking into account both path and current speed), assuming it's assigned to AMR B.

[0085] Finally, tasks are assigned to the remaining idle AMR Cs. At this point, the "shortest path first" strategy is adopted, and among tasks #3, #4, and #5, the one closest to the current location of the AMR C (e.g., #3) is selected for assignment.

[0086] Output: Tasks #4 and #5 are placed in the waiting queue. Once AMR A or B completes task release, the scheduling engine will restart the above process, allocating tasks from the queue to the released AMR according to the order in which tasks were received or the insertion of new urgent tasks.

[0087] Through the above mechanism, the scheduling strategy of this system is flexible, configurable, and scalable, and can dynamically adapt to the diverse business needs of the laboratory, thereby optimizing resource utilization and ensuring critical tasks.

[0088] In addition to the first aspect, the multidimensional dynamic scheduling engine also includes: a strategy selector.

[0089] The strategy selector automatically matches and activates the corresponding scheduling strategy template from the configurable strategy template library based on the task type attribute in the structured task object.

[0090] Understandably, the strategy selector, acting as the "strategy scheduling hub" of the multi-dimensional dynamic scheduling engine, is specifically responsible for analyzing the structured task objects generated by the task access and parsing module. Its key decision-making anchor is the task type attribute within that object. Task type is the highest-level classification of the task's fundamental purpose and specific requirements, such as "general material handling," "constant temperature sample transportation," "hazardous chemical transfer," "empty vehicle recovery," and "loading and unloading docking for high-precision instruments." Behind each task type lies a differentiated business priority, risk control requirements, and resource matching logic. The strategy selector has a built-in or accessible predefined mapping rule library, which clearly defines that "when task type X, scheduling strategy Y should be used preferentially (or in combination)." When a new structured task enters the scheduling queue, the strategy selector immediately "diagnoses" it, identifies its task type, and then, based on the mapping rules, automatically and precisely matches and activates one or more of the most suitable scheduling strategy templates from the configurable strategy template library.

[0091] In systems without a strategy selector, all tasks may be forced to use the same scheduling logic (such as always using "shortest path first"), which obviously cannot meet the needs of complex laboratory scenarios. For example, in a "constant temperature sample transportation" task, the primary requirement is to ensure temperature stability throughout the transportation process. Therefore, "highest capability matching priority" (selecting an AMR equipped with a constant temperature carrier and normal temperature control) is far more important than "shortest path first." On the other hand, in an "urgent additional testing" task, the core requirement is speed. Therefore, "earliest deadline priority" or a combination of high-priority "shortest path first" becomes a better choice. The strategy selector ensures a high degree of alignment between scheduling logic and essential business requirements through automated scenario judgment. In this way, by adaptively selecting strategies for different tasks, the system can more accurately meet the specific constraints of each task (such as time, temperature, and safety), thereby significantly improving the completion quality and reliability of critical tasks. Moreover, it no longer relies on administrators manually switching strategies for different tasks, but achieves fully automatic strategy configuration based on task attributes, enabling the system to intelligently cope with diverse and dynamic task flows in the laboratory. Furthermore, when the lab introduces new services or task types, administrators only need to configure the corresponding policy template reference for the new task type in the policy selector's mapping rule library, without needing to modify the core code of the scheduling engine. This reduces the system's operational complexity and supports the smooth expansion of business functions.

[0092] Example 3 This embodiment enhances the overall system architecture of Embodiment 1 and the intelligent scheduling core of Embodiment 2 with significant functional improvements. The core difference and improvement lies in the addition of a crucial sub-module to the central scheduling layer 20, in addition to the task access and parsing module, the multi-dimensional dynamic scheduling engine, and the data closed-loop management module: the traffic coordination and route planning module.

[0093] The traffic coordination and path planning module connects with the multi-dimensional dynamic scheduling engine and all composite robots. It is used to perform collaborative path planning based on the global map and the real-time location of all composite robots, reserve passage windows for composite robots, and realize early warning and dynamic avoidance of collisions.

[0094] The introduction of this traffic coordination and path planning module aims to solve complex dynamic coordination problems that may arise when multiple composite robots operate in parallel within a limited laboratory space, such as path conflicts, intersection deadlocks, regional congestion, and efficiency bottlenecks. Specifically: The Traffic Coordination and Route Planning module acts as the planner within the central dispatch layer 20, responsible for overall spatial resource management and real-time traffic control. It does not replace the AMR's local obstacle avoidance function, but rather performs forward-looking collaborative planning and control at a higher system level.

[0095] Connection relationship: such as Figure 1As shown, this module maintains a real-time, bidirectional data connection with the multidimensional dynamic scheduling engine and all online composite robots. It receives task allocation decisions from the multidimensional dynamic scheduling engine (i.e., "dispatch robot A from point X to point Y") and provides feasibility verification and optimal path solutions for these decisions. Simultaneously, it feeds back the path planning results (such as estimated time) to the scheduling engine as input for evaluating and optimizing subsequent scheduling decisions.

[0096] The traffic coordination and path planning module connects with all the composite robots, continuously receiving precise real-time location, speed, orientation, and status reported by each robot; at the same time, it sends them collaboratively optimized path point sequences, speed suggestions, and passage instructions.

[0097] Based on a high-precision global digital map of the laboratory (including passageways, doors, intersections, equipment docking areas, restricted areas, etc.) and real-time data streams from all robots, this module achieves the following core functions: Collaborative Path Planning: After the multi-dimensional dynamic scheduling engine assigns tasks, this module does not simply calculate an isolated, static shortest path for a single robot. Instead, it comprehensively considers the existing task paths, real-time positions, and future planned journeys of all robots to perform global optimization calculations. Its goal is to avoid potential conflicts and may proactively plan slightly longer but smoother paths for some robots in exchange for maximizing the overall system throughput. For example, it might instruct a robot to wait briefly or detour to avoid an intersection that will be used by multiple robots.

[0098] Time-space reservation: This is a key mechanism for achieving conflict-free and efficient operation. For critical resource points on the map, such as narrow corridors, one-way passages, elevator entrances, or equipment docking points, the module treats them as "scarce resources" requiring reservation. When planning a path for a robot, a specific "time window" is requested for that resource point. The system checks whether this time window is already occupied by other robots; only after a successful reservation is the path finally confirmed. This is equivalent to allocating a dedicated travel time slot for each robot in critical sections, fundamentally preventing head-on collisions and deadlocks.

[0099] Collision Warning and Dynamic Avoidance: Despite proactive planning, unexpected events may still occur during actual operation (such as temporary robot malfunctions or accidental intrusion of personnel or other moving objects). The module performs millisecond-level collision risk prediction by monitoring the position and status of all robots in real time. Once a potential conflict risk is detected (e.g., two robots are expected to meet at the same coordinate point in 5 seconds), it immediately issues a tiered warning to the relevant robots and the central monitoring station. Simultaneously, it initiates dynamic replanning, calculating and issuing new, safe avoidance paths or stopping instructions for one or more affected robots until the risk is eliminated. This constitutes the safety baseline for system operation.

[0100] Compared to systems that only have intelligent scheduling but lack centralized traffic coordination (Examples 1 and 2), this embodiment can upgrade from single task and resource matching to collaborative orchestration of multiple agents in the spatiotemporal dimension; through reservation system and real-time early warning, the probability of collisions or blockages between robots and between robots and the environment is reduced, thereby integrating isolated mobile units into an organic and collaborative group, solving the core challenge of efficient and safe parallel operation of autonomous mobile robots in high-density and dynamic environments.

[0101] Example 4 In this embodiment, the composite robot in the execution layer 30 is equipped with a modular docking mechanism; the modular docking mechanism includes a quick-change carrier adapter component and / or a robotic arm component for performing loading and unloading actions; The composite robot is also equipped with a standard equipment communication interface, which is used for two-way communication with the test equipment to perform control handshake, status confirmation and action coordination signals when it arrives at the target test equipment.

[0102] Understandably, a composite robot is not merely a mobile platform; its core capabilities lie in its modular docking mechanism at the front end, which can actively perform operations. This mechanism is an integrated electromechanical system, primarily comprising two key components: The quick-change carrier adapter, which serves as the robot's hand or tray, is used to carry and secure a variety of laboratory material containers, such as microplates, cell culture dishes, reagent bottles, and sample tube racks. Its "quick-change" feature means that the robot can switch between different carrier sizes in seconds using a highly efficient automated or semi-automated mechanism (such as pneumatic locking, electromagnetic adsorption, or mechanical latching). For example, it can quickly switch from a general-purpose carrier for transporting 96-well plates to a rotor carrier adapted to a specific brand of centrifuge. This design enables a single robot to serve multiple devices and handle a variety of materials, improving the versatility of the equipment and return on investment.

[0103] Robotic arm components for performing loading and unloading operations: For scenarios requiring complex operations such as opening and closing doors, pressing buttons, inserting and removing samples, or performing precise pipetting, the composite robot also integrates or can be equipped with a multi-degree-of-freedom robotic arm. The end effector of this robotic arm can integrate grippers, suction cups, and specialized tool heads, enabling it to perform a series of precise operations like a human hand, such as opening equipment doors, transferring samples from carriers to internal workstations, and pressing start buttons. The robotic arm's trajectory and movements can be pre-programmed or guided by the equipment in real time.

[0104] By configuring different carriers and tool heads, robots of the same model can flexibly adapt to automated testing equipment (such as biochemical analyzers, nucleic acid extractors, and vibration test benches) of different brands, models, and interfaces in the laboratory, achieving flexible adaptation of physical interfaces. This is the foundation for building large-scale flexible automated production lines.

[0105] Understandably, based on this, the composite robot is configured with a standard equipment communication interface. This does not refer to a specific physical port (such as an Ethernet port or RS-2), but rather a unified, high-level communication protocol and application specification. This interface is specifically used for automated, bidirectional information exchange with the control system of the target testing equipment when the robot arrives. Its standardized interaction process typically includes: Control handshake: When the robot approaches the device, it actively sends a "request docking" signal. The device responds with a status such as "ready" or "busy, please wait", thus establishing a communication link.

[0106] Status confirmation: The robot acquires key information such as the current working status of the equipment (idle, running, faulty), the status of the hatch (open / closed), and the status of the internal workstations (empty / full).

[0107] Two-way communication of motion coordination signals: For example, the robot sends "I am in position, request to open the hatch"; after the equipment controls the hatch to open, it replies "Hatch open, material feeding is possible"; after the robot controls the robotic arm to complete the loading, it sends "Loading complete, material ID is XXX"; the equipment then replies "Material received, starting testing, estimated time XX minutes". The entire process is like a dialogue between two intelligent agents, strictly orderly, and with confirmation at each step.

[0108] This interface can be implemented over industrial Ethernet (such as Profinet, EtherCAT) or general TCP / IP networks, using a unified middleware (such as OPC UA) or a defined lightweight RESTful API / message queue (such as MQTT) protocol. The key is to encapsulate or map the proprietary control commands of different equipment manufacturers into standardized service calls.

[0109] When the robot carrying the task arrives at a high-throughput screening instrument, its complete workflow is as follows: The robot learns the target equipment model through the scheduling system, and automatically selects and loads the corresponding microplate carrier and tool head for opening and closing the hatch.

[0110] Navigate to the pre-set docking point in front of the device.

[0111] Send an access request to the filter via the standard device communication interface.

[0112] After receiving the "access allowed" response from the screening instrument, the robot drives the robotic arm to perform a sequence of actions: "open the door - extend in - place the microplate - exit - close the door".

[0113] After each action is completed, the robot sends a status signal to the device and waits for confirmation feedback from the device to ensure synchronized operation.

[0114] Once the sample is in place, the robot notifies the device that "the sample is in place," and the device replies "test started." The robot then updates its task status to "waiting for results" or proceeds to the next task point.

[0115] In this way, by combining the hardware and software of modular docking mechanisms and standard equipment communication interfaces, the composite robot is transformed from a passive transporter into an intelligent agent that can actively interact with the laboratory environment. This is the cornerstone for building an unmanned operation ecosystem for smart laboratories.

[0116] In conjunction with the first aspect, the data closed-loop management module includes: a full-process data collector, a structured data encapsulation unit, and an upstream system synchronization interface.

[0117] The end-to-end data acquisition device is used to collect task execution data from the composite robot and testing equipment in real time. The task execution data includes timestamps, location trajectories, ambient temperature and humidity, vehicle barcode recognition results, equipment docking logs, and test result data.

[0118] The structured data encapsulation unit, connected to the end-to-end data acquisition unit, is used to encapsulate task execution data according to a predefined format, generating traceable digital transportation and testing credentials.

[0119] The upstream system synchronization interface connects to the structured data encapsulation unit and is used to transmit digital transportation and testing credentials and task status back to the corresponding system in the upstream business layer in real time.

[0120] This data closed-loop management module is the central hub of the entire system, from physical execution to information fusion and business feedback. It aims to systematically solve the core problem of operational data not forming a structured closed loop, transforming discrete operational data into high-value assets that can drive business decisions and ensure quality traceability. This module consists of three logically coherent and sequentially collaborating sub-modules, working together to complete the entire value chain from data acquisition and processing to data feedback.

[0121] The end-to-end data acquisition system, through various sensors and communication interfaces deployed on the composite robot body, vehicle, and testing equipment, constructs a real-time data acquisition network covering the entire task execution chain. The acquired task execution data is characterized by multi-source nature, real-time performance, and structured format, specifically including: Spatiotemporal and identity data: precise timestamp sequences, continuous location trajectory points (used to reconstruct transportation routes), and vehicle barcode recognition results (to confirm material identity).

[0122] Environmental and process data: Environmental temperature and humidity records during transportation (crucial for temperature-controlled transportation), and complete equipment docking logs for interactions with testing equipment (such as records of each step from "handshake request - docking permission - robotic arm extension - placement completed - hatch closing").

[0123] Results data: Raw or aggregated test results data (such as pass / fail, measured values) obtained from the test equipment.

[0124] The data collector continuously gathers these heterogeneous data streams at high frequency and low latency, ensuring that no detail that may affect quality traceability or process analysis is missed, providing rich and accurate raw materials for subsequent processing.

[0125] The structured data encapsulation unit receives the raw data stream from the data collector and cleans, correlates, reorganizes, and encapsulates it according to a predefined data model and format that conforms to industry standards or internal specifications (such as definitions based on JSON Schema or XML Schema). Specifically: Cleaning and Association: Data from different sources and at different times are precisely associated using key identifiers such as task ID, sample ID, and robot ID to form a data set that describes the overall picture of a single task and has consistent internal logic.

[0126] Encapsulation and Signing: The associated data is encapsulated into an indivisible data packet containing its own metadata (such as generation time and version number). Typically, this unit uses cryptographic techniques such as hash algorithms to generate a unique digital fingerprint (hash value) for the data packet, or attaches a timestamp signature. This process generates a "digital transport and testing credential" defined by the system. The core characteristics of this credential are tamper-proof and traceability: any subsequent modification to the credential content will result in a hash value mismatch, which can be easily detected by the system, ensuring the authenticity and integrity of the data.

[0127] Generating credentials is not the end goal; returning the data value to the business system is the final link in the closed loop. The upstream system synchronization interface is responsible for transmitting the encapsulated digital credentials and the current task status back to the corresponding system in the upstream business layer 10 in real time and accurately through standardized application programming interfaces (APIs). Specifically: Intelligent routing: The interface intelligently determines whether to synchronize data to LIMS, MES, or WMS, or simultaneously to multiple systems, based on the content of the voucher (such as the associated order number and material type).

[0128] Status synchronization: The task status (such as "completed" or "abnormal interruption") is updated to the upstream system in real time, driving its business process to jump automatically (for example, the sample status in LIMS is automatically updated from "pending inspection" to "in inspection" and then to "completed").

[0129] Voucher archiving: Complete digital vouchers are stored as attachments or associated records in the upstream system's database, so that when viewing a test report in LIMS, one can simultaneously access full-dimensional data such as the temperature and humidity curves of the sample throughout the transportation process, the transfer timeline, and screenshots of docking operation videos.

[0130] In addition to the first aspect, the system also includes: a visual monitoring and interactive terminal.

[0131] The visual monitoring and interactive terminal is connected to the central dispatch layer 20 to display the real-time location of all composite robots in the system, task queues, task execution progress, and test equipment status; and provides interactive interfaces for manual task initiation, task cancellation, emergency pause, and status query.

[0132] The visual monitoring and interactive terminal is logically independent of the aforementioned three-layer architecture, but is deeply integrated physically and in communication. It establishes stable bidirectional communication connections with the various core modules of the central dispatch layer 20 (especially the dispatch engine and data closed-loop management module) via a high-speed local area network or dedicated data bus. This connection enables it to subscribe to all dynamic data streams within the system in real time, while also delivering operator instructions to the central dispatch layer 20 accurately and in real time.

[0133] This visualization monitoring and interactive terminal provides an integrated graphical user interface (GUI) designed to transform the complex states of multi-robot systems into readily understandable visual information, enabling a comprehensive overview. Its visualization capabilities cover at least the following key dimensions: Global geographic view: Display the precise location, orientation, and movement trajectory of all composite robots in real time and dynamically on a high-precision digital map of the laboratory. Different colors or icons can distinguish the robot's status (such as running, idle, charging, malfunction).

[0134] Task and Resource Monitoring: Clearly displays the task queues for the entire system and individual robots, including lists of tasks to be executed, tasks in progress, and tasks that have been suspended, along with their key attributes (priority, deadline). Simultaneously, it monitors and displays the real-time status of each test device (idle, running, alarm, under maintenance).

[0135] Process progress view: Visually display the execution progress of each task in the form of progress bars, Gantt charts, or timelines. For example, it can display detailed statuses such as "50% of material collection completed", "On its way to the test equipment", and "Equipment docking in progress".

[0136] While achieving a high degree of automation, the system must retain necessary and convenient human control to handle unexpected events or perform special operations. This terminal provides a complete set of hardware and software interfaces: Task Initiation: Allows operators to bypass the upstream system and directly create and issue urgent or temporary transportation tasks through the terminal interface, manually specifying materials, origin, destination and requirements.

[0137] Process control: Provides key safety control functions such as task cancellation (abrupt a scheduled or executing task) and emergency pause / resume (one-click pause or resume the movement of all robots or robots in a specified area).

[0138] Information Query: Supports in-depth query and retrieval of historical and real-time data for any robot, task, or device, such as viewing the complete temperature and humidity record of a transportation or the recent utilization report of a device.

[0139] The Visual Monitoring and Interactive Terminal 40 bridges the gap between fully automated systems and laboratory management needs by combining in-depth status visualization with flexible human interaction capabilities. It is also an indispensable control center and information hub for building human-machine trust and ensuring the safe, efficient, and stable operation of the entire smart laboratory unmanned operation system.

[0140] In conjunction with the first aspect, the task access and parsing module connects to the upstream business layer through a standardized application programming interface and / or event message bus to achieve automatic triggering and parsing of task instructions and reverse synchronization of task status.

[0141] The task access and parsing module does not employ a single, fixed connection method, but rather provides a flexible and robust dual-channel integration solution: a standardized application programming interface (API) and an event message bus. These two methods can be used individually or in combination to adapt to the technical architecture and performance requirements of different upstream systems. Specifically: Integration based on standardized application programming interfaces (APIs) is a typical request-response synchronous integration pattern. The task access and parsing module exposes a set of well-defined API endpoints conforming to RESTful specifications or standards such as gRPC. When an upstream business layer 10 (such as LIMS or MES) needs to initiate a task, it actively pushes the task instructions in structured data (such as JSON format) to this system by calling the corresponding API (e.g., POST / api / v1 / tasks). After the API call is triggered, the module immediately receives the request, performs authentication and data format verification, and initiates the parsing process. Simultaneously, the module can use the same API connection or callback URL to reverse-synchronize the task reception confirmation, execution status (e.g., "assigned to robot," "completed"), and final result back to the calling system. This approach offers direct interaction and strong real-time performance, making it suitable for scenarios requiring immediate confirmation and strong transactional consistency.

[0142] Integration based on an event message bus is a publish-subscribe asynchronous, loosely coupled integration model, more suitable for event-driven architectures. The system and the upstream business layer 10 share a unified event message bus (such as Apache Kafka, RabbitMQ, or AWS Event Bridge). The upstream system does not directly call APIs, but instead publishes business events (such as "sample registered" or "production work order released") as messages to specific topics. The task access and parsing module acts as a subscriber, listening to these topics. Once a relevant event message is detected, the task generation logic is automatically triggered. For example, when the LMS publishes a "Sample.Registered" event, and the event content indicates that the sample needs to be tested, the module automatically creates a corresponding "sample transferred to testing equipment" task. Reverse synchronization of status and results is also achieved by the system publishing events such as "Task.Completed" to the bus; the upstream system can subscribe to these events to know the progress. This approach is thoroughly decoupled, better able to handle traffic spikes and brief system downtime, and offers excellent scalability.

[0143] The task access and parsing module, employing an advanced integration model of standardized APIs and an event message bus, acts as an intelligent gateway connecting the information domain (business systems) and the physical domain (robots and equipment) of the smart laboratory. It not only enables automatic injection of task flows but also constructs a digital bridge for bidirectional empowerment and cyclical optimization between business operations and execution through two-way, real-time data synchronization, providing the technical guarantee for the system's collaborative capabilities.

[0144] Example 5 Secondly, this application provides a composite robot management method for smart laboratories, combining...Figure 2 As shown, this method is applied to the system described above. The method includes: S110 receives task instructions, which originate from automatic triggering by the upstream business layer or active initiation by a manual terminal.

[0145] This step is the logical starting point and entry point of the management method, marking the initialization of a specific work process. This step is specifically executed by the task access and parsing module in the system, whose core function is to act as an open and reliable gateway to receive work requests from both internal and external sources.

[0146] The source of task instructions is dual, reflecting the system's design principles of balancing automation and flexibility: First, they originate from automatic triggering at the upstream business layer, an ideal model for achieving a fully automated, closed-loop process. When a new testing order is created in the Laboratory Information Management System (LIMS), or a process in the Manufacturing Execution System (MES) is completed and needs to be transferred to the next stage, these upstream business systems, based on preset business rules, will automatically and in real-time push task instructions containing information such as materials, destinations, and requirements to this system through standardized application programming interfaces (APIs) or by publishing structured events to a unified event message bus. This seamlessly embeds logistics instructions into the business flow. Second, they originate from proactive initiation at human terminals, providing a necessary channel for the system to respond to temporary, urgent, and special operational needs or for system debugging. Operators can manually create and issue task instructions through dedicated applications on handheld tablets (PADs) or physical button boxes deployed in the laboratory's visual monitoring and interactive terminals. This ensures that the system can operate silently as a core component of the background automated process, while also responding to real-time human control in the foreground, exhibiting high environmental adaptability and operability.

[0147] S120 parses the task instructions, extracts task features, and generates a structured task object containing task type, start and end coordinates, material attributes, priority, and deadline.

[0148] Upon receiving the original instruction, the system immediately enters the crucial information transformation stage. This step is still undertaken by the task access and parsing module, which aims to transform the original instructions received in step S110, which may have different formats, redundant information, or implicit information, into structured task objects with clear semantics and standard formats that can be uniformly processed within the system.

[0149] The parsing process begins with syntax analysis and data cleaning to extract explicit parameters, such as material numbers and target location names specified in the text. Then, semantic understanding and contextual inference are performed by integrating a business rule base and knowledge graph to supplement implicit features. For example, the system can automatically associate the physicochemical properties of a material number (e.g., "requires 4℃ constant temperature storage" or "belongs to highly toxic chemicals"), or determine the required task type based on the target location (e.g., "sent to ICP-MS" implies a "precision instrument loading" type of docking operation). The resulting structured task object is a data entity encapsulating complete operational requirements. Its core dimensions must include: task type (driving subsequent strategy selection), start and end coordinates (physical location based on a high-precision digital map of the laboratory), material properties (determining specific transportation and processing requirements), business priority (for prioritization in case of resource conflicts), and deadline (defining the task's time constraints). The output of this step provides standardized, high-quality information input for all subsequent data-driven intelligent decision-making.

[0150] S130: Obtain a real-time status snapshot of all available composite robots in the execution layer. The status snapshot includes at least position, battery level, vehicle compatibility, and health status.

[0151] This step is completed collaboratively by the communication subsystem of the system and execution layer 30. The system obtains dynamic operational data from the controller of each composite robot in the network in real time through a stable wireless network (such as Wi-Fi 6 or a 5G private network) via polling or event subscription, and aggregates this data to generate a global, instantaneously consistent real-time state snapshot. This snapshot is not a static report, but a continuously refreshed dynamic data view, which covers at least the following for each robot: real-time position and heading angle (usually obtained by fusing data from laser SLAM, visual odometry, and encoders, accurate to the centimeter level), current battery power and health status (State of Charge, SoC), vehicle compatibility and status (including the currently mounted vehicle type and specific vehicle status, such as whether the current temperature of a thermostat vehicle is within the set range), and robot health status (based on status codes reported by the robot's self-diagnostic system, such as "normal," "driver overheat warning," "LiDAR malfunction," etc.). Obtaining comprehensive and accurate real-time status snapshots enables the central dispatch system to clearly grasp the precise location, ammunition (power) status, and combat readiness of every combat unit on the battlefield, just like a commander, laying a solid factual foundation for subsequent intelligent dispatch decisions.

[0152] S140 inputs the structured task object and real-time status snapshot into the multi-dimensional dynamic scheduling engine, performs multi-objective evaluation based on the preset strategy library, and dynamically selects the target composite robot.

[0153] This step is the core of the intelligent decision-making process of the entire method. It is executed by the multi-dimensional dynamic scheduling engine. The structured task object generated in step S120 (which defines the task requirements) and the real-time state snapshot obtained in step S130 (which defines the resource supply) are used as dual inputs to drive the built-in multi-dimensional dynamic task scheduling strategy library to perform complex multi-objective optimization calculations. Finally, the optimal target composite robot to perform this task is dynamically selected.

[0154] The decision-making process is highly systematic: First, the strategy selector automatically matches and activates one or more of the most suitable basic strategy templates from the strategy library based on the "task type" attribute of the task object (e.g., for the "constant temperature transportation" task, a combination of "highest capability matching priority" and "shortest path priority" strategies is activated). Next, the engine filters and scores all available robots based on the activated strategies. The scoring process is a multi-objective trade-off, potentially considering multiple dimensions such as task urgency (priority, deadline), execution efficiency (estimated path duration, current robot speed), resource suitability (vehicle matching, robotic arm capabilities), system energy consumption (remaining robot battery power, estimated task power consumption), and load balancing (length of task queues for each robot). The scoring engine uses weighted scoring, fuzzy evaluation, or more advanced reinforcement learning models to calculate a comprehensive score for each candidate robot. Finally, the robot with the highest score is selected as the target composite robot. This decision is not static. If a higher-priority task is inserted before the task is executed, or if the target robot suddenly malfunctions, the engine will immediately trigger dynamic rescheduling, re-evaluate, and possibly replace the execution unit, ensuring the system's agility and robustness in the face of changes.

[0155] S150 sends a sequence of composite instructions to the selected target composite robot. The sequence of instructions controls the target composite robot to perform the following actions in sequence: going to the material picking point, verifying and loading materials, navigating to the target testing equipment, performing automatic docking, waiting for the test to be completed, unloading materials, and returning to the standby point.

[0156] Once a target robot is selected, the central scheduling system generates a detailed and ordered sequence of compound instructions for it and sends it out via a highly reliable wireless communication link. This sequence of instructions is an ordered set of atomic operations, logically forming a complete task script that controls the robot to autonomously complete the entire process from departure to return to its original position. Head to Pickup Point: Based on global path planning, navigate to the designated warehouse location or workstation.

[0157] Verify and load materials: Upon arrival, the robot's vision system scans the material identification and compares it with the task instructions for verification. Once confirmed, the system triggers the actuator (such as lifting forks or belt conveyor) to complete the loading and updates the material status in the system to "in transit".

[0158] Navigate to target testing equipment: Replan the route to safely and efficiently move to the designated docking point of the target equipment (such as a biochemical analyzer).

[0159] Performing automatic docking: This is the key to demonstrating the capabilities of a "hybrid" robot. The robot interacts with the equipment control system through a standard equipment communication interface. After a successful software handshake, it can control its modular robotic arm components to perform a series of precision operations such as opening and closing doors, placing samples, and starting equipment.

[0160] Waiting for test completion: The robot can enter standby mode or be dispatched by the system to perform other short tasks while monitoring the equipment status.

[0161] Unloading materials and returning to the standby point: After receiving the test completion signal, the robot performs the unloading operation (or transfers the materials to the next stage), and then autonomously navigates to the idle standby area or charging pile according to the instructions of the scheduling system.

[0162] This series of actions is executed automatically under the monitoring of the central dispatch layer 20, realizing end-to-end unmanned operation of the work process.

[0163] S160 continuously collects and structures the status and environmental data reported by the target composite robot throughout the entire task execution cycle, and synchronizes the data to the upstream business layer at key nodes and at the end of the task to update the material status and close the task process.

[0164] This step ensures a closed-loop process that is traceable, auditable, and optimizable, and is managed throughout the entire task by the data closed-loop management module. Throughout the entire lifecycle of the target robot's task execution, the system performs intensive data processing in parallel: Continuous data acquisition: Receive and record various types of data reported by the robot in real time, including high-frequency pose data, status events of key actions (such as "successful pickup" and "docking started"), environmental sensor readings (temperature and humidity curves throughout the transportation process), and raw or pre-processed result data fed back from testing equipment.

[0165] Structured Encapsulation: The structured data encapsulation unit cleans, times-aligns, and logically correlates these raw data streams from different sources and timelines, weaving them into a complete storyline using key fields such as task ID and sample ID. Subsequently, it encapsulates the data according to a predefined, industry-standard data model (such as based on JSON-LD or XML Schema), attaching a digital timestamp and hash value to generate an immutable digital transport and testing certificate. This certificate electronically reproduces the entire process of "who, when, where, what, under what conditions, delivered to which device, and what result."

[0166] Synchronization and Closed Loop: At key milestones of the task (such as material loading verification passing, test initiation, and test completion) and at the final end of the task, the upstream system synchronization interface is triggered. It automatically and in real time transmits the current task status (such as "completed") and the packaged digital credentials back to the corresponding system in the upstream business layer 10 via a standard API (such as updating the sample testing records in the LIMS with results and transportation conditions). This action ensures that the information status within the business system remains strictly consistent with the execution results in the physical world, thereby completely closing the flow of the task at the information level. This not only enables automatic driving of business flows (such as automatically triggering report review) but also provides complete and reliable data assets for subsequent quality audits, process analysis, and continuous optimization.

[0167] In conjunction with the second aspect, step S140 includes: S141, Based on the task priority and deadline in the structured task object, the available composite robots are screened according to their urgency to obtain an initial robot set.

[0168] This sub-step is the first layer of filtering in the multi-dimensional dynamic scheduling engine's decision-making process. Its goal is to quickly identify and ensure the timely completion of critical tasks with high timeliness requirements.

[0169] The multi-dimensional dynamic scheduling engine first focuses on two key time-related attributes in structured task objects: task priority and deadline. Task priority is a qualitative or quantitative urgency indicator assigned by the business system (e.g., "urgent," "high," "normal," or levels 1-5); the deadline is the latest absolute time the task must be completed. The multi-dimensional dynamic scheduling engine combines these two attributes with the current system time to calculate and filter robots currently in the "idle" or "about to be idle" state based on urgency. The logic is that for a high-priority task or one nearing its deadline, selecting a capable robot that requires a long time to complete its current task before being released is unreasonable. Therefore, the filtering tends to exclude robots with excessively long current task queues, late expected release times, slow movement speeds, or speed limitations due to low battery. Through this step, the system quickly narrows down the decision-making scope, obtaining an initial set of robots that are considered capable of responding promptly to the urgent needs of the task in the time dimension. This ensures that the central dispatch level allocates resources to the most urgent tasks immediately, thus avoiding the risk of critical tasks being delayed due to queuing.

[0170] S142, based on the vehicle or environmental requirements required by the material attributes in the structured task object, calculate the matching degree with the capability tags of each composite robot in the initial robot set, and filter out composite robots with a matching degree higher than a preset threshold to form a candidate robot set.

[0171] After initial screening based on time, step S142 rigorously matches the task's quality and compliance requirements, a crucial step in ensuring the safe and accurate transportation of special materials. The multi-dimensional dynamic scheduling engine analyzes the physical carrier or environmental requirements explicitly stated by the material attributes in the structured task object. For example, "requires constant temperature transportation at 2-8℃" requires the robot to be equipped with a temperature-controlled carrier that is functioning normally; "belongs to fragile items" requires the robot to be equipped with a carrier with shock absorption or to perform smooth, low-speed movements; "target is a high-throughput screening instrument" requires the robot's end effector to accurately dock with the microporous plate.

[0172] Subsequently, the system will verify the capability tags of each robot in the initial robot set one by one. Capability tags are predefined and maintained digital profiles that clearly record each robot's supported vehicle types, sensor configurations (such as whether temperature and humidity monitoring is available), robotic arm accuracy level, and other static capabilities, as well as the current status of the vehicle (such as the current temperature of the incubator). The engine will calculate the matching degree; for example, a perfect match to the incubator requirement earns full marks, while a lack of this function earns zero marks. The system presets a matching degree threshold (e.g., 80%), and only robots scoring above this threshold can pass this round of screening and form the candidate robot set. This step acts as a "technical qualification" threshold for the task, fundamentally preventing serious quality and safety incidents such as using ordinary vehicles to transport incubator samples or using unprotected robots to handle hazardous materials, and forcibly ensuring the compliance of the work process and the integrity of the samples from the perspective of resource allocation.

[0173] S143, calculate and score the candidate robot set based on at least one optimization objective among path efficiency, composite robot energy consumption and global task load balancing, and determine the final target composite robot based on the scoring results.

[0174] After the first two rounds of screening, the robots in the candidate robot set have met the standards in terms of timeliness and capability compliance. Step S143 is the final stage of scheduling decision-making, and its goal is to select an executor (i.e., the target composite robot) from these qualified candidate robots to optimize the overall system performance. At this point, the multi-dimensional dynamic scheduling engine will perform multi-dimensional calculations and scoring on the robots in the candidate set according to the preset optimization strategy. Common optimization objectives include: Path efficiency: Calculate the distance from each robot's current position to the task start point, as well as the estimated total path length and time for executing the entire task, and select the shortest path or the one with the least time.

[0175] Robot energy consumption: Taking into account the robot's current battery level, the expected power consumption of the task, and the convenience of returning to the charging station, we tend to select robots with sufficient power or high energy efficiency after the task is completed, in order to balance the battery life of the cluster.

[0176] Global task load balancing: Evaluate the historical and current task load of each robot and tend to assign new tasks to robots with shorter current total task queues to avoid uneven workloads and improve the overall utilization and responsiveness of the cluster.

[0177] Multidimensional dynamic scheduling engines typically employ a weighted scoring model, assigning weights to one or more of the aforementioned objectives and calculating a comprehensive score for each candidate robot.

[0178] Ultimately, the robot with the highest score is identified as the target composite robot. For example, in a scenario where overall efficiency is more important, "path efficiency" might be given the highest weight; while in a scenario where extended unattended operation time is required, "energy consumption" optimization becomes more critical. This step reflects the global optimization wisdom of the scheduling system, which not only focuses on the fastest completion of individual tasks but also on the long-term, stable, and efficient operation of the entire robot cluster. Through local optimization of each scheduling decision, it continuously drives the system towards a globally optimal state.

[0179] Combination Figure 4 As shown, the entire process of material delivery and testing tasks based on AMR (Autonomous Mobile Robot) is systematically demonstrated, which can be divided into the following stages: First, task initiation and scheduling decisions: The process begins at the manual operation terminal, where staff issue delivery requests via PAD or physical buttons. Upon receiving this call instruction, the middle-level scheduling layer of the scheduling system first parses the task type, then generates a specific task and allocates resources. Subsequently, it dispatches the AMR (Automatic Transporter) to the automated warehouse or pallet buffer area to perform the pickup operation.

[0180] Next, material acquisition and anomaly handling: After the AMR arrives at the pickup point, it obtains material information through the barcode recognition system. If the recognition is successful, the AMR receives the palletized materials and enters the task execution state; if the recognition fails, the anomaly information is immediately reported, the system interrupts the current task, and the AMR returns to the standby area.

[0181] Then, the test task is executed: after the materials are successfully acquired, the AMR is dispatched to the target test site and docked with the test equipment or conveyor line. The test equipment performs the test according to the procedure and automatically records the test results to ensure traceability of the process.

[0182] Next, system status synchronization and task management: After testing, the Enterprise System (ES / WAS) updates the material status, and the Logistics Execution System (LES) updates the task status synchronously. If an interruption occurs during production, the system will display the task status in real time. Furthermore, the system performs data interaction at key nodes to ensure information consistency.

[0183] Finally, the task loop and termination check: After a task is completed, the system determines whether to continue executing a new task. If "No" is selected, AMR will return to the standby area, and the task will officially end; if "Yes" is selected, it will re-enter the scheduling loop and continue executing subsequent tasks.

[0184] Thirdly, embodiments of this application provide an electronic device, combined with Figure 3 As shown, the electronic device includes a memory 131 and a processor 130. The memory 131 stores a computer program, and the processor 130 runs the computer program to make the electronic device perform the above-described method.

[0185] Furthermore, combined Figure 3 The electronic device shown also includes a bus 132 and a communication interface 133, with the processor 130, the communication interface 133 and the memory 131 connected via the bus 132.

[0186] The memory 131 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 133 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The bus 132 may be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0187] Processor 130 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 130 or by instructions in software form. Processor 130 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 131, and processor 130 reads the information in memory 131 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0188] Fourthly, embodiments of this application provide a readable storage medium storing computer program instructions, which are read and executed by a processor to perform the above-described method.

[0189] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0190] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0191] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0192] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0193] Finally, it should be noted that the above embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A composite robot management system for use in smart laboratories, characterized in that, include: Upstream business layer, central scheduling layer, and execution layer; The upstream business layer includes a laboratory information management system, a manufacturing execution system, and a warehouse management system, which are used to issue at least one of the following material transfer or testing task instructions: The central scheduling layer includes: a task access and parsing module, a multi-dimensional dynamic scheduling engine, and a data closed-loop management module; The task access and parsing module is used to receive and parse task instructions from the upstream business layer or human terminal through a standardized interface, and generate a structured task object containing task type, start and end point, material attributes and priority; the multi-dimensional dynamic scheduling engine is used to dynamically calculate and allocate target mobile robots according to the characteristics of the structured task object and the real-time status of the mobile robots in the execution layer; the data closed-loop management module is used to structurally record the task process data and synchronize the status and results to the upstream business layer. The execution layer includes at least one composite robot and at least one testing device, used to receive and execute action instructions issued by the central scheduling layer, complete material handling, automatic docking with the testing device, and reporting of test results.

2. The system according to claim 1, characterized in that, The multidimensional dynamic task scheduling strategy library in the multidimensional dynamic scheduling engine includes: The task feature abstraction unit is used to parse structured task objects into feature vectors containing task type, priority, deadline, material attributes, start and end coordinates, and required vehicle type. The composite robot capability and status management unit is used to maintain the capability tag set and real-time status snapshot of each composite robot. The capability tag set includes the supported vehicle types and load capacity, and the real-time status snapshot includes the current position, real-time battery level, and health status. A configurable strategy template library stores multiple scheduling strategy templates, including the shortest path first strategy, the earliest deadline first strategy, the highest capacity matching degree first strategy, and the optimal overall energy consumption strategy. The multi-objective strategy scoring engine is connected to the task feature abstraction unit, the composite robot capability and state management unit, and the configurable strategy template library, respectively. It is used to perform multi-dimensional weighted scoring on candidate composite robots based on the selected strategy template and output the optimal allocation decision.

3. The system according to claim 2, characterized in that, The multi-dimensional dynamic scheduling engine also includes: A strategy selector that automatically matches and activates the corresponding scheduling strategy template from the configurable strategy template library based on the task type attribute in the structured task object.

4. The system according to claim 1, characterized in that, The central scheduling layer also includes: The traffic coordination and path planning module is connected to the multi-dimensional dynamic scheduling engine and all composite robots. It is used to perform collaborative path planning based on the global map and the real-time location of all composite robots, reserve passage time windows for the composite robots, and realize early warning and dynamic avoidance of collisions.

5. The system according to claim 1, characterized in that, The composite robot in the execution layer is equipped with a modular docking mechanism; the modular docking mechanism includes a quick-change carrier adapter component and / or a robotic arm component for performing loading and unloading actions; The composite robot is also equipped with a standard device communication interface, which is used to conduct two-way communication with the test equipment for control handshake, status confirmation and action coordination signals when it arrives at the target test equipment.

6. The system according to claim 1, characterized in that, The data closed-loop management module includes: The full-process data acquisition device is used to collect task execution data from the composite robot and testing equipment in real time. The task execution data includes: timestamps, location trajectories, ambient temperature and humidity, vehicle barcode recognition results, equipment docking logs, and test result data. The structured data encapsulation unit, connected to the full-process data collector, is used to encapsulate the task execution data according to a predefined format to generate traceable digital transportation and testing certificates. The upstream system synchronization interface is connected to the structured data encapsulation unit and is used to transmit the digital transportation and testing credentials and task status back to the corresponding system of the upstream business layer in real time.

7. The system according to claim 1, characterized in that, The system also includes: The visual monitoring and interactive terminal is connected to the central scheduling layer to display the real-time location of all the composite robots in the system, the task queue, the execution progress of each task, and the status of the test equipment; and provides interactive interfaces for manual task initiation, task cancellation, emergency pause, and status query.

8. The system according to any one of claims 1 to 7, characterized in that, The task access and parsing module connects to the upstream business layer through a standardized application programming interface and / or event message bus to realize automatic triggering and parsing of task instructions and reverse synchronization of task status.

9. A method for managing composite robots applied in a smart laboratory, characterized in that, The method is applied to the system as described in any one of claims 1 to 8, and the method comprises: Receive task instructions, which are automatically triggered by the upstream business layer or actively initiated by a manual terminal; The task instructions are parsed, task features are extracted, and a structured task object containing task type, start and end coordinates, material attributes, priority, and deadline is generated. Obtain real-time status snapshots of all available composite robots in the execution layer. The status snapshots include at least position, battery level, vehicle compatibility, and health status. The structured task object and the real-time state snapshot are input into the multi-dimensional dynamic scheduling engine, and multi-objective evaluation is performed based on the preset strategy library to dynamically select the target composite robot. A sequence of composite instructions is issued to the selected target composite robot. The sequence of composite instructions controls the target composite robot to sequentially perform the following actions: going to the material picking point, verifying and loading materials, navigating to the target testing equipment, performing automatic docking, waiting for the test to be completed, unloading materials, and returning to the standby point. Throughout the entire task execution cycle, the status data and environmental data reported by the target composite robot are continuously collected and structured. At key nodes and at the end of the task, the data is synchronized to the upstream business layer to update the material status and close the task process.

10. The method according to claim 9, characterized in that, The steps of inputting the structured task object and the real-time state snapshot into the multi-dimensional dynamic scheduling engine, performing multi-objective evaluation based on a pre-set strategy library, and dynamically selecting the target composite robot include: Based on the task priority and deadline in the structured task object, the available composite robots are filtered by urgency to obtain an initial set of robots; Based on the vehicle or environmental requirements required by the material attributes in the structured task object, the matching degree is calculated with the capability tags of each composite robot in the initial robot set, and composite robots with matching degrees higher than a preset threshold are selected to form a candidate robot set. For the candidate robot set, a score is calculated based on at least one optimization objective among path efficiency, composite robot energy consumption, and global task load balancing, and the final target composite robot is determined based on the score results.

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