Transfer process control system in sample pretreatment

Abstract

The invention relates to the technical field of task scheduling, in particular to a transfer process control system in sample pretreatment, which comprises a sample information management module, a sample state tracking module, a path scheduling module, a sample environment control module, a sample treatment flow collaboration module and a transfer process control module, task disturbance is dynamically sensed through a double-layer intelligent scheduling architecture composed of a strategy scheduling layer and a path execution layer, and a scheduling sequence is adjusted in real time through a layered population collaborative guidance algorithm, so that the task response efficiency and the overall operation flexibility of the system are improved; according to the method, an asymptotic path planning algorithm based on bidirectional potential induced search is introduced, and Bezier curve fitting is adopted in a path optimization stage, so that collaborative optimization of path smoothing and dynamic obstacle avoidance is realized; a temperature and humidity combined adjustment algorithm based on fuzzy control is introduced into the sample environment control module, and environment parameters such as temperature and humidity in the transfer process are sensed and adjusted in real time according to sample types and detection requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of task scheduling, and in particular to a transport process control system in sample pre-processing. Background Art

[0002] In many scenarios such as medical testing, food testing, and environmental monitoring, sample pretreatment is a key step to ensure the accuracy and timeliness of testing. The existing sample pretreatment transfer management system lacks a unified sample information management and dynamic scheduling mechanism. Key parameters such as sample status, priority, and testing requirements are not effectively integrated during the transfer process, resulting in delayed task response and low transfer efficiency. Existing path planning methods are mostly based on static rules or single-objective optimization, and cannot take into account the dynamic trade-offs of multiple factors such as real-time performance, path length, equipment status, and sample priority. Environmental parameters such as temperature and humidity during the transfer process are not perceived and intelligently controlled in real time, which can easily lead to sample denaturation or failure, affecting the accuracy of subsequent testing. Summary of the Invention

[0003] In response to the above situation, in order to overcome the defects of the prior art, the present invention provides a sample pre-processing transfer process control system. To address the problem of lack of sample information management and dynamic scheduling, the present invention dynamically perceives task disturbances through a two-layer intelligent scheduling architecture consisting of a strategy scheduling layer and a path execution layer, and adjusts the scheduling sequence in real time through a hierarchical population collaborative guidance algorithm, thereby improving the task response efficiency and the flexibility of the overall system operation, and overcoming the problems of delayed response and low transfer efficiency of traditional systems; to address the problem of lack of multi-factor trade-offs in path planning, the present invention introduces an asymptotic path planning algorithm based on bidirectional potential induced search, and adopts Bezier curve fitting in the path optimization stage to achieve collaborative optimization of path smoothing and dynamic obstacle avoidance; to address the problem that environmental parameters are not intelligently controlled during the transfer process, the present invention sets a sample environment control module and introduces a temperature and humidity joint adjustment algorithm based on fuzzy control, which can perceive and adjust environmental parameters such as temperature and humidity in the transfer process in real time according to the sample type and detection requirements.

[0004] The technical solution adopted by the present invention is as follows: The present invention provides a sample pre-processing transfer process control system, which includes a sample information management module, a sample status tracking module, a path scheduling module, a sample environment control module, a sample processing flow coordination module and a transfer process control module, specifically including the following contents: The sample information management module collects, numbers and stores sample information, including sample ID, sampling time, current location, target location, priority and detection type; The sample status tracking module detects the status information of the sample in transit in real time, including the location node, current processing status and environmental parameters, and receives the sample transit task, including the task number, sample ID, target location, priority and detection type; The path scheduling module receives the target location, priority and detection type of the sample, takes the sample transfer task as the scheduling unit, and obtains the optimal transfer path of the sample according to the multi-objective optimization algorithm based on heuristic search; The sample environment control module introduces a temperature and humidity joint adjustment algorithm based on fuzzy control to adjust the environmental parameters during the transfer process; The transfer process control module uses the temperature and humidity joint adjustment algorithm based on fuzzy control according to the execution status information to obtain abnormal conditions and sends them to the path scheduling module to regenerate the scheduling execution plan.

[0005] Furthermore, in the path scheduling module, the sample transfer task is used as the scheduling unit, and the optimal transfer path of the sample is obtained according to the multi-objective optimization algorithm based on heuristic search, which specifically includes the following steps: Step S1: Initialize the two-layer architecture, using a two-layer intelligent scheduling architecture, including an upper layer and a lower layer. The upper layer is the policy scheduling layer, and the lower layer is the path execution layer. The path execution layer includes a path planning part and an execution control part. Step S2: Environmental modeling, constructing a spatial path network graph, which includes nodes and edges. Nodes include loading and unloading locations, transfer points, and obstacle avoidance areas, and edges represent the connection relationship of feasible paths. Step S3: Task reception, parsing of sample transfer tasks, triggering of the policy scheduling layer, calling of the hierarchical population collaborative guidance algorithm, and obtaining of the task scheduling sequence; Step S4: Path planning initialization: According to the task scheduling sequence, the path planning process is initialized, the path planning part of the path execution layer is triggered, the asymptotic algorithm based on bidirectional potential induced search is called, and the sampling parameters are set, including sampling radius, maximum step size and bias probability; Step S5: searching for paths, executing an asymptotic algorithm based on bidirectional potential induced search, sampling and connecting in the spatial path network graph, and generating an initial transit path; Step S6: Path optimization, using Bezier curves to smooth the initial transfer path to obtain a processed path; Step S7: Path output, outputting the processed path to the execution control part of the path execution layer.

[0006] Furthermore, in step S1, a two-layer intelligent scheduling architecture is adopted, which specifically includes the following contents: Strategy scheduling layer: including task parsing, disturbance perception, scheduling optimization and task publishing interface; Path execution layer: performs path search and device control, including path planning part and execution control part; Furthermore, the policy scheduling layer specifically includes the following: The task analysis part analyzes the sample transfer task; The disturbance sensing part senses disturbance events, including dynamic insertion, cancellation, replacement, and equipment failure; The scheduling optimization part introduces a hierarchical population collaborative guidance algorithm to dynamically generate task scheduling sequences based on sample transfer tasks and disturbance events; Task release interface: sends the task scheduling sequence to the path execution layer; Furthermore, the path execution layer specifically includes the following: Path planning part: loading the asymptotic algorithm based on bidirectional potential induced search, path sampling and connection strategy, and performing sampling parameter determination; Execution control part: receives the processed path finally generated by the path planning module.

[0007] Furthermore, in step S3, a hierarchical population collaborative guidance algorithm is called, which specifically includes the following steps: Step S31: Task reception, receiving the sample transfer task; Step S32: Task parsing, parsing the sample transfer task into fields, including sample ID, current location information, target location, task priority, time limit and special requirements, to obtain the parsed task; Step S33: The policy scheduling layer is triggered to package the parsed tasks into a scheduling request, construct a scheduling task graph, and initialize the scheduling environment, including the spatial path network graph and the transit resource status; Step S34: mobilizing a hierarchical population collaborative guidance algorithm, including an outer layer and an inner layer. The outer layer uses a genetic algorithm to generate multiple task sequence combinations, and screens the scheduling sequence through a fitness function. The inner layer uses an ant colony optimization algorithm to optimize the scheduling sequence to obtain a task scheduling sequence. Step S35: Output, sending the task scheduling sequence to the task publishing interface.

[0008] Furthermore, in step S5, an asymptotic algorithm based on bidirectional potential induction search is executed, which specifically includes the following steps: Step S51: Input preparation, build the starting tree and target tree, each tree includes a node set and an edge set, set the current expansion node, environment information, current step size, maximum step size, minimum step size, starting position, end position, adaptive parameters and neighborhood radius; Step S52: Calculate the potential energy function and gradient, use the current expanded node to calculate the potential energy and gradient, and calculate the gradient of the potential energy function at the current expanded node; Step S53: Adaptive step size adjustment, adjusting the current step size according to the modulus of the gradient of the potential energy function at the current expansion node. The formula used is as follows: ; in, Indicates the current step size, is the minimum step size, is the maximum step length, is the step size attenuation factor, is the modulus of the gradient of the potential energy function at the current expansion node; Step S54: Re-expand, adjust the current expanded node according to the gradient direction, and obtain a new expanded node position. The formula used is as follows: ; ; in, Indicates the current expanded node position, To expand the displacement vector, For the new expansion node location; Step S55: Node connection, connect the new expanded node position to the nearest node in the target tree, perform collision detection, if there is no collision, perform connection, add the connection to the edge set, if there is a collision, Join the node set and Add edge set, Perform collision detection as a new node of the current tree. If there is a collision, iterate step S52 to step S55, set the maximum number of iterations, input the new expanded node position into step S52 and iterate until the connection is completed; Step S56: Bidirectional connection. When the starting tree and the target tree are connected, path segment 1 is formed from the starting point to the connection point along the starting tree, and path segment 2 is formed from the end point to the connection point along the target tree, and they are merged to form the initial transit path.

[0009] The beneficial effects achieved by the present invention using the above scheme are as follows: (1) To address the problem of lack of sample information management and dynamic scheduling, the present invention adopts a two-layer intelligent scheduling architecture consisting of a strategy scheduling layer and a path execution layer to dynamically perceive task disturbances and adjust the scheduling sequence in real time through a hierarchical population collaborative guidance algorithm, thereby improving the task response efficiency and the flexibility of the overall system operation, and overcoming the problems of delayed response and low transfer efficiency of traditional systems; (2) To address the problem of lack of multi-factor trade-offs in path planning, the present invention introduces an asymptotic path planning algorithm based on bidirectional potential induced search and adopts Bezier curve fitting in the path optimization stage to achieve the coordinated optimization of path smoothing and dynamic obstacle avoidance. (3) To address the problem that environmental parameters are not intelligently controlled during the transfer process, the present invention sets up a sample environment control module and introduces a temperature and humidity joint adjustment algorithm based on fuzzy control. According to the sample type and detection requirements, the temperature and humidity and other environmental parameters during the transfer process are sensed and adjusted in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is a schematic diagram of a sample transport process control system proposed by the present invention; Figure 2 This is a flow chart of the path scheduling module proposed in the present invention.

[0011] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0012] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0013] Example 1, see Figure 1 The present invention provides a sample pre-processing and transfer process control system, which includes a sample information management module, a sample status tracking module, a path scheduling module, a sample environment control module, a sample processing flow coordination module and a transfer process control module; In this example, a serum sample numbered SMP_20250609_001 was collected at sampling site A at 9:00 AM, with the goal of arriving at pre-processing center B for centrifugation by 9:20 AM. The sample was identified by the system as a high-priority sample and assigned the "biochemical fast track" test type. The sample information management module automatically collects the above sample information, including: Sample ID: SMP_20250609_001; Sampling time: 09:00; Current node position: point A; Target node location: point B; Priority: High Test Type: Biochemical-Fast Track; The system enters the information into the sample database; The sample status tracking module starts tracking the status of the sample in real time after it enters the transfer process: Use RFID positioning system and temperature and humidity sensors to continuously update the sample location node (e.g. A → channel C → B); Collect temperature and humidity information inside the sample storage cabin in real time and upload it; If the temperature and humidity exceed the standard or the transmission path is abnormal, the system will generate an abnormal record and send it to the dispatch center; Based on the received transfer task parameters, the route scheduling module uses an improved A* heuristic search algorithm combined with a multi-objective optimization strategy to balance the following factors for route planning: Current path length; Sample priority; Availability of equipment passed through; Current channel congestion status; Plan the route: "Point A → Channel C (cold chain support) → Point B" and specify transfer device D for execution; The sample environment control module activates the fuzzy control algorithm to adjust the temperature and humidity in real time while the sample is being transported through the transfer cabin: It is detected that the current cabin temperature has risen to 28°C and the humidity has reached 75%; Start the air cooling system and dehumidification module for adjustment; The fuzzy control strategy flexibly adjusts the response speed and adjustment range according to the degree of deviation from the expected value (24°C, 60%); If the system detects an abnormality in transfer equipment D during transportation (such as wheel jam or temperature control failure), the transfer process control module will push the abnormal information to the path scheduling module in real time, automatically start the backup path and equipment (equipment E, path "A → Channel D → B"), and regenerate the scheduling plan to ensure that the samples are delivered safely and on time to the target node.

[0014] Example 2, see Figure 2 This embodiment is based on the above embodiment. The path scheduling module, in this embodiment, the serum sample with sample number SMP_20250609_001 needs to be transported from point X in the sampling area to point Y in the pretreatment laboratory, specifically includes the following steps: Step S1: Initialize the two-layer architecture and start the two-layer scheduling architecture. The policy scheduling layer is ready to receive task instructions. The path execution layer starts the path planning and control module and is ready to receive path planning tasks. Step S2: Environmental modeling: Pre-constructing a spatial network diagram of the hospital sampling area and transfer routes. Nodes include sampling point X, multiple transfer points (such as corridor intersections), loading and unloading points (pretreatment laboratory Y), and obstacle avoidance areas (equipment storage areas). The path scheduling module loads this network diagram to prepare for path planning. Step S3: Task reception: The sample information management module collects the SMP_20250609_001 sample information and pushes the task information to the path scheduling module. The policy scheduling layer calls the hierarchical population collaborative guidance algorithm, combines the priority and device status, and generates a task execution sequence. SMP_20250609_001 is set as the priority. Step S4: Path planning initialization: The path execution layer starts the path planning part according to the task sequence, sets the sampling radius to 2 meters, the maximum step length to 1.5 meters, and the bias probability to 0.7, and prepares to execute the bidirectional potential induction search asymptotic algorithm; Step S5: Path search. The algorithm searches for paths simultaneously from sampling point X and target point Y, dynamically samples spatial nodes, avoids obstacle avoidance areas, uses potential functions to adjust the sampling direction, approaches the target point, connects sampling points, forms a feasible path, and finally obtains the initial path. Step S6: Path optimization, using Bezier curves to smooth the initial transfer path to obtain a processed path; Step S7: Path output. The automatic transport vehicle starts from sampling point X according to the path instruction and goes to the pretreatment laboratory Y along the planned path. During the journey, the system monitors the transportation status and environmental parameters in real time to ensure the safety of the samples.

[0015] Embodiment 3: This embodiment is based on the above embodiment. In step S1, a two-layer intelligent scheduling architecture is adopted, which specifically includes the following contents: Strategy scheduling layer: including task parsing, disturbance perception, scheduling optimization and task publishing interface; Path execution layer: performs path search and device control, including path planning part and execution control part.

[0016] Embodiment 4: This embodiment is based on the above embodiment, and the policy scheduling layer specifically includes the following contents: The task analysis part analyzes the sample transfer task; The disturbance sensing part senses disturbance events, including dynamic insertion, cancellation, replacement, and equipment failure; The scheduling optimization part introduces a hierarchical population collaborative guidance algorithm to dynamically generate task scheduling sequences based on sample transfer tasks and disturbance events; Task release interface: sends the task scheduling sequence to the path execution layer; The path execution layer specifically includes the following: Path planning part: loading the asymptotic algorithm based on bidirectional potential induced search, path sampling and connection strategy, and performing sampling parameter determination; Execution control part: receives the processed path finally generated by the path planning module.

[0017] Embodiment 5, based on the above embodiment, the bidirectional potential induction search asymptotic algorithm includes the following steps: Initialization: Create two trees, one starting from the starting point X and the other starting from the end point Y, and prepare environment information and parameters; Calculate potential energy: Calculate "attraction" and "repulsion" based on the current position to determine the direction of travel; Adjust step length: Automatically adjust the moving step length according to the potential energy gradient to avoid collision; Update node: Move along the gradient direction to generate a new node position; Connection detection: try to connect the new node to the nearest node of the other tree to detect collision; Iterative expansion: If there is a collision, continue to adjust the node position and step size and repeat the expansion; if there is no collision, complete the connection; Path synthesis: Connect the paths of the starting tree and the ending tree to form a complete path.

[0018] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0019] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

[0020] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A sample pre-treatment transport process control system, characterized by: It includes sample information management module, sample status tracking module, path scheduling module, sample environment control module, sample processing flow coordination module and transfer process control module, specifically including the following contents: The sample information management module collects, numbers and stores sample information; The sample status tracking module detects the status information of the sample in transit in real time, including the location node, current processing status and environmental parameters, and receives the sample transit task, including the task number, sample ID, target location, priority and detection type; The path scheduling module receives the target location, priority and detection type of the sample, takes the sample transfer task as the scheduling unit, and obtains the optimal transfer path of the sample according to the multi-objective optimization algorithm based on heuristic search; The sample environment control module introduces a temperature and humidity joint adjustment algorithm based on fuzzy control to adjust the environmental parameters during the transfer process; The transfer process control module uses the temperature and humidity joint adjustment algorithm based on fuzzy control according to the execution status information to obtain abnormal conditions and sends them to the path scheduling module to regenerate the scheduling execution plan.

2. A sample pre-treatment and transport process control system according to claim 1, characterized in that: In the path scheduling module, the sample transfer task is used as the scheduling unit, and the optimal transfer path of the sample is obtained according to the multi-objective optimization algorithm based on heuristic search. Specifically, the following steps are included: Step S1: Initialize the two-layer architecture, using a two-layer intelligent scheduling architecture, including an upper layer and a lower layer. The upper layer is the policy scheduling layer, and the lower layer is the path execution layer. The path execution layer includes a path planning part and an execution control part. Step S2: Environmental modeling, constructing a spatial path network graph, which includes nodes and edges. Nodes include loading and unloading locations, transfer points, and obstacle avoidance areas, and edges represent the connection relationship of feasible paths. Step S3: Task reception, parsing of sample transfer tasks, triggering of the policy scheduling layer, calling of the hierarchical population collaborative guidance algorithm, and obtaining of the task scheduling sequence; Step S4: Path planning initialization: According to the task scheduling sequence, the path planning process is initialized, the path planning part of the path execution layer is triggered, the asymptotic algorithm based on bidirectional potential induced search is called, and the sampling parameters are set, including sampling radius, maximum step size and bias probability; Step S5: searching for paths, executing an asymptotic algorithm based on bidirectional potential induced search, sampling and connecting in the spatial path network graph, and generating an initial transit path; Step S6: Path optimization, using Bezier curves to smooth the initial transfer path to obtain a processed path; Step S7: Path output, outputting the processed path to the execution control part of the path execution layer.

3. The sample pre-treatment and transport process control system according to claim 2, characterized in that: In step S1, a two-layer intelligent scheduling architecture is adopted, which specifically includes the following contents: Strategy scheduling layer: including task parsing, disturbance perception, scheduling optimization and task publishing interface; Path execution layer: performs path search and device control, including path planning part and execution control part; The policy scheduling layer specifically includes the following: The task analysis part analyzes the sample transfer task; The disturbance sensing part senses disturbance events, including dynamic insertion, cancellation, replacement, and equipment failure; The scheduling optimization part introduces a hierarchical population collaborative guidance algorithm to dynamically generate task scheduling sequences based on sample transfer tasks and disturbance events; Task release interface: sends the task scheduling sequence to the path execution layer; The path execution layer specifically includes the following: Path planning part: loading the asymptotic algorithm based on bidirectional potential induced search, path sampling and connection strategy, and performing sampling parameter determination; Execution control part: receives the processed path finally generated by the path planning module.

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