A sample pre-treatment transfer process control system

Abstract

The present application relates to the technical field of task scheduling, in particular to a sample pretreatment transfer process control system, which comprises a sample information management module, a sample state tracking module, a path scheduling module, a sample environment control module, a sample processing flow coordination module and a transfer process control module, the present application comprises a double-layer intelligent scheduling architecture composed of a strategy scheduling layer and a path execution layer, dynamically perceives task disturbance, 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 operation of the system; the present application introduces an asymptotic path planning algorithm based on bidirectional potential induced search, and adopts a Bezier curve fitting in the path optimization stage, thereby realizing the collaborative optimization of path smoothing and dynamic obstacle avoidance; the present application introduces a temperature and humidity joint regulation algorithm based on fuzzy control in the sample environment control module, and perceives and adjusts the temperature and humidity and other environmental parameters in the transfer process in real time according to the sample type and detection requirements.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of task scheduling, in particular to a sample pretreatment transfer process control system. BACKGROUND

[0002] In medical examination, food detection, environmental monitoring and other scenes, sample pretreatment is a key step to ensure the accuracy and timeliness of detection. The existing sample pretreatment transfer management system lacks unified sample information management and dynamic scheduling mechanism. The key parameters of sample state, priority and detection requirement cannot be effectively integrated in the transfer process, resulting in delayed task response and low transfer efficiency. The existing path planning method is mostly based on static rules or single-objective optimization, which cannot balance real-time performance, path length, equipment state and sample priority, and the environmental parameters such as temperature and humidity in the transfer process are not real-time perceived and intelligently controlled, which may cause sample denaturation or failure and affect the accuracy of subsequent detection. SUMMARY

[0003] In view of the above situation, in order to overcome the defects of the prior art, the present application provides a sample pretreatment transfer process control system. In view of the problem of lack of sample information management and dynamic scheduling, the present application adopts a double-layer intelligent scheduling architecture composed of a strategy scheduling layer and a path execution layer, dynamically perceives task disturbance, and adjusts the scheduling sequence in real time through a hierarchical population collaborative guidance algorithm, improves the task response efficiency and the flexibility of the whole system operation, and overcomes the problems of delayed response and low transfer efficiency of the traditional system. In view of the problem that the path planning lacks multi-factor balancing, the present application introduces an asymptotic path planning algorithm based on bidirectional potential induced search, and adopts a Bezier curve fitting in the path optimization stage, realizing the collaborative optimization of path smoothing and dynamic obstacle avoidance. In view of the problem that the environmental parameters in the transfer process are not intelligently controlled, the present application sets a sample environment control module, introduces a temperature and humidity joint regulation algorithm based on fuzzy control, and perceives and adjusts the environmental parameters such as temperature and humidity in the transfer process in real time according to the sample type and detection requirement.

[0004] The technical scheme adopted by the present application is as follows: the present application provides a sample pretreatment transfer process control system, which comprises a sample information management module, a sample state tracking module, a path scheduling module, a sample environment control module, a sample processing flow collaborative module and a transfer process control module, and specifically comprises the following contents:

[0005] The sample information management module collects, numbers and stores sample information, and the sample information includes sample ID, sampling time, current position, target position, priority and detection type;

[0006] The sample state tracking module detects the state information of the sample in the transfer process in real time, including a location node, a current processing state and an environmental parameter, receives a sample transfer task, including a task number, a sample ID, a target location, a priority and a detection type;

[0007] The path scheduling module receives the target location, the priority and the detection type of the sample, takes the sample transfer task as a scheduling unit, and obtains an optimal transfer path of the sample according to a multi-objective optimization algorithm based on heuristic search;

[0008] The sample environment control module introduces a temperature and humidity joint adjustment algorithm based on fuzzy control to adjust the environmental parameter in the transfer process.

[0009] The transfer process control module obtains an abnormal condition by using the temperature and humidity joint adjustment algorithm based on fuzzy control according to the execution state information, and sends the abnormal condition to the path scheduling module to regenerate a scheduling execution plan.

[0010] Further, in the path scheduling module, the sample transfer task is taken as a scheduling unit, and an optimal transfer path of the sample is obtained according to a multi-objective optimization algorithm based on heuristic search, specifically including the following steps:

[0011] Step S1: double-layer architecture initialization, a double-layer intelligent scheduling architecture is adopted, including an upper layer and a lower layer, the upper layer is a strategy scheduling layer, and the lower layer is a path execution layer, the path execution layer includes a path planning part and an execution control part;

[0012] Step S2: environmental modeling, a spatial path network graph is constructed, including nodes and edges, the nodes include loading and unloading sites, transfer points and obstacle avoidance areas, and the edges represent feasible path connection relationships;

[0013] Step S3: task receiving, the sample transfer task is parsed, the strategy scheduling layer is triggered, a hierarchical population collaborative guidance algorithm is called, and a task scheduling sequence is obtained;

[0014] Step S4: path planning initialization, the path planning process is initialized according to the task scheduling sequence, the path planning part of the path execution layer is triggered, an asymptotic algorithm based on bidirectional potential induced search is called, and sampling parameters are set, including a sampling radius, a maximum step length and a bias probability;

[0015] Step S5: search path, the asymptotic algorithm based on bidirectional potential induced search is executed to sample and connect in the spatial path network graph, and an initial transfer path is generated;

[0016] Step S6: path optimization, the initial transfer path is smoothed by using a Bezier curve to obtain a processed path;

[0017] Step S7: path output, output the processed path to the execution control part of the path execution layer.

[0018] Further, in step S1, a double-layer intelligent scheduling architecture is adopted, which specifically includes the following contents:

[0019] The policy scheduling layer includes a task analysis part, a disturbance perception part, a scheduling optimization part, and a task release interface.

[0020] The path execution layer performs path search and device control, including a path planning part and an execution control part.

[0021] Further, the policy scheduling layer specifically includes the following contents:

[0022] The task analysis part analyzes the sample transfer task.

[0023] The disturbance perception part perceives disturbance events, including dynamic insertion, cancellation, replacement, and device failure.

[0024] The scheduling optimization part introduces a hierarchical population collaborative guidance algorithm, dynamically generates a task scheduling sequence based on sample transfer tasks and disturbance events.

[0025] The task release interface: issues the task scheduling sequence to the path execution layer.

[0026] Further, the path execution layer specifically includes the following contents:

[0027] The path planning part: loads the asymptotic algorithm based on bidirectional potential induced search, path sampling and connection strategy, and performs sampling parameter determination.

[0028] The execution control part: receives the processed path generated by the path planning module.

[0029] Further, in step S3, the hierarchical population collaborative guidance algorithm is called, which specifically includes the following steps:

[0030] Step S31: task reception, receiving the sample transfer task.

[0031] Step S32: task analysis, analyzing the sample transfer task into fields, including sample ID, current location information, target location, task priority, time limit, and special requirements, to obtain the analyzed task.

[0032] Step S33: policy scheduling layer triggering, packaging the analyzed task into a scheduling request, constructing a scheduling task graph, initializing a scheduling environment, including a spatial path network graph and a transfer resource state.

[0033] 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.

[0034] Step S35: Output, sending the task scheduling sequence to the task publishing interface.

[0035] Furthermore, in step S5, an asymptotic algorithm based on bidirectional potential induction search is executed, which specifically includes the following steps:

[0036] 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;

[0037] 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;

[0038] 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:

[0039] ;

[0040] 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;

[0041] 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:

[0042] ;

[0043] ;

[0044] in, Indicates the current expanded node position, To expand the displacement vector, For the new expansion node location;

[0045] 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 Join the edge set, and Perform collision detection as a new node of the current tree, if there is a collision, iterate steps S52 to S55, set the maximum number of iterations, input the new expansion node position into step S52 for iteration, until the connection is completed;

[0046] Step S56: bidirectional connection, when the starting tree and the target tree are connected, a path segment 1 is formed along the starting tree from the starting point to the connection point, a path segment 2 is formed along the target tree from the ending point to the connection point, and an initial transfer path is obtained by merging.

[0047] The beneficial effects achieved by the above-mentioned scheme are as follows:

[0048] (1) In view of the problem of lack of sample information management and dynamic scheduling, the two-layer intelligent scheduling architecture composed of the strategy scheduling layer and the path execution layer is used to dynamically perceive task disturbance, and the hierarchical population collaborative guidance algorithm is used to adjust the scheduling sequence in real time, thereby improving the task response efficiency and the flexibility of the overall system operation, and overcoming the problems of response lag and low transfer efficiency of the traditional system;

[0049] (2) In view of the problem that the path planning lacks multi-factor weighting, the asymptotic path planning algorithm based on bidirectional potential induced search is introduced, and the Bezier curve fitting is used in the path optimization stage, so as to realize the collaborative optimization of path smoothing and dynamic obstacle avoidance;

[0050] (3) In view of the problem that the environmental parameters in the transfer process are not intelligently controlled, the sample environment control module is set, the temperature and humidity joint regulation algorithm based on fuzzy control is introduced, and the temperature and humidity and other environmental parameters in the transfer process are perceived and adjusted in real time according to the sample type and detection requirements. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 A schematic diagram of a sample pretreatment transfer process control system is provided for the present application;

[0052] Figure 2 A flowchart of the path scheduling module is provided for the present application.

[0053] The accompanying drawings are used to provide a further understanding of the present application, and constitute a part of the specification, together with the embodiments of the present application, to explain the present application, and do not constitute a limitation on the present application. DETAILED DESCRIPTION

[0054] With reference to the drawings and the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0055] Embodiment one, refer to Figure 1 The present application provides a sample pre-treatment transfer process control system, which comprises a sample information management module, a sample state tracking module, a path scheduling module, a sample environment control module, a sample processing flow coordination module and a transfer process control module.

[0056] In this embodiment, the serum sample with sample number SMP_20250609_001 is collected at sampling point A at 9:00 am, and the target is to arrive at pre-treatment center B for centrifugal processing before 9:20 am. The sample is identified as a high-priority sample, and the detection type is "biochemical rapid channel";

[0057] The sample information management module automatically collects the above sample information, including:

[0058] Sample ID: SMP_20250609_001;

[0059] Sampling time: 09:00;

[0060] Current node position: A point;

[0061] Target node position: B point;

[0062] Priority: high;

[0063] Detection type: biochemical - rapid channel;

[0064] The system enters the information into the sample database;

[0065] The sample state tracking module starts to track the state of the sample in real time after the sample enters the transfer process:

[0066] Using an RFID positioning system and a temperature and humidity sensor, the position node (such as A → channel C → B) where the sample is located is continuously updated;

[0067] The temperature and humidity information in the sample storage cabin is collected in real time and uploaded;

[0068] If the temperature and humidity are out of standard or the transmission path is abnormal, the system will generate an abnormal record and send it to the scheduling center;

[0069] The path scheduling module, based on the received transfer task parameters, adopts an improved A* heuristic search algorithm combined with a multi-objective optimization strategy to balance the following multiple factors for path planning:

[0070] Current path length;

[0071] Sample priority;

[0072] Availability of devices along the path;

[0073] Current channel congestion status;

[0074] The planned path is: "Point A → Channel C (cold chain support) → Point B", and the transfer device D is designated to execute it;

[0075] The sample environment control module starts a fuzzy control algorithm to adjust the temperature and humidity in real time during the transportation of the sample through the transfer cabin:

[0076] The current cabin temperature is detected to rise to 28°C and the humidity reaches 75%;

[0077] The air cooling system and dehumidification module are started for adjustment;

[0078] The fuzzy control strategy flexibly adjusts the response speed and adjustment amplitude according to the degree of deviation from the expected value (24°C, 60%);

[0079] If during transportation, the system detects that the transfer device D has an abnormality (such as wheel set jamming, temperature control failure), the transfer process control module will push the abnormal information to the path scheduling module in real time, automatically start the standby path and device (device E, path "A → Channel D → B"), regenerate the scheduling plan, and ensure the safe and on-time delivery of the sample to the target node.

[0080] Example Two, see Figure 2 Based on the above example, the path scheduling module, in this example, the serum sample with sample number SMP_20250609_001 needs to be transferred from the sampling area X to the pretreatment laboratory Y, which includes the following steps:

[0081] Step S1: Initialize the double-layer architecture, start the double-layer scheduling architecture, and the strategy 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;

[0082] Step S2: Environment modeling, pre-construct the spatial network graph of the hospital sampling area and the transfer path, the nodes include the sampling point X, multiple transfer points (such as corridor intersections), loading and unloading points (pretreatment laboratory Y) and obstacle avoidance areas (device storage area), the path scheduling module loads the network graph to prepare for path planning;

[0083] Step S3: Task receiving, the sample information management module collects the sample information of SMP_20250609_001, the task information is pushed to the path scheduling module, the strategy scheduling layer calls the hierarchical population collaborative guidance algorithm, generates a task execution sequence in combination with the priority and the device state, and SMP_20250609_001 is set as a priority processing;

[0084] Step S4: Path planning initialization, the path execution layer starts the path planning part according to the task sequence, sets a sampling radius of 2 meters, a maximum step length of 1.5 meters, and a bias probability of 0.7, and is ready to execute the bidirectional potential induced search asymptotic algorithm;

[0085] Step S5: Search path, the algorithm simultaneously searches paths from the sampling point X and the target point Y, dynamically samples space nodes, avoids obstacle avoidance areas, adjusts the sampling direction by using the potential function, approaches the target point, connects the sampling points, forms a feasible path, and finally obtains an initial path;

[0086] Step S6: Path optimization, the initial transfer path is smoothed by using a Bessel curve to obtain a processed path;

[0087] Step S7: Path output, the automatic transport vehicle starts from the sampling point X and goes to the pre-processing laboratory Y along the planned path according to the path instruction, the system monitors the transport state and environmental parameters in real time during the journey to ensure the safety of the sample.

[0088] Example three, which is based on the above example, in step S1, a double-layer intelligent scheduling architecture is used, which specifically includes the following contents:

[0089] The strategy scheduling layer includes a task analysis part, a disturbance perception part, a scheduling optimization part and a task release interface;

[0090] The path execution layer: performs path search and device control, including a path planning part and an execution control part.

[0091] Example four, which is based on the above example, the strategy scheduling layer specifically includes the following contents:

[0092] The task analysis part analyzes the sample transfer task;

[0093] The disturbance perception part perceives disturbance events, including dynamic insertion, cancellation, replacement and device failure;

[0094] The scheduling optimization part introduces a hierarchical population collaborative guidance algorithm, dynamically generates a task scheduling sequence based on the sample transfer task and the disturbance event;

[0095] The task release interface: the task scheduling sequence is sent to the path execution layer;

[0096] The path execution layer specifically includes the following:

[0097] The path planning part: load the asymptotic algorithm based on bidirectional potential field induced search, path sampling and connection strategy, and determine the sampling parameters;

[0098] The execution control part: receive the processed path finally generated by the path planning module.

[0099] Embodiment five, which is based on the above-mentioned embodiments, the bidirectional potential field induced search asymptotic algorithm includes the following steps:

[0100] Initialization: build two trees, one from the starting point X and one from the ending point Y, prepare the environment information and parameters;

[0101] Calculate the potential energy: calculate the "attractive force" and "repulsive force" according to the current position to determine the direction of movement;

[0102] Adjust the step size: automatically adjust the movement step size according to the potential energy gradient to avoid collision;

[0103] Update the node: move along the gradient direction to generate a new node position;

[0104] Connection detection: try to connect the new node to the nearest node of the opposite tree, and detect whether there is a collision;

[0105] 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;

[0106] Path synthesis: connect the paths of the starting point tree and the ending point tree to form a complete path.

[0107] It should be noted that in this paper, relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0108] Although embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

[0109] The above describes the present application and its embodiments, which are not limited, and the drawings only show one of the embodiments of the present application, and the actual structure is not limited thereto. In general, if a person skilled in the art is inspired thereby, without departing from the purpose of the present application, without creative design, similar structure and embodiments of the technical solution are not creative, and should belong to the protection scope of the present application.

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; 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; In step S3, the 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; 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.

2. The sample pre-treatment and transport process control system according to claim 1, 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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