Rotor identification method and device based on magnetic suspension flexible assembly line

Abstract

The invention relates to a rotor identification method and device based on a magnetic suspension flexible assembly line, and the method comprises the steps: activating a pre-generated topological model; periodically scanning data of a slave station driver mover encoder, obtaining real-time position and speed data of a mover, and mapping the real-time position and speed data to corresponding coordinates; performing noise filtering and smoothing processing on the data; selecting a corresponding recognition algorithm according to the line body size, the bus communication frequency, the mover preset task sequence and the processed speed data; performing association analysis on the processed data to generate rotor track data; checking the trajectory data, and performing early warning if the trajectory data is abnormal; and the data passing the verification is updated to a database, a control instruction set is generated based on the topological model coordinates, the mover motion state and external instruction parameters, and the control instruction set is output to a path planning system, so that multi-mover high-precision real-time identification and dynamic adaptation control are realized, and support is provided for asynchronous scheduling.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing technology, and in particular to a mover identification method and device based on a magnetic levitation flexible assembly line. Background Art

[0002] With the growing market demand for customized and personalized products, small-batch, diversified production models are becoming mainstream. To address this trend, companies need to frequently switch product types during the production process. However, in traditional conveyor line systems, the design of the production line is often customized based on the specific processing technology. Every time the product type changes, the production line needs to be redesigned and adjusted, which not only increases the cost of production line upgrades but also prolongs production preparation time. In contrast, flexible conveyor systems utilize a modular and structured design approach, offering extremely high reusability. With flexible conveyor systems, companies can retain existing intelligent equipment and only need to replace the operating logic, significantly reducing the cost and time of production line adjustments.

[0003] In magnetic levitation flexible conveying systems, the asynchronous motion of multiple movers is a core feature. This asynchronous motion relies on accurate identification and control of multiple movers. Therefore, mover identification technology has become a key technology for magnetic levitation flexible conveying systems. However, existing mover identification methods often struggle to balance accuracy and real-time performance when faced with complex linear shapes, high-speed moving movers, and diverse application scenarios, resulting in numerous challenges in practical application. Summary of the Invention

[0004] The main purpose of the present invention is to provide a mover identification method and device based on a magnetic levitation flexible assembly line, by solving the problem of high-precision identification and dynamic adaptive control in the asynchronous operation scenario of multiple movers in the magnetic levitation flexible assembly line, through intelligent algorithm selection, time-space correlation analysis and multi-level verification mechanism, to achieve high-precision real-time identification and dynamic adaptive control of multiple movers in complex production environments, and provide high-flexibility and high-reliability technical support for asynchronous scheduling in complex production scenarios.

[0005] To achieve the above object, the present invention provides a mover identification method based on a magnetic levitation flexible assembly line, comprising the following steps:

[0006] Activate a pre-generated topology model, which is generated based on a virtual configuration file of the pipeline controller. The configuration file includes line size data, bus communication frequency data, and preset task sequence data for each mover.

[0007] Periodically scan the rotor encoder data of each slave drive, obtain the real-time position and speed data of each rotor, and map the position data of each rotor to the corresponding coordinates of the topological model;

[0008] Perform noise filtering and smoothing processing on the real-time position and speed data of the rotor;

[0009] Based on the linear dimension data, bus communication frequency data, preset task sequence data of each rotor, and the processed rotor speed data, select the corresponding recognition algorithm;

[0010] Perform correlation analysis on the processed real-time position and speed data of the rotor according to the selected recognition algorithm to generate rotor trajectory data;

[0011] Verify the rotor trajectory data. If the verification is abnormal, trigger an alarm;

[0012] Update the rotor trajectory data that passes the verification to the rotor real-time status database, and generate a control instruction set based on the coordinates of the topological model and the rotor motion state, combined with external instruction parameters, and output it to the path planning system.

[0013] Further, before the step of activating the pre-generated topological model, it further includes:

[0014] Parse the linear dimension data in the configuration file of the pipeline controller;

[0015] Select the corresponding topological template according to the linear dimension data. The topological template includes a ring template, a rectangular template, and a custom polygon template;

[0016] Based on the selected topological template, load the motion constraint data of each rotor to generate a digital linear topological model including normalized virtual coordinates.

[0017] Further, the step of periodically scanning the rotor encoder data of each slave drive includes:

[0018] Initiate periodic data polling for each slave drive to obtain the real-time position and speed data of each rotor encoder;

[0019] Add a timestamp identifier to the data corresponding to each rotor, and synchronize the data of different drives in time sequence;

[0020] Based on the synchronized rotor speed data, map the real-time position of each rotor to the normalized virtual coordinates of the topological model.

[0021] Further, the step of performing noise filtering and smoothing processing on the real-time position and speed data of the rotor includes:

[0022] Dynamically adjust the window length of data processing according to the real-time speed data of each mover. The higher the mover speed, the smaller the window, to adapt to the requirement of capturing the trajectory details of high-speed movers;

[0023] Perform weighted average processing on the position and speed data within the window. The weight decreases with the timeliness of the data to suppress noise interference and enhance trajectory continuity.

[0024] Further, the step of selecting the corresponding recognition algorithm includes:

[0025] When the system computing power is evaluated to reach the pre-set sufficient computing power threshold and the current topological form is not homeomorphic to the circular topological form, select the improved Hungarian algorithm. The improved Hungarian algorithm can adapt to any topological form. Once it is detected that the current topological form is not homeomorphic to the circular topological form, the improved Hungarian algorithm must be selected;

[0026] When the bus communication frequency is monitored to be lower than the preset frequency threshold, or the hardware stability is lower than the preset stability threshold and multiple verifications are required based on the system operation requirements, select the frame matching algorithm;

[0027] When the system computing power is evaluated to be lower than the preset computing power standard and the corresponding topological structure is analyzed and determined to be a simple structure homeomorphic to the circle, select the sequence tracking algorithm.

[0028] Further, the step of performing correlation analysis on the processed real-time position and speed data of the mover according to the selected recognition algorithm includes:

[0029] If the selected recognition algorithm is the improved Hungarian algorithm, optimize the matching priority between the mover and the topological coordinate nodes based on the real-time position change of the mover to solve the allocation conflict when the trajectories of multiple movers cross;

[0030] If the frame matching algorithm is selected, extract the displacement features of the mover in adjacent data frames to enhance the continuity matching accuracy of the mover position across frames;

[0031] If the sequence tracking algorithm is selected, determine the logical order of the mover operation based on the current position, speed of the mover and the preset task sequence.

[0032] Further, the step of verifying the mover trajectory data includes:

[0033] Verify the integrity of the data frame. If the verification code deviation exceeds the allowable value, mark it as an abnormal frame;

[0034] Verify the continuity of the mover trajectory. If the acceleration exceeds the allowable range, determine that the trajectory has a jump;

[0035] Verify whether the mover arrangement order conforms to the preset rules, and trigger an early warning if the minimum safety distance is violated.

[0036] Further, after the step of verifying the abnormality, the following steps are further included:

[0037] Execute a retransmission request for the abnormal frame, and mark the corresponding mover as offline if the retransmission fails;

[0038] Start trajectory backtracking analysis for the mover with trajectory jump, and perform collision verification with the trajectories of neighboring movers;

[0039] Trigger a hierarchical early warning according to the deviation degree, including recording the abnormality, sending an audible and visual alarm or freezing the mover.

[0040] Further, the step of generating the control instruction set includes:

[0041] Assign an independent data version number to each mover, and use the optimistic lock mechanism to solve the concurrent writing conflict;

[0042] Based on the mover motion state, topological coordinates, and external instruction parameters, calculate the predicted path for the next N cycles;

[0043] Generate a dynamic control instruction set according to the predicted path, and the instruction set includes braking instructions, speed adjustment instructions, and path replanning instructions.

[0044] The present invention also provides a mover identification device based on a magnetic levitation flexible pipeline, including:

[0045] A topology construction module for activating a pre-generated topology model;

[0046] A data mapping module for periodically scanning the mover encoder data of each slave station driver, obtaining the real-time position and speed data of each mover, and mapping the position data of each mover to the corresponding coordinates of the topology model;

[0047] A data processing module for filtering out noise and smoothing the real-time position and speed data of the mover;

[0048] An algorithm matching module for selecting a corresponding identification algorithm based on the line body size data, bus communication frequency data, preset task sequence data of each mover, and the processed mover speed data;

[0049] A data processing module for performing correlation analysis on the processed real-time position and speed data of the mover according to the selected identification algorithm to generate mover trajectory data;

[0050] A data verification module for verifying the mover trajectory data, and triggering an early warning if the verification is abnormal;

[0051] A data application module is used to update the verified mover trajectory data to the mover real-time status database, and generate a control instruction set based on the coordinates of the topological model and the mover motion status, combined with external instruction parameters, and output it to the path planning system.

[0052] The present invention also provides a computer device, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps of the above-mentioned mover recognition method based on the magnetic levitation flexible pipeline are implemented.

[0053] The mover recognition method and device based on the magnetic levitation flexible pipeline provided by the present invention have the following beneficial effects: By constructing a topological model, the present invention can accurately map the mover position, provide an accurate coordinate reference for recognition, greatly improve the mover positioning accuracy in a complex pipeline environment, and reduce the assembly error rate. In terms of data acquisition, the mover encoder data is scanned periodically and synchronized, and at the same time, the data processing is dynamically optimized according to the real-time speed of the mover, improving the data quality and processing efficiency, saving time, and contributing to the efficient operation of the pipeline. At the same time, in terms of algorithm selection, it can be flexibly adapted according to various data characteristics. Compared with a single algorithm, the present invention can achieve efficient mover recognition and trajectory generation under different working conditions. Then, for abnormal situations, through multi-dimensional verification, problems such as trajectory jumps and too small spacing can be detected and processed in a timely manner, ensuring the safety and stability of the pipeline and reducing downtime losses. In the system control link, the verified trajectory data is updated to the database, and precise control instructions are generated using various mechanisms, improving the production fluency and coordination, and ultimately improving the production efficiency and product quality. Description of the Drawings

[0054] Figure 1 is a schematic flow chart of the mover recognition method based on the magnetic levitation flexible pipeline in an embodiment of the present invention;

[0055] Figure 2 is a structural block diagram of the mover recognition device based on the magnetic levitation flexible pipeline in an embodiment of the present invention.

[0056] The realization, functional characteristics and advantages of the object of the present invention will be further described with reference to the embodiments and the drawings. Detailed Embodiments

[0057] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, and are not used to limit the present invention.

[0058] Refer to Figure 1 , which is a schematic flow chart of a mover recognition method based on the magnetic levitation flexible pipeline proposed by the present invention, and includes the following steps:

[0059] S1. Activate the pre-generated topology model;

[0060] S2. Periodically scan the mover encoder data of each slave drive to obtain the real-time position and speed data of each mover, and map the position data of each mover to the corresponding coordinates of the topology model;

[0061] S3. Filter out noise and smooth the real-time position and speed data of the mover;

[0062] S4. Based on the line body size data, bus communication frequency data, preset task sequence data of each mover, and the processed mover speed data, select the corresponding recognition algorithm;

[0063] S5. Perform correlation analysis on the processed real-time position and speed data of the mover according to the selected recognition algorithm to generate mover trajectory data;

[0064] S6. Verify the mover trajectory data. If the verification is abnormal, trigger an alarm;

[0065] S7. Update the verified mover trajectory data to the mover real-time status database, and based on the coordinates of the topology model and the mover motion status, combined with external instruction parameters, generate a control instruction set and output it to the path planning system.

[0066] As described in step S1 above, before implementing the mover recognition method, it is necessary to first construct a topology model. First, parse the line body size data in the pipeline controller configuration file, select a suitable topology template from the circular template, rectangular template, and custom polygon template according to this data, and then load the motion constraint data of each mover to generate a digital line body topology model containing normalized virtual coordinates. This model is the basic framework of the entire mover recognition process. Activating the line body topology model provides a unified coordinate system and structural reference for the subsequent steps, facilitating the association of the actual operation data of the mover with it.

[0067] As described in step S2 above, by initiating periodic data polling for each slave drive, the real-time position and speed data of each mover encoder can be obtained. To ensure the accuracy and traceability of the data, a timestamp identifier is added to the data corresponding to each mover, and the data of different drives is synchronized in time sequence. After synchronization, based on the synchronized mover speed data, the real-time position of each mover is mapped to the normalized virtual coordinates of the topology model activated in step S1. This step realizes the docking of the actual operation state data of the mover with the model coordinate system, enabling the subsequent processing and analysis of the mover data to be based on a unified coordinate reference and laying a foundation for accurately identifying the mover trajectory.

[0068] As described in step S3 above, the window length of data processing is dynamically adjusted according to the real-time speed data of each mover. The higher the speed, the smaller the window, which can adapt to the need of high-speed movers to capture trajectory details. Then, weighted average processing is performed on the position and speed data within the window, and the weights decrease with the timeliness of the data, so as to suppress noise interference and enhance trajectory continuity. Through this processing, noise interference in the collected data can be removed, making the position and speed data of the mover smoother and more accurate, and providing a reliable data basis for the accurate application of subsequent recognition algorithms.

[0069] As described in step S4 above, when the system computing power is evaluated to reach the pre-set sufficient computing power threshold and the current topological form is not homeomorphic to the ring topological form, the improved Hungarian algorithm is selected. Among them, the improved Hungarian algorithm can adapt to any topological form, and the non-homeomorphism of the ring topological form means that the current topological form is different from the ring topology in terms of topological structure and cannot be continuously deformed into the ring topological form. Once it is detected that the current topological form is not homeomorphic to the ring topological form, the improved Hungarian algorithm must be selected, which can achieve efficient operation under the conditions of system computing power and topological form. When the bus communication frequency is monitored to be lower than the preset frequency threshold, or the hardware stability is lower than the preset stability threshold and multiple checks are required based on the system operation requirements, the frame matching algorithm is selected; this ensures the stable operation of the system in case of communication or hardware anomalies. When the system computing power is evaluated to be lower than the preset computing power standard and the corresponding topological structure is analyzed and determined to be a simple structure homeomorphic to the ring, that is, when the conditions such as few structural components and simple connection relationships are met, the sequence tracking algorithm is selected to effectively process the simple topological structure with limited computing power.

[0070] As described in step S5 above, if the selected recognition algorithm is the improved Hungarian algorithm, the matching priority between the mover and the topological coordinate nodes is optimized based on the real-time position change of the mover to solve the allocation conflict when the trajectories of multiple movers cross, so as to accurately associate the mover position data to generate a trajectory; if the frame matching algorithm is selected, the displacement features of the mover in adjacent data frames are extracted to enhance the continuity matching accuracy of the mover position across frames, and the mover trajectory is obtained through the processing of multiple frames of data; if the sequence tracking algorithm is selected, based on the current position, speed and preset task sequence of the mover, the logical order of the mover operation is determined, and then coherent mover trajectory data is generated. Through step S5, the processed mover data is deeply analyzed according to the selected algorithm to generate trajectory data reflecting the mover movement path.

[0071] As described in step S6 above, first, perform integrity verification on the data frames of the mover trajectory data. If the checksum deviation exceeds the allowable value, mark it as an abnormal frame. Then, verify the continuity of the mover trajectory. If the acceleration exceeds the allowable range, it is determined that the trajectory has a jump. Finally, verify whether the mover arrangement order conforms to the preset rules. If the minimum safety distance is violated, a warning is triggered. Through multi-dimensional data verification, possible errors or abnormal situations in the mover trajectory data can be detected in a timely manner, ensuring the reliability of the monitoring of the mover operation status.

[0072] As described in step S7 above, when updating the data that passes the verification to the database, assign an independent data version number to each mover and use the optimistic lock mechanism to solve concurrent write conflicts. Based on the mover motion state, topological coordinates, and external instruction parameters, calculate the predicted path for the next N cycles; generate a dynamic control instruction set according to the predicted path, and the instruction set includes braking instructions, speed adjustment instructions, and path replanning instructions. If the verification is abnormal, send a retransmission request for the abnormal frame. If the retransmission fails, mark the corresponding mover as an offline state; start trajectory backtracking analysis for the mover with a trajectory jump and perform collision verification with the trajectories of adjacent movers; trigger hierarchical warnings according to the deviation degree, including recording the abnormality, sending an audible and visual alarm, or freezing the mover. This step realizes the use of accurate mover trajectory data to update the database, provides real-time status information for the system, and generates a control instruction set based on the mover status and outputs it to the path planning system to achieve precise control of the maglev flexible production line.

[0073] In a specific embodiment, in an electronic product manufacturing workshop, 15 movers are distributed on a circular maglev production line with a circumference of 60 meters to carry various electronic components. The line body size data obtained from the configuration file of the production line controller is a circular layout, with a radius of 9.55 m and a bus communication frequency of 1 kHz. The preset task sequence of the movers adopts a cyclic distribution mode. By monitoring the CPU occupancy rate (92%) and memory usage rate (85%) of the monitoring control server, it is determined that the current system computing power is 48%, which is significantly lower than the preset sufficient computing power threshold (80%); perform morphological comparison between the circular layout and the standard circular topology, and confirm that there is a continuous bijective mapping relationship (i.e., homeomorphic) between the two; select the circular template from the topological template library, load the mover motion constraints (maximum speed 1.5 m / s, centripetal acceleration limit 0.6 m / s²), and generate a digital topological model with the 3 o'clock position (9.55, 0) as the origin.

[0074] After the model construction is completed, activate the topological model. Scan the mover encoder data of each slave drive at a 1-millisecond cycle to obtain the real-time position and speed data of each mover. For example, the drive where mover C3 is located feeds back its encoder data, showing the real-time position as (4.775000, 8.270000) and the speed as 1.2 m / s; the position of mover D7 is (-9.549000, 0.001000) and the speed is 0.8 m / s, etc. Each mover data contains coordinates with micron-level precision. For example, the position data of mover E2 is accurate to (6.752000, 6.752000). Add IEEE 1588 precise timestamps to all data and control the timing deviation of different drives within ±50 microseconds through the PTP protocol. Based on the synchronized data, accurately map the positions of each mover to the normalized virtual coordinates of the topological model.

[0075] Filter and smooth the noise of the real-time position and speed data of the movers. Take mover F5 as an example, whose speed is 1.0 m / s, and the system automatically selects a 7-point sliding window for processing. The sequence of position data within the current window is (8.270100, 4.774900), (8.270095, 4.774910), (8.270088, 4.774915), (8.270082, 4.774920), (8.270078, 4.774925), (8.270073, 4.774930), (8.270070, 4.774935). Perform weighted averaging according to the exponentially decaying weights, with the weight of the latest data being 0.35, and finally obtain the smoothed position as (8.270085, 4.774920).

[0076] Based on the condition that the system computing power is insufficient (48%) and the topology is homeomorphic, automatically select the sequence tracking algorithm as the main recognition method. When the algorithm runs, it only predicts the trajectory for the next 200 ms (the original design is 500 ms), and the detection range is reduced to the adjacent 3 movers (the original global detection). When the distance between mover G4 and the adjacent mover H8 is less than the safety value of 0.5 m for 5 consecutive cycles, the system automatically switches to the frame matching algorithm for cross-frame continuity verification, and uses SIFT feature point matching to ensure the trajectory coherence, with the matching accuracy reaching ±10 microns.

[0077] During the trajectory verification process, when it is detected that the position difference of mover J3 reaches 0.000125 m (125 microns) for two consecutive frames, exceeding the allowable threshold of 95 microns, it is immediately marked as a trajectory jump anomaly. If the same mover triggers 3 jump warnings within 1 second, the automatic isolation program is started, marking the mover as offline and sending a maintenance request.

[0078] When the mover K9 approaches the annular connection point (within the range of coordinates (9.55, 0) ± 0.1 m), the hierarchical protection mechanism is triggered in sequence: First, the speed is limited to less than 0.3 m / s; if it continues to move towards the boundary, an emergency stop command (EtherCAT command 0x6040 = 0x000B) is sent within 10 ms; when the final position exceeds (9.55 ± 0.005) m, the drive enable is immediately cut off and the out-of-bounds fault code 0x8F01 is reported.

[0079] The improved Hungarian algorithm is only used for offline trajectory analysis when the engineer manually enables the diagnostic mode. During online operation, the system strictly adopts a working mode with the sequence tracking algorithm as the main and the frame matching algorithm as the auxiliary. The control instructions of all movers are managed by the optimistic lock mechanism to ensure that the system can still operate stably under a low computing power state of 45%.

[0080] Refer to Figure 2 , which is the structural block diagram of the mover identification device based on the magnetic levitation flexible pipeline in an embodiment of the present invention, including:

[0081] The topology construction module is used to activate the pre-generated topology model;

[0082] The data mapping module is used to periodically scan the mover encoder data of each slave drive, obtain the real-time position and speed data of each mover, and map the position data of each mover to the corresponding coordinates of the topology model;

[0083] The data processing module is used to filter out noise and smooth the real-time position and speed data of the mover;

[0084] The algorithm matching module is used to select the corresponding identification algorithm based on the line body size data, bus communication frequency data, preset task sequence data of each mover, and the processed mover speed data;

[0085] The data processing module is used to perform correlation analysis on the processed real-time position and speed data of the mover according to the selected identification algorithm to generate mover trajectory data;

[0086] The data verification module is used to verify the mover trajectory data, and if the verification is abnormal, an early warning is triggered;

[0087] The data application module is used to update the verified mover trajectory data to the mover real-time status database, and based on the coordinates of the topology model and the mover motion state, combined with external instruction parameters, generate a control instruction set and output it to the path planning system.

[0088] For the specific implementation of each module in the above device example, please refer to that described in the above method embodiment and will not be elaborated here.

[0089] In summary, the present invention activates a pre-generated topological model, which is virtualized based on the configuration file of a pipeline controller. The configuration file includes line body size data, bus communication frequency data, and preset task sequence data of each mover. It periodically scans the mover encoder data of each slave drive to obtain the real-time position and speed data of each mover, and maps the position data of each mover to the corresponding coordinates of the topological model. It filters out noise and smooths the real-time position and speed data of the mover. Based on the line body size data, bus communication frequency data, preset task sequence data of each mover, and the processed mover speed data, it selects a corresponding recognition algorithm. It performs correlation analysis on the processed real-time position and speed data of the mover according to the selected recognition algorithm to generate mover trajectory data. It verifies the mover trajectory data. If the verification is abnormal, it triggers an alarm. It updates the verified mover trajectory data to the mover real-time status database, and based on the coordinates of the topological model and the mover motion state, combined with external instruction parameters, it generates a control instruction set and outputs it to the path planning system, achieving the purpose of high-precision real-time recognition and dynamic adaptation control of multiple movers in a complex production environment, and providing high-flexibility and high-reliability technical support for asynchronous scheduling in complex production scenarios.

[0090] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above embodiments of the methods. Among them, any reference to a memory, storage, database, or other medium provided by the present invention and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0091] It should be noted that in this text, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, apparatus, article or method including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent in such a process, apparatus, article or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, apparatus, article or method including that element.

[0092] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be included in the patent protection scope of the present invention by the same token.

Claims

1. A mover identification method based on a magnetic levitation flexible pipeline, characterized in that Including the following steps: Activate a pre-generated topology model, which is virtualized and generated based on the configuration file of the pipeline controller. The configuration file includes line body size data, bus communication frequency data, and preset task sequence data for each mover; Periodically scan the mover encoder data of each slave drive, obtain the real-time position and speed data of each mover, and map the position data of each mover to the corresponding coordinates of the topology model; Perform noise filtering and smoothing processing on the real-time position and speed data of the movers; Based on the line body size data, bus communication frequency data, preset task sequence data for each mover, and the processed mover speed data, select the corresponding recognition algorithm; Perform correlation analysis on the processed real-time position and speed data of the movers according to the selected recognition algorithm to generate mover trajectory data; Verify the mover trajectory data. If the verification is abnormal, trigger an alarm; Update the verified mover trajectory data to the mover real-time status database, and generate a control instruction set based on the coordinates of the topology model and the mover motion status, combined with external instruction parameters, and output it to the path planning system.

2. The mover recognition method based on a maglev flexible pipeline according to claim 1, characterized in that Before the step of activating the pre-generated topology model, it further includes: Analyze the line body size data in the configuration file of the pipeline controller; Select the corresponding topology template according to the line body size data. The topology template includes a ring template, a rectangular template, and a custom polygon template; Based on the selected topology template, load the motion constraint data of each mover to generate a digital line body topology model including normalized virtual coordinates.

3. The mover identification method based on a maglev flexible pipeline according to claim 1, wherein The step of periodically scanning the mover encoder data of each slave drive includes: Initiate periodic data polling for each slave drive to obtain the real-time position and speed data of each mover encoder; Add a timestamp identifier to the data corresponding to each mover, and synchronize the data of different drives in time sequence; Based on the synchronized mover speed data, map the real-time position of each mover to the normalized virtual coordinates of the topology model.

4. The mover recognition method based on a maglev flexible pipeline according to claim 1, characterized in that The step of performing noise filtering and smoothing processing on the real-time position and speed data of the movers includes: Dynamically adjust the window length of data processing according to the real-time speed data of each mover. The higher the mover speed, the smaller the window, to adapt to the need to capture the trajectory details of high-speed movers; Perform weighted average processing on the position and speed data within the window. The weight decreases with the timeliness of the data to suppress noise interference and enhance trajectory continuity.

5. The mover identification method based on a magnetic levitation flexible pipeline according to claim 1, wherein The step of selecting the corresponding recognition algorithm includes: When the system computing power is evaluated to reach a pre-set sufficient computing power threshold and the current topology form is not homeomorphic to the ring topology form, select the improved Hungarian algorithm. The improved Hungarian algorithm can adapt to any topology form. Once it is detected that the current topology form is not homeomorphic to the ring topology form, the improved Hungarian algorithm must be selected; When the bus communication frequency is monitored to be lower than the preset frequency threshold, or the hardware stability is lower than the preset stability threshold and multiple verifications are required based on the system operation requirements, select the frame matching algorithm; When the system computing power is evaluated to be lower than the preset computing power standard, and the corresponding topological structure is analyzed and determined to be a simple structure homeomorphic to a ring, the sequence tracking algorithm is selected.

6. The mover recognition method based on a magnetic levitation flexible pipeline according to claim 1, characterized in that The step of performing correlation analysis on the processed real-time position and speed data of the mover according to the selected recognition algorithm includes: If the selected recognition algorithm is the improved Hungarian algorithm, the matching priority of the mover and the topological coordinate nodes is optimized based on the real-time position change of the mover to solve the allocation conflict when the trajectories of multiple movers cross; If the frame matching algorithm is selected, the displacement features of the mover in adjacent data frames are extracted to enhance the continuity matching accuracy of the cross-frame mover positions; If the sequence tracking algorithm is selected, the logical order of the mover operation is determined based on the current position, speed of the mover, and the preset task sequence.

7. The mover identification method based on a magnetic levitation flexible pipeline according to claim 1, characterized in that The step of verifying the mover trajectory data includes: Verifying the integrity of the data frame, and marking it as an abnormal frame if the deviation of the check code exceeds the allowable value; Verifying the continuity of the mover trajectory, and determining it as a trajectory jump if the acceleration exceeds the allowable range; Verifying whether the arrangement order of the movers conforms to the preset rules, and triggering an alarm if the minimum safety distance is violated.

8. The mover identification method based on a magnetic levitation flexible pipeline according to claim 1, characterized in that, After the step of verifying the abnormality, it further includes: Executing a retransmission request for the abnormal frame, and marking the corresponding mover as an offline state if the retransmission fails; Starting trajectory backtracking analysis for the mover with a trajectory jump, and performing collision verification with the trajectories of adjacent movers; Triggering a hierarchical alarm according to the deviation degree, including recording the abnormality, sending an audible and visual alarm, or freezing the mover.

9. The mover recognition method based on a maglev flexible pipeline according to claim 1, wherein The step of generating the control instruction set includes: Assigning an independent data version number to each mover, and using an optimistic lock mechanism to solve the concurrent writing conflict; Calculating the predicted path for the next N cycles based on the mover motion state, topological coordinates, and external instruction parameters; Generating a dynamic control instruction set according to the predicted path, and the instruction set includes braking instructions, speed adjustment instructions, and path replanning instructions.

10. A mover recognition device based on a magnetic levitation flexible pipeline, characterized in that, It includes: A topology construction module for activating a pre-generated topology model; A data mapping module for periodically scanning the mover encoder data of each slave drive to obtain the real-time position and speed data of each mover, and mapping the position data of each mover to the corresponding coordinates of the topology model; A data processing module for filtering out noise and smoothing the real-time position and speed data of the mover; An algorithm matching module for selecting a corresponding recognition algorithm based on the line body size data, bus communication frequency data, preset task sequence data of each mover, and the processed mover speed data; A data processing module for performing correlation analysis on the processed real-time position and speed data of the mover according to the selected recognition algorithm to generate mover trajectory data; A data verification module for verifying the mover trajectory data, and triggering an alarm if the verification is abnormal; A data application module for updating the verified mover trajectory data to the mover real-time status database, and generating a control instruction set based on the coordinates of the topology model and the mover motion state, and combining external instruction parameters, and outputting it to the path planning system.

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