Intelligent sorting control method and system for automobile parts

Abstract

The invention relates to the technical field of automatic sorting control, and discloses an intelligent sorting control method and system for automobile parts, and the method comprises the steps: obtaining a production plan, extracting key task parameters of the parts according to the production plan, and obtaining a sorting path according to the key task parameters; label identification and physical detection are carried out on the parts according to the key task parameters, and control constraints are generated based on label identification and physical detection; and generating a time path according to the sorting path and the control constraint, and issuing and executing the time path to realize intelligent sorting control of the parts. According to the method, dynamic scheduling can be achieved only through existing detection data and control parameters, a sensor or a complex hardware module does not need to be additionally arranged, the method has the advantages of being simple in structure, high in integration and capable of being directly deployed in an existing sorting production line, and the intelligent level and operation flexibility of an automobile part sorting system are remarkably improved on the whole.

Description

Technical Field

[0001] This invention relates to the field of automatic sorting control technology, specifically to an intelligent sorting control method and system for automotive parts. Background Technology

[0002] As the automation and flexibility of the automotive manufacturing supply chain continue to improve, vehicle assembly plants and parts warehousing centers have widely adopted intelligent sorting systems to achieve automated storage, retrieval, and path scheduling of parts. However, existing sorting control technologies generally rely on fixed-cycle or average-speed control, primarily using barcode scanning or RFID identification to match parts and then sequentially placing them via conveyor belts or robotic arms. While these methods can achieve automated sorting, they still present the following prominent problems in scenarios where multiple parts are handled simultaneously: First, path control lacks individual difference constraints. Existing systems typically control all components at a uniform speed and fixed release interval, without considering the differences in mass, structural strength, and fragility of different parts. This can easily lead to problems such as heavy components impacting lighter components or brittle components being damaged by vibration.

[0003] Second, the sorting cycle lacks a timing coordination mechanism. Most existing methods are based on static path planning or first-in-first-out queuing rules, without establishing dynamic scheduling and control of path resources in the time dimension. This can easily lead to path congestion, task waiting, or conflict release when multiple channels are running in parallel.

[0004] Third, the detection results are disconnected from the control strategy. In some sorting systems with detection functions, the label recognition or detection results are only used to determine the model of the parts, and are not deeply coupled with the control algorithm, so dynamic control and path adaptive adjustment based on the differences in the characteristics of the parts cannot be achieved.

[0005] Therefore, existing technologies cannot simultaneously achieve adaptive control based on component characteristics and time-based scheduling for multi-task synchronous release within the same system. To address these issues, this invention proposes an intelligent sorting control method for automotive components that combines detection constraints with path-based time-based scheduling. By introducing control constraint parameters at the path scheduling layer, it achieves dynamic coordination of sorting cycle time and individualized control for operational safety. Summary of the Invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] To solve the above technical problems, the present invention provides the following technical solution: an intelligent sorting control method for automotive parts, comprising: acquiring a production plan; extracting key task parameters of the parts according to the production plan, wherein the key task parameters include the target workstation, arrival time limit and required part type; and acquiring a sorting path according to the key task parameters. Based on key task parameters, the components are labeled and physically inspected. Control constraints are generated based on the label identification and physical inspection, including a speed limit, a minimum safe time interval, and a minimum safe distance interval. For each component, the predicted transit time is calculated based on the control constraints. The latest entry time of the component is obtained by subtracting the predicted transit time from the arrival time limit. The components are sorted from smallest to largest according to their latest entry time to obtain the entry order. The time point when the component enters the sorting path and the predicted arrival time are calculated based on the control constraints. The entry order of the components, the time point when the component enters the sorting path, and the predicted arrival time are used as timed paths and issued and executed.

[0008] As a preferred embodiment of the intelligent sorting control method for automotive parts described in this invention, the step of obtaining the sorting path based on key task parameters includes: obtaining the storage location of the parts based on the required part type, taking the storage location of the parts as the starting point of the sorting path, taking the target workstation as the ending point of the sorting path, taking the path between the starting point and the ending point as the sorting path, and obtaining the length of the sorting path.

[0009] As a preferred embodiment of the intelligent sorting control method for automotive parts described in this invention, the step of tag identification and physical detection of parts based on key task parameters includes: reading the part tag before picking up the part, parsing the part tag to obtain the part type, quality range and frequency band set, comparing the part type with the required part type, picking up the part and performing physical detection if the part type is the same as the required part type, and marking the part as an abnormal part if the part type is different from the required part type or the part tag parsing fails. The physical detection is divided into quality detection and vibration detection. The quality detection includes performing a micro-acceleration action in the vertical direction after grasping the component. The micro-acceleration action has a fixed displacement, acceleration and duration. During the micro-acceleration process, the vertical resultant force and vertical acceleration of the components are collected, and the equivalent mass is calculated based on the vertical resultant force and vertical acceleration. The equivalent quality is compared with the quality range. If the equivalent quality is within the quality range, the quality test passes; if the equivalent quality is outside the quality range, the quality test fails. The vibration detection includes setting the detection area and electromagnetic shock intensity of the component, positioning the component in the detection area after grasping it, and subjecting the detection area to an electromagnetic shock at the set electromagnetic shock intensity. Accelerometer data acquisition is initiated synchronously with the electromagnetic impact moment t as the zero point, and the acquisition time window is set. Internal vibration acceleration; The amplitude of vibration acceleration is calculated using fast Fourier transform, and the k local maxima with the largest amplitude are selected according to the magnitude of the vibration acceleration. The k local maxima are compared with the frequency band set. Each local maxima has a corresponding frequency band range in the frequency band set. If all local maxima are within the corresponding frequency band range, the vibration detection passes. If there is a local maxima outside the corresponding frequency band range, the vibration detection fails.

[0010] As a preferred embodiment of the intelligent sorting control method for automotive parts described in this invention, the step of generating control constraints based on tag recognition and physical detection includes: calculating the center and half-width of the frequency band range of the first local maximum value based on the first local maximum value and the frequency band range of the first local maximum value, and calculating the main frequency band offset. ; Where z represents the offset of the main frequency band; This means restricting the value to between 0 and 1; Indicates the center; This represents the first local maximum value; Indicates half width; Below The more z there are, the larger z becomes, with a maximum value of 1. Higher than Ignoring fragility, z is set to 0; Let the segmentation frequency be the center. Using the center as the boundary, calculate the low-frequency energy bias: ; in, Indicates the energy of the lower half-band; This represents the lower limit of the frequency band range of the first local maximum. Indicates the amplitude of vibration acceleration; Indicates to Perform differential calculations; Indicates high half-band energy; This represents the upper limit of the frequency band range of the first local maximum. ; Where R represents the proportion of low-frequency energy; the vulnerability index is calculated by weighting and fusing the main frequency band offset and the low-frequency energy offset. The equivalent quality and vulnerability indices are calculated based on the velocity limit, minimum safe time interval, and minimum safe distance interval, and then scaled accordingly. ; ; according to and Maximum computation speed: ; in, Indicates equivalent quality scaling; This indicates taking the minimum value; Indicates reference mass, the average mass of the component; Indicates equivalent mass; Indicates the equivalent mass scaling parameter; This indicates a scaling of the vulnerability index; This represents the scaling parameter for the vulnerability index. Indicators representing vulnerability; Indicates the upper limit of speed; This indicates the actual speed limit determined by the actual equipment. The minimum safe time interval and minimum safe distance interval are calculated and expressed as follows: ; ; in, Indicates the minimum safe time interval; This represents the minimum reference time interval, a constant set according to the actual equipment. This represents the quality correction factor; This represents the vulnerability correction factor; Indicates the minimum safe distance interval; The speed limit, minimum safe time interval, and minimum safe distance interval are output as control constraints.

[0011] As a preferred embodiment of the intelligent sorting control method for automotive parts described in this invention, the step of calculating the predicted transit time of the parts based on control constraints includes assuming the speed of the parts on the sorting path is... Divide the length of the sorting path by Calculate the predicted transit time for components; The step of calculating the time point of the component entering the sorting path and the predicted arrival time based on the control constraints includes binding the control constraints to the sorting path, and recording the time point of the component entering the sorting path as... The time point at which the next component enters the sorting path is represented as: ; ; in, Indicates candidate time point a; Indicates the current time point; Indicates candidate time point b; Indicates candidate time point c; This indicates the time point at which the next component enters the sorting path; express Take the maximum value among the three; The predicted arrival time is equal to the time when the part enters the sorting path plus the predicted transit time of the part. Using the time point when the parts enter the sorting path, the predicted arrival time, and the speed of the parts on the sorting path as control parameters, the sorting path with control parameters is used as a time-based path.

[0012] An intelligent sorting control system for automotive parts using any of the methods described in this invention, wherein: a data acquisition module acquires a production plan, extracts key task parameters of the parts based on the production plan, and acquires a sorting path based on the key task parameters; The detection module performs label recognition and physical inspection on components based on key task parameters, and generates control constraints based on label recognition and physical inspection. The control module generates a timed path based on the sorting path and control constraints, issues and executes the timed path, and realizes intelligent sorting control of parts.

[0013] The beneficial effects of this invention are as follows: By introducing a dynamic constraint mechanism based on detection results into the sorting control, the method of this invention enables the sorting system to achieve adaptive operation control and safe scheduling based on the quality and structural differences of different parts. Through control constraints, the system can maintain the rhythm coordination and spatial safety of each part's operation even under high-concurrency task conditions, avoiding path congestion, task conflicts, and collisions between parts, thus significantly improving the safety and stability of the sorting process. This method can achieve dynamic scheduling based solely on existing detection data and control parameters, without the need for additional sensors or complex hardware modules. It has the advantages of simple structure, high integration, and direct deployment in existing sorting production lines, significantly improving the overall intelligence level and operational flexibility of automotive parts sorting systems. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is an overall flowchart of an intelligent sorting control method for automotive parts provided in Embodiment 1 of the present invention. Detailed Implementation

[0016] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0017] Example 1, referring to Figure 1 As an embodiment of the present invention, an intelligent sorting control method for automotive parts is provided, comprising: S1: Obtain the production plan, extract the key task parameters of the parts based on the production plan, and obtain the sorting path based on the key task parameters.

[0018] To ensure that subsequent sorting path generation and timing scheduling are based on a unified and traceable task baseline, key task parameters for each component are acquired and solidified starting from the production plan. The production plan refers to an electronic data set issued by the upper-level production management system to guide the assembly sequence and material delivery within a certain production cycle. It includes at least the vehicle assembly sequence, the component requirements for each assembly station, and the corresponding arrival time requirements. The upper-level production management system can be a Manufacturing Execution System (MES) or an enterprise management system (such as an Enterprise Resource Planning (ERP) system); this invention does not limit its specific implementation form.

[0019] Through a data exchange channel established with the upper-level production management system, production plans are actively retrieved at fixed time intervals or passively received when plan change events occur. The data exchange channel can employ common industrial integration methods such as file delivery or network service interfaces. After receiving the production plan, to avoid confusion between different batches of data, the system records the source system, reception time, and plan batch number for each reception.

[0020] After obtaining the production plan, key task parameters are extracted from it and bound to specific parts to form task records. Key task parameters include only three items: first, the target workstation, which is the assembly workstation or its preceding buffer position where the part should be delivered, used to clarify the final destination of sorting and transportation; second, the arrival time limit, which is the specific time or remaining time for the part to arrive at the target workstation according to the production plan requirements, used to constrain the feasibility of subsequent paths and sequences; and third, the required part type, which is the type of part required by this production plan.

[0021] When the upper-level production management system adjusts the production plan, it only updates the three key task parameters mentioned above for the affected task records, and retains the component labels and time records before the update, thereby maintaining the consistency and traceability of the task chain.

[0022] Furthermore, based on the target workstation in the key task parameters, the sorting path corresponding to that target workstation is retrieved from the system's preset path library. A sorting path refers to the complete transport route of a component from its current storage location, through a predetermined conveying and sorting system, to the target workstation. During the system deployment phase, the sorting path is configured by on-site engineering technicians based on the factory's material handling layout and workstation distribution, and stored electronically in the path library. The establishment of the path library is a one-time system configuration task and does not change with individual tasks; that is, given the required components and the target workstation, the sorting path is fixed.

[0023] In actual automotive assembly production, sorting paths are not generated individually for each part, but rather defined by part category or target workstation. That is, multiple parts belonging to the same category or heading to the same target workstation share the same sorting path. This design is because the conveying and sorting equipment on the production line is a fixed system shared by multiple tasks, and the path division method should correspond to the equipment layout, rather than being dynamically generated for each individual material. Therefore, this invention, during execution, only calls the corresponding standard path from the path library based on the target workstation indicated by the task, without performing sorting path calculation or optimization. Subsequent sorting path scheduling is based on the time progress of parts under fixed sorting path conditions, such as when to perform sorting and conveying, when the sorting path is idle, and how to avoid conflicts. Main and backup paths can be pre-configured for the target workstation in the path library, but under normal sorting conditions, parts only proceed along the main path; only when the main path experiences equipment malfunction or congestion will it switch to the backup path to continue execution.

[0024] After determining the sorting path, sensors deployed along the path collect real-time data on the path's transit time and occupied time slots. The system records the timestamps of each part passing the sensors and calculates the real-time transit time based on these timestamps, representing the average transport time of the sorting path under current conditions. Simultaneously, based on the issued production plan, the system generates the occupied time slots for the sorting path in the near future, indicating which time intervals of the sorting path are occupied by other tasks and which are idle within the future timeframe.

[0025] S2: Perform label recognition and physical inspection on components based on key task parameters, and generate control constraints based on label recognition and physical inspection.

[0026] Furthermore, before grabbing the parts, the parts are identified by a reading device, and the parts labels are parsed to obtain the part type, mass range, frequency band set and unique identifier.

[0027] The parsing results are validated. Validation includes: completeness check, to confirm whether the component labels are complete and without gaps; and duplicate check, to confirm whether the same component with the same unique identifier has appeared repeatedly recently. Failure to pass any of these checks indicates an abnormal label recognition.

[0028] The parsed component types are matched with the critical task parameters to confirm whether the component type is the same as the required component type in the critical task parameters. If the component type is the same as the required component type, the component is captured and physical detection is performed. If the component type is different from the required component type or the component tag parsing fails, it is marked as an abnormal component.

[0029] It should be noted that component labels can be QR codes or Radio Frequency Identification (RFID) tags. The labeling process is completed when the component enters the warehousing system. Specifically, after the component passes inspection upon arrival, it is automatically labeled or manually affixed to a visible location on the component, and the label is then coded and registered in the warehousing system. Once the label is bound, the QR code or RFID tag becomes the component's identifier, used in subsequent label identification steps to read and match the component's identity information.

[0030] Furthermore, after grasping the parts, physical testing is performed, which includes quality testing and vibration testing. The quality testing includes calculating the equivalent mass of the parts by performing a vertical micro-acceleration action with set parameters, and then comparing the equivalent mass with the mass range for testing.

[0031] Specifically, after clamping in place, the system remains stationary for 50-100ms to allow it to reach a steady state. Then, it performs a micro-acceleration motion in the vertical direction. This micro-acceleration motion has fixed displacement, acceleration, and duration; for example, the displacement amplitude range is 1-2mm, and the acceleration amplitude range is 0.5-1.5m / s². 2 The duration of the action is in the range of 80-120ms. Fixing the displacement, acceleration, and duration serves several purposes: first, it ensures that the micro-acceleration conditions are consistent for each test; second, keeping the displacement and acceleration within a small range allows for sufficient inertial signals to be obtained without causing clamping slippage or impact to the equipment; and third, the shorter duration keeps the measured response mainly in the low-frequency range, reducing interference from environmental and equipment vibrations.

[0032] During the micro-acceleration process, the vertical resultant force and vertical acceleration of the components are collected. A stable interval is selected in the middle of the micro-acceleration process, and representative values ​​(e.g., median or average value after removing extreme values) of the vertical resultant force and vertical acceleration within this interval are calculated. Based on this, the equivalent mass of the components is obtained. ; in, Indicates equivalent mass; The vertical resultant force is represented by ; g represents the acceleration due to gravity, taken as 9.81 m / s². 2 ; It represents vertical acceleration; z represents the z-axis, i.e., the vertical direction.

[0033] The calculated equivalent quality is compared with the quality range. If the equivalent quality is within the quality range, the quality inspection passes; if the equivalent quality is outside the quality range, the quality inspection fails.

[0034] It should be noted that the vertical resultant force of the components refers to the supporting reaction force provided by the end of the robotic arm (the device for gripping and sorting components) on the components in the vertical direction. It is preferably measured directly by a force sensor installed at the end. When there is no force sensor at the end, it can be obtained by converting the joint motor current into the calibrated torque-end force.

[0035] The vertical acceleration of a component refers to its actual acceleration in the vertical direction. It is preferably measured by an inertial measurement unit (IMU, including accelerometers) fixed on the robotic arm along the vertical axis. When the controller can provide an end-effector acceleration estimate that is consistent with the vertical axis of the robotic arm, this estimate can also be used directly. The vertical direction is based on the direction of gravity on site.

[0036] Furthermore, the vibration testing is only performed on components with high rigidity or hollow structures that require high material or internal support strength during assembly, such as engine housings, suspension connectors, cast aluminum brackets, brake drums, and hollow plastic parts. These components, even with similar appearances, may have material substitutions or internal molding differences. Failure to differentiate them would affect assembly quality; therefore, vibration response testing is necessary to confirm their structural consistency.

[0037] During vibration testing, components are fixed in a positioning robotic arm to maintain a stable posture, and a single electromagnetic shock induces measurable vibration. Electromagnetic shock refers to the process of using the instantaneous magnetic force generated by an electromagnetic coil to drive a metal impactor (also known as an impact rod or impact hammer) to move along a guide axis, creating a controllable transient mechanical contact with the surface of the component to generate a structural vibration signal. The intensity and direction of the electromagnetic shock are fixed during the installation and commissioning phase, and each test is performed according to the same parameters.

[0038] An electromagnetic shock of set intensity is applied to the detection area, and acceleration acquisition is synchronously started at the electromagnetic shock time t as the zero point. The acquisition time window is set accordingly. Vibration acceleration within.

[0039] The vibration acceleration is bandpass filtered, and the filtered vibration acceleration is analyzed using Fast Fourier Transform (FFT) to calculate the amplitude. The k largest local maxima are selected based on the magnitude of the vibration acceleration. A local maxima is defined as a value where the amplitudes on both the left and right sides are smaller than the local maximum.

[0040] The k local maxima are compared with the frequency band set. Each local maxima has a corresponding frequency band range in the frequency band set. If all local maxima are within the corresponding frequency band range, the vibration detection passes. If there is a local maxima outside the corresponding frequency band range, the vibration detection fails.

[0041] It should be noted that a frequency band set is a collection of multiple frequency band ranges, and each local maximum value has a corresponding frequency band range.

[0042] The reason for adopting this vibration detection method is that there are stable differences in the natural frequency distribution between parts that are similar in appearance but differ in structure or material. By detecting the area where the local maximum value falls, different models or materials can be distinguished, enabling rapid confirmation of structural consistency. At the same time, the detection time is short and it is not sensitive to on-site noise, making it suitable for continuous execution in automotive parts sorting sites.

[0043] Based on the results of label recognition and physical detection, a sorting decision is generated. When both label recognition and physical detection pass, the part is marked as normal release and the established sorting path remains unchanged. When either detection result fails, the part is marked as a return part and sent to the abnormal processing channel or the abnormal part temporary storage area.

[0044] Control constraints are generated based on tag recognition and physical detection, using equivalent mass as a parameter reflecting the load characteristics of components. Local maxima from vibration detection output are converted into vulnerability indices, serving as indicators of component structural stability and vibration resistance.

[0045] Specifically, calculate the center and half-width of the frequency band range of the first local maximum. The center is the average of the upper and lower limits of the frequency band range of the first local maximum, and the half-width is half of the interpolation of the upper and lower limits. First, check if the first local maximum is too low relative to the center of the frequency band range, expressed as: ; Where z represents the offset of the main frequency band; This means restricting the value to between 0 and 1; Indicates the center; This represents the first local maximum value; Indicates half width. Below The more z there are, the larger z becomes, with a maximum value of 1. Higher than Regardless of its fragility, z takes 0.

[0046] Let the segmentation frequency be the center. Divide the frequency band into a lower half-band and a higher half-band, using the center as the boundary. Then, sum the energy of the vibration acceleration amplitude in the lower half-band and the higher half-band (which can be understood as the area of ​​the square of the amplitude) respectively: ; in, Indicates the energy of the lower half-band; This represents the lower limit of the frequency band range of the first local maximum. Indicates the amplitude of vibration acceleration; Indicates to Perform differential calculations; Indicates high half-band energy; This represents the upper limit of the frequency band range of the first local maximum. Calculate the low-frequency energy bias: ; Here, R represents the proportion of low-frequency energy. The more energy is biased towards low frequencies, the larger R is.

[0047] The vulnerability index is obtained by weighting and summing the main frequency band offset and the proportion of low frequency energy.

[0048] The upper speed limit, minimum safe time interval, and minimum safe distance interval are calculated based on equivalent mass and vulnerability indices. The greater the equivalent mass and the higher the vulnerability index, the greater the need to reduce speed and increase intervals. Specifically, the equivalent mass and vulnerability indices are scaled: ; ; ; in, Indicates equivalent quality scaling; This indicates taking the minimum value; Indicates reference mass, the average mass of the component; Indicates equivalent mass; Indicates the equivalent mass scaling parameter; This indicates a scaling of the vulnerability index; This represents the scaling parameter for the vulnerability index. Indicators representing vulnerability; Indicates the upper limit of speed; This indicates the actual speed limit determined by the actual equipment.

[0049] Calculate the minimum safe time interval and minimum safe distance interval, starting from the baseline minimum time interval (a pre-set constant) of the sorting path. Only add time for overweight items, and add additional time for vulnerability items, expressed as: ; ; in, Indicates the minimum safe time interval; Indicates the minimum reference time interval; This represents the quality correction factor; This represents the vulnerability correction factor; This indicates the minimum safe distance interval. and Obtained through on-site calibration. The speed limit, minimum safe time interval, and minimum safe distance interval are output as control constraints.

[0050] S3: For each component, calculate the predicted transit time based on the control constraints, subtract the predicted transit time from the arrival time limit to obtain the latest entry time of the component, sort the latest entry times of the components from smallest to largest to obtain the entry order of the components, calculate the time point when the component enters the sorting path and the predicted arrival time based on the control constraints, and issue and execute the component entry order, the time point when the component enters the sorting path and the predicted arrival time as a timed path.

[0051] Furthermore, for each component, the corresponding sorting path and the length of the sorting path are obtained, and the speed of the component on the sorting path is set as the upper speed limit. Divide the length of the sorting path by Calculate the predicted transit time for each component, and subtract the predicted transit time from the actual transit time to obtain the latest entry time for the component.

[0052] For multiple parts to be sorted along the same sorting path, the parts are sorted in ascending order based on their latest entry time.

[0053] After obtaining the entry sequence of the parts, the system calculates the entry time and predicted arrival time of each part along the entry sequence. With the sorting path remaining unchanged, the system limits the running speed, entry timing, and spatial spacing of individual parts through control constraints, thereby establishing an independent and non-interfering transportation time interval for each part in the time dimension.

[0054] Specifically, the control constraints obtained from the preceding steps are invoked, including the speed limit, minimum safe time interval, and minimum safe distance interval, and these parameters are used as constraint inputs for sorting path scheduling. The speed limit is used to restrict the maximum running speed of parts on the path; the minimum safe time interval is used to determine the release interval between two adjacent parts in time; and the minimum safe distance interval is used to convert it into the minimum spatial distance between adjacent parts to ensure a safe distance during the sorting process.

[0055] The time point at which the parts enter the sorting path is recorded as... The time point at which the next component enters the sorting path is represented as: ; ; in, Indicates candidate time point a; Indicates the current time point; Indicates candidate time point b; Indicates candidate time point c; This indicates the time point at which the next component enters the sorting path; express Take the maximum value among the three.

[0056] It should be noted that when determining the entry time of the current component, this invention does not directly follow the entry order of components, but rather uses a rolling decision based on the maximum value of three candidate time points: first, the current system time point, reflecting the on-site status; second, the entry time of the previous component plus the minimum safety time interval, ensuring congestion and interference prevention in the time domain; and third, the entry time of the previous component plus the time calculated from the minimum safety distance interval, ensuring safe vehicle distance in the spatial domain. Taking the maximum value of these three factors means that the release timing of this component always conforms to the determined time of the previous component and the safety constraints of this component itself. Therefore, under the same priority sequence, the actual release time will be dynamically adjusted according to "when the previous component entered," "the size of the constraints of this component," and "changes in the current time," rather than being fixed as a static queue progression. This method of calculating each item individually, writing the calculation results back in real time, and forming new constraints on subsequent items allows the system to achieve adaptive release in both time and space dimensions based on the control constraints of each component, while keeping the sorting path unchanged. This ensures safe intervals and maximizes the approximation of the earliest executable time, demonstrating the intelligence and on-site availability of sorting scheduling.

[0057] The predicted arrival time is equal to the time when the part enters the sorting path plus the predicted transit time of the part; the part entry sequence, the time when the part enters the sorting path, and the predicted arrival time are issued and executed as a timed path.

[0058] During execution, the system sends the timed path to the field control (such as WCS / PLC / robot), and queues the parts one by one according to the generated entry order. When a part reaches its entry time point, the corresponding gate / sorter is opened to allow it to enter the line. After entering, it executes in a closed loop according to control constraints along the entire sorting path: the running speed is limited by the speed limit and cannot be exceeded; the time distance and vehicle distance with the previous part are monitored by the minimum safe time interval / minimum safe distance interval, and if it approaches the threshold, it automatically decelerates or pauses briefly to restore the safe distance; the arrival, stop and deviation during task execution are corrected and recorded by the controller in real time. After completion, the occupied area of ​​the part is written back / released from the timed path for subsequent parts to avoid and for the continuous rolling of the system cycle.

[0059] In summary, the intelligent control of this invention is manifested in the following aspects: First, control constraints are generated online for each component through tag recognition and physical detection, enabling components with different qualities or structural characteristics to obtain differentiated speed limits, time intervals, and distance requirements on the same path; Second, the entry sequence is driven by the latest entry time data, ensuring that tasks with tighter deadlines enter first; Third, the determination of the entry time point adopts a rolling recursive method of taking the largest of multiple candidate constraints, where the determined time point of the previous component forms an immediate constraint on the next component, forming a traceable and verifiable dynamic rhythm coordination. Therefore, even with a fixed path topology, the system can still adaptively schedule multiple components based on detection constraints and task deadlines, significantly improving the safety, timeliness, and throughput efficiency of the sorting process.

[0060] Example 2, in an exemplary embodiment, also provides an intelligent sorting control system for automotive parts, including, The data acquisition module obtains the production plan, extracts key task parameters of parts based on the production plan, and obtains the sorting path based on the key task parameters.

[0061] The detection module performs label recognition and physical inspection on components based on key task parameters, and generates control constraints based on label recognition and physical inspection.

[0062] The control module generates a timed path based on the sorting path and control constraints, issues and executes the timed path, and realizes intelligent sorting control of parts.

[0063] If the above functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0064] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0065] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0066] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0067] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for intelligent sorting and control of automotive parts, characterized in that, include: Obtain the production plan, extract key task parameters of parts based on the production plan, the key task parameters include target workstation, arrival time limit and required part type, and obtain the sorting path based on the key task parameters; Based on key task parameters, the components are labeled and physically inspected. Control constraints are generated based on the label identification and physical inspection, including a speed limit, a minimum safe time interval, and a minimum safe distance interval. For each component, the predicted transit time is calculated based on the control constraints. The latest entry time of the component is obtained by subtracting the predicted transit time from the arrival time limit. The components are sorted from smallest to largest according to their latest entry time to obtain the entry order. The time point when the component enters the sorting path and the predicted arrival time are calculated based on the control constraints. The entry order of the components, the time point when the component enters the sorting path, and the predicted arrival time are used as timed paths and issued and executed.

2. The intelligent sorting control method for automotive parts as described in claim 1, characterized in that: The step of obtaining the sorting path based on key task parameters includes obtaining the storage location of the parts based on the required part type, taking the storage location of the parts as the starting point of the sorting path, taking the target workstation as the ending point of the sorting path, taking the path between the starting point and the ending point as the sorting path, and obtaining the length of the sorting path.

3. The intelligent sorting control method for automotive parts as described in claim 2, characterized in that: The step of tag identification and physical detection of components based on key task parameters includes: reading component tags before grabbing components, parsing component tags to obtain component type, quality range and frequency band set, comparing component type with required component type, grabbing component and performing physical detection if component type is the same as required component type, and marking abnormal component if component type is different from required component type or component tag parsing fails. The physical detection is divided into quality detection and vibration detection. The quality detection includes performing a micro-acceleration action in the vertical direction after grasping the component. The micro-acceleration action has a fixed displacement, acceleration and duration. During the micro-acceleration process, the vertical resultant force and vertical acceleration of the components are collected, and the equivalent mass is calculated based on the vertical resultant force and vertical acceleration. The equivalent quality is compared with the quality range. If the equivalent quality is within the quality range, the quality test passes; if the equivalent quality is outside the quality range, the quality test fails. The vibration detection includes setting the detection area and electromagnetic shock intensity of the component, positioning the component in the detection area after grasping it, and subjecting the detection area to an electromagnetic shock at the set electromagnetic shock intensity. Accelerometer data acquisition is initiated synchronously with the electromagnetic impact moment t as the zero point, and the acquisition time window is set. Internal vibration acceleration; The amplitude of vibration acceleration is calculated using fast Fourier transform, and the k local maxima with the largest amplitude are selected according to the magnitude of the vibration acceleration. The k local maxima are compared with the frequency band set. Each local maxima has a corresponding frequency band range in the frequency band set. If all local maxima are within the corresponding frequency band range, the vibration detection passes. If there is a local maxima outside the corresponding frequency band range, the vibration detection fails.

4. The intelligent sorting control method for automotive parts as described in claim 3, characterized in that: The control constraints generated based on tag recognition and physical detection include: calculating the center and half-width of the frequency band range of the first local maximum based on the first local maximum and its frequency band range; and calculating the main frequency band offset. ； Where z represents the offset of the main frequency band; This means restricting the value to between 0 and 1; Indicates the center; This represents the first local maximum value; Indicates half width; Below The more z there are, the larger z becomes, with a maximum value of 1. Higher than Ignoring fragility, z is set to 0; Let the segmentation frequency be the center. Using the center as the boundary, calculate the low-frequency energy bias: ； in, Indicates the energy of the lower half-band; This represents the lower limit of the frequency band range of the first local maximum. Indicates the amplitude of vibration acceleration; Indicates to Perform differential calculations; Indicates high half-band energy; This represents the upper limit of the frequency band range of the first local maximum. ； Where R represents the proportion of low-frequency energy; the vulnerability index is calculated by weighting and fusing the main frequency band offset and the low-frequency energy offset. The equivalent quality and vulnerability indices are calculated based on the velocity limit, minimum safe time interval, and minimum safe distance interval, and then scaled accordingly. ； ； according to and Maximum computation speed: ； in, Indicates equivalent quality scaling; This indicates taking the minimum value; Indicates reference mass, the average mass of the component; Indicates equivalent mass; Indicates the equivalent mass scaling parameter; This indicates a scaling of the vulnerability index; This represents the scaling parameter for the vulnerability index. Indicators representing vulnerability; Indicates the upper limit of speed; This indicates the actual speed limit determined by the actual equipment. The minimum safe time interval and minimum safe distance interval are calculated and expressed as follows: ； ； in, Indicates the minimum safe time interval; This represents the minimum reference time interval, a constant set according to the actual equipment. This represents the quality correction factor; This represents the vulnerability correction factor; Indicates the minimum safe distance interval; The speed limit, minimum safe time interval, and minimum safe distance interval are output as control constraints.

5. The intelligent sorting control method for automotive parts as described in claim 4, characterized in that: The step of calculating the predicted transit time of components based on control constraints includes assuming the speed of the components on the sorting path is... Divide the length of the sorting path by Calculate the predicted transit time for components; The step of calculating the time point of the component entering the sorting path and the predicted arrival time based on the control constraints includes binding the control constraints to the sorting path, and recording the time point of the component entering the sorting path as... The time point at which the next component enters the sorting path is represented as: ； ； in, Indicates candidate time point a; Indicates the current time point; Indicates candidate time point b; Indicates candidate time point c; This indicates the time point at which the next component enters the sorting path; express Take the maximum value among the three; The predicted arrival time is equal to the time when the part enters the sorting path plus the predicted transit time of the part. Using the time point when the parts enter the sorting path, the predicted arrival time, and the speed of the parts on the sorting path as control parameters, the sorting path with control parameters is used as a time-based path.

6. An intelligent sorting control system for automotive parts, applied to the intelligent sorting control method for automotive parts according to any one of claims 1 to 5, characterized in that, include, The data acquisition module obtains the production plan, extracts key task parameters of parts based on the production plan, and obtains the sorting path based on the key task parameters. The detection module performs label recognition and physical inspection on components based on key task parameters, and generates control constraints based on label recognition and physical inspection. The control module generates a timed path based on the sorting path and control constraints, issues and executes the timed path, and realizes intelligent sorting control of parts.

Images