Mes system for a stator production line

Abstract

The application discloses an MES system for a stator production line, and relates to the technical field of intelligent manufacturing execution systems, aiming to solve the problem of parameter mismatch, process skipping and poor product quality consistency in stator production caused by the fact that a general MES system lacks stator-specific process control logic. The system is adapted to 14 processes in the whole process of stator production, and is internally provided with a stator-specific process control module, including a stator model-specific process parameter library, a process error-proofing unit and a parameter dynamic adjustment unit, and is further provided with a device adaptation layer and a process parameter version management unit. The parameter library realizes accurate storage and self-optimization of process parameters of different stator models, the error-proofing unit implements closed-loop error-proofing for high-risk processes, and the dynamic adjustment unit guarantees the stability of key process parameters, so that accurate, standardized and intelligent control of the whole process of stator production is finally realized, the production efficiency and product qualification rate are effectively improved, and the system is adapted to the large-scale production demand of stators.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing execution systems, and in particular to a MES system for stator production lines. Background Technology

[0002] As the core component of an electric motor, the stator's production process involves 14 exclusive and closely related steps (core feeding, alignment, winding, pre-circling, rounding welding, inspection, marking, outer diameter inspection, lead wire bending, busbar assembly, terminal pre-clamping, terminal wire cutting, terminal welding, and stator performance testing). Among these, the precision of process parameters in winding, rounding welding, and terminal processing directly determines the stator's performance and quality. Busbar assembly and terminal pre-clamping are prone to model mismatch and process skipping issues due to their high operational complexity.

[0003] However, existing general-purpose MES systems (such as the "MES system" published under CN116109263A) have a core deficiency in stator production scenarios: the lack of dedicated logic for process control makes them unable to meet the precise control requirements of stator production. The process management module of CN116109263A only vaguely "determines the process flow and operation route" and does not design control functions for the specific process characteristics of stator production. There is no dedicated storage and real-time comparison mechanism for key stator process parameters such as winding tension and welding temperature, making it impossible to achieve precise matching of process parameters for different stator models. It also lacks error prevention rules (such as model matching verification and judgment of completion of previous processes) for error-prone processes such as busbar assembly and terminal pre-clamping. This leads to problems such as parameter mismatch (such as using the winding parameters of type A stator to produce type B stator) and process skipping (such as cutting wire directly without completing terminal pre-clamping) in stator production. The product quality is inconsistent and seriously affects the pass rate and stability of stator production. Summary of the Invention

[0004] This invention provides a dedicated MES system adapted to the entire stator production process. Through a dedicated stator process control module, it achieves accurate parameter storage, closed-loop error prevention and dynamic adjustment of processes, solving the problems of parameter mismatch, process skipping and poor product quality consistency caused by the lack of dedicated stator process control logic in general-purpose MES systems.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: The system is compatible with the entire stator production process, which at least includes core feeding, alignment, winding, pre-circling, rounding welding, inspection, marking, outer diameter inspection, lead wire bending, busbar assembly, terminal pre-clamping, terminal wire cutting, terminal welding, and stator performance testing. The system is characterized by a built-in stator-specific process control module, which includes: A dedicated process parameter library for each stator model is used to structure and store the characteristic process parameters and parameter constraint rules of each production process of different stator models. It establishes a coupling relationship model of parameters between different processes of the same stator model, and realizes a parameter self-optimization and update mechanism based on production batch data, quality inspection feedback and equipment operating status, and continuously optimizes parameter configuration through algorithm iteration. The process error prevention unit is used to implement closed-loop error prevention and control for characteristic processes with strong correlation in the stator production process. It includes precondition verification, operation compliance verification, and cross-process collaborative error prevention logic. The parameter dynamic adjustment unit is used to implement full-cycle parameter control for key processes that affect the core performance of the product during stator production. By collecting process parameters and equipment operation data in real time, combined with preset parameter constraint rules and dynamic deviation judgment algorithms, it is used to identify parameter fluctuation trends, realize closed-loop adjustment after parameters exceed the threshold, and predict and preventively adjust deviation risks based on parameter change gradients.

[0006] Preferably, the stator model-specific process parameter library is constructed according to a hierarchical structure of stator model, process type, and parameter category, wherein: The process types must include at least winding, welding, and terminal processing. The parameter categories include number of turns, tension, and linear speed parameters for the winding process; temperature, current, and time parameters for the welding process; and pre-clamping pressure and wire cutting length parameters for the terminal processing process. Each parameter group contains three types of characteristic parameters: base value, fluctuation threshold, and adjustment step size. All parameters are uniquely mapped to a specific stator model.

[0007] Preferably, the parameter coupling relationship model in the stator model-specific process parameter library is used to establish the correlation constraint logic of process parameters between different processes of the same stator model, specifically including: For the winding process and the subsequent round welding process, an adaptation constraint between winding tension and welding temperature is established, and the adjustment range of welding temperature is simultaneously limited when the winding tension increases. For the terminal pre-clamping process and the terminal welding process, a synergistic relationship between pre-clamping pressure and welding current is established. When the pre-clamping pressure exceeds the preset range, the welding current reference value is adjusted synchronously.

[0008] Preferably, the implementation logic of the parameter self-optimization update mechanism includes: Regularly collect stator production batch data, quality inspection feedback data, and equipment operating status data. The production batch data includes the actual process parameters of each process and the equipment running time. The quality inspection feedback data includes the number of non-conforming products and the type of defects in each process. The equipment operating status data includes the equipment load rate and the degree of component wear. Data correlation analysis was used to identify the mapping relationship between parameter configuration and quality and equipment status, and to statistically analyze the correlation between key parameter fluctuations and stator quality defects. Based on the analysis results, parameter optimization suggestions are generated, and parameters that deviate from the optimal range are iteratively corrected. After correction, the optimization effect was verified through small-batch trial production. If the verification was successful, the corresponding configuration in the parameter library was updated.

[0009] Preferably, the specific process of the algorithm iterative optimization is as follows: The gradient descent algorithm is used to optimize the parameter configuration, with the dual objective functions being the highest stator finished product qualification rate and the lowest equipment energy consumption. Production batch data, quality inspection feedback data, and equipment operating status data are used as input variables for the algorithm. During each iteration, the optimization direction is adjusted based on the actual production effect of the previous parameter configuration, gradually reducing the deviation between the parameters and the optimal range; The iteration cycle is set according to the production batch. The algorithm is iterated once after each preset production batch is completed, ensuring that the parameter configuration is dynamically adapted to changes in production conditions.

[0010] Preferably, the implementation logic of the process error prevention unit includes: Feature process identification: Quantitative evaluation is carried out from three dimensions: process correlation, quality impact weight, and historical error frequency. Processes with risk values ​​greater than the set risk threshold are selected as error prevention target processes. The feature processes include at least busbar assembly, terminal pre-clamping, and terminal wire cutting. The process correlation refers to the material / parameter dependence with upstream and downstream processes. The quality impact weight is the proportion of the impact on stator performance. Precondition verification: For each feature process, at least one associated precondition process is preset. The associated precondition process for busbar assembly is lead wire bending, the associated precondition process for terminal preclamping is busbar assembly, and the associated precondition process for terminal wire cutting is terminal preclamping. The completion status (completed / incomplete) and quality qualification (qualified / unqualified) of the precondition process are verified through the production data chain. The verification is considered successful when both are completed and qualified. Operational compliance verification: The unique model identification information on the stator carrier is read through identification technology to obtain the model data of the stator currently being processed. The MD5 value of the process parameter set corresponding to the model is compared with the parameter set actually loaded at the current workstation. If the MD5 values ​​are consistent, the matching is deemed qualified. The identification technology includes at least QR code identification and RFID identification. Anomaly Handling Mechanism: When the preconditions are not met or the parameters are mismatched, a multi-level warning is triggered and the workstation is locked; unlocking requires the corresponding authorized personnel to log in to the system and submit an anomaly handling report containing cause analysis and handling measures. The system will automatically unlock the workstation after recording and confirming the report.

[0011] Preferably, the implementation logic of the parameter dynamic adjustment unit includes: Key processes were selected, with winding, circular welding, and terminal welding identified as the processes for parameter adjustment. Real-time data acquisition: Obtaining actual process parameters and equipment operating status data through interfaces with production equipment; Deviation determination involves comparing the actual process parameters with the standard parameters of the corresponding model in the parameter library, calculating the deviation value, and comparing it with the fluctuation threshold. Closed-loop control: When a parameter is found to be out of tolerance, a step-by-step adjustment instruction is automatically generated according to the parameter category and sent to the production equipment. After adjustment, parameters are continuously collected at a preset frequency and deviation judgment is repeated until the parameter returns to the threshold range. Then, the adjustment is stopped and the complete adjustment process is recorded in the parameter change file.

[0012] Preferably, the generation rule for the step-by-step adjustment command is as follows: Winding process parameters: When the number of turns exceeds the tolerance, the winding machine counting module is corrected based on the degree of deviation. After adjustment, the parameters are re-acquired and judged after the current coil winding cycle ends. When the tension or linear speed exceeds the tolerance, the output of the corresponding controller is adjusted in stages according to the preset ratio, and the coupling relationship between the two is verified synchronously. Welding process parameters: When the temperature exceeds the tolerance, adjust the heating module power according to the conversion relationship calibrated by the equipment, and re-judge the deviation after the temperature stabilizes; when the current exceeds the tolerance, adjust the output value in stages and monitor the temperature fluctuation range simultaneously; when the time exceeds the tolerance, correct the timing module to ensure that it is compatible with the temperature and current parameters. Terminal welding process parameters: The adjustment logic for temperature and current deviations refers to the corresponding parameter rules for round welding and is adapted to the characteristics of terminal welding equipment; when adjusting the current, welding pressure fluctuations are additionally monitored to ensure that the pressure is within the preset stable range.

[0013] Preferably, the stator-specific process control module includes an equipment adaptation layer, which comprises: Universal Protocol Conversion Interface: Identifies and adapts the native communication protocols of different brands of winding machines, welding equipment, and terminal processing equipment. Through built-in protocol parsing and conversion logic, it converts the device-specific protocols into the system's universal communication protocols, enabling stable access and bidirectional data transmission between various devices and the system. Data interaction specifications: Define the data format and interaction sequence for process parameter acquisition, equipment status acquisition, and adjustment command issuance to avoid data transmission format incompatibility or timing errors; Abnormal reconnection mechanism: Real-time monitoring of the communication link status between the system and the equipment. When a communication interruption is detected, the disconnection detection process is initiated and the reconnection mechanism is automatically triggered. During the reconnection period, critical production data that has not been transmitted and process adjustment instructions to be issued are cached. After the communication is restored, the transmission is resumed in the order of first retransmitting historical data and then issuing unexecuted instructions, so as to ensure the integrity of production data and the continuity of process control.

[0014] Preferably, the stator-specific process control module further includes a process parameter version management unit, the specific functions of which include: Change Log: Automatically records every change to the process parameters of each stator model. The change information includes at least the specific time of parameter modification, the identity of the user who performed the modification operation, the reason for triggering the parameter change, and the effective scope of the changed parameter. Retrospective query: Supports searching by stator model and production batch to find the version of process parameters used when the corresponding batch of stators was produced, and to view the change history, original parameter values ​​and modification basis of the parameters in that version; Historical reuse: Extracts verified and effective process parameter combinations from historical parameter versions and generates reuse suggestions; when producing stators of the same model or type, users can directly call historical parameter versions and confirm their effectiveness without reconfiguring basic parameters.

[0015] The beneficial effects of this invention are: 1. This invention achieves precise matching of process parameters for different stator models through a hierarchical storage and unique mapping mechanism of a stator model-specific process parameter library. At the same time, it relies on a parameter coupling relationship model to ensure the coordination of process parameters throughout the entire process, thereby avoiding production defects caused by parameter mismatch from the source and significantly improving the accuracy and adaptability of stator production processes.

[0016] 2. This invention relies on the three-dimensional feature process identification, precondition verification, and multi-level anomaly handling mechanism of the process error prevention unit to accurately identify high-risk processes, eliminate process skipping and operational compliance issues. At the same time, through hierarchical unlocking of permissions and anomaly traceability design, it standardizes the anomaly handling process, effectively reduces quality risks caused by human error, and ensures the standardization and stability of the stator production process.

[0017] 3. This invention utilizes the real-time data acquisition, quantitative deviation judgment, and step-by-step closed-loop adjustment functions of the parameter dynamic adjustment unit to achieve dynamic correction and preventive adjustment of key process parameters. Combined with the parameter self-optimization and update mechanism to adapt to changes in production conditions, it continuously optimizes parameter configuration, significantly improves the stability of process parameters, ensures the consistency of stator product quality, and reduces equipment energy consumption and production losses.

[0018] In summary, the invention addresses the pain points of general-purpose MES systems in stator production scenarios, such as parameter mismatch, process skipping, and poor quality consistency, through the collaborative operation of the stator-specific process control module. It achieves precise, standardized, and intelligent control of the entire stator production process, improving production efficiency and product qualification rate while enhancing the system's adaptability and scalability to different production conditions. Attached Figure Description

[0019] Figure 1 This invention provides an architectural principle block diagram of a MES system for an electronic production line. Figure 2 The flowchart for the self-optimization and updating of process parameters provided by this invention; Figure 3 This invention provides a closed-loop logic block diagram for process error prevention. Figure 4 The closed-loop control block diagram of the parameter dynamic adjustment unit provided by the present invention. Detailed Implementation

[0020] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. It should be noted that these descriptions are for the purpose of aiding understanding the present invention, but do not constitute a limitation thereof. Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0021] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The singular forms “group,” “class,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0022] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this disclosure, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0023] Please refer to the following: Figures 1 to 4This invention proposes a MES system for electronic production lines. This system is adapted to the entire stator production process, which at least includes core loading, alignment, winding, pre-circling, rounding welding, inspection, marking, outer diameter inspection, lead wire bending, busbar assembly, terminal pre-clamping, terminal wire cutting, terminal welding, and stator performance testing. The system has a built-in stator-specific process control module, which includes: A dedicated process parameter library for each stator model is used to structure and store the characteristic process parameters and parameter constraint rules of each production process of different stator models. It establishes a coupling relationship model of parameters between different processes of the same stator model, and realizes a parameter self-optimization and update mechanism based on production batch data, quality inspection feedback and equipment operating status, and continuously optimizes parameter configuration through algorithm iteration. The process error prevention unit is used to implement closed-loop error prevention and control for characteristic processes with strong correlation in the stator production process. It includes precondition verification, operation compliance verification, and cross-process collaborative error prevention logic. The parameter dynamic adjustment unit is used to implement full-cycle parameter control for key processes that affect the core performance of the stator during production. By collecting process parameters and equipment operation data in real time, combined with preset parameter constraint rules and dynamic deviation judgment algorithms, it identifies parameter fluctuation trends, realizes closed-loop adjustment after parameters exceed the threshold, and performs deviation risk prediction and preventive adjustment based on parameter change gradient, ensuring that process parameters are always within a stable range and guaranteeing the consistency of stator product quality.

[0024] It should be noted that the basic parameter configuration of the stator-specific process control module is required for software initialization, including: Stator Model and Process Configuration: Enter all stator models supported by the production line (such as Y2-132S-4, Y2-160M-2, etc.) into the system, and associate each model with the 14 processes of the entire process, clarifying the execution order and logical relationship of the processes, and providing a basis for the precondition verification of the process error prevention unit; Basic data entry for the parameter library: Enter the basic values, fluctuation thresholds, and adjustment steps for the process parameters of each stator model according to the hierarchical structure of "Stator Model - Process Type - Parameter Category". For example, for the winding process of the Y2-132S-4 stator, the basic value for the number of turns is calculated as 36 turns using the electromagnetic design formula, the fluctuation threshold is set to ±1 turn, and the adjustment step is 1 turn; the basic value for tension is set to 12N based on the tensile strength of φ0.8mm enameled wire and the manufacturer's recommended range, the fluctuation threshold is ±0.6N, and the adjustment step is 0.2N. Coupling relationship model configuration: Preset parameter association rules between different processes of the same stator model in the parameter library. For example, the coupling relationship between the winding tension and the round welding temperature of the Y2-132S-4 stator: when the winding tension exceeds the range of 12±0.3N, the adjustment range of the welding temperature is automatically limited to 320-350℃ (the original standard adjustment range is 300-360℃), avoiding quality conflicts between processes caused by the adjustment of a single parameter; Error prevention rule configuration: Set the associated preceding processes of the characteristic process (busbar assembly associated with lead wire bending, terminal pre-clamping associated with busbar assembly, terminal wire cutting associated with terminal pre-clamping), risk assessment threshold (a process with a risk value ≥80 points on a 100-point scale is judged as an error prevention target process), and the parameter set range for MD5 value comparison (covering all characteristic parameters of the corresponding model for this process). Dynamic adjustment parameter configuration: Set the parameter acquisition frequency (1Hz) for key processes (winding, circular welding, terminal welding), the fluctuation threshold reference rule in the deviation judgment formula, and the preset ratio of the step adjustment command (the first adjustment is 40% of the fluctuation threshold, the second adjustment is 60%, and the machine will stop if the standard is not met after 3 consecutive adjustments). Equipment adaptation configuration: In the equipment adaptation layer, enter the native communication protocol type of each production equipment (such as the winding machine using the Modbus-RTU protocol and the welding equipment using the Profinet protocol), configure the protocol conversion rules, data interaction format (process parameters are transmitted in JSON format, and the fields include parameter name, value, and collection timestamp) and interaction timing (the timeout for system request → equipment response is set to 500ms).

[0025] In this invention, the stator model-specific process parameter library is constructed according to a hierarchical structure of stator model, process type, and parameter category, wherein: The process types must include at least winding, welding, and terminal processing. The parameter categories include number of turns, tension, and linear speed parameters for the winding process; temperature, current, and time parameters for the welding process; and pre-clamping pressure and wire cutting length parameters for the terminal processing process. Each parameter group contains three types of characteristic parameters: base value, fluctuation threshold, and adjustment step size. All parameters are uniquely mapped to a specific stator model. By taking into account the process differences of different stator models, the characteristic process parameters and constraint rules of each process are stored in a structured manner. When a production task is issued, the system automatically retrieves the corresponding parameter set according to the current production model and sends it to the relevant equipment to ensure that the equipment starts and runs according to the exclusive process, thus avoiding the risk of parameter mismatch from the source.

[0026] The method for determining the basic values ​​of each parameter group is as follows: The basic value of the number of turns is calculated using electromagnetic design formulas based on the stator phase voltage, power supply frequency, air gap magnetic flux density, effective cross-sectional area of ​​stator slots, and winding coefficient. Basic tension value: Set according to the enameled wire diameter, material tensile strength and the manufacturer's recommended range, combined with the winding tightness requirements; Linear velocity base value: The inverse relationship is determined by the number of turns, and set in combination with the equipment operating efficiency and winding accuracy requirements; Base temperature value: determined by superimposing a preset compensation temperature on the melting point of the welding material; The baseline current value is determined based on the temperature-current correlation characteristics and equipment power parameter calibration. Base time value: set based on welding penetration requirements and reference to verification data of similar stator processes; Pre-clamping pressure base value: calculated using a linear formula based on the conductor cross-sectional area, terminal material hardness, and minimum clamping pressure; Basic value for wire cutting length: determined by adding a preset allowance length to the terminal crimping depth as a reference. Each parameter group contains three types of characteristic parameters: base value, fluctuation threshold, and adjustment step size. All parameters are uniquely mapped to a specific stator model.

[0027] In this invention, the parameter coupling relationship model in the stator model-specific process parameter library is used to establish the correlation constraint logic of process parameters between different processes of the same stator model, specifically including: For the winding process and the subsequent round welding process, an adaptation constraint between winding tension and welding temperature is established, and the adjustment range of welding temperature is simultaneously limited when the winding tension increases. For the terminal pre-clamping process and the terminal welding process, a synergistic relationship between pre-clamping pressure and welding current is established. When the pre-clamping pressure exceeds the preset range, the welding current reference value is adjusted synchronously. The model addresses potential quality issues in subsequent processes caused by adjusting parameters in a single process by establishing parameter correlation constraints between different processes of the same stator model. For example, when tension parameters fluctuate during the winding process, the coupled model synchronously limits the temperature adjustment range for the coil welding process, ensuring coordinated adaptation between coil winding and welding processes and guaranteeing the consistency of stator electromagnetic performance and mechanical strength.

[0028] In this invention, the implementation logic of the parameter self-optimization update mechanism includes: Regularly collect stator production batch data, quality inspection feedback data, and equipment operating status data. Production batch data includes actual process parameters and equipment running time for each process. Quality inspection feedback data includes the number and type of defective products for each process. Equipment operating status data includes equipment load rate and component wear level. Data correlation analysis was used to identify the mapping relationship between parameter configuration and quality and equipment status, and to statistically analyze the correlation between key parameter fluctuations and stator quality defects. Based on the analysis results, parameter optimization suggestions are generated, and parameters that deviate from the optimal range are iteratively corrected. After correction, the optimization effect is verified through small-batch trial production. If the verification is successful, the corresponding configuration in the parameter library is updated. Based on batch data, quality feedback data, and equipment status data continuously collected during the production process, parameter configuration is continuously optimized through algorithm iteration. This mechanism can dynamically adapt to changes in production conditions, such as parameter adaptation deviations caused by equipment aging and process adjustment needs caused by fluctuations in raw material characteristics, ensuring that process parameters are always within the optimal range and continuously improving product qualification rate and production efficiency.

[0029] In this invention, the specific process of algorithm iterative optimization is as follows: The gradient descent algorithm is used to optimize the parameter configuration, with the dual objective functions being the highest stator finished product qualification rate and the lowest equipment energy consumption. Production batch data, quality inspection feedback data, and equipment operating status data are used as input variables for the algorithm. During each iteration, the optimization direction is adjusted based on the actual production effect of the previous parameter configuration, gradually reducing the deviation between the parameters and the optimal range; The iteration cycle is set according to the production batch. The algorithm is iterated once after each preset production batch is completed, ensuring that the parameter configuration is dynamically adapted to changes in production conditions.

[0030] In this invention, the implementation logic of the process error prevention unit includes: Featured Process Identification: A quantitative assessment is conducted based on three dimensions: process relevance, quality impact weight, and historical error frequency. Processes with risk values ​​exceeding a set risk threshold are selected as target processes for error prevention. Featured processes include at least busbar assembly, terminal pre-clamping, and terminal wire cutting. Process relevance refers to the material / parameter dependence on upstream and downstream processes, and quality impact weight is the proportion of impact on stator performance. This quantitative assessment, based on process relevance, quality impact weight, and historical error frequency, accurately identifies target processes for error prevention. This selection logic ensures that error prevention resources are concentrated on core risk points, avoiding indiscriminate error prevention that leads to reduced efficiency. Furthermore, by dynamically updating the assessment data, it adapts to changes in process risks during production. Pre-condition verification: For each feature process, at least one associated pre-condition process is preset. For example, the pre-condition process associated with busbar assembly is lead wire bending, the pre-condition process associated with terminal pre-clamping is busbar assembly, and the pre-condition process associated with terminal wire cutting is terminal pre-clamping. The completion status (completed / incomplete) and quality compliance (qualified / unqualified) of the pre-condition process are verified through the production data chain. Verification is considered successful when both conditions are met. A unique pre-condition process is preset for each feature process through pre-condition verification. The completion status and quality compliance of the pre-condition process are traced through the production data chain. The current process is only allowed to start when both conditions are met. This mechanism eliminates skipping steps in the process flow and solves product assembly defects caused by process connection errors. Operational compliance verification: The unique model identification information on the stator carrier is read using identification technology to obtain the model data of the stator currently being processed. The MD5 value of the process parameter set corresponding to this model is compared with the actual parameter set loaded at the current workstation. If the MD5 values ​​match, the matching is deemed successful. Identification technology includes at least QR code recognition and RFID recognition. The model information of the stator currently being processed is obtained through identification technology, and the consistency of the process parameter set corresponding to this model is verified against the actual parameter set loaded at the workstation. Operation is only allowed after verification is passed to ensure accurate matching of parameters and model, avoiding process deviations caused by human error or incorrect equipment parameters. Anomaly Handling Mechanism: When preconditions are not met or parameters are mismatched, multi-level warnings are triggered and the workstation is locked. Unlocking requires authorized personnel to log in to the system and submit an anomaly handling report containing cause analysis and remedial measures. After system confirmation, the workstation is automatically unlocked. Through this anomaly handling mechanism, warnings at corresponding levels are triggered and workstations are locked for anomalies of varying severity, preventing the escalation of anomalies. Simultaneously, the hierarchical unlocking and anomaly report submission mechanism ensures the standardization and traceability of anomaly handling, forming a complete error-prevention closed loop consisting of discovery, locking, handling, and unlocking. It should be noted that the multi-level early warning system is divided into three fixed levels: audible and visual alarms, terminal message push notifications, and SMS notifications to management personnel. The system automatically determines and triggers the corresponding level of early warning based on the severity of the anomaly. Only an audible and visual alarm is triggered when there is a slight deviation in the parameters; When the parameters deviate moderately, an audible and visual alarm and a terminal message push are triggered. When a preceding process is missing or parameters are completely mismatched, an audible and visual alarm, terminal message push, and SMS notification to management personnel are triggered to ensure that abnormal situations are promptly perceived and handled by the corresponding authorized personnel.

[0031] In this invention, the implementation logic of the parameter dynamic adjustment unit includes: Key processes were selected, with winding, circular welding, and terminal welding identified as the processes for parameter adjustment. Real-time data acquisition involves obtaining actual process parameters and equipment operating status data through an interface with the production equipment. The actual process parameters include the actual number of turns, actual tension, and actual linear speed of the winding process; the actual temperature, actual current, and actual time of the round welding process; the actual temperature and actual current of the terminal welding process; and the equipment operating status code (specifically, 0 indicates standby, 1 indicates operation, and 2 indicates abnormality). Deviation determination involves retrieving the standard parameters corresponding to the stator model from the stator model's dedicated process parameter library. The actual process parameters Standard parameters of the corresponding model in the parameter library Compare and calculate the absolute deviation between the actual parameters and the standard parameters. The formula is: Where X represents the actual process parameters of any process; then the deviation judgment formula is used. Calculate the degree of deviation D. When D≤1, the parameter is considered normal. When D>1, the parameter is out of tolerance, and the adjustment operation corresponding to the out-of-tolerance parameter needs to be initiated. The purpose of this step is to quantify the degree of deviation, avoid frequent adjustment due to small fluctuations, and ensure that out-of-tolerance parameters are accurately identified. Closed-loop control: When a parameter is found to be out of tolerance, a step-wise adjustment command is automatically generated based on the parameter category and sent to the production equipment. After adjustment, parameters are continuously collected at a preset frequency and deviation judgment is repeated until the parameter returns to the threshold range. Then, adjustment is stopped and the complete adjustment process is recorded in the parameter change file. The specific logic is as follows: For winding process parameters, specific adjustment rules are matched according to parameter type (number of turns, tension, linear speed): when the number of turns exceeds the tolerance, the winding machine counting module is corrected based on the degree of deviation. After adjustment, the parameters are re-acquired and judged after the current coil winding cycle ends; when the tension or linear speed exceeds the tolerance, the output of the corresponding controller is adjusted in stages according to the preset ratio, and the coupling relationship between the two is checked simultaneously to avoid the adjustment of a single parameter causing abnormalities in other parameters.

[0032] For welding process parameters (temperature, current, time), when the temperature exceeds the tolerance, the power of the heating module is adjusted according to the conversion relationship calibrated by the equipment, and the deviation is re-judged after the temperature stabilizes; when the current exceeds the tolerance, the output value is adjusted in stages, and the temperature fluctuation range is monitored simultaneously; when the time exceeds the tolerance, the timing module is corrected to ensure that the adjustment is compatible with the temperature and current parameters and to avoid abnormal heat input.

[0033] For the terminal welding process parameters, the adjustment logic for temperature and current deviations refers to the corresponding parameter rules for round welding, but is adapted to the characteristics of the terminal welding equipment; when adjusting the current, welding pressure fluctuations are additionally monitored to ensure that the pressure is within the preset stable range.

[0034] After all adjustment instructions are issued, parameters are continuously collected at a preset frequency and deviation judgment is repeated until the parameters return to the threshold range. Then, the adjustment is stopped and the complete adjustment process is recorded in the parameter change file. Based on the parameter change gradient analysis, the parameter fluctuation trend is predicted and the adjustment action is triggered in advance to control the parameter deviation in the early stage. This function breaks through the passive mode of adjusting after exceeding the tolerance in the traditional way, further improves the stability of process parameters, and ensures the consistency of stator product quality.

[0035] In this invention, the generation rule for the step-by-step adjustment command is as follows: Winding process parameters: When the number of turns exceeds the tolerance, the winding machine counting module is corrected based on the degree of deviation. After adjustment, the parameters are re-acquired and judged after the current coil winding cycle ends. When the tension or linear speed exceeds the tolerance, the output of the corresponding controller is adjusted in stages according to the preset ratio, and the coupling relationship between the two is verified synchronously. Welding process parameters: When the temperature exceeds the tolerance, adjust the heating module power according to the conversion relationship calibrated by the equipment, and re-judge the deviation after the temperature stabilizes; when the current exceeds the tolerance, adjust the output value in stages and monitor the temperature fluctuation range simultaneously; when the time exceeds the tolerance, correct the timing module to ensure that it is compatible with the temperature and current parameters. Terminal welding process parameters: The adjustment logic for temperature and current deviations refers to the corresponding parameter rules for round welding and is adapted to the characteristics of terminal welding equipment; when adjusting the current, welding pressure fluctuations are additionally monitored to ensure that the pressure is within the preset stable range.

[0036] It should be noted that the preset ratio of the step-by-step adjustment command follows the following rules: the initial adjustment range is the first preset ratio of the fluctuation threshold. If the deviation still exceeds the threshold after adjustment, a second adjustment is performed according to the second preset ratio. If the parameter does not return to the normal range after a preset number of consecutive adjustments, the equipment shutdown protection is triggered and the warning level is upgraded.

[0037] In this invention, the stator-specific process control module has an equipment adaptation layer, which includes: Universal Protocol Conversion Interface: Identifies and adapts the native communication protocols of different brands of winding machines, welding equipment, and terminal processing equipment. Through built-in protocol parsing and conversion logic, it converts the device-specific protocols into the system's universal communication protocols, enabling stable access and bidirectional data transmission between various devices and the system. Data interaction specifications: Define the data format (including data field definitions, field types and lengths) and interaction sequence for process parameter acquisition, equipment status acquisition, and adjustment command issuance to avoid data transmission format incompatibility or timing disorder; Abnormal reconnection mechanism: Real-time monitoring of the communication link status between the system and equipment. When a communication interruption is detected, the disconnection detection process (including link fault location) is initiated and the reconnection mechanism is automatically triggered. During the reconnection period, critical production data (including process parameters and equipment status data) that have not been transmitted and process adjustment instructions to be issued are cached. After communication is restored, the transmission is resumed in the order of first retransmitting historical data and then issuing unexecuted instructions, so as to ensure the integrity of production data and the continuity of process control.

[0038] In this invention, the stator-specific process control module also includes a process parameter version management unit, used to record the change history of process parameters for each stator model, including parameter modification time, modifying user, reason for change, and scope of effect. It supports parameter retrospective query based on production batches and reuse of historical versions. Specific functions include: Change Log: Automatically records every change to the process parameters of each stator model. The change information includes at least the specific time of parameter modification, the user identity who performed the modification operation, the reason for triggering the parameter change (such as process optimization, quality improvement, equipment upgrade), and the scope of effect of the changed parameter (such as specified production batch, specific production line, all models). Retrospective query: Supports searching by stator model and production batch to find the version of process parameters used when the corresponding batch of stators was produced. It also allows you to view the change history, original parameter values ​​and modification basis of the parameters in that version. Historical reuse: It can extract the verified and effective combination of process parameters from historical parameter versions and generate reuse suggestions; when producing stators of the same model or type, users can directly call the historical parameter version and confirm its effectiveness without reconfiguring the basic parameters.

[0039] In this invention, the stator-specific process control module further includes a process parameter version management unit, the specific functions of which include: Change Log: Automatically records every change to the process parameters of each stator model. The change information includes at least the specific time of parameter modification, the user identity who performed the modification operation, the reason for triggering the parameter change (process optimization, quality improvement, equipment upgrade, etc.), and the scope of effect of the changed parameter (specified production batch, specific production line, all models, etc.). Retrospective query: Supports searching by stator model and production batch to find the version of process parameters used when the corresponding batch of stators was produced, and to view the change history, original parameter values ​​and modification basis of the parameters in that version; Historical reuse: Extracts verified and effective process parameter combinations from historical parameter versions and generates reuse suggestions; when producing stators of the same model or type, users can directly call historical parameter versions and confirm their effectiveness without reconfiguring basic parameters.

[0040] All formulas in this invention are based on dimensionless numerical calculations. Dimensionlessness can be achieved using conventional methods such as standardization, the specific methods of which will not be elaborated here. The formulas are obtained by collecting a large amount of production data and fitting it with software simulation, which can fit the actual production scenario to the greatest extent. The preset parameters in the formulas can be flexibly set by those skilled in the art according to specific production conditions, equipment characteristics and product requirements.

[0041] The embodiments of the present invention can be implemented entirely or partially through software, hardware, firmware, or any combination thereof. When implemented in software, it can be presented entirely or partially as a computer program product. The computer program product includes computer instructions or programs, which, when loaded or executed on a computer (including general-purpose computers, special-purpose computers, computer networks, and other programmable devices), can produce the processes or functions of the embodiments of the present invention. The computer instructions can be stored in a computer-readable storage medium or transmitted between different storage media via wired or wireless means (such as infrared, microwave, etc.).

[0042] In the embodiments of this invention, the process numbers do not represent the order of execution. The execution order is determined by the function and internal logic and does not constitute a limitation on the implementation process. The units and algorithm steps of each example can be implemented by a combination of electronic hardware, computer software, and electronic hardware. The specific implementation method depends on the application scenario and design constraints of the technical solution and does not exceed the protection scope of this invention.

[0043] The systems, apparatus, and methods disclosed in this invention can be implemented in other forms. The unit division is only a logical functional division; other division methods may be used in actual implementation. The couplings and communication connections shown or discussed can be achieved through indirect couplings or communication connections via interfaces, devices, or units, and can take the form of electrical, mechanical, etc. The units described as separate components may or may not be physically separated. Components shown as units may or may not be physical units, and can be concentrated in one place or distributed across multiple network units. Some or all units can be selected to achieve the purpose of this solution according to requirements.

[0044] Each functional unit can be integrated into a processing unit, or exist as a separate physical entity, or two or more units can be integrated into one unit. If the function is implemented as a software functional unit and sold or used independently, it can be stored in a computer-readable storage medium (including USB flash drives, portable hard drives, ROM, RAM, magnetic disks, optical disks, and other media capable of storing program code), which contains several instructions to cause a computer device to execute all or part of the steps of the methods of the various embodiments of the present invention.

[0045] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and these variations still fall within the protection scope of the present invention.

Claims

1. A MES system for a stator production line, the system being adapted to the entire stator production process, the entire stator production process including at least core feeding, alignment, winding, pre-circling, rounding welding, inspection, marking, outer diameter inspection, lead wire bending, busbar assembly, terminal pre-clamping, terminal wire cutting, terminal welding, and stator performance testing, characterized in that, The system has a built-in stator-specific process control module, which includes: A dedicated process parameter library for each stator model is used to structure and store the characteristic process parameters and parameter constraint rules of each production process of different stator models. It establishes a coupling relationship model of parameters between different processes of the same stator model, and realizes a parameter self-optimization and update mechanism based on production batch data, quality inspection feedback and equipment operating status, and continuously optimizes parameter configuration through algorithm iteration. The process error prevention unit is used to implement closed-loop error prevention and control for characteristic processes with strong correlation in the stator production process. It includes precondition verification, operation compliance verification, and cross-process collaborative error prevention logic. The parameter dynamic adjustment unit is used to implement full-cycle parameter control for key processes that affect the core performance of the product during stator production. By collecting process parameters and equipment operation data in real time, combined with preset parameter constraint rules and dynamic deviation judgment algorithms, it is used to identify parameter fluctuation trends, realize closed-loop adjustment after parameters exceed the threshold, and predict and preventively adjust deviation risks based on parameter change gradients.

2. The MES system for a stator production line according to claim 1, characterized in that, The stator model-specific process parameter library is constructed according to a hierarchical structure of stator model, process type, and parameter category, wherein: The process types must include at least winding, welding, and terminal processing. The parameter categories include number of turns, tension, and linear speed parameters for the winding process; temperature, current, and time parameters for the welding process; and pre-clamping pressure and wire cutting length parameters for the terminal processing process. Each parameter group contains three types of characteristic parameters: base value, fluctuation threshold, and adjustment step size. All parameters are uniquely mapped to a specific stator model.

3. The MES system for a stator production line according to claim 1, characterized in that, The parameter coupling relationship model in the stator model-specific process parameter library is used to establish the correlation constraint logic of process parameters between different processes of the same stator model, specifically including: For the winding process and the subsequent round welding process, an adaptation constraint between winding tension and welding temperature is established, and the adjustment range of welding temperature is simultaneously limited when the winding tension increases. For the terminal pre-clamping process and the terminal welding process, a synergistic relationship between pre-clamping pressure and welding current is established. When the pre-clamping pressure exceeds the preset range, the welding current reference value is adjusted synchronously.

4. The MES system for a stator production line according to claim 1, characterized in that, The implementation logic of the parameter self-optimization update mechanism includes: Regularly collect stator production batch data, quality inspection feedback data, and equipment operating status data. The production batch data includes the actual process parameters of each process and the equipment running time. The quality inspection feedback data includes the number of non-conforming products and the type of defects in each process. The equipment operating status data includes the equipment load rate and the degree of component wear. Data correlation analysis was used to identify the mapping relationship between parameter configuration and quality and equipment status, and to statistically analyze the correlation between key parameter fluctuations and stator quality defects. Based on the analysis results, parameter optimization suggestions are generated, and parameters that deviate from the optimal range are iteratively corrected. After correction, the optimization effect was verified through small-batch trial production. If the verification was successful, the corresponding configuration in the parameter library was updated.

5. The MES system for a stator production line according to claim 1, characterized in that, The specific process of algorithm iterative optimization is as follows: The gradient descent algorithm is used to optimize the parameter configuration, with the dual objective functions being the highest stator finished product qualification rate and the lowest equipment energy consumption. Production batch data, quality inspection feedback data, and equipment operating status data are used as input variables for the algorithm. During each iteration, the optimization direction is adjusted based on the actual production effect of the previous parameter configuration, gradually reducing the deviation between the parameters and the optimal range; The iteration cycle is set according to the production batch. The algorithm is iterated once after each preset production batch is completed, ensuring that the parameter configuration is dynamically adapted to changes in production conditions.

6. The MES system for a stator production line according to claim 1, characterized in that, The implementation logic of the process error prevention unit includes: Feature process identification: Quantitative evaluation is carried out from three dimensions: process correlation, quality impact weight, and historical error frequency. Processes with risk values ​​greater than the set risk threshold are selected as error prevention target processes. The feature processes include at least busbar assembly, terminal pre-clamping, and terminal wire cutting. The process correlation refers to the material / parameter dependence with upstream and downstream processes. The quality impact weight is the proportion of the impact on stator performance. Precondition verification: For each feature process, at least one associated precondition process is preset. The associated precondition process for busbar assembly is lead wire bending, the associated precondition process for terminal preclamping is busbar assembly, and the associated precondition process for terminal wire cutting is terminal preclamping. The completion status (completed / incomplete) and quality qualification (qualified / unqualified) of the precondition process are verified through the production data chain. The verification is considered successful when both are completed and qualified. Operational compliance verification: The unique model identification information on the stator carrier is read through identification technology to obtain the model data of the stator currently being processed. The MD5 value of the process parameter set corresponding to the model is compared with the parameter set actually loaded at the current workstation. If the MD5 values ​​are consistent, the matching is deemed qualified. The identification technology includes at least QR code identification and RFID identification. Anomaly Handling Mechanism: When the preconditions are not met or the parameters are mismatched, a multi-level warning is triggered and the workstation is locked; unlocking requires the corresponding authorized personnel to log in to the system and submit an anomaly handling report containing cause analysis and handling measures. The system will automatically unlock the workstation after recording and confirming the report.

7. A MES system for a stator production line according to claim 1, characterized in that, The implementation logic of the parameter dynamic adjustment unit includes: Key processes were selected, with winding, circular welding, and terminal welding identified as the processes for parameter adjustment. Real-time data acquisition: Obtaining actual process parameters and equipment operating status data through interfaces with production equipment; Deviation determination involves comparing the actual process parameters with the standard parameters of the corresponding model in the parameter library, calculating the deviation value, and comparing it with the fluctuation threshold. Closed-loop control: When a parameter is found to be out of tolerance, a step-by-step adjustment instruction is automatically generated according to the parameter category and sent to the production equipment. After adjustment, parameters are continuously collected at a preset frequency and deviation judgment is repeated until the parameter returns to the threshold range. Then, the adjustment is stopped and the complete adjustment process is recorded in the parameter change file.

8. A MES system for a stator production line according to claim 7, characterized in that, The generation rule for the step-by-step adjustment command is as follows: Winding process parameters: When the number of turns exceeds the tolerance, the winding machine counting module is corrected based on the degree of deviation. After adjustment, the parameters are re-acquired and judged after the current coil winding cycle ends. When the tension or linear speed exceeds the tolerance, the output of the corresponding controller is adjusted in stages according to the preset ratio, and the coupling relationship between the two is verified synchronously. Welding process parameters: When the temperature exceeds the tolerance, adjust the heating module power according to the conversion relationship calibrated by the equipment, and re-judge the deviation after the temperature stabilizes; when the current exceeds the tolerance, adjust the output value in stages and monitor the temperature fluctuation range simultaneously; when the time exceeds the tolerance, correct the timing module to ensure that it is compatible with the temperature and current parameters. Terminal welding process parameters: The adjustment logic for temperature and current deviations refers to the corresponding parameter rules for round welding and is adapted to the characteristics of terminal welding equipment; when adjusting the current, welding pressure fluctuations are additionally monitored to ensure that the pressure is within the preset stable range.

9. A MES system for a stator production line according to claim 1, characterized in that, The stator-specific process control module has an equipment adaptation layer, which includes: Universal Protocol Conversion Interface: Identifies and adapts the native communication protocols of different brands of winding machines, welding equipment, and terminal processing equipment. Through built-in protocol parsing and conversion logic, it converts the device-specific protocols into the system's universal communication protocols, enabling stable access and bidirectional data transmission between various devices and the system. Data interaction specifications: Define the data format and interaction sequence for process parameter acquisition, equipment status acquisition, and adjustment command issuance; Abnormal reconnection mechanism: Real-time monitoring of the communication link status between the system and the equipment. When a communication interruption is detected, the disconnection detection process is initiated and the reconnection mechanism is automatically triggered. During the reconnection period, critical production data that has not been transmitted and process adjustment instructions that are yet to be issued are cached. After communication is restored, the transmission will be resumed in the order of first retransmitting historical data and then sending subsequent unexecuted instructions.

10. A MES system for a stator production line according to claim 1, characterized in that, The stator-specific process control module also includes a process parameter version management unit, the specific functions of which include: Change Log: Automatically records every change to the process parameters of each stator model. The change information includes at least the specific time of parameter modification, the identity of the user who performed the modification operation, the reason for triggering the parameter change, and the effective scope of the changed parameter. Retrospective query: Supports searching by stator model and production batch to find the version of process parameters used when the corresponding batch of stators was produced, and to view the change history, original parameter values ​​and modification basis of the parameters in that version; Historical reuse: Extracts verified and effective process parameter combinations from historical parameter versions and generates reuse suggestions; when producing stators of the same model or type, users can directly call historical parameter versions and confirm their effectiveness without reconfiguring basic parameters.

Images