Tag gun pin production efficiency optimization method and system based on industrial big data

Abstract

The invention relates to the technical field of hang tag gun pins, and particularly discloses a hang tag gun pin production efficiency optimization method and system based on industrial big data, and the method comprises the steps: collecting production line dynamic parameters and process compliance parameters, and carrying out the calculation to obtain an efficiency deviation reflecting the beat stability; and determining dynamic standard process operation parameters by combining the quality coordination factor and the equipment health degree factor, calculating equipment operation deviation parameters by integrating the equipment instantaneous working condition, the adjustable performance, the vibration risk and the order time requirement, and then performing collaborative analysis on the efficiency deviation, the standard process parameters and the equipment operation deviation. A system quality risk coefficient is introduced, a comprehensive optimization parameter is generated, a target process parameter is obtained through mapping according to the comprehensive optimization parameter, finally, a cooperative control instruction subjected to environment interference compensation is generated based on the target parameter and issued to an execution unit, and dynamic cooperative optimization of the stamping efficiency and the process quality is achieved.

Description

Technical Field

[0001] This invention relates to the field of tag gun needle technology, and in particular to a method and system for optimizing the production efficiency of tag gun needles based on industrial big data. Background Technology

[0002] Hang tag pins are key components used to attach product hang tags in industries such as apparel, bags, and handicrafts. Their production efficiency and quality directly impact the overall production rhythm and costs of downstream industries. Traditional optimization of hang tag pin production efficiency mainly relies on fixed-cycle production line settings, experience-based equipment maintenance cycles, and offline sampling inspection quality control models. The physical essence of this model lies in treating the production system as a static or quasi-static process, maintaining operation through preset, unchanging parameters (such as fixed stamping frequency, regular maintenance plans, and fixed sampling rates). However, the actual production process involves many dynamic uncertainties: the hardness and thickness of the upstream raw materials may fluctuate slightly; stamping dies will naturally wear down with use; different batches of orders may have different process requirements for the size, tip angle, etc. of the gun needles; and the health status of the production equipment itself is also changing in real time. Existing control methods based on fixed parameters are difficult to adapt to these dynamic changes in real time, resulting in production efficiency not always being maintained at its optimal state. Specifically, when the raw material parameters change, a fixed stamping frequency may cause equipment overload or underload, affecting output and increasing energy consumption; failure to detect die wear and machine tool vibration in real time may lead to increased defect rates or sudden equipment failures, causing unplanned downtime; the lack of coordinated optimization of multiple objectives such as production efficiency, equipment health, and process quality often results in optimization measures being ineffective, such as overusing equipment to increase output at the expense of equipment life and product qualification rate. Therefore, a production efficiency optimization method for hang tag gun needles based on industrial big data is needed to solve the above problems. Summary of the Invention

[0003] The purpose of this invention is to provide a method for optimizing the production efficiency of hang tag needles based on industrial big data, including: Obtain the real-time operating parameters of the hangtag gun needle production system, wherein the real-time operating parameters include production line dynamic response parameters and process compliance status parameters when the order and production process are synchronized; The production response cycle time and production fluctuation factor are obtained based on the production line dynamic response parameters, and the efficiency deviation is obtained based on the production response cycle time and the production fluctuation factor. Based on the process compliance status parameters, obtain the key dimension sequence and geometric tolerance values, and based on the key dimension sequence and geometric tolerance values, obtain the standard process operation parameters; The system status parameters of the production equipment are obtained, wherein the system status parameters include instantaneous operating condition parameters and adjustable performance parameters, and the equipment operating deviation parameters are obtained based on the instantaneous operating condition parameters and the adjustable performance parameters. Based on the efficiency deviation, the standard process operating parameters, and the equipment operating deviation parameters, comprehensive optimization parameters are obtained, and target process parameters are obtained based on the comprehensive optimization parameters. Control commands for the production equipment are generated based on the target process parameters, and these commands are applied to the execution unit of the production equipment to perform synergistic optimization of stamping efficiency and process quality.

[0004] Preferably, the step of obtaining the production response cycle time and production fluctuation factor based on the production line dynamic response parameters, and obtaining the efficiency deviation based on the production response cycle time and production fluctuation factor, includes: Based on the production line dynamic response parameters, obtain multiple real-time production cycles and multiple cycle change rates within a preset time window; The average real-time cycle time is obtained based on the multiple real-time production cycle times, and the average real-time cycle time is used as the production response cycle time. The real-time response time variance is obtained based on the multiple real-time production timescales and production response timescales, and the efficiency characteristic value is obtained based on the real-time response time variance and the multiple real-time production timescales. The system obtains the real-time material flow topology and work-in-process quantity data of the production line segment where the production equipment is located, and obtains the system load balancing coefficient based on the real-time material flow topology and the work-in-process quantity data. The comprehensive efficiency fluctuation index is obtained based on the system load balancing coefficient and the efficiency characteristic value. The average beat change rate is obtained based on the multiple beat change rates described; Obtain the rated cycle time variation rate, and obtain the production fluctuation factor based on the rated cycle time variation rate, the comprehensive efficiency fluctuation index, and the average cycle time variation rate; The efficiency deviation is obtained based on the production response cycle and the production fluctuation factor.

[0005] Preferably, the step of obtaining the key dimension sequence and geometric tolerance values ​​based on the process compliance status parameters, and obtaining the standard process operating parameters based on the key dimension sequence and the geometric tolerance values, includes: Based on the process compliance status parameters, obtain the real-time needle tip diameter sequence, needle body diameter sequence, and needle shoulder angle sequence; The corresponding key dimension sequence is obtained based on the real-time needle tip diameter sequence, needle body diameter sequence, and needle shoulder angle sequence; The real-time coaxiality error value and the real-time straightness error value are obtained based on the process compliance status parameters, and the geometric tolerance value is obtained based on the real-time coaxiality error value and the real-time straightness error value. The system acquires real-time process stability data of adjacent and related equipment in the production system and misjudgment rate data of key detection stations, and obtains the system quality coordination factor based on the real-time process stability data and the misjudgment rate data. The real-time vibration spectrum data and historical fatigue accumulation data of the core moving parts of the production equipment are obtained, and the comprehensive health factor of the equipment is obtained based on the real-time vibration spectrum data and the historical fatigue accumulation data. Standard process operating parameters are obtained based on the critical dimension sequence, the geometric tolerance values, the system quality synergy factor, and the equipment comprehensive health factor.

[0006] Preferably, the step of obtaining the equipment operating deviation parameters based on the instantaneous operating condition parameters and the adjustable performance parameters includes: Obtain the real-time spindle speed and real-time spindle torque from the instantaneous operating condition parameters, and obtain the operating condition deviation factor based on the real-time spindle speed and real-time spindle torque; Obtain the servo response time constant and feed rate coefficient from the adjustable performance parameters, and obtain the performance adjustment factor based on the servo response time constant and the feed rate coefficient; Based on the waveform of the spindle's real-time rotational speed change over multiple consecutive sampling periods, the spindle vibration characteristic spectrum is obtained through Fast Fourier Transform, and the dominant vibration frequency and vibration amplitude are extracted from the spindle vibration characteristic spectrum. The process fluctuation risk coefficient is obtained based on the dominant vibration frequency and the vibration amplitude. Obtain the time requirement feature parameters corresponding to the current production order. The time requirement feature parameters include the delivery time scale of urgent orders and the production time scale of regular orders. Obtain multi-objective optimization factors based on the delivery time scale of urgent orders and the production time scale of regular orders. Equipment operating deviation parameters are obtained based on the multi-objective optimization factor, the operating condition deviation factor, and the performance adjustment factor.

[0007] Preferably, the step of obtaining comprehensive optimization parameters based on the efficiency deviation, the standard process operating parameters, and the equipment operating deviation parameters, and obtaining target process parameters based on the comprehensive optimization parameters, includes: Acquire the quality defect pattern characteristic parameters of the entire production system process, including the frequency of dominant defect type, the damping ratio of defect-related processes, and the process modal influence factor; The system quality risk coefficient is obtained based on the frequency of the dominant defect type, the damping ratio of the defect-related process, and the modal influence factor of the process. The first optimization synergy factor is obtained based on the efficiency deviation and the standard process operating parameters; A second optimization synergy factor is obtained based on the equipment operating deviation parameters and the standard process operating parameters; The comprehensive optimization parameters are obtained based on the system quality risk coefficient, the first optimization synergy factor, and the second optimization synergy factor. The target process parameters are obtained by matching and referencing the preset process parameter mapping table with the comprehensive optimization parameters.

[0008] Preferably, the step of generating control commands for the production equipment based on the target process parameters, and applying the control commands to the execution unit of the production equipment to perform synergistic optimization of stamping efficiency and process quality, includes: Generate stamping efficiency control instructions and process quality control instructions based on the target process parameters; Real-time interference parameters of the production environment are obtained, including the temperature and humidity change rate of the workshop environment, the power grid voltage fluctuation rate, and the gas source pressure fluctuation rate. An environmental interference compensation coefficient is obtained based on the temperature and humidity change rate of the workshop environment, the power grid voltage fluctuation rate, and the gas source pressure fluctuation rate. Based on the environmental interference compensation coefficient, the stamping efficiency control command is subjected to anti-interference compensation to obtain the compensated stamping efficiency control command. The process quality control command is subjected to anti-interference compensation based on the environmental interference compensation coefficient to obtain the compensated process quality control command, and a collaborative optimization command is obtained based on the compensated stamping efficiency control command and the compensated process quality control command. The collaborative optimization command is applied to the execution unit of the production equipment to adjust the stamping frequency of the stamping press and the feed accuracy of the die, so as to achieve collaborative optimization of stamping efficiency and process quality.

[0009] This application also provides a system for optimizing the production efficiency of hang tag needles based on industrial big data, including: The first acquisition module is used to acquire the real-time operating parameters of the hangtag gun needle production system, wherein the real-time operating parameters include production line dynamic response parameters and process compliance status parameters when the order is synchronized with the production process; The second acquisition module is used to acquire the production response cycle time and production fluctuation factor based on the production line dynamic response parameters, and to acquire the efficiency deviation based on the production response cycle time and the production fluctuation factor. The third acquisition module is used to acquire key dimension sequences and geometric tolerance values ​​based on the process compliance status parameters, and to acquire standard process operating parameters based on the key dimension sequences and geometric tolerance values. The fourth acquisition module is used to acquire system status parameters of the production equipment, wherein the system status parameters include instantaneous operating condition parameters and adjustable performance parameters, and to acquire equipment operating deviation parameters based on the instantaneous operating condition parameters and the adjustable performance parameters; The fifth acquisition module is used to acquire comprehensive optimization parameters based on the efficiency deviation, the standard process operating parameters, and the equipment operating deviation parameters, and to acquire target process parameters based on the comprehensive optimization parameters. The control module is used to generate control instructions for the production equipment based on the target process parameters, and to apply the control instructions to the execution unit of the production equipment to perform synergistic optimization of stamping efficiency and process quality.

[0010] Preferably, the second acquisition module includes: The first acquisition unit is used to acquire multiple real-time production cycles and multiple cycle change rates within a preset time window based on the production line dynamic response parameters. The second acquisition unit is used to acquire an average real-time cycle time based on the multiple real-time production cycle times, and use the average real-time cycle time as the production response cycle time. The third acquisition unit is used to acquire the real-time response cycle variance value based on the multiple real-time production cycle counts and production response cycle counts, and to acquire the efficiency characteristic value based on the real-time response cycle variance value and the multiple real-time production cycle counts. The fourth acquisition unit is used to acquire the real-time material flow topology and work-in-process quantity data of the production line segment where the production equipment is located, and to acquire the system load balancing coefficient based on the real-time material flow topology and the work-in-process quantity data. The fifth acquisition unit is used to acquire the comprehensive efficiency fluctuation index based on the system load balancing coefficient and the efficiency characteristic value; The average beat change rate is obtained based on the multiple beat change rates described; The sixth acquisition unit is used to acquire the rated cycle time change rate and acquire the production fluctuation factor based on the rated cycle time change rate, the comprehensive efficiency fluctuation index, and the average cycle time change rate. The seventh acquisition unit is used to acquire the efficiency deviation based on the production response cycle time and the production fluctuation factor.

[0011] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0012] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0013] The beneficial effects of this application are as follows: First, this application obtains the real-time operating parameters of the production system, including response parameters reflecting the dynamics of the production cycle and compliance status parameters reflecting the product's compliance with process requirements. By analyzing the dynamic response parameters of the production line, the production response cycle and the production fluctuation factor characterizing the fluctuation risk are calculated, thereby obtaining the comprehensive efficiency deviation. At the same time, key dimensions and geometric tolerances are extracted based on the process compliance parameters, and combined with the quality coordination factor reflecting the overall stability of the production line and the health factor reflecting the equipment status, the standard process operating parameters are dynamically determined. Second, the instantaneous operating conditions and adjustable performance parameters of the equipment are collected, and vibration risk and order time requirements are integrated to calculate the equipment operating deviation parameters. Subsequently, the efficiency deviation, standard process parameters, and equipment operating deviation are analyzed collaboratively, and a system quality risk coefficient is introduced to generate comprehensive optimization parameters. Based on this, the target process parameters are obtained by querying the mapping table. Finally, based on the target parameters, a collaborative control command after environmental interference compensation is generated and issued to the equipment execution unit to achieve dynamic collaborative optimization of stamping efficiency and process quality. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of a method flow according to an embodiment of this application.

[0015] Figure 2 This is a schematic diagram of the system structure according to an embodiment of this application.

[0016] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0018] like Figure 1 - Figure 2 As shown, this application provides a method for optimizing the production efficiency of hang tag needles based on industrial big data, including: A method for optimizing the production efficiency of hang tag needles based on industrial big data includes: S1. Obtain the real-time operating parameters of the tag gun needle production system, wherein the real-time operating parameters include production line dynamic response parameters and process compliance status parameters when the order is synchronized with the production process; S2. Obtain the production response cycle time and production fluctuation factor based on the production line dynamic response parameters, and obtain the efficiency deviation based on the production response cycle time and the production fluctuation factor; S3. Obtain the key dimension sequence and geometric tolerance values ​​based on the process compliance status parameters, and obtain the standard process operation parameters based on the key dimension sequence and geometric tolerance values; S4. Obtain system status parameters of the production equipment, wherein the system status parameters include instantaneous operating condition parameters and adjustable performance parameters, and obtain equipment operating deviation parameters based on the instantaneous operating condition parameters and the adjustable performance parameters; S5. Obtain comprehensive optimization parameters based on the efficiency deviation, the standard process operating parameters, and the equipment operating deviation parameters, and obtain target process parameters based on the comprehensive optimization parameters; S6. Generate control instructions for the production equipment based on the target process parameters, and apply the control instructions to the execution unit of the production equipment to perform synergistic optimization of stamping efficiency and process quality.

[0019] As described in steps S1-S6 above, existing tag needle production control relies on fixed parameters set offline, which cannot be updated in real time. When raw material prices fluctuate, molds wear out, or order specifications change, the control strategy still executes based on the old parameters, leading to low production efficiency or quality deviations. This application first clarifies that real-time operating parameters include production line dynamic response parameters and process compliance status parameters. Among them, production line dynamic response parameters mainly reflect the changes in production cycle time over time, such as the single-piece production time and cycle time change rate at different times; process compliance status parameters focus on the degree of conformity between production output and order process requirements, including the measured values ​​of key product dimensions and geometric tolerance data. High-precision sensors, such as photoelectric counters, machine vision dimensional measuring instruments, and laser profilometers, are deployed at key nodes of the production line (such as the stamping press exit and online inspection stations). These sensors need to have sampling capabilities that match the production cycle time to ensure that they can capture dynamic changes in the production status. Secondly, the raw data collected by the sensors is transmitted in real time to the data processing unit of the Manufacturing Execution System (MES) through an industrial IoT gateway. The data processing unit first filters and removes outliers from the raw data, using a moving average filtering algorithm to eliminate random interference. Then, it performs data verification to ensure the data is within a reasonable process range, ultimately obtaining accurate real-time operating parameters for the production system. By proactively acquiring comprehensive parameters encompassing production line dynamics and process status, the system can capture dynamic characteristics in real time, providing a precise data foundation for subsequent optimization decisions and avoiding control inaccuracies caused by information lag.

[0020] Secondly, the production response cycle time and production fluctuation factor are obtained based on the production line dynamic response parameters, and the efficiency deviation is obtained based on the production response cycle time and the production fluctuation factor. In this way, by introducing the production fluctuation factor and comprehensively considering the statistical characteristics of the production cycle time and the system load balancing state, the deviation of the actual production efficiency from the ideal state can be quantified more accurately.

[0021] Based on the process compliance status parameters, key dimension sequences and geometric tolerance values ​​are obtained, and standard process operating parameters are obtained based on the key dimension sequences and geometric tolerance values. In this way, by combining system quality coordination factors and equipment comprehensive health factors, the overall quality fluctuations of the production line and the health status of the equipment itself are incorporated into the standard parameter calculation, making the standard process operating parameters more in line with the actual production scenario, and taking into account both quality consistency and equipment safety.

[0022] The system status parameters of the production equipment are obtained, and the equipment operation deviation parameters are obtained based on the instantaneous operating condition parameters and the adjustable performance parameters. By introducing the process fluctuation risk coefficient and multi-objective optimization factors, and comprehensively considering the instantaneous operating condition, adjustable performance, vibration risk and order time requirements of the equipment, the deviation between the current equipment operating state and the ideal state can be fully and accurately quantified, providing a comprehensive status basis for subsequent precise optimization.

[0023] Based on the efficiency deviation, the standard process operating parameters, and the equipment operating deviation parameters, comprehensive optimization parameters are obtained, and target process parameters are obtained based on the comprehensive optimization parameters. In this way, by introducing a system quality risk coefficient, the synergistic influence of comprehensive efficiency deviation, standard process operating parameters, and equipment operating deviation parameters is considered to construct comprehensive optimization parameters. Then, the target parameters are determined by combining them with a preset process parameter mapping table, so that the target process parameters can accurately match quality risks, efficiency requirements, and equipment status, thereby improving the effectiveness of the optimization strategy.

[0024] Control commands for the production equipment are generated based on the target process parameters, and these commands are applied to the execution unit of the production equipment to achieve synergistic optimization of stamping efficiency and process quality. By generating efficiency control and quality control commands separately and introducing an environmental interference compensation coefficient to compensate for the control commands, synergistic optimization of the two is achieved. At the same time, environmental fluctuation interference is offset, the robustness and accuracy of the control commands are improved, and the stability and pass rate of production output are guaranteed.

[0025] In one embodiment, step S2, which involves obtaining the production response cycle time and production fluctuation factor based on the production line dynamic response parameters, and obtaining the efficiency deviation based on the production response cycle time and production fluctuation factor, includes: S201. Obtain multiple real-time production cycles and multiple cycle change rates within a preset time window based on the production line dynamic response parameters. S202. Obtain the average real-time cycle time based on the multiple real-time production cycle times, and use the average real-time cycle time as the production response cycle time. S203. Obtain the real-time response cycle variance value based on the multiple real-time production cycle counts and production response cycle counts, and obtain the efficiency characteristic value based on the real-time response cycle variance value and the multiple real-time production cycle counts. S204. Obtain the real-time material flow topology and work-in-process quantity data of the production line segment where the production equipment is located, and obtain the system load balancing coefficient based on the real-time material flow topology and the work-in-process quantity data. S205. Obtain the comprehensive efficiency fluctuation index based on the system load balancing coefficient and the efficiency characteristic value; S206. Obtain the average beat change rate based on the multiple beat change rates; S207. Obtain the rated cycle time change rate, and obtain the production fluctuation factor based on the rated cycle time change rate, the comprehensive efficiency fluctuation index, and the average cycle time change rate. S208. Obtain the efficiency deviation based on the production response cycle time and the production fluctuation factor.

[0026] As described in steps S201-S208 above, existing cycle time data acquisition often uses fixed-interval counting or short-term statistics, which is easily affected by instantaneous production anomalies (such as material jams), leading to distortion in subsequent efficiency analysis. Therefore, this application obtains multiple real-time production cycle times (single-piece production time) and multiple cycle time change rates (time difference between adjacent cycle times) within a preset time window (e.g., 5 minutes) based on the production line dynamic response parameters. This preset time window and multi-data-point acquisition method can reflect the overall dynamic change trend of the cycle time over a period of time, providing more reliable raw data for subsequent calculations.

[0027] The average real-time beat rate is obtained from multiple real-time production beat rates and used as the production response beat rate. For example, within a 5-minute window, N beat rate data points are obtained using a photoelectric counter and a timer, and their arithmetic mean is calculated as the average real-time beat rate. The real-time response beat rate variance is obtained from the multiple real-time production beat rates and production response beat rates. This value is obtained by averaging the sum of the squares of the differences between each real-time production beat rate and the production response beat rate (i.e., the average real-time beat rate), and then taking the square root of this average. The physical significance of this step is to quantify the fluctuation and dispersion of the production beat rate; the larger the variance value, the more unstable the production. Then, an efficiency characteristic value is obtained from the real-time response beat rate variance value and multiple real-time production beat rates. The efficiency characteristic value is calculated by dividing the production response beat rate by the real-time response beat rate variance value. This value comprehensively reflects the average speed and stability of production; the larger the ratio, the faster the average beat rate and the smaller the fluctuation, indicating better efficiency characteristics.

[0028] The system acquires real-time material flow topology (e.g., connections between preceding and succeeding workstations, buffer capacity) and real-time work-in-process (WIP) quantity data for critical workstation buffers within the production line segment where the production equipment is located. The system load balancing coefficient is calculated by dividing the total effective capacity of all synchronous production units within the region by their total load demand. The total load demand is reflected by the ratio of the real-time WIP quantity on critical connecting lines to the line's rated throughput capacity. This coefficient quantifies the smoothness of material flow and load pressure within the production line segment; a higher coefficient value indicates a greater capacity of the system to absorb cycle time adjustments.

[0029] The comprehensive efficiency fluctuation index is obtained based on the system load balancing coefficient and the efficiency characteristic value. This index is calculated by multiplying the efficiency characteristic value by the system load balancing coefficient. This index integrates the equipment's own cycle time performance with the overall production line load status, more comprehensively reflecting the risk level of efficiency fluctuations. A high index value indicates greater optimization potential and lower risk when the efficiency characteristic is good and the system load is light.

[0030] The average cycle time variation rate is obtained based on multiple cycle time variation rates. The rated cycle time variation rate (the maximum permissible cycle time variation rate set according to equipment capacity and process safety) is obtained. The production fluctuation factor is obtained based on the rated cycle time variation rate, the comprehensive efficiency fluctuation index, and the average cycle time variation rate. The production fluctuation factor is calculated by dividing the average cycle time variation rate by the rated cycle time variation rate, and then multiplying by the comprehensive efficiency fluctuation index. This factor quantifies the product effect of the severity of cycle time variation (relative to rated capacity) and comprehensive efficiency fluctuation; the higher the dynamic risk, the larger the factor value.

[0031] Finally, the efficiency deviation is obtained based on the production response takt time and the production fluctuation factor. The efficiency deviation is calculated by adding the number 1 to the production fluctuation factor, and then multiplying it by the absolute value of the difference between the production response takt time and the preset rated takt time. The efficiency deviation obtained in this way not only includes a static assessment of the deviation from the average takt time, but also amplifies the deviation weight under unstable and high-load conditions through the production fluctuation factor, providing a more sensitive and accurate basis for subsequent optimization.

[0032] In one embodiment, step S3, which involves obtaining the critical dimension sequence and geometric tolerance values ​​based on the process compliance status parameters, and obtaining the standard process operating parameters based on the critical dimension sequence and the geometric tolerance values, includes: S301. Obtain the real-time needle tip diameter sequence, needle body diameter sequence, and needle shoulder angle sequence based on the process compliance status parameters. S302. Obtain the corresponding key dimension sequence based on the real-time needle tip diameter sequence, needle body diameter sequence, and needle shoulder angle sequence; S303. Obtain the real-time coaxiality error value and the real-time straightness error value according to the process compliance status parameters, and obtain the form and position tolerance value according to the real-time coaxiality error value and the real-time straightness error value; S304. Obtain real-time process stability data of adjacent related equipment and misjudgment rate data of key detection stations in the production system, and obtain the system quality coordination factor based on the real-time process stability data and the misjudgment rate data. S305. Obtain real-time vibration spectrum data and historical fatigue accumulation data of the core moving parts of the production equipment, and obtain the comprehensive health factor of the equipment based on the real-time vibration spectrum data and the historical fatigue accumulation data. S306. Obtain standard process operating parameters based on the critical dimension sequence, the geometric tolerance value, the system quality coordination factor, and the equipment comprehensive health factor.

[0033] As described in steps S301-S306 above, the process compliance status parameters are derived from the online inspection system database, containing measurement data uploaded by each inspection station. In S301-S302, the needle tip diameter, needle body diameter, and needle shoulder angle are sampled by the machine vision system in each production cycle, forming a sequence of measurement values ​​from the most recent cycles. The average of these sequences is calculated to obtain the current set of critical dimensions. This step extracts the feature dimensions representing the current process level from massive point data.

[0034] In S303, the coaxiality and straightness errors of the needle are measured using a laser profilometer. The average of the most recent measurements is taken as the current geometric tolerance value. This step quantifies the shape and positional accuracy of the product.

[0035] In S304, real-time status (such as fault alarms and speed fluctuations) of upstream feeders, downstream sorters, and other related equipment, as well as the false judgment rate reported by the visual inspection station (obtained through periodic calibration with standard parts), are acquired from the Manufacturing Execution System (MES). The system quality coordination factor is calculated as follows: First, a coefficient reflecting the overall stability of the system is obtained based on the real-time operating data of adjacent synchronous production units and the transmission capacity data of key logistics channels; simultaneously, a coefficient reflecting the reliability of quality monitoring is obtained based on the real-time process stability data and the false judgment rate data; finally, these two coefficients are weighted and fused to obtain the system quality coordination factor. This factor reflects the overall production line environment's ability to coordinate in ensuring the quality of the final product; the factor value decreases when surrounding equipment is unstable or detection is unreliable.

[0036] In S305, real-time vibration spectra are collected by vibration sensors installed on the press spindle and slide, and characteristic frequency amplitudes are extracted. Historical fatigue accumulation data is obtained from the equipment maintenance system, including the total number of stamping operations and recent replacement records of major components. The calculation method for the equipment's comprehensive health factor is as follows: First, the relative deviation between the real-time junction temperature data and the preset ambient junction temperature value is calculated, and combined with the maximum allowable junction temperature value, a sub-factor reflecting the current thermal state is obtained; second, based on the comparison between historical thermal fatigue accumulation data and the rated life, a sub-factor reflecting cumulative damage is obtained; finally, these two sub-factors are combined according to preset weights to obtain the equipment's comprehensive health factor. This factor quantifies the impact of the equipment's own mechanical and thermal state on its ability to maintain process accuracy.

[0037] In S306, standard process operating parameters are obtained based on the key dimension sequence, geometric tolerance values, system quality coordination factor, and equipment comprehensive health factor. These parameters are no longer fixed drawing tolerances but dynamically adjusted "operating benchmarks." Specifically, the obtained voltage amplitude is multiplied by the system stability coordination factor and the equipment health attenuation factor to obtain the standard voltage amplitude; the obtained reactive power is multiplied by the system stability coordination factor and the equipment health attenuation factor to obtain the standard reactive power; the standard voltage amplitude and standard reactive power together constitute the standard motor operating parameters. Through this step, the standard process operating parameters incorporate the constraints of the overall stability of the production line and equipment health, making subsequent optimization objectives more aligned with actual production conditions and maximizing production capacity potential while ensuring quality.

[0038] In one embodiment, step S4, which involves obtaining the equipment operating deviation parameter based on the instantaneous operating condition parameter and the adjustable performance parameter, includes: S401. Obtain the real-time spindle speed and real-time spindle torque from the instantaneous operating condition parameters, and obtain the operating condition deviation factor based on the real-time spindle speed and real-time spindle torque; S402. Obtain the inertial time constant and damping coefficient from the adjustable dynamic characteristic parameters, and obtain the dynamic adjustment factor based on the inertial time constant and the damping coefficient; S403. Based on the variation trend of the rotor angular velocity in multiple consecutive control cycles, obtain the virtual shaft system torsional vibration characteristic spectrum through fast Fourier transform, and extract the dominant torsional vibration frequency and torsional vibration amplitude from the virtual shaft system torsional vibration characteristic spectrum. S404. Obtain the virtual shaft system torsional vibration risk coefficient based on the dominant torsional vibration frequency and the torsional vibration amplitude; S405. Obtain the time scale characteristic parameters corresponding to the current operating condition of the power grid. The time scale characteristic parameters include the transient stability time scale and the dynamic oscillation time scale. Obtain the multi-time scale coordination factor based on the transient stability time scale and the dynamic oscillation time scale. S406. Obtain the operation deviation parameters based on the multi-timescale coordination factor, the motion deviation factor, and the dynamic adjustment factor.

[0039] As described in steps S401-S406 above, in S401, the feedback signal from the spindle servo drive is read in real time by the CNC system to obtain the real-time spindle speed and real-time torque. The operating condition deviation factor is calculated as follows: the ratio of the absolute difference between the real-time speed and the rated speed to the rated speed is calculated, and then the ratio of the absolute difference between the real-time torque and the rated torque to the rated torque is added together. This factor quantifies the degree of deviation between the current actual operating point of the equipment and the set rated state.

[0040] In S402, the servo response time constant and feed rate coefficient are adjustable parameters, read from the controller parameter table. The performance adjustment factor is calculated as follows: the ratio of the absolute difference between the real-time inertial time constant and the ideal inertial time constant to the ideal constant, plus the ratio of the absolute difference between the real-time damping coefficient and the ideal damping coefficient to the ideal coefficient, and then summing these two ratios. This factor quantifies the gap between the current control parameters and the theoretical optimal configuration.

[0041] In S403, time-frequency analysis is performed on the spindle speed signal. The real-time spindle speed waveform is acquired over multiple consecutive sampling periods, and its vibration characteristic spectrum is obtained through Fast Fourier Transform (FFT). The frequency component with the largest amplitude is identified from this spectrum, and its frequency is recorded as the dominant vibration frequency, with the corresponding amplitude recorded as the torsional vibration amplitude.

[0042] In S404, the process fluctuation risk coefficient is obtained based on the dominant vibration frequency and the torsional vibration amplitude. The calculation method is as follows: the dominant vibration frequency is divided by a preset upper frequency threshold to obtain a ratio; the torsional vibration amplitude is divided by a preset upper amplitude threshold to obtain another ratio; finally, these two ratios are multiplied to obtain the process fluctuation risk coefficient. This coefficient quantifies the potential threat of equipment vibration to process stability; the closer the frequency or amplitude is to the upper limit, the higher the risk coefficient.

[0043] In S405, the attributes of the current production order are retrieved from the order management system. Time requirement characteristic parameters are defined: delivery timescales for urgent orders (e.g., the ratio of remaining time to total duration), and production timescales for regular orders (e.g., planning flexibility). The multi-timescale coordination factor is calculated as follows: the transient stable timescale and the dynamic oscillation timescale are normalized separately, then multiplied by their respective preset weighting coefficients, and finally the two weighted results are summed. This factor can adjust the weight of the optimization objective between response speed and stability according to the current operating conditions.

[0044] In S406, equipment operating deviation parameters are obtained based on the multi-timescale coordination factor, the operating condition deviation factor, the performance adjustment factor, and the process fluctuation risk coefficient. The calculation method is as follows: the operating condition deviation factor, the performance adjustment factor, and the process fluctuation risk coefficient are added together, and then multiplied by the multi-timescale coordination factor to obtain the final equipment operating deviation parameter. This parameter comprehensively reflects the overall operating state deviation of the equipment under time constraints, unifying instantaneous operating conditions, adjustable performance, and vibration risk into an evaluation index whose weight changes with the urgency of the order.

[0045] In one embodiment, step S5, which involves obtaining comprehensive optimization parameters based on the efficiency deviation, the standard process operating parameters, and the equipment operating deviation parameters, and then obtaining target process parameters based on the comprehensive optimization parameters, includes: S501. Obtain the quality defect mode characteristic parameters of the entire production system process. The quality defect mode characteristic parameters include the frequency of dominant defect type, the damping ratio of defect-related processes, and the process mode influence factor. S502. Obtain the system quality risk coefficient based on the frequency of the dominant defect type, the damping ratio of the defect-related process, and the modal influence factor of the process. S503. Obtain the first coordination factor based on the frequency deviation and the standard motor operating parameters; S504. Obtain the second coordination factor based on the operating deviation parameter and the standard motor operating parameter; S505. Obtain abnormal coordination parameters based on the regional oscillation risk coefficient, the first coordination factor, and the second coordination factor; S506. Refer to the preset dynamic characteristic mapping table with the abnormal coordination parameters to obtain the target dynamic characteristic parameters.

[0046] As described in steps S501-S506 above, in S501, recent full-process quality data is extracted from the Statistical Process Control (SPC) module of the Quality Management System (QMS). Analysis yields: the frequency of dominant defect types (e.g., the percentage of occurrences of "needle-point burrs"); the damping ratio of defect-related processes (obtained by analyzing the rate of decay of fluctuations in related process parameters before and after a defect occurs; a small damping ratio indicates that the defect in that process is prone to triggering a chain reaction of fluctuations); and the process modal influence factor (obtained through correlation analysis of the contribution of the stamping process parameters to each defect type).

[0047] In S502, the system quality risk coefficient is obtained based on the frequency of the dominant defect type, the damping ratio of the defect-related process, and the modal influence factor of the process. The calculation method is to multiply the frequency of the dominant defect type, the reciprocal of the damping ratio of the defect-related process, and the modal influence factor of the process. This coefficient integrates the defect occurrence rate, the ease of defect propagation, and the responsibility level of this process, comprehensively assessing the current quality risk level faced by the production system.

[0048] In step S503, a first optimization synergy factor is obtained based on the efficiency deviation and the standard process operating parameters. It is calculated by dividing the efficiency deviation by one-tenth of the ideal operating threshold related to efficiency in the standard process operating parameters. This factor quantifies the magnitude of the current efficiency deviation relative to the theoretical efficiency benchmark.

[0049] In S504, a second optimization coordination factor is obtained based on the equipment operating deviation parameters and the standard process operating parameters. It is calculated by dividing the equipment operating deviation parameters by the ideal deviation threshold related to the equipment state in the standard process operating parameters. This factor quantifies the magnitude of the current equipment deviation relative to the health baseline.

[0050] In S505, a comprehensive optimization parameter is obtained based on the system quality risk coefficient, the first optimization synergy factor, and the second optimization synergy factor. The calculation method is as follows: the system quality risk coefficient, the first optimization synergy factor, and the second optimization synergy factor are each multiplied by a preset weight value, and then the three weighted results are summed to obtain the comprehensive optimization parameter. This parameter is a comprehensive quantitative indicator that balances quality risk, efficiency requirements, and equipment status.

[0051] In S506, the preset process parameter mapping table is obtained through historical big data analysis, process simulation, and expert experience calibration. The table establishes a correspondence between different comprehensive optimization parameter value ranges and a set of recommended target process parameters (such as target stamping frequency, target die feed speed, and target spindle speed correction). The calculated comprehensive optimization parameter values ​​are searched and matched in the mapping table. If they fall within a certain range, the corresponding target process parameter is output; if they are between two ranges, the corresponding target process parameter is calculated using linear interpolation. This step transforms the abstract comprehensive optimization requirements into specific, executable process parameter settings, providing clear instructions for final control.

[0052] In one embodiment, step S6, which generates control commands for the production equipment based on the target process parameters and applies the control commands to the execution unit of the production equipment to perform synergistic optimization of stamping efficiency and process quality, includes: S601. Generate stamping efficiency control instructions and process quality control instructions based on the target process parameters; S602. Obtain real-time interference parameters of the production environment, wherein the real-time interference parameters include the temperature and humidity change rate of the workshop environment, the power grid voltage fluctuation rate, and the gas source pressure fluctuation rate, and obtain the environmental interference compensation coefficient based on the temperature and humidity change rate of the workshop environment, the power grid voltage fluctuation rate, and the gas source pressure fluctuation rate. S603. Perform anti-interference compensation on the stamping efficiency control command according to the environmental interference compensation coefficient to obtain the compensated stamping efficiency control command. S604. Perform anti-interference compensation on the process quality control command according to the environmental interference compensation coefficient to obtain the compensated process quality control command, and obtain the collaborative optimization command according to the compensated stamping efficiency control command and the compensated process quality control command. S605. The collaborative optimization instruction is applied to the execution unit of the production equipment to adjust the stamping frequency of the stamping press and the feed accuracy of the die, so as to achieve collaborative optimization of stamping efficiency and process quality.

[0053] As described in steps S601-S605 above, in S601, instructions are generated according to the target process parameters. For example, the stamping efficiency control instruction mainly reflects the target stamping frequency, which is directly converted into the F code of the CNC system or the speed setpoint of the servo drive. The process quality control instruction mainly reflects the target die feed speed and possible fine-tuning compensation amount, which is converted into the position loop or speed loop correction instruction of the servo axis.

[0054] In S602, real-time interference parameters—temperature and humidity change rate, power grid voltage fluctuation rate, and air source pressure fluctuation rate—are acquired through workshop environmental sensors, a power grid quality analyzer, and a barometric pressure sensor. The environmental interference compensation coefficient is calculated as follows: First, the voltage harmonic distortion rate, current harmonic distortion rate, and interharmonic content are each divided by their corresponding rated thresholds to obtain three ratios. Then, these three ratios are multiplied by preset weighting coefficients and summed. Finally, the sum is subtracted by a number to obtain the environmental interference compensation coefficient. This coefficient quantifies the overall intensity of environmental interference; when any interference parameter exceeds the standard, the compensation coefficient decreases.

[0055] In S603-S604, compensation is performed on two types of control commands. Harmonic suppression compensation is applied to the inertial torque control signal based on the environmental interference compensation coefficient. The method is as follows: first, harmonic components are separated from the original control signal; then, these harmonic components are multiplied by the difference between a digital factor and the environmental interference compensation coefficient to obtain the harmonic components that need to be suppressed; finally, the original control signal is subtracted from the harmonic components that need to be suppressed to obtain the compensated control signal. The synchronous torque control signal is compensated using the exact same steps to obtain the compensated synchronous torque control signal. The basic principle of compensation is: when environmental interference is large (compensation coefficient is small), in order to maintain stability, the fluctuation components (analog harmonics) in the control signal should be suppressed to a greater extent.

[0056] In S605, the compensated efficiency and quality commands are synergistically integrated. The synergistic optimization command is obtained by adding the compensated stamping efficiency control command and the compensated process quality control command according to a preset weight ratio. For example, when the efficiency command has a higher weight, the optimization leans more towards increasing output; when the quality command has a higher weight, the optimization leans more towards ensuring accuracy. Finally, the synergistic optimization command is sent to the main driver, servo driver, and other execution units of the stamping press via a fieldbus (such as PROFINET, EtherCAT) to adjust the equipment's operating parameters in real time, thereby achieving a balance and synergistic optimization of production efficiency and process quality under dynamically changing internal and external conditions.

[0057] This application also provides a system for optimizing the production efficiency of hang tag needles based on industrial big data, including: The first acquisition module is used to acquire the real-time operating parameters of the hangtag gun needle production system, wherein the real-time operating parameters include production line dynamic response parameters and process compliance status parameters when the order is synchronized with the production process; The second acquisition module is used to acquire the production response cycle time and production fluctuation factor based on the production line dynamic response parameters, and to acquire the efficiency deviation based on the production response cycle time and the production fluctuation factor. The third acquisition module is used to acquire key dimension sequences and geometric tolerance values ​​based on the process compliance status parameters, and to acquire standard process operating parameters based on the key dimension sequences and geometric tolerance values. The fourth acquisition module is used to acquire system status parameters of the production equipment, wherein the system status parameters include instantaneous operating condition parameters and adjustable performance parameters, and to acquire equipment operating deviation parameters based on the instantaneous operating condition parameters and the adjustable performance parameters; The fifth acquisition module is used to acquire comprehensive optimization parameters based on the efficiency deviation, the standard process operating parameters, and the equipment operating deviation parameters, and to acquire target process parameters based on the comprehensive optimization parameters. The control module is used to generate control instructions for the production equipment based on the target process parameters, and to apply the control instructions to the execution unit of the production equipment to perform synergistic optimization of stamping efficiency and process quality.

[0058] In one embodiment, the second acquisition module includes: The first acquisition unit is used to acquire multiple real-time production cycles and multiple cycle change rates within a preset time window based on the production line dynamic response parameters. The second acquisition unit is used to acquire an average real-time cycle time based on the multiple real-time production cycle times, and use the average real-time cycle time as the production response cycle time. The third acquisition unit is used to acquire the real-time response cycle variance value based on the multiple real-time production cycle counts and production response cycle counts, and to acquire the efficiency characteristic value based on the real-time response cycle variance value and the multiple real-time production cycle counts. The fourth acquisition unit is used to acquire the real-time material flow topology and work-in-process quantity data of the production line segment where the production equipment is located, and to acquire the system load balancing coefficient based on the real-time material flow topology and the work-in-process quantity data. The fifth acquisition unit is used to acquire the comprehensive efficiency fluctuation index based on the system load balancing coefficient and the efficiency characteristic value; The sixth acquisition unit is used to acquire the average beat change rate based on the multiple beat change rates; The seventh acquisition unit is used to acquire the rated cycle time change rate and acquire the production fluctuation factor based on the rated cycle time change rate, the comprehensive efficiency fluctuation index, and the average cycle time change rate. The eighth acquisition unit is used to acquire the efficiency deviation based on the production response cycle time and the production fluctuation factor.

[0059] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0060] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0061] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0062] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0063] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for optimizing the production efficiency of hang tag gun needles based on industrial big data, characterized in that, include: Obtain the real-time operating parameters of the hangtag gun needle production system, wherein the real-time operating parameters include production line dynamic response parameters and process compliance status parameters when the order and production process are synchronized; The production response cycle time and production fluctuation factor are obtained based on the production line dynamic response parameters, and the efficiency deviation is obtained based on the production response cycle time and the production fluctuation factor. Based on the process compliance status parameters, obtain the key dimension sequence and geometric tolerance values, and based on the key dimension sequence and geometric tolerance values, obtain the standard process operation parameters; The system status parameters of the production equipment are obtained, wherein the system status parameters include instantaneous operating condition parameters and adjustable performance parameters, and the equipment operating deviation parameters are obtained based on the instantaneous operating condition parameters and the adjustable performance parameters. Based on the efficiency deviation, the standard process operating parameters, and the equipment operating deviation parameters, comprehensive optimization parameters are obtained, and target process parameters are obtained based on the comprehensive optimization parameters. Control commands for the production equipment are generated based on the target process parameters, and these commands are applied to the execution unit of the production equipment to perform synergistic optimization of stamping efficiency and process quality.

2. The method for optimizing the production efficiency of hang tag gun needles based on industrial big data according to claim 1, characterized in that, The steps of obtaining the production response cycle time and production fluctuation factor based on the production line dynamic response parameters, and obtaining the efficiency deviation based on the production response cycle time and production fluctuation factor, include: Based on the production line dynamic response parameters, obtain multiple real-time production cycles and multiple cycle change rates within a preset time window; The average real-time cycle time is obtained based on the multiple real-time production cycle times, and the average real-time cycle time is used as the production response cycle time. The real-time response time variance is obtained based on the multiple real-time production timescales and production response timescales, and the efficiency characteristic value is obtained based on the real-time response time variance and the multiple real-time production timescales. The system obtains the real-time material flow topology and work-in-process quantity data of the production line segment where the production equipment is located, and obtains the system load balancing coefficient based on the real-time material flow topology and the work-in-process quantity data. The comprehensive efficiency fluctuation index is obtained based on the system load balancing coefficient and the efficiency characteristic value. The average beat change rate is obtained based on the multiple beat change rates described; Obtain the rated cycle time variation rate, and obtain the production fluctuation factor based on the rated cycle time variation rate, the comprehensive efficiency fluctuation index, and the average cycle time variation rate; The efficiency deviation is obtained based on the production response cycle and the production fluctuation factor.

3. The method for optimizing the production efficiency of hangtag gun needles based on industrial big data according to claim 1, characterized in that, The steps of obtaining the key dimension sequence and geometric tolerance values ​​based on the process compliance status parameters, and obtaining the standard process operating parameters based on the key dimension sequence and the geometric tolerance values, include: Based on the process compliance status parameters, obtain the real-time needle tip diameter sequence, needle body diameter sequence, and needle shoulder angle sequence; The corresponding key dimension sequence is obtained based on the real-time needle tip diameter sequence, needle body diameter sequence, and needle shoulder angle sequence; The real-time coaxiality error value and the real-time straightness error value are obtained based on the process compliance status parameters, and the geometric tolerance value is obtained based on the real-time coaxiality error value and the real-time straightness error value. The system acquires real-time process stability data of adjacent and related equipment in the production system and misjudgment rate data of key detection stations, and obtains the system quality coordination factor based on the real-time process stability data and the misjudgment rate data. The real-time vibration spectrum data and historical fatigue accumulation data of the core moving parts of the production equipment are obtained, and the comprehensive health factor of the equipment is obtained based on the real-time vibration spectrum data and the historical fatigue accumulation data. Standard process operating parameters are obtained based on the critical dimension sequence, the geometric tolerance values, the system quality synergy factor, and the equipment comprehensive health factor.

4. The method for optimizing the production efficiency of hang tag gun needles based on industrial big data according to claim 1, characterized in that, The step of obtaining the equipment operating deviation parameters based on the instantaneous operating condition parameters and the adjustable performance parameters includes: Obtain the real-time spindle speed and real-time spindle torque from the instantaneous operating condition parameters, and obtain the operating condition deviation factor based on the real-time spindle speed and real-time spindle torque; Obtain the servo response time constant and feed rate coefficient from the adjustable performance parameters, and obtain the performance adjustment factor based on the servo response time constant and the feed rate coefficient; Based on the waveform of the spindle's real-time rotational speed change over multiple consecutive sampling periods, the spindle vibration characteristic spectrum is obtained through Fast Fourier Transform, and the dominant vibration frequency and vibration amplitude are extracted from the spindle vibration characteristic spectrum. The process fluctuation risk coefficient is obtained based on the dominant vibration frequency and the vibration amplitude. Obtain the time requirement feature parameters corresponding to the current production order. The time requirement feature parameters include the delivery time scale of urgent orders and the production time scale of regular orders. Obtain multi-objective optimization factors based on the delivery time scale of urgent orders and the production time scale of regular orders. Equipment operating deviation parameters are obtained based on the multi-objective optimization factor, the operating condition deviation factor, and the performance adjustment factor.

5. The method for optimizing the production efficiency of hangtag gun needles based on industrial big data according to claim 1, characterized in that, The step of obtaining comprehensive optimization parameters based on the efficiency deviation, the standard process operating parameters, and the equipment operating deviation parameters, and obtaining target process parameters based on the comprehensive optimization parameters, includes: Acquire the quality defect pattern characteristic parameters of the entire production system process, including the frequency of dominant defect type, the damping ratio of defect-related processes, and the process modal influence factor; The system quality risk coefficient is obtained based on the frequency of the dominant defect type, the damping ratio of the defect-related process, and the modal influence factor of the process. The first optimization synergy factor is obtained based on the efficiency deviation and the standard process operating parameters; A second optimization synergy factor is obtained based on the equipment operating deviation parameters and the standard process operating parameters; The comprehensive optimization parameters are obtained based on the system quality risk coefficient, the first optimization synergy factor, and the second optimization synergy factor. The target process parameters are obtained by matching and referencing the preset process parameter mapping table with the comprehensive optimization parameters.

6. The method for optimizing the production efficiency of hang tag gun needles based on industrial big data according to claim 1, characterized in that, The step of generating control commands for the production equipment based on the target process parameters, and applying the control commands to the execution unit of the production equipment to perform synergistic optimization of stamping efficiency and process quality, includes: Generate stamping efficiency control instructions and process quality control instructions based on the target process parameters; Real-time interference parameters of the production environment are obtained, including the temperature and humidity change rate of the workshop environment, the power grid voltage fluctuation rate, and the gas source pressure fluctuation rate. An environmental interference compensation coefficient is obtained based on the temperature and humidity change rate of the workshop environment, the power grid voltage fluctuation rate, and the gas source pressure fluctuation rate. Based on the environmental interference compensation coefficient, the stamping efficiency control command is subjected to anti-interference compensation to obtain the compensated stamping efficiency control command. The process quality control command is subjected to anti-interference compensation based on the environmental interference compensation coefficient to obtain the compensated process quality control command, and a collaborative optimization command is obtained based on the compensated stamping efficiency control command and the compensated process quality control command. The collaborative optimization command is applied to the execution unit of the production equipment to adjust the stamping frequency of the stamping press and the feed accuracy of the die, so as to achieve collaborative optimization of stamping efficiency and process quality.

7. A system for optimizing the production efficiency of hang tag needles based on industrial big data, characterized in that, include: The first acquisition module is used to acquire the real-time operating parameters of the hangtag gun needle production system, wherein the real-time operating parameters include production line dynamic response parameters and process compliance status parameters when the order is synchronized with the production process; The second acquisition module is used to acquire the production response cycle time and production fluctuation factor based on the production line dynamic response parameters, and to acquire the efficiency deviation based on the production response cycle time and the production fluctuation factor. The third acquisition module is used to acquire key dimension sequences and geometric tolerance values ​​based on the process compliance status parameters, and to acquire standard process operating parameters based on the key dimension sequences and geometric tolerance values. The fourth acquisition module is used to acquire system status parameters of the production equipment, wherein the system status parameters include instantaneous operating condition parameters and adjustable performance parameters, and to acquire equipment operating deviation parameters based on the instantaneous operating condition parameters and the adjustable performance parameters; The fifth acquisition module is used to acquire comprehensive optimization parameters based on the efficiency deviation, the standard process operating parameters, and the equipment operating deviation parameters, and to acquire target process parameters based on the comprehensive optimization parameters. The control module is used to generate control instructions for the production equipment based on the target process parameters, and to apply the control instructions to the execution unit of the production equipment to perform synergistic optimization of stamping efficiency and process quality.

8. The tag gun needle production efficiency optimization system based on industrial big data according to claim 7, characterized in that, The second acquisition module includes: The first acquisition unit is used to acquire multiple real-time production cycles and multiple cycle change rates within a preset time window based on the production line dynamic response parameters. The second acquisition unit is used to acquire an average real-time cycle time based on the multiple real-time production cycle times, and use the average real-time cycle time as the production response cycle time. The third acquisition unit is used to acquire the real-time response cycle variance value based on the multiple real-time production cycle counts and production response cycle counts, and to acquire the efficiency characteristic value based on the real-time response cycle variance value and the multiple real-time production cycle counts. The fourth acquisition unit is used to acquire the real-time material flow topology and work-in-process quantity data of the production line segment where the production equipment is located, and to acquire the system load balancing coefficient based on the real-time material flow topology and the work-in-process quantity data. The fifth acquisition unit is used to acquire a comprehensive efficiency fluctuation index based on the system load balancing coefficient and the efficiency characteristic value; and to acquire an average cycle time change rate based on multiple cycle time change rates. The sixth acquisition unit is used to acquire the rated cycle time change rate and acquire the production fluctuation factor based on the rated cycle time change rate, the comprehensive efficiency fluctuation index, and the average cycle time change rate. The seventh acquisition unit is used to acquire the efficiency deviation based on the production response cycle time and the production fluctuation factor.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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