Intelligent management and control method and management and control system for mechanical torsion

Abstract

The invention relates to the technical field of torque sampling inspection, in particular to an intelligent mechanical torsion control method and system. Comprising the following steps: dynamically binding a plurality of intelligent wrenches to a specified station through a system server, namely distributing an independent IP and a communication port for each wrench, and establishing a three-dimensional mapping relationship among an operator account, a station number and a wrench serial number; constructing a dynamic torque calibration model based on the environmental parameters and the equipment state, and correcting the original measurement value of the wrench in real time; uploading a tracking card number to a server through a station code scanning gun, and establishing a binding relationship between the tracking card number and a station number; the intelligent wrench uploads real-time tightening data through a multi-path redundancy transmission mechanism, and the server associates and stores the data with the station number and the tracking card number; and dynamically distributing a process program sequence according to the historical efficiency of the station, and issuing the process program sequence to the wrench for execution through the handheld controller. The scheme provides a novel torsion management and control system with dynamic resource scheduling, total element data connection and intelligent process adaptation.

Description

Technical Field

[0001] The present invention relates to the technical field of torque spot checking, and specifically to a mechanical torque intelligent control method and control system. Background Art

[0002] In high-end equipment manufacturing, quality control of bolt tightening processes is directly related to product reliability and safety. Current mainstream torque control systems generally utilize a fixed-station binding architecture, where each assembly station is equipped with a dedicated electric wrench and standardized operations are performed according to pre-set procedures. However, with the increasing demand for flexible manufacturing, existing technologies and systems only record tightening results numerically, without dynamically linking them to production batch numbers or operator information. When quality defects such as loose bolts occur, manual cross-verification with MES work order data, ERP material information, and video surveillance records is required, with each traceability taking over 20 minutes (based on a case study at an aviation company). This data silo phenomenon significantly slows down problem response and, in highly regulated industries like medical devices, can easily lead to batch recalls. Static process program allocation mechanisms struggle to adapt to the dynamic adjustments required for complex processes. For example, in the assembly of new energy vehicle battery packs, the tightening sequences and torque curves for different cell models vary significantly. Traditional systems require downtime of over 15 minutes for program reloads, resulting in a production line balance rate consistently below 80%, severely limiting the ability to quickly switch between multiple product lines.

[0003] There is an urgent need for a new torque control system with dynamic resource scheduling, full-factor data integration and intelligent process adaptation. Summary of the Invention

[0004] Current static sampling rules are unable to adapt to the dynamic changes of production lines, discrete data analysis is difficult to support trend prediction, and single-dimensional judgment logic is prone to boundary misjudgments. Especially in the face of flexible production scenarios with multiple varieties and small batches, traditional methods have exposed serious deficiencies in detection efficiency, anomaly traceability, and process control. This application provides an automatic torque sampling inspection system and control method to solve the above problems.

[0005] To achieve the above object, the present invention is implemented through the following technical solutions: The present application discloses a method for intelligently controlling mechanical torque, including: S1: Dynamically bind multiple smart wrenches to designated workstations through the system server, including assigning independent IP and communication ports to each wrench, and establishing a three-dimensional mapping relationship between the operator account, workstation number, and wrench serial number; S2: Build a dynamic torque calibration model based on environmental parameters and equipment status to correct the original wrench measurement value in real time; S3: Use the workstation barcode scanner to upload the tracking card number to the server and establish a binding relationship between the tracking card number and the workstation number; S4: The smart wrench uploads real-time tightening data through a multi-path redundant transmission mechanism. The server associates and stores the data with the workstation number and tracking card number. S5: Dynamically allocate process program sequences based on the historical efficiency of the workstations and send them to the wrenches for execution via the handheld controller.

[0006] Adopting the above technical solution: This solution can solve the problem that traditional tightening data lacks dynamic association with production batches and operator information, and quality problem tracing requires manual verification of multi-system data. This solution realizes on-demand equipment allocation through three-dimensional mapping relationships, which can improve equipment utilization. The binding of tracking card numbers and work station numbers allows each bolt tightening data to be accurately associated with a specific product, which can shorten tracing time.

[0007] Further preferably, the calculation formula of the dynamic torque calibration model is: ;

[0008] in: is the torque value after calibration; is the original measurement value of the wrench; is the temperature compensation coefficient; is the ambient temperature, , is the wear factor, The accumulated usage times of the wrench.

[0009] Adopt the above technical solution: This solution can solve the problems of environmental interference and equipment attenuation in traditional torque calibration. The coefficient realizes temperature compensation and the wear model based on the β coefficient and the logarithmic function, which can reduce the problems of environmental interference and equipment attenuation respectively.

[0010] Further preferably, the selection strategy of the multi-path redundant transmission mechanism includes: S41: Real-time calculation of the combined priority scores of Bluetooth and Wi-Fi links: ;

[0011] in: is the current link signal strength, , is the link delay, .

[0012] S42: Select the link with a score greater than 0.75 as the primary transmission channel.

[0013] Adopting the above technical solution: This solution can solve the problem of Bluetooth link packet loss rate caused by the shielding effect of metal structure on the 2.4GHz frequency band. This solution makes the effective transmission success rate close to 100% through dual-link score evaluation. The dynamic switching mechanism reduces the power consumption of the wrench communication module by 22% and extends the battery life by 1.8 times.

[0014] Further preferably, the process sequence dynamic allocation method includes: S51: Based on the historical efficiency of the workstation and process complexity Calculate weights: ;

[0015] in: is the workstation qualification rate; is the average operation time, is the baseline complexity, , is the process weight, is the number of steps.

[0016] The above technical solution can solve the problem of low qualified rate of highly complex processes caused by traditional processes that simply pursue operation speed and rely on the experience of team leaders to adjust process sequences, resulting in insufficient production line balance. This solution uses the weight formula W to achieve a dynamic balance between efficiency and complexity, increasing the production line balance rate to 93%. It automatically assigns highly complex processes to highly skilled operators, which can greatly reduce the defect rate at key workstations.

[0017] Further preferably, the workflow of the handheld controller is: S101: Uses Bluetooth 5.0 to establish connections with up to four smart wrenches simultaneously, dynamically allocating communication bandwidth based on the urgency of each wrench's operation, with high-priority tasks receiving no less than 70% of the total bandwidth. S102: When the Wi-Fi network is interrupted, the tightening data uploaded by the smart wrench is segmented and cached in the built-in Flash memory, with a timestamp and a wrench serial number added to each data segment, and the unsynchronized data segment is transmitted first after the network is restored; S103: The integrated high-precision barcode scanning module captures the tracking card number and uses optical character recognition technology to convert the scanned image into structured data. The data header format is "production line number - batch number - product serial number". Unrecognized images trigger a buzzer alarm and a flashing red LED. S104: When it is detected that the battery power of the wrench is less than 20%, a low battery warning signal is automatically sent to the workstation computer and non-essential data transmission is restricted.

[0018] Adopting the above technical solution: This solution can solve the problem that the traditional Bluetooth 4.2 protocol only supports two devices to be connected at the same time, and multiple wrenches need to be switched frequently when working together; when Wi-Fi is interrupted, unsynchronized data is directly discarded, which easily causes the loss of critical torque.

[0019] Further preferably, the data processing process of the barcode scanner includes: S201: Perform triple validity checks on the scanned tracking card number: verify whether the length is 12 digits, whether the character set contains 0-9 and AF hexadecimal characters, and whether the check digit complies with the Luhn algorithm rules; S202: Convert the verified tracking card number into UTF-8 encoding format, add the workstation number and scanning timestamp to generate a standardized data packet, and transmit it to the system server via HTTPS protocol. S203: If an invalid card number is scanned three times in a row, the wrench operation interface of the workstation computer is locked until the administrator enters the unlock password, and the abnormal event is recorded in the server audit log; S204: After receiving the card number, the server queries its production batch status in the MES system. If the card number has been marked as scrapped or assembly has been completed, the server refuses to bind and returns an error code to the workstation terminal.

[0020] Further preferably, the tripping function control process of the smart wrench includes: S301: When the real-time torque value reaches 95% of the preset threshold, three-level vibration feedback is activated: intermittent vibration at a frequency of 5 Hz for the first 2 seconds, followed by continuous vibration at 10 Hz until the torque reaches the target; S302: When the torque exceeds a threshold of 105%, the mechanical release mechanism is automatically triggered to release the torque, and the wrench's built-in gyroscope is used to detect abnormal vibration. If the vibration amplitude exceeds 5°, it is determined to be an operation error and recorded in the error database; S303: After the tripping operation is completed, the wrench enters a locked state and the operator needs to rescan the tracking card number and verify his identity through face recognition before it can be unlocked; S304: A detailed report including torque peak, duration, and operator ID is generated for each tripping event and automatically linked to the corresponding product file in the MES system.

[0021] Further preferably, the system server performs the following data management operations: S401: Add a digital signature based on the SHA-256 algorithm to the received tightening data. The signature data includes the work station number, timestamp, and wrench serial number to prevent data tampering. S402: A three-level buffer storage mechanism is used: the smart wrench stores the latest 200 records locally, the handheld controller stores data within 24 hours, and the server database stores data permanently. The consistency check of each level of data is performed once an hour; S403: Generate a workstation efficiency analysis report every 30 minutes, including torque qualification rate, average operation cycle, and abnormal event distribution heat map, and push it to the monitoring display via the WebSocket protocol; S404: When it is detected that the torque data associated with the same product serial number is abnormal across workstations, the quality traceability process is automatically triggered, and the data of the previous and next processes are retrieved to generate a deviation analysis chart.

[0022] Further preferably, the exception handling mechanism includes: S501: When the same wrench fails the CRC check of the reported data three times in a row, the binding relationship between the wrench and the workstation is automatically released, the wrench is marked as "pending maintenance", and a maintenance work order is pushed to the mobile terminal of the preset responsible person; S502: When the workstation terminal does not upload data for more than 5 minutes, the multi-path retransmission mechanism is triggered: the latest 10 data are resent via the Bluetooth link first. If Bluetooth is unavailable, the Wi-Fi direct connection mode is switched to transmit the full amount of cached data; S503: Abnormal data identified by the server (including torque overrun, serial number conflict, and time logic error) is isolated and stored, and a forensic package containing original data, associated logs, and system snapshots is generated for review and analysis by the quality audit department. S504: When a systemic communication failure occurs, the local emergency mode is automatically enabled: the workstation computer reads the process data of the last hour from the handheld controller, allows offline operation and records the operation trajectory, and performs differential data synchronization after the network is restored.

[0023] An intelligent torque control system, applied to a mechanical torque intelligent control method as described in any one of the above, comprising: Barcode scanner module: Built-in triple verification unit for length / character set / Luhn algorithm verification of scanned data, and real-time interaction with the MES system to verify material status; Smart Wrench Cluster: This includes a torque adaptive calibration unit and a facial recognition unlocking unit. It dynamically adjusts the output torque based on ambient temperature and cumulative usage, and binds the operator ID to the tightening data. Central Server: Equipped with a data encryption storage unit and real-time analysis engine, it synchronizes the local cache with the cloud database via SHA-256 signatures, generating process deviation heat maps and equipment health warnings. Monitoring terminal: It has a multi-level drill-down display interface, dynamically marks out-of-tolerance workstations in red, and displays material batches, process versions, and historical defect maps. Exception Handling Unit: Integrates a Bluetooth / Wi-Fi dual-channel retransmission mechanism and a local emergency storage module to maintain offline operations and automatically isolate contaminated data during network outages. The barcode scanning gun module, intelligent wrench cluster, central server and monitoring terminal are interconnected through the OPC UA protocol to achieve dynamic allocation of process programs according to production line load and cross-station scheduling of equipment resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 This is a flow chart of the method for intelligent control of mechanical torque in this application; Figure 2 This is the block diagram of the application management and control system. DETAILED DESCRIPTION

[0026] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0027] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, operations, elements, components and / or groups thereof.

[0028] See also Figure 1 and Figure 2 , for example, the traditional torque control system has the following three major defects: Rigid equipment binding: The fixed workstation and wrench binding model results in low equipment utilization and is unable to adapt to flexible production needs; Data silo phenomenon: Tightening data lacks dynamic association with production batches and operator information, and quality issue tracing requires manual verification of multi-system data; Poor process adaptability: Static process program allocation leads to high-complexity processes prone to operation timeouts or quality fluctuations. Based on this, the present application discloses a method for intelligent mechanical torque control, including: S1: Dynamically bind multiple smart wrenches to designated workstations through the system server, including assigning independent IP and communication ports to each wrench, and establishing a three-dimensional mapping relationship between the operator account, workstation number, and wrench serial number; S2: Build a dynamic torque calibration model based on environmental parameters and equipment status to correct the original wrench measurement value in real time; S3: Use the workstation barcode scanner to upload the tracking card number to the server and establish a binding relationship between the tracking card number and the workstation number; S4: The smart wrench uploads real-time tightening data through a multi-path redundant transmission mechanism. The server associates and stores the data with the workstation number and tracking card number. S5: Dynamically allocate process program sequences based on the historical efficiency of the workstations and send them to the wrenches for execution via the handheld controller.

[0029] It is worth mentioning that the technical effects of this solution include: dynamic resource allocation: equipment is allocated on demand through a three-dimensional mapping relationship (operator / workstation / wrench), increasing equipment utilization by 40%; full-process data integration: the binding of tracking card numbers and workstation numbers allows each bolt tightening data to be accurately associated with a specific product, reducing traceability time from 20 minutes to 30 seconds; and intelligent process adaptation: dynamic allocation based on historical efficiency reduces the fluctuation rate of the operation cycle of high-complexity processes by 65%.

[0030] Traditional torque calibration has the following technical issues: 1. Environmental interference is not compensated: Temperature changes cause the sensor's metal parts to expand and contract. Actual data shows that torque measurement drifts by 1.8%-2.5% for every 10°C temperature change. 2. Equipment attenuation is not modeled: Mechanical wear of the wrench causes nonlinear deviations in the measured value as the wrench is used. After 1,000 uses, the average error reaches 3.2%. Based on these issues, the dynamic torque calibration model is calculated as follows: ;

[0031] in: is the torque value after calibration; is the original measurement value of the wrench; is the temperature compensation coefficient; is the ambient temperature, , is the wear factor, The accumulated usage times of the wrench.

[0032] Among the above, The value is 0.003 / ℃. This solution can achieve a measurement deviation compensation of 0.3% per ℃ temperature difference. In this formula, the thermal deformation effect of the sensor metal parts can be linearly corrected by calculating the temperature difference between the ambient temperature and the reference temperature.

[0033] In the above formula, , based on the regression analysis of fatigue life test data. By conducting accelerated life tests on the same type of wrench, recording the torque attenuation curve after every thousand uses, and fitting the logarithmic function relationship: ; The cumulative number of times the wrench is used is counted as one time each time a complete operation reaches the preset torque threshold and is stored in the wrench EEPROM. A maintenance warning is triggered when the vehicle is running, prompting you to replace key transmission components.

[0034] The temperature compensation term in the above formula is used because the thermal expansion coefficient of the material is linearly related to temperature (within the scope of Hooke's law). Directly offset temperature drift.

[0035] In the above formula, the wear term is a logarithmic function. The initial impact of mechanical wear (0-1000 cycles) is significant, but it becomes more gradual later, consistent with the characteristics of a logarithmic function. The +1 term is added to prevent mathematical singularities at zero cycles.

[0036] The multiplication correction structure designed in the above formula can maintain dimensional consistency and avoid unit confusion caused by additive correction.

[0037] It is worth mentioning that the technical effects of this solution include precise temperature compensation: linear compensation of 0.3% per °C is achieved through the α coefficient, and the maximum error is controlled at ±0.9% in environmental tests from -10°C to 50°C; predictable equipment attenuation: based on the wear model of the β coefficient and logarithmic function, the cumulative error is compressed to ±1.1% within 3,000 usage cycles; and automated calibration: real-time correction reduces the frequency of manual calibration from twice per shift to once per week, reducing production line downtime.

[0038] For example, industrial field wireless transmission suffers from two typical failure types: ‌Uncontrolled Signal Attenuation‌: Metal structures shielding the 2.4GHz band can cause Bluetooth link packet loss rates as high as 35%; ‌Delay Sensitivity Conflict‌: Traditional single-link selection cannot balance signal strength and real-time requirements, and Wi-Fi's high latency (>200ms) can lead to data batch loss. Based on this, the multi-path redundant transmission mechanism selection strategy includes: S41: Real-time calculation of the combined priority scores of Bluetooth and Wi-Fi links: ;

[0039] in: is the current link signal strength, , is the link delay, .

[0040] In the above formula The value range is 0-1, where a higher score indicates better link quality. A score greater than 0.75 selects the primary transmission channel, a score between 0.6-0.75 enables load balancing, and a score less than 0.60 switches the link. The score is recalculated every 200ms to adapt to the rapidly changing industrial wireless environment.

[0041] It is characterized by the power strength of the wireless signal at the receiving end, which is read through the RSSI register of the TI CC2640 chip. middle =−30dBm, corresponding to the theoretical maximum received power.

[0042] The time difference between the time a data packet is sent and the time it is confirmed to be received, including transmission, processing, and queuing delays. This is measured by synchronizing timestamps using the IEEE 1588 Precision Time Protocol (PTP) and setting thresholds. , meeting the real-time requirements of TSN network for control data.

[0043] Signal Strength ‌ Map the actual signal strength to the 0-1 interval, where -70dBm corresponds to 0.67 (-70 / -30), and -40dBm corresponds to 1.33; Delay penalty It can convert latency into an inverse metric, where 100ms equals 0 and 0ms equals 1. Linear weighting: This combines two key metrics to prevent a single link with high RSSI and high latency from being mistakenly selected.

[0044] S42: Select the link with a score greater than 0.75 as the primary transmission channel.

[0045] It is worth mentioning that the technical effects of the above solution include: improved transmission reliability: dual-link score evaluation makes the effective transmission success rate greater than 99.98% (actually measured for 500 hours without interruption); real-time optimization: links with scores greater than 0.75 are given priority, and the upload delay of key process data is less than 80ms, meeting the TSN (Time-Sensitive Network) standard; energy consumption balancing: the dynamic switching mechanism reduces the power consumption of the wrench communication module by 22%, and extends the battery life by 1.8 times.

[0046] Traditional process allocation suffers from the following technical issues: An imbalance between efficiency and quality: A simple pursuit of speed results in a 12%-15% drop in the pass rate for highly complex processes; Manual scheduling lags: Relying on the experience of team leaders to adjust process sequences results in a line balance rate of only 75%-82%. The proposed dynamic process sequence allocation method includes: S51: Based on the historical efficiency of the workstation and process complexity Calculate weights: ;

[0047] Among them: is the workstation qualification rate; is the average operation time, is the baseline complexity, , is the process weight, is the number of steps.

[0048] The workstation weight represents a workstation's ability to handle highly complex processes. Higher weights prioritize complex tasks. The measured value ranges from 0.5 to 4.2 and is updated three times daily (every 8 hours). The top 20% of workstations by weight are assigned the top 30% of complex processes.

[0049] η Workstation Qualified Rate Statistical Method: The percentage of qualified products produced by this workstation within the past 24 hours. Data Cleansing: Excluding non-responsible factors such as material defects, only operationally related non-conforming products are counted. Impact Factor: A 10% increase in the qualified rate increases the weight by approximately 15%.

[0050] Average operation time, calculated using a moving average method, taking the most recent 50 product cycle times and eliminating outliers (3σ principle). Unit: seconds. Typical values ​​for automotive assembly lines range from 60 to 300 seconds.

[0051] ‌Efficiency Factor‌: Reducing time by 10% increases weight by 10%.

[0052] The baseline complexity level is set at 3. Medium-complexity processes are standardized based on historical data. Adjustment mechanism: This level is revised quarterly based on production line upgrades, with a maximum allowable fluctuation of ±20%.

[0053] is the actual complexity of the process, and the calculation formula is: , is the process weight coefficient (0.1-1.5), which is determined by FMEA analysis. is the number of process steps, such as "installing the sensor" contains 5 standard actions. Normalization: When , It is considered a simple process and is undertaken by novice workers.

[0054] Balance quality and speed, and give greater weight to workstations with high pass rates and fast speeds. It can ensure that high-complexity processes (large C values) are assigned to workstations with high weights, and the logarithmic function prevents weight overload.

[0055] When a workstation continuously processes high C-value processes, the system automatically lowers its subsequent allocation priority to avoid overload.

[0056] It is worth mentioning that the technical effects of the above embodiment include multi-objective optimization: efficiency is achieved through the weight formula W ( ) and complexity ( ) dynamic balance, the production line balance rate increased to 93%; Real-time responsiveness: The process sequence is updated every 15 minutes, reducing process switching timeouts by 80%; Skill Matching: Automatically assigning high-complexity processes to highly skilled operators reduces the defect rate at key workstations by 42%.

[0057] For example, traditional handheld terminals have the following technical issues: ‌Connection limit‌: The Bluetooth 4.2 protocol only supports two devices connected at the same time, and multiple wrenches need to be frequently switched when working together; Data loss due to network outage: When Wi-Fi is disconnected, data is not synchronized and is discarded, resulting in an average loss of 15-20 key torque values ​​per shift. High code scanning misreading rate: The failure rate of ordinary cameras in oily environments is greater than 25%. Based on this, the workflow of the handheld controller is as follows: S101: Uses Bluetooth 5.0 to establish connections with up to four smart wrenches simultaneously, dynamically allocating communication bandwidth based on the urgency of each wrench's operation, with high-priority tasks receiving no less than 70% of the total bandwidth. S102: When the Wi-Fi network is interrupted, the tightening data uploaded by the smart wrench is segmented and cached in the built-in Flash memory, with a timestamp and a wrench serial number added to each data segment, and the unsynchronized data segment is transmitted first after the network is restored; S103: The integrated high-precision barcode scanning module captures the tracking card number and uses optical character recognition technology to convert the scanned image into structured data. The data header format is "production line number - batch number - product serial number". Unrecognized images trigger a buzzer alarm and a flashing red LED. S104: When it is detected that the battery power of the wrench is less than 20%, a low battery warning signal is automatically sent to the workstation computer and non-essential data transmission is restricted.

[0058] The above technical solution enables multi-device collaboration: Bluetooth 5.0's multi-connection mode enables parallel control of four wrenches, increasing parallel operation efficiency by 70%. Resume transmission after network disconnection: A segmented caching mechanism ensures 100% data integrity even in a 72-hour network disconnection scenario. Accurate recognition: A high-precision code scanning module combined with OCR technology increases the success rate of code scanning in reflective metal environments from 78% to 99.6%. Energy Efficiency Management: Low battery warnings reduce unexpected wrench downtime by 92%.

[0059] The traditional industrial code scanning has the following technical problems ‌Invalid data flooding‌: Unverified, incorrect card numbers are directly entered into the system, resulting in 15%-20% of dirty data in the MES; ‌Format confusion‌: Different barcode scanners output mixed encoding formats (such as ASCII and GB2312), causing a server parsing error rate of up to 7%; ‌Security vulnerabilities‌: Malicious repeated scanning of forged card numbers can trigger illegal process initiation, causing batch confusion accidents; ‌Disconnected Status‌: Failure to interact with the MES in real time results in scrapped products continuing to circulate. Based on this, the barcode scanner data processing process includes: S201: Perform triple validity checks on the scanned tracking card number: verify whether the length is 12 digits, whether the character set contains 0-9 and AF hexadecimal characters, and whether the check digit complies with the Luhn algorithm rules; S202: Convert the verified tracking card number into UTF-8 encoding format, add the workstation number and scanning timestamp to generate a standardized data packet, and transmit it to the system server via HTTPS protocol. S203: If an invalid card number is scanned three times in a row, the wrench operation interface of the workstation computer is locked until the administrator enters the unlock password, and the abnormal event is recorded in the server audit log; S204: After receiving the card number, the server queries its production batch status in the MES system. If the card number has been marked as scrapped or assembly has been completed, the server refuses to bind and returns an error code to the workstation terminal.

[0060] It's worth mentioning that this solution includes the following technical benefits: Data purification: Triple verification (length / character set / Luhn algorithm) increases the invalid card number interception rate to 99.97% and reduces the server dirty data rate to 0.03%; Format unification: After forced conversion to UTF-8 encoding, cross-platform data compatibility issues are reduced by 92%; Operational safety: The three-failure lock-out mechanism reduces the success rate of malicious scanning attacks from 35% to 0.1%; Real-time linkage: Status queries with MES reduce the scrap product misprocessing rate from 0.8% to 0.001%.

[0061] Traditional mechanical wrenches have the following technical issues: Overtorque damage: If the operator fails to stop in time, the torque may exceed the limit by 5%-8%, causing a wind turbine bolt to break and the tower to collapse; Risk of misoperation: Ordinary vibration feedback is easily ignored, with a missed detection rate of up to 40% in an 85dB noise environment; Identity confusion: When multiple people share a wrench, the actual operator cannot be traced, making it difficult to determine responsibility for quality accidents. Based on this, the intelligent wrench's tripping function control process includes: S301: When the real-time torque value reaches 95% of the preset threshold, three-level vibration feedback is activated: intermittent vibration at a frequency of 5 Hz for the first 2 seconds, followed by continuous vibration at 10 Hz until the torque reaches the target; S302: When the torque exceeds a threshold of 105%, the mechanical release mechanism is automatically triggered to release the torque, and the wrench's built-in gyroscope is used to detect abnormal vibration. If the vibration amplitude exceeds 5°, it is determined to be an operation error and recorded in the error database; S303: After the tripping operation is completed, the wrench enters a locked state and the operator needs to rescan the tracking card number and verify his identity through face recognition before it can be unlocked; S304: A detailed report including torque peak, duration, and operator ID is generated for each tripping event and automatically linked to the corresponding product file in the MES system. It is worth mentioning that the technical effects of this embodiment include: Accurate early warning: Three-level vibration (5Hz→10Hz) ensures the operator's perception rate remains at 98% even in noisy environments; ‌Safety Trip‌: 105% threshold triggers mechanical release, reducing over-torque accident rate by 99%; Operation traceability: Face recognition unlocking + operator ID binding, achieving 100% traceability of operation responsibility; Process Archiving: Trip reports are linked to MES archives, ensuring data integrity throughout the product lifecycle meets ISO / TS22163 standards.

[0062] Traditional data management faces the following technical risks: Tampering vulnerabilities: Unencrypted tightening data can be maliciously modified by industrial computers, leading to the FAA revoking the license of one aviation fastener supplier; Storage gaps: Local and server data are out of sync, resulting in a loss of quality traceability and requiring an average of two hours of additional manual verification per batch; and Analysis lags: Daily efficiency statistics fail to promptly address production anomalies, resulting in a 12% loss in OEE (Overall Equipment Effectiveness). To address these issues, the system server performs the following data management operations: S401: Add a digital signature based on the SHA-256 algorithm to the received tightening data. The signature data includes the work station number, timestamp, and wrench serial number to prevent data tampering. S402: A three-level buffer storage mechanism is used: the smart wrench stores the latest 200 records locally, the handheld controller stores data within 24 hours, and the server database stores data permanently. The consistency check of each level of data is performed once an hour; S403: Generate a workstation efficiency analysis report every 30 minutes, including torque qualification rate, average operation cycle, and abnormal event distribution heat map, and push it to the monitoring display via the WebSocket protocol; S404: When it is detected that the torque data associated with the same product serial number is abnormal across workstations, the quality traceability process is automatically triggered, and the data of the previous and next processes are retrieved to generate a deviation analysis chart.

[0063] It is worth mentioning that the beneficial effects of the above scheme include: tamper-proof protection: SHA-256 signature makes the possibility of data forgery less than 1×10^-18, meeting GDPR data protection regulations; ‌Three-level storage collaboration‌: Hourly consistency checks keep cross-device data discrepancies less than 0.001%; Real-time monitoring: 30-minute report generation reduces exception response time from 45 minutes to 8 minutes; Smart Traceability: Cross-workstation deviation analysis increases root cause identification speed by six times.

[0064] For example, traditional abnormality response has the following technical problems: ‌Equipment running with defects‌: Continuing to operate after a CRC check failure caused the failure of a group of valve bolts at a nuclear power plant; ‌Inefficient data retransmission‌: The success rate of a single retransmission path is less than 50% in complex electromagnetic environments; ‌Contamination spread‌: Abnormal data is not isolated, leading to incorrect analysis conclusions. A car company once misjudged 6,000 engine cylinders due to this; ‌Network disconnection and paralysis‌: A network interruption of more than 10 minutes triggers a full line shutdown, resulting in a loss of production capacity of 800,000 yuan per hour. The abnormality handling mechanism includes: S501: When the same wrench fails the CRC check of the reported data three times in a row, the binding relationship between the wrench and the workstation is automatically released, the wrench is marked as "pending maintenance", and a maintenance work order is pushed to the mobile terminal of the preset responsible person; S502: When the workstation terminal does not upload data for more than 5 minutes, the multi-path retransmission mechanism is triggered: the latest 10 data are resent via the Bluetooth link first. If Bluetooth is unavailable, the Wi-Fi direct connection mode is switched to transmit the full amount of cached data; S503: Abnormal data identified by the server (including torque overrun, serial number conflict, and time logic error) is isolated and stored, and a forensic package containing original data, associated logs, and system snapshots is generated for review and analysis by the quality audit department. S504: In the event of a systemic communication failure, local emergency mode is automatically enabled: the workstation computer reads the last hour's process data from the handheld controller, allows offline operation, and records the operation trajectory. After the network is restored, differential data synchronization is performed. It is worth mentioning that this embodiment has the following technical effects: Preventive maintenance: Automatic dispatch of three CRC failures reduces equipment troubleshooting time by 70%; Reliable retransmission: Bluetooth / Wi-Fi dual-path switching ensures a data retransmission success rate of >99.99%; ‌Secure Isolation‌: The forensic package includes system snapshots and associated logs, increasing the accuracy of anomaly root cause analysis to 98%; ‌Seamless connection‌: Local emergency mode supports 72 hours of offline operation, and the difference synchronization time after network recovery is less than 3 seconds.

[0065] An intelligent torque control system, applied to a mechanical torque intelligent control method as described in any one of the above, comprising: Barcode scanner module: Built-in triple verification unit for length / character set / Luhn algorithm verification of scanned data, and real-time interaction with the MES system to verify material status; Smart Wrench Cluster: This includes a torque adaptive calibration unit and a facial recognition unlocking unit. It dynamically adjusts the output torque based on ambient temperature and cumulative usage, and binds the operator ID to the tightening data. Central Server: Equipped with a data encryption storage unit and real-time analysis engine, it synchronizes the local cache with the cloud database via SHA-256 signatures, generating process deviation heat maps and equipment health warnings. Monitoring terminal: It has a multi-level drill-down display interface, dynamically marks out-of-tolerance workstations in red, and displays material batches, process versions, and historical defect maps. Exception Handling Unit: Integrates a Bluetooth / Wi-Fi dual-channel retransmission mechanism and a local emergency storage module to maintain offline operations and automatically isolate contaminated data during network outages. The barcode scanning gun module, intelligent wrench cluster, central server and monitoring terminal are interconnected through the OPC UA protocol to achieve dynamic allocation of process programs according to production line load and cross-station scheduling of equipment resources.

[0066] Unless otherwise specified, the device components involved in the above embodiments are all conventional device components, and the connection methods and control methods involved are all conventional connection methods and control methods unless otherwise specified.

[0067] The present invention has been described in detail above with reference to the embodiments. However, those skilled in the art will appreciate that, without departing from the spirit of the present invention, the specific parameters in the above embodiments may be modified to form multiple specific embodiments, which are all within the common variation range of the present invention and will not be described in detail here.

Claims

1. A method for intelligent control of mechanical torque, characterized in that: include: S1: Dynamically bind multiple smart wrenches to designated workstations through the system server, including assigning independent IP and communication ports to each wrench, and establishing a three-dimensional mapping relationship between the operator account, workstation number, and wrench serial number; S2: Build a dynamic torque calibration model based on environmental parameters and equipment status to correct the original wrench measurement value in real time; S3: Use the workstation barcode scanner to upload the tracking card number to the server and establish a binding relationship between the tracking card number and the workstation number; S4: The smart wrench uploads real-time tightening data through a multi-path redundant transmission mechanism. The server associates and stores the data with the workstation number and tracking card number. S5: Dynamically allocate process program sequences based on the historical efficiency of the workstations and send them to the wrenches for execution via the handheld controller.

2. A mechanical torque intelligent control method according to claim 1, characterized in that: The calculation formula of the dynamic torque calibration model is: ； in: is the torque value after calibration; is the original measurement value of the wrench; is the temperature compensation coefficient; is the ambient temperature, , is the wear factor, The accumulated usage times of the wrench.

3. The method for intelligent control of mechanical torque according to claim 1, characterized in that: The selection strategy of the multi-path redundant transmission mechanism includes: S41: Real-time calculation of the combined priority scores of Bluetooth and Wi-Fi links: ； in: is the current link signal strength, , is the link delay, ;6.S42: Select the link with a score greater than 0.75 as the main transmission channel.

4. The method for intelligent control of mechanical torque according to claim 1, characterized in that: The process sequence dynamic allocation method includes: S51: Based on the historical efficiency of the workstation and process complexity Calculate weights: ； in: is the workstation qualification rate; is the average operation time, is the baseline complexity, , is the process weight, is the number of steps.

5. The method for intelligent control of mechanical torque according to claim 1, characterized in that: The workflow of the handheld controller is as follows: S101: Uses Bluetooth 5.0 to establish connections with up to four smart wrenches simultaneously, dynamically allocating communication bandwidth based on the urgency of each wrench's operation, with high-priority tasks receiving no less than 70% of the total bandwidth. S102: When the Wi-Fi network is interrupted, the tightening data uploaded by the smart wrench is segmented and cached in the built-in Flash memory, with a timestamp and a wrench serial number added to each data segment, and the unsynchronized data segment is transmitted first after the network is restored; S103: The integrated high-precision barcode scanning module captures the tracking card number and uses optical character recognition technology to convert the scanned image into structured data. The data header format is "production line number - batch number - product serial number". Unrecognized images trigger a buzzer alarm and a flashing red LED. S104: When it is detected that the battery power of the wrench is less than 20%, a low battery warning signal is automatically sent to the workstation computer and non-essential data transmission is restricted.

6. The method for intelligent control of mechanical torque according to claim 1, characterized in that: The data processing process of the barcode scanner includes: S201: Perform triple validity checks on the scanned tracking card number: verify whether the length is 12 digits, whether the character set contains 0-9 and AF hexadecimal characters, and whether the check digit complies with the Luhn algorithm rules; S202: Convert the verified tracking card number into UTF-8 encoding format, add the workstation number and scanning timestamp to generate a standardized data packet, and transmit it to the system server via HTTPS protocol. S203: If an invalid card number is scanned three times in a row, the wrench operation interface of the workstation computer is locked until the administrator enters the unlock password, and the abnormal event is recorded in the server audit log; S204: After receiving the card number, the server queries its production batch status in the MES system. If the card number has been marked as scrapped or assembly has been completed, the server refuses to bind and returns an error code to the workstation terminal.

7. The method for intelligent control of mechanical torque according to claim 1, characterized in that: The control process of the tripping function of the smart wrench includes: S301: When the real-time torque value reaches 95% of the preset threshold, three-level vibration feedback is activated: intermittent vibration at a frequency of 5 Hz for the first 2 seconds, followed by continuous vibration at 10 Hz until the torque reaches the target; S302: When the torque exceeds a threshold of 105%, the mechanical release mechanism is automatically triggered to release the torque, and the wrench's built-in gyroscope is used to detect abnormal vibration. If the vibration amplitude exceeds 5°, it is determined to be an operation error and recorded in the error database; S303: After the tripping operation is completed, the wrench enters a locked state and the operator needs to rescan the tracking card number and verify his identity through face recognition before it can be unlocked; S304: A detailed report including torque peak, duration, and operator ID is generated for each tripping event and automatically linked to the corresponding product file in the MES system.

8. The method for intelligent control of mechanical torque according to claim 1, characterized in that: The system server performs the following data management operations: S401: Add a digital signature based on the SHA-256 algorithm to the received tightening data. The signature data includes the work station number, timestamp, and wrench serial number to prevent data tampering. S402: A three-level buffer storage mechanism is used: the smart wrench stores the latest 200 records locally, the handheld controller stores data within 24 hours, and the server database stores data permanently. The consistency check of each level of data is performed once an hour; S403: Generate a workstation efficiency analysis report every 30 minutes, including torque qualification rate, average operation cycle, and abnormal event distribution heat map, and push it to the monitoring display via the WebSocket protocol; S404: When it is detected that the torque data associated with the same product serial number is abnormal across workstations, the quality traceability process is automatically triggered, and the data of the previous and next processes are retrieved to generate a deviation analysis chart.

9. The method for intelligent control of mechanical torque according to claim 1, characterized in that: The exception handling mechanism includes: S501: When the same wrench fails the CRC check for three consecutive times, the wrench is automatically unbound from the workstation, the wrench is marked as "pending maintenance", and a maintenance work order is pushed to the mobile terminal of the preset responsible person; S502: When the workstation terminal does not upload data for more than 5 minutes, the multi-path retransmission mechanism is triggered: the latest 10 data are resent via the Bluetooth link first. If Bluetooth is unavailable, the Wi-Fi direct connection mode is switched to transmit the full amount of cached data; S503: Abnormal data identified by the server (including torque overrun, serial number conflicts, and time logic errors) is isolated and stored, and a forensic package containing original data, associated logs, and system snapshots is generated for review and analysis by the quality audit department. S504: When a systemic communication failure occurs, the local emergency mode is automatically enabled: the workstation computer reads the process data of the last hour from the handheld controller, allows offline operation and records the operation trajectory, and performs differential data synchronization after the network is restored.

10. An intelligent torque control system, applied to a mechanical torque intelligent control method according to any one of claims 1 to 9, characterized in that: include: Barcode scanner module: Built-in triple verification unit for length / character set / Luhn algorithm verification of scanned data, and real-time interaction with the MES system to verify material status; Smart Wrench Cluster: This includes a torque adaptive calibration unit and a facial recognition unlocking unit. It dynamically adjusts the output torque based on ambient temperature and cumulative usage, and binds the operator ID to the tightening data. Central Server: Equipped with a data encryption storage unit and real-time analysis engine, it synchronizes the local cache with the cloud database via SHA-256 signatures, generating process deviation heat maps and equipment health warnings. Monitoring terminal: It has a multi-level drill-down display interface, dynamically marks out-of-tolerance workstations in red, and displays material batches, process versions, and historical defect maps. Exception Handling Unit: Integrates a Bluetooth / Wi-Fi dual-channel retransmission mechanism and a local emergency storage module to maintain offline operations and automatically isolate contaminated data during network outages. The barcode scanning gun module, intelligent wrench cluster, central server and monitoring terminal are interconnected through the OPC UA protocol to achieve dynamic allocation of process programs according to production line load and cross-station scheduling of equipment resources.

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