A screw fastening work management method and system

Abstract

The application is suitable for the technical field of intelligent manufacturing, and provides a screw fastening operation management method and system, the system comprises: a state data acquisition module, an adaptive twist adjustment module, an intelligent warning module, and a data recording module. By establishing and applying an environmental factor relationship model, not only can the influence of current environmental state data on fastening parameters be accurately analyzed, but also the change of future environmental conditions can be predicted, so as to adjust the torque setting in advance, and ensure the precision and efficiency of the fastening operation. The significant advantage of this step is that it dynamically optimizes the torque parameter by combining real-time data and prediction data, reduces the operation interruption or quality problems caused by environmental changes, automatically generates torque adjustment instructions, reduces manual intervention, and improves the consistency and efficiency of the operation. In addition, data-driven decision support is also provided, which provides a scientific basis for the fastening operation, helps to continuously optimize the operation process and improve the operation quality.

Description

Technical Field

[0001] This invention belongs to the field of intelligent manufacturing technology, and in particular relates to a screw tightening operation management method and system. Background Technology

[0002] Intelligent manufacturing technology is a comprehensive field that integrates information technology, automation technology, artificial intelligence, machine learning, the Internet of Things (IoT), big data analytics, and other cutting-edge technologies to achieve intelligent, automated, and efficient manufacturing processes. Within intelligent manufacturing, screw tightening operation management is a highly automated and intelligent process that utilizes advanced technologies and systems to ensure the accuracy, efficiency, and reliability of tightening operations. It can significantly improve production efficiency, reduce human error, ensure product quality, and support continuous optimization of the production process. This intelligent management approach is a crucial component of the transformation of modern manufacturing towards efficient, precise, and sustainable production models.

[0003] Traditional fastening operations often overlook the impact of environmental factors on torque settings, leading to unstable fastening results. Because torque settings are not adjusted in a timely manner to adapt to environmental changes, work interruptions or quality problems frequently occur due to improper torque. Furthermore, the lack of effective data support and predictive mechanisms makes it difficult to guarantee operational consistency and efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a screw tightening operation management method, which aims to solve the technical problems existing in the prior art as identified in the background art.

[0005] This invention is implemented as follows: a screw tightening operation management method, the method comprising the following steps:

[0006] Control and receive changes in working environment status data collected by sensors, and acquire real-time data of torque tools during fastening operations;

[0007] An environmental impact analysis model is established, and the impact on fastening parameters is analyzed by combining current environmental status data. Future environmental conditions are predicted, and the optimal torque parameters under these conditions are calculated to determine the optimal torque setting.

[0008] It monitors environmental conditions and current torque data in real time, compares them with the predicted future environmental conditions and optimal torque settings, determines whether there is a deviation from the prediction results, and issues a warning when there is a deviation from the prediction results and the current environmental conditions conflict with the torque data.

[0009] Record information for each alert, including the alert time, type, and subsequent handling results.

[0010] As a further aspect of the present invention, the control and reception of changes in the working environment state data collected by the sensor, and the acquisition of real-time data of the torque tool during the fastening operation, specifically includes:

[0011] Monitor and record changes in the working environment, and record operational data and data fluctuations during fastening operations;

[0012] Collect the specific location and tightening sequence of each screw, and track the progress of the tightening operation.

[0013] As a further aspect of the present invention, the prediction of future environmental conditions and the optimal torque parameters under these environmental conditions, and the calculation of the optimal torque setting, specifically include:

[0014] Establish an environmental factor relationship model, analyze the impact of environmental status data on fastening parameters, and predict future environmental condition changes under the current environmental status data;

[0015] By combining current environmental data and real-time torque data, the current optimal torque parameter is calculated, and based on the predicted environmental conditions, the optimal torque parameter in the future environment is calculated, and a future time tag is added to the optimal torque parameter in the future environment.

[0016] Obtain the current time and compare it with a future time tag. Extract the torque parameter corresponding to the matching future time tag and generate a torque adjustment command.

[0017] As a further aspect of the present invention, the step of issuing a warning when the predicted result deviates and the current environmental conditions conflict with the torque data specifically includes:

[0018] The system analyzes current environmental status data, real-time torque data, and the current time in real time, and compares the analyzed data with the future environmental condition changes and optimal torque parameters corresponding to the matching future time tags to determine whether there is a deviation from the prediction results.

[0019] When the data deviates from the prediction result and the current environmental conditions conflict with the torque data, a warning message is issued, and a proportional range is set. At the same time, corresponding warning content is set for each proportional range, the degree of data deviation is calculated, and a matching warning content is issued according to the proportional range.

[0020] Another object of the present invention is to provide a screw tightening operation management system, the system comprising:

[0021] The status data acquisition module is used to control and receive changes in working environment status data collected by sensors, and to acquire real-time data of the torque tool during fastening operations.

[0022] The adaptive torsion adjustment module is used to establish an environmental impact analysis model. It combines current environmental status data to analyze the impact on fastening parameters, predict future environmental conditions, and the optimal torque parameters under these conditions, and calculate the optimal torque setting.

[0023] The intelligent warning module is used to monitor environmental conditions and current torque data in real time, compare them with the predicted future environmental conditions and optimal torque settings, determine whether there is a deviation from the prediction results, and issue a warning when there is a deviation from the prediction results and the current environmental conditions conflict with the torque data.

[0024] The data logging module is used to record information for each alert, including the alert time, type, and subsequent processing results.

[0025] As a further embodiment of the present invention, the status data acquisition module includes:

[0026] The operation data acquisition unit is used to monitor and record data changes in the operation environment, and to record operational data and data fluctuations during the fastening operation;

[0027] The location data tracking unit is used to collect the specific location and tightening sequence of each screw and track the progress of the tightening operation.

[0028] As a further embodiment of the present invention, the adaptive distortion adjustment module includes:

[0029] The environmental impact analysis unit is used to establish a model of the relationship between environmental factors, analyze the impact of environmental status data on fastening parameters, and predict future changes in environmental conditions under the current environmental status data.

[0030] The torque optimization calculation unit is used to combine current environmental state data and real-time torque data to calculate the current optimal torque parameters, and to calculate the optimal torque parameters in future environments based on predicted environmental conditions, and to add future time tags to the optimal torque parameters in future environments.

[0031] The torque adjustment execution unit is used to obtain the current time, compare it with the future time tag, extract the torque parameters corresponding to the matching future time tag, and generate torque adjustment instructions.

[0032] As a further embodiment of the present invention, the intelligent warning module includes:

[0033] The secondary monitoring unit is used to analyze the current environmental status data, real-time torque data, and current time in real time, and compare the analyzed data with the future environmental condition changes and optimal torque parameters corresponding to the matching future time tags to determine whether there is a deviation from the prediction result.

[0034] The warning logic judgment unit is used to issue warning information when the data deviates from the prediction result and the current environmental conditions conflict with the torque data. It sets a proportion range and sets corresponding warning content for each proportion range, calculates the degree of data deviation, and issues a matching warning content according to the proportion range.

[0035] The beneficial effects of this invention are:

[0036] By establishing and applying environmental factor relationship models, we can not only accurately analyze the impact of current environmental data on tightening parameters, but also predict future changes in environmental conditions, thereby adjusting torque settings in advance to ensure the accuracy and efficiency of tightening operations. The significant advantage of this step lies in its ability to dynamically optimize torque parameters by combining real-time and predicted data, reducing work interruptions or quality issues caused by environmental changes. Simultaneously, it automatically generates torque adjustment commands, reducing manual intervention and improving work consistency and efficiency. Furthermore, data-driven decision support provides a scientific basis for tightening operations, contributing to continuous optimization of work processes and improvement of work quality. Attached Figure Description

[0037] Figure 1 A flowchart illustrating a screw tightening operation management method provided in an embodiment of the present invention;

[0038] Figure 2 A flowchart illustrating how to control and receive changes in working environment status data collected by sensors and acquire real-time data of torque tools during fastening operations, as provided in an embodiment of the present invention.

[0039] Figure 3 A flowchart for calculating the optimal torque setting based on the prediction of future environmental conditions and the optimal torque parameters under these environmental conditions, provided for embodiments of the present invention;

[0040] Figure 4 A flowchart for issuing a warning when the prediction result is deviated from and the current environmental conditions conflict with the torque data, provided for an embodiment of the present invention;

[0041] Figure 5 A structural block diagram of a screw fastening operation management system provided in an embodiment of the present invention;

[0042] Figure 6 This is a structural block diagram of the status data acquisition module provided in an embodiment of the present invention;

[0043] Figure 7 This is a structural block diagram of the adaptive distortion adjustment module provided in an embodiment of the present invention;

[0044] Figure 8 This is a structural block diagram of the intelligent warning module provided in an embodiment of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0046] It is understood that the terms "first," "second," etc., used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are used only to distinguish one element from another. For example, without departing from the scope of this application, a first script may be referred to as a second script, and similarly, a second script may be referred to as a first script.

[0047] Figure 1 A flowchart of a screw tightening operation management method provided in an embodiment of the present invention is shown below. Figure 1 As shown, a screw tightening operation management method includes:

[0048] S100 controls and receives changes in working environment status data collected by sensors, and acquires real-time data of torque tools during fastening operations.

[0049] This step uses a temperature sensor to monitor ambient temperature in real time and record temperature change trends. A humidity sensor monitors ambient humidity and records humidity changes and their impact on the operation. In specific applications, a barometric pressure sensor is used to monitor barometric pressure changes and assess their potential impact on the fastening operation.

[0050] Simultaneously, it records the torque output values ​​of the torque tool in real time, including average torque and peak torque, and analyzes the fluctuations in the torque data to assess the tool's stability and work quality. It also records the operation time for tightening each screw to analyze work efficiency. Position sensors or marking systems are used to record the specific position of each screw, ensuring that tightening is performed in the predetermined order and avoiding structural problems caused by incorrect sequence.

[0051] Through the detailed implementation of this step, the screw tightening operation management system can achieve comprehensive monitoring of the working environment and operation process, ensuring that the operation is carried out efficiently, accurately and safely.

[0052] S200: Establish an environmental impact analysis model, combine current environmental status data to analyze the impact on fastening parameters, predict future environmental conditions and the optimal torque parameters under these conditions, and calculate the optimal torque setting.

[0053] This step collects historical environmental data and corresponding fastening operation results, including environmental factors such as temperature, humidity, and air pressure. It then uses statistical analysis or machine learning methods to construct a model relating these environmental factors to the fastening parameters. Current environmental data is input into this model, which outputs the specific impact of environmental factors on the fastening parameters, such as the adjustment amount for the torque value.

[0054] Time series analysis is used to predict changes in environmental conditions over a future period. A predictive model is constructed by combining historical data and current trends to forecast environmental changes. Optimal torque parameters are calculated using an environmental factor relationship model, taking into account factors such as tool performance and material properties, to optimize torque parameter settings.

[0055] Finally, based on the predicted future environmental conditions, an environmental factor relationship model is used to calculate the optimal torque parameters for the future environment. A corresponding time tag is added to the calculated future torque parameters for future use. When using this optimal torque parameter, the current time is obtained and compared with the future time tag. The matching future time tag is extracted, and a torque adjustment command is generated based on the torque parameter corresponding to the matching time tag.

[0056] This step, by establishing and applying an environmental factor relationship model, not only accurately analyzes the impact of current environmental data on tightening parameters but also predicts future changes in environmental conditions. This allows for proactive adjustments to torque settings, ensuring the precision and efficiency of tightening operations. A significant advantage of this step is that it dynamically optimizes torque parameters by combining real-time and predicted data, reducing work interruptions or quality issues caused by environmental changes. Simultaneously, it automatically generates torque adjustment commands, minimizing manual intervention and improving work consistency and efficiency. Furthermore, data-driven decision support provides a scientific basis for tightening operations, contributing to continuous optimization of workflows and improved work quality.

[0057] The S300 monitors environmental conditions and current torque data in real time, compares them with the predicted future environmental conditions and optimal torque settings, determines whether there is a deviation from the prediction results, and issues a warning when there is a deviation from the prediction results and the current environmental conditions conflict with the torque data.

[0058] This step compares the real-time data with the predicted future environmental conditions and optimal torque parameters to assess whether there are any deviations, determine whether the data deviates from the prediction results, and evaluate the degree of deviation.

[0059] When data deviates from the predicted results, and environmental conditions conflict with the torque data, the system automatically triggers an alert. Based on the degree of deviation, multiple proportional ranges are set, each corresponding to a different alert level. Furthermore, customized alert content is provided for each proportional range to ensure that operators can take appropriate measures based on the severity of the alert.

[0060] Finally, based on the current environmental data, the optimal torque parameters are calculated to ensure the accuracy of the tightening operation. Based on the calculated optimal torque parameters, torque adjustment instructions are generated to guide operators or automatic torque adjustment tools, ensuring that torque data can still be adjusted in a timely manner in the event of abnormalities.

[0061] This step, through real-time monitoring and analysis of environmental and torque data, enables timely detection and response to the impact of environmental changes on fastening operations, ensuring continuity and quality. Its significant advantage lies in its precise deviation detection and warning system, which effectively prevents potential problems during operations and reduces errors and rework caused by environmental changes. Furthermore, by setting proportional ranges and customizing warning content, it provides more refined operational guidance, helping operators quickly and accurately respond to deviations of varying degrees. In addition, real-time calculation of optimal torque parameters and generation of adjustment commands further enhances the automation level and response speed of the operation, ensuring high efficiency and precision in fastening.

[0062] S400 records information for each alert, including the alert time, type, and subsequent processing results.

[0063] Figure 2 The flowchart provided in this embodiment of the invention describes the control and reception of changes in working environment state data collected by sensors, and the acquisition of real-time data of the torque tool during fastening operations. Figure 2 As shown, the control and reception of changes in working environment status data collected by sensors, and the acquisition of real-time data of the torque tool during fastening operations, specifically include:

[0064] S110 monitors and records changes in the working environment, and records operational data and data fluctuations during fastening operations;

[0065] S120 collects the specific location and tightening sequence of each screw and tracks the progress of the tightening operation.

[0066] Figure 3 A flowchart for calculating the optimal torque setting based on the prediction of future environmental conditions and the optimal torque parameters under these environmental conditions, as provided in this embodiment of the invention, is shown below. Figure 3 As shown, the calculation of the optimal torque setting, based on the prediction of future environmental conditions and the optimal torque parameters under those conditions, specifically includes:

[0067] S210, Establish an environmental factor relationship model, analyze the impact of environmental status data on fastening parameters, and predict future environmental condition changes under the current environmental status data;

[0068] In step S210, the functional expression of the environmental factor relationship model is:

[0069] M = a1·T 3 +b1·H 2 +c1·T 2 ·H+d1·T·cos(e1·H)+f1·sin(g1·T)+h1·V+i1·T·H·V+j1

[0070] Where M represents the torque parameter in the environmental factor relationship model, T represents the current ambient temperature, H represents the current ambient humidity, V represents the current ambient vibration level, and a1, b1, c1, d1, e1, f1, g1, h1, i1, and j1 all represent the parameters of the first model.

[0071] It should be noted that the parameters of each of the first models mentioned above represent the degree of influence of different environmental factors on the torque parameters and the interactions between them. The environmental factor relationship model not only considers the direct effects of temperature, humidity, and vibration, but also considers their complex interactions through cross terms. This type of model can more accurately predict the changes in torque parameters under different environmental conditions.

[0072] S220 combines current environmental data and real-time torque data to calculate the current optimal torque parameters, and calculates the optimal torque parameters for future environments based on predicted environmental conditions, and adds a future time tag to the optimal torque parameters for future environments.

[0073] In step S220, the calculation function expression corresponding to the current optimal torque parameter is:

[0074] M opt =a2·T 2 +b2·H 2 +c2·T·H+d2·M cur +e2·T·M cur +f2·H·M cur +g2

[0075] Among them, M opt Indicates the current optimal torque parameter, M cur This represents real-time torque data, and a2, b2, c2, d2, e2, f2, and g2 all represent parameters of the second model.

[0076] Furthermore, in step S220, the calculation function expression corresponding to the optimal torque parameter under the future environment is:

[0077]

[0078] Among them, M fut T represents the optimal torque parameter under future conditions. pred H represents the predicted future temperature. pred The predicted future humidity is represented by a3, b3, c3, d3, e3, f3, and g3, which all represent third-level model parameters. As a supplementary note, the model formulas for the current optimal torque parameter and the optimal torque parameter under future conditions consider the complex interaction between environmental state and real-time torque on the optimal torque parameter by including a cross term. Furthermore, different parameter sets are used for current conditions and for future conditions to distinguish the impact of current and future environments.

[0079] S230: Obtain the current time and compare it with the future time tag, extract the torque parameter corresponding to the matching future time tag, and generate a torque adjustment command.

[0080] In step S230, the calculation function expression for the torque adjustment command is:

[0081]

[0082] Where A represents the torque adjustment command, t cur t represents the current time. fut，n M represents the nth future time tag. fut，n ε represents the torque parameter corresponding to the nth future time tag, N represents the total number of future time tags; ε represents a positive coefficient used to control the slope of the logistic regression function to adjust the sensitivity of the time comparison; e represents the base of the natural logarithm.

[0083] It should be noted that torque adjustment command A is a weighted sum of matching torque parameters extracted based on a comparison of the current time and future time stamps. Furthermore, the function expression uses a logistic regression function. To calculate the similarity between the current time and each future time tag. When t cur Approaching t fut，n When t approaches 1, the value of the function approaches 1, indicating a high degree of time matching; when t... cur Stay away from t fut，n When the time is right, the value of the function approaches 0, indicating a time mismatch.

[0084] At the same time, through To calculate the weights for time alignment, ensuring that when t cur With t fut，n When they are exactly the same, the weight is 1; when t cur With tfut，n As the difference increases, the weight gradually decreases to 0.

[0085] Finally, the torque adjustment instruction A is obtained by multiplying the torque parameter corresponding to each future time tag by its weight and summing all the results. This torque adjustment instruction takes into account the influence of all future time tags and weights each torque parameter according to the degree of matching between the current time and these tags.

[0086] Figure 4 The flowchart provided in this embodiment of the invention provides a warning when the current environmental conditions deviate from the predicted result and conflict with the torque data, and calculates the current optimal torque parameters. Figure 4 As shown, the warning issued when the deviation from the predicted result is found, and the current environmental conditions conflict with the torque data, specifically includes:

[0087] S310 analyzes the current environmental status data, real-time torque data, and current time in real time, and compares the analyzed data with the future environmental condition changes and optimal torque parameters corresponding to the matching future time tags to determine whether there is a deviation from the prediction result;

[0088] In step S310, the functional expression for the deviation from the prediction result is:

[0089]

[0090] Where, f(E) cur M cur , t cur E fut M fut E represents the function value that deviates from the predicted result. cur E represents the current environmental state data vector. fut (t cur ) indicates at the current time point t cur The corresponding predicted value of future environmental conditions, M fut (t cur ) indicates at the current time point t cur The corresponding predicted future torque data, σ E σ represents the standard deviation of environmental state data. M This represents the standard deviation of the torque data.

[0091] As a supplementary explanation, by calculating the function value that deviates from the prediction result, the degree of deviation between the current data and the predicted data can be quantified, thereby determining whether to issue a warning. If the function value that deviates from the prediction result exceeds a certain preset threshold, it indicates that there is a significant deviation between the current data and the prediction result, and the system can issue a warning message at this time.

[0092] S320 issues a warning message when the data deviates from the prediction result and the current environmental conditions conflict with the torque data. It also sets a proportional range and sets corresponding warning content for each proportional range, calculates the degree of data deviation, and issues a matching warning content according to the proportional range.

[0093] Figure 5 A structural block diagram of a screw tightening operation management system provided in an embodiment of the present invention is shown below. Figure 5 As shown, a screw tightening operation management system includes:

[0094] The status data acquisition module 100 is used to control and receive changes in working environment status data collected by the sensor, and to acquire real-time data of the torque tool during fastening operations.

[0095] This module uses a temperature sensor to monitor ambient temperature in real time and record temperature change trends. It also uses a humidity sensor to monitor ambient humidity, record humidity changes and their impact on operations. In specific applications, a barometric pressure sensor is used to monitor barometric pressure changes and assess their potential impact on fastening operations.

[0096] Simultaneously, it records the torque output values ​​of the torque tool in real time, including average torque and peak torque, and analyzes the fluctuations in the torque data to assess the tool's stability and work quality. It also records the operation time for tightening each screw to analyze work efficiency. Position sensors or marking systems are used to record the specific position of each screw, ensuring that tightening is performed in the predetermined order and avoiding structural problems caused by incorrect sequence.

[0097] Through the detailed implementation of this module, the screw fastening operation management system can achieve comprehensive monitoring of the working environment and operation process, ensuring that the operation is carried out efficiently, accurately and safely.

[0098] The adaptive torsion adjustment module 200 is used to establish an environmental impact analysis model, combine current environmental status data, analyze the impact on fastening parameters, predict future environmental conditions, and the optimal torque parameters under these environmental conditions, and calculate the optimal torque setting.

[0099] This module collects historical environmental data and corresponding fastening operation results, including environmental factors such as temperature, humidity, and air pressure. It then uses statistical analysis or machine learning methods to construct a relationship model between environmental factors and fastening parameters. Current environmental data is input into the environmental factor relationship model, which outputs the specific impact of environmental factors on fastening parameters, such as the adjustment amount for torque values.

[0100] Time series analysis is used to predict changes in environmental conditions over a future period. A predictive model is constructed by combining historical data and current trends to forecast environmental changes. Optimal torque parameters are calculated using an environmental factor relationship model, taking into account factors such as tool performance and material properties, to optimize torque parameter settings.

[0101] Finally, based on the predicted future environmental conditions, an environmental factor relationship model is used to calculate the optimal torque parameters for the future environment. A corresponding time tag is added to the calculated future torque parameters for future use. When using this optimal torque parameter, the current time is obtained and compared with the future time tag. The matching future time tag is extracted, and a torque adjustment command is generated based on the torque parameter corresponding to the matching time tag.

[0102] This module, by establishing and applying a model of the relationship between environmental factors, can not only accurately analyze the impact of current environmental data on tightening parameters, but also predict future changes in environmental conditions, thereby adjusting torque settings in advance to ensure the accuracy and efficiency of tightening operations. A significant advantage of this module is that it dynamically optimizes torque parameters by combining real-time and predicted data, reducing work interruptions or quality issues caused by environmental changes. Simultaneously, it automatically generates torque adjustment commands, reducing manual intervention and improving work consistency and efficiency. Furthermore, through data-driven decision support, it provides a scientific basis for tightening operations, contributing to continuous optimization of work processes and improvement of work quality.

[0103] The intelligent warning module 300 is used to monitor environmental conditions and current torque data in real time, compare them with the predicted future environmental conditions and optimal torque settings, determine whether there is a deviation from the prediction results, and issue a warning when there is a deviation from the prediction results and the current environmental conditions conflict with the torque data.

[0104] This module compares real-time data with predicted future environmental conditions and optimal torque parameters to assess whether there are any deviations, determine whether the data deviates from the prediction results, and evaluate the degree of deviation.

[0105] When data deviates from the predicted results, and environmental conditions conflict with the torque data, the system automatically triggers an alert. Based on the degree of deviation, multiple proportional ranges are set, each corresponding to a different alert level. Furthermore, customized alert content is provided for each proportional range to ensure that operators can take appropriate measures based on the severity of the alert.

[0106] Finally, based on the current environmental data, the optimal torque parameters are calculated to ensure the accuracy of the tightening operation. Based on the calculated optimal torque parameters, torque adjustment instructions are generated to guide operators or automatic torque adjustment tools, ensuring that torque data can still be adjusted in a timely manner in the event of abnormalities.

[0107] This module monitors and analyzes environmental and torque data in real time, enabling it to promptly detect and respond to the impact of environmental changes on fastening operations, ensuring continuity and quality. Its significant advantage lies in its precise deviation detection and warning system, which effectively prevents potential problems during operations and reduces errors and rework caused by environmental changes. Furthermore, by setting proportional ranges and customizing warning content, it provides more refined operational guidance, helping operators respond quickly and accurately to deviations of varying degrees. In addition, real-time calculation of optimal torque parameters and generation of adjustment commands further enhances the level of automation and response speed, ensuring high efficiency and precision in fastening operations.

[0108] The data recording module 400 is used to record information for each alert, including the alert time, type, and subsequent processing results.

[0109] Figure 6 This is a structural block diagram of the status data acquisition module provided in an embodiment of the present invention, as shown below. Figure 6 As shown, the status data acquisition module includes:

[0110] The operation data acquisition unit 110 is used to monitor and record data changes in the operation environment, and to record operation data and data fluctuations during the fastening operation;

[0111] The position data tracking unit 120 is used to collect the specific position and tightening sequence of each screw and track the progress of the tightening operation.

[0112] Figure 7 This is a structural block diagram of the adaptive distortion adjustment module provided in an embodiment of the present invention, as shown below. Figure 7 As shown, the adaptive distortion adjustment module includes:

[0113] The environmental impact analysis unit 210 is used to establish a model of the relationship between environmental factors, analyze the impact of environmental status data on fastening parameters, and predict future changes in environmental conditions under the current environmental status data.

[0114] The torque optimization calculation unit 220 is used to combine the current environmental state data and real-time torque data to calculate the current optimal torque parameters, and to calculate the optimal torque parameters in the future environment based on the predicted environmental conditions, and to add a future time tag to the optimal torque parameters in the future environment.

[0115] The torque adjustment execution unit 230 is used to obtain the current time, compare it with the future time tag, extract the torque parameters corresponding to the matching future time tag, and generate a torque adjustment command.

[0116] Figure 8 The structural block diagram of the intelligent warning module provided in the embodiment of the present invention is as follows: Figure 8 As shown, the intelligent warning module includes:

[0117] The secondary monitoring unit 310 is used to analyze the current environmental status data, real-time torque data and current time in real time, and compare the analyzed data with the future environmental condition changes and optimal torque parameters corresponding to the matching future time tags to determine whether it deviates from the prediction results.

[0118] The warning logic judgment unit 320 is used to issue a warning message when the data deviates from the prediction result and the current environmental conditions conflict with the torque data. It sets a proportion range and sets corresponding warning content for each proportion range, calculates the degree of data deviation, and issues a matching warning content according to the proportion range.

[0119] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0120] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application 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 various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0121] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0122] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0123] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for managing screw tightening operations, characterized in that, The method includes the following steps: Control and receive changes in working environment status data collected by sensors, and acquire real-time data of torque tools during fastening operations; An environmental impact analysis model is established, and the impact on fastening parameters is analyzed by combining current environmental status data. Future environmental conditions are predicted, and the optimal torque parameters under these conditions are calculated to determine the optimal torque setting. It monitors environmental conditions and current torque data in real time, compares them with the predicted future environmental conditions and optimal torque settings, determines whether there is a deviation from the prediction results, and issues a warning when there is a deviation from the prediction results and the current environmental conditions conflict with the torque data. Record information for each alert, including the alert time, type, and subsequent handling results; Specifically, the control and reception of changes in the working environment status data collected by the sensor, and the acquisition of real-time data of the torque tool during the fastening operation, include: Monitor and record changes in the working environment, and record operational data and data fluctuations during fastening operations; Collect the specific location and tightening sequence of each screw, and track the progress of the tightening operation.

2. The screw tightening operation management method according to claim 1, characterized in that, The calculation of the optimal torque setting, based on the predicted future environmental conditions and the optimal torque parameters under those conditions, specifically includes: Establish an environmental factor relationship model, analyze the impact of environmental status data on fastening parameters, and predict future environmental condition changes under the current environmental status data; By combining current environmental data and real-time torque data, the current optimal torque parameter is calculated, and based on the predicted environmental conditions, the optimal torque parameter in the future environment is calculated, and a future time tag is added to the optimal torque parameter in the future environment. Obtain the current time and compare it with a future time tag. Extract the torque parameter corresponding to the matching future time tag and generate a torque adjustment command.

3. The screw tightening operation management method according to claim 2, characterized in that, In the steps of establishing a model of the relationship between environmental factors, the functional expression of the model is: M=a1·T 3 +b1·H 2 +c1·T 2 ·H+d1·T·cos(e1·H)+f1·sin(g1·T)+h1·V+i1·T·H·V+j1 Where M represents the torque parameter in the environmental factor relationship model, T represents the current ambient temperature, H represents the current ambient humidity, V represents the current ambient vibration level, and a1, b1, c1, d1, e1, f1, g1, h1, i1, and j1 all represent the parameters of the first model.

4. The screw tightening operation management method according to claim 3, characterized in that, The calculation function expression corresponding to the current optimal torque parameter is: M opt =a2·T 2 +b2·H 2 +c2·T·H+d2·M cur +e2·T·M cur +f2·H·M cur +g2 Among them, M opt Indicates the current optimal torque parameter, M cur This represents real-time torque data, and a2, b2, c2, d2, e2, f2, and g2 all represent parameters of the second model. The calculation function expression for the optimal torque parameter under future conditions is: Among them, M fut T represents the optimal torque parameter under future conditions. pred H represents the predicted future temperature. pred The value represents the predicted future humidity. a3, b3, c3, d3, e3, f3, and g3 all represent parameters of the third model.

5. The screw tightening operation management method according to claim 4, characterized in that, The warning issued when the deviation from the predicted result is found, and the current environmental conditions conflict with the torque data, specifically includes: The system analyzes current environmental status data, real-time torque data, and the current time in real time, and compares the analyzed data with the future environmental condition changes and optimal torque parameters corresponding to the matching future time tags to determine whether there is a deviation from the prediction results. When the data deviates from the prediction result and the current environmental conditions conflict with the torque data, a warning message is issued, and a proportional range is set. At the same time, corresponding warning content is set for each proportional range, the degree of data deviation is calculated, and a matching warning content is issued according to the proportional range.

6. The screw tightening operation management method according to claim 5, characterized in that, The calculation function expression for the torque adjustment command is: Where A represents the torque adjustment command, t cur t represents the current time. fut，n M represents the nth future time tag. fut，n ε represents the torque parameter corresponding to the nth future time tag, N represents the total number of future time tags; ε represents a positive coefficient used to control the slope of the logistic regression function to adjust the sensitivity of the time comparison; e represents the base of the natural logarithm.

7. The screw tightening operation management method according to claim 6, characterized in that, In the process of analyzing current environmental status data, real-time torque data, and the current time in real time, and comparing the analyzed data with the future environmental condition changes and optimal torque parameters corresponding to the matching future time tags to determine whether there is a deviation from the prediction results; The functional expression for the deviation from the predicted result is: Where, f(E) cur M cur , t cur E fut M fut E represents the function value that deviates from the predicted result. cur E represents the current environmental state data vector. fut (t cur ) indicates at the current time point t cu r corresponds to the predicted value of future environmental conditions, M fut (t cur ) indicates at the current time point t cur The corresponding predicted future torque data, σ E σ represents the standard deviation of environmental state data. M This represents the standard deviation of the torque data.

8. A screw tightening operation management system, characterized in that, The system, employing the screw tightening operation management method as described in any one of claims 1 to 7, comprises: The status data acquisition module is used to control and receive changes in working environment status data collected by sensors, and to acquire real-time data of the torque tool during fastening operations. The adaptive torsion adjustment module is used to establish an environmental impact analysis model. It combines current environmental status data to analyze the impact on fastening parameters, predict future environmental conditions, and the optimal torque parameters under these conditions, and calculate the optimal torque setting. The intelligent warning module is used to monitor environmental conditions and current torque data in real time, compare them with the predicted future environmental conditions and optimal torque settings, determine whether there is a deviation from the prediction results, and issue a warning when there is a deviation from the prediction results and the current environmental conditions conflict with the torque data. The data logging module is used to record information for each alert, including the alert time, type, and subsequent processing results; The status data acquisition module includes: The operation data acquisition unit is used to monitor and record data changes in the operation environment, and to record operational data and data fluctuations during the fastening operation; The location data tracking unit is used to collect the specific location and tightening sequence of each screw and track the progress of the tightening operation.

9. A screw tightening operation management system according to claim 8, characterized in that, The adaptive distortion adjustment module includes: The environmental impact analysis unit is used to establish a model of the relationship between environmental factors, analyze the impact of environmental status data on fastening parameters, and predict future changes in environmental conditions under the current environmental status data. The torque optimization calculation unit is used to combine current environmental state data and real-time torque data to calculate the current optimal torque parameters, and to calculate the optimal torque parameters in future environments based on predicted environmental conditions, and to add future time tags to the optimal torque parameters in future environments. The torque adjustment execution unit is used to obtain the current time, compare it with the future time tag, extract the torque parameters corresponding to the matching future time tag, and generate torque adjustment instructions.

10. A screw tightening operation management system according to claim 9, characterized in that, The intelligent alert module includes: The secondary monitoring unit is used to analyze the current environmental status data, real-time torque data, and current time in real time, and compare the analyzed data with the future environmental condition changes and optimal torque parameters corresponding to the matching future time tags to determine whether there is a deviation from the prediction result. The warning logic judgment unit is used to issue warning information when the data deviates from the prediction result and the current environmental conditions conflict with the torque data. It sets a proportion range and sets corresponding warning content for each proportion range, calculates the degree of data deviation, and issues a matching warning content according to the proportion range.

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