An intelligent pre-control system of electric welding machine with real-time monitoring function

Abstract

The present application relates to the field of electric welding machine, especially to a kind of electric welding machine intelligent pre-control system with real-time monitoring function, including data acquisition module, data processing module, fault analysis module, improvement information generation module, user interaction module and remote control module, data acquisition module is used to collect the various data of electric welding machine, data processing module is used to the data collected is preprocessed, fault analysis module is used to analyze whether electric welding machine exists fault, fault analysis module is also used to generate accuracy index, improvement information generation module is used to generate improvement information, user interaction module is used to receive user instruction, remote control module is used to the remote control of electric welding machine.The present application is analyzed in real time to electric welding machine by adopting defect analysis model, it is favorable to obtain the defect condition of electric welding machine in first time, compared with artificial analysis speed is faster and workload is smaller.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of electric welding machine, and particularly to an intelligent pre-control system of electric welding machine with real-time monitoring function. BACKGROUND

[0002] Electric welding technology is an important process in modern manufacturing industry, which is widely used in automobile manufacturing, shipbuilding industry, aerospace, construction engineering and mechanical equipment fields. With the rapid development of industrial technology, electric welding machine has evolved from traditional simple equipment to complex equipment with intelligent function. By monitoring the electric welding machine during use, the safety of use can be improved.

[0003] The prior art such as CN118795842A discloses an electric welding machine energy-saving control system based on weld monitoring, which relates to the field of electric welding technology. The system includes: determining an initial control scheme; taking the scheme parameter control dimension and the external intervention dimension as the benchmark, and taking the welding standard as the constraint, performing dimension joint energy-saving optimization; establishing a programmable control module and a digital controller; establishing a communication connection between the digital controller and the intelligent central control system, and performing electric welding brake control; taking the weld quality and control energy consumption as the benchmark, performing abnormal control decision and positioning pre-adjustment control point; based on the pre-adjustment control point, responding to the digital controller, and performing electric welding feedback control.

[0004] Another typical prior art such as CN105537725A discloses an electric welding machine welding position automatic monitoring method, which includes the following steps: step one, detection point layout and detector installation: M detection points are laid out, each detection point is equipped with a temperature detection and position detection unit; step two, detection area division: the welding area between two welded workpieces is divided into M detection areas from front to back through M detection points; step three, detection point position detection and detection area sorting; step four, starting welding; step five, analysis and processing of temperature data of each detection area: the temperature data detected by multiple detection areas are analyzed and processed from front to back, the analysis and processing process of the temperature data detected by each detection area is as follows: current analysis detection point designation, temperature data detection and synchronous analysis processing, and current detection area temperature data analysis processing completion judgment.

[0005] CN118527769B. The system comprises a monitoring background, a monitoring terminal, a communication module, an electric welding machine monitoring circuit, and a relay control module. The monitoring background is used to manage the identity information of the user of the electric welding machine, receives the monitoring data uploaded by the electric welding machine monitoring circuit, and issues control instructions to the relay control module according to the monitoring data. The monitoring terminal is used to verify the identity of the user of the electric welding machine and issue control instructions to the relay control module according to the verification result. The electric welding machine monitoring circuit is used to collect and upload monitoring data in real time when the electric welding machine is in use. The relay control module is connected to the signal line of the thermal protection circuit of the electric welding machine and is used to enable or cut off the thermal protection signal of the electric welding machine according to the control instructions.

[0006] Currently, the existing electric welding machine monitoring process can only determine whether a certain monitoring data exceeds the set range, and it is difficult to determine the fault type according to the monitoring data, which requires additional analysis by the staff. In order to solve the problems existing in the field, the present application is made. SUMMARY

[0007] The present application aims to overcome the shortcomings of the prior art and provides an intelligent pre-control system for electric welding machines with real-time monitoring function.

[0008] In order to overcome the shortcomings of the prior art, the present application adopts the following technical solutions:

[0009] An intelligent pre-control system for electric welding machines with real-time monitoring function, comprising a data acquisition module, a data processing module, a fault analysis module, an improvement information generation module, a user interaction module, and a remote control module. The data acquisition module is used to collect various data of the electric welding machine. The data processing module is used to preprocess the data collected by the data acquisition module. The fault analysis module is used to analyze whether the electric welding machine has a fault and the type of the fault according to the preprocessed data. The fault analysis module is also used to generate an accuracy index to determine the accuracy of the analyzed fault type. The improvement information generation module is used to generate improvement information according to the analysis result of the fault analysis module. The user interaction module is used to display the analysis result and the improvement information and receive user instructions. The remote control module is used to remotely control the electric welding machine according to the received user instructions.

[0010] Further, the data acquisition module comprises a sensor group and a signal acquisition unit, the sensor group is used for acquiring various data generated by the electric welding machine during the working process, and the signal acquisition unit is used for receiving the signals sent by the sensor group; the data processing module comprises a data standardization unit and a filtering and noise reduction unit, the data standardization unit is used for converting the form of the data received by the signal acquisition unit into a form conforming to the input standard of the fault analysis module, and the filtering and noise reduction unit is used for filtering and noise reduction on the data after standardization processing.

[0011] Further, the fault analysis module comprises a process database, a defect analysis model and a calculation unit, the process database is used for saving the electric welding machine data when various processes are completed and the problem data corresponding to various electric welding machine defects, wherein the electric welding machine data does not include problem data, the defect analysis model is used for real-time analyzing whether the electric welding machine has defects and the type of defects according to the data processed by the data processing module and the data saved by the process database, and the calculation unit is used for calculating the accuracy index according to the data processed by the data processing module, the data saved by the process database and the analysis result of the defect analysis model.

[0012] Further, the improvement information generation module comprises a judgment unit, an improvement information database, a matching unit and an improvement information output unit, the judgment unit is used for judging whether the improvement information needs to be generated according to the analysis result of the fault analysis module, the improvement information database is used for saving the improvement information corresponding to various defect types, the matching unit is used for matching the acquired defect type with the data in the improvement information database when the improvement information needs to be generated and inputting the matching result as the improvement information into the improvement information output unit, and the improvement information output unit is used for fitting the matching result into the set template and outputting the acquired improvement information through the template.

[0013] Further, the user interaction module comprises a display unit and a user input unit, the display unit is used for displaying the analysis result of the fault analysis module and the improvement information generated by the improvement information generation module, and the user input unit is used for receiving the user instruction.

[0014] Further, the remote control module comprises an instruction conversion unit and a communication unit, the instruction conversion unit is used for converting the format of the user instruction received by the user input unit into a format suitable for communication with the electric welding machine, and the communication unit is used for sending the user instruction in the converted format to the electric welding machine.

[0015] Further, the working process of the system comprises the following steps:

[0016] S1, the data acquisition module collects various data of the electric welding machine in a working state;

[0017] S2, the data processing module processes various data collected by the data acquisition module;

[0018] S3, the fault analysis module analyzes whether the electric welding machine has defects according to the processed data, and sends the analysis result to the improvement information generation module;

[0019] S4, the improvement information generation module determines whether to generate improvement information, if yes, generates the improvement information, and executes the next step, otherwise, returns to S1;

[0020] S5, the user interaction module displays the generated improvement information, and the user inputs the user instruction according to the improvement information and actual demand;

[0021] S6, the user input unit receives the user instruction, and the remote control module remotely controls the electric welding machine according to the user instruction.

[0022] Further, the fault analysis module analyzing whether the electric welding machine has defects comprises the following steps:

[0023] S31, the defect analysis model matches the current data of the electric welding machine in this work with the data in the process database, and judges the process type of the electric welding machine at present;

[0024] S32, judges the deviation index of each corresponding data of the electric welding machine at the current time and the recognized process type at the corresponding time;

[0025] S33, judges whether the deviation index of each data of the electric welding machine at the current time is less than the deviation index threshold, if yes, there is no defect, and the process is ended, otherwise, there is a defect, and the next step is executed;

[0026] S34, the defect analysis model obtains the defect type corresponding to the data type from the process database according to the data whose deviation index is greater than the deviation index threshold;

[0027] S35, the calculation unit calculates the accuracy index.

[0028] The beneficial effects obtained by the present application are: 1. By adopting the defect analysis model to analyze the defects of the electric welding machine in real time, the defect condition of the electric welding machine can be obtained in the first time, which is faster than manual analysis and has smaller workload, and by generating the improvement information, the work personnel can adjust the electric welding machine according to the improvement information, and the adjustment progress is accelerated.

[0029] 2. The accuracy of the defect analysis model is evaluated by setting the accuracy index, which is beneficial to improve the reliability of the defect analysis model, and warns the staff when the accuracy is low, so as to avoid the staff from taking the wrong adjustment mode for the electric welding machine. BRIEF DESCRIPTION OF DRAWINGS

[0030] The application can be further understood from the following description made with reference to the drawings. The components in the drawings are not necessarily drawn to scale, but emphasis is instead placed upon illustrating the principles of the embodiments. Like reference numerals designate like parts in the different views.

[0031] Figure 1 The structure of the present application is shown in the schematic diagram.

[0032] Figure 2 The workflow of the present application is shown in the flowchart.

[0033] Figure 3 The flowchart of the fault analysis module of the present application is shown in the flowchart.

[0034] Figure 4 The relationship between the accuracy index threshold and the number of related data in the error items whose deviation index is less than the deviation index threshold and the number of related data in the correct items whose deviation index is less than the deviation index threshold is shown in the graph. DETAILED DESCRIPTION

[0035] The following is an embodiment of the present application by a specific example, and the advantages and effects of the present application can be understood by the person skilled in the art from the disclosure of the specification. The present application can be implemented or applied by other different embodiments, and each detail in the specification can be modified and changed based on different views and applications without departing from the spirit of the present application. In addition, the drawings of the present application are only simple schematic illustrations, not the actual size, and the prior declaration is made. The following embodiments will further illustrate the related technical content of the present application in detail, but the disclosed content is not used to limit the protection scope of the present application.

[0036] Example 1: According to Figure 1 , Figure 2 and Figure 3The embodiment provides an intelligent pre-control system of an electric welding machine with a real-time monitoring function, which comprises a data acquisition module, a data processing module, a fault analysis module, an improvement information generation module, a user interaction module and a remote control module, the data acquisition module is used for acquiring various data of the electric welding machine, the data processing module is used for preprocessing the data acquired by the data acquisition module, the fault analysis module is used for analyzing whether the electric welding machine has a fault and the type of the fault according to the preprocessed data, the fault analysis module is also used for generating an accuracy index to judge the accuracy degree of the analyzed fault type, the improvement information generation module is used for generating improvement information according to the analysis result of the fault analysis module, the user interaction module is used for displaying the analysis result and the improvement information and receiving a user instruction, and the remote control module is used for remotely controlling the electric welding machine according to the received user instruction.

[0037] Furthermore, the data acquisition module comprises a sensor group and a signal acquisition unit, the sensor group is used for acquiring various data generated by the electric welding machine in a working process, and the signal acquisition unit is used for receiving signals sent by the sensor group; the data processing module comprises a data standardization unit and a filtering and noise reduction unit, the data standardization unit is used for converting the form of the data received by the signal acquisition unit into a form conforming to the input standard of the fault analysis module, and the filtering and noise reduction unit is used for filtering and noise reduction on the data after standardization processing.

[0038] Furthermore, the fault analysis module comprises a process database, a defect analysis model and a calculation unit, the process database is used for saving electric welding machine data when various processes are completed and problem data corresponding to various electric welding machine defects, wherein the electric welding machine data does not include problem data, the defect analysis model is used for analyzing whether the electric welding machine has a defect and the type of the defect according to the data processed by the data processing module and the data saved by the process database in real time, and the calculation unit is used for calculating the accuracy index according to the data processed by the data processing module, the data saved by the process database and the analysis result of the defect analysis model.

[0039] Specifically, the process database is summarized and refined by a person skilled in the art according to years of production experience and actual data, and the defect analysis model is trained by a person skilled in the art through the process database and past defects (various data corresponding thereto).

[0040] Specifically, the analysis result of the fault analysis module comprises the analysis result of the defect analysis model and the accuracy index.

[0041] Further, the improvement information generation module comprises a judging unit, an improvement information database, a matching unit and an improvement information output unit, the judging unit is used for judging whether improvement information needs to be generated according to the analysis result of the fault analysis module, the improvement information database is used for saving the improvement information corresponding to various defect types, the matching unit is used for matching the obtained defect type with the data in the improvement information database when the improvement information needs to be generated and inputting the matching result as the improvement information into the improvement information output unit, and the improvement information output unit is used for fitting the matching result into a set template and outputting the obtained improvement information through the template.

[0042] Specifically, different defect types have one-to-one matching improvement information in the improvement information database. Since the system is real-time monitoring and the corresponding improvement information is generated immediately after detecting the defect, the defect analysis module will only analyze one kind of defect in one analysis.

[0043] Specifically, when the analysis result of the defect analysis model is that there is no defect, the judging unit considers that the improvement information does not need to be generated; when the analysis result of the defect analysis model is that there is a defect and the accuracy index is greater than or equal to the set accuracy index threshold, the judging unit considers that the improvement information needs to be generated; when the analysis result of the defect analysis model is that there is a defect and the accuracy index is less than the set accuracy index threshold, the judging unit considers that the generated improvement information may have errors and the staff needs to additionally confirm the defect type, and the judging unit sends a warning signal to the user interaction module.

[0044] Specifically, the greater the accuracy index, the greater the accuracy of the analysis result of the defect analysis model, and the accuracy index threshold is obtained by the person skilled in the art from the correct items in the past analysis results of the defect analysis model (whether correct is judged by the person skilled in the art), selecting the minimum value as the accuracy index threshold.

[0045] Specifically, the past analysis results are judged by the staff whether the analysis result is correct, not by the defect analysis model according to the accuracy index.

[0046] Further, the user interaction module comprises a display unit and a user input unit, the display unit is used for displaying the analysis result of the fault analysis module and the improvement information generated by the improvement information generation module, and the user input unit is used for receiving user instructions.

[0047] Specifically, the display unit can also display the corresponding warning information according to the warning signal sent by the judging unit.

[0048] Further, the remote control module comprises an instruction conversion unit and a communication unit, the instruction conversion unit is used for converting the format of the user instruction received by the user input unit into a format suitable for communication with the electric welding machine, and the communication unit is used for sending the user instruction in the converted format to the electric welding machine.

[0049] Further, the workflow of the system comprises the following steps:

[0050] S1, the data acquisition module acquires various data of the electric welding machine in the working state;

[0051] S2, the data processing module processes the various data acquired by the data acquisition module;

[0052] S3, the fault analysis module analyzes whether the electric welding machine has defects according to the processed data, and sends the analysis result to the improvement information generation module;

[0053] S4, the improvement information generation module judges whether the improvement information needs to be generated, if yes, the improvement information is generated, and the next step is executed, otherwise, it returns to S1;

[0054] S5, the user interaction module displays the generated improvement information, and the user inputs the user instruction according to the improvement information and actual demand;

[0055] S6, the user input unit receives the user instruction, and the remote control module remotely controls the electric welding machine according to the user instruction.

[0056] Specifically, the remote control module converts the format of the user instruction so that the user instruction meets the requirements of the communication protocol between the electric welding machine and the communication unit, and then opens or closes the power supply of the electric welding machine or adjusts various working parameters of the electric welding machine through the communication unit, thereby remotely controlling the electric welding machine to overcome the defect problem of the electric welding machine.

[0057] Further, the fault analysis module analyzing whether the electric welding machine has defects comprises the following steps:

[0058] S31, the defect analysis model matches the current data of the electric welding machine in this working with the data in the process database to judge the current process type of the electric welding machine;

[0059] Specifically, the defect analysis model calculates the matching similarity index of the current process type of the electric welding machine and various processes in the process database with reference to the following formula, taking the process type a as an example:

[0060] ;

[0061] wherein, This is a similarity index comparing the current process type of the welding machine with the process type α in the process database. This index characterizes the degree of similarity between the two; the larger the index, the higher the similarity. A represents the number of relevant data points collected from the welding machine at the current moment. The welding machine data includes various parameters of the welding machine, including but not limited to the power output current, power output voltage, and welding position temperature, excluding problematic data. e is a natural constant. Let a be the value of the a-th welding machine data at the current moment. For process type α and The reference value of data of the same data type at the corresponding time.

[0062] Specifically, when the matching similarity index between the current process type of the welding machine and all process types in the process database is less than the matching similarity index threshold, or when the number of types of the current welding machine data is different from the number of types of welding machine data of all process types in the process database, it is considered that the process type cannot be determined and a warning signal is issued to the user.

[0063] For the corresponding time, the following is an example: for instance, if the welding machine has been working for 10 seconds, then... For process type α, when the operation reaches the 10th second, it is related to... The values ​​of data of the same data type; the data type includes, but is not limited to, the power supply output current, the power supply output voltage, the temperature of the welding position, etc.

[0064] Specifically, the defect analysis unit determines the process type with the highest similarity index in the process database as the current process type of the welding machine;

[0065] S32, Determine the deviation index of the corresponding data of the welding machine at the current moment from the corresponding data of the identified process type at the corresponding moment;

[0066] Specifically, taking the a-th relevant data point as an example, the deviation index is calculated according to the following formula:

[0067] ;

[0068] in, The deviation index corresponds to the data of the a-th welding machine, and is used to characterize the degree of deviation between the data of the a-th welding machine and its corresponding data in the identified process type. The larger the deviation index, the greater the degree of deviation. The value of the data corresponding to the a-th welding machine data in the identified process type at the corresponding time. Let be the value of the a-th welding machine data at the current moment.

[0069] S33, judging whether the deviation indexes of the data of the electric welding machine at the current time are all less than the deviation index threshold value, if yes, there is no defect, ending, otherwise, there is a defect, executing the next step;

[0070] Specifically, the deviation index threshold value is set by the person skilled in the art according to the error tolerance of the electric welding machine to each type of data during the working process, i.e. the influence degree of each type of data on the working of the electric welding machine, the greater the influence degree of the data on the working of the electric welding machine, the smaller the deviation index threshold value corresponding to the type of data.

[0071] S34, the defect analysis model acquires the defect type corresponding to the data type from the process database according to the data whose deviation index is greater than the deviation index threshold value;

[0072] Specifically, the process database stores the data types and process types corresponding to different defect types, when the data type and the process type are known, the defect type can be acquired by matching with the defect types in the process database.

[0073] S35, the calculation unit calculates the accuracy index.

[0074] Specifically, the accuracy index can be calculated according to the following formula:

[0075] ;

[0076] ;

[0077] Wherein, ZQZB is the accuracy index, the greater the index, the higher the accuracy of the analysis, SG is the data quality factor, e is the natural constant, T is the time of the electric welding machine currently worked, is the total time of the data missing of the a th electric welding machine data in the data collection process, is the set update frequency of the a th electric welding machine data, is the actual update frequency of the a th electric welding machine data, is the process recognition accuracy parameter, A is the number of the electric welding machine data of the electric welding machine collected at the current time, is the deviation index accuracy parameter of the a th electric welding machine data, is the correlation weight of the a-th relevant data, which is set by a person skilled in the art between 0 and 2 according to the correlation degree between the relevant data and the process type currently identified by the electric welder, and the greater the correlation degree, the greater the weight; for example, in the welding process, the current and voltage of the electrode and the temperature of the welding position need to be considered, at this time the corresponding correlation weight of the current and voltage of the electrode and the temperature of the welding position is set to 2, in the cooling process, the current and voltage of the electrode have no effect on the cooling process, at this time the corresponding correlation weight of the current and voltage of the electrode is set to 0, and the weight of the temperature of the welding position is set to 2.

[0078] The process identification accuracy parameter can be calculated according to the following formula:

[0079] ;

[0080] ;

[0081] wherein, is the process type identification accuracy parameter, H is the information entropy parameter of the identified process type, when there is no past data, the value of H is set to 1, when there is past data, is the number of prediction errors in the past prediction, is the number of correct predictions in the past prediction, A is the number of electric welder data collected at the current time, T is the time the electric welder has worked, is the correlation weight of the a-th electric welder data, e is the natural constant, is the normalized at the value of t time, is the normalized at the value of t time;

[0082] Specifically, the normalization method adopts "minimum-maximum normalization", wherein the minimum value is the minimum value of the a-th relevant data recorded in the process database, and the maximum value is the maximum value of the a-th relevant data recorded in the process database, and the normalized range is 0 to 1.

[0083] Taking the a-th relevant data as an example, the deviation accuracy parameter can be calculated according to the following formula:

[0084] ;

[0085] wherein, is the deviation accuracy parameter of the a-th electric welder data, T is the time the electric welder has worked, e is the natural constant, is the normalized at the value of t time, is the normalized at the value of t time.

[0086] The beneficial effects of the present scheme are: 1. By adopting the defect analysis model to analyze the defects of the electric welding machine in real time, the defect situation of the electric welding machine can be obtained in the first time, which is faster than manual analysis and has less workload, and by generating improvement information, the electric welding machine can be adjusted according to the improvement information, and the adjustment progress is accelerated.

[0087] 2. By setting the accuracy index to evaluate the accuracy of the defect analysis model, the reliability of the defect analysis model can be improved, and when the accuracy is low, the staff is warned to avoid taking the wrong adjustment method for the electric welding machine.

[0088] Embodiment two: this embodiment should be understood as including all the features of any one of the preceding embodiments, and further improving on the basis thereof, and the method for obtaining the accuracy index threshold value has been given in embodiment one, but the accuracy index threshold value obtained by this method may be too large or too small when the past data is too small, which may cause the judgment to be wrong, in order to avoid the above situation, this embodiment proposes another method for obtaining the accuracy index threshold value when the past data is less than the threshold value, the threshold value is set by the person skilled in the art according to experience, when the past data is less than the threshold value, only the method of this embodiment is used, and when it is greater than or equal to the threshold value, only the method of embodiment one is used, the accuracy index threshold value of this embodiment is obtained by the following formula:

[0089] ;

[0090] Wherein, YZ is the obtained accuracy index threshold value, D is the eccentricity coefficient of the staff, is the maximum accuracy index of the analysis error in the past analysis results of the defect analysis model (whether it is wrong is judged by the person skilled in the art), is the number of electric welding machine data whose deviation index is less than the deviation index threshold value in the maximum accuracy index, is the minimum accuracy index of the analysis correct in the past analysis results of the defect analysis model (whether it is wrong is judged by the person skilled in the art), is the number of electric welding machine data whose deviation index is less than the deviation index threshold value in the minimum accuracy index.

[0091] Specifically, the eccentricity coefficient is set by the person skilled in the art according to the tolerance of the person skilled in the art to the misjudgment of the defect analysis model, if the worker considers that the severity of misjudging the correct result as the wrong result is greater than the severity of misjudging the wrong result as the correct result, D is set to 0.6, if the worker considers that the severity of misjudging the correct result as the wrong result is less than the severity of misjudging the wrong result as the correct result, D is set to 0.4, if the worker considers that the severity of misjudging the correct result as the wrong result is equal to the severity of misjudging the wrong result as the correct result, D is set to 0.5.

[0092] As shown in Figure 4 , Figure 4 D is 0.5, 0.8, 1.4, the relationship diagram of the accuracy index threshold value and the number of related data in the error items with the deviation index less than the deviation index threshold value and the number of related data in the correct items with the deviation index less than the deviation index threshold value.

[0093] The beneficial effects of the embodiment are: by setting the accuracy index threshold value by comprehensively considering the error items with the maximum accuracy index and the correct items with the minimum accuracy index, a less biased accuracy index threshold value can be obtained when there is less past data, and the calculated accuracy index helps the user to judge whether to perform the generated improvement information, which helps to improve the accuracy of the judgment.

[0094] The above disclosed content is only the preferred feasible embodiment of the present application, and does not limit the protection scope of the present application, so any equivalent technical change made by applying the content of the present application specification and drawings is included in the protection scope of the present application, and in addition, the elements can be updated as the technology develops. The above units are only examples, and the person skilled in the art can design and use corresponding units according to actual needs when implementing the scheme.

Claims

1. An intelligent pre-control system for welding machines with real-time monitoring function, characterized in that, The system includes a data acquisition module, a data processing module, a fault analysis module, an improvement information generation module, a user interaction module, and a remote control module. The data acquisition module is used to collect various data from the welding machine. The data processing module is used to preprocess the data collected by the data acquisition module. The fault analysis module is used to analyze whether the welding machine has a fault and the type of fault based on the preprocessed data. The fault analysis module is also used to generate an accuracy index to determine the accuracy of the analyzed fault type. The improvement information generation module is used to generate improvement information based on the analysis results of the fault analysis module. The user interaction module is used to display the analysis results and the improvement information and receive user commands. The remote control module is used to remotely control the welding machine according to the received user commands. The system's workflow includes the following steps: S1, the data acquisition module collects various data from the welding machine in operation; S2, the data processing module processes the various data collected by the data acquisition module; S3, the fault analysis module analyzes whether the welding machine has defects based on the processed data and sends the analysis results to the improvement information generation module; S4, Improvement information generation module determines whether improvement information needs to be generated. If yes, it generates improvement information and proceeds to the next step; otherwise, it returns to S1. S5, the user interaction module displays the generated improvement information, and the user inputs user commands based on the improvement information and actual needs; S6, The user input unit receives user instructions, and the remote control module remotely controls the welding machine according to the user instructions; The fault analysis module analyzes whether the welding machine has defects, including the following steps: S31, The defect analysis model matches the current data of the welding machine in this operation with the data in the process database to determine the current process type of the welding machine; S32, Determine the deviation index of the corresponding data of the welding machine at the current moment from the corresponding data of the identified process type at the corresponding moment; S33, Determine whether the deviation indicators of all data of the welding machine at the current moment are all less than the deviation indicator threshold. If they are all less than the threshold, there is no defect and the process ends. Otherwise, there is a defect and the process continues to the next step. S34, The defect analysis model retrieves the corresponding defect type from the process database based on the data type of the data whose deviation index is greater than the deviation index threshold. S35, Calculation accuracy index of the calculation unit; The accuracy index can be calculated using the following formula: ； ； Where ZQZB is the accuracy index; the larger the index, the higher the accuracy of the analysis. SG is the data quality factor, e is the natural constant, and T is the current operating time of the welding machine. Let be the total time spent during the data acquisition process for the data of the a-th welding machine when data was missing. The update frequency for the data of the a-th welding machine is set. Let a be the actual update frequency of the data for the a-th welding machine. Here, A represents the process identification accuracy parameter, and A is the number of welding machine data points collected at the current moment. Let a be the accuracy parameter for the deviation index of the a-th welding machine data. Let a be the relevance weight of the a-th relevant data point; The process identification accuracy parameter is calculated according to the following formula: ； ； in, H is the accuracy parameter for process type identification, and H is the information entropy parameter for the identified process type. When there is no past data, the value of H is set to 1; when there is past data, H is set to 1. This represents the number of incorrect predictions in past forecasts. Let A be the number of correct predictions in past forecasts, A be the number of welding machine data points collected at the current moment, and T be the current operating time of the welding machine. Let e ​​be the relevance weight of the a-th welding machine data point, and e be the natural constant. For normalized The value at time t, For normalized The value at time t; The accuracy parameter of the deviation index for the a-th welding machine data is calculated according to the following formula: ； in, Let T be the accuracy parameter for the deviation index of the a-th welding machine data, T be the current working time of the welding machine, and e be the natural constant. For normalized The value at time t, For normalized The value at time t.

2. The intelligent pre-control system for welding machines with real-time monitoring function according to claim 1, characterized in that, The data acquisition module includes a sensor group and a signal acquisition unit. The sensor group is used to acquire various data generated by the welding machine during operation, and the signal acquisition unit is used to receive signals sent by the sensor group. The data processing module includes a data standardization unit and a filtering and noise reduction unit. The data standardization unit is used to convert the data received by the signal acquisition unit into a form that conforms to the input standard of the fault analysis module, and the filtering and noise reduction unit is used to filter and reduce noise in the standardized data.

3. The intelligent pre-control system for an electric welding machine with real-time monitoring function according to claim 2, characterized in that, The fault analysis module includes a process database, a defect analysis model, and a calculation unit. The process database is used to store welding machine data when completing various processes and problem data corresponding to various welding machine defects. The welding machine data does not include problem data. The defect analysis model is used to analyze in real time whether the welding machine has defects and the types of defects based on the data processed by the data processing module and the data stored in the process database. The calculation unit is used to calculate accuracy indicators based on the data processed by the data processing module, the data stored in the process database, and the analysis results of the defect analysis model.

4. The intelligent pre-control system for welding machines with real-time monitoring function according to claim 3, characterized in that, The improvement information generation module includes a judgment unit, an improvement information database, a matching unit, and an improvement information output unit. The judgment unit is used to determine whether improvement information needs to be generated based on the analysis results of the fault analysis module. The improvement information database is used to store improvement information corresponding to various defect types. The matching unit is used to match the acquired defect type with the data in the improvement information database when improvement information needs to be generated and input the matching result into the improvement information output unit. The improvement information output unit is used to fit the matching result into a pre-defined template and output the acquired improvement information through the template.

5. The intelligent pre-control system for an electric welding machine with real-time monitoring function according to claim 4, characterized in that, The user interaction module includes a display unit and a user input unit. The display unit is used to display the analysis results of the fault analysis module and the improvement information generated by the improvement information generation module. The user input unit is used to receive user commands.

6. The intelligent pre-control system for an electric welding machine with real-time monitoring function according to claim 5, characterized in that, The remote control module includes an instruction conversion unit and a communication unit. The instruction conversion unit is used to convert the format of the user instructions received by the user input unit into a format suitable for communication with the welding machine. The communication unit is used to send the converted user instructions to the welding machine.

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