Electric welding machine intelligent pre-control system with real-time monitoring function

Abstract

The invention relates to the field of electric welding machines, in particular to an electric welding machine intelligent pre-control system with a real-time monitoring function, which comprises a data acquisition module, a data processing module, a fault analysis module, an improved information generation module, a user interaction module and a remote control module, the data processing module is used for preprocessing the collected data, the fault analysis module is used for analyzing whether the electric welding machine has faults or not, the fault analysis module is further used for generating an accuracy index, the improved information generation module is used for generating improved information, and the user interaction module is used for receiving a user instruction. And the remote control module is used for remotely controlling the electric welding machine. Real-time defect analysis is carried out on the electric welding machine through the defect analysis model, the defect condition of the electric welding machine can be obtained in the first time, and compared with manual analysis, the speed is higher, and the workload is smaller.

Description

Technical Field

[0001] The present invention relates to the field of welding machines, and in particular to an intelligent pre-control system for welding machines with real-time monitoring function. Background Art

[0002] Welding technology, as an essential process in modern manufacturing, is widely used in fields such as automobile manufacturing, shipbuilding industry, aerospace, construction engineering, and mechanical equipment. With the rapid development of industrial technology, welding machines have evolved from traditional simple devices into complex devices with intelligent functions. Monitoring the welding machine during use can improve the safety of use.

[0003] For example, the prior art of CN118795842A discloses an energy-saving control system for welding machines based on weld monitoring, which relates to the field of welding technology. The system includes: determining an initial control scheme; performing dimension joint energy-saving optimization with the scheme parameter control dimension and the external intervention dimension as the benchmark and the welding standard as the constraint; establishing a programmable control module and establishing a digital controller; establishing a communication connection between the digital controller and the intelligent central control system to perform braking control of the welding machine; making an abnormal control decision with the weld quality and control energy consumption as the benchmark to locate the pre-adjustment control point; and performing welding feedback control in response to the digital controller based on the pre-adjustment control point.

[0004] Another typical prior art of CN105537725A discloses a method for automatically monitoring the welding position of a welding machine, including the steps of: I. Detection point layout and installation of detection devices: arranging M detection points, and each detection point is equipped with a temperature detection and position detection unit; II. Detection area division: dividing the welding area between two workpieces to be welded into M detection areas from front to back through M detection points; III. Detection point position detection and detection area sorting; IV. Starting welding; V. Data analysis and processing of the temperature of each detection area: analyzing and processing the temperature data detected in multiple detection areas from first to last. The analysis and processing process of the temperature data detected in each detection area is as follows: specifying the current analysis detection point, detecting and synchronously analyzing the temperature data, and judging whether the data analysis and processing of the temperature of the current detection area are completed.

[0005] Next, let's look at an existing welding machine monitoring system and method disclosed in the prior art such as CN118527769B. The system includes: a monitoring background, a monitoring terminal, a communication module, a welding machine monitoring circuit, and a relay control module; the monitoring background is used to manage the identity information of the users of the welding machine, receive the monitoring data uploaded by the welding machine monitoring circuit, and issue control instructions to the relay control module according to the monitoring data; the monitoring terminal is used to authenticate the identity of the users of the welding machine and issue control instructions to the relay control module according to the verification result; the welding machine monitoring circuit is used to collect the monitoring data of the welding machine in real time during use and upload it; the relay control module is connected to the signal line of the thermal protection circuit of the welding machine and is used to enable or cut off the thermal protection signal of the welding machine according to the control instruction.

[0006] Currently, during the existing welding machine monitoring process, it is generally only possible to determine whether a certain monitoring data exceeds the set range, and it is difficult to determine the type of fault based on the monitoring data, which requires additional analysis by the staff. To solve the problems commonly existing in this field, the present invention is made. Summary of the Invention

[0007] The purpose of the present invention is to propose an intelligent pre-control system for welding machines with real-time monitoring function in view of the current deficiencies.

[0008] In order to overcome the deficiencies of the prior art, the present invention adopts the following technical solutions: An intelligent pre-control system for welding machines with real-time monitoring function 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 of 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 there is a fault in the welding machine and the type of the fault based on the preprocessed data. The fault analysis module is also used to generate an accuracy index to judge 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 welding machine according to the received user instructions.

[0009] Further, the data acquisition module includes a sensor group and a signal acquisition unit. The sensor group is used to collect various data generated by the electric welding machine during operation, and the signal acquisition unit is used to receive the 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 form of the data received by the signal acquisition unit into a form that meets the input standard of the fault analysis module, and the filtering and noise reduction unit is used to filter and reduce the noise of the data after the standardization process.

[0010] Further, the fault analysis module includes a process database, a defect analysis model, and a calculation unit. The process database is used to store the data of the electric welding machine when completing various processes and the problem data corresponding to various defects of the electric welding machine. Among them, the data of the electric welding machine does not include problem data. The defect analysis model is used to analyze in real time whether there are defects in the electric welding machine 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 the accuracy index based on the data processed by the data processing module, the data stored in the process database, and the analysis result of the defect analysis model.

[0011] Further, 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 judge whether it is necessary to generate improvement information based on the analysis result of the fault analysis module. The improvement information database is used to store the improvement information corresponding to various defect types. The matching unit is used to match the obtained defect type with the data in the improvement information database when it is necessary to generate improvement information and input the matching result as the improvement information into the improvement information output unit. The improvement information output unit is used to put the matching result into a set template and output the obtained improvement information through the template.

[0012] Further, the user interaction module includes a display unit and a user input unit. The display unit is used to display 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 to receive user instructions.

[0013] Further, 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 communicating with the electric welding machine, and the communication unit is used to send the user instructions with the converted format to the electric welding machine.

[0014] Further, the working process of the system includes the following steps: S1, The data acquisition module collects various data of the electric welding machine in the working state; S2, The data processing module processes the various data collected by the data acquisition module; S3, The fault analysis module analyzes whether there are defects in the electric welding machine based on the processed data, and sends the analysis result to the improvement information generation module; S4, The improvement information generation module determines whether it is necessary to generate improvement information. If so, 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 a user instruction according to the improvement information and actual requirements; 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.

[0015] Furthermore, the steps for the fault analysis module to analyze whether there are defects in the electric welding machine include the following: S31, The defect analysis model matches the current data of the electric welding machine during this operation with the data in the process database to determine the current process type of the electric welding machine; S32, Determine the deviation index of the corresponding data of the electric welding machine at the current moment and the identified process type at the corresponding moment; S33, Determine whether the deviation indexes of the various data of the electric welding machine at the current moment are all less than the deviation index threshold. If they are all less, there are no defects and the process ends. Otherwise, there are defects and proceed to the next step; S34, The defect analysis model obtains the corresponding defect type from the process database according to the data type of the data with the deviation index greater than the deviation index threshold; S35, The calculation unit calculates the accuracy index.

[0016] The beneficial effects achieved by the present invention are as follows: 1. By using the defect analysis model to perform real-time defect analysis on the electric welding machine, it is beneficial to obtain the defect situation of the electric welding machine in the first time, with a faster speed and less workload compared to manual analysis. At the same time, by generating improvement information, it is beneficial for the staff to adjust the electric welding machine according to the improvement information, accelerating the adjustment progress.

[0017] 2. By setting the accuracy index to evaluate the accuracy of the defect analysis model for defect analysis, it is beneficial to improve the reliability of the defect analysis model, warning the staff when the accuracy is low to avoid the staff taking incorrect adjustment methods for the electric welding machine. Description of the Drawings

[0018] The present invention can be further understood from the following description in conjunction with the drawings. The components in the drawings are not necessarily drawn to scale, but the emphasis is on showing the principles of the embodiments. In different views, the same reference numerals designate the same parts.

[0019] Figure 1 This is a schematic structural diagram of the present invention.

[0020] Figure 2 This is a flowchart of the working process of the present invention.

[0021] Figure 3 This is a flowchart for the fault analysis module of the present invention to analyze whether there are defects in the electric welding machine.

[0022] Figure 4 This is a relationship diagram of the accuracy index threshold of the present invention, the number of relevant data with the deviation index less than the deviation index threshold in the error items, and the number of relevant data with the deviation index less than the deviation index threshold in the correct items. Detailed implementation manners

[0023] The following are specific embodiments to illustrate the implementation manners of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Additionally, the drawings of the present invention are only for simple schematic illustration and are not drawn according to actual dimensions, hereby declared in advance. The following implementation manners will further detail the related technical content of the present invention, but the disclosed content is not used to limit the protection scope of the present invention.

[0024] Embodiment 1: According to Figure 1 , Figure 2 and Figure 3 , this embodiment provides an intelligent pre-control system for an electric welding machine with real-time monitoring function, including 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 there are faults in the electric welding machine and the types of faults based on the preprocessed data. The fault analysis module is also used to generate an accuracy index to judge 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.

[0025] Furthermore, the data acquisition module includes a sensor group and a signal acquisition unit. The sensor group is used to collect various data generated during the operation of the electric welding machine, and the signal acquisition unit is used to receive the 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 form of the data received by the signal acquisition unit into a form that meets the input standard of the fault analysis module, and the filtering and noise reduction unit is used to filter and reduce the noise of the data after the standardization process.

[0026] Furthermore, the fault analysis module includes a process database, a defect analysis model, and a calculation unit. The process database is used to store the data of the electric welding machine when completing various processes and the problem data corresponding to various defects of the electric welding machine. Among them, the data of the electric welding machine does not include problem data. The defect analysis model is used to analyze in real time whether there are defects in the electric welding machine 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 the accuracy index based on the data processed by the data processing module, the data stored in the process database, and the analysis result of the defect analysis model.

[0027] Specifically, the process database is summarized and refined by those skilled in the art based on years of production experience and actual data. The defect analysis model is trained by those skilled in the art through the process database and the existing defects (and their corresponding data).

[0028] Specifically, the analysis result of the fault analysis module includes the analysis result of the defect analysis model and the accuracy index.

[0029] Furthermore, 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 judge whether it is necessary to generate improvement information according to the analysis result of the fault analysis module. The improvement information database is used to store the improvement information corresponding to various defect types. The matching unit is used to match the obtained defect type with the data in the improvement information database when it is necessary to generate improvement information and input the matching result as the improvement information into the improvement information output unit. The improvement information output unit is used to put the matching result into a set template and output the obtained improvement information through the template.

[0030] Specifically, there is corresponding improvement information in the improvement information database for each different defect type. Since the system is for real-time monitoring and corresponding improvement information will be generated immediately when a defect is detected, only one type of defect will be analyzed in one analysis by the defect analysis module.

[0031] Specifically, when the analysis result of the defect analysis model indicates no defect, the judgment unit deems that there is no need to generate improvement information; when the analysis result of the defect analysis model indicates a defect and the accuracy index is greater than or equal to the set accuracy index threshold, the judgment unit deems that improvement information needs to be generated; when the analysis result of the defect analysis model indicates a defect and the accuracy index is less than the set accuracy index threshold, the judgment unit deems that improvement information needs to be generated but the generated improvement information may be incorrect, and additional confirmation of the defect type by the staff is required. The judgment unit sends a warning signal to the user interaction module.

[0032] Specifically, the larger the accuracy index, the higher the accuracy of the analysis result of the defect analysis model is considered. The accuracy index threshold is obtained by those skilled in the art by comprehensively considering the correct items in the past analysis results of the defect analysis model (whether it is correct is judged by those skilled in the art), obtaining the corresponding accuracy index, and selecting the minimum value among them as the accuracy index threshold.

[0033] Specifically, the past analysis results are judged by the staff whether the analysis results are correct, rather than being judged by the defect analysis model according to the accuracy index.

[0034] Furthermore, the user interaction module includes a display unit and a user input unit. The display unit is used to display 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 to receive user instructions.

[0035] Specifically, the display unit can also display corresponding warning information according to the warning signal sent by the judgment unit.

[0036] Furthermore, 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 communicating with the electric welding machine, and the communication unit is used to send the user instructions with the converted format to the electric welding machine.

[0037] Furthermore, the working process of the system includes the following steps: S1. The data acquisition module acquires various data of the electric welding machine in the working state; S2. The data processing module processes the various data acquired by the data acquisition module; S3. The fault analysis module analyzes whether the electric welding machine has defects based on the processed data and sends the analysis result to the improvement information generation module; S4. The improvement information generation module determines whether improvement information needs to be generated. If so, 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 a user instruction based on the improvement information and actual requirements; 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.

[0038] 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 turns on or off the power supply of the electric welding machine or adjusts various working parameters of the electric welding machine through the communication unit, so as to realize remote control of the electric welding machine to overcome the defects of the electric welding machine.

[0039] Furthermore, the fault analysis module analyzes whether there are defects in the electric welding machine, including the following steps: S31, The defect analysis model matches the current data of the electric welding machine during this work with the data in the process database to judge the current process type of the electric welding machine; Specifically, the defect analysis model calculates the matching similarity index between 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 α as an example: ; Among them, is the matching similarity index between the current process type of the electric welding machine and the process type α in the process database. This index is used to characterize the similarity between the two. The larger the index, the higher the similarity. A is the number of relevant data of the electric welding machine collected at the current moment. The electric welding machine data of the electric welding machine are various parameters of the electric welding machine, including but not limited to the current output by the power supply, the voltage output by the power supply, the temperature at the welding position, etc., and does not include problem data. e is the natural constant. is the value of the a-th electric welding machine data at the current moment. is the reference value at the corresponding moment of the data with the same data type as in the process type α; Specifically, when the matching similarity index between the current process type of the electric welding machine and all process types in the process database is less than the matching similarity index threshold, or when the number of data types of the current electric welding machine data is not the same as the number of data types of the electric 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 sent to the user.

[0040] For the corresponding moment, the following is an example. For example, if the current working time of the electric welding machine is 10s, then is the value of the data with the same data type as when the process type α reaches the 10th second during the work; the data types include but not limited to the current output by the power supply, the voltage output by the power supply, the temperature at the welding position, etc. Specifically, the defect analysis unit determines the process type currently used by the welding machine as the process type with the largest matching similarity index in the process database; S32. Determine the deviation index of each corresponding data of the welding machine at the current moment from the corresponding data of the identified process type at the corresponding moment; Specifically, taking the a-th relevant data as an example, the deviation index is calculated according to the following formula: ; where, is the deviation index corresponding to the a-th welding machine data, which is used to characterize the deviation degree between the a-th welding machine data and its corresponding data in the identified process type. The larger the deviation index, the greater the deviation degree. is the value of the data corresponding to the a-th welding machine data in the identified process type at the corresponding moment. is the value of the a-th welding machine data at the current moment.

[0041] S33. Determine whether the deviation indexes of all data of the welding machine at the current moment are all smaller than the deviation index threshold. If they are all smaller, there is no defect, and the process ends. Otherwise, there is a defect, and proceed to the next step; Specifically, the deviation index threshold is set for each type of data by those skilled in the art according to the tolerance degree of the welding machine to the errors of various types of data during operation, that is, the influence degree of various types of data on the operation of the welding machine. The greater the influence degree of the data on the operation of the welding machine, the smaller the deviation index threshold corresponding to this type of data.

[0042] S34. The defect analysis model obtains the defect type corresponding to the data type according to the data type of the data with a deviation index greater than the deviation index threshold from the process database; Specifically, the process database stores the data types and process types corresponding to different defect types. When the data type and process type are known, the defect type can be obtained by matching with the defect types in the process database.

[0043] S35. The calculation unit calculates the accuracy index.

[0044] Specifically, the accuracy index can be calculated according to the following formula: ; ; where ZQZB is the accuracy index. The larger this index, the higher the accuracy of the analysis. SG is the data quality factor, e is the natural constant, T is the time that the welding machine has been operating currently, is the total time during which data loss occurs for the a-th welding machine data during data acquisition. 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 identification accuracy parameter, A is the number of electric welding machine data collected at the current moment, is the deviation index accuracy parameter of the a-th electric welding machine data, is the correlation weight of the a-th related data, and this weight is set by those skilled in the art between 0 and 2 according to the correlation degree between the related data and the process type currently identified by the electric welding machine. The greater the correlation degree, the greater its weight. For example, during the welding process, the current and voltage of the electrode and the temperature at the welding position need to be considered. At this time, the correlation weights corresponding to the current and voltage of the electrode and the temperature at the welding position are set to 2. During the cooling process, the current and voltage of the electrode have no effect on the cooling process. At this time, the correlation weights corresponding to the current and voltage of the electrode are set to 0, and the weight of the temperature at the welding position is set to 2.

[0045] The process identification accuracy parameter can be calculated according to the following formula: ; ; 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 past predictions, is the number of correct predictions in past predictions, A is the number of electric welding machine data collected at the current moment, T is the time that the electric welding machine has worked currently, is the correlation weight of the a-th electric welding machine data, e is the natural constant, is the normalized value at time t, is the normalized value at time t; Specifically, the normalization method adopts "min-max normalization". The minimum value is the minimum value recorded in the process database for the a-th related data, and the maximum value is the maximum value recorded in the process database for the a-th related data. The normalized range is from 0 to 1.

[0046] Taking the a-th related data as an example, the deviation index accuracy parameter can be calculated according to the following formula: ; wherein, is the deviation index accuracy parameter of the a-th electric welding machine data, T is the time that the electric welding machine has worked currently, e is the natural constant, The value at time t after normalization at time t, The value at time t after normalization at time t.

[0047] Beneficial effects of this solution: 1. By using a defect analysis model to perform real-time defect analysis on the welding machine, it is beneficial to obtain the defect situation of the welding machine in a timely manner. Compared with manual analysis, the speed is faster and the workload is smaller. At the same time, by generating improvement information, it is beneficial for the staff to adjust the welding machine according to the improvement information, accelerating the adjustment progress.

[0048] 2. By setting an accuracy index to evaluate the accuracy of the defect analysis model for defect analysis, it is beneficial to improve the reliability of the defect analysis model. When the accuracy is low, it warns the staff to avoid the staff taking incorrect adjustment methods for the welding machine.

[0049] Embodiment 2: This embodiment should be understood as including all the features of any one of the foregoing embodiments and further improving on this basis. It also lies in that a method for obtaining an accuracy index threshold has been given in Embodiment 1, but when the past data is too small, the obtained accuracy index threshold may be too large or too small, which may lead to misjudgment in this determination. To avoid the above situation, this embodiment proposes another method for obtaining the accuracy index threshold when the past data volume is less than the threshold. The threshold mentioned here is set by those skilled in the art according to experience. When the past data volume is less than the threshold, only the method of this embodiment is used for the accuracy index threshold, and only the method of Embodiment 1 is used when it is greater than or equal to the threshold. The method of this embodiment obtains the accuracy index threshold through the following formula: ; where YZ is the obtained accuracy index threshold, D is the eccentricity coefficient of the staff, is the maximum accuracy index of incorrect analysis in the past analysis results of the defect analysis model (whether it is incorrect is judged by those skilled in the art), is the number of welding machine data with a deviation index less than the deviation index threshold in this maximum accuracy index, is the minimum accuracy index of correct analysis in the past analysis results of the defect analysis model (whether it is incorrect is judged by those skilled in the art), is the number of welding machine data with a deviation index less than the deviation index threshold in this minimum accuracy index.

[0050] Specifically, the eccentricity coefficient is set by those skilled in the art according to their tolerance for misjudgment situations in the defect analysis model. If the staff believes that the severity of misjudging the correct result as an incorrect result is greater than that of misjudging the incorrect result as a correct result, D is set to 0.6. If the staff believes that the severity of misjudging the correct result as an incorrect result is less than that of misjudging the incorrect result as a correct result, D is set to 0.4. If the staff believes that the severity of misjudging the correct result as an incorrect result is equal to that of misjudging the incorrect result as a correct result, D is set to 0.5.

[0051] As Figure 4 shown, Figure 4 assuming D is 0.5, is 0.8, is 1.4, the figure shows the relationship between the accuracy index threshold, the number of relevant data with a deviation index less than the deviation index threshold in the incorrect items, and the number of relevant data with a deviation index less than the deviation index threshold in the correct items.

[0052] The beneficial effects of this embodiment: By comprehensively considering the incorrect item with the largest accuracy index and the correct item with the smallest accuracy index to set the accuracy index threshold, it is beneficial to obtain an accuracy index threshold with a smaller degree of bias when there is less historical data. The calculated accuracy index helps users determine whether to execute according to the generated improvement information, which is beneficial to improving the accuracy of judgment.

[0053] The content disclosed above is only the preferred feasible embodiment of the present invention, and does not limit the protection scope of the present invention. Therefore, all equivalent technical changes made by using the content of the specification and drawings of the present invention are included in the protection scope of the present invention. In addition, with the development of technology, the elements therein can be updated. The above units are only examples, and those skilled in the art can make different designs according to actual needs and adopt corresponding units when implementing this solution.

Claims

1. An intelligent pre-control system for electric welding machine with real-time monitoring function, characterized in that: It 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 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 judge 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.

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

3. The intelligent pre-control system for electric welding machine with real-time monitoring function according to claim 2 is 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 the welding machine data when various processes are completed and the problem data corresponding to various welding machine defects, wherein 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 that exist 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 the accuracy index 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 electric welding machine with real-time monitoring function according to claim 3 is 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 judge whether it is necessary to generate improvement information 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 it is necessary to generate improvement information and input the matching result as improvement information into the improvement information output unit. The improvement information output unit is used to fit the matching result into a set template and output the acquired improvement information through the template.

5. The intelligent pre-control system for electric welding machine with real-time monitoring function according to claim 4 is 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 result 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 instructions.

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

7. The intelligent pre-control system for electric welding machine with real-time monitoring function according to claim 6 is characterized in that: The workflow of the system includes the following steps: S1, the data acquisition module collects various data of the welding machine in working state; S2, the data processing module processes the data collected by the data acquisition module; S3, the fault analysis module analyzes whether the welding machine has defects according to the processed data, and sends the analysis results to the improvement information generation module; S4, the improvement information generation module determines whether it is necessary to generate improvement information, if so, generates improvement information and executes the next step, otherwise, returns to S1; S5, the user interaction module displays the generated improvement information, and the user inputs user instructions according to the improvement information and actual needs; S6, the user input unit receives the user instruction, and the remote control module remotely controls the welding machine according to the user instruction.

8. The intelligent pre-control system for electric welding machine with real-time monitoring function according to claim 7 is characterized in that: 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 electric welder in this work with the data in the process database to determine the current process type of the electric welder; S32, determining the deviation index of each corresponding data of the electric welding machine at the current moment and the identified process type at the corresponding moment; S33, judging whether the deviation indexes of various data of the electric welding machine at the current moment are all less than the deviation index threshold value, if they are all less than, there is no defect, and the process ends; otherwise, there is a defect, and the process proceeds to the next step; S34, the defect analysis model obtains the defect type corresponding to the data type from the process database according to the data type of the data whose deviation index is greater than the deviation index threshold; S35, the calculation unit calculates the accuracy index.

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