Intelligent supervision system of welding robot

Abstract

The invention discloses an intelligent supervision system for a welding robot, and the system comprises an equipment information collection module which is used for collecting the state information of equipment; the environment information acquisition module is used for acquiring operation environment information of the welding robot; the welding information acquisition module is used for acquiring welding information; the operator acquisition module is used for acquiring operator information; the data processing module is used for processing the equipment state information to obtain equipment supervision information; processing the operation environment information to obtain environment supervision information; processing the welding information to generate welding supervision information; processing the operator information to obtain personnel supervision information; the information sending module is used for generating the equipment supervision information, the environment supervision information, the welding supervision information and the personnel supervision information and then sending the information to a preset receiving terminal. According to the invention, the welding robot can be better supervised.

Description

Technical Field

[0001] The present invention relates to the field of monitoring systems, and in particular to an intelligent monitoring system for a welding robot. Background Art

[0002] With the rapid development of manufacturing toward intelligent and automated processes, welding robots are increasingly being used in modern industrial production. However, traditional welding robot systems face numerous challenges in practical applications, such as unstable welding quality, frequent equipment failures, poor environmental adaptability, and varying operator skill levels. These issues not only impact production efficiency but also increase costs and safety risks. Therefore, a monitoring system is needed to oversee welding robots and ensure both efficiency and safety.

[0003] The existing supervision system has a single supervision type and a low level of intelligence, which has a certain impact on the use of the supervision system. Therefore, an intelligent supervision system for welding robots is proposed. Summary of the Invention

[0004] The technical problem to be solved by the present invention is: how to solve the problem that the existing supervision system has a single supervision type and a low degree of intelligence, which has a certain impact on the use of the supervision system, and provides an intelligent supervision system for a welding robot.

[0005] The present invention solves the above-mentioned technical problems through the following technical solutions, which include: Device information collection module, used to collect device status information; Environmental information collection module, used to collect operating environment information of the welding robot; Welding information collection module, used to collect welding information; Operator collection module, used to collect operator information; The data processing module is used to process the equipment status information and obtain equipment supervision information; Process the operating environment information and obtain environmental supervision information; Process welding information and generate welding supervision information; Process operator information and obtain personnel supervision information; The information sending module is used to generate equipment supervision information, environmental supervision information, welding supervision information and personnel supervision information, and then send the above information to a preset receiving terminal.

[0006] Furthermore, the specific process of the device information collection module collecting device status information is as follows: The status information of the welding robot is collected in real time through sensors and communication interfaces, including the position, posture, speed, acceleration and temperature of the robotic arm.

[0007] Furthermore, the specific process of processing the device status information and obtaining the device supervision information is as follows: Extract the position of the robotic arm, which is the end position of the robotic arm; Calculate the deviation between the end position of the robot arm and the target position: , , ; in, 、 、 is the current end position of the robotic arm, 、 、 is the target position of the end of the robotic arm; Extract the posture of the robotic arm, which is the angle of each joint of the robotic arm, and calculate the deviation between the joint angle and the target angle; ,in is the angle of each joint of the current robotic arm, is the target angle, i=1, 2, ... n, n is the number of joints; Extract the velocity and acceleration, and calculate whether the absolute values ​​of the velocity and acceleration are abnormal; Continuously collect m times of velocity and m times of acceleration; Extract the number of times that the speed deviates from the standard speed by more than the preset range, and mark it as T1; Extract the number of accelerations whose deviations from the standard acceleration exceed the preset range, and mark it as T2; Continuously collect temperature m times, extract the number of temperatures whose deviation from the standard temperature exceeds the preset temperature range, and mark it as T3; when If the deviation between any one of the values ​​and the corresponding standard value exceeds the preset value, the device supervision information is generated, which is a position deviation warning; when If the value is greater than the preset value, the device supervision information is generated. In this case, the device supervision information is a posture deviation warning. When T1 is greater than the preset value, the device supervision information is generated. At this time, the device supervision information is a speed abnormality warning; When T2 is greater than the preset value, the device supervision information is generated. At this time, the device supervision information is an acceleration abnormality warning; When T3 is greater than the preset value, device supervision information is generated. At this time, the device supervision information is a temperature abnormality warning.

[0008] Furthermore, the specific process of the environmental information collection module for collecting environmental information is as follows: Collect the position information of the welding robots, mark the position information of the welding robots in the welding project plan, and select the position of the leftmost robot, the rightmost robot, the topmost robot, and the bottommost robot; Mark the position of the leftmost robot as point A1, the position of the rightmost robot as point A2, the position of the topmost robot as point A3, and the position of the bottommost robot as point A4; Connect point A1 and point A2 to obtain line segment L1, connect point A2 and point A3 to obtain line segment L2, connect point A3 and point A4 to obtain line segment L3, and connect point A4 and point A1 to obtain line segment L4; Line segments L1, L2, L3 and L4 enclose a monitoring area K; Measure the area of ​​the monitoring area K. When the area of ​​the area K is greater than the preset value a1, select at least four monitoring points; When the area of ​​region K is between the preset values ​​a1 and a2, at least three monitoring points are selected; When the area of ​​region K is smaller than the preset value a2, at least two monitoring points are selected; Each monitoring point is equipped with a temperature sensor, a humidity sensor and a harmful gas monitoring sensor; The distance between each monitoring point shall not be less than the preset value; Each monitoring point is set with the same collection interval. After collecting the preset time, the average value of the temperature information collected within the preset time, the average value of the humidity information and the average value of the harmful gas concentration are calculated; The mean of temperature information, the mean of humidity information and the mean of harmful gas concentration constitute environmental information.

[0009] Furthermore, the specific process of processing environmental information to generate environmental supervision information is as follows: Extract environmental information and obtain the average temperature information from it. When the average temperature information collected in a single time exceeds the preset warning value q1, environmental supervision information is generated. At this time, the content of the environmental supervision information is abnormal ambient temperature; Extract environmental information. When the average value of the temperature information collected multiple times exceeds the preset value q2 and reaches a preset threshold, environmental monitoring information is generated. At this time, the content of the environmental monitoring information is that the ambient temperature is abnormal. Extract environmental information and obtain the mean value of humidity information from it. When the mean value of humidity information collected in a single time exceeds the preset warning value e1, environmental supervision information is generated. At this time, the content of the environmental supervision information is abnormal environmental humidity; When the average value of the temperature information collected multiple times exceeds the preset value e2 for more times than the preset value, environmental supervision information is generated. At this time, the content of the environmental supervision information is abnormal ambient humidity; Extract environmental information and obtain the average concentration of harmful gases. When the average concentration of harmful gases is greater than a preset value, environmental supervision information is generated. In this case, the content of the environmental supervision information is that the environmental gas is abnormal. At the same time, the welding quality information under different temperatures and humidity is monitored in real time. The welding quality information includes the number of poor welds and the number of excellent welds. When the number of poor welds at any temperature value or humidity value exceeds the preset value, environmental supervision information is generated. At this time, the content of the environmental supervision information is to set a new temperature warning value or a new humidity warning value.

[0010] Furthermore, the specific process of processing the welding information to obtain the welding supervision information is as follows: Welding information is welding quality information, including welding quality abnormalities and welding quality normalities. Welding quality information of at least x products is randomly selected. When the number of abnormalities in the welding quality information of x products exceeds the preset value, welding supervision information is generated. Furthermore, the process of obtaining the welding quality information is as follows: Extract a welding product and first perform weld appearance quality inspection. Use an industrial camera to take real-time pictures of the weld to obtain the weld appearance image. Then, obtain the weld pixel width and weld pixel height from the weld appearance image. Weld seam width = pixel width ÷ pixel resolution; Weld seam height = pixel height ÷ pixel resolution; Then, the acoustic sensor collects the acoustic signal during the welding process, analyzes the frequency and intensity of the acoustic signal, and analyzes the spectrum of the acoustic signal through Fourier transform to identify abnormal frequency components; Then the energy spectral density of the acoustic signal is calculated; ; Where s(t) is the sound signal and f is the frequency; Then, the width and height of the weld are extracted, and the width and height of the weld are imported into a pre-established mapping set, and the corresponding score U of the width and height of the weld is retrieved from the mapping set; Then, the energy spectrum density P(f) is imported into the pre-established mapping set, and the corresponding energy spectrum density P(f) score K is retrieved from the mapping set; Assign U correction value G1, assign K correction value G2, G1+G2=1, G1>G2; The comprehensive score Uk is obtained through the formula U×G1+K×G2=Uk; When the comprehensive score Uk is greater than or equal to the preset value, it means that there is no abnormality in the welding quality.

[0011] Furthermore, the specific process of processing the operator information and obtaining the personnel supervision information is as follows: Extract operator information, which includes operator theoretical knowledge information and actual operation welding abnormality information; When the operator's theoretical knowledge information is less than the preset value for more than the preset number of times, personnel supervision information is generated; When the number of abnormal welding information in actual operation exceeds the preset number within the preset time, personnel supervision information is generated.

[0012] Furthermore, the process of obtaining the operator information is as follows: Operator theoretical knowledge information: Theoretical knowledge test questions are sent to operators regularly. Operators are required to answer within a preset time. After answering, they are automatically scored and a single theoretical score is obtained, which is the operator's theoretical knowledge information; The actual operation welding abnormality information means that the welding quality of the operator within the preset time period is greater than the preset value in terms of the welding abnormality probability.

[0013] Compared to existing technologies, this invention offers the following advantages: The welding robot's intelligent monitoring system improves welding quality stability. By collecting welding process parameters and quality information in real time, the system can quickly identify abnormalities during welding and issue timely warnings. For example, if the welding current or voltage exceeds a preset range, the system will immediately generate an abnormality warning, prompting the operator to make adjustments to avoid welding defects caused by parameter fluctuations. The system comprehensively scores weld quality and uses the results to determine whether the weld quality is abnormal. This data-based evaluation method can more objectively reflect weld quality, helping operators to promptly identify potential problems and optimize the welding process, thereby improving the stability and consistency of weld quality.

[0014] The system comprehensively monitors the welding robot's operating environment, including temperature, humidity, and hazardous gas concentrations. By strategically placing sensors within the monitoring area and dynamically adjusting the number of monitoring points based on the area, the system accurately captures environmental information, ensuring the welding process is carried out in a suitable environment. Prompt response to environmental anomalies: When environmental parameters exceed preset ranges, the system generates environmental monitoring information, alerting the operator to take appropriate action.

[0015] The system not only collects the operator's theoretical knowledge information, but also records any welding anomalies during actual operation. This comprehensive personnel information collection method can more accurately reflect the operator's actual operation level and knowledge mastery. By processing the operator's information, the system can generate personnel supervision information. When the operator's theoretical knowledge score is lower than the preset value, or the number of welding anomalies in actual operation is too many, the system will remind the relevant personnel to provide training or adjust their positions. This data-based personnel supervision method helps to improve the operator's operational standardization and reduce the impact of human factors on welding quality; To improve production efficiency and management, the system comprehensively processes collected information on equipment status, environment, welding, and personnel, generating detailed supervisory information. This information is sent in real time to a pre-set receiving terminal, providing comprehensive and accurate production data support for production managers, helping them make quick decisions, optimize production processes, and improve production efficiency, making the system even more worthy of widespread use. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0017] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process. However, the protection scope of the present invention is not limited to the following embodiment.

[0018] like Figure 1 As shown, this embodiment provides a technical solution: an intelligent monitoring system for a welding robot, comprising: Device information collection module, used to collect device status information; Environmental information collection module, used to collect operating environment information of the welding robot; Welding information collection module, used to collect welding information; Operator collection module, used to collect operator information; The data processing module is used to process the equipment status information and obtain equipment supervision information; Process the operating environment information and obtain environmental supervision information; Process welding information and generate welding supervision information; Process operator information and obtain personnel supervision information; The information sending module is used to generate equipment supervision information, environmental supervision information, welding supervision information and personnel supervision information, and then send the above information to a preset receiving terminal.

[0019] The specific process of the device information collection module collecting device status information is as follows: The status information of the welding robot is collected in real time through sensors and communication interfaces, including the position, posture, speed, acceleration and temperature of the robotic arm.

[0020] The specific process of processing device status information and obtaining device supervision information is as follows: Extract the position of the robotic arm, which is the end position of the robotic arm; Calculate the deviation between the end position of the robot arm and the target position: , , ; in, 、 、 is the current end position of the robotic arm, 、 、 is the target position of the end of the robotic arm; Extract the posture of the robotic arm, which is the angle of each joint of the robotic arm, and calculate the deviation between the joint angle and the target angle; ,in is the angle of each joint of the current robotic arm, is the target angle, i=1, 2, ... n, n is the number of joints; Extract the velocity and acceleration, and calculate whether the absolute values ​​of the velocity and acceleration are abnormal; Continuously collect m times of velocity and m times of acceleration; Extract the number of times that the speed deviates from the standard speed by more than the preset range, and mark it as T1; Extract the number of accelerations whose deviations from the standard acceleration exceed the preset range, and mark it as T2; Continuously collect temperature m times, extract the number of temperatures whose deviation from the standard temperature exceeds the preset temperature range, and mark it as T3; m≥10; when If the deviation between any one of the values ​​and the corresponding standard value exceeds the preset value, the device supervision information is generated, which is a position deviation warning; when If the value is greater than the preset value, the device supervision information is generated. In this case, the device supervision information is a posture deviation warning. When T1 is greater than the preset value, the device supervision information is generated. At this time, the device supervision information is a speed abnormality warning; When T2 is greater than the preset value, the device supervision information is generated. At this time, the device supervision information is an acceleration abnormality warning; When T3 is greater than the preset value, the device supervision information is generated. At this time, the device supervision information is a temperature abnormality warning; The above process improves the accuracy and reliability of equipment operation; By collecting the real-time position of the robot arm and the posture of each joint, and calculating the deviation from the target position and posture, the system can accurately monitor the robot arm's motion accuracy. This precise monitoring ensures that the welding robot operates according to the preset trajectory, reducing welding quality issues caused by position or posture deviations. Improve the stability and consistency of welding quality and reduce welding defects caused by robot arm motion errors, such as weld offset and lack of fusion; By continuously collecting speed and acceleration data and counting the number of times their deviations exceed preset limits (T1 and T2), the system can promptly detect anomalies in the robot's motion. For example, a sudden change in speed or acceleration could indicate a problem with the robot's drive system or an unexpected interference during the welding process. This allows for early detection of potential equipment failures, reducing the risk of damage, extending equipment life, and avoiding production interruptions caused by sudden failures. By continuously collecting temperature data and counting the number of times the temperature deviates from a preset range (T3), the system can monitor the thermal status of the robotic arm in real time. Abnormal temperatures may indicate motor overload, cooling system failure, or excessive ambient temperature. Promptly detecting temperature anomalies can prevent equipment damage or performance degradation caused by high temperatures and ensure that the equipment operates within a safe temperature range. The system uses sensors to collect real-time equipment status data and rapidly processes it to generate supervisory information (such as position deviation warnings and speed anomaly warnings). This information is fed back to operators or management systems in real time, helping them make quick decisions and enabling refined equipment management, reducing production losses caused by equipment failures or anomalies, and improving production efficiency.

[0021] The system can dynamically adjust equipment operating parameters or optimize maintenance plans based on real-time data. For example, if a speed anomaly is detected, the system can automatically adjust the welding speed or alert the operator to conduct an inspection. This improves the adaptability and flexibility of the equipment, optimizes its operating status, and reduces maintenance costs.

[0022] Improve the safety and stability of the production process. By monitoring equipment status in real time and generating warning messages, the system can proactively detect potential equipment failures. For example, when temperature anomalies or speed deviations exceed preset values, the system will issue an alert, prompting operators to conduct inspections and maintenance. This reduces the risk of sudden equipment failures, avoids production accidents caused by equipment failures, and improves the safety of the production process.

[0023] Reduce maintenance costs and downtime. Real-time monitoring of equipment status and the generation of alerts provide data support for preventive maintenance. Operators can plan maintenance based on the equipment's actual operating status, avoiding unexpected equipment failures, reducing downtime, lowering maintenance costs, and improving equipment availability and production efficiency. By promptly detecting and addressing equipment anomalies, the system reduces excessive wear and damage, extends equipment life, reduces equipment replacement frequency, and reduces equipment investment costs.

[0024] The specific process of the environmental information collection module for collecting environmental information is as follows: Collect the position information of the welding robots, mark the position information of the welding robots in the welding project plan, and select the position of the leftmost robot, the rightmost robot, the topmost robot, and the bottommost robot; Mark the position of the leftmost robot as point A1, the position of the rightmost robot as point A2, the position of the topmost robot as point A3, and the position of the bottommost robot as point A4; Connect point A1 and point A2 to obtain line segment L1, connect point A2 and point A3 to obtain line segment L2, connect point A3 and point A4 to obtain line segment L3, and connect point A4 and point A1 to obtain line segment L4; Line segments L1, L2, L3 and L4 enclose a monitoring area K; Measure the area of ​​the monitoring area K. When the area of ​​the area K is greater than the preset value a1, select at least four monitoring points; When the area of ​​region K is between the preset values ​​a1 and a2, at least three monitoring points are selected; When the area of ​​region K is smaller than the preset value a2, at least two monitoring points are selected; Each monitoring point is equipped with a temperature sensor, a humidity sensor and a harmful gas monitoring sensor; The distance between each monitoring point shall not be less than the preset value; Each monitoring point is set with the same collection interval. After collecting the preset time, the average value of the temperature information collected within the preset time, the average value of the humidity information and the average value of the harmful gas concentration are calculated; The mean of temperature information, the mean of humidity information and the mean of harmful gas concentration constitute environmental information.

[0025] Furthermore, the specific process of processing environmental information to generate environmental supervision information is as follows: Extract environmental information and obtain the average temperature information from it. When the average temperature information collected in a single time exceeds the preset warning value q1, environmental supervision information is generated. At this time, the content of the environmental supervision information is abnormal ambient temperature; Extract environmental information. When the average value of the temperature information collected multiple times exceeds the preset value q2 and reaches a preset threshold, environmental monitoring information is generated. At this time, the content of the environmental monitoring information is that the ambient temperature is abnormal. Extract environmental information and obtain the mean value of humidity information from it. When the mean value of humidity information collected in a single time exceeds the preset warning value e1, environmental supervision information is generated. At this time, the content of the environmental supervision information is abnormal environmental humidity; When the average value of the temperature information collected multiple times exceeds the preset value e2 for more times than the preset value, environmental supervision information is generated. At this time, the content of the environmental supervision information is abnormal ambient humidity; Extract environmental information and obtain the average concentration of harmful gases. When the average concentration of harmful gases is greater than a preset value, environmental supervision information is generated. In this case, the content of the environmental supervision information is that the environmental gas is abnormal. At the same time, the welding quality information under different temperatures and humidity levels is monitored in real time. The welding quality information includes the number of poor welds and the number of excellent welds. When the number of poor welds at any temperature or humidity value exceeds the preset value, environmental supervision information is generated. At this time, the content of the environmental supervision information is to set a new temperature warning value or a new humidity warning value; This process improves the stability of welding quality. By monitoring welding quality information (including the number of poor and excellent welds) under different temperature and humidity conditions in real time, the system can analyze the specific impact of environmental parameters on welding quality. For example, if the number of poor welds under certain temperature or humidity conditions exceeds a preset value, the system will generate an environmental monitoring message, indicating that the environmental warning value needs to be adjusted. This data-based correlation analysis can help optimize the welding environment, reduce welding quality problems caused by environmental factors, and thus improve the stability and consistency of welding quality.

[0026] When it is detected that the welding quality is greatly affected by environmental parameters, the system will recommend setting new temperature or humidity warning values ​​to adapt to actual production needs.

[0027] The dynamic adjustment mechanism can better adapt to changes in the production environment, ensure that the welding process is always under the best environmental conditions, and further improve the welding quality.

[0028] Improve the safety of the production process, monitor the concentration of harmful gases in real time, and immediately generate environmental supervision information to indicate abnormal environmental gases when the concentration exceeds the preset value.

[0029] Real-time monitoring and early warning functions can promptly detect the leakage or accumulation of harmful gases, remind operators to take ventilation or other safety measures, reduce the harm of harmful gases to the health of operators, and improve the safety of the production process.

[0030] Improve the flexibility and adaptability of environmental monitoring. By dividing the monitoring area according to the location information of the welding robot and dynamically adjusting the number of monitoring points according to the area of ​​the area, the system can flexibly adapt to welding production lines of different sizes and layouts. Dynamic division and adjustment mechanisms improve the flexibility and adaptability of environmental monitoring, ensuring the rational allocation of monitoring resources while avoiding resource waste due to insufficient or excessive monitoring points. Optimization of monitoring point spacing and collection intervals; The system requires that the distance between monitoring points must not be less than the preset value and that the same collection interval be set to ensure monitoring continuity and data stability. The optimization mechanism can improve the accuracy and reliability of monitoring data and reduce data fluctuations caused by too small distances between monitoring points or inconsistent collection intervals. Improve monitoring efficiency and economy, reasonably arrange monitoring points, and dynamically adjust the number of monitoring points according to the size of the monitoring area to ensure that unnecessary monitoring points are reduced while meeting monitoring needs; Reasonable arrangement of monitoring points can reduce monitoring costs, improve monitoring efficiency, and ensure the comprehensiveness and accuracy of monitoring; The system calculates the average of the temperature, humidity and harmful gas concentration collected within a preset time period, reduces the impact of instantaneous data fluctuations, improves the stability of monitoring data, improves the reliability of monitoring data, reduces false alarms and missed alarms, and further improves monitoring efficiency.

[0031] The environmental information collection and processing described above significantly improves welding quality stability, production process safety, and the flexibility and adaptability of environmental monitoring through dynamic division of monitoring areas, rational placement of monitoring points, real-time monitoring of environmental parameters, and analysis of the relationship between environmental parameters and welding quality. This also reduces monitoring costs and improves monitoring efficiency. This refined, data-based monitoring and management approach provides strong support for efficient and intelligent welding production.

[0032] The specific process of processing welding information to obtain welding supervision information is as follows: Welding information is welding quality information, including welding quality abnormalities and welding quality normalities. Welding quality information of at least x products is randomly selected. When the number of abnormalities in the welding quality information of x products exceeds the preset value, welding supervision information is generated. The process of obtaining the welding quality information is as follows: Extract a welding product and first perform weld appearance quality inspection. Use an industrial camera to take real-time pictures of the weld to obtain the weld appearance image. Then, obtain the weld pixel width and weld pixel height from the weld appearance image. Weld seam width = pixel width ÷ pixel resolution; Weld seam height = pixel height ÷ pixel resolution; Then, the acoustic sensor collects the acoustic signal during the welding process, analyzes the frequency and intensity of the acoustic signal, and analyzes the spectrum of the acoustic signal through Fourier transform to identify abnormal frequency components; Then the energy spectral density of the acoustic signal is calculated; ; Where s(t) is the sound signal and f is the frequency; Then, the width and height of the weld are extracted, and the width and height of the weld are imported into a pre-established mapping set, and the corresponding score U of the width and height of the weld is retrieved from the mapping set; Then, the energy spectrum density P(f) is imported into the pre-established mapping set, and the corresponding energy spectrum density P(f) score K is retrieved from the mapping set; Assign U correction value G1, assign K correction value G2, G1+G2=1, G1>G2; The comprehensive score Uk is obtained through the formula U×G1+K×G2=Uk; When the comprehensive score Uk is greater than or equal to the preset value, it means that there is no abnormality in the welding quality; Improve the accuracy of welding quality inspections by combining weld appearance inspection (capturing weld images with industrial cameras) and acoustic signal analysis (collecting acoustic signals with acoustic sensors) to comprehensively assess weld quality from both visual and acoustic perspectives. This multi-dimensional inspection approach can more comprehensively identify potential issues in the welding process. Compared to single-method inspections, multi-dimensional inspection can significantly improve the accuracy and reliability of welding quality inspections, reducing misjudgments and missed detections.

[0033] Precise image processing and analysis: By calculating the pixel width and height of the weld and converting them into actual dimensions, the weld's geometric parameters can be accurately measured. This precise measurement method can effectively determine whether the weld size meets process requirements.

[0034] Improve the accuracy of weld appearance quality inspections and reduce quality issues caused by substandard weld dimensions. Acoustic signal spectrum analysis: Analyze the acoustic signal spectrum using Fourier transform, identify abnormal frequency components, and calculate the energy spectral density of the acoustic signal. This spectrum analysis method can detect internal defects (such as porosity and lack of fusion) that may occur during welding.

[0035] Improve the ability to detect internal welding quality, discover potential defects in time, and ensure welding quality.

[0036] To improve the scientific nature of welding quality assessment, the weld appearance score (U) and the acoustic signal score (K) are combined and weighted by assigning different correction values ​​(G1 and G2) to obtain a comprehensive score (Uk). This comprehensive scoring mechanism can more scientifically reflect the overall level of welding quality.

[0037] Compared with single indicator evaluation, the comprehensive scoring mechanism can reflect the welding quality more comprehensively and avoid misjudgment due to the randomness of a single indicator.

[0038] By setting G1 > G2, the importance of weld appearance quality in the comprehensive scoring process is emphasized, while also taking into account the acoustic signal evaluation results. This weighting adjustment mechanism allows for flexible adjustment of scoring criteria based on actual production needs. Technical Effect: Improves the flexibility and adaptability of welding quality assessments, ensuring that assessment results meet actual production needs.

[0039] Improve production efficiency and management. Randomly select at least x products for welding quality assessment. When the number of abnormalities exceeds a preset value, welding supervision information is generated. This random sampling method reduces inspection workload while ensuring inspection coverage. Technical Effect: Improves inspection efficiency and reduces inspection costs while ensuring the overall quality level of the production process. When the comprehensive score Uk falls below the preset value, welding supervision information is immediately generated to alert operators to make adjustments. This real-time feedback mechanism promptly identifies quality issues, reduces production delays caused by quality issues, improves production process response speed, and reduces the impact of quality issues on production efficiency.

[0040] The use of mapping sets allows weld dimensions and acoustic signal energy spectral density to be imported into pre-established mapping sets, allowing for rapid retrieval of corresponding scores. This enables rapid and accurate quality assessment, improves data processing efficiency, reduces manual intervention, and ensures objectivity and consistency in assessment results.

[0041] Based on the comprehensive score (Uk), preset welding quality values ​​are dynamically adjusted. This dynamic adjustment mechanism optimizes quality standards based on actual production conditions and improves production management. It provides data-driven decision support, helping managers optimize production processes and improve welding quality based on actual data.

[0042] By comprehensively evaluating weld appearance and acoustic signals, abnormalities in the welding process can be promptly identified and optimized. This comprehensive evaluation method can effectively reduce the frequency of welding quality issues, improve the stability of welding quality, and reduce rework and scrap caused by quality issues.

[0043] The specific process of processing operator information and obtaining personnel supervision information is as follows: Extract operator information, which includes operator theoretical knowledge information and actual operation welding abnormality information; When the operator's theoretical knowledge information is less than the preset value for more than the preset number of times, personnel supervision information is generated; When the number of abnormal welding information in actual operation exceeds the preset number within the preset time, personnel supervision information is generated.

[0044] The process of obtaining the operator information is as follows: Operator theoretical knowledge information: Theoretical knowledge test questions are sent to operators regularly. Operators are required to answer within a preset time. After answering, they are automatically scored and a single theoretical score is obtained, which is the operator's theoretical knowledge information; The actual operation welding abnormality information means that the welding quality of the operator within the preset time is greater than the preset value. Improving operators' professionalism by regularly sending theoretical knowledge tests and automatically scoring them allows for timely monitoring of operators' mastery of welding theory. This approach identifies weaknesses in operators' knowledge, helping them to promptly supplement and consolidate their theoretical knowledge, improve their professionalism, and thus better guide practical operations. When an operator's theoretical knowledge score falls below a preset value for more than a preset number of times, a personnel supervision message is generated. This dynamic feedback mechanism alerts managers to operators' knowledge gaps and arranges timely training. Through targeted training, operators' theoretical knowledge level is improved, reducing operational errors caused by insufficient knowledge.

[0045] Improve welding quality stability and assess actual operating performance: By monitoring the operator's welding anomaly rate within a preset time period, it can directly reflect their actual operating level. When the welding anomaly rate exceeds the preset value, a personnel supervision message is generated, reminding management to pay attention to the operator's actual operating performance, promptly identify problems in the operator's actual operation, reduce welding quality issues caused by improper operation, and improve welding quality stability.

[0046] Evaluating operators' theoretical knowledge and practical performance simultaneously provides a more comprehensive reflection of their overall capabilities. This comprehensive evaluation ensures that operators not only possess a solid theoretical foundation but can also apply it in actual operations, improving overall welding quality and reducing quality fluctuations caused by insufficient operator skills.

[0047] To improve production efficiency and management, the system generates real-time personnel supervision information, alerting managers to take timely action. This real-time feedback mechanism can quickly identify potential problems, reduce production delays caused by operator problems, improve production process response speed, and reduce the impact of quality issues on production efficiency.

[0048] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0049] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0050] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. An intelligent monitoring system for a welding robot, characterized in that: include: Device information collection module, used to collect device status information; Environmental information collection module, used to collect operating environment information of the welding robot; Welding information collection module, used to collect welding information; Operator collection module, used to collect operator information; The data processing module is used to process the equipment status information and obtain equipment supervision information; Process the operating environment information and obtain environmental supervision information; Process welding information and generate welding supervision information; Process operator information and obtain personnel supervision information; The information sending module is used to generate equipment supervision information, environmental supervision information, welding supervision information and personnel supervision information, and then send the above information to a preset receiving terminal.

2. The intelligent monitoring system for a welding robot according to claim 1, characterized in that: The specific process of the device information collection module collecting device status information is as follows: The status information of the welding robot is collected in real time through sensors and communication interfaces, including the position, posture, speed, acceleration and temperature of the robotic arm.

3. The intelligent monitoring system for a welding robot according to claim 2, characterized in that: The specific process of processing device status information and obtaining device supervision information is as follows: Extract the position of the robotic arm, which is the end position of the robotic arm; Calculate the deviation between the end position of the robot arm and the target position: ， ， ； in, 、 、 is the current end position of the robotic arm, 、 、 is the target position of the end of the robotic arm; Extract the posture of the robotic arm, which is the angle of each joint of the robotic arm, and calculate the deviation between the joint angle and the target angle; ,in is the angle of each joint of the current robotic arm, is the target angle, i=1, 2, ... n, n is the number of joints; Extract the velocity and acceleration, and calculate whether the absolute values ​​of the velocity and acceleration are abnormal; Continuously collect m times of velocity and m times of acceleration; Extract the number of times that the speed deviates from the standard speed by more than the preset range, and mark it as T1; Extract the number of accelerations whose deviations from the standard acceleration exceed the preset range, and mark it as T2; Continuously collect temperature m times, extract the number of temperatures whose deviation from the standard temperature exceeds the preset temperature range, and mark it as T3; when If the deviation between any one of the values ​​and the corresponding standard value exceeds the preset value, the device supervision information is generated, which is a position deviation warning; when If the value is greater than the preset value, the device supervision information is generated. In this case, the device supervision information is a posture deviation warning. When T1 is greater than the preset value, the device supervision information is generated. At this time, the device supervision information is a speed abnormality warning; When T2 is greater than the preset value, the device supervision information is generated. At this time, the device supervision information is an acceleration abnormality warning; When T3 is greater than the preset value, device supervision information is generated. At this time, the device supervision information is a temperature abnormality warning.

4. The intelligent monitoring system for a welding robot according to claim 1, characterized in that: The specific process of the environmental information collection module for collecting environmental information is as follows: Collect the position information of the welding robots, mark the position information of the welding robots in the welding project plan, and select the position of the leftmost robot, the rightmost robot, the topmost robot, and the bottommost robot; Mark the position of the leftmost robot as point A1, the position of the rightmost robot as point A2, the position of the topmost robot as point A3, and the position of the bottommost robot as point A4; Connect point A1 and point A2 to obtain line segment L1, connect point A2 and point A3 to obtain line segment L2, connect point A3 and point A4 to obtain line segment L3, and connect point A4 and point A1 to obtain line segment L4; Line segments L1, L2, L3, and L4 enclose a monitoring area K; Measure the area of ​​the monitoring area K. When the area of ​​the area K is greater than the preset value a1, select at least four monitoring points; When the area of ​​region K is between the preset values ​​a1 and a2, at least three monitoring points are selected; When the area of ​​region K is smaller than the preset value a2, at least two monitoring points are selected; Each monitoring point is equipped with a temperature sensor, a humidity sensor and a harmful gas monitoring sensor; The distance between each monitoring point shall not be less than the preset value; Each monitoring point is set with the same collection interval. After collecting the preset time, the average value of the temperature information collected within the preset time, the average value of the humidity information and the average value of the harmful gas concentration are calculated; The mean of temperature information, the mean of humidity information and the mean of harmful gas concentration constitute environmental information.

5. The intelligent monitoring system for a welding robot according to claim 4, characterized in that: The specific process of processing environmental information to generate environmental supervision information is as follows: Extract environmental information and obtain the average temperature information from it. When the average temperature information collected in a single time exceeds the preset warning value q1, environmental supervision information is generated. At this time, the content of the environmental supervision information is abnormal ambient temperature; Extract environmental information. When the average value of the temperature information collected multiple times exceeds the preset value q2 and reaches a preset threshold, environmental monitoring information is generated. At this time, the content of the environmental monitoring information is that the ambient temperature is abnormal. Extract environmental information and obtain the mean value of humidity information from it. When the mean value of humidity information collected in a single time exceeds the preset warning value e1, environmental supervision information is generated. At this time, the content of the environmental supervision information is abnormal environmental humidity; When the average value of the temperature information collected multiple times exceeds the preset value e2 for more times than the preset value, environmental supervision information is generated. At this time, the content of the environmental supervision information is abnormal ambient humidity; Extract environmental information and obtain the average concentration of harmful gases. When the average concentration of harmful gases is greater than a preset value, environmental supervision information is generated. In this case, the content of the environmental supervision information is that the environmental gas is abnormal. At the same time, the welding quality information under different temperatures and humidity is monitored in real time. The welding quality information includes the number of poor welds and the number of excellent welds. When the number of poor welds at any temperature value or humidity value exceeds the preset value, environmental supervision information is generated. At this time, the content of the environmental supervision information is to set a new temperature warning value or a new humidity warning value.

6. The intelligent monitoring system for a welding robot according to claim 1, characterized in that: The specific process of processing welding information to obtain welding supervision information is as follows: The welding information is welding quality information, including welding quality abnormalities and welding quality normalities. The welding quality information of at least x products is randomly selected. When the number of abnormalities in the welding quality information of x products exceeds a preset value, welding supervision information is generated.

7. The intelligent monitoring system for a welding robot according to claim 6, characterized in that: The process of obtaining the welding quality information is as follows: Extract a welding product and first perform weld appearance quality inspection. Use an industrial camera to take real-time pictures of the weld to obtain the weld appearance image. Then, obtain the weld pixel width and weld pixel height from the weld appearance image. Weld seam width = pixel width ÷ pixel resolution; Weld seam height = pixel height ÷ pixel resolution; Then, the acoustic sensor collects the acoustic signal during the welding process, analyzes the frequency and intensity of the acoustic signal, and analyzes the spectrum of the acoustic signal through Fourier transform to identify abnormal frequency components; Then the energy spectral density of the acoustic signal is calculated; ； Where s(t) is the sound signal and f is the frequency; Then, the width and height of the weld are extracted, and the width and height of the weld are imported into a pre-established mapping set, and the corresponding score U of the width and height of the weld is retrieved from the mapping set; Then, the energy spectrum density P(f) is imported into the pre-established mapping set, and the corresponding energy spectrum density P(f) score K is retrieved from the mapping set; Assign U correction value G1, assign K correction value G2, G1+G2=1, G1>G2; The comprehensive score Uk is obtained through the formula U×G1+K×G2=Uk; When the comprehensive score Uk is greater than or equal to the preset value, it means that there is no abnormality in the welding quality.

8. The intelligent monitoring system for a welding robot according to claim 1, characterized in that: The specific process of processing operator information and obtaining personnel supervision information is as follows: Extract operator information, which includes operator theoretical knowledge information and actual operation welding abnormality information; When the operator's theoretical knowledge information is less than the preset value for more than the preset number of times, personnel supervision information is generated; When the number of abnormal welding information in actual operation exceeds the preset number within the preset time, personnel supervision information is generated.

9. The intelligent monitoring system for a welding robot according to claim 8, characterized in that: The process of obtaining the operator information is as follows: Operator theoretical knowledge information: Theoretical knowledge test questions are sent to operators regularly. Operators are required to answer within a preset time. After answering, they are automatically scored and a single theoretical score is obtained, which is the operator's theoretical knowledge information; The actual operation welding abnormality information means that the welding quality of the operator within the preset time period is greater than the preset value in terms of the welding abnormality probability.

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