Detection Method and System for Equipment Environment Based on Fiber Bragg Grating Sensors

Abstract

The present invention discloses a detection method and system for the equipment environment based on fiber Bragg grating sensors. The present invention relates to the technical field of detection methods for equipment environments. The comprehensive environment of the equipment is determined based on the internal production environment of the equipment, the production images of the equipment, and the external environment of the equipment. The internal production environment of the equipment, the production images of the equipment, and the external environment of the equipment are overall managed, achieving precise detection of the comprehensive environment of the equipment and improving the accuracy of the comprehensive environment of the equipment. Therefore, the production level of the equipment is determined based on the detection of the comprehensive environment of the equipment and the real-time images of the materials produced by the equipment; if the production level of the equipment is lower than the preset production level threshold, the abnormal part within the equipment is determined based on the abnormal detection of the equipment, and the autonomous regulation of the abnormal part is triggered, realizing the autonomous regulation of the abnormal part and fully considering the production level of the equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of detection methods for equipment environments, and particularly to a detection method and system for equipment environments based on fiber Bragg grating sensors. Background Art

[0002] With the development of technology, equipment, as one of the cores of production materials, is applied in various factories. Automated equipment is also a type of equipment. In the prior art, the internal environment of the equipment is monitored in real time, and the internal images of the equipment are collected. Based on the recognition of the internal images of the equipment, the comprehensive environment of the equipment is determined. However, relying solely on the internal images of the equipment to determine the comprehensive environment of the equipment results in low accuracy of the comprehensive environment of the equipment. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art. The present invention provides a detection method and system for equipment environments based on fiber Bragg grating sensors.

[0004] An embodiment of the present invention provides a detection method for equipment environments based on fiber Bragg grating sensors, including:

[0005] Determining the working state of the equipment based on the current working mode of the equipment, multiple working parameters of the equipment, and the aging level of the equipment;

[0006] If the working state of the equipment is in the online working state, then determine the internal production environment of the equipment according to the three-dimensional model of the equipment, the spatial positions of each fiber Bragg grating sensor relative to the equipment, and the sensing signals of each fiber Bragg grating sensor;

[0007] Determining the comprehensive environment of the equipment based on the internal production environment of the equipment, the production images of the equipment, and the external environment of the equipment;

[0008] Determining the production level of the equipment according to the detection of the comprehensive environment of the equipment and the real-time images of the materials produced by the equipment;

[0009] If the production level of the equipment is lower than the preset production level threshold, then determine the abnormal part inside the equipment based on the abnormal detection of the equipment, and trigger the autonomous regulation of the abnormal part.

[0010] An embodiment of the present invention provides a detection system for equipment environments based on fiber Bragg grating sensors. The detection system for equipment environments based on fiber Bragg grating sensors is applied to the above detection method for equipment environments based on fiber Bragg grating sensors. The detection system for equipment environments based on fiber Bragg grating sensors includes:

[0011] A working state module, configured to determine the working state of the equipment based on the current working mode of the equipment, multiple working parameters of the equipment, and the aging level of the equipment;

[0012] Internal production environment module, which is used to determine the internal production environment of the device according to the three-dimensional model of the device, the spatial positions of each fiber Bragg grating sensor relative to the device, and the sensing signals of each fiber Bragg grating sensor if the working state of the device is in the online working state;

[0013] Comprehensive environment module, which is used to determine the comprehensive environment of the device based on the internal production environment of the device, the production image of the device, and the external environment of the device;

[0014] Production level module, which is used to determine the production level of the device according to the comprehensive environment of the device and the detection of the real-time image of the material produced by the device;

[0015] Abnormal detection module, which is used to determine the abnormal part in the device based on the abnormal detection of the device and trigger the autonomous regulation of the abnormal part if the production level of the device is lower than the preset production level threshold.

[0016] Compared with the prior art, the beneficial effects of the present invention are:

[0017] In the embodiment of the present invention, through the method in the embodiment of the present invention, the working state of the device is determined based on the current working mode of the device, multiple working parameters of the device, and the aging level of the device; if the working state of the device is in the online working state, the internal production environment of the device is determined according to the three-dimensional model of the device, the spatial positions of each fiber Bragg grating sensor relative to the device, and the sensing signals of each fiber Bragg grating sensor; the comprehensive environment of the device is determined based on the internal production environment of the device, the production image of the device, and the external environment of the device, realizing the multiple interactions of the internal production environment of the device, the production image of the device, and the external environment of the device, overall controlling the internal production environment of the device, the production image of the device, and the external environment of the device, realizing the accurate detection of the comprehensive environment of the device, and improving the accuracy of the comprehensive environment of the device.

[0018] Therefore, the production level of the device is determined according to the comprehensive environment of the device and the detection of the real-time image of the material produced by the device; if the production level of the device is lower than the preset production level threshold, the abnormal part in the device is determined based on the abnormal detection of the device, and the autonomous regulation of the abnormal part is triggered, realizing the autonomous regulation of the abnormal part and fully considering the production level of the device. Description of the Drawings

[0019] Figure 1 It is a schematic flow chart of the method for detecting the device environment based on fiber Bragg grating sensors in the embodiment of the present invention;

[0020] Figure 2It is a schematic flowchart of step S11 in the method for detecting the device environment based on fiber Bragg grating sensors in an embodiment of the present invention;

[0021] Figure 3 It is a schematic flowchart of step S12 in the method for detecting the device environment based on fiber Bragg grating sensors in an embodiment of the present invention;

[0022] Figure 4 It is a schematic flowchart of step S13 in the method for detecting the device environment based on fiber Bragg grating sensors in an embodiment of the present invention;

[0023] Figure 5 It is a schematic flowchart of step S14 in the method for detecting the device environment based on fiber Bragg grating sensors in an embodiment of the present invention;

[0024] Figure 6 It is a schematic flowchart of step S15 in the method for detecting the device environment based on fiber Bragg grating sensors in an embodiment of the present invention;

[0025] Figure 7 It is a schematic diagram of the structural composition of the device environment detection system based on fiber Bragg grating sensors in an embodiment of the present invention. Detailed implementation manners

[0026] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention.

[0027] Please refer to Figures 1 to 7 , a method for detecting the device environment based on fiber Bragg grating sensors, which is applied to the detection scenario of the device environment based on fiber Bragg grating sensors; the method for detecting the device environment based on fiber Bragg grating sensors includes:

[0028] Step S11: Determine the working state of the device based on the current working mode of the device, multiple working parameters of the device, and the aging level of the device;

[0029] Step S12: If the working state of the device is in the online working state, determine the internal production environment of the device according to the three-dimensional model of the device, the spatial positions of each fiber Bragg grating sensor relative to the device, and the sensing signals of each fiber Bragg grating sensor;

[0030] Step S13: Determine the comprehensive environment of the device based on the internal production environment of the device, the production image of the device, and the external environment of the device;

[0031] Step S14: Determine the production level of the device according to the comprehensive environment of the device and the detection of the real-time image of the materials produced by the device;

[0032] Step S15: If the production level of the device is lower than the preset production level threshold, determine the abnormal part within the device based on the anomaly detection of the device, and trigger the autonomous regulation of the abnormal part.

[0033] Reference Figure 2 , in step S11, determine the working state of the device based on the current working mode of the device, multiple working parameters of the device, and the aging level of the device.

[0034] In the specific implementation process of the present invention, the specific steps are as follows:

[0035] S111: Collect the model information of the device, determine the current working mode of the device according to the model information of the device, the working signal of the device, and the working mode matching table, and dynamically collect multiple working parameters of the device.

[0036] S112: Determine the aging level of the device according to the production time of the device, the working record of the device, and the external form of the device.

[0037] S113: In the multiple interactions of the current working mode of the device, multiple working parameters of the device, and the aging level of the device, determine the first state coefficient based on the current working mode of the device and multiple working parameters of the device, determine the second state coefficient based on the current working mode of the device and the aging level of the device, and determine the working state of the device according to the first state coefficient, the second state coefficient, and the preset working state matching table. The working state of the device includes the online working state, the mid-way standby state, or the stop state.

[0038] In the embodiment of the present application, collect the model information of the device, determine the current working mode of the device according to the model information of the device, the working signal of the device, and the working mode matching table, and dynamically collect multiple working parameters of the device, which takes into account the overall consideration of the model information of the device, the working signal of the device, and the working mode matching table, and ensures the accuracy of the current working mode of the device.

[0039] At this time, collect the model information of the device, read the model information from the physical label or electronic label of the device. At the same time, access the working mode matching table, which is stored in the control system of the device or the remote server; search for the entry in the table that matches the device model and the working signal; read the device working mode information in the matching entry, and determine the list of working parameters to be collected according to the current working mode of the device; through the sensor or communication interface of the device, read the values of these working parameters in real time; store the read working parameter values in the local or remote database for subsequent analysis.

[0040] Specifically, assume there is an injection molding machine on an automated production line, and it is necessary to determine its current working mode and collect working parameters; the model information read from the physical label of the injection molding machine is "XYZ-1234"; accessing the working mode matching table of the injection molding machine, it is found that when the device of model "XYZ-1234" receives specific working signals (such as start signal, pause signal, etc.), it will enter different working modes; currently, the injection molding machine is receiving a stable operation signal, and the matching working mode is found to be "continuous production mode".

[0041] According to the "continuous production mode" of the injection molding machine, determine that the working parameters to be collected include injection temperature, injection pressure, injection time, mold temperature, etc.; through the sensor interface of the injection molding machine, read the values of these working parameters in real time. For example, the injection temperature is 200°C, the injection pressure is 50 MPa, the injection time is 10 seconds, and the mold temperature is 60°C; store these working parameter values in a remote database for subsequent analysis and optimization of the performance of the injection molding machine.

[0042] Furthermore, determine the aging level of the device based on the production time of the device, the working records of the device, and the external form of the device, taking into account the overall production time, working records, and external form of the device, ensuring the accuracy of the aging level of the device.

[0043] At this time, obtain the production date of the device as the basic data for evaluating the aging degree of the device; consult the factory records or labels of the device, and this information will include the production date or serial number of the device; at the same time, understand the usage history of the device, including working time, working load, fault records, etc., to evaluate the wear and aging of the device.

[0044] Access the maintenance records or log systems of the device, and these records contain the historical working data of the device; analyze the working time of the device, especially the continuous working time and the cumulative working time, to evaluate the fatigue degree of the device; check the fault records of the device, especially the faults that occur repeatedly or the components that need frequent repair, which are signs of device aging; evaluate the working load of the device, such as working pressure, temperature range, vibration level, etc., to determine whether the device has experienced excessive use or a harsh working environment.

[0045] Visually inspect the appearance and physical state of the device to identify signs of aging such as wear, corrosion, and deformation. At this time, conduct a comprehensive visual inspection of the device, including the shell, connecting parts, display screen, buttons, etc.; check whether the device has obvious scratches, dents, rust or corrosion signs; observe whether the connecting parts of the device are loose or damaged, and whether the cables and lines are aged or worn; check whether the display screen or indicator lights of the device are working properly, and whether the buttons or control panels are responsive.

[0046] Based on the production time and usage time of the equipment, preliminarily estimate the age and expected life of the equipment; combine the work records and failure history of the equipment to evaluate the wear degree and potential risks of the equipment; according to the results of the external form inspection of the equipment, identify any obvious signs of aging or potential problems; comprehensively consider the above information and use the preset aging level evaluation criteria or models to determine the aging level of the equipment.

[0047] Specifically, assume there is a numerically controlled machine tool that has been used for 5 years and its aging level needs to be evaluated; check the factory records of the numerically controlled machine tool and find that its production date is 2018; access the maintenance log system of the numerically controlled machine tool and find that the equipment has accumulated more than 4,000 hours of work in the past 5 years and has experienced several minor failures, mainly tool wear and coolant leakage problems; the working load of the equipment is relatively stable and mainly operates within the normal working temperature and pressure range.

[0048] Conduct a comprehensive visual inspection of the numerically controlled machine tool and find that there are some slight scratches and dents on its outer shell, but the overall structure is still firm; the connecting parts and cables look a bit aged, but there are no obvious breaks or damages; the display screen and control panel work normally and the buttons respond sensitively.

[0049] Combining the production time (5 years), cumulative working time (more than 4,000 hours), failure history (minor failures, mainly wear of vulnerable parts) of the equipment and the results of the external form inspection (slight scratches and dents, aging of connecting parts and cables), evaluate the aging level of the numerically controlled machine tool as "moderate aging"; this means that although the equipment can still operate, it requires more frequent maintenance and inspection, especially for the replacement and repair of vulnerable parts and connecting parts.

[0050] Therefore, in the multiple interactions of the current working mode of the equipment, multiple working parameters of the equipment, and the aging level of the equipment, determine the first state coefficient based on the current working mode of the equipment and multiple working parameters of the equipment, determine the second state coefficient based on the current working mode of the equipment and the aging level of the equipment, and determine the working state of the equipment according to the first state coefficient, the second state coefficient, and the preset working state matching table. The working state of the equipment includes the online working state, the mid-way standby state, or the stop state, which takes into account the overall consideration of the first state coefficient, the second state coefficient, and the preset working state matching table to ensure the accuracy of the working state of the equipment.

[0051] At this time, conduct multiple interactions on the current working mode of the equipment, multiple working parameters of the equipment, and the aging level of the equipment, and introduce the first state coefficient and the second state coefficient.

[0052] For the first state coefficient, based on the current working mode of the device and multiple working parameters of the device, determine the first state coefficient. At this time, according to the current working mode of the device, select a set of key working parameters related thereto; perform normalization processing on each working parameter so that its value falls within a preset range (such as 0 - 1); according to the normalized working parameter values, calculate a comprehensive score or coefficient, that is, the first state coefficient; the first state coefficient is obtained by weighted summation.

[0053] For the second state coefficient, based on the current working mode of the device and the aging level of the device, determine the second state coefficient. At this time, according to the current working mode and aging level of the device, select a set of aging indicators related thereto; perform quantitative evaluation on each aging indicator to obtain a quantitative value; according to the quantitative value, calculate a comprehensive score or coefficient, that is, the second state coefficient; the calculation method of this coefficient is similar to that of the first state coefficient, but the considered factors are different.

[0054] Furthermore, combine the first state coefficient and the second state coefficient and a preset working state matching table to determine the current working state of the device. At this time, access the preset working state matching table, which contains the device working states corresponding to different combinations of state coefficients; compare the calculated first state coefficient and second state coefficient with the values in the matching table; according to the closest matching result, determine the current working state of the device, such as the online working state, the mid - way standby state or the stop state.

[0055] Specifically, suppose there is a cooling system in a data center and it is necessary to determine its current working state; for the first state coefficient, the current working mode is the "normal operation mode"; the key working parameters include the temperature, flow rate and pressure of the cooling medium; perform normalization processing on these parameters. For example, the temperature of the cooling medium is normalized to 0.8 (indicating that the current temperature is at the 80% position within the preset range); through the weighted average method, the calculated first state coefficient is 0.9 (indicating that the device operates well in the current working mode).

[0056] For the second state coefficient, the aging level of the device is "moderate aging"; the aging indicators include the energy efficiency ratio, leakage rate and vibration level of the cooling system; perform quantitative evaluation on these indicators. For example, the energy efficiency ratio has decreased by 10%, the leakage rate has increased slightly, and the vibration level is within the normal range; through comprehensive evaluation, the calculated second state coefficient is 0.7 (indicating that the performance of the device has decreased due to aging).

[0057] Access the preset working status matching table; in the working status matching table, find the matching item that is closest to the first status coefficient of 0.9 and the second status coefficient of 0.7; according to the matching result, determine that the current working status of the device is "online working status, but pay attention to the impact of aging"; this means that although the device is still running normally, due to the impact of aging, more frequent maintenance and inspections are required to ensure its continuous and stable operation.

[0058] In an embodiment of the present application, assume there is a generator whose current working mode is "normal operation mode", the key working parameters include output voltage, output current and oil temperature, and the aging indicators include operating hours and insulation resistance.

[0059] Calculate the first status coefficient, output voltage: 0.9 (normalized value), weight: 0.4; output current: 0.85 (normalized value), weight: 0.3; oil temperature: 0.95 (normalized value), weight: 0.3; first status coefficient = 0.9×0.4 + 0.85×0.3 + 0.95×0.3 = 0.885.

[0060] Calculate the second status coefficient, quantization and weight of aging indicators: operating hours: 8000 hours (quantization score: 0.8, assuming the total life is 10000 hours), weight: 0.6; insulation resistance: 10 MΩ (quantization score: 0.9, assuming the normal range is 10 - 100 MΩ), weight: 0.4; second status coefficient = 0.8×0.6 + 0.9×0.4 = 0.84.

[0061] Both the first status coefficient of 0.885 and the second status coefficient of 0.84 fall within the ranges of "0.7 - 0.9" and "0.5 - 0.8"; therefore, the current working status of the device is "midway standby status".

[0062] Preset working status matching table:

[0063] First state coefficient Second state coefficient Working state >0.9 >0.8 Online working state 0.7-0.9 0.5-0.8 Intermediate standby state <0.7 <0.5 Stop state or maintenance

[0064] Reference Figure 3 , in step S12, if the working status of the device is in the online working status, then determine the internal production environment of the device according to the three-dimensional model of the device, the spatial positions of each fiber grating sensor relative to the device, and the sensing signals of each fiber grating sensor.

[0065] In the specific implementation process of the present invention, the specific steps are as follows:

[0066] S121: Determine the three-dimensional model of the device according to the model information of the device, the external form of the device, and the preset model database.

[0067] S122: If the working state of the device is in the online working state, determine the spatial positions of the fiber Bragg grating sensors relative to the device based on the spatial detection of the device and each fiber Bragg grating sensor;

[0068] S123: Each fiber Bragg grating sensor is distributed at different positions of the device, and determine the sensing signals of each fiber Bragg grating sensor based on the dynamic detection of each fiber Bragg grating sensor;

[0069] S124: Determine multiple internal environment features based on the multi-identification of the three-dimensional model of the device, the spatial positions of each fiber Bragg grating sensor relative to the device, and the sensing signals of each fiber Bragg grating sensor, and determine the internal production environment of the device based on the multiple internal environment features and the internal production area of the device;

[0070] In the embodiments of the present application, determine the three-dimensional model of the device according to the model information of the device, the external form of the device, and the preset model database, which incorporates the overall consideration of the model information of the device, the external form of the device, and the preset model database, ensuring the accuracy of the three-dimensional model of the device.

[0071] At this time, introduce the model information of the device, the external form of the device, and the preset model database, visually inspect the appearance and structure of the device to assist in confirming or correcting the three-dimensional model obtained from the model information; search for the three-dimensional model that matches the device model and form in the model database. At the same time, use the model information of the device as a keyword to search for the matching three-dimensional model in the model database; if there are multiple matching items in the database, further screen according to the external form characteristics of the device, and then select the most suitable three-dimensional model from the model database to represent the device. Optionally, compare the actual external form of the device with the three-dimensional model in the database and select the most suitable model.

[0072] Specifically, assume there is an industrial robot with a model number of XYZ-1234, and a three-dimensional model needs to be determined for it; read its model number as XYZ-1234 from the label of the industrial robot; conduct a comprehensive visual inspection of the industrial robot and find that it has six degrees of freedom, an arm length of about 1.5 meters, and an end effector is a gripper; record the overall shape, color, and the positions and forms of each joint of the industrial robot.

[0073] Search for the keyword "XYZ-1234" in the model database and find multiple matching solid models; further filter the matching models according to the external morphological characteristics of the industrial robot, such as arm length, number of joints, and end effector type; select a solid model that best matches the actual morphology of the industrial robot from the filtered models; this model has six degrees of freedom, the arm length matches the actual arm length of the industrial robot, and the end effector is a gripper, which is consistent with the industrial robot; confirm that this model is the solid model of the industrial robot and use it for subsequent analysis and visualization work.

[0074] Further, if the working state of the device is in the online working state, then determine the spatial positions of the respective fiber Bragg grating sensors relative to the device based on the spatial detection of the device and the respective fiber Bragg grating sensors, ensuring the accuracy of the spatial positions of the respective fiber Bragg grating sensors relative to the device.

[0075] At this time, confirm whether the device is currently in the online working state through the control system or status indicator light of the device; if the device is not in the online working state, wait or start the device to the online state.

[0076] If the working state of the device is in the online working state, then perform spatial detection on the device and the respective fiber Bragg grating sensors. At this time, check the installation positions of the fiber Bragg grating sensors to ensure that they are firmly fixed on the device and the connection lines are not damaged; start the spatial detection system, such as a laser rangefinder, a 3D scanner, or a machine vision system, etc.; scan or measure the device according to the requirements of the detection system to obtain the spatial position data of the fiber Bragg grating sensors; ensure that the device remains stationary during the detection process to avoid measurement errors caused by device movement.

[0077] Convert the detected spatial position data into three-dimensional coordinates relative to the device; perform necessary calibration and adjustment on the coordinate data according to the actual size and morphology of the device; save the processed spatial position data for subsequent analysis and use.

[0078] Specifically, assume there is a wind turbine on which multiple fiber Bragg grating sensors are installed to monitor the strain of the blades; now, it is necessary to determine the spatial positions of these sensors relative to the wind turbine; through the control system of the wind turbine, confirm that it is currently in the online working state and the blades are rotating; since the spatial detection needs to be carried out when the blades are stationary, the operation of the wind turbine is temporarily stopped.

[0079] Check the fiber Bragg grating sensors on the blade to confirm that they are firmly fixed on the blade surface and the connection lines are undamaged; confirm that the connection between the sensors and the detection system is normal and can transmit detection data; use a 3D scanner to scan the blade of the wind turbine to obtain the 3D point cloud data of the blade surface; during the scanning process, ensure that the wind turbine remains stationary to avoid measurement errors caused by blade rotation; according to the scanning results, identify the specific positions of the fiber Bragg grating sensors on the blade surface.

[0080] Use data processing software to convert the scanned 3D point cloud data into a 3D coordinate system relative to the wind turbine; calibrate and adjust the coordinate data according to the actual size and shape of the blade; mark the specific positions of the fiber Bragg grating sensors in the processed coordinate data for subsequent analysis and use.

[0081] Furthermore, the fiber Bragg grating sensors are distributed at different positions of the device, and the induction signals of each fiber Bragg grating sensor are determined based on the dynamic detection of each fiber Bragg grating sensor, ensuring the accuracy of the induction signals of each fiber Bragg grating sensor.

[0082] At this time, distribute the fiber Bragg grating sensors at different key positions of the device to comprehensively monitor the operating state of the device. At the same time, determine the installation positions of the fiber Bragg grating sensors according to the structural characteristics and monitoring requirements of the device; firmly fix the fiber Bragg grating sensors on the device to ensure that they can stably collect data; ensure that the connection lines of the sensors are undamaged and can smoothly transmit induction signals.

[0083] Start the dynamic detection system connected to the fiber Bragg grating sensors to collect and process induction signals in real time. At the same time, confirm that the power supply and signal connection of the dynamic detection system are normal; start the detection system and set the corresponding sampling frequency and data storage parameters; ensure that the detection system can stably receive and process the induction signals from the fiber Bragg grating sensors during operation.

[0084] Furthermore, the operating parameters of the device, such as strain, temperature, pressure, etc., are collected in real time through the fiber Bragg grating sensors. At this time, during the operation of the device, the fiber Bragg grating sensors will generate corresponding spectral changes according to the physical changes (such as strain and temperature changes) at their locations; the dynamic detection system monitors these spectral changes, converts them into digital signals for collection and storage; ensure that the collected induction signals are accurate, continuous, and can reflect the actual operating state of the device.

[0085] Process and analyze the collected sensing signals to extract useful information for equipment monitoring and diagnosis. At this time, perform preprocessing operations such as filtering and denoising on the collected data; according to the characteristics of fiber Bragg grating sensors and the monitoring requirements of the equipment, extract characteristic parameters related to the operating state of the equipment; store and back up the processed sensing signals and characteristic parameters for subsequent analysis and use.

[0086] Therefore, determine multiple internal environment characteristics based on the three-dimensional model of the equipment, the spatial positions of each fiber Bragg grating sensor relative to the equipment, and the multiple identifications of the sensing signals of each fiber Bragg grating sensor, and determine the internal production environment of the equipment based on the multiple internal environment characteristics and the internal production areas of the equipment. It incorporates the overall consideration of the three-dimensional model of the equipment, the spatial positions of each fiber Bragg grating sensor relative to the equipment, and the multiple identifications of the sensing signals of each fiber Bragg grating sensor, ensuring the accuracy of the multiple internal environment characteristics.

[0087] At this time, integrate information such as the three-dimensional model of the equipment, the spatial positions of fiber Bragg grating sensors, and sensing signals to provide a comprehensive and accurate data basis for subsequent analysis. At the same time, ensure that an accurate three-dimensional model of the equipment has been obtained, which should be able to reflect the internal structure, layout, and production areas of the equipment; mark the spatial positions of fiber Bragg grating sensors on the three-dimensional model according to the determined positions in the previous steps; collect and organize the sensing signals of all fiber Bragg grating sensors to ensure the accuracy and timeliness of the signals.

[0088] By analyzing the sensing signals of fiber Bragg grating sensors and combining with the three-dimensional model of the equipment and the sensor positions, determine multiple internal environment characteristics of the equipment; at this time, according to the characteristics and working principles of fiber Bragg grating sensors, interpret the physical quantities represented by the sensing signals (such as temperature, strain, pressure, etc.); combine with the three-dimensional model of the equipment and the sensor positions to analyze the spatial distribution and change trends of these physical quantities inside the equipment; according to the analysis results, determine multiple internal environment characteristics of the equipment, such as temperature gradient, stress distribution, vibration mode, etc.

[0089] Based on the internal environment characteristics of the equipment, analyze whether the internal production area of the equipment is in a suitable production state; at this time, determine the scope of the internal production area of the equipment, which is based on the three-dimensional model of the equipment and the production process requirements; analyze the impact of the internal environment characteristics on the production area, such as whether the temperature is too high or too low, whether the stress is concentrated, whether the vibration is too large, etc.; evaluate whether these impacts lead to a decrease in production efficiency, product quality problems, or equipment failures, etc.

[0090] Furthermore, based on the above analysis, determine the internal production environment of the equipment and propose corresponding improvement measures. At this time, according to the analysis results of the internal environment characteristics and production areas, comprehensively evaluate the internal production environment of the equipment; identify problems or potential risks existing in the internal production environment, such as local overheating, stress concentration areas, vibration sources, etc.; for these problems or risks, propose specific improvement measures, such as adjusting the production process, optimizing the equipment layout, strengthening maintenance, etc.

[0091] Specifically, assume there is a large injection molding machine, on which multiple fiber Bragg grating sensors are installed to monitor parameters such as temperature, strain, and pressure in the mold area; now, it is necessary to determine the internal production environment of the injection molding machine based on this information; an accurate three-dimensional model of the injection molding machine has been obtained, which details the internal structure, mold area, and layout of each component of the injection molding machine; the fiber Bragg grating sensors have been installed at key positions in the mold area and marked on the three-dimensional model; all sensor induction signals have been collected and sorted, including time series data of parameters such as temperature, strain, and pressure.

[0092] By analyzing the induction signals of the fiber Bragg grating sensors, it is found that there is an obvious gradient distribution of temperature in the mold area, and local overheating occurs at some positions; the strain data shows that the mold bears a large mechanical stress during the injection molding process, especially during the mold opening and closing operations; the pressure sensors detect that the pressure distribution inside the mold is uneven, with local high-pressure areas.

[0093] The internal production area of the injection molding machine is mainly concentrated in the mold area, which is responsible for key process steps such as plastic melting, injection, and molding; local overheating causes plastic material degradation, affecting product quality; stress concentration accelerates mold wear and shortens the service life; uneven pressure distribution leads to poor product molding.

[0094] Based on the above analysis, it is determined that there are problems such as local overheating, stress concentration, and uneven pressure distribution in the internal production environment of the injection molding machine; for these problems, the following improvement measures are proposed: optimize the mold cooling system to ensure uniform mold temperature distribution; adjust the injection molding process parameters to reduce the mechanical stress borne by the mold; improve the mold design to optimize the pressure distribution.

[0095] In an embodiment of the present application, the internal environment matching table:

[0096] Sensor number Position description Inductive signal type Signal value Internal environment characteristics Influence of production area S1 Top of the mold Temperature 200°C High temperature Cause plastic degradation S2 Bottom of the mold Temperature 180°C Moderate temperature Normal S3 Injection cylinder Pressure 10 MPa High pressure Affect injection speed S4 Mold opening and closing position Strain 0.2% Low strain Normal S5 Cooling water channel Temperature 25°C Low temperature Normal cooling effect

[0097] According to the internal environment matching table, it is determined that there are problems such as overheating at the top of the mold and high pressure in the injection cylinder in the internal production environment of the equipment, and these characteristics have an adverse impact on the production area.

[0098] Refer to Figure 4, in step S13, determine the comprehensive environment of the device based on the internal production environment of the device, the production image of the device, and the external environment of the device;

[0099] In the specific implementation process of the present invention, the specific steps are as follows:

[0100] S131: Detect the production process of the device in real time and collect the production image of the device;

[0101] S132: Mark the current position of the device and determine the external environment of the device according to the environmental detection of the current position of the device;

[0102] S133: In the environmental control of the device, determine the first environmental feature of the device according to the internal production environment of the device and the production image of the device, determine the second environmental feature of the device according to the internal production environment of the device and the external environment of the device, and determine the comprehensive environment of the device based on the multiple interactions of the first environmental feature, the second environmental feature, and the transition area from the internal environment to the external environment in the device.

[0103] In the embodiment of the present application, detecting the production process of the device in real time and collecting the production image of the device; marking the current position of the device and determining the external environment of the device according to the environmental detection of the current position of the device ensure the accuracy of the external environment of the device.

[0104] At this time, detect the production process of the device in real time, and set the parameters of the monitoring device according to the actual situation of the production process to capture key production images. At the same time, set parameters such as the frame rate, exposure time, and white balance of the monitoring device according to factors such as the production rhythm and lighting conditions; ensure that clear and accurate images can be captured in key production links.

[0105] During the production process, collect the production image of the device in real time to provide data support for subsequent analysis; start the monitoring device and start collecting production images in real time; ensure that the image data can be transmitted to the monitoring center or storage device in real time for subsequent viewing and analysis. Further, establish an image database and store the collected production images according to time, device, production link, etc.; ensure the security and accessibility of the image data and provide convenience for subsequent analysis.

[0106] At the same time, mark the current position of the device, and select appropriate environmental sensors (such as temperature and humidity sensors, light sensors, gas sensors, etc.) according to the location of the device and the characteristics of the production environment; deploy the sensors at key positions around the device to ensure that the external environmental conditions can be detected comprehensively and accurately; at the same time, connect the sensors to the data acquisition system to obtain environmental data in real time.

[0107] Real-time collection of equipment external environment data provides data support for subsequent environmental analysis. At this time, start the environmental sensor and start real-time collection of external environment data; ensure the accuracy and real-time nature of data collection so as to timely reflect changes in the equipment's external environment; at the same time, record the collected data in a database or data warehouse for subsequent query and analysis.

[0108] Analyze the characteristics and changing trends of the equipment's external environment based on the collected environmental data; process and analyze the collected environmental data; identify outliers, trends, and patterns in the environmental data to determine the characteristics and changing trends of the equipment's external environment; and record the analysis results in a report or data visualization tool for subsequent review and decision-making.

[0109] Based on the environmental data and analysis results, the external environmental conditions of the equipment are determined. At this time, based on the environmental data and analysis results, the external environment of the equipment is evaluated to see whether it is suitable for equipment operation and production requirements. Potential environmental risks or problems, such as excessive temperature, excessive humidity, and excessive concentration of harmful gases, are identified. At the same time, the evaluation results are recorded in the equipment management system so that corresponding measures can be taken to improve or optimize the equipment.

[0110] Specifically, suppose there is an electronic product manufacturing plant with an automated assembly line. In order to determine the external environment of the equipment on the assembly line, follow the steps below: install RFID tags or Bluetooth locators at key locations of the assembly line (such as equipment entrances, exits, workbenches, etc.), and use corresponding readers or receivers to mark the current location of the equipment; ensure that the positioning accuracy is high enough to accurately reflect the actual location of the equipment in the assembly line; at the same time, record the location information in the equipment management system or database.

[0111] According to the layout of the assembly line and the location of the equipment, deploy temperature and humidity sensors, light sensors, and harmful gas sensors at key locations around the equipment (such as above the workbench, around the equipment, etc.); connect the sensors to the data acquisition system to ensure that environmental data can be collected in real time. Start the environmental sensor and start collecting real-time external environmental data of the assembly line equipment; ensure the accuracy and real-time nature of data collection so that changes in environmental conditions can be reflected in a timely manner; at the same time, record the collected data in a database or data warehouse.

[0112] Process and analyze the collected environmental data; for example, use statistical methods to identify outliers and trends in the environmental data, and use machine learning methods to identify patterns and association rules in the environmental data; through analysis, it is found that the temperature in a certain area of the assembly line is too high, exceeding the normal operating range of the equipment; according to the environmental data and the analysis results, evaluate whether the external environment of the assembly line equipment is suitable for the equipment operation and production requirements; determine that there is an overheating problem in this area, which has an adverse impact on the normal operation of the equipment and the product quality; at the same time, record the evaluation results in the equipment management system and notify the relevant departments to take corresponding measures for improvement or optimization, such as adding air conditioning equipment, adjusting the production line layout, etc.

[0113] Furthermore, in the environmental control of the equipment, determine the first environmental feature of the equipment according to the internal production environment of the equipment and the production image of the equipment, and determine the second environmental feature of the equipment according to the internal production environment of the equipment and the external environment of the equipment. Determine the comprehensive environment of the equipment based on the multiple interactions of the first environmental feature, the second environmental feature, and the transition area from the internal environment to the external environment in the equipment; realizing the multiple interactions of the first environmental feature, the second environmental feature, and the transition area from the internal environment to the external environment in the equipment, and further accurately controlling the comprehensive environment of the equipment.

[0114] At this time, in the environmental control of the equipment, the first environmental feature and the second environmental feature are introduced. For the first environmental feature, determine the first environmental feature of the equipment according to the internal production environment of the equipment and the production image of the equipment. At this time, use built-in sensors (such as temperature sensors, humidity sensors, pressure sensors, etc.) to monitor environmental parameters such as temperature, humidity, and pressure inside the equipment; use image processing technology (such as object tracking) to analyze the production image of the equipment and extract key information, such as material flow status, equipment operation status, product quality, etc.; combine the internal environmental monitoring data and the analysis results of the production image to extract the first environmental feature of the equipment, and the first environmental feature includes temperature distribution, humidity level, material flow speed, equipment vibration frequency, etc.

[0115] For the second environmental feature, determine the second environmental feature of the equipment according to the internal production environment of the equipment and the external environment of the equipment. At this time, use external sensors (such as temperature sensors, humidity sensors, light sensors, gas sensors, etc.) to monitor environmental parameters such as temperature, humidity, light intensity, and harmful gas concentration around the equipment; analyze the interaction between the internal environment and the external environment, such as heat exchange, gas flow, light influence, etc.; this requires using physical models or simulation technologies to simulate these interactions; combine the external environmental monitoring data and the analysis results of the interaction between the internal environment and the external environment to extract the second environmental feature of the equipment, and these features include temperature difference, humidity gradient, light influence degree, harmful gas diffusion range, etc.

[0116] Identify the transition regions from the internal environment to the external environment of the device and analyze the multiple interactions in these regions; at this time, according to the structure and function of the device, identify the transition regions between the internal environment and the external environment, such as the inlets, outlets, heat dissipation holes, ventilation openings, etc. of the device; analyze the multiple interactions in these transition regions, such as heat transfer, gas exchange, material flow, etc.; this requires the use of advanced detection techniques or simulation models to accurately describe these interactions; evaluate the impact of these interactions on the internal environment and external environment of the device, as well as their impact on the operating efficiency and product quality of the device.

[0117] Based on the first environmental characteristics, the second environmental characteristics, and the multiple interactions in the transition regions, comprehensively evaluate the overall environmental condition of the device; at this time, integrate the data of the first environmental characteristics, the second environmental characteristics, and the multiple interactions in the transition regions into a comprehensive evaluation model; use the comprehensive evaluation model to evaluate the overall environmental condition of the device to output comprehensive evaluation results, including the environmental health condition of the device, potential environmental risk points, recommended improvement measures, etc.

[0118] Specifically, assume there is a food processing enterprise with an automated packaging machine; to determine the comprehensive environmental condition of this packaging machine, the following steps are taken:

[0119] Determine the first environmental characteristics: Use built-in sensors to monitor the temperature, humidity, and air pressure inside the packaging machine; analyze the production images of the packaging machine to extract information such as the material flow state, packaging film tension, and product arrangement; extract the first environmental characteristics, such as the uniformity of the internal temperature distribution, whether the humidity level is appropriate, and the air pressure stability.

[0120] Determine the second environmental characteristics: Use external sensors to monitor the temperature, humidity, and light intensity around the packaging machine; analyze the interactions between the packaging machine and the external environment, such as heat exchange and gas flow; extract the second environmental characteristics, such as the temperature difference around the packaging machine, whether the humidity gradient is obvious, and the impact of light on the packaging materials.

[0121] Introduce the transition regions, identify the transition regions such as the inlets, outlets, heat dissipation holes, and ventilation openings of the packaging machine; analyze the multiple interactions in these regions, such as heat transfer, gas exchange, and material flow; evaluate the impact of these interactions on the internal environment and external environment of the packaging machine, as well as their impact on the packaging efficiency and product quality.

[0122] Integrate the multiple interaction data of the first environmental feature, the second environmental feature, and the transition region into a comprehensive evaluation model; use the comprehensive evaluation model to evaluate the overall environmental condition of the packaging machine; output a comprehensive evaluation result, indicating the environmental health condition of the packaging machine (such as whether there are problems such as overheating, excessive humidity, or excessive harmful gases), potential environmental risk points (such as material blockage, poor heat dissipation, etc.), and recommended improvement measures (such as adding ventilation equipment, adjusting the position of the packaging machine, etc.).

[0123] In an embodiment of the present application, an example of feature matching is as follows:

[0124] Reference Figure 5 , in step S14, determine the production grade of the equipment according to the comprehensive environment of the equipment and the detection of the real-time image of the material produced by the equipment;

[0125] In the specific implementation process of the present invention, the specific steps are as follows:

[0126] S141: Collect the comprehensive environment of the equipment, and the comprehensive environment of the equipment is dynamically adjusted as the working mode of the equipment is adjusted;

[0127] S142: Locate the discharge port of the equipment, and determine the real-time image of the material produced by the equipment according to the real-time shooting of the discharge port of the equipment. Determine the quality grade of the material based on the image recognition of the real-time image of the material produced by the equipment. The quality grade of the material is comprehensively judged according to the form of the material produced by the equipment and the surface quality of the material produced by the equipment;

[0128] S143: Correlate the comprehensive environment of the equipment and the quality grade of the material, and determine the production grade of the equipment according to the comprehensive environment of the equipment, the quality grade of the material, and the production grade matching table.

[0129] In an embodiment of the present application, collect the comprehensive environment of the equipment, and the comprehensive environment of the equipment is dynamically adjusted as the working mode of the equipment is adjusted; locate the discharge port of the equipment, and determine the real-time image of the material produced by the equipment according to the real-time shooting of the discharge port of the equipment. Determine the quality grade of the material based on the image recognition of the real-time image of the material produced by the equipment. The quality grade of the material is comprehensively judged according to the form of the material produced by the equipment and the surface quality of the material produced by the equipment;

[0130] At this time, the comprehensive environment of the equipment is introduced, and subsequent control of the comprehensive environment of the equipment is carried out. At the same time, locate the discharge port of the equipment, use high-precision measuring tools (such as laser rangefinders, 3D scanners) to measure the equipment, and determine the accurate position of the discharge port; mark the position of the discharge port on the equipment for installing a camera or an image acquisition device; according to the layout and production process of the equipment, select the best shooting angle and position to ensure that a complete material image can be captured.

[0131] Install a high-definition camera or image acquisition device at the discharge port to ensure good connection between the camera and the image acquisition system; set the shooting parameters of the camera, such as resolution, frame rate, exposure time, etc., to obtain clear and stable images; enable the real-time shooting function and transmit the images captured by the camera to the image recognition system for processing in real time.

[0132] Perform preprocessing operations on the images, such as denoising and enhancing contrast, to improve the image quality; apply image recognition methods (such as convolutional neural networks) to process the images and identify the morphology and surface features of the materials; extract key features of the materials, such as size, shape, color, texture, etc., to provide a basis for subsequent quality grade determination.

[0133] Comprehensively judge the quality grade of the materials based on the morphology and surface quality of the materials; at this time, formulate the quality grade determination standard for the materials, clarify the morphology and surface quality requirements of the materials under different quality grades; compare the extracted material features with the determination standard, and determine the quality grade of the materials according to the comparison results; record the quality grade information of the materials and associate it with the production batch, time, etc. of the materials for subsequent tracking and analysis.

[0134] Specifically, suppose there is an automotive parts manufacturing enterprise, and there is an injection molding machine used to produce automotive parts; in order to determine the quality grade of the parts produced by the injection molding machine, the following steps are taken: use a laser rangefinder to measure the injection molding machine to determine the exact position of the discharge port (mold outlet); mark the position of the discharge port on the injection molding machine and install a high-definition camera beside it; adjust the shooting angle and position of the camera to ensure that the complete part image can be captured.

[0135] Enable the real-time shooting function of the camera and transmit the captured part images to the image recognition system in real time; set the resolution of the camera to 1920x1080 and the frame rate to 30fps to ensure the clarity and stability of the images. Perform denoising and contrast enhancement processing on the captured part images to improve the image quality; apply a convolutional neural network to process the images and identify the morphology (such as size, shape) and surface features (such as scratches, bubbles, defects, etc.) of the parts; extract the key features of the parts, such as length, width, height, surface roughness, etc.

[0136] Formulate the judgment criteria for the quality grades of components, such as Grade A (perfect), Grade B (minor defects), Grade C (serious defects), etc.; compare the extracted component features with the judgment criteria, such as whether the dimensions are within the allowable range, whether there are obvious scratches or bubbles on the surface, etc.; according to the comparison results, determine that the quality grade of the component is Grade A (perfect) and record this information; at the same time, associate the component with the production batch, time, etc. for subsequent tracking and analysis.

[0137] Furthermore, correlate the comprehensive environment of the equipment and the quality grades of the materials, and determine the production grade of the equipment according to the comprehensive environment of the equipment, the quality grades of the materials, and the production grade matching table, which incorporates the overall consideration of the comprehensive environment of the equipment, the quality grades of the materials, and the production grade matching table, ensuring the accuracy of the production grade of the equipment.

[0138] At this time, integrate the equipment comprehensive environment data and the material quality grade data and establish the association between them; at the same time, extract the historical data and real-time data of the equipment comprehensive environment from the database, including parameters such as temperature, humidity, air pressure, vibration, etc.; at the same time, extract the data of the material quality grade, including information such as the form and surface quality of materials in different batches; use data association technologies (such as timestamp matching, batch number matching, etc.) to correspond the environment data with the material quality grade data to form an associated data set.

[0139] According to different combinations of the equipment comprehensive environment and the material quality grade, formulate a production grade matching table. At this time, analyze the historical data to find the correlation rules between the equipment comprehensive environment, the material quality grade, and the production results (such as the yield rate, failure rate); according to these rules, divide the equipment comprehensive environment and the material quality grade into different intervals or grades; assign a production grade to each interval or grade combination, such as Grade One (excellent), Grade Two (good), Grade Three (average), etc.; record these grade combinations and the corresponding production grades in the matching table.

[0140] Determine the production grade of the current equipment according to the real-time collected equipment comprehensive environment data and material quality grade data; at this time, compare the real-time collected data with the intervals or grades in the matching table; find the interval or grade combination that best matches the real-time data; determine the production grade of the current equipment according to the production grade corresponding to this combination in the matching table.

[0141] Specifically, assume there is an electronics manufacturing enterprise with an automated production line for assembling mobile phones. To determine the production level of this production line, the following steps are taken: Extract the real-time environmental data collected by various sensors on the production line from the database, such as temperature, humidity, air pressure, etc.; at the same time, extract the quality level data of the materials during the mobile phone assembly process, such as whether there are scratches on the screen, whether the camera is installed correctly, etc.; use the batch number and timestamp to correspond the environmental data with the material quality level data to form an associated dataset.

[0142] Analyze the historical data and find that when the environmental temperature of the production line is between 20 - 25°C and the humidity is between 40% - 60%RH, the material quality level is relatively high and the yield rate is also relatively high; divide the environmental temperature into three intervals: below 20°C, 20 - 25°C, above 25°C; divide the humidity into three intervals: below 40%RH, 40% - 60%RH, above 60%RH; divide the material quality level into two levels: qualified and unqualified; formulate a matching table. For example, when the environmental temperature is between 20 - 25°C, the humidity is between 40% - 60%RH and the material quality level is qualified, the production level is level one; other combinations are assigned corresponding production levels according to the analysis results of the historical data.

[0143] Collect the real-time environmental data and material quality level data of the production line in real time; compare the real-time data with the intervals or levels in the matching table and find that the current environmental temperature is 23°C, the humidity is 50%RH and the material quality level is qualified; according to the matching table, determine that the production level of the current production line is level one.

[0144] In an embodiment of the present application, collect the comprehensive environmental data of the equipment (such as temperature, humidity, vibration, etc.) and the quality level data of the materials; formulate a production level matching table based on historical data and industry experience; this table associates different equipment environmental intervals and material quality level combinations with specific production levels; monitor the comprehensive environment of the equipment and the material quality level in real time, compare them with the intervals in the matching table, and find the most matching combination to determine the current production level.

[0145] Production level matching table:

[0146] Equipment environment (temperature) Equipment environment (humidity) Material quality grade Production grade 20-25°C 40%-60%RH Grade A First grade 25-30°C 40%-60%RH Grade A Second grade <20 °C or >30 °C Any value Any grade Third grade Any value >60%RH Grade B or C Third grade

[0147] Note: In this example, when the equipment temperature is between 20 - 25°C, the humidity is between 40% - 60%RH, and the material quality level is A grade, the production level is determined to be level one; if the equipment temperature exceeds this range, or the humidity is too high, or the material quality level drops, the production level will be correspondingly reduced.

[0148] Reference Figure 6, in step S15, if the production level of the device is lower than the preset production level threshold, determine the abnormal part in the device based on the abnormal detection of the device, and trigger the autonomous regulation of the abnormal part;

[0149] In the specific implementation process of the present invention, the specific steps are as follows:

[0150] S151: Determine the preset production level threshold based on the model information of the device and the aging level of the device, and compare the production level of the device with the preset production level threshold;

[0151] S152: If the production level of the device is lower than the preset production level threshold, determine the corresponding production level difference based on the production level of the device and the preset production level threshold, and determine the abnormal part in the device based on the production level difference, the comprehensive environment of the device, and the real-time image of the material produced by the device;

[0152] S153: Determine the corresponding autonomous regulation event based on the type of the abnormal part in the device, the location of the abnormal part, and the regulation matching table, and trigger the autonomous regulation of the abnormal part according to the autonomous regulation event.

[0153] In the embodiment of the present application, determining the preset production level threshold based on the model information of the device and the aging level of the device, and comparing the production level of the device with the preset production level threshold, introduce the production level of the device and the preset production level threshold.

[0154] At this time, obtain the specific model of the device and the current aging state for subsequent search of the corresponding preset production level threshold. At the same time, obtain the model information of the device from the device management system or the device label, including the manufacturer, model code, production date, etc.; evaluate the aging level of the device according to factors such as the operation time of the device, maintenance records, and component wear conditions; the aging level is a qualitative description (such as new, slightly aged, moderately aged, severely aged) or a quantitative score (such as 1-10 points).

[0155] According to the model information and aging level of the device, search for the corresponding preset production level threshold in the database or experience table; at this time, use the model information and aging level of the device as the query conditions for retrieval in the database; if there is a corresponding record in the database, extract the preset production level threshold in the record; if there is no corresponding record in the database, estimate a reasonable preset production level threshold according to experience or industry standards.

[0156] Evaluate whether the production performance of the current device meets the standard so as to take necessary control measures subsequently. At this time, obtain the production level data of the current device from the production monitoring system; compare the production level of the device with the preset production level threshold to determine whether the production performance of the device meets the standard; if the production level of the device is higher than or equal to the preset production level threshold, it is considered that the production performance of the device meets the standard; if the production level of the device is lower than the preset production level threshold, it is considered that the production performance of the device does not meet the standard and further control measures need to be taken.

[0157] Specifically, suppose there is a food manufacturing enterprise with a packaging machine model XYZ - 123; to determine the preset production level threshold of this packaging machine and make a comparison, operate according to the following steps: Obtain the model information of the packaging machine as XYZ - 123 and the production date as 2018 from the device label; evaluate its aging level as "moderate aging" according to the operating time and maintenance records of the packaging machine.

[0158] In the device management system database, use the model information "XYZ - 123" and the aging level "moderate aging" as query conditions for retrieval; find the corresponding record and extract the preset production level threshold as 85 points.

[0159] Obtain the production level data of the current packaging machine from the production monitoring system as 80 points; compare 80 points with the preset production level threshold of 85 points and find that the production level of the device is lower than the preset threshold; therefore, it is considered that the production performance of this packaging machine does not meet the standard and further control measures need to be taken, such as adjusting device parameters, optimizing the production process or performing device maintenance, etc., to improve its production level.

[0160] Furthermore, if the production level of the device is lower than the preset production level threshold, determine the corresponding production level difference based on the production level of the device and the preset production level threshold, and determine the abnormal part inside the device based on the production level difference, the comprehensive environment of the device, and the real - time image of the material produced by the device. Considering the overall production level difference, the comprehensive environment of the device, and the real - time image of the material produced by the device, it ensures the accuracy of the abnormal part inside the device.

[0161] At this time, quantify the gap between the production performance of the device and the preset standard to provide a basis for subsequent analysis and control. At this time, obtain the production level of the device and the preset production level threshold from step S151; calculate the production level difference, that is, the preset production level threshold minus the production level of the device; for example, if the preset production level threshold is 90 points and the production level of the device is 80 points, then the production level difference is 10 points.

[0162] Understand the current working environment status of the device to determine whether environmental factors have an impact on the production performance of the device; at this time, collect the comprehensive environmental data of the device, including parameters such as temperature, humidity, vibration, and air pressure; analyze whether these parameters are within the normal range and whether they are related to the production grade difference; for example, if the device temperature is too high, it will cause the performance of some components to decline, thereby affecting the production grade.

[0163] By observing the real-time image of the materials produced by the device, detect existing quality problems or production abnormalities; at this time, obtain the real-time image of the materials from the production monitoring system; conduct quality inspections on the image, such as observing whether the materials have defects, whether the sizes are consistent, and whether the colors are uniform; link the quality problems in the image with the production grade difference to determine whether there is a decline in production performance caused by material problems.

[0164] Based on the above analysis, determine which parts inside the device have problems resulting in a decline in the production grade; at this time, conduct a comprehensive analysis according to the production grade difference, the results of the comprehensive environmental analysis of the device, and the inspection of the real-time image of the materials; after excluding environmental factors and material problems, focus on the device itself, such as components like sensors, controllers, and actuators; determine the abnormal part inside the device that most likely causes the decline in the production grade.

[0165] Specifically, assume there is an automobile manufacturing plant with a stamping machine model ABC-567; in step S151, it has been determined that the production grade of this stamping machine is 82 points, while the preset production grade threshold is 90 points, so the production grade difference is 8 points; next, conduct a detailed analysis according to step S152:

[0166] Preset production grade threshold: 90 points; Device production grade: 82 points; Production grade difference: 90 - 82 = 8 points. At the same time, the comprehensive environmental data collected for the device includes: temperature 25°C (normal range is 20 - 30°C), humidity 45%RH (normal range is 40% - 60%RH), vibration 0.2m / s² (normal range is <0.5m / s²); Analysis result: The comprehensive environmental parameters of the device are all within the normal range, so environmental factors are not the main reason for the decline in the production grade.

[0167] The real-time image of the materials obtained from the production monitoring system shows that the dimensions of the stamped automobile parts are consistent, the surfaces are flat, and there are no obvious defects; Analysis result: The material quality is good and is not the reason for the decline in the production grade; Based on the above analysis, exclude environmental factors and material problems, and focus on the stamping machine itself; further observe the operating state of the stamping machine and find that the movement trajectory of the stamping head has a slight deviation and the punching force is unstable; Therefore, determine that the abnormal parts of the stamping machine are the stamping head and the related control system.

[0168] Therefore, based on the type of the abnormal part within the device, the location of the abnormal part, and the regulation matching table, the corresponding autonomous regulation event is determined, and the autonomous regulation of the abnormal part is triggered according to this autonomous regulation event, ensuring the accuracy of the autonomous regulation event and achieving the autonomous regulation of the abnormal part.

[0169] At this time, it is clear which parts inside the device have problems and the specific locations of these problems, providing an accurate target for subsequent regulation; at the same time, obtain the type (such as sensor failure, motor overheating, control system error, etc.) and location (such as the top of the device, a certain module inside, the transmission system, etc.) of the abnormal part within the device from step S152; ensure that the type and location information of the abnormal part are accurate so that subsequent regulation measures can be accurately implemented.

[0170] According to the type and location of the abnormal part, find the corresponding autonomous regulation event and regulation measure in the regulation matching table; at this time, the regulation matching table is a predefined database or table that lists the autonomous regulation events and regulation measures corresponding to different devices and different abnormal parts; use the type and location of the abnormal part as the query conditions to perform a search in the regulation matching table; after finding the matching autonomous regulation event and regulation measure, record the relevant information for subsequent execution.

[0171] Clarify the autonomous regulation event to be executed to ensure that the regulation measure can have an effect on the specific problem. At this time, according to the search result of the regulation matching table, determine the autonomous regulation event that matches the current abnormal part; the autonomous regulation event includes adjusting device parameters, starting standby components, sending alarm information, triggering an emergency shutdown, etc.; ensure that the autonomous regulation event matches the nature of the problem of the abnormal part.

[0172] Execute the autonomous regulation measure to solve the abnormal problem inside the device and restore the normal production performance of the device; at this time, according to the determined autonomous regulation event, trigger the corresponding regulation measure; this involves operations such as sending a control signal to the device, starting a standby system, and adjusting device settings; monitor the execution situation of the regulation measure to ensure that the abnormal part is properly handled; after the regulation measure is executed, re-evaluate the production level of the device to verify the regulation effect.

[0173] Specifically, suppose there is a chemical plant with a reactor model XYZ - 987; in step S152, it has been determined that the abnormal part of the reactor is the temperature sensor of the heating system, and this sensor is located in the heating chamber of the reactor; next, perform detailed operations according to step S153:

[0174] Type of abnormal part: Temperature sensor failure; Location of abnormal part: Inside the heating chamber of the reactor; At the same time, use "temperature sensor failure" and "inside the heating chamber" as query conditions to retrieve in the regulation matching table; The found autonomous regulation event is "Start the standby temperature sensor and turn off the faulty sensor".

[0175] According to the retrieval result of the regulation matching table, determine the autonomous regulation event as "Start the standby temperature sensor and turn off the faulty sensor"; At the same time, send a control signal to the reactor to start the standby temperature sensor; At the same time, turn off the faulty temperature sensor to prevent it from continuing to affect the normal operation of the heating system; Monitor the operation of the standby temperature sensor to ensure that the heating system can accurately measure and control the temperature; After the regulation measures are completed, re-evaluate the production level of the reactor and find that the production level has increased, verifying the effectiveness of the regulation measures.

[0176] In an embodiment of the present application, in step S152, the type of abnormal part (such as motor overheating, sensor failure, etc.) and the specific location inside the device have been determined; For example, the abnormal part is the "temperature sensor of the reactor heating system", and the location is "inside the heating chamber".

[0177] Autonomous regulation event matching table:

[0178] Use the type of abnormal part (such as "sensor failure") and location (such as "heating system") as query conditions to find the corresponding autonomous regulation event in the regulation matching table; In this example, the found autonomous regulation event is "Start the standby sensor and turn off the faulty sensor".

[0179] At the same time, the temperature sensor of the reactor heating system fails, and the location is inside the heating chamber; The found autonomous regulation event is "Start the standby sensor and turn off the faulty sensor"; Assume the sensor failure weight is 5 and the heating chamber weight is 2, with a score of 10 points, and perform regulation preferentially; Start the standby temperature sensor and turn off the faulty sensor; Send a control signal to the reactor to execute the autonomous regulation measures; Monitor the operation of the standby temperature sensor to ensure that the heating system can accurately measure and control the temperature; Re-evaluate the production level of the reactor and find that the production performance has returned to normal.

[0180] Please refer to Figure 7 , Figure 7 is a schematic structural diagram of the detection system for the device environment based on fiber Bragg grating sensors in the embodiment of the present invention; The detection system for the device environment based on fiber Bragg grating sensors includes:

[0181] The working state module 21 is used to determine the working state of the device based on the current working mode of the device, multiple working parameters of the device, and the aging level of the device;

[0182] The internal production environment module 22 is configured to, if the working state of the device is in the online working state, determine the internal production environment of the device according to the three-dimensional model of the device, the spatial positions of the respective fiber Bragg grating sensors relative to the device, and the sensing signals of the respective fiber Bragg grating sensors;

[0183] The comprehensive environment module 23 is configured to determine the comprehensive environment of the device based on the internal production environment of the device, the production image of the device, and the external environment of the device;

[0184] The production level module 24 is configured to determine the production level of the device according to the comprehensive environment of the device and the detection of the real-time image of the material produced by the device;

[0185] The anomaly detection module 25 is configured to, if the production level of the device is lower than the preset production level threshold, determine the anomalous part within the device based on the anomaly detection of the device and trigger the autonomous regulation of the anomalous part.

[0186] For any combination of the technical features of the above embodiments, for the sake of brevity of description, not all combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

Claims

1. A detection method for the device environment based on fiber Bragg grating sensors, characterized in that, Including: Determine the working state of the device based on the current working mode of the device, multiple working parameters of the device, and the aging level of the device, including: collecting the model information of the device, determining the current working mode of the device according to the model information of the device, the working signal of the device, and the working mode matching table, and dynamically collecting multiple working parameters of the device; determining the aging level of the device according to the production time of the device, the working record of the device, and the external form of the device; in the multiple interactions of the current working mode of the device, multiple working parameters of the device, and the aging level of the device, determine the first state coefficient based on the current working mode of the device and multiple working parameters of the device, determine the second state coefficient based on the current working mode of the device and the aging level of the device, and determine the working state of the device according to the first state coefficient, the second state coefficient, and the preset working state matching table. The working state of the device includes an online working state, an intermediate standby state, or a stop state; If the working state of the device is in the online working state, determine the internal production environment of the device according to the three-dimensional model of the device, the spatial positions of each fiber Bragg grating sensor relative to the device, and the induction signals of each fiber Bragg grating sensor; Determine the comprehensive environment of the device based on the internal production environment of the device, the production image of the device, and the external environment of the device; Determine the production level of the device according to the comprehensive environment of the device and the detection of the real-time image of the material produced by the device, including: collecting the comprehensive environment of the device, and the comprehensive environment of the device is dynamically adjusted with the adjustment of the working mode of the device; locate the discharge port of the device, and determine the real-time image of the material produced by the device according to the real-time shooting of the discharge port of the device, and determine the quality level of the material based on the image recognition of the real-time image of the material produced by the device. The quality level of the material is comprehensively judged according to the form of the material produced by the device and the surface quality of the material produced by the device; correlate the comprehensive environment of the device and the quality level of the material, and determine the production level of the device according to the comprehensive environment of the device, the quality level of the material, and the production level matching table; If the production level of the device is lower than the preset production level threshold, determine the abnormal part in the device based on the abnormal detection of the device, and trigger the autonomous regulation of the abnormal part.

2. The detection method of the device environment based on the fiber grating sensor according to claim 1, characterized in that, The description that if the working state of the device is in the online working state, determine the internal production environment of the device according to the three-dimensional model of the device, the spatial positions of each fiber Bragg grating sensor relative to the device, and the induction signals of each fiber Bragg grating sensor, includes: Determine the three-dimensional model of the device according to the model information of the device, the external form of the device, and the preset model database; If the working state of the device is in the online working state, determine the spatial positions of each fiber Bragg grating sensor relative to the device based on the spatial detection of the device and each fiber Bragg grating sensor; Each fiber Bragg grating sensor is distributed at different positions of the device, and determine the induction signals of each fiber Bragg grating sensor based on the dynamic detection of each fiber Bragg grating sensor; Determine multiple internal environmental characteristics based on the three-dimensional model of the device, the spatial positions of each fiber Bragg grating sensor relative to the device, and the sensing signals of each fiber Bragg grating sensor, and determine the internal production environment of the device based on the multiple internal environmental characteristics and the internal production area of the device.

3. The detection method of the device environment based on the fiber grating sensor according to claim 1, characterized in that, The determination of the comprehensive environment of the device based on the internal production environment of the device, the production image of the device, and the external environment of the device includes: Detect the production process of the device in real time and collect the production image of the device. Mark the current position of the device and determine the external environment of the device according to the environmental detection of the current position of the device.

4. The detection method of the device environment based on the fiber Bragg grating sensor according to any one of claims 1 to 3, characterized in that, The determination of the comprehensive environment of the device based on the internal production environment of the device, the production image of the device, and the external environment of the device further includes: In the environmental control of the device, determine the first environmental characteristic of the device according to the internal production environment of the device and the production image of the device, determine the second environmental characteristic of the device according to the internal production environment of the device and the external environment of the device, and determine the comprehensive environment of the device based on the multiple interactions of the first environmental characteristic, the second environmental characteristic, and the transition area from the internal environment to the external environment in the device.

5. The detection method of the device environment based on the fiber Bragg grating sensor according to claim 1, characterized in that, If the production level of the device is lower than the preset production level threshold, then determine the abnormal part in the device based on the abnormality detection of the device and trigger the autonomous regulation of the abnormal part, including: Determine the preset production level threshold based on the model information of the device and the aging level of the device, and compare the production level of the device with the preset production level threshold. If the production level of the device is lower than the preset production level threshold, then determine the corresponding production level difference based on the production level of the device and the preset production level threshold, and determine the abnormal part in the device based on the production level difference, the comprehensive environment of the device, and the real-time image of the material produced by the device.

6. The detection method for the device environment based on the fiber grating sensor according to claim 1 or 5, characterized in that, If the production level of the device is lower than the preset production level threshold, then determine the abnormal part in the device based on the abnormality detection of the device and trigger the autonomous regulation of the abnormal part, further includes: Determine the corresponding autonomous regulation event based on the type of the abnormal part in the device, the location of the abnormal part, and the regulation matching table, and trigger the autonomous regulation of the abnormal part according to the autonomous regulation event.

7. A detection system for the device environment based on fiber Bragg grating sensors, characterized in that, The detection system of the device environment based on the fiber Bragg grating sensor is applied to the detection method of the device environment based on the fiber Bragg grating sensor as described in any one of claims 1-6. The detection system of the device environment based on the fiber Bragg grating sensor includes: The working status module is used to determine the working status of the device based on the current working mode of the device, multiple working parameters of the device, and the aging level of the device, including: collecting the model information of the device, determining the current working mode of the device according to the model information of the device, the working signal of the device, and the working mode matching table, and dynamically collecting multiple working parameters of the device; determining the aging level of the device according to the production time of the device, the working record of the device, and the external form of the device; in the multiple interactions of the current working mode of the device, multiple working parameters of the device, and the aging level of the device, determining a first state coefficient based on the current working mode of the device and multiple working parameters of the device, determining a second state coefficient based on the current working mode of the device and the aging level of the device, and determining the working status of the device according to the first state coefficient, the second state coefficient, and the preset working status matching table, and the working status of the device includes an online working status, an intermediate standby status, or a stop status; The internal production environment module is used to determine the internal production environment of the device according to the three-dimensional model of the device, the spatial position of each fiber Bragg grating sensor relative to the device, and the sensing signal of each fiber Bragg grating sensor if the working status of the device is in the online working status; The comprehensive environment module is used to determine the comprehensive environment of the device based on the internal production environment of the device, the production image of the device, and the external environment of the device; The production level module is used to determine the production level of the device according to the comprehensive environment of the device and the detection of the real-time image of the material produced by the device, including: collecting the comprehensive environment of the device, and the comprehensive environment of the device is dynamically adjusted with the adjustment of the working mode of the device; locating the discharge port of the device, and determining the real-time image of the material produced by the device according to the real-time shooting of the discharge port of the device, determining the quality level of the material based on the image recognition of the real-time image of the material produced by the device, and the quality level of the material is comprehensively judged according to the form of the material produced by the device and the surface quality of the material produced by the device; correlating the comprehensive environment of the device and the quality level of the material, and determining the production level of the device according to the comprehensive environment of the device, the quality level of the material, and the production level matching table; The abnormality detection module is used to determine the abnormal part in the device based on the abnormality detection of the device and trigger the autonomous regulation of the abnormal part if the production level of the device is lower than the preset production level threshold.

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