Method and system for detecting equipment environment based on fiber grating sensor

Abstract

The invention discloses an equipment environment detection method and system based on a fiber grating sensor, and relates to the technical field of equipment environment detection methods, and the comprehensive environment of equipment is determined based on the internal production environment of the equipment, the production image of the equipment and the external environment of the equipment. The internal production environment of the equipment, the production image of the equipment and the external environment of the equipment are integrally controlled, accurate detection of the comprehensive environment of the equipment is achieved, and the accuracy of the comprehensive environment of the equipment is improved. Therefore, the production grade of the equipment is determined according to the comprehensive environment of the equipment and the detection of the real-time image of the material produced by the equipment; and if the production grade of the equipment is lower than the preset production grade threshold value, determining the abnormal part in the equipment based on the abnormal detection of the equipment, and triggering the autonomous regulation and control of the abnormal part, thereby realizing the autonomous regulation and control of the abnormal part, and fully considering the production grade of the equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of device environment detection methods, and in particular to a device environment detection method and system based on a fiber grating sensor. Background Art

[0002] With the development of science and technology, equipment is one of the core of production materials and is used in various factories. Automation equipment is also a kind of equipment. In the existing technology, the internal environment of the equipment is monitored in real time, and the internal image of the equipment is collected. The comprehensive environment of the equipment is determined based on the recognition of the internal image of the equipment. However, the comprehensive environment of the equipment is determined only by the internal image of the equipment, resulting 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 method and system for detecting device environment based on a fiber grating sensor.

[0004] The embodiment of the present invention provides a method for detecting a device environment based on a fiber grating sensor, comprising: Determining an operating state of the device based on a current operating mode of the device, a plurality of operating parameters of the device, and an aging level of the device; If the working state of the equipment is in an online working state, the internal production environment of the equipment is determined according to the three-dimensional model of the equipment, the spatial position of each fiber grating sensor relative to the equipment, and the sensing signal of each fiber grating sensor; determining a comprehensive environment of the device based on an internal production environment of the device, a production image of the device, and an external environment of the device; Determine the production level of the equipment based on the comprehensive environment of the equipment and the real-time image detection 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 in the equipment is determined based on the abnormal detection of the equipment, and autonomous regulation of the abnormal part is triggered.

[0005] The embodiment of the present invention provides a device environment detection system based on a fiber Bragg grating sensor, the device environment detection system based on a fiber Bragg grating sensor is applied to the above-mentioned device environment detection method based on a fiber Bragg grating sensor, and the device environment detection system based on a fiber Bragg grating sensor includes: A working status module, 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; 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 grating sensor relative to the device, and the sensing signal of each fiber grating sensor if the working state of the device is in the online working state; A comprehensive environment module, for determining a comprehensive environment of the device based on an internal production environment of the device, a production image of the device, and an external environment of the device; The production grade module is used to determine the production grade of the equipment based on the comprehensive environment of the equipment and the detection of real-time images of the materials produced by the equipment; The anomaly detection module is used to determine the abnormal part in the equipment based on the anomaly detection of the equipment and trigger autonomous regulation of the abnormal part if the production level of the equipment is lower than the preset production level threshold.

[0006] Compared with the prior art, the present invention has the following beneficial effects: In an 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 an online working state, the internal production environment of the device is determined according to the three-dimensional model of the device, the spatial position of each fiber grating sensor relative to the device and the sensing signal of each fiber 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, thereby realizing multiple interactions of the internal production environment of the device, the production image of the device and the external environment of the device, performing overall management and control of the internal production environment of the device, the production image of the device and the external environment of the device, realizing accurate detection of the comprehensive environment of the device and improving the accuracy of the comprehensive environment of the device.

[0007] Therefore, the production level of the equipment is determined based on the comprehensive environment of the equipment and the detection of 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 in the equipment is determined based on the abnormal detection of the equipment, and autonomous regulation of the abnormal part is triggered, thereby realizing autonomous regulation of the abnormal part and fully considering the production level of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 is a flow chart of a method for detecting a device environment based on a fiber grating sensor in an embodiment of the present invention; Figure 2 is a flow chart of step S11 in the method for detecting a device environment based on a fiber grating sensor in an embodiment of the present invention; Figure 3 is a flow chart of step S12 in the method for detecting a device environment based on a fiber grating sensor in an embodiment of the present invention; Figure 4 is a flow chart of step S13 in the method for detecting a device environment based on a fiber grating sensor in an embodiment of the present invention; Figure 5 is a flow chart of step S14 in the method for detecting a device environment based on a fiber grating sensor in an embodiment of the present invention; Figure 6 is a flow chart of step S15 in the method for detecting a device environment based on a fiber grating sensor in an embodiment of the present invention; Figure 7 It is a schematic diagram of the structural composition of a device environment detection system based on a fiber grating sensor in an embodiment of the present invention. DETAILED DESCRIPTION

[0009] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0010] See also Figures 1 to 7 A method for detecting a device environment based on a fiber grating sensor is applied to a detection scenario of a device environment based on a fiber grating sensor; the method for detecting a device environment based on a fiber grating sensor comprises: Step S11: determining 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; Step S12: if the working state of the equipment is in an online working state, the internal production environment of the equipment is determined according to the three-dimensional model of the equipment, the spatial position of each fiber grating sensor relative to the equipment, and the sensing signal of each fiber grating sensor; Step S13: determining 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; Step S14: determining the production level 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; Step S15: if the production level of the equipment is lower than the preset production level threshold, an abnormal part in the equipment is determined based on the abnormal detection of the equipment, and autonomous regulation of the abnormal part is triggered; refer to Figure 2 , in step S11, determining 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; In the specific implementation process of the present invention, the specific steps are: S111: Collecting 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; S112: Determine the aging level of the equipment according to the production time of the equipment, the working record of the equipment and the external appearance of the equipment; 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, a first state coefficient is determined based on the current working mode of the device and the multiple working parameters of the device, a second state coefficient is determined based on the current working mode of the device and the aging level of the device, and the working state of the device is determined according to the first state coefficient, the second state coefficient and a preset working state matching table. The working state of the device includes an online working state, a mid-way standby state or a stopped state.

[0011] In an embodiment of the present application, the model information of the device is collected, the current working mode of the device is determined based on the model information of the device, the working signal of the device and the working mode matching table, and multiple working parameters of the device are dynamically collected, which is compatible with the overall consideration of the model information of the device, the working signal of the device and the working mode matching table, thereby ensuring the accuracy of the current working mode of the device.

[0012] At this time, the model information of the device is collected and read from the physical tag or electronic tag of the device. At the same time, the working mode matching table is accessed, and the working mode matching table is stored in the control system of the device or the remote server; the entry matching the device model and the working signal is searched in the table; the device working mode information in the matching entry is read, and the list of working parameters that need to be collected is determined according to the current working mode of the device; the values ​​of these working parameters are read in real time through the sensor or communication interface of the device; the read working parameter values ​​are stored in a local or remote database for subsequent analysis.

[0013] Specifically, suppose there is an injection molding machine on an automated production line, and its current working mode needs to be determined and working parameters collected; the model information "XYZ-1234" is read from the physical label of the injection molding machine; the working mode matching table of the injection molding machine is accessed, and it is found that the "XYZ-1234" model device will enter different working modes when receiving specific working signals (such as start signals, pause signals, etc.); currently, the injection molding machine is receiving a stable operating signal, and the matching working mode is found to be "continuous production mode".

[0014] According to the "continuous production mode" of the injection molding machine, the working parameters that need to be collected include injection temperature, injection pressure, injection time, mold temperature, etc.; through the sensor interface of the injection molding machine, the values ​​of these working parameters are read in real time, for example, the injection temperature is 200°C, the injection pressure is 50MPa, the injection time is 10 seconds, and the mold temperature is 60°C; these working parameter values ​​are stored in a remote database for subsequent analysis and optimization of the performance of the injection molding machine.

[0015] Furthermore, the aging level of the equipment is determined according to the production time of the equipment, the working record of the equipment and the external shape of the equipment, which is compatible with the overall consideration of the production time of the equipment, the working record of the equipment and the external shape of the equipment, thereby ensuring the accuracy of the aging level of the equipment.

[0016] At this time, obtain the production date of the equipment as the basic data for evaluating the degree of equipment aging; check the factory records or labels of the equipment, which will include the production date or serial number of the equipment; at the same time, understand the usage history of the equipment, including working hours, workload, fault records, etc., to evaluate the wear and aging of the equipment.

[0017] Access the equipment's maintenance records or log system, which contain the equipment's historical operating data; analyze the equipment's operating hours, especially continuous operating hours and cumulative operating hours, to assess the equipment's fatigue level; check the equipment's failure records, especially recurring failures or parts that require frequent repairs, which are signs of equipment aging; assess the equipment's workload, such as operating pressure, temperature range, vibration level, etc., to determine whether the equipment has experienced excessive use or a harsh working environment.

[0018] Visually inspect the appearance and physical condition 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 casing, connecting parts, display, 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 or indicator lights of the device are working properly, and whether the buttons or control panel are responsive.

[0019] Preliminarily estimate the age and expected life of the equipment based on the equipment's production time and usage time; assess the equipment's wear and tear and potential risks based on the equipment's working records and failure history; identify any obvious signs of aging or potential problems based on the equipment's external morphological inspection results; based on the above information, use preset aging grade assessment standards or models to determine the equipment's aging grade.

[0020] Specifically, suppose there is a CNC machine tool that has been in use for 5 years and its aging level needs to be evaluated; after checking the factory records of the CNC machine tool, it is found that its production date is 2018; after accessing the maintenance log system of the CNC machine tool, it is found that the equipment has worked for more than 4,000 hours in the past 5 years, during which it has experienced several minor faults, mainly tool wear and coolant leakage problems; the equipment's workload is relatively stable, and it mainly operates within the normal operating temperature and pressure range.

[0021] A comprehensive visual inspection of the CNC machine revealed that the casing had some minor scratches and dents, but the overall structure was still solid; the connecting components and cables looked a little aged, but there were no obvious breaks or damages; the display and control panel worked properly, and the buttons were responsive.

[0022] Based on the equipment's production time (5 years), cumulative working time (more than 4,000 hours), failure history (minor failures, mainly wear of wearing parts) and external morphology inspection results (minor scratches and dents, aging of connecting parts and cables), the aging level of this CNC machine tool is assessed as "moderately aged", which means that although the equipment is still able to operate, it requires more frequent maintenance and inspection, especially the replacement and repair of wearing parts and connecting parts.

[0023] Therefore, 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, the first state coefficient is determined based on the current working mode of the device and multiple working parameters of the device, the second state coefficient is determined based on the current working mode of the device and the aging level of the device, and the working state of the device is determined according to the first state coefficient, the second state coefficient and a preset working state matching table. The working state of the device includes an online working state, a mid-way standby state or a stopped state, which is compatible with 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 device.

[0024] At this time, multiple interactions are performed on the current working mode of the device, multiple working parameters of the device, and the aging level of the device, and a first state coefficient and a second state coefficient are introduced.

[0025] For the first state coefficient, the first state coefficient is determined based on the current working mode of the device and multiple working parameters of the device. At this time, according to the current working mode of the device, a group of key working parameters related to it are selected; each working parameter is normalized so that its value falls within a preset range (such as 0-1); according to the normalized working parameter value, a comprehensive score or coefficient is calculated, that is, the first state coefficient; the first state coefficient is obtained by weighted summation.

[0026] For the second state coefficient, the second state coefficient is determined based on the current working mode of the device and the aging level of the device. At this time, a group of aging indicators related to the current working mode and aging level of the device are selected; each aging indicator is quantitatively evaluated to obtain a quantitative value; based on the quantitative value, a comprehensive score or coefficient is calculated, namely the second state coefficient; the calculation method of this coefficient is similar to that of the first state coefficient, but different factors are considered.

[0027] Furthermore, the current working state of the device is determined by combining the first state coefficient and the second state coefficient and a preset working state matching table. At this time, the preset working state matching table is accessed, which contains the working states of the device corresponding to different state coefficient combinations; the calculated first state coefficient and second state coefficient are compared with the values ​​in the matching table; based on the closest matching result, the current working state of the device is determined, such as online working state, midway standby state or stop state.

[0028] Specifically, suppose there is a cooling system in a data center, and its current working status needs to be determined; for the first state coefficient, the current working mode is "normal operating mode"; the key working parameters include the temperature, flow rate and pressure of the cooling medium; these parameters are normalized, for example, the temperature of the cooling medium is normalized to 0.8 (indicating that the current temperature is at 80% of the preset range); through the weighted average method, the first state coefficient is calculated to be 0.9 (indicating that the equipment is operating well in the current working mode).

[0029] For the second state coefficient, the aging level of the equipment is "moderate aging"; the aging indicators include the energy efficiency ratio, leakage rate and vibration level of the cooling system; these indicators are quantitatively evaluated, for example, the energy efficiency ratio has dropped by 10%, the leakage rate has increased slightly, and the vibration level is within the normal range; through comprehensive evaluation, the second state coefficient is calculated to be 0.7 (indicating that the performance of the equipment has declined due to aging).

[0030] Access the preset working status matching table; in the working status matching table, find the closest match to the first state coefficient 0.9 and the second state coefficient 0.7; based on the matching result, determine that the current working status of the device is "online working status, but pay attention to the influence of aging"; this means that although the device is still operating normally, due to the influence of aging, more frequent maintenance and inspection are required to ensure its continued stable operation.

[0031] In one embodiment of the present application, it is assumed that there is a generator whose current operating mode is "normal operating mode", key operating parameters include output voltage, output current and oil temperature, and aging indicators include operating hours and insulation resistance.

[0032] Calculate the first state 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 state coefficient = 0.90.4 + 0.850.3 +0.95*0.3 = 0.885.

[0033] Calculate the second state coefficient, quantify the aging indicators and weights: operating hours: 8000 hours (quantitative score: 0.8, assuming the total life is 10000 hours), weight: 0.6; insulation resistance: 10MΩ (quantitative score: 0.9, assuming the normal range is 10-100MΩ), weight: 0.4; second state coefficient = 0.80.6 + 0.90.4 = 0.84.

[0034] The first state coefficient 0.885 and the second state coefficient 0.84 both fall within the ranges of "0.7-0.9" and "0.5-0.8"; therefore, the current working state of the device is "mid-way standby state".

[0035] Preset working status matching table: First state coefficient The second state coefficient Working status >0.9 >0.8 Online work status 0.7-0.9 0.5-0.8 Mid-term standby mode <0.7 <0.5 Stop state or maintenance refer to Figure 3 , in step S12, if the working state of the equipment is in an online working state, the internal production environment of the equipment is determined according to the three-dimensional model of the equipment, the spatial position of each fiber grating sensor relative to the equipment, and the sensing signal of each fiber grating sensor; In the specific implementation process of the present invention, the specific steps are: S121: determining a three-dimensional model of the device according to the model information of the device, the external shape of the device, and a preset model database; S122: if the working state of the device is in an online working state, determining the spatial position of each fiber Bragg grating sensor relative to the device based on the spatial detection of the device and each fiber Bragg grating sensor; S123: Distributing the fiber grating sensors at different locations of the device, and determining the sensing signals of the fiber grating sensors based on the dynamic detection of the fiber grating sensors; S124: determining a plurality of internal environment features based on a three-dimensional model of the device, a spatial position of each fiber grating sensor relative to the device, and multiple identifications of sensing signals of each fiber grating sensor, and determining an internal production environment of the device based on the plurality of internal environment features and an internal production area of ​​the device; In an embodiment of the present application, the three-dimensional model of the device is determined based on the model information of the device, the external form of the device and a preset model database, which is compatible with the overall consideration of the model information of the device, the external form of the device and a preset model database to ensure the accuracy of the three-dimensional model of the device.

[0036] At this time, the model information of the device, the external form of the device, and the preset model database are introduced. The appearance and structure of the device are visually inspected to assist in confirming or correcting the three-dimensional model obtained from the model information; the three-dimensional model matching the device model and form is searched in the model database, and at the same time, the model information of the device is used as a keyword to search for matching three-dimensional models in the model database; if there are multiple matching items in the database, further screening is performed based on the external form characteristics of the device, and then the most suitable three-dimensional model is selected from the model database to represent the device. Optionally, the actual external form of the device is compared with the three-dimensional model in the database, and the most suitable model is selected.

[0037] Specifically, suppose there is an industrial robot with model number XYZ-1234, and a three-dimensional model needs to be determined for it; read the model number 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 approximately 1.5 meters, and an end effector is a gripper; record the overall shape, color, and position and form of each joint of the industrial robot.

[0038] Search the keyword "XYZ-1234" in the model database to find multiple matching three-dimensional models; further screen the matching models according to the external morphological features of the industrial robot, such as arm length, number of joints and end effector type; select a three-dimensional model that best matches the actual morphology of the industrial robot from the screened models; the model has six degrees of freedom, the arm length is consistent with the actual arm length of the industrial robot, and the end effector is a gripper, which is consistent with the industrial robot; confirm that the model is a three-dimensional model of the industrial robot, and use it for subsequent analysis and visualization work.

[0039] Furthermore, if the device is in an online working state, the spatial position of each fiber grating sensor relative to the device is determined based on the spatial detection of the device and each fiber grating sensor, thereby ensuring the accuracy of the spatial position of each fiber grating sensor relative to the device.

[0040] At this time, confirm whether the device is currently in an online working state through the device's control system or status indicator light; if the device is not in an online working state, wait or start the device to an online state.

[0041] If the equipment is in an online working state, perform spatial detection on the equipment and each fiber grating sensor. At this time, check the installation position of the fiber grating sensor to ensure that it is firmly fixed on the equipment and the connecting line is not damaged; start the spatial detection system, such as a laser rangefinder, a 3D scanner or a machine vision system; scan or measure the equipment according to the requirements of the detection system to obtain the spatial position data of the fiber grating sensor; ensure that the equipment remains stationary during the detection process to avoid measurement errors caused by equipment movement.

[0042] 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 shape of the device; save the processed spatial position data for subsequent analysis and use.

[0043] Specifically, suppose there is a wind turbine on which multiple fiber grating sensors are installed to monitor the strain of the blades; now, it is necessary to determine the spatial position of these sensors relative to the wind turbine; through the control system of the wind turbine, it is confirmed that it is currently in an online working state and the blades are rotating; because spatial detection needs to be performed when the blades are stationary, the operation of the wind turbine is temporarily stopped.

[0044] Check the fiber grating sensor on the blade to confirm that it is firmly fixed on the blade surface and the connecting line is not damaged; confirm that the connection between the sensor and the detection system is normal and can transmit detection data; use a 3D scanner to scan the blades of the wind turbine to obtain 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; based on the scanning results, identify the specific position of the fiber grating sensor on the blade surface.

[0045] 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 blades. Mark the specific position of the fiber grating sensor in the processed coordinate data for subsequent analysis and use.

[0046] Furthermore, each fiber grating sensor is distributed at a different position of the device, and the sensing signal of each fiber grating sensor is determined based on the dynamic detection of each fiber grating sensor, thereby ensuring the accuracy of the sensing signal of each fiber grating sensor.

[0047] At this time, the fiber grating sensors are distributed in different key positions of the equipment to comprehensively monitor the operating status of the equipment. At the same time, the installation position of the fiber grating sensor is determined according to the structural characteristics of the equipment and monitoring requirements; the fiber grating sensor is firmly fixed on the equipment to ensure that it can collect data stably; ensure that the connection line of the sensor is not damaged and can smoothly transmit the sensing signal.

[0048] Start the dynamic detection system connected to the fiber Bragg grating sensor to collect and process the sensing 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 sensing signals from the fiber Bragg grating sensor during operation.

[0049] Furthermore, the fiber Bragg grating sensor is used to collect the operating parameters of the equipment in real time, such as strain, temperature, pressure, etc. At this time, during the operation of the equipment, the fiber Bragg grating sensor will produce corresponding spectral changes according to the physical changes (such as strain and temperature changes) at its location; the dynamic detection system monitors these spectral changes and converts them into digital signals for collection and storage; ensuring that the collected sensing signals are accurate and continuous and can reflect the actual operating status of the equipment.

[0050] The collected sensing signals are processed and analyzed to extract useful information for equipment monitoring and diagnosis. At this time, the collected data is pre-processed by filtering, denoising and other operations; according to the characteristics of the fiber grating sensor and the monitoring requirements of the equipment, the characteristic parameters related to the equipment operation status are extracted; the processed sensing signals and characteristic parameters are stored and backed up for subsequent analysis and use.

[0051] Therefore, multiple internal environmental features are determined based on the three-dimensional model of the equipment, the spatial position of each fiber grating sensor relative to the equipment, and multiple identifications of the sensing signals of each fiber grating sensor, and the internal production environment of the equipment is determined based on the multiple internal environmental features and the internal production area of ​​the equipment. This is compatible with the overall consideration of the three-dimensional model of the equipment, the spatial position of each fiber grating sensor relative to the equipment, and multiple identifications of the sensing signals of each fiber grating sensor, thereby ensuring the accuracy of multiple internal environmental features.

[0052] At this point, the three-dimensional model of the equipment, the spatial position of the fiber Bragg grating sensor, the sensing signal and other information are integrated 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 reflect the internal structure, layout and production area of ​​the equipment; mark the spatial position of the fiber Bragg grating sensor determined in the previous step on the three-dimensional model; collect and organize the sensing signals of all fiber Bragg grating sensors to ensure the accuracy and timeliness of the signals.

[0053] By analyzing the sensing signals of the fiber Bragg grating sensor, combined with the three-dimensional model of the equipment and the sensor position, multiple internal environmental characteristics of the equipment are determined; at this time, according to the characteristics and working principles of the fiber Bragg grating sensor, the physical quantities represented by the sensing signals (such as temperature, strain, pressure, etc.) are interpreted; combined with the three-dimensional model of the equipment and the sensor position, the spatial distribution and change trend of these physical quantities inside the equipment are analyzed; based on the analysis results, multiple internal environmental characteristics of the equipment are determined, such as temperature gradient, stress distribution, vibration mode, etc.

[0054] According to the internal environmental 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 environmental 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 reduced production efficiency, product quality problems or equipment failures, etc.

[0055] Furthermore, based on the above analysis, the internal production environment status of the equipment is determined, and corresponding improvement measures are proposed. At this time, the internal production environment of the equipment is comprehensively evaluated based on the analysis results of the internal environment characteristics and the production area. Problems or potential risks in the internal production environment are identified, such as local overheating, stress concentration areas, vibration sources, etc.; specific improvement measures are proposed for these problems or risks, such as adjusting the production process, optimizing equipment layout, and strengthening maintenance.

[0056] Specifically, suppose there is a large injection molding machine on which multiple fiber grating sensors are installed to monitor parameters such as temperature, strain and pressure in the mold area; now, the internal production environment of the injection molding machine needs to be determined based on this information; an accurate three-dimensional model of the injection molding machine has been obtained, which shows in detail the internal structure of the injection molding machine, the mold area and the layout of each component; fiber grating sensors have been installed at key locations in the mold area and marked on the three-dimensional model; the sensing signals of all sensors have been collected and organized, including time series data of parameters such as temperature, strain and pressure.

[0057] By analyzing the sensing signals of the fiber grating sensor, it was found that there was an obvious gradient distribution of temperature in the mold area, and local overheating occurred in some locations; the strain data showed that the mold was subjected to greater mechanical stress during the injection molding process, especially during the opening and closing of the mold; the pressure sensor detected that the pressure distribution inside the mold was uneven, and there were local high-pressure areas.

[0058] 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 melting, injection and molding of plastics; local overheating causes degradation of plastic materials and affects product quality; stress concentration accelerates mold wear and shortens service life; uneven pressure distribution leads to poor product molding.

[0059] Based on the above analysis, it is determined that the internal production environment of the injection molding machine has problems such as local overheating, stress concentration and uneven pressure distribution. To address 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 on the mold; improve mold design and optimize pressure distribution.

[0060] In one embodiment of the present application, the internal environment matching table: Sensor No. Position Description Induction signal type Signal value Internal environment characteristics Production area impact S1 Mold top temperature 200°C high temperature Causes plastic degradation S2 Mold bottom temperature 180°C Moderate temperature normal S3 Injection cylinder pressure 10 MPa high pressure Influence of injection speed S4 Mould opening and closing strain 0.2% Low strain normal S5 cooling water channel temperature 25°C Low temperature Normal cooling effect According to the internal environment matching table, it was determined that the internal production environment of the equipment had problems such as overheating of the mold top and high pressure of the injection cylinder, and these characteristics had an adverse effect on the production area.

[0061] refer to Figure 4 , in step S13, determining 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; In the specific implementation process of the present invention, the specific steps are: S131: Real-time detection of the production process of the equipment and collection of production images of the equipment; S132: Mark the current location of the device, and determine the external environment of the device based on the environment detection of the current location of the device; S133: In the environmental control of the device, the first environmental feature of the device is determined according to the internal production environment of the device and the production image of the device, the second environmental feature of the device is determined according to the internal production environment of the device and the external environment of the device, and the comprehensive environment of the device is determined based on 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.

[0062] In an embodiment of the present application, the production process of the equipment is detected in real time, and the production image of the equipment is collected; the current position of the equipment is marked, and the external environment of the equipment is determined based on the environmental detection of the current position of the equipment, thereby ensuring the accuracy of the external environment of the equipment.

[0063] At this time, the production process of the equipment is detected in real time. According to the actual situation of the production process, the parameters of the monitoring equipment are set to capture key production images. At the same time, according to factors such as production rhythm and lighting conditions, the frame rate, exposure time, white balance and other parameters of the monitoring equipment are set to ensure that clear and accurate images can be captured in key production links.

[0064] During the production process, the production images of the equipment are collected in real time to provide data support for subsequent analysis; the monitoring equipment is started to start the real-time collection of production images; it is ensured that the image data can be transmitted to the monitoring center or storage device in real time for subsequent viewing and analysis. Furthermore, an image database is established to store the collected production images according to time, equipment, production links, etc.; the security and accessibility of the image data are ensured to facilitate subsequent analysis.

[0065] At the same time, mark the current location of the equipment, and select appropriate environmental sensors (such as temperature and humidity sensors, light sensors, gas sensors, etc.) according to the location of the equipment and the characteristics of the production environment; deploy sensors at key locations around the equipment to ensure that external environmental conditions can be fully and accurately detected; at the same time, connect the sensors to the data acquisition system to obtain environmental data in real time.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] Process and analyze the collected environmental data; for example, use statistical methods to identify outliers and trends in environmental data, and use machine learning methods to identify patterns and association rules in environmental data; through analysis, find that the temperature in a certain section of the assembly line is too high, exceeding the range of normal equipment operation; based on environmental data and analysis results, evaluate whether the external environment of the assembly line equipment is suitable for equipment operation and production requirements; determine that there is an overheating problem in this section, which has an adverse effect on the normal operation of the equipment and product quality; at the same time, record the evaluation results in the equipment management system, and notify relevant departments to take corresponding measures to improve or optimize, such as adding air-conditioning equipment, adjusting the production line layout, etc.

[0072] Furthermore, in the environmental control of the equipment, the first environmental characteristic of the equipment is determined according to the internal production environment of the equipment and the production image of the equipment, the second environmental characteristic of the equipment is determined according to the internal production environment of the equipment and the external environment of the equipment, and the comprehensive environment of the equipment is determined 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 equipment; 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 equipment are realized, and the comprehensive environment of the equipment is further accurately controlled.

[0073] At this time, the first environmental characteristics and the second environmental characteristics are introduced in the environmental control of the equipment. For the first environmental characteristics, the first environmental characteristics of the equipment are determined according to the internal production environment of the equipment and the production image of the equipment. At this time, built-in sensors (such as temperature sensors, humidity sensors, pressure sensors, etc.) are used to monitor the temperature, humidity, pressure and other environmental parameters inside the equipment; image processing technology (such as object tracking) is used to analyze the production image of the equipment to extract key information, such as material flow status, equipment operation status, product quality, etc.; combined with the internal environment monitoring data and production image analysis results, the first environmental characteristics of the equipment are extracted. The first environmental characteristics include temperature distribution, humidity level, material flow speed, equipment vibration frequency, etc.

[0074] For the second environmental characteristics, the second environmental characteristics of the equipment are determined according to the internal production environment of the equipment and the external environment of the equipment. At this time, external sensors (such as temperature sensors, humidity sensors, light sensors, gas sensors, etc.) are used to monitor the temperature, humidity, light intensity, harmful gas concentration and other environmental parameters around the equipment; the interaction between the internal environment and the external environment is analyzed, such as heat exchange, gas flow, light influence, etc.; this requires the use of physical models or simulation technology to simulate these interactions; combined with the external environment monitoring data and the interactive analysis results of the internal environment and the external environment, the second environmental characteristics of the equipment are extracted. These characteristics include temperature difference, humidity gradient, degree of light influence, diffusion range of harmful gases, etc.

[0075] Identify the transition area from the internal environment of the equipment to the external environment, and analyze the multiple interactions in these areas; at this time, based on the structure and function of the equipment, identify the transition area between the internal environment and the external environment, such as the inlet and outlet of the equipment, heat dissipation holes, vents, etc.; analyze the multiple interactions in these transition areas, such as heat transfer, gas exchange, material flow, etc.; this requires the use of advanced detection technology or simulation models to accurately describe these interactions; evaluate the degree of impact of these interactions on the internal and external environments of the equipment, as well as their impact on equipment operating efficiency and product quality.

[0076] Based on the multiple interactions of the first environmental characteristic, the second environmental characteristic, and the transition area, the overall environmental condition of the equipment is comprehensively evaluated; at this time, the multiple interaction data of the first environmental characteristic, the second environmental characteristic, and the transition area are integrated into a comprehensive evaluation model; the comprehensive evaluation model is used to evaluate the overall environmental condition of the equipment to output a comprehensive evaluation result, including the environmental health status of the equipment, potential environmental risk points, and recommended improvement measures.

[0077] Specifically, suppose there is a food processing company with an automated packaging machine. To determine the comprehensive environmental conditions of this packaging machine, follow these steps: 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 material flow status, packaging film tension and product arrangement; extract the first environmental characteristics, such as the uniformity of internal temperature distribution, whether the humidity level is appropriate, and the stability of air pressure.

[0078] Determine the second environmental characteristics: Use external sensors to monitor the temperature, humidity, and light intensity around the packaging machine; analyze the interaction 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 packaging materials.

[0079] Transition zones are introduced to identify transition zones such as inlets and outlets, heat dissipation holes and vents of packaging machines; multiple interactions in these zones are analyzed, such as heat transfer, gas exchange and material flow; and the extent of the impact of these interactions on the internal and external environments of the packaging machine, as well as their impact on packaging efficiency and product quality, are evaluated.

[0080] The multiple interaction data of the first environmental characteristics, the second environmental characteristics and the transition area are integrated into a comprehensive evaluation model; the comprehensive evaluation model is used to evaluate the overall environmental status of the packaging machine; the comprehensive evaluation results are output to indicate the environmental health status of the packaging machine (such as whether there are problems such as overheating, overhumidity 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 location of the packaging machine, etc.).

[0081] In one embodiment of the present application, the feature matching table is as follows: refer to Figure 5 , in step S14, the production level of the equipment is determined based on the comprehensive environment of the equipment and the detection of the real-time image of the material produced by the equipment; In the specific implementation process of the present invention, the specific steps are: S141: Collecting the comprehensive environment of the device, where the comprehensive environment of the device is dynamically regulated as the working mode of the device is regulated; S142: Locate the material outlet of the equipment, and determine the real-time image of the material produced by the equipment based on the real-time shooting of the material outlet of the equipment, and 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 shape of the material produced by the equipment and the surface quality of the material produced by the equipment; S143: Associating the comprehensive environment of the equipment and the quality grade of the material, and determining 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.

[0082] In the embodiment of the present application, the comprehensive environment of the equipment is collected, and the comprehensive environment of the equipment is dynamically regulated as the working mode of the equipment is regulated; the discharge port of the equipment is located, and the real-time image of the material produced by the equipment is determined based on the real-time shooting of the discharge port of the equipment, and the quality grade of the material is determined based on the image recognition of the real-time image of the material produced by the equipment, and the quality grade of the material is comprehensively judged according to the shape of the material produced by the equipment and the surface quality of the material produced by the equipment; At this point, the comprehensive environment of the equipment is introduced, and the comprehensive environment of the equipment is subsequently controlled. At the same time, the discharge port of the equipment is located, and the equipment is measured using high-precision measurement tools (such as laser rangefinders, 3D scanners) to determine the exact position of the discharge port; the position of the discharge port is marked on the equipment to facilitate the installation of a camera or image acquisition device; based on the layout of the equipment and the production process, the best shooting angle and position are selected to ensure that the complete material image can be captured.

[0083] Install a high-definition camera or image acquisition device at the discharge port and ensure that the camera is well connected to the image acquisition system; set the camera's shooting parameters, such as resolution, frame rate, exposure time, etc., to obtain clear and stable images; enable the real-time shooting function to transmit the images captured by the camera to the image recognition system in real time for processing.

[0084] Perform pre-processing operations such as denoising and contrast enhancement on the image to improve image quality; apply image recognition methods (such as convolutional neural networks) to process the image to identify the morphology and surface characteristics of the material; extract key features of the material such as size, shape, color, texture, etc. to provide a basis for subsequent quality grade determination.

[0085] The quality grade of the material is comprehensively judged based on the material's shape and surface quality. At this time, a material quality grade judgment standard is formulated to clarify the material shape and surface quality requirements under different quality grades. The extracted material characteristics are compared with the judgment standard, and the material quality grade is determined based on the comparison results. The material quality grade information is recorded and associated with the material's production batch, time, etc., to facilitate subsequent tracking and analysis.

[0086] Specifically, suppose there is an automobile parts manufacturing company, which has an injection molding machine used to produce automobile parts; in order to determine the quality grade of the parts produced by the injection molding machine, follow the steps below: use a laser rangefinder to measure the injection molding machine to determine the exact location of the discharge port (mold exit); mark the location of the discharge port on the injection molding machine and install a high-definition camera next to it; adjust the shooting angle and position of the camera to ensure that the complete image of the parts can be captured.

[0087] Enable the real-time shooting function of the camera to transmit the captured component images to the image recognition system in real time; set the camera resolution to 1920x1080 and the frame rate to 30fps to ensure the clarity and stability of the image. Perform denoising and contrast enhancement on the captured component images to improve image quality; apply convolutional neural networks to process the images to identify the morphology (such as size, shape) and surface features (such as scratches, bubbles, defects, etc.) of the components; extract key features of the components, such as length, width, height, surface roughness, etc.

[0088] Establish quality grade criteria for parts, such as Grade A (flawless), Grade B (minor defects), Grade C (serious defects), etc.; compare the extracted component features with the criteria, such as whether the size is within the allowable range, whether there are obvious scratches or bubbles on the surface, etc.; based on the comparison results, determine that the quality grade of the component is Grade A (flawless), and record this information; at the same time, associate the component with the production batch, time, etc. to facilitate subsequent tracking and analysis.

[0089] Furthermore, the comprehensive environment of the equipment and the quality grade of the materials are associated, and the production grade of the equipment is determined based on the comprehensive environment of the equipment, the quality grade of the materials and the production grade matching table, which is compatible with the overall consideration of the comprehensive environment of the equipment, the quality grade of the materials and the production grade matching table to ensure the accuracy of the production grade of the equipment.

[0090] At this time, the equipment comprehensive environment data and material quality grade data are integrated and the association between them is established; at the same time, the historical data and real-time data of the equipment comprehensive environment are extracted from the database, including parameters such as temperature, humidity, air pressure, vibration, etc.; at the same time, the material quality grade data are extracted, including information such as the morphology and surface quality of different batches of materials; data association technology (such as timestamp matching, batch number matching, etc.) is used to correspond the environmental data with the material quality grade data to form an associated data set.

[0091] According to the different combinations of equipment comprehensive environment and material quality grades, a production grade matching table is prepared. At this time, historical data is analyzed to find out the correlation between the equipment comprehensive environment and material quality grades and production results (such as yield rate, failure rate); according to these rules, the equipment comprehensive environment and material quality grades are divided into different intervals or grades; a production grade is assigned to each interval or grade combination, such as level one (excellent), level two (good), level three (general), etc.; these grade combinations and corresponding production grades are recorded in the matching table.

[0092] The production grade of the current equipment is determined based on the comprehensive equipment environment data and material quality grade data collected in real time. At this time, the data collected in real time is compared with the intervals or grades in the matching table. The interval or grade combination that best matches the real-time data is found. The production grade of the current equipment is determined based on the production grade corresponding to the combination in the matching table.

[0093] Specifically, suppose there is an electronic product manufacturing company, which has an automated production line for assembling mobile phones. To determine the production grade of this production line, follow these steps: extract real-time environmental data collected by various sensors on the production line from the database, such as temperature, humidity, air pressure, etc.; extract quality grade data of materials during the mobile phone assembly process, such as whether the screen has scratches, whether the camera is installed correctly, etc.; use batch number and timestamp to match environmental data with material quality grade data to form a related data set.

[0094] By analyzing historical data, it was found that when the ambient temperature of the production line was 20-25℃ and the humidity was 40%-60%RH, the material quality grade was higher and the yield rate was also higher; the ambient temperature was divided into three intervals: below 20℃, 20-25℃, and above 25℃; the humidity was divided into three intervals: below 40%RH, 40%-60%RH, and above 60%RH; the material quality grade was divided into two levels: qualified and unqualified; a matching table was prepared, such as when the ambient temperature was 20-25℃, the humidity was 40%-60%RH and the material quality grade was qualified, the production grade was level one; other combinations were assigned corresponding production grades based on the analysis results of historical data.

[0095] Collect the environmental data and material quality grade data of the production line in real time; compare the real-time data with the intervals or grades in the matching table, and find that the current ambient temperature is 23°C, the humidity is 50%RH, and the material quality grade is qualified; according to the matching table, determine that the production grade of the current production line is level one.

[0096] In one embodiment of the present application, comprehensive environmental data of the equipment (such as temperature, humidity, vibration, etc.) and quality grade data of the materials are collected; a production grade matching table is prepared based on historical data and industry experience; this table associates different equipment environmental intervals and material quality grade combinations with specific production grades; the comprehensive environment and material quality grades of the equipment are monitored in real time, and they are compared with the intervals in the matching table to find the best matching combination, thereby determining the current production grade.

[0097] Production grade table: Equipment environment (temperature) Equipment environment (humidity) Material quality grade Production level 20-25°C 40%-60%RH A-level Level 1 25-30°C 40%-60%RH A-level Level 2 <20°C or >30°C Any value Any level Level 3 Any value >60%RH B or C Level 3 Note: In this example, when the equipment temperature is between 20-25°C, the humidity is between 40%-60%RH, and the material quality grade is A, the production grade is determined to be Level 1; if the equipment temperature exceeds this range, or the humidity is too high, or the material quality grade decreases, the production grade will be reduced accordingly.

[0098] refer to Figure 6, in step S15, if the production level of the equipment is lower than the preset production level threshold, an abnormal part in the equipment is determined based on the abnormal detection of the equipment, and autonomous regulation of the abnormal part is triggered; In the specific implementation process of the present invention, the specific steps are: S151: determining a 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; S152: if the production level of the equipment is lower than the preset production level threshold, determining a corresponding production level difference based on the production level of the equipment and the preset production level threshold, and determining an abnormal part in the equipment based on the production level difference, the comprehensive environment of the equipment, and a real-time image of the material produced by the equipment; S153: Determine a corresponding autonomous control event based on the type of the abnormal part in the device, the location of the abnormal part, and the control matching table, and trigger autonomous control of the abnormal part according to the autonomous control event.

[0099] In an embodiment of the present application, a preset production grade threshold is determined based on the model information of the device and the aging level of the device, and the production grade of the device and the preset production grade threshold are compared, thereby introducing the production grade of the device and the preset production grade threshold.

[0100] At this point, obtain the specific model and current aging status of the equipment in order to subsequently find the corresponding preset production level threshold. At the same time, obtain the model information of the equipment from the equipment management system or equipment tag, including manufacturer, model code, production date, etc.; evaluate the aging level of the equipment based on factors such as the equipment's operating time, maintenance records, and component wear; 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).

[0101] According to the model information and aging level of the equipment, the corresponding preset production level threshold is searched in the database or experience table; at this time, the model information and aging level of the equipment are used as query conditions to search in the database; if there is a corresponding record in the database, the preset production level threshold in the record is extracted; if there is no corresponding record in the database, a reasonable preset production level threshold is estimated based on experience or industry standards.

[0102] Evaluate whether the production performance of the current equipment is up to standard so as to take necessary regulatory measures later. At this time, obtain the production grade data of the current equipment from the production monitoring system; compare the production grade of the equipment with the preset production grade threshold to determine whether the production performance of the equipment is up to standard; if the production grade of the equipment is higher than or equal to the preset production grade threshold, it is considered that the production performance of the equipment is up to standard; if the production grade of the equipment is lower than the preset production grade threshold, it is considered that the production performance of the equipment is not up to standard and further regulatory measures are required.

[0103] Specifically, suppose there is a food manufacturing company, which has a packaging machine with model number XYZ-123; in order to determine the preset production level threshold of the packaging machine and make a comparison, follow the steps below: obtain the model information of the packaging machine from the equipment label as XYZ-123, and the production date is 2018; based on the operating time and maintenance record of the packaging machine, evaluate its aging level as "moderate aging".

[0104] In the equipment management system database, the model information "XYZ-123" and the aging level "moderate aging" are used as query conditions for retrieval; the corresponding records are found, and the preset production level threshold of 85 points is extracted.

[0105] The production grade data of the current packaging machine obtained from the production monitoring system is 80 points; by comparing 80 points with the preset production grade threshold of 85 points, it is found that the production grade of the equipment is lower than the preset threshold; therefore, it is considered that the production performance of the packaging machine does not meet the standards, and further regulatory measures are needed, such as adjusting equipment parameters, optimizing production processes or performing equipment maintenance, to improve its production grade.

[0106] Furthermore, if the production level of the equipment is lower than a preset production level threshold, the corresponding production level difference is determined based on the production level of the equipment and the preset production level threshold, and the abnormal part in the equipment is determined based on the production level difference, the comprehensive environment of the equipment and the real-time image of the material produced by the equipment. This takes into account the overall consideration of the production level difference, the comprehensive environment of the equipment and the real-time image of the material produced by the equipment, thereby ensuring the accuracy of the abnormal part in the equipment.

[0107] At this time, the gap between the production performance of the equipment and the preset standard is quantified to provide a basis for subsequent analysis and regulation. At this time, the production grade of the equipment and the preset production grade threshold are obtained from step S151; the production grade difference is calculated, that is, the preset production grade threshold minus the production grade of the equipment; for example, if the preset production grade threshold is 90 points and the production grade of the equipment is 80 points, the production grade difference is 10 points.

[0108] Understand the current working environment status of the equipment to determine whether environmental factors have an impact on the production performance of the equipment; at this time, collect comprehensive environmental data of the equipment, including temperature, humidity, vibration, air pressure and other parameters; analyze whether these parameters are within the normal range and whether they are associated with the production grade difference; for example, if the equipment temperature is too high, it will cause the performance of some components to decline, thus affecting the production grade.

[0109] By observing the real-time images of materials produced by the equipment, quality problems or production anomalies can be discovered. At this time, real-time images of the materials are obtained from the production monitoring system. Quality checks are performed on the images, such as observing whether the materials have defects, whether the sizes are consistent, whether the colors are uniform, etc. The quality problems in the images are linked to the production grade differences to determine whether there is a decline in production performance due to material problems.

[0110] Based on the above analysis, determine which parts of the equipment have problems that lead to a decline in production levels. At this time, conduct a comprehensive analysis based on the production level difference, the results of the comprehensive environmental analysis of the equipment, and the real-time image inspection of the materials. After excluding environmental factors and material problems, focus on the equipment itself, such as sensors, controllers, actuators and other components. Determine the abnormal parts inside the equipment that are most likely to cause a decline in production levels.

[0111] Specifically, suppose there is an automobile manufacturing plant, in which there is a stamping machine of model ABC-567; in step S151, it has been determined that the production grade of the stamping machine is 82 points, and the preset production grade threshold is 90 points, so the production grade difference is 8 points; next, a detailed analysis is performed according to step S152: Preset production level threshold: 90 points; equipment production level: 82 points; production level difference: 90 - 82 = 8 points. At the same time, the collected equipment comprehensive environmental data include: 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 results: the equipment comprehensive environmental parameters are within the normal range, so environmental factors are not the main reason for the decline in production level.

[0112] The real-time images of the materials obtained from the production monitoring system showed that the stamped automotive parts were of uniform size, smooth surface and without obvious defects. The analysis results showed that the material quality was good and was not the reason for the decline in production grade. Based on the above analysis, environmental factors and material problems were excluded and the focus was placed on the stamping machine itself. Further observation of the operating status of the stamping machine revealed that the motion trajectory of the stamping head was slightly offset and the stamping force was unstable. Therefore, it was determined that the abnormal parts of the stamping machine were the stamping head and the related control system.

[0113] Therefore, the corresponding autonomous control event is determined based on the type of abnormal part in the equipment, the location of the abnormal part and the control matching table, and the autonomous control of the abnormal part is triggered according to the autonomous control event, thereby ensuring the accuracy of the autonomous control event and realizing the autonomous control of the abnormal part.

[0114] At this point, it is necessary to identify which parts of the equipment have problems and the specific locations of these problems, so as to provide accurate targets for subsequent regulation. At the same time, the type (such as sensor failure, motor overheating, control system error, etc.) and location (such as the top of the equipment, an internal module, transmission system, etc.) of the abnormal parts in the equipment are obtained from step S152. Ensure that the type and location information of the abnormal parts are accurate so that subsequent regulation measures can be implemented accurately.

[0115] According to the type and location of the abnormal part, the corresponding autonomous control events and control measures are found in the control matching table; at this time, the control matching table is a predefined database or table, which lists the autonomous control events and control measures corresponding to different devices and different abnormal parts; use the type and location of the abnormal part as the query condition to search in the control matching table; after finding the matching autonomous control events and control measures, record the relevant information for subsequent execution.

[0116] Clarify the autonomous control events to be executed to ensure that the control measures can produce effects on specific problems. At this time, determine the autonomous control events that match the current abnormal part based on the retrieval results of the control matching table; autonomous control events include adjusting equipment parameters, starting spare parts, sending alarm information, triggering emergency shutdown, etc.; ensure that the autonomous control events match the nature of the problem in the abnormal part.

[0117] Execute autonomous control measures to solve abnormal problems inside the equipment and restore the normal production performance of the equipment; at this time, trigger corresponding control measures based on the determined autonomous control events; this involves operations such as sending control signals to the equipment, starting the backup system, and adjusting equipment settings; monitor the execution of control measures to ensure that the abnormal parts are properly handled; after the control measures are executed, re-evaluate the production level of the equipment to verify the control effect.

[0118] Specifically, suppose there is a chemical plant, in which there is a reactor of 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 the sensor is located in the heating chamber of the reactor; next, detailed operations are performed according to step S153: 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 search in the control matching table; The matching autonomous control event found is "start the backup temperature sensor and turn off the faulty sensor".

[0119] According to the retrieval results of the control matching table, the autonomous control event is determined to be "starting the backup temperature sensor and turning off the faulty sensor"; at the same time, a control signal is sent to the reactor to start the backup temperature sensor; at the same time, the faulty temperature sensor is turned off to prevent it from continuing to affect the normal operation of the heating system; the operation of the backup temperature sensor is monitored to ensure that the heating system can accurately measure and control the temperature; after the control measures are implemented, the production level of the reactor is re-evaluated, and it is found that the production level has improved, which verifies the effectiveness of the control measures.

[0120] In one embodiment of the present application, in step S152, the type of abnormal part in the equipment (such as motor overheating, sensor failure, etc.) and the specific location 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".

[0121] Autonomous control event matching table: Use the type (such as "sensor failure") and location (such as "heating system") of the abnormal part as query conditions to find the corresponding autonomous control event in the control matching table; in this example, the matching autonomous control event "Start the backup sensor and turn off the faulty sensor" is found.

[0122] At the same time, the temperature sensor of the reactor heating system is faulty and located in the heating chamber. The matching autonomous control event is found to be "start the backup sensor and turn off the faulty sensor". Assuming the sensor fault weight is 5 and the heating chamber weight is 2, the score is 10 and control is given priority. Start the backup temperature sensor and turn off the faulty sensor. Send a control signal to the reactor to execute autonomous control measures. Monitor the operation of the backup 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.

[0123] See also Figure 7 , Figure 7 : is a schematic diagram of the structure of a device environment detection system based on a fiber grating sensor in an embodiment of the present invention; the device environment detection system based on a fiber grating sensor includes: A working status module 21, configured to determine the working status of the device based on the current working mode of the device, a plurality of working parameters of the device, and an aging level of the device; The internal production environment module 22 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 grating sensor relative to the device, and the sensing signal of each fiber grating sensor if the working state of the device is in the online working state; A comprehensive environment module 23, for determining a comprehensive environment of the device based on an internal production environment of the device, a production image of the device, and an external environment of the device; The production grade module 24 is used to determine the production grade of the equipment based on the comprehensive environment of the equipment and the detection of real-time images of materials produced by the equipment; The abnormality detection module 25 is used to determine the abnormal part in the equipment based on the abnormality detection of the equipment and trigger the autonomous regulation of the abnormal part if the production level of the equipment is lower than the preset production level threshold.

[0124] The technical features of the above embodiments are arbitrarily combined. In order to make the description concise, 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, they should be considered to be within the scope of this specification.

Claims

1. A method for detecting a device environment based on a fiber grating sensor, characterized in that: include: Determining an operating state of the device based on a current operating mode of the device, a plurality of operating parameters of the device, and an aging level of the device; If the working state of the equipment is in an online working state, the internal production environment of the equipment is determined according to the three-dimensional model of the equipment, the spatial position of each fiber grating sensor relative to the equipment, and the sensing signal of each fiber grating sensor; determining a comprehensive environment of the device based on an internal production environment of the device, a production image of the device, and an external environment of the device; Determine the production level of the equipment based on the comprehensive environment of the equipment and the real-time image detection of the materials produced by the equipment, including: collecting the comprehensive environment of the equipment, and dynamically adjusting the comprehensive environment of the equipment 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 based on the real-time shooting of the discharge port of the equipment, and 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 based on the shape of the material produced by the equipment and the surface quality of the material produced by the equipment; If the production level of the equipment is lower than the preset production level threshold, the abnormal part in the equipment is determined based on the abnormal detection of the equipment, and autonomous regulation of the abnormal part is triggered.

2. The method for detecting device environment based on fiber grating sensor according to claim 1, characterized in that: The determining 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 includes: 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; Determine the aging level of the equipment based on the production time of the equipment, the working record of the equipment and the external appearance of the equipment; 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, a first state coefficient is determined based on the current working mode of the device and the multiple working parameters of the device, a second state coefficient is determined based on the current working mode of the device and the aging level of the device, and the working state of the device is determined according to the first state coefficient, the second state coefficient and a preset working state matching table. The working state of the device includes an online working state, a mid-way standby state or a stopped state.

3. The method for detecting device environment based on fiber grating sensor according to claim 2, characterized in that: If the working state of the equipment is in an online working state, the internal production environment of the equipment is determined according to the three-dimensional model of the equipment, the spatial position of each fiber grating sensor relative to the equipment, and the sensing signal of each fiber grating sensor, including: Determine the three-dimensional model of the device according to the model information of the device, the external shape of the device and a preset model database; If the working state of the device is in an online working state, the spatial position of each fiber grating sensor relative to the device is determined based on the spatial detection of the device and each fiber grating sensor; The fiber grating sensors are distributed at different positions of the device, and the sensing signals of the fiber grating sensors are determined based on the dynamic detection of the fiber grating sensors; Based on the three-dimensional model of the equipment, the spatial position of each fiber grating sensor relative to the equipment and multiple identifications of the sensing signals of each fiber grating sensor, multiple internal environmental features are determined, and the internal production environment of the equipment is determined based on the multiple internal environmental features and the internal production area of ​​the equipment.

4. The method for detecting device environment based on fiber grating sensor according to claim 1, characterized in that: The determining 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 equipment in real time and collect the production images of the equipment; The current location of the device is marked, and the external environment of the device is determined based on the environment detection of the current location of the device.

5. The method for detecting device environment based on fiber grating sensor according to any one of claims 1 to 4, characterized in that: Determining 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 also includes: In the environmental control of the equipment, the first environmental characteristics of the equipment are determined according to the internal production environment of the equipment and the production image of the equipment, the second environmental characteristics of the equipment are determined according to the internal production environment of the equipment and the external environment of the equipment, and the comprehensive environment of the equipment is determined based on multiple interactions of the first environmental characteristics, the second environmental characteristics and the transition area from the internal environment to the external environment in the equipment.

6. The method for detecting device environment based on fiber grating sensor according to claim 1, characterized in that: The method of determining the production level 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 also includes: The comprehensive environment of the equipment and the quality grade of the materials are associated, and the production grade of the equipment is determined based on the comprehensive environment of the equipment, the quality grade of the materials and the production grade matching table.

7. The method for detecting device environment based on fiber grating sensor according to claim 1, characterized in that: If the production level of the equipment is lower than the preset production level threshold, an abnormal part in the equipment is determined based on the abnormal detection of the equipment, and autonomous regulation of the abnormal part is triggered, including: Determine a 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 equipment is lower than the preset production level threshold, the corresponding production level difference is determined based on the production level of the equipment and the preset production level threshold, and the abnormal part within the equipment is determined based on the production level difference, the comprehensive environment of the equipment and the real-time image of the material produced by the equipment.

8. The method for detecting device environment based on fiber grating sensor according to claim 1 or 7, characterized in that: If the production level of the equipment is lower than the preset production level threshold, the abnormal part in the equipment is determined based on the abnormal detection of the equipment, and the autonomous regulation of the abnormal part is triggered, which also includes: Based on the type of the abnormal part in the device, the location of the abnormal part and the control matching table, a corresponding autonomous control event is determined, and autonomous control of the abnormal part is triggered according to the autonomous control event.

9. A device environment detection system based on fiber grating sensor, characterized in that: The device environment detection system based on the fiber Bragg grating sensor is applied to the device environment detection method based on the fiber Bragg grating sensor as claimed in any one of claims 1 to 8, and the device environment detection system based on the fiber Bragg grating sensor comprises: A working status module, 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; 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 grating sensor relative to the device, and the sensing signal of each fiber grating sensor if the working state of the device is in the online working state; A comprehensive environment module, for determining a comprehensive environment of the device based on an internal production environment of the device, a production image of the device, and an external environment of the device; The production grade module is used to 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, including: collecting the comprehensive environment of the equipment, and dynamically adjusting the comprehensive environment of the equipment as the working mode of the equipment is adjusted; locating the discharge port of the equipment, and determining the real-time image of the material produced by the equipment according to the real-time shooting of the discharge port of the equipment, and determining 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 shape of the material produced by the equipment and the surface quality of the material produced by the equipment; The anomaly detection module is used to determine the abnormal part in the equipment based on the anomaly detection of the equipment and trigger autonomous regulation of the abnormal part if the production level of the equipment is lower than the preset production level threshold.

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