An explosion-proof on-line stress monitoring system and stress monitoring method from -40°C to 600°C

Abstract

The present invention provides an explosion-proof on-line stress monitoring system and a stress monitoring method from -40°C to 600°C. The high-temperature alloy substrate of the stress monitoring system eliminates the thermal expansion difference through seamless welding, combines with a temperature compensation algorithm to dynamically correct the thermal drift error, and ensures the monitoring accuracy under extreme temperatures. A magnetic coupling isolator is used to cut off the direct electrical connection between the power management module and the signal processing module, and the power consumption of the monitoring module is dynamically managed through a time-sharing power supply strategy, minimizing the power consumption to compress the total system power consumption within the explosion-proof certification threshold. While ensuring the real-time monitoring and rapid alarm capabilities of high-risk equipment, the system battery life is significantly extended, effectively balancing the contradictory requirements of safety protection and energy consumption control, and realizing remote data transmission without manual intervention through the RS485 communication module, ensuring personnel safety.

Description

Technical Field

[0001] The present invention relates to the field of stress monitoring, and particularly to an explosion-proof on-line stress monitoring system and a stress monitoring method from -40°C to 600°C. Background Art

[0002] In high-temperature, low-temperature, and flammable and explosive industrial scenarios, existing stress monitoring technologies mainly collect equipment deformation signals through strain gauges. Their working principle relies on a single protective layer (such as a metal shell) to resist environmental impacts, adopts a continuous power supply mode to maintain circuit operation, and collects data at a fixed frequency. However, the existing protective structure lacks hierarchical heat insulation and explosion-proof design. Extreme temperatures can easily cause heat conduction or bursting of the shell, and the internal circuit becomes unstable due to temperature rise. Secondly, traditional monitoring devices stack circuit components, which easily leads to uneven heat dissipation and serious electromagnetic interference, increasing the risk of equipment overheating in high-temperature environments and having limited battery life. The fixed acquisition strategy cannot dynamically optimize the monitoring frequency according to the stress distribution of the equipment, and continuous high-frequency acquisition in the whole area causes waste of resources. Summary of the Invention

[0003] In view of the above problems, the present application provides an explosion-proof on-line stress monitoring system and a stress monitoring method from -40°C to 600°C, which can meet the requirements of real-time stress monitoring of storage tanks, pressure vessels, pressure pipelines, etc.

[0004] To achieve the above object, in a first aspect, the present invention provides an explosion-proof on-line stress monitoring system from -40°C to 600°C, including an explosion-proof housing, a first circuit module, a second circuit module, a third circuit module and a strain sensing module. The explosion-proof housing includes an alumina ceramic protective layer, a stainless steel armor layer and a nano-porous heat insulation layer from outside to inside. The first circuit module is arranged inside the explosion-proof housing and includes a signal processing module, an instrumentation amplifier, a low-pass filter, an analog-to-digital converter and a control unit. The signal processing module is electrically connected to the instrumentation amplifier, the low-pass filter and the analog-to-digital converter in sequence, and the analog-to-digital converter is electrically connected to the control unit. The second circuit module is arranged inside the explosion-proof housing and is adjacent to the first circuit module. The second circuit module includes a battery and a power management module. The battery is electrically connected to the power management module. The power management module is configured to manage the circuit by time sharing. The power management module is electrically connected to the signal processing module, the instrumentation amplifier, the low-pass filter, the analog-to-digital converter and the control unit through a magnetic coupling isolator. The third circuit module is arranged on the top of the explosion-proof housing. The third circuit module includes an RS485 communication module. The RS485 communication module is electrically connected to the control unit, and the RS485 communication module is also connected to an external cable through an insulating terminal. The strain sensing module is arranged at the bottom of the explosion-proof housing. The strain sensing module is electrically connected to the signal processing module. The strain sensing module includes a ceramic substrate strain gauge, a temperature sensor and a high-temperature resistant alloy substrate. The high-temperature resistant alloy substrate is welded to the outer surface of the device to be measured. The high-temperature resistant alloy substrate has a first groove, and the opening of the first groove faces the device to be measured. The ceramic substrate strain gauge is embedded in the first groove, and the ceramic substrate strain gauge abuts against the device to be measured. The temperature sensor is in contact connection with the ceramic substrate strain gauge.

[0005] In some embodiments, the power management module is configured to supply power to the instrumentation amplifier and the analog-to-digital converter when a first preset condition is met. The first preset condition is when deforming signals of the device to be measured are collected.

[0006] And / or, the power management module is configured to selectively supply power to the first circuit module and the third circuit module when a second preset condition is met. The second preset condition is that the stress value calculated by the control unit according to the collected deforming signals is within a preset stress threshold range.

[0007] In some embodiments, an air buffer layer is provided between the nano-porous heat insulation layer and the first circuit module, and the air buffer layer is filled with inert gas.

[0008] In a second aspect, the present invention further provides an explosion-proof on-line stress monitoring method from -40°C to 600°C, which is applicable to the stress monitoring system described in the first aspect. The number of stress monitoring systems is multiple, and the multiple stress monitoring systems are distributed on the outer surface of the device to be measured in a preset manner. The method includes:

[0009] Obtain the characteristic information of the device under test, construct a stress distribution model based on the characteristic information, and generate a preset acquisition strategy according to the position information of the point to be measured and the stress distribution model. The characteristic information includes at least one of the structural parameters, device category, material parameters, and environmental parameters of the device under test;

[0010] Timely collect and obtain the deformation signals of the device under test at the point to be measured collected by the local strain sensing module according to the preset acquisition strategy;

[0011] Preprocess the deformation signals, including numerical amplification, filtering and noise reduction, and analog-to-digital conversion, to obtain multiple first strain information;

[0012] At the same time, obtain the first temperature information collected by the current strain sensing module, and correct the first strain information according to the first temperature information to obtain multiple second strain information;

[0013] And input the multiple second strain information into the stress distribution model;

[0014] Perform an anomaly judgment on each second strain information in the stress distribution model. If the stress value of the second strain information exceeds the preset stress threshold, record it as abnormal strain information;

[0015] Obtain the position information, temperature value, and device status of the abnormal strain information and generate a first abnormal signal, and send the first abnormal signal to the server through the RS485 communication module. The first abnormal signal is configured as a MODBUS protocol data frame.

[0016] In some embodiments, correcting the first strain information according to the first temperature information to obtain multiple second strain information includes:

[0017] Perform temperature correction on the first strain information according to the first temperature information to obtain the first correction information, which is represented by formula (1). Formula (1) is as follows:

[0018] ε co =ε ra ×[1+α(T cu -T re )]+β(T cu -T r e ) 2

[0019] In formula (1), ε co is the first correction information, ε ra is the first strain information, α is the first-order temperature coefficient, T cu is the first temperature information, β is the second-order temperature coefficient, and T re is the calibration reference temperature;

[0020] Perform Kalman filtering on the first correction information to obtain second strain information, including:

[0021] Construct a first state equation, which is represented by formula (2), and formula (2) is as follows:

[0022] x k = x k-1 + w k ;

[0023] In formula (2), x k is the second strain information at the k-th moment, x k-1 is the second strain information at the (k - 1)-th moment, w k is the process noise caused by environmental vibration or electromagnetic interference at the k-th moment, w k ~ N(0, Q), where Q is the covariance matrix of the process noise;

[0024] Construct a first observation equation, which is represented by formula (3), and formula (3) is as follows:

[0025] z k = x k + v k ;

[0026] In formula (3), z k is the first correction information at the k-th moment, v k is the observation noise caused by circuit noise or temperature compensation residuals at the k-th moment, v k ~ N(0, R), where R is the covariance matrix of the observation noise.

[0027] In some embodiments, the first strain information is corrected according to the first temperature information to obtain a plurality of second strain information, including:

[0028] Perform Kalman filtering on the first strain information to obtain second correction information, including:

[0029] Construct a second state equation, which is represented by formula (4), and formula (4) is as follows:

[0030] x k ' = x k-1 '+ w k ';

[0031] In formula (4), x k ' is the second correction information at the k-th moment, x k-1 ' is the second correction information at the (k - 1)-th moment, w k ' is the process noise caused by environmental vibration or electromagnetic interference at the k-th moment, w k' to N'(0, Q'), where Q' is the covariance matrix of the process noise;

[0032] Construct a second observation equation, which is represented by formula (5) as follows:

[0033] z k ' = x k ' + v k ';

[0034] In formula (5), z k ' is the first strain information at the k-th moment, and v k ' is the observation noise caused by circuit noise or temperature compensation residuals at the k-th moment. v k ' ~ N'(0, R'), where R' is the covariance matrix of the observation noise;

[0035] Perform temperature correction on the second correction information according to the first temperature information to obtain the second strain information, which is represented by formula (6) as follows:

[0036] ε co ' = ε ra ' × [1 + α'(T cu ' - T re ')] + β'(T cu ' - T re '); 2

[0037] In formula (6), ε co ' is the second strain information, ε ra ' is the second correction information, α' is the first-order temperature coefficient, T cu ' is the first temperature information, β' is the second-order temperature coefficient, and T re ' is the calibration reference temperature.

[0038] In some embodiments, the preset acquisition strategy is obtained through the following steps:

[0039] Perform stress simulation calculations on each measurement point in the stress distribution model to obtain multiple stress simulation values;

[0040] Divide the position information of multiple measurement points into a high-stress set, a medium-stress set, and a low-stress set according to the stress simulation values;

[0041] Set the measurement points in the high-stress set to collect deformation signals at the first preset frequency, set the measurement points in the medium-stress set to collect deformation signals at the second preset frequency, and set the measurement points in the low-stress set to collect deformation signals at the third preset frequency;

[0042] Judging one by one whether the change rate of the deformation signal is within the range of the preset change rate threshold;

[0043] If not, record the position information corresponding to the deformation signal as abnormal position information, and obtain the set type to which the abnormal position information belongs. The set type includes one of the high stress set, the medium stress set, and the low stress set. If the abnormal position information belongs to the low stress set, divide it into the medium stress set. If the abnormal position information belongs to the medium stress set, divide it into the high stress set;

[0044] If so, determine whether the set type to which the position information of the current deformation signal originally belongs is consistent with the set type to which it currently belongs;

[0045] If the set type to which the position information of the deformation signal originally belongs is inconsistent with the set type to which it currently belongs, divide the position information of the deformation signal into the set type to which it originally belongs.

[0046] In some embodiments, determine one by one whether the change rate of the deformation signal is within the range of the preset change rate threshold. If not, the preset acquisition strategy is also obtained through the following steps:

[0047] Record the position information corresponding to the deformation signal as abnormal position information, and obtain all the position information within the verification area to be verified divided by taking the abnormal position information as the center and the preset size as the radius, and record it as auxiliary position information;

[0048] Construct an abnormal stress verification set, and divide the abnormal position information and the auxiliary position information into the abnormal stress verification set;

[0049] Set the deformation signal to be collected at the fourth preset frequency for the measurement points in the abnormal stress verification set.

[0050] In some embodiments, perform an abnormal judgment on each second strain information in the stress distribution model. If the stress value of the second strain information exceeds the preset stress threshold, record it as abnormal strain information, including:

[0051] Obtain the second stress information corresponding to the deformation signal of each measurement point in the abnormal stress verification set within the preset time period, and record it as the first verification stress information;

[0052] Input the first verification stress information into the stress distribution model for dynamic simulation to judge the stress change state within the verification area;

[0053] Perform state information matching on the stress change state. The state information includes the normal fluctuation state, the abnormal fluctuation state, and the emergency fluctuation state;

[0054] When the stress change state belongs to the normal fluctuation state, divide the position information in the abnormal stress verification set into the set type to which it originally belongs;

[0055] When the stress change state belongs to the abnormal fluctuation state, the first abnormal signal is configured to be generated according to the position information and temperature value in the abnormal stress verification set;

[0056] When the stress change state belongs to the emergency fluctuation state, the first abnormal signal is configured to be generated according to the position information and temperature value in the abnormal stress verification set, and further, a first alarm message is generated and sent to the server.

[0057] In some embodiments, the method further includes:

[0058] The stress detectors corresponding to the position information in the medium stress set and the low stress set are in deep sleep when not collecting data;

[0059] The stress detectors corresponding to the position information in the high stress set are in light sleep when not collecting data.

[0060] Different from the prior art, the above technical solution has the following beneficial effects:

[0061] The present invention provides an explosion-proof on-line stress monitoring system from -40°C to 600°C, which can realize safe and reliable real-time stress monitoring capabilities in extreme temperature environments. Through the multi-layer composite explosion-proof housing structure, combined with the synergistic effect of alumina ceramics, stainless steel armor and nano-porous heat insulation materials, it effectively resists extreme temperature shocks from -40°C to 600°C and external deflagration risks, ensuring the stable operation of internal electronic components. The split circuit design physically isolates the core processing module, power supply module and communication module, cooperates with magnetic coupling isolation technology to block circuit interference, and combines an intelligent time-sharing power supply mechanism to dynamically control the working states of the first circuit module, the second circuit module and the third circuit module according to signal acquisition requirements, significantly reducing the overall energy consumption while ensuring measurement accuracy. The strain sensing module is directly welded to the device surface through a high-temperature resistant alloy substrate, and with the dual detection structure of an embedded ceramic substrate strain gauge and a contact temperature sensor, it realizes synchronous and accurate acquisition of deformation signals and temperature data in high-temperature environments.

[0062] The above description of the invention content is only an overview of the technical solution of this application. In order to enable those of ordinary skill in the art to understand the technical solution of this application more clearly, and then to implement it according to the content recorded in the description and the drawings, and in order to make the above objects, other objects, features and advantages of this application more easily understood, the following is described in conjunction with the specific embodiments and drawings of this application. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The drawings are only used to illustrate the principles, implementation methods, applications, features and effects of the specific embodiments of the present invention and other related contents, and should not be considered as a limitation to this application.

[0064] In the accompanying drawings of the specification:

[0065] Figure 1 is the specific circuit diagram of the on-line stress monitoring system described in the specific embodiment;

[0066] Figure 2 is the schematic cross-sectional structure diagram of the explosion-proof enclosure described in the specific embodiment;

[0067] Figure 3 is the specific structure schematic diagram of the strain sensing module described in the specific embodiment;

[0068] Figure 4 is the partial circuit diagram of the on-line stress monitoring system described in the specific embodiment;

[0069] Figure 5 is the simplified circuit diagram of the strain bridge described in the specific embodiment;

[0070] Figure 6 is the step schematic diagram of steps S101 to S107 of the on-line stress monitoring method described in the specific embodiment.

[0071] The description of the reference numerals involved in the above-mentioned accompanying drawings is as follows:

[0072] 1. Explosion-proof enclosure;

[0073] 11. Alumina ceramic protective layer;

[0074] 12. Stainless steel armored layer;

[0075] 13. Nano-porous heat insulation layer;

[0076] 14. Air buffer layer;

[0077] 2. First circuit module;

[0078] 21. Signal processing module;

[0079] 22. Instrumentation amplifier;

[0080] 23. Low-pass filter;

[0081] 24. Analog-to-digital converter;

[0082] 25. Control unit;

[0083] 3. Second circuit module;

[0084] 31. Battery;

[0085] 32. Power management module;

[0086] 4. Third circuit module;

[0087] 41. RS485 communication module;

[0088] 5. Strain sensing module;

[0089] 51. Ceramic substrate strain gauge;

[0090] 52. Temperature sensor;

[0091] 53. High-temperature resistant alloy substrate;

[0092] 6. Device under test. Specific embodiments

[0093] To illustrate in detail the possible application scenarios, technical principles, specific implementable solutions, achievable objectives and effects of this application, etc., the following is a detailed description in conjunction with the listed specific embodiments and accompanied by drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of this application, so they are only examples and cannot be used to limit the protection scope of this application.

[0094] Referring to "embodiments" herein means that the specific features, structures or characteristics described in conjunction with the embodiments may be included in at least one embodiment of this application. The term "embodiment" appearing in various positions in the specification does not necessarily refer to the same embodiment, nor is it particularly limited to the independence or relevance with other embodiments. In principle, in this application, as long as there is no technical contradiction or conflict, the technical features mentioned in each embodiment can be combined in any way to form corresponding implementable technical solutions.

[0095] Unless otherwise defined, the meanings of the technical terms used herein are the same as those commonly understood by those skilled in the technical field to which this application belongs; the use of the relevant terms herein is only for describing specific embodiments and is not intended to limit this application.

[0096] In the description of this application, the term "and / or" is an expression used to describe the logical relationship between objects, indicating that there can be three relationships, for example, A and / or B, which means: the existence of A, the existence of B, and the simultaneous existence of A and B. In addition, the character " / " herein generally represents an "or" logical relationship between the associated objects before and after.

[0097] In this application, terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual quantity, primary-secondary or order relationship between these entities or operations.

[0098] In the absence of further limitations, in this application, the use of the terms "comprising", "including", "having" or other similar open-ended expressions in a statement is intended to cover non-exclusive inclusion. These expressions do not exclude the possibility that there may be additional elements in the process, method or product that includes the said elements. Thus, in a process, method or product that includes a series of elements, it may include not only those defined elements, but also other elements not explicitly listed, or elements inherent to such a process, method or product.

[0099] Similar to the understanding in the Examination Guidelines, in this application, expressions such as "greater than", "less than", "exceeding" are understood not to include the number itself; expressions such as "above", "below", "within" are understood to include the number itself. In addition, in the description of the embodiments of this application, the meaning of "a plurality of" is two or more (including two). Similar expressions related to "many", such as "multiple groups", "multiple times", etc., are understood in the same way, unless otherwise specifically defined.

[0100] In the description of the embodiments of this application, the spatial-related expressions used, such as "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "perpendicular", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the specific embodiment or the drawing. This is only for the convenience of describing the specific embodiments of this application or for the reader's understanding, rather than indicating or implying that the device or component referred to must have a specific position, a specific orientation, or be constructed or operated in a specific orientation. Therefore, it should not be construed as a limitation on the embodiments of this application.

[0101] The processor described in the embodiments of the present application can be implemented by hardware, firmware, software, or a combination thereof, and can use circuits, one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), central processing units (CPUs), controllers, microcontrollers, microprocessors, or at least one of other physical, biological, or chemical structures capable of implementing functions similar to or equivalent to those of the above-listed processors, such as biological neurons, quantum computing units, DNA computing units, etc., so that the processor can execute some steps, all steps, or any combination of the steps mentioned in the computer programs or methods involved in the various embodiments of the present application.

[0102] The computer program involved in the embodiment can be stored in a computer-readable storage medium, which includes but is not limited to magnetic disks, magnetic tapes, magnetic cards, floppy disks, flash memories, optical discs, optical cards, read-only memories (ROMs), random access memories (RAMs), erasable programmable ROMs (EPROMs), and electrically erasable programmable ROMs (EEPROMs), etc. It also includes other biological, physical, or chemical structures that can achieve the same or equivalent functions as the above-listed storage media, such as units with information storage capabilities like DNA, RNA, proteins, etc. In a specific embodiment, the storage medium involved can be one of the above medium types or a combination of the above medium types. In different embodiments, the computer program involved in the embodiment can be stored centrally in a single medium or distributed among multiple media. The memory containing the computer-readable storage medium can be a non-volatile memory or a random access memory. These computer-readable storage media can be built into the device or connected to the device involved in the embodiment as an external device or a part of an external device. In some embodiments, the memory with the computer-readable storage medium is deployed locally; in other embodiments, a scheme of deploying the memory away from the processor can also be adopted, such as a network-attached memory accessed via an RF circuit or an external port and a communication network, where the communication network can be the Internet, one or more internal networks, local area networks (LANs), wide area wireless networks (WLANs), storage area networks (SANs), etc., or a suitable combination thereof, as long as the computer device can access the memory. In addition, the computer program involved in the embodiment can be stored in plaintext / ciphertext form or designed as training data and integrated and recombined implicitly and stored in the parameter states of a deep neural network or other machine learning models through model training.

[0103] The stress monitoring system shown in this embodiment is applicable to any scenario that requires stress monitoring. Preferably, it is applicable to special equipment. Specifically, special equipment includes equipment such as boilers, pressure vessels (including gas cylinders), pressure pipelines, elevators, lifting machinery, passenger ropeways, large-scale amusement facilities, etc., which pose greater risks to personal and property safety. Further, it also includes the safety accessories and safety protection devices belonging to them.

[0104] The stress monitoring system shown in this embodiment is adapted to stress monitoring in a special environment from -40 degrees Celsius to 600 degrees Celsius.

[0105] Please refer to Figures 1 to 5, To achieve the above object, in the first aspect, this embodiment provides an explosion-proof on-line stress monitoring system from -40°C to 600°C, including an explosion-proof housing 1, a first circuit module 2, a second circuit module 3, a third circuit module 4 and a strain sensing module 5. The explosion-proof housing 1 includes an alumina ceramic protective layer 11, a stainless steel armor layer 12 and a nano-porous heat insulation layer 13 from outside to inside; the first circuit module 2 is arranged inside the explosion-proof housing 1 and includes a signal processing module 21, an instrumentation amplifier 22, a low-pass filter 23, an analog-to-digital converter 24, and a control unit 25. The signal processing module 21 is electrically connected to the instrumentation amplifier 22, the low-pass filter 23 and the analog-to-digital converter 24 in sequence, and the analog-to-digital converter 24 is electrically connected to the control unit 25; the second circuit module 3 is arranged inside the explosion-proof housing 1 and is adjacent to the first circuit management module. The second circuit module 3 includes a battery 31 and a power management module 32. The battery 31 is electrically connected to the power management module 32. The power management module 32 is configured to manage the circuit in a time-sharing manner. The power management module 32 is electrically connected to the signal processing module 21, the instrumentation amplifier 22, the low-pass filter 23, the analog-to-digital converter 24, and the control unit 25 through a magnetic coupling isolator; the third circuit module 4 is arranged on the top of the explosion-proof housing 1. The third circuit module 4 includes an RS485 communication module 41. The RS485 communication module 41 is electrically connected to the control unit 25, and the RS485 communication module 41 is also connected to an external cable through an insulating terminal; the strain sensing module 5 is arranged at the bottom of the explosion-proof housing 1. The strain sensing module 5 is electrically connected to the signal processing module 21. The strain sensing module 5 includes a ceramic substrate strain gauge 51, a temperature sensor 52 and a high-temperature resistant alloy substrate 53. The high-temperature resistant alloy substrate 53 is welded to the outer surface of the device under test 6. The high-temperature resistant alloy substrate 53 has a first groove, and the opening of the first groove faces the device under test 6. The ceramic substrate strain gauge 51 is embedded in the first groove, and the ceramic substrate strain gauge 51 abuts against the device under test. The temperature sensor 52 is in contact connection with the ceramic substrate strain gauge 51.

[0106] In this embodiment, the explosion-proof housing 1 includes an alumina ceramic protective layer 11, a stainless steel armor layer 12 and a nano-porous heat insulation layer 13 from outside to inside, achieving hierarchical heat insulation, ensuring that even when the external temperature reaches 550°C, the internal circuit can still maintain a temperature ≤ 80°C, and realizing stable protection in the range of -40 to 600°C. In addition, the joints of the explosion-proof housing 1 adopt a metal-ceramic composite sealing ring, and helium mass spectrometry leak detection (leak rate < 1×10 -9 Pa·m 3 / s), and can pass the GB 3836.4 Ex ia IICT4 explosion-proof certification. Further, the thickness of the nano-porous heat insulation layer 13 is 5 mm, and the thermal conductivity ≤ 0.03 W / (m·K), which is coated around the internal circuit modules (i.e., the first circuit module 2 and the second circuit module 3). In addition, the internal circuit of the explosion-proof shell 1 meets the safety requirements, with the total capacitance ≤ 10 nF, the total inductance ≤ 10 μH, and a fast-fusing fuse is connected in series at the power input end, with a rated current of 30 mA and a response time ≤ 0.1 ms to prevent sparks caused by short circuits.

[0107] The first circuit module 2, the second circuit module 3, and the third circuit module 4 are physically isolated by function, optimizing the circuit layout and reducing the uneven heat dissipation and mutual interference. The second circuit module 3 is closely attached to the side wall of the explosion-proof shell 1 to dissipate heat using the stainless steel armored layer 12; the third circuit module 4 is independently placed at the top to avoid the influence of cable bending stress on the internal circuit. Further, the battery 31 of the second circuit module 3 is a 12V lithium thionyl chloride battery 31 (capacity ≥ 19 Ah), and the power management module 32 is electrically connected to the signal processing module 21, the instrumentation amplifier 22, the low-pass filter 23, the analog-to-digital converter 24, and the control unit 25 through a magnetic coupling isolator. The instrumentation amplifier 22 can be INA128; the low-pass filter 23 can be a second-order Butterworth low-pass filter (cut-off frequency 50 Hz) to eliminate power frequency interference; the magnetic coupling isolator is arranged at the junction of the side wall and the central area of the explosion-proof shell 1, and the direct electrical connection between the power management area and the signal processing area is cut off through the magnetic coupling device to reduce the influence of each component and improve safety; the power management module 32 provides dynamic power for the signal processing module 21 and the communication module through a time-sharing management circuit. The third circuit module 4 includes an RS485 communication module 41. The RS485 communication module 41 is an integrated isolation type ADM2483 chip, supporting the MODBUS-RTU protocol, with a baud rate adaptive range of 9600 - 115200 bps. The interface terminal between the RS485 communication module 41 and the external cable adopts a metal-ceramic composite sealing structure, and the sealing structure is embedded in the top groove of the explosion-proof shell 1. The RS485 communication module 41 provides online communication for providing an alarm when monitoring an anomaly.

[0108] The ceramic substrate strain gauge 51 of the strain sensing module 5 is a zirconia toughened ceramic, with a nano-aluminum oxide layer covering its surface to prevent high-temperature oxidation and mechanical wear. The gauge factor GF = 2.1 ± 0.1. The high-temperature resistant alloy substrate 53 is used to collect stress signals. Its material is Inconel 718 (temperature resistance 700 °C), and it is laser welded to the surface of the device under test through a high-temperature solder (Au-Sn alloy) to form a seamless connection, avoiding deformation caused by thermal expansion differences and eliminating the thermal stress error of the traditional installation method. The temperature sensor 52 is set as a PT100 temperature sensor 52, which is installed closely to the ceramic substrate strain gauge 51 and connected to the signal processing module 21 through a flexible wire, for collecting the ambient temperature in real time and correcting the stress value through a temperature compensation algorithm, which is beneficial to achieving precise monitoring.

[0109] The stress sensor provided in this embodiment includes an explosion-proof housing 1, a first circuit module 2, a second circuit module 3, a third circuit module 4, and a strain sensing module 5. The measuring range reaches ±5000 με, and the minimum resolution is 1 με. It can measure the stress values of each measurement point of storage tanks, pressure vessels, pressure pipelines, etc., and can prevent explosion risks. No electric sparks or high temperatures will be generated to cause accidents when used in flammable and explosive chemical environments. The high-temperature resistant alloy substrate 53 and the ceramic substrate strain gauge 51 eliminate thermal expansion differences through seamless welding, cooperate with the PT100 temperature sensor 52 installed closely to collect the ambient temperature in real time, and combine with the compensation algorithm to dynamically correct the thermal drift error to ensure that the stress value is not interfered by extreme temperatures from -40 °C to 600 °C. At the same time, the alumina ceramic protective layer 11, stainless steel armor layer 12, and nano-porous heat insulation layer 13 of the explosion-proof housing 1 gradually block the external high temperature, making the internal circuit temperature stable ≤ 80 °C, ensuring the stable operation of the core module in a wide temperature range. The magnetic coupling isolator is used to cut off the direct electrical connection between the power management module 32 and the signal processing module 21, and transfer energy through magnetic coupling, avoiding sparks caused by power fluctuations or faults. At the same time, the time-sharing power supply strategy dynamically distributes the energy of the battery 31, reducing circuit heating and mutual interference. In addition, the RS485 module uses a metal-ceramic composite sealing interface and an isolated communication chip to achieve remote data transmission without manual intervention in flammable and explosive environments. Its adaptive baud rate and anti-interference protocol ensure stable signal transmission under complex working conditions, avoiding the risk of personnel entering dangerous areas for operation. The three work together to significantly improve the stress monitoring accuracy and safety in high-temperature flammable and explosive environments.

[0110] In some embodiments, the power management module 32 is configured to supply power to the instrumentation amplifier 22 and the analog-to-digital converter 24 when a first preset condition is met, and the first preset condition is when collecting the deformation signal of the device under test 6;

[0111] And / or, when the power management module 32 is configured to meet the second preset condition, it selectively supplies power to the first circuit module 2 and the third circuit module 4. The second preset condition is that the stress value calculated by the control unit 25 based on the collected deformation signal is within the preset stress threshold range.

[0112] In this embodiment, the power management module 32 does not adopt a continuous power supply mode, but provides dynamic power for the signal processing module 21 and the communication module through a time-sharing management circuit. For the convenience of understanding, the working mode of the time-sharing management circuit is divided into working modes under the first preset condition and the second preset condition.

[0113] Specifically, the first preset condition can be understood as follows: when a deformation signal appears in the device under test 6, the power management module 32 resumes power supply to the instrumentation amplifier 22 and the analog-to-digital converter 24, can continuously collect relevant information, and also resumes power supply to the third circuit module 4, so that the third circuit module 4 can timely upload the collected deformation signal or the stress value calculated from the deformation signal. In other words, the first preset condition means that when a deformation signal is collected once, the power of the entire stress monitoring system is started, and at the same time, the third circuit module 4 can selectively upload the stress value according to the actual operating mode.

[0114] The second preset condition can be understood as follows: when the stress monitoring system collects a deformation signal, the power management module 32 only supplies power to the electrical components required for signal collection, specifically including the instrumentation amplifier 22 and the analog-to-digital converter 24. Then the collected deformation signal will reach the control unit 25 after passing through the instrumentation amplifier 22 and the analog-to-digital converter 24, which is a complete signal collection link; at this time, the instrumentation amplifier 22 and the analog-to-digital converter 24 turn off the power after the collection is completed, and the control unit 25 performs logical operations and then judges whether the stress value collected from the deformation signal is within the preset stress threshold range. If so, it means that the stress value is normal, and then the third circuit module 4 (that is, the RS485 communication module 41) can be turned on to upload this stress value to the cloud, meeting the automatic collection, upload and maintenance of the normalized data of stress monitoring; further, if not, it means that the stress value is abnormal. At this time, the second preset condition is not met, and the control unit 25 can control the power management module 32 to start a signal collection operation for the entire stress monitoring system according to actual needs. The specific content will be described later. This method can reduce unnecessary consumption, save power supply expenditure, achieve long-term battery life, can meet the power supply duration of more than half a year, and can limit the total circuit energy within the explosion-proof certification threshold.

[0115] It should be noted that the first preset condition and the second preset condition are not mutually exclusive. The first preset condition and the second preset condition can be satisfied simultaneously or selectively. This embodiment does not limit this.

[0116] This embodiment achieves a balance between energy conservation and safety through a dynamic time-sharing power supply strategy, significantly extending the device's battery life while ensuring monitoring reliability. When the device under test 6 deforms to trigger signal acquisition, the power management module 32 automatically activates the power supply to the instrumentation amplifier 22 and the analog-to-digital converter 24 to ensure the complete acquisition of the deformation signal. If the stress value is within the normal threshold range, only the basic acquisition function is maintained, and the power supply to non-essential modules (such as the communication unit) is cut off to reduce energy consumption. When the device is in a stable state, high-power circuits are further turned off, and basic monitoring is maintained through intermittent fixed-point acquisition to avoid energy loss caused by continuous power supply. This mechanism dynamically compresses the total circuit energy consumption to the lowest required level, not only meeting the strict restrictions on circuit energy in the explosion-proof certification but also reducing device heating and electromagnetic interference through precise power supply timing control to ensure intrinsic safety. In case of an abnormality, full-function power supply is immediately restored to support real-time communication and alarm, taking into account the dual requirements of low power consumption and long battery life and emergency response, and achieving efficient and reliable continuous monitoring under complex working conditions.

[0117] In some embodiments, an air buffer layer 14 is provided between the nano-porous thermal insulation layer 13 and the first circuit module 2, and the air buffer layer 14 is filled with an inert gas.

[0118] In this embodiment, the thickness of the air buffer layer 14 is 2 - 5 mm, and the inert gas includes helium, neon, argon, krypton, xenon, radon, and oganesson. Preferably, the inert gas in this embodiment is helium or argon. The inert gas blocks the direct conduction of external high temperature to the internal circuit module, and together with the nano-porous thermal insulation layer 13, forms a gradient thermal insulation structure, significantly reducing the damage to the core circuit caused by extreme high temperatures of 600°C. On the other hand, the inert gas isolates oxygen and corrosive substances, preventing circuit oxidation or short circuits, and at the same time reducing the temperature drift error between the temperature sensor 52 and the strain gauge, improving the measurement accuracy in the wide temperature range of -40°C to 600°C. The air buffer layer 14 of this embodiment absorbs mechanical shock and explosion pressure waves through the gas compression characteristics, can enhance the anti-explosion ability of the explosion-proof housing 1, and further optimizes the compactness and reliability of the explosion-proof housing 1 through physical isolation.

[0119] Please refer to Figure 6 , in a second aspect, this embodiment also provides an explosion-proof on-line stress monitoring method from -40 degrees to 600 degrees, applicable to the stress monitoring system described in the first aspect. The number of stress monitoring systems is multiple, and the multiple stress monitoring systems are distributed on the outer surface of the device under test in a preset manner. The method includes:

[0120] S101. Obtain the characteristic information of the device under test, construct a stress distribution model based on the characteristic information, and generate a preset acquisition strategy according to the position information of the measurement points and the stress distribution model. The characteristic information includes at least one of the structural parameters, device category, material parameters, and environmental parameters of the device under test;

[0121] S102. Regularly collect the deformation signals of the device under test at the measurement points collected by the local strain sensing module according to the preset collection strategy;

[0122] S103. Preprocess the deformation signals, including numerical amplification, filtering and noise reduction, and analog-to-digital conversion, to obtain multiple first strain information;

[0123] S104. Simultaneously obtain the first temperature information collected by the current strain sensing module, and correct the first strain information according to the first temperature information to obtain multiple second strain information;

[0124] S105. Input the multiple second strain information into the stress distribution model;

[0125] S106. Perform anomaly judgment on each second strain information in the stress distribution model. If the stress value of the second strain information exceeds the preset stress threshold, it is recorded as abnormal strain information;

[0126] S107. Obtain the position information, temperature value and device status of the abnormal strain information and generate a first abnormal signal, and send the first abnormal signal to the server through the RS485 communication module. The first abnormal signal is configured as a MODBUS protocol data frame.

[0127] Corresponding to the stress monitoring system described in the first aspect, this embodiment provides a stress monitoring method. Among them, the number of stress monitoring systems is multiple, and the multiple stress monitoring systems are distributed on the outer surface of the device under test according to a preset method. Preferably, the distance between two adjacent stress monitoring systems does not exceed 5 times the wall thickness of the device under test, and is arranged in a ring or grid pattern. Further, the preset method can be a position distribution method obtained based on historical data or simulation calculation. For example, based on the historical stress data of the past 30 days, calculate the variance σ of the stress values of multiple dense measurement points of the device under test 2 , according to the weight distribution formula Calculate the position information distribution method, which is the preset method, and install the stress monitoring system accordingly in the actual application stage.

[0128] In step S101, the device to be tested is a device that needs to monitor the stress value, for example, it can be a boiler under high pressure working state. The characteristic information includes the structural parameters of the device to be tested, such as the overall size information, wall thickness data, etc. The equipment category is the model of the device to be tested, which is convenient for subsequent maintenance. The material parameter can be understood as the material used to make the entire device to be tested. The environmental parameter is the environmental parameter of the device to be tested in the working state, which may involve the corresponding values ​​of various categories such as high temperature, high pressure, low temperature, low pressure, humidity, and dryness. The stress distribution model refers to the model of the device to be tested that is digitized in the form of software modeling to facilitate subsequent stress calculation, monitoring and other needs. Further, the construction of the stress distribution model shown in this embodiment is based on finite element analysis software or other multivariate dynamic analysis software, and the grid is divided according to the CAD model of the device to be tested, and the characteristic information of the device to be tested is loaded, including pressure, temperature and other information; the position information of the test point and the stress distribution model are generated by iteratively solving the equation Ku=F, where K is the stiffness matrix, u is the displacement vector, and F is the load vector. The point to be tested is a point detected by a stress monitoring system. A device to be tested may have multiple points to be tested or only one point to be tested. This embodiment does not limit this and can be set according to actual needs. The preset acquisition strategy is the acquisition strategy described later. The preset acquisition strategy can ensure accurate monitoring of the device to be tested under limited energy consumption, rather than blindly taking values ​​frequently, to achieve reasonable utilization of resources.

[0129] In step S102, the local strain sensing module, i.e. the information of the local stress monitoring system, collects the deformation signals of some test points according to the preset collection strategy, instead of collecting all the signals, so as to realize the time-sharing start-up in the whole monitoring process, and further combine the time-sharing start-up function of the power management module of the stress monitoring system to realize the standardized and refined management of energy consumption from large to small, and realize long-term endurance; the detection of deformation signals is based on the principle that the material produces elastic deformation after being stressed, and is realized through the ceramic base strain gauge of the stress monitoring system. Please refer to Figure 5 The ceramic base strain gauge is fixed on the surface of the device to be tested, and its resistance value changes linearly with the deformation (ΔR=K·ε), which is converted into an electrical signal, namely the deformation signal, by the Wheatstone strain bridge.

[0130] In step S103, the preprocessing corresponds to the data preprocessing function implemented in hardware by the aforementioned stress monitoring system. Specifically, each deformation signal is numerically amplified by the instrument amplifier of the aforementioned stress detector, filtered and denoised by a low-pass filter, and converted into analog-to-digital by an analog-to-digital converter, thereby obtaining the first strain information.

[0131] In step S104, the first strain information is corrected according to the first temperature information collected by the strain sensing module to obtain a plurality of second strain information. The second strain information obtained after correction solves the monitoring error caused by temperature, making the monitoring result more accurate. The specific correction process is described in detail later. It should be noted that this correction process can also be understood as an online preprocessing of the first strain information from the perspective of logical operation, and at the same time integrates the error problem caused by temperature drift, making the data more accurate.

[0132] In steps S105 and S106, a plurality of second strain information is input into the stress distribution model for stress simulation calculation. In the stress distribution model, an abnormality judgment is made for each second strain information. If the stress value of the second strain information exceeds the preset stress threshold, it is recorded as abnormal strain information. Specifically, when making an abnormality judgment on the second strain information in the stress distribution model, first, a dynamic stress threshold library established based on device structure parameters, material characteristics, and environmental data is used. By comparing the corrected second strain value with the preset stress threshold (including the gradient threshold after temperature correction), it is judged whether it exceeds the limit. This step dynamically adjusts the threshold range according to the actual working conditions of the device. For example, different threshold values are set for the welding area and the non-welding area according to the difference in the thermal expansion coefficients of different materials. The model integrates multi-dimensional parameters to achieve intelligent threshold matching, improves the sensitivity of abnormality detection, avoids misjudgment of a single threshold in a high and low temperature alternating environment, and ensures reliable identification of early abnormalities such as the initiation of microcracks under extreme conditions of -40°C to 600°C.

[0133] In step S107, the temperature value is the first temperature information synchronously obtained when the current abnormal strain information is collected, and the device state is the operating state of the current device under test. Preferably, the device state can also include the operating parameters of the current device under test, which is convenient for subsequent problem analysis and troubleshooting. The first abnormal signal is configured as a MODBUS protocol data frame. Preferably, the format of the MODBUS protocol data frame is as follows: [device address][function code][stress value (4-byte floating point)][temperature value (2-byte)][CRC check]. The MODBUS protocol data frame has strong standardization compatibility, a compact structure, high transmission efficiency, built-in verification to ensure data reliability, is suitable for industrial environments, and is convenient for integration and maintenance.

[0134] Based on the stress distribution model constructed by finite element, this embodiment dynamically generates an optimal acquisition strategy in combination with the device characteristic parameters. It reasonably distributes multiple monitoring systems through a preset grid or circular layout, optimizes the position distribution by weighting the variance of historical data, ensures the coverage density of key areas, and enhances the ability to capture anomalies. The time-sharing acquisition strategy synchronously coordinates the low-power operation of multiple nodes, reduces the overall energy consumption to extend the battery life. At the same time, it eliminates noise interference through signal amplification, filtering, and analog-to-digital conversion, and improves the signal-to-noise ratio of the original deformation signal. The temperature compensation mechanism dynamically correlates the real-time acquired temperature data with the strain information, corrects the measurement deviation caused by the thermal expansion of the material, and significantly improves the accuracy of the stress value. In the anomaly judgment stage, a dynamic threshold is used in combination with the predicted value of the stress distribution model for double verification. When the threshold is exceeded, the position, temperature, and device status are automatically associated to generate a standardized anomaly signal, which is remotely transmitted through the isolated communication module using an anti-interference protocol, avoiding manual entry into dangerous areas for investigation, and realizing a full-process automated closed-loop from data acquisition, processing to early warning. The method provided in this embodiment, through the deep cooperation of the model and the hardware, takes into account the monitoring accuracy, response speed, and intrinsic safety under complex working conditions, and provides a reliable early warning ability for stress anomalies in high-temperature explosion-proof scenarios.

[0135] In some embodiments, correcting the first strain information according to the first temperature information to obtain a plurality of second strain information includes:

[0136] Performing temperature correction on the first strain information according to the first temperature information to obtain a first correction information, which is represented by formula (1), and formula (1) is as follows:

[0137] ε co =ε ra ×[1 + α(T cu -T re )] + β(T cu -T re ) 2

[0138] In formula (1), ε co is the first correction information, ε ra is the first strain information, α is the first-order temperature coefficient, T cu is the first temperature information, β is the second-order temperature coefficient, and T re is the calibration reference temperature;

[0139] Performing Kalman filtering on the first correction information to obtain second strain information, including:

[0140] Constructing a first state equation, which is represented by formula (2), and formula (2) is as follows:

[0141] x k =x k-1 +w k ;

[0142] In formula (2), x k is the second strain information at the k-th moment, and x k-1 is the second strain information at the (k - 1)-th moment, and w k is the process noise caused by environmental vibration or electromagnetic interference at the k-th moment, and w k ~N(0, Q), where Q is the covariance matrix of the process noise;

[0143] Construct the first observation equation, which is expressed by formula (3) as follows:

[0144] z k = x k + v k ;

[0145] In formula (3), z k is the first correction information at the k-th moment, and v k is the observation noise caused by circuit noise or temperature compensation residual at the k-th moment, and v k ~N(0, R), where R is the covariance matrix of the observation noise.

[0146] In this embodiment, after collecting the deformation signal of the device under test, first perform numerical amplification, then perform analog-to-digital conversion to obtain the first strain information, then perform temperature correction to obtain the first correction information, and finally perform filtering and noise reduction on the first correction information to obtain the second strain information. Specifically, the temperature correction uses the temperature compensation formula, and the filtering and noise reduction uses the Kalman filter formula.

[0147] The first correction information is expressed by formula (1) as:

[0148] ε co = ε ra × [1 + α(T cu - T re )] + β(T cu - T re ) 2

[0149] Among them, the first-order temperature coefficient α and the second-order temperature coefficient β are calibrated through the following steps: Apply a known stress to the device under test in an incubator and record the output values of the strain gauges at different temperatures; Fit α and β by the least squares method to make the compensated error ≤ 1 με to ensure the monitoring accuracy.

[0150] Perform Kalman filtering on the first correction information, and the covariance matrix

[0151] Q of the process noise and the covariance matrix R of the observation noise are dynamically adjusted. Specifically, the covariance matrix Q of the process noise is expressed by the following formula:

[0152] Q = Q 0 × [1 + 0.01(T cu - T re )];

[0153] Wherein, Q 0 is the reference process noise covariance matrix, which characterizes the inherent noise characteristics of the stress distribution model itself without temperature deviation (such as the state prediction uncertainty caused by mechanical vibration, sensor drift, etc.), and, when the temperature rises, relaxes the process noise constraint.

[0154] The covariance matrix R of the observation noise is expressed by the following formula:

[0155] R = R 0 × [1 + 0.05ΔT gradlent ;

[0156] Wherein, R 0 is the reference observation noise covariance matrix, which characterizes the inherent measurement error of the sensor without temperature gradient (ΔT gradlent = 0), usually determined by the sensor calibration experiment, ΔT gradlent is the temperature gradient change value, and, when the temperature gradient changes greatly, increases the observation noise weight.

[0157] The covariance matrix Q of the process noise and the covariance matrix R of the observation noise are associated with the temperature change, which can solve the failure problem of traditional fixed parameters in the variable temperature scenario.

[0158] In this embodiment, the first strain information is first corrected for temperature and then processed by Kalman filtering, which is beneficial to providing more accurate and stable deformation monitoring in a complex environment. Specifically, the influence of temperature change on the deformation signal will cause measurement error. First performing temperature correction can effectively eliminate the interference of temperature change on the deformation signal and make the signal closer to the true value; while Kalman filtering is a signal processing method based on statistical estimation, and its performance depends on the quality of the input signal. The signal after temperature correction removes the temperature-related noise and drift, which can simplify the model and computational complexity of Kalman filtering, enabling Kalman filtering to more effectively process the remaining random noise, effectively improving the accuracy and stability of stress deformation detection, while simplifying the system design and enhancing its adaptability in a complex environment.

[0159] In some embodiments, correcting the first strain information according to the first temperature information to obtain a plurality of second strain information includes:

[0160] Performing Kalman filtering on the first strain information to obtain second correction information, including:

[0161] Construct a second state equation, which is represented by formula (4) as follows:

[0162] x k ′ = x k-1 ′ + w k ′;

[0163] In formula (4), x k ′ is the second correction information at the k-th moment, x k-1 ′ is the second correction information at the (k - 1)-th moment, w k ′ is the process noise caused by environmental vibration or electromagnetic interference at the k-th moment, w k ′ ~ N′(0, Q′), where Q′ is the covariance matrix of the process noise;

[0164] Construct a second observation equation, which is represented by formula (5) as follows:

[0165] z k ′ = x k ′ + v k ′;

[0166] In formula (5), z k ′ is the first strain information at the k-th moment, v k ′ is the observation noise caused by circuit noise or temperature compensation residuals at the k-th moment, v k ′ ~ N′(0, R′), where R′ is the covariance matrix of the observation noise;

[0167] Perform temperature correction on the second correction information according to the first temperature information to obtain the second strain information, which is represented by formula (6) as follows:

[0168] ε co = ε ra ′ × [1 + α′(T cu ′ - T re ′)] + β′(T cu ′ - T re ′) 2

[0169] In formula (6), ε co ′ is the second strain information, ε ra ′ is the second correction information, α′ is the first-order temperature coefficient, T cu ′ is the first temperature information, β′ is the second-order temperature coefficient, T re ′ is the calibration reference temperature.

[0170] Distinguished from the temperature correction principle described above, in this embodiment, after collecting the deformation signal of the device under test, numerical amplification is first performed, followed by analog-to-digital conversion to obtain the first strain information. Then, the first strain information is filtered and denoised to obtain the second correction information. Finally, the second correction information is temperature-corrected to obtain the second strain information. The filtering and denoising use the Kalman filter formula, and the temperature correction uses the temperature compensation formula.

[0171] For filtering and denoising the first strain information, the covariance matrix Q′ of the process noise and the covariance matrix R′ of the observation noise of the Kalman filter are dynamically adjusted. Specifically, the covariance matrix Q′ of the process noise is expressed by the following formula:

[0172] Q′ = Q 0 ′ × [1 + 0.01(T cu ′ - T re ′)];

[0173] Among them, Q 0 ′ is the reference process noise covariance matrix, which characterizes the inherent noise characteristics of the stress distribution model itself without temperature deviation (such as state prediction uncertainty caused by mechanical vibration, sensor drift, etc.), and, when the temperature rises, relaxes the process noise constraint.

[0174] The covariance matrix R′ of the observation noise is expressed by the following formula:

[0175] R′ = R0 ′ × [1 + 0.05ΔT gradlent ;

[0176] Among them, R 0 ′ is the reference observation noise covariance matrix, which characterizes the inherent measurement error of the sensor without temperature gradient (ΔT gradlent ′ = 0), usually determined by the sensor calibration experiment, ΔT gradlent ′ is the temperature gradient change value, and, when the temperature gradient changes greatly, increases the observation noise weight.

[0177] The covariance matrix Q′ of the process noise and the covariance matrix R′ of the observation noise are associated with the temperature change, which can solve the problem of the failure of traditional fixed parameters in the variable temperature scenario.

[0178] The second correction information is temperature-corrected to obtain the second strain information. The second strain information is expressed by formula (6) as:

[0179] ε co ′ = ε ra ′ × [1 + α′(T cu ′ - T re ′)] + β′(T cu ′ - Tre ′) 2

[0180] Among them, the first-order temperature coefficient α' and the second-order temperature coefficient β' are calibrated through the following steps: Apply a known stress to the device under test in an incubator, and record the output values of the strain gauges at different temperatures; Fit α' and β' by the least squares method to make the error after compensation ≤ 1 με to ensure the monitoring accuracy.

[0181] In this embodiment, through the sequential processing of first filtering and denoising and then temperature compensation, the accuracy and stability of strain monitoring are significantly improved. Based on dynamically adjusting the process noise covariance matrix Q' and the observation noise covariance matrix R', the Kalman filter effectively suppresses the noise interference in different temperature scenarios: Q' is linearly extended based on the temperature deviation (T cu ′ - T re ′), relaxing the state prediction uncertainty constraint caused by material expansion and increased vibration at high temperatures to avoid overfitting; R' amplifies the observation noise weight according to the temperature gradient ΔT gradlent ′, enhancing the robustness of the filter to local sudden changes when the surface temperature distribution of the device is uneven. Through the dynamic noise model, both the high-frequency characteristics of the true deformation signal are retained, and the interferences such as environmental vibration and circuit noise are filtered out, providing the second correction information ε rα ′ with a high signal-to-noise ratio for subsequent temperature correction. The temperature compensation adopts a second-order polynomial model to accurately quantify the non-linear coupling effect between temperature and strain with the calibrated first-order temperature coefficient α' and second-order temperature coefficient β'. Through the sequential processing of first filtering and denoising and then temperature compensation, the aliasing amplification of temperature residual noise in the filtering link is avoided, and at the same time, it is ensured that the error of the second strain information ε co ′ after compensation is ≤ 1 με in the range of -40°C to 600°C. Especially in the case of rapid high and low temperature alternating working conditions, the true mechanical strain and thermal strain can still be reliably separated, providing a high-fidelity data basis for stress overrun judgment.

[0182] It should be noted that whether to perform temperature correction first or Kalman filtering first needs to be determined according to the specific application scenario. When the temperature compensation is linear and does not require iterative processing, the method of performing temperature correction first and then Kalman filtering is adopted, and the deformation monitoring will be more accurate and stable; when the temperature compensation involves complex calculations or requires the data after filtering and denoising, Kalman filtering needs to be performed first, and then temperature correction to improve the efficiency of deformation monitoring.

[0183] In some embodiments, the preset acquisition strategy is obtained through the following steps:

[0184] Perform stress simulation calculations on each point to be measured in the stress distribution model to obtain multiple stress simulation values;

[0185] Divide the position information of multiple points to be measured into a high-stress set, a medium-stress set, and a low-stress set according to the stress simulation values;

[0186] Set the points to be measured in the high-stress set to collect deformation signals at a first preset frequency, set the points to be measured in the medium-stress set to collect deformation signals at a second preset frequency, and set the points to be measured in the low-stress set to collect deformation signals at a third preset frequency;

[0187] Judgment is made one by one whether the change rate of the deformation signal is within the range of a preset change rate threshold;

[0188] If not, record the position information corresponding to the deformation signal as abnormal position information, and obtain the set type to which the abnormal position information belongs. The set type includes one of the high-stress set, the medium-stress set, and the low-stress set. If the abnormal position information belongs to the low-stress set, divide it into the medium-stress set. If the abnormal position information belongs to the medium-stress set, divide it into the high-stress set;

[0189] If so, judge whether the set type to which the position information of the current deformation signal initially belongs is consistent with the set type to which it currently belongs;

[0190] If the set type to which the position information of the deformation signal initially belongs is inconsistent with the set type to which it currently belongs, divide the position information of the deformation signal into the set type to which it initially belongs.

[0191] In this embodiment, the stress simulation calculation can be obtained based on finite element analysis software, and specifically can include the corresponding finite element calculation formula. It should be noted that the change rate of the deformation signal in this embodiment can be understood as the change rate corresponding to the stress value after calculating the stress value of the deformation signal. Specifically, after meshing the device to be measured, load pressure and temperature parameters, iteratively solve the equation Ku = F, calculate the stress distribution through the displacement of each point to be measured, correct the boundary conditions in combination with real-time monitoring data, and generate dynamic stress simulation values. Further, based on historical data, calculate the mean μ and standard deviation σ of the stress values of each point to be measured. The preset change rate threshold is set to μ ± 3σ and updated every 24 hours. Then, divide the position information of multiple points to be measured into a high-stress set, a medium-stress set, and a low-stress set according to the stress simulation values.

[0192] The first preset frequency, the second preset frequency, and the third preset frequency are statistically analyzed based on historical data. Specifically, the peak-valley difference, fluctuation frequency, and change rate of the stress values at each measurement point to be measured are statistically analyzed through historical data, and the standard deviation or coefficient of variation of the stress values at the measurement points to be measured is calculated. For example, the measurement points with the top 20% in terms of standard deviation or coefficient of variation are classified as high-fluctuation regions and grouped into the high-stress set. It should be noted that the first preset frequency needs to cover more than 90% of the fluctuation cycles to ensure that at least 3 - 5 data points are collected for each complete fluctuation cycle; the measurement points with the middle 60% in terms of standard deviation or coefficient of variation are classified as medium-fluctuation regions, corresponding to the medium-stress set, and the second preset frequency needs to cover more than 50% of the fluctuation cycles to ensure that at least 2 - 3 data points are collected for each complete fluctuation cycle; the measurement points with the bottom 20% in terms of standard deviation or coefficient of variation are classified as low-frequency fluctuation regions, and the third preset frequency needs to cover more than 10% of the fluctuation cycles to ensure that at least 1 data point is collected for each complete fluctuation cycle;

[0193] When the collected change rate exceeds the preset change rate threshold, it indicates that abnormal deformation has occurred at the position of this measurement point to be measured. Then, the position information of this position is marked as abnormal position information, and the set type to which this measurement point belongs is upgraded by one level, that is, from the medium-stress set to the high-stress set, from the low-stress set to the medium-stress set, and the high-stress set remains in the high-stress set. This step can correspondingly increase the monitoring frequency of this measurement point to ensure high-frequency collection of abnormal deformation and timely detection of risks; correspondingly, when the collected change rate does not exceed the preset change rate threshold, it indicates that the deformation signal of the current measurement point to be measured is in a normal state, and then the set type to which it belongs is not changed, and the monitoring frequency of the current frequency can be maintained.

[0194] Furthermore, if an abnormality occurs in a measurement point during one collection, the set division of this measurement point is changed to increase the collection frequency of the abnormal position information. If this measurement point maintains a normal fluctuation range in subsequent collections, it is judged whether the set type to which the position information of the current deformation signal originally belongs is the current set type. If not, it means that the position information of this measurement point did not originally belong to this set, and then it should be restored to the set to which this measurement point originally belonged. For example, measurement point A originally belonged to the low-stress set, but stress abnormality occurred during one monitoring, then measurement point A was scheduled to the medium-stress set. During the second monitoring, the stress of measurement point A is a normal fluctuation value, then it is judged whether the set type to which measurement point A originally belonged is the medium-stress set. Under the above conditions, measurement point A will be rescheduled from the medium-stress set back to the low-stress set.

[0195] In this embodiment, through a dynamic hierarchical acquisition strategy and an adaptive adjustment mechanism, the efficiency of stress monitoring and the abnormal response ability are significantly improved. Based on the stress simulation values, the points to be measured are divided into a high-stress set, a medium-stress set, and a low-stress set, and the acquisition frequencies are differentially set according to the historical fluctuation characteristics, which not only avoids the waste of resources caused by over-monitoring of low-risk areas but also ensures the data integrity of high-fluctuation areas. Combining the real-time change rate with a preset threshold to dynamically adjust the attribution of the set of points to be measured, when an abnormality occurs, it is upgraded to a higher-frequency set for enhanced monitoring, and after returning to normal, it reverts to the initial set to restore the baseline acquisition strategy, realizing the elastic allocation of monitoring resources. While ensuring high-frequency tracking of high-risk areas, it reduces unnecessary data redundancy. In addition, by collaborating with the finite element model and real-time data to correct the characteristic information of the device to be measured, the stress simulation value is more in line with the actual working conditions, providing a dynamic benchmark for the classification strategy, and ensuring that the monitoring priorities can still be accurately divided when the device deforms or the temperature load changes, effectively balancing the monitoring accuracy and system energy consumption.

[0196] In some embodiments, it is determined one by one whether the change rate of the deformation signal is within the range of a preset change rate threshold. If not, the preset acquisition strategy is also obtained through the following steps:

[0197] Record the position information corresponding to the deformation signal as abnormal position information, and obtain all the position information within the verification area to be divided with the abnormal position information as the center and a preset size as the radius, which is recorded as auxiliary position information;

[0198] Construct an abnormal stress verification set, and divide the abnormal position information and the auxiliary position information into the abnormal stress verification set;

[0199] Set the points to be measured in the abnormal stress verification set to collect deformation signals at a fourth preset frequency.

[0200] In this embodiment, the accuracy and response efficiency of deformation monitoring are improved through a dynamic abnormal linkage mechanism, while reducing the amount of redundant data and strengthening the risk warning ability. When the change rate of a single point to be measured exceeds the preset change rate threshold, it indicates that abnormal deformation has occurred at this position. At this time, the system automatically delimits the verification area with the abnormal position information at this place as the center and constructs an abnormal stress verification set for abnormal linkage acquisition.

[0201] Specifically, when abnormal deformation signals are detected at a certain position information, several adjacent position information will be activated to enter high-frequency acquisition together. The deformation signals of the auxiliary positions are synchronously acquired at a high frequency through the fourth preset frequency, forming a local dense monitoring network. The collected data is uploaded to the server through the RS485 communication module to determine whether there is a problem with the structure at this place and further prevent risks.

[0202] This embodiment focuses on dynamically adjusting the acquisition strategy for abnormal areas, avoiding energy consumption waste caused by global high-frequency sampling. At the same time, it uses cross-validation of multi-node data to distinguish real deformation from instantaneous interference, reducing the misjudgment rate. The abnormal verification data is uploaded to the server in real time through the communication module, and combined with spatial correlation analysis to quickly locate the evolution trend of structural hidden dangers, and achieve early warning before the risk spreads. The intelligent frequency switching and regional linkage not only ensure the complete capture of key abnormal information, but also maintain the low-power operation of the overall system through targeted resource allocation, taking into account both monitoring sensitivity and long-term stability, providing hierarchical and adaptive security guarantees for high-risk equipment.

[0203] In some embodiments, for each second strain information in the stress distribution model, if the stress value of the second strain information exceeds the preset stress threshold, it is recorded as abnormal strain information, including:

[0204] Obtain the second stress information corresponding to the deformation signal of each measurement point in the abnormal stress verification set within the preset time period, and record it as the first verification stress information;

[0205] Input the first verification stress information into the stress distribution model for dynamic simulation to judge the stress change state in the area to be verified;

[0206] Perform state information matching on the stress change state, and the state information includes normal fluctuation state, abnormal fluctuation state, and emergency fluctuation state;

[0207] When the stress change state belongs to the normal fluctuation state, divide the position information in the abnormal stress verification set into the set type it originally belongs to;

[0208] When the stress change state belongs to the abnormal fluctuation state, the first abnormal signal is configured to be generated according to the position information and temperature value in the abnormal stress verification set;

[0209] When the stress change state belongs to the emergency fluctuation state, the first abnormal signal is configured to be generated according to the position information and temperature value in the abnormal stress verification set, and a first alarm information is generated and sent to the server.

[0210] In this embodiment, dynamic simulation is performed by inputting the first verification stress information (stress values including time series) into the stress distribution model. Based on the current stiffness matrix K and the measured load F, the displacement field u is iteratively solved and the real-time stress distribution is calculated. At the same time, the deviation between the theoretical value and the actual value is compared by combining subsequent monitoring data. If the difference exceeds the tolerance threshold, the material parameters (such as elastic modulus) or boundary conditions (such as contact constraints) are inversely optimized and adjusted. After updating the model, a new round of stress field solution is carried out, and the cycle is iterated until the simulation result converges with the measured stress change trend (such as gradient direction, fluctuation frequency), and finally the dynamically corrected stress state and abnormal evolution path are output.

[0211] After dynamic simulation, the stress change state in the area to be verified is judged. When the stress change state belongs to the normal fluctuation state, it indicates that there is no abnormal deformation in the position information in the abnormal stress verification set, and there is no need to adjust its set type. When the stress change state belongs to the abnormal fluctuation state, it indicates that there is an abnormal risk, and the corresponding temperature value is recorded. When the stress change state belongs to the emergency fluctuation state, it indicates that there is a high-risk explosion risk, and the corresponding position information and temperature value are recorded and sent to the server, and the first alarm information is sent to prompt the relevant personnel to deal with it as soon as possible. This embodiment realizes the accurate identification and hierarchical response of stress anomalies through dynamic model iteration and multi-level risk classification mechanism, significantly improving the monitoring reliability and emergency efficiency in high-risk environments. In the abnormal verification stage, based on the dynamic interaction between real-time monitoring data and the stress distribution model, the local stress field distribution is corrected by iteratively solving the stiffness matrix and load parameters, and the model accuracy is optimized by combining time series data to ensure that the simulation result is consistent with the measured trend and eliminate the risk of single-point misjudgment. The stress state matching adopts a multi-level classification strategy. The normal fluctuation automatically removes the abnormal mark to reduce redundant alarms. The abnormal fluctuation is associated with the temperature to generate a warning signal, and the emergency fluctuation triggers a high-risk alarm and pushes the position information to the server, forming a gradient response. The adaptive adjustment of the dynamic model and the state classification work together, enhancing the abnormal path prediction ability through parameter optimization and reducing the system load through hierarchical processing, ensuring that resources are concentrated on real risk points, realizing the full-process closed-loop management from abnormal preliminary screening, model verification to hierarchical alarm, effectively balancing the monitoring sensitivity and operation stability, and providing intelligent risk control support for explosion-proof scenarios.

[0212] In some embodiments, the method further includes:

[0213] The stress detectors corresponding to the position information in the high-stress set and the low-stress set are in deep sleep when not collecting data;

[0214] The stress detector corresponding to the position information in the high-stress set is in light sleep when not collecting data.

[0215] In this embodiment, the stress detectors corresponding to the position information in the medium stress set and the low stress set are in deep sleep when not collecting information, and the power consumption is ≤1μA at this time; the stress detectors corresponding to the position information in the high stress set are in shallow sleep when not collecting information, and are ready to wake up quickly, and the power consumption is ≤10μA at this time.

[0216] This embodiment optimizes energy consumption distribution through a hierarchical sleep strategy, taking into account the response speed of key areas and overall endurance. Detectors with medium and low stress collections enter deep sleep during non-collection periods to minimize energy consumption; high stress collection detectors maintain a shallow sleep state to ensure that they can be quickly awakened and put into monitoring when an abnormality occurs. This mechanism dynamically adjusts the sleep depth according to the stress risk level, which not only reduces redundant power consumption in non-critical areas, but also ensures the real-time monitoring needs of high-risk points, achieving a precise balance between system energy consumption and safety response, and significantly extending the continuous operation cycle of the equipment.

[0217] Different from the prior art, the above technical solution has the following beneficial effects:

[0218] The present invention provides an explosion-proof online stress monitoring system of -40 to 600 degrees, which can realize safe and reliable real-time stress monitoring capability in extreme temperature environments. Through the multi-layer composite explosion-proof shell structure, combined with the synergistic effect of alumina ceramics, stainless steel armor and nanoporous thermal insulation materials, and the air buffer layer filled with internal inert gas, it can effectively resist the extreme temperature shock of -40°C to 600°C and the risk of external explosion, ensuring the stable operation of internal electronic components. By adopting a split circuit design to physically isolate the core processing module, power supply module and communication module, and cooperating with magnetic coupling isolation technology to block circuit interference, combined with an intelligent time-sharing power supply mechanism, the working state of each module is dynamically controlled according to the signal acquisition requirements, while ensuring the measurement accuracy and significantly reducing the overall energy consumption. The strain sensing module is directly welded to the surface of the equipment through a high-temperature resistant alloy substrate, and cooperates with the dual detection structure of embedded ceramic substrate strain gauges and contact temperature sensors to realize the synchronous and accurate acquisition of deformation signals and temperature data in high temperature environments.

[0219] In addition, the present invention also provides a stress monitoring method suitable for the above-mentioned stress monitoring system, which dynamically formulates the acquisition strategy by constructing a stress distribution model, divides the monitoring area based on the stress simulation value and sets the differentiated acquisition frequency, combines the temperature compensation algorithm with the Kalman filter technology to eliminate environmental interference, and realizes the accurate correction of multi-dimensional data. At the same time, a dynamic verification mechanism for abnormal stress is established, and the classification of the test points is automatically adjusted according to the real-time data and the graded warning is triggered, which not only ensures the response sensitivity of the monitoring system, but also optimizes the energy consumption management of the equipment. The system extends the battery life of the equipment while maintaining the all-weather monitoring capability through the coordination of the intelligent sleep mode and dynamic frequency adjustment.

[0220] Finally, it should be noted that although the above embodiments have been described in the text and drawings of the specification of this application, the patent protection scope of this application cannot be limited thereby. Any technical solutions obtained by equivalent structure or equivalent process substitution or modification based on the substantial concept of this application and using the content recorded in the text and drawings of the specification of this application, as well as the direct or indirect implementation of the technical solutions of the above embodiments in other related technical fields, etc., are all included in the patent protection scope of this application.

Claims

1. A -40 to 600 degree explosion-proof online stress monitoring system, characterized in that: include: Explosion-proof shell, from outside to inside, consists of alumina ceramic protective layer, stainless steel armor layer and nano-porous thermal insulation layer; A first circuit module is arranged inside the explosion-proof housing, the first circuit module includes a signal processing module, an instrument amplifier, a low-pass filter, an analog-to-digital converter, and a control unit, the signal processing module is electrically connected to the instrument amplifier, the low-pass filter, and the analog-to-digital converter in sequence, and the analog-to-digital converter is electrically connected to the control unit; A second circuit module is arranged adjacent to the first circuit module, the second circuit module is electrically connected to the first circuit module via a magnetic coupling isolator, the second circuit module includes a battery and a power management module, and the power management module is configured as a time-sharing management circuit; A third circuit module is arranged on the top of the explosion-proof housing, and the third circuit module includes an RS485 communication module, the RS485 communication module is electrically connected to the control unit, and the RS485 communication module is also connected to an external cable through an insulating terminal; A strain sensing module is arranged at the bottom of the explosion-proof housing, the strain sensing module is electrically connected to the signal processing module to monitor the stress value of the device to be tested in real time, and is used to send the monitored stress signal to the signal processing module in real time, and communicate with the control unit, the strain sensing module includes a ceramic substrate strain gauge, a temperature sensor and a high-temperature resistant alloy substrate, the high-temperature resistant alloy substrate is welded to the outer surface of the device to be tested, the opening of the first groove arranged on the high-temperature resistant alloy substrate faces the device to be tested, the ceramic substrate strain gauge is embedded in the first groove, the ceramic substrate strain gauge is abutted against the device to be tested, and is used to collect stress signals, and the temperature sensor is in contact with the ceramic substrate strain gauge and is used to collect temperature signals; The power management module is configured to supply power to the instrument amplifier and the analog-to-digital converter when a first preset condition is met, and the first preset condition is when a deformation signal of the device under test is collected; The power management module is configured to selectively supply power to one of the first circuit module and the third circuit module when a second preset condition is met, and the second preset condition is that a stress value calculated by the control unit according to the collected deformation signal is within a preset stress threshold range; An air buffer layer is provided between the nanoporous heat insulation layer and the first circuit module, and the air buffer layer is filled with an inert gas.

2. A -40 to 600 degree explosion-proof online stress monitoring method, characterized in that: The stress monitoring system according to claim 1, wherein the number of the stress monitoring systems is multiple, and the multiple stress monitoring systems are distributed on the outer surface of the device to be tested in a preset manner, and the method comprises: Acquire characteristic information of the device under test, construct a stress distribution model according to the characteristic information, and generate a preset acquisition strategy according to the position information of the test point and the stress distribution model, wherein the characteristic information includes at least one of the structural parameters, device category, material parameters and environmental parameters of the device under test; According to the preset acquisition strategy, the deformation signal of the device under test at the point to be tested is acquired by the local strain sensing module at a regular time; Preprocessing the deformation signal, wherein the preprocessing includes numerical amplification, filtering and noise reduction, and analog-to-digital conversion, to obtain a plurality of first strain information; At the same time, first temperature information currently collected by the strain sensing module is obtained, and the first strain information is corrected according to the first temperature information to obtain a plurality of second strain information; and inputting a plurality of the second strain information into the stress distribution model; In the stress distribution model, each of the second strain information is judged to be abnormal, and if the stress value of the second strain information exceeds a preset stress threshold, it is recorded as abnormal strain information; The position information, temperature value and device status of the abnormal strain information are obtained and a first abnormal signal is generated. The first abnormal signal is sent to the service end through the RS485 communication module. The first abnormal signal is configured as a MODBUS protocol data frame.

3. The -40 to 600 degree explosion-proof online stress monitoring method as claimed in claim 2, characterized in that: The first strain information is modified according to the first temperature information to obtain a plurality of second strain information including: The first strain information is temperature corrected according to the first temperature information to obtain first correction information, which is expressed by formula (1). The formula (1) is as follows: e co =e ra ×[1+α(T cu -T re )]+β(T cu -T re ) 2 In formula (1), ε co is the first correction information, ε ra is the first strain information, α is the first-order temperature coefficient, T cu is the first temperature information, β is the second-order temperature coefficient, T re is the calibration reference temperature; Performing Kalman filtering on the first correction information to obtain the second strain information includes: A first state equation is constructed, and the first state equation is expressed by formula (2), and the formula (2) is as follows: x k =x k-1 +w k ; In formula (2), x k is the second strain information at the kth moment, x k-1 is the second strain information at the k-1th moment, w k is the process noise caused by environmental vibration or electromagnetic interference at the kth moment, w k ~N(0,Q), Q is the covariance matrix of process noise; A first observation equation is constructed. The first observation equation is expressed by formula (3). The formula (3) is as follows: z k =x k +v k ; In formula (3), z k is the first correction information at the kth moment, v k The observed noise at the kth moment is caused by circuit noise or temperature compensation residual, v k ~N(0,R), R is the covariance matrix of the observation noise.

4. The -40 to 600 degree explosion-proof online stress monitoring method as claimed in claim 2, characterized in that: The first strain information is modified according to the first temperature information to obtain a plurality of second strain information including: Performing Kalman filtering on the first strain information to obtain second correction information includes: A second state equation is constructed, and the second state equation is expressed by formula (4), and the formula (4) is as follows: x k '=x k-1 '+w k '; In formula (4), x k ' is the second correction information at the kth moment, x k-1 ' is the second correction information at the k-1th moment, w k ' is the process noise caused by environmental vibration or electromagnetic interference at the kth moment, w k '~N'(0,Q'), Q' is the covariance matrix of process noise; A second observation equation is constructed, and the second observation equation is expressed by formula (5), and the formula (5) is as follows: z k '=x k '+v k '; In formula (5), z k ' is the first strain information at the kth moment, v k 'The observed noise caused by circuit noise or temperature compensation residual at the kth moment, v k '~N'(0,R'), R' is the covariance matrix of the observation noise; The second correction information is temperature corrected according to the first temperature information to obtain the second strain information, which is expressed by formula (6). The formula (6) is as follows: e co '=e ra '×[1+α'(T cu '-T re ')]+β'(T cu '-T re ') 2 In formula (6), ε co ' is the second strain information, ε ra ' is the second correction information, α' is the first-order temperature coefficient, T cu ' is the first temperature information, β' is the second-order temperature coefficient, T re ' is the calibration reference temperature.

5. The -40 to 600 degree explosion-proof online stress monitoring method as claimed in claim 2, characterized in that: The preset acquisition strategy is obtained by the following steps: Perform stress simulation calculation on each test point in the stress distribution model to obtain multiple stress simulation values; Dividing the position information of the plurality of test points into a high stress set, a medium stress set and a low stress set according to the stress simulation value; The test points in the high stress set are set to collect deformation signals according to a first preset frequency, the test points in the medium stress set are set to collect deformation signals according to a second preset frequency, and the test points in the low stress set are set to collect deformation signals according to a third preset frequency; Determining whether the change rate of the deformation signal is within a preset change rate threshold value one by one; If not, the position information corresponding to the deformation signal is recorded as abnormal position information, and the set type to which the abnormal position information belongs is obtained, and the set type includes one of a high stress set, a medium stress set, and a low stress set. If the abnormal position information belongs to the low stress set, it is divided into the medium stress set; if the abnormal position information belongs to the medium stress set, it is divided into the high stress set; If yes, determining whether the set type to which the position information of the current deformation signal originally belongs is consistent with the set type to which it currently belongs; If the set type to which the position information of the deformation signal originally belongs is inconsistent with the set type to which it currently belongs, the position information of the deformation signal is classified into the set type to which it originally belongs.

6. The -40 to 600 degree explosion-proof online stress monitoring method according to claim 5, characterized in that: It is determined one by one whether the change rate of the deformation signal is within the range of the preset change rate threshold. If not, the preset acquisition strategy is also obtained by the following steps: Recording the position information corresponding to the deformation signal as abnormal position information, and obtaining all position information in the area to be verified divided by a preset radius with the abnormal position information as the center, and recording it as auxiliary position information; Constructing an abnormal stress verification set, and dividing the abnormal position information and the auxiliary position information into the abnormal stress verification set; The test points in the abnormal stress verification set are set to collect deformation signals at a fourth preset frequency.

7. The -40 to 600 degree explosion-proof online stress monitoring method according to claim 6, characterized in that In the stress distribution model, each of the second strain information is judged to be abnormal. If the stress value of the second strain information exceeds a preset stress threshold, it is recorded as abnormal strain information, including: Acquire second stress information corresponding to the deformation signal of each of the test points in the abnormal stress verification set within a preset time period, and record it as first verification stress information; Inputting the first verification stress information into the stress distribution model for dynamic simulation to determine the stress change state in the area to be verified; Performing state information matching on the stress change state, wherein the state information includes a normal fluctuation state, an abnormal fluctuation state, and an emergency fluctuation state; When the stress change state belongs to a normal fluctuation state, the position information in the abnormal stress verification set is divided into the set type to which it originally belongs; When the stress change state belongs to an abnormal fluctuation state, the first abnormal signal is configured to be generated according to the position information and the temperature value in the abnormal stress verification set; When the stress change state belongs to an emergency fluctuation state, the first abnormal signal is configured to be generated according to the position information and the temperature value in the abnormal stress verification set, and to generate a first alarm message, and send the first alarm message to the service end.

8. The -40 to 600 degree explosion-proof online stress monitoring method according to claim 5, characterized in that: The method further comprises: The stress detectors corresponding to the position information in the medium stress set and the low stress set are in deep sleep when not collecting data; The stress detector corresponding to the position information in the high stress set is in shallow sleep when not collecting information.

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