-40 DEG C to 600 DEG C anti-explosion online stress monitoring system and stress monitoring method

Abstract

According to the-40 DEG C to 600 DEG C anti-explosion online stress monitoring system and the stress monitoring method provided by the invention, a high-temperature-resistant alloy substrate of the stress monitoring system eliminates thermal expansion difference through seamless welding, and dynamically corrects thermal drift error in combination with a temperature compensation algorithm, so that the monitoring precision at an extreme temperature is ensured; the direct electrical connection between the power management module and the signal processing module is cut off by adopting a magnetic coupling isolator, the energy consumption of the monitoring module is dynamically managed through a time-sharing power supply strategy, the power consumption is reduced to the maximum extent, the total energy consumption of the system is compressed to be within an anti-explosion authentication threshold value, and the real-time monitoring and rapid alarm capability of high-risk equipment is guaranteed. According to the utility model, the system endurance is obviously prolonged, the contradictory requirements of safety protection and energy consumption control are effectively balanced, remote data transmission without manual intervention is realized through the RS485 communication module, and the personnel safety is ensured.

Description

Technical Field

[0001] The present invention relates to the field of stress monitoring, and in particular to an explosion-proof online stress monitoring system and a stress monitoring method at a temperature ranging from -40 to 600 degrees. Background Art

[0002] In high-temperature, low-temperature, and flammable and explosive industrial scenarios, existing stress monitoring technology mainly collects equipment deformation signals through strain gauges. Its working principle relies on a single protective layer (such as a metal casing) to resist environmental impact, uses a continuous power supply mode to maintain circuit operation, and collects data at a fixed frequency. However, the existing protective structure lacks hierarchical insulation and explosion-proof design. Extreme temperatures can easily cause heat conduction or bursting of the casing, and the internal circuit becomes unstable due to temperature rise; secondly, traditional monitoring equipment stacks circuit components, which can easily lead to uneven heat dissipation and severe electromagnetic interference, exacerbating the risk of equipment overheating in high-temperature environments, and limiting battery life; fixed collection strategies cannot dynamically optimize the monitoring frequency according to the equipment stress distribution, and continuous high-frequency collection in the entire area causes a waste of resources. Summary of the invention

[0003] In view of the above problems, the present application provides a -40 to 600 degrees explosion-proof online stress monitoring system and stress monitoring method, which can meet the needs of real-time stress monitoring of storage tanks, pressure vessels and pressure pipelines.

[0004] To achieve the above-mentioned objectives, in a first aspect, the present invention provides an explosion-proof online stress monitoring system of -40 degrees to 600 degrees, comprising an explosion-proof shell, a first circuit module, a second circuit module, a third circuit module and a strain sensing module, wherein the explosion-proof shell is composed of an alumina ceramic protective layer, a stainless steel armor layer and a nanoporous thermal insulation layer from the outside to the inside; the first circuit module is arranged inside the explosion-proof shell, comprising 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; the second circuit module is arranged inside the explosion-proof shell and is adjacent to the first circuit management module, the second circuit module comprises a battery and a power management module, the battery is electrically connected to the power management module, the power management module is configured as a time-sharing management circuit, and the power supply The management module is electrically connected to the signal processing module, the instrument 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 at 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 base 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 high-temperature resistant alloy substrate has a first groove, the opening of the first groove is arranged toward the device to be tested, the ceramic base strain gauge is embedded in the first groove, the ceramic base strain gauge is abutted against the device to be tested, and the temperature sensor is in contact with the ceramic base strain gauge.

[0005] In some embodiments, 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; 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, and the second preset condition is that the stress value calculated by the control unit based on the collected deformation signal is placed within a preset stress threshold range.

[0006] In some embodiments, an air buffer layer is provided between the nanoporous thermal insulation layer and the first circuit module, and the air buffer layer is filled with an inert gas.

[0007] In a second aspect, the present invention further provides a -40 to 600 degrees explosion-proof online stress monitoring method, which is applicable to the stress monitoring system described in the first aspect, wherein 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 tested in a preset manner, and the method comprises: Acquire characteristic information of the device to be tested, construct a stress distribution model based on the characteristic information, and generate a preset acquisition strategy based on the location 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 to be tested; According to the preset acquisition strategy, the deformation signal of the device under test at the test point acquired by the local strain sensing module is acquired at a fixed time; Preprocessing the deformation signal, including 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 collected by the current 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 second strain information into the stress distribution model; In the stress distribution model, each 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. The location 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.

[0008] In some embodiments, 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). Formula (1) is as follows:

[0009] In formula (1), For the first correction information, is the first strain information, is the first-order temperature coefficient, is the first temperature information, is the second-order temperature coefficient, is the calibration reference temperature; Performing Kalman filtering on the first correction information to obtain second strain information includes: The first state equation is constructed. The first state equation is expressed by formula (2). Formula (2) is as follows: ; In formula (2), For the The second strain information at the moment, For the The second strain information at the moment, For the Process noise caused by environmental vibration or electromagnetic interference at all times, , is the covariance matrix of process noise; Construct the first observation equation, which is expressed by formula (3). Formula (3) is as follows: ; In formula (3), For the The first revised information at the moment, No. The observation noise caused by circuit noise or temperature compensation residual at each moment, , is the covariance matrix of the observation noise.

[0010] In some embodiments, 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: The second state equation is constructed. The second state equation is expressed by formula (4). Formula (4) is as follows: ; In formula (4), For the The second correction information of the time, For the The second correction information of the time, For the Process noise caused by environmental vibration or electromagnetic interference at all times, , is the covariance matrix of process noise; The second observation equation is constructed. The second observation equation is expressed by formula (5). Formula (5) is as follows: ; In formula (5), For the The first response information at the moment, No. The observation noise caused by circuit noise or temperature compensation residual at each moment, , 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). Formula (6) is as follows:

[0011] In formula (6), is the second strain information, For the second correction information, is the first-order temperature coefficient, is the first temperature information, is the second-order temperature coefficient, is the calibration reference temperature.

[0012] In some embodiments, 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; According to the stress simulation values, the position information of the multiple test points is divided into a high stress set, a medium stress set and a low stress set; 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; Determine one by one whether the change rate of the deformation signal is within the range of a preset change rate threshold; 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. 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 a low stress set, it is divided into a medium stress set. If the abnormal position information belongs to a medium stress set, it is divided into a high stress set. If yes, 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; 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.

[0013] In some embodiments, 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 further obtained by the following steps: Record the position information corresponding to the deformation signal as abnormal position information, and obtain all position information in the area to be verified divided by a preset size as a radius with the abnormal position information as the center, and record it as auxiliary position information; An abnormal stress verification set is constructed, and abnormal position information and auxiliary position information are divided 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.

[0014] In some embodiments, performing abnormality judgment on each second strain information in the stress distribution model, if the stress value of the second strain information exceeds a preset stress threshold, recording it as abnormal strain information includes: Obtaining second stress information corresponding to the deformation signal of each test point in the abnormal stress verification set within a preset time period, recorded 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; Matching state information of stress change state, the state information includes normal fluctuation state, abnormal fluctuation state and emergency fluctuation state; When the stress change state belongs to the 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 the first alarm information and send the first alarm information to the service end.

[0015] In some embodiments, the method further comprises: The stress detector corresponding to the position information in the medium stress set and the low stress set is in deep sleep when not collecting; The stress detector corresponding to the position information in the high stress set is in shallow sleep when not collecting.

[0016] Different from the prior art, the above technical solution has the following beneficial effects: 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, 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. The split circuit design is adopted to physically isolate the core processing module, power supply module and communication module, and the magnetic coupling isolation technology is used to block circuit interference. Combined with the intelligent time-sharing power supply mechanism, the working status of the first circuit module, the second circuit module and the third circuit 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 the dual detection structure of the embedded ceramic substrate strain gauge and the contact temperature sensor is used to realize the synchronous and accurate acquisition of deformation signals and temperature data in high temperature environments.

[0017] The above-mentioned records related to the invention content are only an overview of the technical solution of the present application. In order to enable ordinary technicians in the field to more clearly understand the technical solution of the present application, and then implement it according to the text of the specification and the contents recorded in the drawings, and to make the above-mentioned purpose and other purposes, features and advantages of the present application easier to understand, the following is an explanation in combination with the specific implementation mode and drawings of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings are only used to illustrate the principles, implementation methods, applications, characteristics and effects of the specific embodiments of the present invention and other related contents, and shall not be considered as limiting the present application.

[0019] In the drawings of the specification: Figure 1 A specific circuit diagram of the online stress monitoring system described in the specific implementation method; Figure 2 It is a schematic diagram of the cross-sectional structure of the explosion-proof housing described in the specific implementation method; Figure 3 It is a schematic diagram of the specific structure of the strain sensing module described in the specific implementation method; Figure 4 It is a partial circuit diagram of the online stress monitoring system described in the specific implementation method; Figure 5 A circuit diagram of the strain bridge described in the specific implementation method; Figure 6 It is a schematic diagram of steps S101 to S107 of the online stress monitoring method described in the specific implementation manner.

[0020] The reference numerals in the above drawings are described as follows: 1. Explosion-proof housing; 11. Alumina ceramic protective layer; 12. Stainless steel armor layer; 13. Nanoporous thermal insulation layer; 14. Air buffer layer; 2. A first circuit module; 21. Signal processing module; 22. Instrumentation amplifier; 23. Low pass filter; 24. Analog-to-digital converter; 25. Control unit; 3. The second circuit module; 31. Battery; 32. Power management module; 4. The third circuit module; 41. RS485 communication module; 5. Strain sensing module; 51. Ceramic substrate strain gauge; 52. Temperature sensor; 53. High temperature resistant alloy substrate; 6. Equipment under test. DETAILED DESCRIPTION

[0021] In order to explain in detail the possible application scenarios, technical principles, specific schemes that can be implemented, and the purposes and effects that can be achieved, the following is a detailed description of the specific embodiments listed in conjunction with the accompanying drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0022] Reference to "embodiment" herein means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present application. The term "embodiment" appearing in various places in the specification does not necessarily refer to the same embodiment, nor does it particularly limit its independence or association with other embodiments. In principle, in the present application, as long as there is no technical contradiction or conflict, the various technical features mentioned in the embodiments can be combined in any way to form a corresponding implementable technical solution.

[0023] Unless otherwise defined, the technical terms used in this document have the same meanings as those generally understood by those skilled in the art to which this application belongs; the use of relevant terms in this document is only for describing specific embodiments and is not intended to limit this application.

[0024] In the description of this application, the term "and / or" is an expression used to describe the logical relationship between objects, indicating that three relationships may exist, for example, A and / or B, which means: A exists, B exists, and A and B exist at the same time. In addition, the character " / " in this article generally indicates that the objects before and after are in an "or" logical relationship.

[0025] In the present application, terms such as “first” and “second” are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship of quantity, priority or sequence between these entities or operations.

[0026] Without further limitations, in this application, the words "include", "comprises", "has" or other similar open-ended expressions used in sentences are intended to cover non-exclusive inclusion. These expressions do not exclude the presence of additional elements in the process, method or product including the elements, so that the process, method or product including a series of elements may include not only those limited elements, but also other elements not explicitly listed, or also include elements inherent to such process, method or product.

[0027] Similar to the understanding in the Examination Guidelines, in this application, expressions such as "greater than", "less than", "exceed" and the like are understood to exclude the number itself; expressions such as "above", "below", "within" and the like are understood to include the number itself. In addition, in the description of the embodiments of this application, "multiple" means more than two (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups", "multiple times", etc., unless otherwise clearly and specifically limited.

[0028] In the description of the embodiments of the present application, space-related expressions used, such as "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "vertical", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicate the orientation or position relationship based on the orientation or position relationship shown in the specific embodiments or drawings, and are only for the convenience of describing the specific embodiments of the present application or facilitating the reader's understanding, and do not indicate or imply that the referred device or component must have a specific position, a specific orientation, or be constructed or operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present application.

[0029] The processor described in the embodiments of the present application can be implemented by hardware, firmware, software or a combination thereof, and can use a circuit, a single or multiple application-specific integrated circuits (Application Specific Integrated Circuit, ASIC), a digital signal processor (Digital Signal Processor, DSP), a digital signal processing device (Digital Signal Processing Device, DSPD), a programmable logic device (Programmable Logic Device, PLD), a field programmable gate array (Field Programmable Gate Array, FPGA), a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, at least one of a microprocessor, and also includes other physical, biological or chemical structures that can achieve similar or equivalent functions to the processors listed above, such as biological neurons, quantum computing units, DNA computing units, etc., so that the processor can execute some or all of the steps in the computer program or method involved in the various embodiments of the present application, or any combination of the steps mentioned therein.

[0030] The computer program involved in the embodiment can be stored in a computer device readable storage medium, which includes but is not limited to a disk, a tape, a magnetic card, a floppy disk, a flash memory, an optical disk, an optical card, a read-only memory (ROM), a random access memory (RAM), an erasable programmable ROM (EPROM) and an electrically erasable programmable ROM (EEPROM), etc., and also includes other biological, physical or chemical structures that can achieve the same or equivalent functions as the storage media listed above, such as DNA, RNA, protein and other units with information storage capabilities. In a specific embodiment, the storage medium involved can be one of the above-mentioned media types, or a combination of the above-mentioned media types. In different embodiments, the computer program involved in the embodiment can be stored in a single medium in a centralized manner, or it can be stored in multiple media in a distributed manner. The memory containing the computer device readable storage medium can be a non-volatile memory or a random access memory. These computer device readable storage media can be built into the device, or they can be connected to the device involved in the embodiment as an external device or a part of an external device. In some embodiments, a memory with a computer device readable storage medium is deployed locally; in other embodiments, a solution of deploying the memory away from the processor may also be adopted, such as a network attached memory accessed via an RF circuit or an external port and a communication network, wherein the communication network may be the Internet, one or more intranets, a local area network (LAN), a wide area network (WLAN), a storage area network (SAN), 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 may be stored in plaintext / ciphertext form, or may be designed as training data, which may be integrated and reorganized through model training and implicitly stored in the parameter state of a deep neural network or other machine learning model.

[0031] The stress monitoring system shown in this embodiment is applicable to any scenario requiring stress monitoring, preferably, to special equipment. Specifically, special equipment includes boilers, pressure vessels (including gas cylinders), pressure pipelines, elevators, lifting machinery, passenger ropeways, large amusement facilities, and other equipment that poses a greater risk to personal and property safety, and further includes their own safety accessories and safety protection devices.

[0032] The stress monitoring system shown in this embodiment is suitable for stress monitoring in a special environment of -40 degrees Celsius to 600 degrees Celsius.

[0033] See also Figures 1 to 5To achieve the above-mentioned purpose, in the first aspect, the present embodiment provides a -40 to 600 degree explosion-proof online stress monitoring system, comprising 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, wherein the explosion-proof housing 1 is composed of an alumina ceramic protective layer 11, a stainless steel armor layer 12 and a nanoporous thermal insulation layer 13 from the outside to the inside; the first circuit module 2 is arranged inside the explosion-proof housing 1, and comprises a signal processing module 21, an instrument amplifier 22, a low-pass filter 23, an analog-to-digital converter 24 and a control unit 25, wherein the signal processing module 21 is electrically connected to the instrument 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 arranged adjacent to the first circuit management module, wherein the second circuit module 3 comprises a battery 31 and a power management module 32, wherein the battery 31 is electrically connected to the power management module 32, and the power management module 32 is configured as a time-sharing management circuit, and the power management module 32 is electrically connected to the power management module 32. The source management module 32 is electrically connected to the signal processing module 21, the instrument 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 at the top of the explosion-proof housing 1, and 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 base 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 to be tested 6, the high-temperature resistant alloy substrate 53 has a first groove, the opening of the first groove is arranged toward the device to be tested 6, the ceramic base strain gauge 51 is embedded in the first groove, the ceramic base strain gauge 51 is abutted against the device to be tested, and the temperature sensor 52 is in contact with the ceramic base strain gauge 51.

[0034] In this embodiment, the explosion-proof housing 1 is composed of an alumina ceramic protective layer 11, a stainless steel armor layer 12 and a nanoporous thermal insulation layer 13 from the outside to the inside, achieving hierarchical thermal insulation to ensure that even if the external temperature reaches , the internal circuit can still maintain the temperature ,exist In addition, the joints of the explosion-proof housing 1 use metal-ceramic composite sealing rings, which are tested by helium mass spectrometry for leaks (leakage rate ), can pass GB3836.4 Ex ia IIC T4 explosion-proof certification. Further, the thickness of the nanoporous thermal insulation layer 13 is , thermal conductivity , covering the outer periphery of the internal circuit module (i.e., the first circuit module 2 and the second circuit module 3). In addition, the internal circuit of the explosion-proof housing 1 meets the safety requirements, and the total capacitance , Total Inductance , and connect a fast-blow fuse in series at the power input end, with a rated current of , response time , to prevent sparks caused by short circuit.

[0035] The first circuit module 2, the second circuit module 3 and the third circuit module 4 are physically isolated according to their functions, which optimizes the circuit layout and can reduce uneven heat dissipation and mutual interference. The second circuit module 3 is close to the side wall of the explosion-proof housing 1 and uses the stainless steel armor layer 12 to dissipate heat; the third circuit module 4 is independently placed on the top to avoid the bending stress of the cable affecting the internal circuit. Furthermore, the battery 31 of the second circuit module 3 is Lithium thionyl chloride battery 31 (capacity ), 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 (cutoff frequency ), 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 housing 1, and the direct electrical connection between the power management area and the signal processing area is cut off by the magnetic coupling isolation device, reducing the impact of each component and improving safety; the power management module 32 provides dynamic power to the signal processing module 21 and the communication module through the time-sharing management circuit. The third circuit module 4 includes an RS485 communication module 41, which is an integrated isolation type ADM2483 chip, supports the MODBUS-RTU protocol, and the baud rate adaptive range is A metal-ceramic composite sealing structure is used between the interface terminal of the RS485 communication module 41 and the external cable. The sealing structure is embedded in the top groove of the explosion-proof housing 1. The RS485 communication module 41 provides online communication for providing an alarm when monitoring abnormalities.

[0036] The ceramic base strain gauge 51 of the strain sensor module 5 is a zirconia toughened ceramic with a nano-aluminum oxide layer on the surface to prevent high temperature oxidation and mechanical wear. The high-temperature resistant alloy substrate 53 is used to collect stress signals. Its material is Inconel 718 (temperature resistance 700°C). It is laser welded to the surface of the device under test with high-temperature solder (Au-Sn alloy) to form a seamless connection, avoiding deformation caused by thermal expansion differences and eliminating thermal stress errors in traditional installation methods. The temperature sensor 52 is set to a PT100 temperature sensor 52, which is installed close to the ceramic substrate strain gauge 51 and is connected to the signal processing module 21 through a flexible wire. It is used to collect the ambient temperature in real time and correct the stress value through a temperature compensation algorithm, which is conducive to accurate monitoring.

[0037] 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 sensor module 5. The range reaches , minimum resolution , can measure the stress value of each test point of storage tanks, pressure vessels, pressure pipes, etc., and can prevent the risk of explosion. It will not generate electric sparks or high temperature accidents when used in flammable and explosive chemical environments. The high-temperature alloy substrate 53 and the ceramic base strain gauge 51 are seamlessly welded to eliminate the difference in thermal expansion, and the PT100 temperature sensor 52 installed closely collects the ambient temperature in real time. The thermal drift error is dynamically corrected in combination with the compensation algorithm to ensure that the stress value is not affected by extreme temperatures of -40°C to 600°C; at the same time, the alumina ceramic protective layer 11, stainless steel armor layer 12 and nanoporous thermal insulation layer 13 of the explosion-proof housing 1 gradually block the external high temperature to stabilize the internal circuit temperature , ensuring the stable operation of the core module under a wide temperature range; using a magnetic coupling isolator to cut off the direct electrical connection between the power management module 32 and the signal processing module 21, transferring energy through magnetic coupling to avoid sparks caused by power fluctuations or failures, and at the same time, the time-sharing power supply strategy dynamically distributes the energy of the battery 31 to reduce circuit heating and mutual interference; in addition, the RS485 module uses a metal-ceramic composite sealed interface and an isolated communication chip to achieve remote data transmission without human 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 accuracy and safety of stress monitoring in high-temperature flammable and explosive environments.

[0038] In some embodiments, the power management module 32 is configured to supply power to the instrument amplifier 22 and the analog-to-digital converter 24 when a first preset condition is met, and the first preset condition is when a deformation signal of the device under test 6 is collected; And / or, the power management module 32 is configured to selectively supply power to the first circuit module 2 and the third circuit module 4 when a second preset condition is met, and the second preset condition is that the stress value calculated by the control unit 25 based on the collected deformation signal is placed within a preset stress threshold range.

[0039] In this embodiment, the power management module 32 does not adopt a continuous power supply mode, but provides dynamic power to the signal processing module 21 and the communication module through a time-sharing management circuit. For ease of understanding, the working mode of the time-sharing management circuit is divided into working modes that meet the first preset condition and the second preset condition.

[0040] 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 powering the instrument amplifier 22 and the analog-to-digital converter 24, and can continuously collect relevant information. At the same time, the power supply to the third circuit module 4 is also restored, so that the third circuit module 4 can upload the collected deformation signal or the stress value calculated from the deformation signal in a timely manner. In other words, the first preset condition means that the power of the entire stress monitoring system is started when a deformation signal is collected once, and the third circuit module 4 can selectively upload the stress value according to the actual operation mode.

[0041] The second preset condition can be understood as follows: when the stress monitoring system collects deformation signals, the power management module 32 only supplies power to the electrical components required for collecting signals, specifically including the instrument amplifier 22 and the analog-to-digital converter 24. The collected deformation signals will pass through the instrument amplifier 22 and the analog-to-digital converter 24 and then reach the control unit 25, which is a complete signal collection link; at this time, the instrument amplifier 22 and the analog-to-digital converter 24 turn off the power after the collection is completed, and the control unit 25 performs a logical operation to determine whether the stress value collected by the deformation signal is within the preset stress threshold range. If so, it means that the stress value is normal, and the third circuit module 4 (that is, the RS485 communication module 41) can be turned on to upload this stress value to the cloud, so as to meet the automatic collection, uploading and maintenance of normalized data for stress monitoring; further, if not, it means that the stress value is abnormal, and the second preset condition is not met at this time, then the control unit 25 can control the power management module 32 to start the signal collection operation for the entire stress monitoring system according to actual needs, and the specific content is described below. This method can reduce unnecessary consumption, save power supply expenses, achieve long-term battery life, can meet the power supply time of more than half a year, and can limit the total energy of the circuit to within the explosion-proof certification threshold.

[0042] 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 may be satisfied at the same time or one of them may be satisfied. This embodiment does not limit this.

[0043] This embodiment achieves a balance between energy saving and safety through a dynamic time-sharing power supply strategy, significantly extending the device life while ensuring monitoring reliability. When the deformation of the device under test 6 triggers signal acquisition, the power management module 32 automatically activates the instrument amplifier 22 and the analog-to-digital converter 24 to supply power to ensure 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 of non-essential modules (such as communication units) is cut off to reduce energy consumption. When the device is in a stable state, the high-power circuit is 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 energy consumption of the circuit to the minimum required level, which not only meets the strict restrictions on circuit energy for explosion-proof certification, but also reduces device heating and electromagnetic interference through precise power supply timing control to ensure intrinsic safety. In abnormal situations, full-function power supply is immediately restored to support real-time communication alarms, taking into account the dual needs of low power consumption, long battery life and emergency response, and achieving efficient and reliable continuous monitoring under complex working conditions.

[0044] In some embodiments, an air buffer layer 14 is provided between the nanoporous heat-insulating layer 13 and the first circuit module 2 , and the air buffer layer 14 is filled with an inert gas.

[0045] In this embodiment, the thickness of the air buffer layer 14 is Inert gases include helium, neon, argon, krypton, xenon, radon and austenite. 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 forms a gradient insulation structure in combination with the nanoporous insulation layer 13, which significantly reduces the damage of the 600°C extreme high temperature to the core circuit; on the other hand, the inert gas isolates oxygen and corrosive substances, avoids circuit oxidation or short circuit, and reduces the temperature drift error of the temperature sensor 52 and the strain gauge, improving Measurement accuracy in a wide temperature range. The air buffer layer 14 of this embodiment absorbs mechanical shock and explosion pressure waves through gas compression characteristics, which can enhance the explosion resistance of the explosion-proof housing 1 and further optimize the compactness and reliability of the explosion-proof housing 1 through physical isolation.

[0046] See also Figure 6 In a second aspect, the present embodiment further provides a -40 to 600 degree explosion-proof online stress monitoring method, which is applicable to the stress monitoring system described in the first aspect, wherein 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 tested in a preset manner, and the method comprises: S101, acquiring characteristic information of the device to be tested, and constructing a stress distribution model according to the characteristic information, and generating 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 to be tested; S102, acquiring deformation signals of the device under test at the test point collected by the local strain sensing module according to a preset collection strategy; S103, 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; S104, simultaneously acquiring first temperature information collected by the current strain sensing module, and correcting the first strain information according to the first temperature information to obtain a plurality of second strain information; S105, inputting the plurality of second strain information into the stress distribution model; S106, performing abnormality judgment on each second strain information in the stress distribution model, and if the stress value of the second strain information exceeds a preset stress threshold, recording it as abnormal strain information; S107, obtaining the location information, temperature value and device status of the abnormal strain information and generating a first abnormal signal, sending the first abnormal signal to the server through the RS485 communication module, and the first abnormal signal is configured as a MODBUS protocol data frame.

[0047] Corresponding to the stress monitoring system described in the first aspect, this embodiment provides a stress monitoring method, wherein 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 tested 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 to be tested, and they are arranged in a ring or grid shape. Furthermore, the preset method can be a position distribution method based on historical data or simulated calculations. For example, based on the historical stress data of the past 30 days, the variance of the stress values ​​of multiple dense test points of the device to be tested is calculated. , according to the weight distribution formula The calculated position information distribution mode is the preset mode, and the stress monitoring system is installed according to it in the actual application stage.

[0048] 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 conditions. 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 parameters can be understood as the material used to make the entire device to be tested. The environmental parameters are the environmental parameters of the device to be tested under working conditions, which may involve corresponding values ​​of various categories such as high temperature, high pressure, low temperature, low pressure, humidity, and dryness. The stress distribution model refers to a model that is digitized through software modeling of the device to be tested to facilitate subsequent stress calculations, monitoring, and other needs. Furthermore, the construction of the stress distribution model shown in this embodiment is based on finite element analysis software or other multivariate dynamics analysis software. 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, including pressure, temperature, and other information, is loaded; by iteratively solving the equation Generate the location information of the test point and the stress distribution model, where: is the stiffness matrix, is the displacement vector, is the load vector. The point to be tested is the 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 ​​multiple times, to achieve rational use of resources.

[0049] 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 ( ), which is converted into an electrical signal, i.e. a deformation signal, by a Wheatstone strain bridge.

[0050] 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.

[0051] In step S104, the first strain information is corrected according to the first temperature information collected by the strain sensing module to obtain multiple 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 realizing online preprocessing of the first strain information from the perspective of logical operation, while integrating the error problem caused by temperature drift, making the data more accurate.

[0052] In step S105 and step S106, multiple second strain information are input into the stress distribution model to simulate and calculate stress. In the stress distribution model, each second strain information is judged to be abnormal. If the stress value of the second strain information exceeds the preset stress threshold, it is recorded as abnormal strain information. Specifically, when the second strain information is judged to be abnormal in the stress distribution model, the dynamic stress threshold library established based on the equipment structure parameters, material properties and environmental data is first used to compare the corrected second strain value with the preset stress threshold (including the gradient threshold after temperature correction) to determine whether it exceeds the limit. This step dynamically adjusts the threshold range in combination with the actual working conditions of the equipment. For example, differentiated thresholds are set for welding areas and non-welding areas based on the difference in thermal expansion coefficients of different materials. Intelligent matching of thresholds is achieved by integrating multi-dimensional parameters in the model, improving the sensitivity of abnormal detection, avoiding misjudgment of a single threshold in a high and low temperature alternating environment, and ensuring that early abnormalities such as microcrack initiation can still be reliably identified under extreme conditions of -40°C~600°C.

[0053] In step S107, the temperature value is the first temperature information synchronously acquired when the current abnormal strain information is collected, and the device state is the operating state of the current device under test monitored. Preferably, the device state may 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 bytes)][CRC check]. The MODBUS protocol data frame is standardized and highly compatible, with a compact structure and efficient transmission. The built-in check ensures data reliability, is suitable for industrial environments, and is easy to integrate and maintain.

[0054] In this embodiment, the stress distribution model constructed based on finite elements is combined with the characteristic parameters of the equipment to dynamically generate the optimal acquisition strategy. Multiple monitoring systems are reasonably allocated through a preset grid or ring layout, and the position distribution is optimized by weighting the historical data variance to ensure the coverage density of key areas and enhance the ability to capture anomalies. The time-sharing acquisition strategy synchronizes and coordinates the low-power operation of multiple nodes, reduces the overall energy consumption to extend the endurance, and eliminates noise interference through signal amplification, filtering and analog-to-digital conversion, thereby improving the signal-to-noise ratio of the original deformation signal. The temperature compensation mechanism dynamically associates the real-time collected 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 abnormal judgment stage, the dynamic threshold is combined with the predicted value of the stress distribution model for double verification. When the threshold is exceeded, the position, temperature and equipment status are automatically associated to generate a standardized abnormal signal, which is remotely transmitted through the isolated communication module with an anti-interference protocol to avoid manual entry into the dangerous area for investigation, and realize the full process from data acquisition, processing to early warning. The method provided in this embodiment takes into account the monitoring accuracy, response speed and inherent safety under complex working conditions through the deep collaboration of the model and hardware, and provides a reliable early warning capability for stress anomalies for high-temperature explosion-proof scenarios.

[0055] In some embodiments, 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). Formula (1) is as follows:

[0056] In formula (1), For the first correction information, is the first strain information, is the first-order temperature coefficient, is the first temperature information, is the second-order temperature coefficient, is the calibration reference temperature; Performing Kalman filtering on the first correction information to obtain second strain information includes: The first state equation is constructed. The first state equation is expressed by formula (2). Formula (2) is as follows: ; In formula (2), For the The second strain information at the moment, For the The second strain information at the moment, For the Process noise caused by environmental vibration or electromagnetic interference at all times, , is the covariance matrix of process noise; Construct the first observation equation, which is expressed by formula (3). Formula (3) is as follows: ; In formula (3), For the The first revised information at the moment, No. The observation noise caused by circuit noise or temperature compensation residual at each moment, , is the covariance matrix of the observation noise.

[0057] In this embodiment, after the deformation signal of the device to be tested is collected, numerical amplification is first performed, followed by analog-to-digital conversion to obtain first strain information, followed by temperature correction to obtain first correction information, and finally filtering and denoising the first correction information to obtain second strain information. Specifically, the temperature correction uses a temperature compensation formula, and the filtering and denoising uses a Kalman filter formula.

[0058] The first correction information is expressed by formula (1):

[0059] Among them, the first-order temperature coefficient and the second-order temperature coefficient Calibration is performed through the following steps: applying known stress to the device under test in a constant temperature box and recording the output values ​​of the strain gauge at different temperatures; fitting by the least squares method and , so that the error after compensation , ensuring the accuracy of monitoring.

[0060] The first correction information is subjected to Kalman filtering, and the covariance matrix of the process noise of Kalman filtering is and the covariance matrix of the observation noise Dynamically adjust, specifically, the covariance matrix of the process noise It is expressed by the following formula: ; in, is the baseline process noise covariance matrix, which characterizes the inherent noise characteristics of the stress distribution model itself when there is no temperature deviation (such as state prediction uncertainty caused by mechanical vibration, sensor drift, etc.), and relaxes the process noise constraints when the temperature increases.

[0061] Covariance matrix of observation noise It is expressed by the following formula: ; in, is the reference observation noise covariance matrix, which characterizes the sensor in the absence of temperature gradient ( ), which is usually determined by sensor calibration experiments. is the temperature gradient change value, and increases the observation noise weight when the temperature gradient changes greatly.

[0062] Covariance matrix of process noise and the covariance matrix of the observation noise Associated with temperature changes, it can solve the failure problem of traditional fixed parameters in variable temperature scenarios.

[0063] In this embodiment, the first strain information is first temperature corrected and then Kalman filter processing is performed, which is conducive to providing more accurate and stable deformation monitoring in complex environments. Specifically, the influence of temperature change on the deformation signal will cause measurement errors. Temperature correction can effectively eliminate the interference of temperature change on the deformation signal and make the signal closer to the true value; 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 temperature-related noise and drift, which can simplify the model and computational complexity of Kalman filtering, so that Kalman filtering can more effectively process the remaining random noise, effectively improve the accuracy and stability of stress deformation detection, and simplify system design to enhance its adaptability in complex environments.

[0064] In some embodiments, 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: The second state equation is constructed. The second state equation is expressed by formula (4). Formula (4) is as follows: ; In formula (4), For the The second correction information of the time, For the The second correction information of the time, For the Process noise caused by environmental vibration or electromagnetic interference at all times, , is the covariance matrix of process noise; The second observation equation is constructed. The second observation equation is expressed by formula (5). Formula (5) is as follows: ; In formula (5), For the The first response information at the moment, No. The observation noise caused by circuit noise or temperature compensation residual at each moment, , 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). Formula (6) is as follows:

[0065] In formula (6), is the second strain information, For the second correction information, is the first-order temperature coefficient, is the first temperature information, is the second-order temperature coefficient, is the calibration reference temperature.

[0066] Different from the temperature correction principle described above, in this embodiment, after the deformation signal of the device to be tested is collected, it is first amplified numerically, then analog-to-digital conversion is performed to obtain the first strain information, then the first strain information is filtered and denoised to obtain the second correction information, and finally the second correction information is temperature corrected to obtain the second strain information. The Kalman filter formula is used for filtering and denoising, and the temperature correction is used for temperature compensation formula.

[0067] Filter and denoise the first strain information, and the covariance matrix of the Kalman filter process noise and the covariance matrix of the observation noise Dynamically adjust, specifically, the covariance matrix of the process noise It is expressed by the following formula: ; in, is the baseline process noise covariance matrix, which characterizes the inherent noise characteristics of the stress distribution model itself when there is no temperature deviation (such as state prediction uncertainty caused by mechanical vibration, sensor drift, etc.), and relaxes the process noise constraints when the temperature increases.

[0068] Covariance matrix of observation noise It is expressed by the following formula: ; in, is the reference observation noise covariance matrix, which characterizes the sensor in the absence of temperature gradient ( ), which is usually determined by sensor calibration experiments. is the temperature gradient change value, and increases the observation noise weight when the temperature gradient changes greatly.

[0069] Covariance matrix of process noise and the covariance matrix of the observation noise Associated with temperature changes, it can solve the failure problem of traditional fixed parameters in variable temperature scenarios.

[0070] The second correction information is subjected to temperature correction to obtain the second strain information, which is expressed by formula (6):

[0071] Among them, the first-order temperature coefficient and the second-order temperature coefficient Calibration is performed through the following steps: applying known stress to the device under test in a constant temperature box and recording the output values ​​of the strain gauge at different temperatures; fitting by the least squares method and , so that the error after compensation , ensuring the accuracy of monitoring.

[0072] This embodiment significantly improves the accuracy and stability of strain monitoring by performing a sequence of filtering, noise reduction, and temperature compensation. and the observation noise covariance matrix Based on this, noise interference in different temperature scenarios is effectively suppressed: Based on the temperature deviation ( Linear expansion, relaxing the uncertainty constraints of state prediction caused by material expansion and increased vibration at high temperatures, avoiding overfitting; According to the temperature gradient The observation noise weight is amplified to enhance the robustness of the filter to local sudden changes when the surface temperature of the equipment is unevenly distributed. Through the dynamic noise model, the high-frequency characteristics of the real deformation signal are retained, and interference such as environmental vibration and circuit noise are filtered out, providing a second correction information with a high signal-to-noise ratio for subsequent temperature correction. The temperature compensation adopts the second-order polynomial model with the calibrated first-order temperature coefficient and the second-order temperature coefficient Accurately quantify the nonlinear coupling effect between temperature and strain. Through the sequential processing of filtering and noise reduction and then temperature compensation, the aliasing amplification of temperature residual noise in the filtering link is avoided, while ensuring the second strain information after compensation exist Error within range , especially under the condition of rapid alternation of high and low temperatures, it can still reliably separate the true mechanical strain and thermally induced strain, providing a high-fidelity data basis for stress over-limit judgment.

[0073] It is worth noting that whether to perform temperature correction first or Kalman filtering first needs to be decided according to the specific application scenario. When temperature compensation is linear and does not require iterative processing, deformation monitoring will be more accurate and stable by first performing temperature correction and then Kalman filtering. When temperature compensation involves complex calculations or requires filtering and denoising data, Kalman filtering is required first, followed by temperature correction, to improve the efficiency of deformation monitoring.

[0074] In some embodiments, 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; According to the stress simulation values, the position information of the multiple test points is divided into a high stress set, a medium stress set and a low stress set; 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; Determine one by one whether the change rate of the deformation signal is within the range of a preset change rate threshold; 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. 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 a low stress set, it is divided into a medium stress set. If the abnormal position information belongs to a medium stress set, it is divided into a high stress set. If yes, 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; 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.

[0075] In this embodiment, the stress simulation calculation can be obtained based on the 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 of the stress value after the deformation signal is calculated. Specifically, the device to be tested is gridded and then loaded with pressure and temperature parameters, and the equation is iteratively solved. , calculate the stress distribution through the displacement of each test point, modify the boundary conditions based on real-time monitoring data, generate dynamic stress simulation values, and further calculate the mean stress value of each test point based on historical data statistics. and standard deviation , the preset change rate threshold is set to , updated every 24 hours, and then divided the location information of multiple test points into high stress set, medium stress set and low stress set according to the stress simulation value.

[0076] The first preset frequency, the second preset frequency and the third preset frequency are analyzed based on historical data statistics. Specifically, the peak-to-valley difference, fluctuation frequency and change rate of the stress values ​​of each test point are statistically analyzed through historical data, and the standard deviation or coefficient of variation of the stress values ​​of the test points is calculated. For example, the test points with the top 20% of the standard deviation or coefficient of variation are classified as high fluctuation areas and are classified 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 test points with the middle 60% of the standard deviation or coefficient of variation are regarded as medium fluctuation areas, 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 test points with the bottom 20% of the standard deviation or coefficient of variation are regarded as low-frequency fluctuation areas, 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.

[0077] When the collected change rate exceeds the preset change rate threshold, indicating that the position of the test point has undergone abnormal deformation, the position information will be marked as abnormal position information, and the set type to which the test point belongs will be 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 will remain in the high stress set. This step can correspondingly increase the monitoring frequency of the test point, ensure high-frequency collection of abnormal deformations, and timely discover risks; correspondingly, when the collected change rate does not exceed the preset change rate threshold, indicating that the deformation signal of the current test point is in a normal state, the set type to which it belongs will not be changed, and the current frequency of monitoring can be maintained.

[0078] Furthermore, if an abnormality occurs at the test point during a collection, the set division of the test point will be changed to increase the collection frequency of abnormal position information. If the test point maintains a normal fluctuation range in subsequent collections, it is determined whether the set type to which the position information of the current deformation signal originally belongs is the set type to which it currently belongs. If not, it means that the position information of the test point did not originally belong to this set, and it should be restored to the set to which the test point originally belonged. For example, if test point A originally belonged to a low stress set, but a stress abnormality occurred during a monitoring, then test point A will be dispatched to a medium stress set. In the second monitoring, the stress of test point A is a normal fluctuation value, then it is determined whether the set type to which test point A originally belonged is a medium stress set. Under the aforementioned conditions, test point A will be reassigned from the medium stress set back to the low stress set.

[0079] This embodiment significantly improves the efficiency and abnormal response capability of stress monitoring through a dynamically graded collection strategy and an adaptive adjustment mechanism. Based on the stress simulation value, the test points are divided into a high stress set, a medium stress set, and a low stress set, and the collection frequency is set differently according to the historical fluctuation characteristics, which not only avoids the waste of resources caused by excessive monitoring of low-risk areas, but also ensures the data integrity of high-volatility areas. The collection strategy of dynamically adjusting the collection of test points in combination with the real-time change rate and the preset threshold, upgrading to a higher frequency set in case of an abnormality to strengthen monitoring, and tracing back to the initial set after returning to normal to restore the baseline collection strategy, realizes the flexible allocation of monitoring resources, and reduces unnecessary data redundancy while ensuring high-frequency tracking in high-risk areas. In addition, the characteristic information of the equipment to be tested is corrected by the finite element model and real-time data in a coordinated manner, so that the stress simulation value is more in line with the actual working conditions, providing a dynamic benchmark for the classification strategy, ensuring that the monitoring priority can still be accurately divided when the equipment is deformed and the temperature load changes, and effectively balancing the monitoring accuracy and system energy consumption.

[0080] In some embodiments, 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 further obtained by the following steps: Record the position information corresponding to the deformation signal as abnormal position information, and obtain all position information in the area to be verified divided by a preset size as a radius with the abnormal position information as the center, and record it as auxiliary position information; An abnormal stress verification set is constructed, and abnormal position information and auxiliary position information are divided 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.

[0081] In this embodiment, the accuracy and response efficiency of deformation monitoring are improved through the dynamic abnormal linkage mechanism, and the risk warning capability is strengthened while reducing the amount of redundant data. When the change rate of a single measured point exceeds the preset change rate threshold, it indicates that abnormal deformation has occurred at that location. At this time, the system automatically demarcates the area to be verified with the abnormal location information as the center, and constructs an abnormal stress verification set for abnormal linkage collection.

[0082] Specifically, when a certain position information detects an abnormal deformation signal, several adjacent position information will be activated to enter high-frequency collection, and the auxiliary position deformation signal will be collected synchronously through the fourth preset frequency to form 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 that location and further prevent risks.

[0083] This embodiment focuses on dynamically adjusting the collection strategy in abnormal areas to avoid energy waste caused by global high-frequency sampling, and uses multi-node data cross-validation to distinguish between real deformation and instantaneous interference to reduce 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, the evolution trend of structural hidden dangers is quickly located to achieve early warning before the risk spreads. 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 monitoring sensitivity and long-term stability, and providing hierarchical and adaptive security protection for high-risk equipment.

[0084] In some embodiments, performing abnormality judgment on each second strain information in the stress distribution model, if the stress value of the second strain information exceeds a preset stress threshold, recording it as abnormal strain information includes: Obtaining second stress information corresponding to the deformation signal of each test point in the abnormal stress verification set within a preset time period, recorded 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; Matching state information of stress change state, the state information includes normal fluctuation state, abnormal fluctuation state and emergency fluctuation state; When the stress change state belongs to the 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 the first alarm information and send the first alarm information to the service end.

[0085] In this embodiment, the dynamic simulation is performed by inputting the first verification stress information (including the stress value of the time series) into the stress distribution model, and using the current stiffness matrix and measured load Based on it, the displacement field is solved iteratively And calculate the real-time stress distribution; at the same time, compare the deviation between the theoretical value and the actual value with the subsequent monitoring data. If the difference exceeds the tolerance threshold, reversely optimize and adjust the material parameters (such as elastic modulus) or boundary conditions (such as contact constraints). After updating the model, a new round of stress field solution is performed. The cycle is iterated until the simulation results converge 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.

[0086] 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 the position information in the abnormal stress verification set has no abnormal deformation and there is no need to adjust its set type; when the stress change state belongs to the abnormal fluctuation state, it indicates that an abnormal risk occurs, and the corresponding temperature value is recorded; when the stress change state belongs to the emergency fluctuation state, it indicates that a high-risk explosion risk occurs, and the corresponding position information and temperature value are recorded and sent to the server, and the first alarm information is sent to prompt relevant personnel to handle it as soon as possible. This embodiment realizes accurate identification and graded response of stress anomalies through dynamic model iteration and multi-level risk classification mechanism, which significantly improves 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 stress distribution model, the local stress field distribution is corrected by iterative solution of stiffness matrix and load parameters, and the model accuracy is optimized in combination with time series data to ensure that the simulation results are consistent with the measured trend and eliminate the risk of single-point misjudgment. The stress state matching adopts a multi-level classification strategy. Conventional fluctuations automatically remove abnormal marks to reduce redundant alarms, abnormal fluctuations are associated with temperature to generate early warning signals, and emergency fluctuations trigger high-risk alarms and push location information to the server to form a gradient response. The adaptive adjustment of the dynamic model and the synergistic effect of state classification not only enhance the abnormal path prediction capability through parameter optimization, but also reduce the system load through hierarchical processing, ensuring that resources are concentrated on real risk points, and realizing full-process closed-loop management from initial screening of abnormalities, model verification to graded alarms, effectively balancing monitoring sensitivity and operational stability, and providing intelligent risk management and control support for explosion-proof scenarios.

[0087] In some embodiments, the method further comprises: The stress detector corresponding to the position information in the medium stress set and the low stress set is in deep sleep when not collecting; The stress detector corresponding to the position information in the high stress set is in shallow sleep when not collecting.

[0088] In this embodiment, the stress detector corresponding to the position information in the medium stress set and the low stress set is in deep sleep when not collecting data. ; The stress detector corresponding to the position information in the high stress set remains in shallow sleep when not collecting, and is ready to wake up quickly. At this time, the power consumption .

[0089] 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.

[0090] Different from the prior art, the above technical solution has the following beneficial effects: 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.

[0091] 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.

[0092] Finally, it should be noted that although the above embodiments have been described in the specification and drawings of this application, this does not limit the scope of patent protection of this application. All technical solutions generated by replacing or modifying equivalent structures or equivalent processes based on the essential concept of this application using the contents recorded in the specification and drawings of this application, as well as directly or indirectly implementing the technical solutions of the above embodiments in other related technical fields, are included in the scope of patent protection 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 management 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 base 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 base strain gauge is embedded in the first groove. The ceramic base strain gauge is abutted against the device to be tested for collecting stress signals. The temperature sensor is in contact with the ceramic base strain gauge for collecting temperature signals.

2. The -40 to 600 degree explosion-proof online stress monitoring system according to claim 1 is characterized in that: 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; And / or, the power management module is configured to selectively supply power to either the first circuit module or 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 based on the collected deformation signal is placed within a preset stress threshold range.

3. The -40 to 600 degree explosion-proof online stress monitoring system as claimed in claim 1, characterized in that: 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.

4. A -40 to 600 degree explosion-proof online stress monitoring method, characterized in that: The stress monitoring system according to any one of claims 1 to 3 is applicable, 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.

5. The -40 to 600 degree explosion-proof online stress monitoring method as claimed in claim 4, 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: In formula (1), For the first correction information, is the first strain information, α is the first-order temperature coefficient, is the first temperature information, β is the second-order temperature coefficient, 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. The first state equation is expressed by formula (2). The formula (2) is as follows: ； In formula (2), is the second strain information at the Kth moment, is the second strain information at the K-1th moment, is the process noise caused by environmental vibration or electromagnetic interference at the Kth moment, , 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: ； In formula (3), is the first correction information at the Kth moment, The observation noise caused by circuit noise or temperature compensation residual at time K is , is the covariance matrix of the observation noise.

6. The -40 to 600 degree explosion-proof online stress monitoring method as claimed in claim 4, 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: ； In formula (4), is the second correction information at the Kth moment, is the second correction information at the K-1th moment, is the process noise caused by environmental vibration or electromagnetic interference at the Kth moment, , is the covariance matrix of process noise; A second observation equation is constructed. The second observation equation is expressed by formula (5). The formula (5) is as follows: ； In formula (5), is the first strain information at the Kth moment, The observation noise caused by circuit noise or temperature compensation residual at time K is , 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: In formula (6), is the second strain information, For the second correction information, is the first-order temperature coefficient, is the first temperature information, is the second-order temperature coefficient, is the calibration reference temperature.

7. The -40 to 600 degree explosion-proof online stress monitoring method according to claim 4, 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.

8. The -40 to 600 degree explosion-proof online stress monitoring method according to claim 7, 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.

9. The -40 to 600 degree explosion-proof online stress monitoring method as claimed in claim 8, 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, the second strain information 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.

10. The -40 to 600 degree explosion-proof online stress monitoring method according to claim 7, 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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