Anti-explosion valve monitoring and alarming method and device based on Internet of Things

Abstract

The invention relates to the technical field of explosion-proof valve safety and alarm, particularly provides an explosion-proof valve monitoring and alarm method and device based on the Internet of Things, and solves the problem that whether a flap door in an explosion-proof valve has sealing leakage cannot be monitored in real time. The method comprises the following steps: installing data monitoring equipment on a rotating shaft; transmitting the deformation data and the strain data to an Internet of Things application server; comparing the deformation data and the strain data with preset standard deformation data and strain data respectively; performing simulation according to the deformation data and the strain data; and receiving information that the flap door is unqualified sent by the Internet of Things application server, triggering an anti-explosion-wave valve alarm instruction according to the information, and processing according to a preset solution. The device comprises a monitoring equipment module, an Internet of Things application server module, an explosion-proof valve analogue simulation model module and an Internet of Things safety equipment module. According to the invention, the efficiency of fault detection and alarm is improved.

Description

Technical Field

[0001] The present invention relates to the field of safety and alarm technology of explosion-proof wave valves, and in particular to an explosion-proof wave valve monitoring and alarm method and device based on the Internet of Things. Background Art

[0002] The explosion-proof valve is an important safety device. Its working principle is to detect the pressure change of the explosion wave through the sensor and control the opening and closing of the valve through the controller to achieve protection. This valve is usually equipped with components such as sensors, controllers and actuators, which can be quickly closed when an explosion occurs to prevent the explosion wave from entering the protection area. The rotating shaft of the pendulum plate is installed on the pendulum plate shaft seat through the pendulum plate bearing. Specifically, the rotating shaft of the pendulum plate is installed on the pendulum plate shaft seat through the pendulum plate bearing, and the pendulum plate shaft seat is located above the two side walls of the caisson. This design allows the pendulum plate to rotate around the steel hinge shaft, thereby achieving a maximum swing angle of approximately ±35°.

[0003] Prior art 1, Chinese patent, application number 202311864173.3 discloses a pendulum-type explosion-proof valve rubber strip peeling strength test device and test method, the peeling strength test device spans across the two sides of the rubber strip bonded to the inner wall of the valve door frame and is connected to the rubber strip, including the peeling strength test device including a support cylinder and a tensile test assembly slidably arranged in the support cylinder, one end of the support cylinder spans across the inner wall of the valve door frame arranged on both sides of the rubber strip, one end of the tensile test assembly can extend along one end of the support cylinder and connect to the rubber strip, and the other end of the tensile test assembly can slide outward along the other end of the support cylinder; read until the set tensile value of the peeling rubber strip is met, repeatedly measure the tensile value to obtain the average tensile value, and then correct the test adhesion value. It can ensure that the peeling angle does not deviate during the test process; avoid human interference in the peeling strength during the test process, although it improves the accuracy of the comprehensive judgment and test results of the valve rubber strip detection test, it cannot monitor in real time whether there is a leakage problem in the valve door in the explosion-proof valve.

[0004] Prior art two, Chinese patent, application number 202311502197.4 relates to the technical field of explosion-proof wave valves, specifically to a bidirectional wave-absorbing explosion-proof wave valve, which is an integral structure, or is composed of several modular units, the overall structure and the modular units both include a valve body assembly and a valve core assembly; the valve body assembly includes two rows of valve plates and a frame structure; the valve core assembly includes several valve core rods, which are movably arranged within a set range and can reach a position where they fit with the valve plates on any side to block the flow channel; an elastic reset structure provides an elastic extrusion force on the valve core rod, and the elastic extrusion force drives the valve core rod to remain between the two rows of valve plates, and at a position spaced apart from the valve plates on both sides. The two-way wave-absorbing and explosion-proof valve can withstand shock waves in both directions, is non-gravity sensitive, and can be installed at any angle. During use, the two-way wave-absorbing and explosion-proof valve has good durability, and all key components can be made of metal structures. Although it is maintenance-free during its service life, the base material is resistant to high temperatures of ≥300℃, and the good structural strength can withstand the impact of greater pressure, but it cannot monitor the problem of inflexible rotation of the door panel in real time.

[0005] Prior art three, Chinese patent, application number 201610184733.4 discloses a suspended plate type explosion-proof wave valve bidirectional monitoring and operation sensor module and explosion-proof wave valve, wherein the suspended plate type explosion-proof wave valve bidirectional monitoring and operation sensor module includes a suspended swing plate biaxial tilt angle monitoring module, a data analysis and processing module and a data transmission module, and can record the swing angle of the suspended swing plate and the total swing time, analyze the number of times and required time of the suspension swing plate closure, the starting and ending position and angle of the swing, judge the working condition of the valve, and automatically and online through the requirements of the specifications (standards) preset in the data analysis and processing module. Rapid and accurate detection of the performance of the valve. Although it is conducive to the subsequent regular maintenance of the valve, ensuring that the moving parts of the suspended plate type explosion-proof wave valve remain in good operating condition and ensuring the wartime engineering protection effect, it cannot monitor the aging of the leaf door and the sealing performance in real time.

[0006] At present, the existing technologies 1, 2 and 3 cannot monitor the inflexibility of the valve leaf rotation in real time, cannot monitor the sealing leakage of the valve leaf in the explosion-proof valve in real time, and cannot monitor the aging of the valve leaf and the sealing performance in real time. Therefore, the present invention provides an explosion-proof valve monitoring and alarm device and method based on the Internet of Things. Summary of the invention

[0007] In order to achieve the above object, the present invention adopts the following technical scheme: In one aspect of the present invention, a method for monitoring and alarming explosion-proof valves based on the Internet of Things is provided, comprising the following steps: Obtain the installation position of the rotating shaft of the explosion-proof wave valve, and install a data monitoring device on the rotating shaft to measure the deformation data and strain data of the rotating shaft; the data monitoring device transmits the deformation data and strain data to the Internet of Things application server through the Internet of Things interconnection interface; The deformation data and strain data are compared with the preset standard deformation data and strain data respectively; when any one of the deformation data and strain data does not meet the standard, the explosion-proof valve simulation model is started; simulation is performed according to the deformation data and strain data, if the valve door cannot be completely sealed, the deformation data and / or strain data of the rotating shaft are unqualified; otherwise, they are qualified; Receive the information that the flap door is unqualified from the IoT application server, trigger the explosion-proof wave flap alarm instruction according to the information, send the explosion-proof wave flap alarm instruction to the IoT security device, and process it according to the preset solution.

[0008] In an optional implementation, the process of acquiring deformation data and strain data includes the following steps: Install the monitoring equipment at the corresponding position of the rotating shaft according to the type of target data; configure the corresponding sampling channel after the monitoring equipment is installed, calibrate each monitoring equipment, compare the output with the standard value, and initialize after the comparison passes; Establish a connection between the monitoring device and the IoT interface, and transmit the deformation data and strain data to the IoT application server with the support of the data transmission protocol; After the deformation data and strain data are collected, a threshold is set for the change amplitude of each sampling point. The deformation data and / or strain data that exceeds the range will be marked as noise and removed. The continuous deformation data and / or strain data window after noise removal is averaged, and the average value of the time series is continuously calculated to eliminate the impact of fluctuations in a short period of time, and obtain preprocessed deformation data and strain data.

[0009] In an optional embodiment, wherein the type refers to strain and deformation; the process of installing at a corresponding position of the rotating shaft includes: Using the relationship between stress and strain, calculate the strain energy density of the rotating axis :

[0010] In the formula, represents the stress tensor, represents the strain tensor, represents the volume to be analyzed; deformation Defined as the change in length of an object after an external force is applied:

[0011] In the formula, Indicates a position after an external force is applied Length; The original length without stress; At a certain position on the axis of rotation, the strain is calculated using the constitutive relation :

[0012] In the formula, Represents radial strain, which is used for the relative deformation of the rotating shaft in the radial direction, the ratio of the deformed length to the original length. represents the hoop strain, describing the relative deformation of the rotation axis in the hoop direction, represents the axial strain of the rotating axis, represents the hoop stress of the rotating shaft, represents the shear stress, which represents the interaction between the rotating axis in the radial and circumferential directions, It represents Young's modulus, which indicates the stiffness of the material and describes the relationship between stress and strain of the rotating shaft when it is subjected to force. It represents Poisson's ratio, which is the ratio of the strain of the rotating axis in one direction to the strain in the vertical direction. It reflects the lateral deformation behavior of the rotating axis when it is under tension or compression. Indicates that the radial strain is calculated through the relationship between the stress in the latitudinal direction and the stress in other directions. It means that the axial strain is calculated through the relationship between the axial stress and the stress in other directions. represents the calculated shear strain, showing the relationship between shear stress and Young's modulus and Poisson's ratio; Define an objective function to minimize the influence of strain energy, deformation and strain on the overall structure of the rotating shaft; the objective function is expressed as:

[0013] In the formula, represents the optimization target, represents the total damage or energy of the device, represents the strain energy density, Indicates absolute deformation, reflecting the deformation amount at a specific position. Indicates strain, reflecting the deformation characteristics of the material. , Represents the weight coefficient, which is used to adjust the influence of strain energy, deformation and strain in the overall target; During the optimization process, the following constraints are followed: Deformation Limits:

[0014] In the formula, It indicates the maximum deformation allowed by the material to avoid structural failure; Boundary conditions: Fixed end conditions:

[0015] Free end conditions:

[0016] A set of solutions is randomly generated, each solution represents the installation location of a monitoring device, and a set of particles is randomly generated, and the position of each particle represents the installation location of the monitoring device; individuals are selected for replication according to fitness; individuals are selected for crossover to generate new solutions; the newly generated solutions are randomly mutated to introduce diversity, and the population is updated with the newly generated individuals; each particle updates its speed according to its own optimal position and the optimal position of the group;

[0017] In the formula, is the inertia weight, and is the learning factor, and is a random number, and are the individual best and global best positions respectively; Update particle positions:

[0018] Reaching the maximum number of iterations: Monitor the changes in the best solution within the set maximum number of iterations; obtain the optimal installation position based on the minimum objective function value.

[0019] In an optional implementation, the process of obtaining the preprocessed deformation data and strain data includes the following steps: For each monitoring point, dynamically set the threshold of the change amplitude; perform multi-level decomposition on the signal of deformation data and / or strain data to extract high-frequency and low-frequency components; compare the high-frequency component with the set noise threshold, if the absolute value of the high-frequency component is greater than the set threshold, the data point is considered to be noise, and is removed or replaced, and the low-frequency component with noise removed and the processed high-frequency component are recombined to form a denoised signal; Introducing a sliding window of fixed length to realize group processing of deformation data and / or strain data; calculating the weighted average of each data point in the window; processing the start and end boundaries of the window; For the continuous deformation data and / or strain data that have been processed by noise, the sliding average is continuously calculated to generate a new time series to smooth the data; the moving standard deviation of each time period is calculated. If the moving standard deviation exceeds the set threshold, an alarm is issued. The alarm content includes the monitoring point identification, supervision time and abnormal data value; the data are classified and stored in the corresponding database of the Internet of Things application server, and a table is established according to the date, monitoring point and data type.

[0020] In an optional implementation, the process of starting the explosion-proof valve simulation model includes the following steps: The standard deformation data and standard strain data of the explosion-proof valve are preset, and the real-time deformation data and strain data obtained from the monitoring equipment are received through the Internet of Things application server; the real-time deformation data are compared with the preset standard deformation data item by item to check whether the real-time value is within the preset standard range; the same comparison operation is performed on the real-time strain data, and the comparison standard is the absolute difference of the specific values; If any of the deformation data or strain data measured in real time is greater than or less than the corresponding standard value and exceeds the set threshold range, it will be recorded as non-compliant with the standard; if any data is confirmed to be non-compliant with the standard, the explosion-proof valve simulation model will be activated to simulate the performance of the valve in the working state; the acquired real-time deformation and strain data will be input into the explosion-proof valve simulation model to evaluate the sealing effect of the valve; Analyze the results of the explosion-proof valve simulation model output. If the valve is leaking or not sealed tightly, it will be marked as unqualified. On the contrary, if the simulation results show that the valve is completely sealed, it will be recorded as qualified.

[0021] In an optional implementation manner, the process of checking whether the real-time value is within a preset standard range includes the following steps: When real-time deformation data is received, an independent thread is triggered to extract standard data. After the thread is started, a connection with the standard data repository is established. The device ID is used as the query condition, an index is established on the device ID field, and the thread constructs a query statement. To perform a query operation, the thread synchronously waits for the return result of the standard data repository; the thread receives the data returned by the database; when the query result is obtained, the thread performs data analysis, and after the analysis is completed, the thread stores the standard deformation data into the memory cache; In the main thread, read the set threshold range, each real-time deformation data, read the real-time deformation data in the memory cache one by one, and extract the standard value according to the device ID matching; calculate the absolute difference between the current real-time deformation data and the standard deformation data, and after calculating each difference, determine whether the difference exceeds the set threshold range.

[0022] In an optional implementation, the process of simulating the performance of the valve in a working state includes the following steps: Input the deformation data and strain data that do not meet the standards into the explosion-proof wave valve simulation model, and use the deformation data and strain data as the initial conditions and boundary conditions of the explosion-proof wave valve simulation model to form a problem domain; Discretize the problem domain into small finite elements for calculation, solve the fluid and solid equations at the same time, and update the interaction force between deformation data and strain data in each iteration. During the calculation process, the interaction between fluid and solid is realized through interface coupling. After the simulation is completed, the simulation results are analyzed in depth, and the performance of the flap door under actual working conditions is predicted through the explosion-proof flap simulation model.

[0023] In an optional implementation, the process of triggering the explosion-proof valve alarm instruction according to the information includes the following steps: The information about the explosion-proof valve failure is received through the IoT application server, and an instant event processing mechanism is established inside the IoT application server; the failure information is parsed to identify the specific type of problem, whether it is caused by the excessive deformation data of the rotating shaft or the excessive strain data; Based on the activation signal received from the event processing mechanism, the corresponding alarm process is triggered; each unqualified situation has a corresponding alarm priority setting. If the deformation data is unqualified, the priority is higher than the strain data unqualified, and the response order is determined according to the set priority; According to the type of non-conformity, the corresponding explosion-proof wave valve alarm instruction is automatically generated, including the urgency of the response, the type of fault and the location identification; the alarm instruction contains the timestamp, equipment ID, and specific fault description information.

[0024] In an optional implementation, the process of identifying a specific problem type includes the following steps: Identify keywords related to equipment status, including exceeding standards, abnormalities, or failures, compare keywords with predefined failure dictionaries, and locate unqualified categories; obtain failure types through the built-in mapping relationship between multiple keywords and failure types, including the association between deformation data and corresponding control methods, and the link between strain data and potential risks; Based on the surrounding information of the received keywords, the potential causes of failure are analyzed. The keyword deformation exceeds the standard and appears in the description, which is associated with the time and operation status information mentioned in the context; The parsed information is classified and marked, and the information of deformation data exceeding the standard is marked as a high-priority fault, and the information of strain data exceeding the standard is marked as a low-priority fault, and stored in the database; the classification adopts a dynamic label mechanism to automatically adjust the priority of each state according to the changes in real-time data.

[0025] Another aspect of the present invention provides an explosion-proof valve monitoring and alarm device based on the Internet of Things, which is used to implement the explosion-proof valve monitoring and alarm method based on the Internet of Things, including: A monitoring device module is used to install sensors on the rotating shaft of the explosion-proof valve to monitor deformation and strain data in real time and transmit the data to the IoT application server through the IoT protocol; The IoT application server module is used to receive and store the deformation data and strain data transmitted from the data monitoring equipment, conduct preliminary analysis and comparison, and determine whether the data meets the standards; The explosion-proof valve simulation model module is used to start the simulation model when the data does not meet the standards, simulate the performance of the valve under actual working conditions, and judge whether it can be completely sealed. If the simulation result shows that the valve cannot be completely sealed, the IoT application server will trigger an alarm instruction and send it to the IoT security device; The IoT safety device module is used to execute preset solutions after receiving alarm instructions, such as closing explosion-proof valves or initiating emergency measures.

[0026] The present invention utilizes real-time monitoring technology to reduce the need for manual inspections, allowing maintenance personnel to focus more on data analysis and troubleshooting, thereby improving work efficiency; uploading data to the cloud or a centralized processing platform facilitates subsequent data processing, analysis, and storage, providing necessary real-time information for decision-making. Through timely detection and comparison, early warnings can be issued before potential equipment failures develop into serious problems, reducing the occurrence of emergencies; simulation provides a scientific way to make judgments, allowing the system to further verify the operating status of the valve through virtual testing, thereby reducing the risks of potential failures in actual operation. Through a rapid alarm and linkage response mechanism, timely measures can be taken to prevent accidents and significantly improve the safe operation of explosion-proof valves; rapid handling of potential problems can effectively reduce economic losses and personal injuries caused by equipment failures, and improve overall safety management results. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a flow chart of the explosion-proof valve monitoring and alarm method based on the Internet of Things provided in Example 1 of the present invention; Figure 2 A flow chart of a process for acquiring deformation data and strain data provided in Example 2 of the present invention; Figure 3 A process diagram of obtaining pre-processed deformation data and strain data provided in Example 3 of the present invention; Figure 4 A process diagram of a simulation model for starting an explosion-proof valve provided in Example 4 of the present invention; Figure 5A process diagram for checking whether a real-time value is within a preset standard range provided in Embodiment 5 of the present invention; Figure 6 A process diagram of the performance of the simulated valve in working state provided in Example 6 of the present invention; Figure 7 A process diagram of triggering an explosion-proof valve alarm instruction according to information provided in Example 7 of the present invention; Figure 8 A process diagram for identifying a specific problem type provided in Embodiment 8 of the present invention; Fig. 9 This is a block diagram of an explosion-proof valve monitoring and alarm device based on the Internet of Things provided in Example 9 of the present invention; Fig.10 A block diagram of an electronic device provided by the present invention; Fig.11 A block diagram of a computer-readable storage medium provided for the present invention. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present invention will be described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0029] In the following, the terms "first", "second", etc. are used only for convenience of description and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0030] In the present invention, unless otherwise clearly specified and limited, the term "connection" should be understood in a broad sense, for example, "connection" can be a fixed mechanical connection, or a detachable mechanical connection, or integrated; or, "connection" can be a direct connection, or an indirect connection through an intermediate medium. In addition, unless otherwise clearly specified and limited, the term "coupling" should be understood in a broad sense, for example, "coupling" can be a direct electrical connection, such as physical contact and electrical conduction between two components, and can also be understood as electrical connection between different components in a circuit structure through physical lines such as copper foil or wires on a printed circuit board (PCB) that can transmit electrical signals to transmit electrical signals; or, "coupling" can be an indirect electrical connection between two components through an intermediate medium; or, "coupling" can be an electrical connection between two components in an air-spaced / non-contact manner, for example, two components are electrically connected by capacitive coupling to transmit electrical signals.

[0031] In the embodiments of the present invention, directional terms such as "up", "down", "left" and "right" may be defined including but not limited to the orientation relative to the schematic placement of the components in the drawings. It should be understood that these directional terms may be relative concepts, which are used for relative description and clarification, and may change accordingly according to the change of the orientation of the components in the drawings.

[0032] The embodiments of the present invention can be used for industrial safety monitoring. In high-risk industries such as petroleum, chemical industry, and electric power, the safety of explosion-proof wave valves is extremely important. They can be used to monitor the operating status of the equipment in real time to ensure the safety of explosion-proof wave valves in actual applications. Equipment environment monitoring is suitable for monitoring explosion-proof equipment in various extreme environments; such as high temperature and high pressure, to ensure that the equipment can work normally in harsh environments. Production process automation, in automated production lines, by real-time monitoring of valve status, the system can automatically adjust the production process according to the data to improve production efficiency. Smart factory construction, in the process of implementing smart factory transformation, can integrate the Internet of Things system to improve the level of intelligent monitoring and management of equipment. Predictive maintenance, through real-time monitoring and data analysis, can predict equipment failure points in advance, perform maintenance, and achieve cost reduction and efficiency improvement.

[0033] The key technical features of the embodiments of the present invention are as follows: a data monitoring device (such as a sensor) is installed on the rotating shaft of the explosion-proof valve to accurately measure the deformation data and strain data of the rotating shaft. This feature ensures the accuracy and real-time nature of the monitoring data; the data monitoring device transmits the acquired data to the IoT application server in real time through the IoT interface; the remote transmission capability of the data is ensured, which provides convenience for subsequent analysis; the deformation and strain data collected in real time are compared with standards, and the simulation model of the explosion-proof valve is started in important situations. The feature provides a scientific and reliable evaluation method to ensure the sealing performance of the valve; when it is detected that the valve cannot be completely sealed, the system can automatically trigger an alarm instruction and send it to the IoT security device. This feature improves the response speed of the system and ensures that safety hazards can be dealt with immediately. According to the preset solution, the alarm can be automatically processed, effectively reducing the need for manual intervention and improving the automation level of the system. The embodiments of the present invention have the characteristics of high efficiency, automation, real-time monitoring and response, and are suitable for a variety of industrial environments, making equipment management more intelligent and safe. The design of key technical features ensures that the system can still operate stably and efficiently in complex and changing environments, significantly improving the safety and reliability of the explosion-proof wave valve; it not only provides a new technical means for equipment protection, but also provides new ideas and solutions for operation and maintenance management in related industrial fields. Example

[0034] like Figure 1As shown, an embodiment of the present invention provides an explosion-proof valve monitoring and alarm method based on the Internet of Things, comprising the following steps: Step S100: obtaining the installation position of the rotating shaft of the explosion-proof wave valve, installing a data monitoring device on the rotating shaft to measure the deformation data and strain data of the rotating shaft; the data monitoring device transmits the deformation data and strain data to the Internet of Things application server through the Internet of Things interconnection interface; Step S200: respectively comparing the deformation data and the strain data with the preset standard deformation data and strain data; when any one of the deformation data and the strain data does not meet the standard, starting the explosion-proof wave valve simulation model; performing simulation according to the deformation data and the strain data, if the valve door cannot be completely sealed, the deformation data and / or strain data of the rotating shaft are unqualified; otherwise, they are qualified; Step S300: receiving information that the flap door is unqualified from the IoT application server, triggering an explosion-proof wave flap alarm instruction according to the information, sending the explosion-proof wave flap alarm instruction to the IoT security device, and processing according to a preset solution.

[0035] In the above embodiment, step S100 installs a data monitoring device on the rotating shaft of the explosion-proof valve to obtain the deformation and strain data of the rotating shaft in real time; the monitoring of the equipment operation status is transformed from passive to active, and key engineering data is captured in time; the collected deformation data and strain data are transmitted to the Internet of Things application server through the Internet of Things interconnection interface to ensure that the data can be uploaded quickly and accurately. The significance achieved: using real-time monitoring technology to reduce the need for manual inspections, so that maintenance personnel can focus more on data analysis and troubleshooting, and improve work efficiency; uploading data to the cloud or centralized processing platform is convenient for subsequent data processing, analysis and storage, and provides necessary real-time information for decision-making. Step S200 can objectively evaluate the performance of the explosion-proof valve by comparing the deformation data and strain data obtained by real-time monitoring with the preset standards; once the data is found to be inconsistent with the standards, it can respond quickly; when abnormal deformation or strain data is detected, the simulation model is started to further analyze the sealing performance of the explosion-proof valve to confirm whether it can work effectively. Significance achieved: Through timely detection and comparison, early warning can be given before the potential equipment failure develops into a serious problem, thereby reducing the occurrence of emergencies; simulation provides a scientific way of judgment, allowing the system to further verify the operating status of the valve through virtual testing, thereby reducing the risk of potential failures in actual operation. Step S300 triggers an alarm instruction based on the data received that the valve cannot be completely sealed to ensure that the system responds immediately; the alarm instruction is sent to the Internet of Things security device, and corresponding processing is performed according to the preset solution to avoid the spread of potential dangers. Significance achieved: Through the rapid alarm and linkage response mechanism, timely measures can be taken to prevent accidents and significantly improve the safe operation of explosion-proof valves; rapid handling of potential problems can effectively reduce economic losses and personal injuries caused by equipment failures and improve the overall safety management effect.

[0036] Technical problems solved by this embodiment In traditional systems, the status of explosion-proof valves mostly depends on manual monitoring and regular inspections, which may lead to delayed response to fault information; this embodiment uses the Internet of Things technology to achieve real-time monitoring of the deformation and strain of the rotating shaft, thereby changing passive monitoring to active monitoring. Traditional monitoring methods are difficult to quickly discover potential faults or safety hazards in explosion-proof valves; this embodiment can detect problems in time when equipment abnormalities occur through standard data comparison and simulation. In the face of equipment failures or safety hazards, traditional systems may not be able to provide rapid alarm and processing mechanisms; this embodiment improves the response speed of the system by automatically triggering alarm instructions and combining preset solutions. Traditional equipment monitoring often lacks in-depth analysis and utilization of data, resulting in decisions based on limited information; this embodiment provides a basis for decision-making support through real-time data collection, transmission and analysis.

[0037] In summary, this embodiment improves the degree of automation through real-time monitoring and data processing, reduces human intervention, and optimizes resource allocation; through real-time monitoring and rapid response to equipment status, the safety and reliability of the system are significantly improved, providing safer protection for industrial applications; the combination of real-time and historical data provides data support for subsequent fault analysis and operation maintenance, making the decision-making process scientific and transparent; a full life cycle management mechanism for equipment from operation to fault warning is established, providing an effective basis for equipment maintenance, updating and improvement; the advantages brought by the Internet of Things technology are fully utilized, heralding a safer and more reliable future for equipment operation. This embodiment uses the Internet of Things technology to realize real-time monitoring, intelligent evaluation, and automatic alarm and response of explosion-proof wave valves, thereby significantly improving the safety and reliability of equipment operation and solving the shortcomings of traditional monitoring methods in real-time, initiative, and response speed; it not only improves the efficiency of fault detection and alarm, but also provides strong data support for the safety management and decision-making of industrial equipment. Example

[0038] like Figure 2 As shown, based on Example 1, the process of acquiring deformation data and strain data in step S100 provided in the embodiment of the present invention specifically includes the following steps: Step S101: Install the monitoring equipment at the corresponding position of the rotating shaft according to the type of target data; after the monitoring equipment is installed, configure the corresponding sampling channel, calibrate each monitoring equipment, compare the output with the standard value, and initialize after the comparison passes; Among them, the type refers to strain and deformation; the process of installing at the corresponding position of the rotating axis includes: Using the relationship between stress and strain, calculate the strain energy density of the rotating axis :

[0039] In the formula, represents the stress tensor, represents the strain tensor, represents the volume to be analyzed; deformation Defined as the change in length of an object after an external force is applied:

[0040] In the formula, Indicates a position after an external force is applied Length; The original length without stress; At a certain position on the axis of rotation, the strain is calculated using the constitutive relation :

[0041] In the formula, Represents radial strain, which is used for the relative deformation of the rotating shaft in the radial direction, the ratio of the deformed length to the original length. represents the hoop strain, describing the relative deformation of the rotation axis in the hoop direction, represents the axial strain of the rotating axis, represents the hoop stress of the rotating shaft, represents the shear stress, which represents the interaction between the rotating axis in the radial and circumferential directions, It represents Young's modulus, which indicates the stiffness of the material and describes the relationship between stress and strain of the rotating shaft when it is subjected to force. It represents Poisson's ratio, which is the ratio of the strain of the rotating axis in one direction to the strain in the vertical direction. It reflects the lateral deformation behavior of the rotating axis when it is under tension or compression. Indicates that the radial strain is calculated through the relationship between the stress in the latitudinal direction and the stress in other directions. It means that the axial strain is calculated through the relationship between the axial stress and the stress in other directions. represents the calculated shear strain, showing the relationship between shear stress and Young's modulus and Poisson's ratio; Define an objective function to minimize the influence of strain energy, deformation and strain on the overall structure of the rotating shaft; the objective function is expressed as:

[0042] In the formula, represents the optimization target, represents the total damage or energy of the device, represents the strain energy density, Indicates absolute deformation, reflecting the deformation amount at a specific position. Indicates strain, reflecting the deformation characteristics of the material. , Represents the weight coefficient, which is used to adjust the influence of strain energy, deformation, and strain in the overall target.

[0043] During the optimization process, the following constraints are followed: Deformation Limits:

[0044] In the formula, It indicates the maximum deformation allowed by the material to avoid structural failure; Boundary conditions: Fixed end conditions:

[0045] Free end conditions:

[0046] Equipment performance requirements: Ensure that the monitoring equipment can capture sufficient strain or deformation information; Generate the initial population: randomly generate a set of solutions, each solution represents the installation location of a monitoring device, randomly generate a set of particles, the position of each particle represents the installation location of the monitoring device; select individuals for replication based on fitness, using methods such as roulette selection or tournament selection; select a certain proportion of individuals for crossover to generate new solutions; randomly mutate the newly generated solutions to introduce diversity and avoid falling into local optimality; update the population with the newly generated individuals; each particle updates the speed based on its own optimal position and the optimal position of the group;

[0047] In the formula, is the inertia weight, and is the learning factor, and is a random number, and are the individual best and global best positions respectively; Update particle positions:

[0048] Reaching the maximum number of iterations: monitoring the changes in the best solution within the set maximum number of iterations; obtaining the optimal installation position, based on the minimum objective function value; Step S102: Establishing a connection between the monitoring device and the Internet of Things interface, and transmitting the deformation data and strain data to the Internet of Things application server under the support of the data transmission protocol; Step S103: After the deformation data and strain data are collected, a threshold is set for the change amplitude of each sampling point. The deformation data and / or strain data that exceeds the range will be marked as noise and the noise will be removed; the continuous deformation data and / or strain data window after the noise is removed is averaged, and the average value of the time series is continuously calculated to eliminate the influence of fluctuations in a short period of time, so as to obtain preprocessed deformation data and strain data.

[0049] In the above embodiment, step S101 installs the monitoring equipment and calibrates it. According to the target data type (strain and deformation), the monitoring equipment is properly installed at the key position of the rotating shaft to ensure that the sensor can collect relevant data at the best position, which directly affects the accuracy and reliability of the subsequent data. By configuring the sampling channel and calibrating it, the difference between the sensor output and the standard value is compared to ensure that the equipment is in the best state before actual application. The calibration not only makes the output data of the sensor within an acceptable range, but also reduces the data collection deviation caused by equipment errors, and enhances the credibility of the data. Significance: It ensures that the monitoring equipment can collect accurate information during actual work, reduces the risk of errors in subsequent data processing, and improves the overall reliability of the monitoring system. High-quality initial data is the basis for further analysis and an important prerequisite for the effective implementation of the subsequent steps of early warning and monitoring. Step S102 establishes data connection and transmission. By establishing a stable connection between the monitoring equipment and the Internet of Things interface, real-time data upload is realized. The use of data transmission protocols (such as MQTT, HTTP) can ensure fast and reliable data transmission. It makes real-time monitoring possible, enables the system to receive data from the monitoring equipment at any time, and supports agile response and decision-making. Significance: Real-time transmission of data ensures the sensitivity of the system to the stimulus conditions. Rapidly transmitted data can speed up fault detection and processing, improve the safety of the overall system, ensure that data can be quickly integrated into the IoT application server, reduce data delay, and make data available for subsequent analysis and decision-making. Step S103 Data noise processing and preprocessing, by setting the amplitude threshold, give priority to eliminating abnormal data that exceeds the predetermined range to ensure data quality; can effectively reduce data noise introduced by environmental interference or equipment errors; the data after noise removal is averaged in a continuous window to form stable time series data, and smoothing processing helps to eliminate fluctuations in a short period of time and significantly reduce the randomness and uncertainty of the data. Significance: Removing noise and fluctuations can enhance the quality of data, facilitate more accurate interpretation and analysis, and help to truly understand the status of the valve; the smoothed data generated after data preprocessing is not only conducive to subsequent fault detection and status assessment, but also lays the necessary foundation for model establishment (such as simulation model), helping to make accurate decisions and effective early warning.

[0050] In summary, the monitoring system of this embodiment can efficiently and accurately collect, transmit and process deformation data and strain data. It not only improves the monitoring capability of the system, but also provides strong data support to ensure the safe operation of the equipment, effectively reducing potential safety hazards. In industrial applications, the monitoring system helps to continuously optimize equipment performance, improve production efficiency, and maintain a safe production environment. Example

[0051] like Figure 3As shown, based on Example 2, the process of obtaining the pre-processed deformation data and strain data in step 103 provided in the embodiment of the present invention specifically includes the following steps: Step S1031: dynamically set a threshold value of the change amplitude for each monitoring point; perform multi-level decomposition on the signal of the deformation data and / or strain data to extract high-frequency and low-frequency components; compare the high-frequency component with the set noise threshold, and if the absolute value of the high-frequency component is greater than the set threshold, the data point is considered to be noise, and is removed or replaced, and the low-frequency component after noise removal and the processed high-frequency component are recombined to form a denoised signal; Step S1032: introducing a sliding window of fixed length to realize grouping processing of deformation data and / or strain data; calculating the weighted average value of each data point in the window; processing the start and end boundaries of the window; Step S1033: For the continuous deformation data and / or strain data that have been processed with noise, continuously calculate their sliding average values ​​to generate a new time series to smooth the data; calculate the moving standard deviation of each time period, and if the moving standard deviation exceeds the set threshold, issue an alarm, which includes the monitoring point identification, supervision time and abnormal data value; classify and store them in the corresponding database of the Internet of Things application server, and establish a table according to date, monitoring point and data type.

[0052] In the above embodiment, step S1031 dynamically sets the noise threshold and multi-level signal processing, and dynamically adjusts the change amplitude threshold of each monitoring point according to the characteristics of the monitoring equipment and the changes in the environment, so as to be able to adapt to the changes in the external environment (such as temperature, humidity, operating conditions, etc.) in real time, which helps to improve the accuracy of noise marking; through wavelet transform and other technologies, multi-level decomposition of the signal is realized, high-frequency and low-frequency components are extracted, and high-frequency noise is helped to be identified and removed, while retaining important low-frequency signals; this decomposition method can accurately identify potential noise components in the signal and effectively improve the quality of the signal. Compare the high-frequency components with the set noise threshold, remove the signal points that exceed the threshold, and form effective data after denoising; ensure the accuracy and reliability of the data, and improve the basis for subsequent analysis. The significance achieved: through dynamic thresholds and signal decomposition, the interference of noise on the data can be minimized, thereby ensuring that the subsequent analysis is based on accurate and reliable data; dynamically adjusting the threshold makes the real-time monitoring system more adaptable and able to cope with complex and changeable monitoring environments. Step S1032 Sliding window technology and weighted average, grouping data through a sliding window of fixed length, dividing the continuously collected data into multiple windows, making the processing process more efficient and orderly; calculating the weighted average in each window, using the Hanning window to ensure that more recent data points account for a larger proportion in the average calculation; effectively smoothing the data helps to reduce the impact of random fluctuations on the results; processing the start and end boundaries of the window to avoid data distortion caused by boundary effects, such as ensuring the continuity and integrity of the data through repeated extension methods. Significance achieved: The weighted average within the window makes the final data sequence smoother, reduces the impact of short-term fluctuations, and makes the long-term trend more obvious; the sliding window facilitates batch processing, simplifies the complexity of subsequent data analysis, and improves the processing efficiency of the system. Step S1033 Sliding average and dynamic evaluation, by sliding average of the noise-processed data, a smooth new time series can be generated to make it easier to identify long trends and patterns; make the characteristics of the data clearer, which is convenient for subsequent analysis; continuously calculate the moving standard deviation of each time period to dynamically monitor the stability of the data; if the standard deviation exceeds the set threshold, trigger an alarm and promptly feedback possible system problems; the processed data is stored in the Internet of Things database by date, monitoring point and data type, which improves the organization and ease of use of the data and facilitates subsequent data analysis and quality tracking. Significance achieved: The setting of the moving standard deviation and the alarm mechanism enable the monitoring system to detect potential problems in advance and ensure the safe operation of equipment and systems; effectively classify the processed data to facilitate subsequent data retrieval and analysis, and improve the flexibility of data application.

[0053] In summary, this embodiment comprehensively solves the noise interference, data fluctuation, management and storage, and real-time feedback problems generated in the process of deformation data and strain data collection and analysis through noise processing, data smoothing, anomaly detection, and effective data management; the monitoring system can not only provide accurate and useful information to support subsequent decision-making, but also ensure the long-term reliability and security of the system. This embodiment implements a set of efficient noise processing and data smoothing mechanisms, which significantly improves the accuracy, reliability, and real-time performance of monitoring data; through these technical means, the system can not only continuously monitor and analyze data, but also provide a solid foundation for subsequent decision support. Example

[0054] like Figure 4 As shown, based on Example 1, the process of starting the explosion-proof valve simulation model in step S200 provided in the embodiment of the present invention specifically includes the following steps: Step S201: presetting standard deformation data and standard strain data of the explosion-proof valve, receiving real-time deformation data and strain data obtained from the monitoring equipment through the Internet of Things application server; comparing the real-time deformation data with the preset standard deformation data item by item to check whether the real-time value is within the preset standard range; performing the same comparison operation on the real-time strain data, and the comparison standard is the absolute difference of the specific values; Step S202: If any of the deformation data or strain data measured in real time is greater than or less than the corresponding standard value and exceeds the set threshold range, it is recorded as not meeting the standard; confirming that any data does not meet the standard, activating the explosion-proof wave valve simulation model, which can simulate the performance of the valve in a working state; inputting the acquired real-time deformation and strain data into the explosion-proof wave valve simulation model to evaluate the sealing effect of the valve; Step S203: Analyze the results of the explosion-proof valve simulation model output. If the valve is leaking or not sealed tightly, it is marked as unqualified. On the contrary, if the simulation result shows that the valve is completely sealed, it is recorded as qualified.

[0055] In the above embodiment, step S201 real-time data acquisition and standard comparison, by presetting the standard deformation data and standard strain data of the explosion-proof valve, a benchmark is established to evaluate the performance and operating status of the equipment; the data from the monitoring equipment is received in real time through the Internet of Things application server to ensure that the working status of the equipment can be understood in time; the deformation data and strain data obtained in real time are compared item by item, and compared with the standard value; the accuracy and reliability of the data can be ensured, and potential anomalies can be quickly discovered. The significance achieved: through real-time monitoring and comparison, the abnormal performance of the equipment can be quickly identified to prevent the expansion of potential risks; provide objective data basis, lay the foundation for subsequent processing or maintenance decisions, and ensure the safety and reliability of the equipment. Step S202 does not meet the standard record and activate the simulation model, establish a judgment mechanism that exceeds the preset threshold, and can accurately identify any non-standard situation in the deformation or strain data; activate the explosion-proof valve simulation model, which actually creates a virtual environment to analyze the performance of the valve under actual working conditions based on real-time data; input the real-time deformation and strain data into the simulation model, which provides necessary information for the subsequent sealing effect analysis. Significance achieved: Once a situation that does not meet the standards is found, the simulation model is immediately activated, which effectively improves the risk warning capability of the equipment and responds to safety hazards in a timely manner; through simulation, the performance of the equipment under specific conditions can be verified without affecting the actual operation of the equipment, providing a scientific basis for equipment maintenance and optimization. Step S203 Simulation result analysis: Analyze the output results of the simulation model to determine whether the valve has leakage or poor sealing; a key link in the entire detection process; mark the status of the valve as qualified or unqualified according to the results to clarify whether the equipment is working safely. Significance achieved: By analyzing the simulation results, it can be determined whether the equipment is in normal working condition, thereby ensuring the safety of the explosion-proof wave valve in use; based on the test results, the company can take corresponding management measures, such as scheduling maintenance, patrol inspections, etc., to ultimately improve the operating efficiency of the equipment and extend its service life.

[0056] In summary, the process of starting the explosion-proof valve simulation model in this embodiment forms an efficient monitoring and management system through real-time monitoring, accurate judgment and scientific analysis. It not only improves the real-time control of the equipment operation status, but also enhances the prediction and processing capabilities of potential risks, providing effective support for ensuring the safety and reliability of industrial production. Enterprises can reduce the risk of accidents and improve the overall safety level and economic benefits of operations. Example

[0057] like Figure 5 As shown, based on Example 4, the process of checking whether the real-time value is within the preset standard range in step S201 provided in the embodiment of the present invention specifically includes the following steps: Step S2011: When real-time deformation data is received, an independent thread is triggered to be responsible for extracting standard data; after the thread is started, a connection with the standard data repository is established; the device ID is used as a query condition, an index is established on the device ID field, and the thread constructs a query statement; Step S2012: perform a query operation, the thread synchronously waits for the return result of the standard data repository; the thread receives the data returned by the database; after obtaining the query result, the thread performs data analysis, and after the analysis is completed, the thread stores the standard deformation data into the memory cache; Step S2013: In the main thread, read the set threshold range, each real-time deformation data, read the real-time deformation data in the memory cache one by one, and extract the standard value according to the device ID matching; calculate the absolute difference between the current real-time deformation data and the standard deformation data, and after calculating each difference, determine whether the difference exceeds the set threshold range.

[0058] In the above embodiment, when receiving real-time deformation data, step S2011 triggers an independent thread to process standard data extraction to ensure that the main thread is not blocked; real-time data monitoring and processing can be performed on a parallel basis, which improves the throughput of the system; the thread establishes a connection with the standard data repository and optimizes the query using the device ID. This lays the foundation for subsequent data extraction. Significance: Through the design of independent threads, the delay of the main program in processing real-time data is reduced, and the responsiveness of the overall system is improved; the use of indexed device ID queries ensures that standard data can be quickly found even in large data sets, reducing delays and resource consumption. Step S2012 The thread will synchronously wait for the query result, which means that other operations will not be affected, but this thread will ensure that the required data is accurately obtained; when the data is obtained, it is parsed and stored in the memory cache, reducing the delay of subsequent queries. Significance: By ensuring that the thread can only continue after receiving a valid result when processing data, inconsistent operations caused by errors on the way are avoided; the standard deformation data is stored in the memory cache, which supports the rapid comparison of subsequent real-time data with standard data, and improves the efficiency of real-time monitoring. Step S2013 statically obtains the preset threshold range, reads the real-time deformation data in the cache one by one, and performs device ID matching to ensure that there is a standard value for comparison; the absolute difference between the real-time data and the standard data is calculated in real time, and the threshold judgment is immediately made. Significance: It can dynamically monitor the real-time deformation data and compare it with the preset standard to quickly determine whether the equipment is in a safe state; through the rapid evaluation of the difference, the system can immediately issue an alarm when an abnormality is found (i.e., when it exceeds the threshold range), reduce potential risks, and improve on-site safety.

[0059] In summary, this embodiment forms an efficient real-time monitoring framework that can respond quickly in a dynamic environment and accurately evaluate the status of the device. Through independent threads, efficient database access, memory cache and real-time comparison, the smoothness and accuracy of data processing are guaranteed, thereby achieving safe operation and maintenance of the equipment. Intelligent monitoring of industrial equipment is of great significance, which can effectively prevent failures and damages and improve the safety and reliability of the overall system. Example

[0060] like Figure 6 As shown, based on Example 4, the process of simulating the performance of the valve in the working state in step S202 provided in the embodiment of the present invention specifically includes the following steps: Step S2021: inputting the deformation data and strain data that do not meet the standards into the explosion-proof wave valve simulation model, and using the deformation data and strain data as the initial conditions and boundary conditions of the explosion-proof wave valve simulation model to form a problem domain; Step S2022: discretize the problem domain into small finite elements for calculation, solve the fluid and solid equations at the same time, and update the interaction force between the deformation data and the strain data in each iteration. In the calculation process, the interaction between the fluid and the solid is realized through interface coupling; Step S2023: After the simulation is completed, the simulation results are analyzed in depth, and the performance of the flap door under actual working conditions is predicted through the explosion-proof flap simulation model.

[0061] In the above embodiment, step S2021 is data input and problem domain formation. By inputting deformation data and strain data that do not meet the standards into the model, it is ensured that the initial conditions and boundary conditions of the model reflect the actual working state of the valve; a complete simulation problem domain is established, including the interaction between solid (valve material) and fluid (medium flowing through the valve). The significance achieved: ensure that the simulation model is consistent with the actual physical process and reflects the deformation and stress state of the valve in the real environment as much as possible; help to accurately define the performance evaluation criteria of the valve, and reduce the deviation between the model and the actual situation through real data-driven simulation. Step S2022 is problem domain discretization and coupling calculation. The overall calculation area is divided into small finite units, so that complex physical problems can be solved by numerical methods; the equations between fluid and solid are solved at the same time, and the interaction force between them is dynamically updated; the interaction between fluid and solid is realized through interface coupling technology, which ensures the physical consistency of the calculation. The significance achieved: discretization can quickly process complex geometry and boundary conditions, making numerical calculations more efficient. More accurate results can be obtained within a limited computing time; the coupled solution process can comprehensively consider the mutual influence of fluids and solids, so as to more realistically simulate the performance of the valve under various working conditions and improve the accuracy of the prediction. Step S2023 Simulation result analysis and performance prediction conducts a detailed analysis of the results obtained after the simulation, such as checking the sealing of the valve and potential leakage points; using the simulation results to predict the performance of the valve under actual working conditions, and identify possible problems and their impacts. Significance achieved: Through an in-depth interpretation of the analysis results, guidance can be provided for actual operations, helping engineers and decision makers make data-based judgments; timely identification and resolution of potential leakage or non-conformity problems, enhancing the safety and reliability of the system, and reducing the risk of failures and accidents; the simulation results can also provide a basis for subsequent design improvements, thereby optimizing the structure of the explosion-proof valve and improving its overall performance.

[0062] In this embodiment, the deformation data that does not meet the standards obtained by real-time monitoring and strain data Input into the simulation model, these data will serve as the initial conditions and boundary conditions of the simulation model:

[0063] Establishment of the problem domain: The problem domain is defined using initial conditions. The problem domain can be considered as a system containing fluid and solid interactions. Discretization of the problem domain Finite element discretization: discretize the problem domain into small finite elements and represent them as a grid structure, usually using FEM (finite element method):

[0064] In the formula, represents the displacement of the node, represents the shape function, represents the displacement at the node; Solving Fluid and Solid Equations Solid Mechanics Equations: In the solid part, use the stress-strain relationship:

[0065] In the formula, is stress, is Young's modulus, For deformation, is the thermal expansion coefficient of the material, is temperature; Fluid dynamics equations: In the fluid part, the continuity equation and the Navier-Stokes equations are used to describe the fluid motion:

[0066] In the formula, represents the fluid velocity, represents the fluid density, Indicates pressure, represents the viscosity of the fluid, represents body force; Iterative calculation: In each iteration, the interaction force between the deformation data and the strain data is updated, and the coupling between the fluid and the solid is realized through the interface coupling technology, which is expressed as:

[0067] In the formula, represents the stress tensor, represents the interface normal vector, represents the interface area; After the simulation is complete, analyze the output results, especially check for leaks; If the valve cannot be completely closed, the following conditions are met:

[0068] In the formula, represents the leakage flow rate, Indicates the set leakage threshold; If the above conditions are met, the deformation data and / or strain data of the rotating shaft are recorded as unqualified, and the data are sent to the Internet of Things application server; conversely, if the valve can be completely sealed, the deformation data and / or strain data are considered qualified.

[0069] In summary, this embodiment can form a complete closed loop, from data input to problem domain formation, and then to coupled solution and result analysis, thereby realizing a comprehensive evaluation of the performance of the explosion-proof wave valve under actual working conditions; it can not only improve safety and reliability, but also provide good data support and practical guidance for future design improvements and real-time monitoring. Example

[0070] like Figure 7 As shown, based on Example 1, the process of triggering the explosion-proof valve alarm instruction according to the information in step S300 provided by the embodiment of the present invention specifically includes the following steps: Step S301: receiving information about the explosion-proof valve being unqualified through the Internet of Things application server, and establishing an instant event processing mechanism inside the Internet of Things application server; parsing the unqualified information, and identifying the specific type of problem, whether it is due to the rotation shaft deformation data exceeding the standard or the strain data exceeding the standard; Step S302: based on the activation signal of the event processing mechanism, trigger the corresponding alarm process; each unqualified situation has a corresponding alarm priority setting. If the deformation data is unqualified, the priority is higher than the strain data unqualified, and the response order is determined according to the set priority; Step S303: Automatically generate corresponding explosion-proof valve alarm instructions according to the type of failure, including the urgency of the response, the type of failure and the location identification, etc.; the alarm instruction contains a timestamp, a device ID, and specific failure description information.

[0071] In the above embodiment, the instant event processing mechanism is established in step S301, and the unqualified information received by the IoT application server will be processed immediately to ensure that the system can quickly respond to potential safety issues; the unqualified information is parsed inside the IoT application server, and the problem type (such as deformation data exceeding the standard or strain data exceeding the standard) is promptly identified, and the problem is quickly classified through the set parsing rules to optimize the subsequent processing process. Significance: The real-time processing mechanism ensures that the fault information can be quickly transmitted and processed, thereby greatly reducing the response time and improving the overall safety; by parsing the unqualified information, the problem can be managed and classified more systematically, which is helpful for subsequent data analysis and improvement of protective measures, and provides data support for decision-making. Step S302 triggers the alarm process and priority setting, and triggers the corresponding alarm process according to the priority set for the unqualified type (the priority of deformation data unqualified is higher than that of strain data unqualified), ensuring that more urgent situations are handled first; the triggering mechanism of the alarm process enables the system to flexibly adjust the response strategy according to the specific situation and quickly allocate resources according to the degree of crisis. Significance: Priority setting can effectively allocate and utilize resources, ensure that the issues that need the most attention are resolved first, and reduce the risk of potential losses; timely response to higher priority faults can prevent the problem from worsening and enhance the overall operational safety of the explosion-proof wave valve. Step S303 automatically generates an alarm instruction, and automatically generates an alarm instruction in a unified format according to the type of non-conformity, including necessary fault information, such as urgency, fault type, equipment ID and timestamp, etc.; the alarm instruction covers detailed information, which helps the recipient to quickly understand the fault situation and take corresponding measures. Significance: The standardized instruction format enables maintenance personnel or automatic response systems to quickly identify the root cause of the fault and reduce processing time; complete alarm information not only helps to deal with current emergencies, but also provides rich information for future data mining and fault analysis, which helps to achieve continuous improvement and optimization of the system.

[0072] In summary, through the comprehensive implementation of the above steps, this embodiment has an efficient real-time response capability and accurate information management in the process of triggering the explosion-proof valve alarm instruction according to information, which significantly improves the system's response efficiency to faults and potential risks. It helps to improve the overall reliability and safety of the explosion-proof valve. The ultimate goal is to achieve continuous monitoring of equipment and more efficient fault prevention to ensure safe operation in industrial environments. Example

[0073] like Figure 8 As shown, based on Example 7, the process of identifying a specific problem type in step S301 provided in this embodiment of the present invention specifically includes the following steps: Step S3011: Identify keywords related to the equipment status, including exceeding the standard, abnormality or failure, etc., compare the keywords with the predefined fault dictionary, and locate the unqualified category; obtain the fault type through the built-in mapping relationship between multiple keywords and fault types, including the association between deformation data and corresponding control methods, and the link between strain data and potential risks; Step S3012: Analyze the potential reasons for the failure based on the surrounding information of the received keyword, and the keyword deformation exceeding the standard appears in the description, which is associated with the time and running status mentioned in the context; Step S3013: classify and mark the parsed information, mark the information of deformation data exceeding the standard as high priority fault, mark the information of strain data exceeding the standard as low priority fault, and store them in the database; the classification adopts a dynamic label mechanism to automatically adjust the priority of each state according to the changes in real-time data.

[0074] In the above embodiment, step S3011 keyword identification and fault type location, using a predefined fault dictionary, can quickly identify keywords related to the equipment status (such as "exceeding the standard", "abnormal" or "fault"), and achieve efficient location of unqualified categories; built-in mapping relationships between multiple keywords and fault types make it possible to directly associate the identified keywords with specific fault types (such as deformation data and strain data) to ensure high accuracy of fault information. Significance: By quickly identifying keywords and classifying faults, the response time to unqualified information can be shortened and the efficiency of equipment management can be improved; accurate keyword identification helps to reduce the false alarm rate and ensure that the system only responds to real fault conditions, thereby improving the reliability of equipment operation. Step S3012 context analysis and potential cause analysis, by analyzing the surrounding information of the received keywords, can extract more context information (such as time and operating status) to help analyze potential fault causes; can evaluate the relationship between keywords and context information, identify whether there are external factors that may cause unqualified, and thus provide a more comprehensive fault cause analysis. Significance: It not only identifies the type of fault, but also digs out the potential causes of the fault, which helps to formulate a comprehensive maintenance and treatment plan and reduce the recurrence of similar problems in the future; through context analysis, the system can effectively predict possible fault trends, provide early warnings, and improve the timeliness and initiative of equipment maintenance. Step S3013 Information classification and dynamic labeling mechanism, the parsed information is classified and labeled to form fault information of different priorities, which is helpful for subsequent processing and decision-making; the dynamic labeling mechanism automatically adjusts the priority of each state according to the real-time data changes, ensuring that the system always responds to faults with the best resource allocation. Significance: By classifying the priority of fault information, it ensures that resource allocation can target the most urgent problems, making fault handling more effective; the dynamic adjustment priority mechanism enables the system to adapt to changes, improves the flexibility and response speed of the system, and responds to emergencies more promptly.

[0075] In summary, this embodiment together constitutes an efficient fault identification and processing mechanism, which identifies faults by combining keywords with context, optimizes response strategies through dynamic priority management, and enhances the intelligence and automation level of the system. It not only improves the efficiency and accuracy of fault handling, but also provides important support for the long-term stable operation of equipment, and promotes the effectiveness of the safety management system as a whole. With the continuous accumulation of equipment status data, more advanced fault prediction and intelligent decision-making capabilities can be achieved in the future, further improving the intelligence level and operational safety of the industrial Internet of Things. Example

[0076] like Fig. 9As shown, on the basis of Embodiments 1 to 9, the explosion-proof wave valve monitoring and alarm device based on the Internet of Things provided by the embodiment of the present invention comprises: a monitoring device module 1, an Internet of Things application server module 2, an explosion-proof wave valve simulation model module 3, and an Internet of Things safety device module 4; The monitoring device module 1 installs a sensor on the rotating shaft of the explosion-proof wave valve to monitor the deformation and strain data in real time, and transmits the data to the Internet of Things application server through the Internet of Things protocol; the Internet of Things application server module 2 receives and stores the deformation data and strain data transmitted from the data monitoring device, performs preliminary analysis and comparison, and determines whether the data meets the standards; the explosion-proof wave valve simulation model module 3 starts the simulation model when the data does not meet the standards, simulates the performance of the leaf door under actual working conditions, and determines whether it can be completely sealed. If the simulation result shows that the leaf door cannot be completely sealed, the Internet of Things application server will trigger an alarm command and send it to the Internet of Things security device; after receiving the alarm command, the Internet of Things security device module 4 executes a preset solution, such as closing the explosion-proof wave valve or initiating emergency measures.

[0077] In the above embodiment, the monitoring device module 1 installs a sensor on the rotating shaft of the explosion-proof wave valve to continuously monitor the deformation and strain data to ensure the real-time and accuracy of data collection; the monitored real-time data is efficiently and safely transmitted to the Internet of Things application server through the Internet of Things protocol to reduce data transmission delay. Significance: Timely acquisition of real-time data on the status of the equipment can provide basic data for subsequent fault warning and maintenance, and help discover potential problems; through real-time monitoring, it can effectively prevent facility failures, improve the safety of overall equipment operation, and reduce accident risks. The Internet of Things application server module 2 receives data from the monitoring device module and effectively stores it to ensure the integrity and traceability of the data before and after the failure occurs; the received deformation and strain data are preliminarily analyzed and compared with the standards to determine whether the data meets the set safety standards. Significance: Through comparative analysis, abnormal data can be quickly identified, the response speed to faults can be improved, and the timeliness of decision-making can be ensured; it provides a basis for the effective storage and analysis of data, helps to accumulate historical data, provides support for subsequent data mining and analysis, and promotes the decision-making quality of equipment management. When the data of explosion-proof valve simulation model module 3 does not meet the standard, the simulation model is started to simulate the performance of the explosion-proof valve under actual working conditions, providing an intuitive performance evaluation; it can determine whether the explosion-proof valve can be completely sealed, so as to estimate whether it can effectively prevent potential risks and threats. Significance: Through simulation, the performance and failure mode of the equipment can be evaluated in a safe environment, providing a basis for the optimization and improvement of the equipment; the rapid judgment ability of the simulation results can directly affect the activation of the alarm mechanism, ensuring that measures are taken before potential risks occur. The IoT security device module 4 can receive alarm instructions from the IoT application server and quickly execute preset solutions (such as closing the explosion-proof valve or starting emergency measures); when the emergency measures are automatically executed, the risk of human error can be reduced and the efficiency of responding to emergencies can be improved. Significance: Ensure that effective emergency measures can be deployed quickly in the event of a failure or abnormality, reducing the losses and risks that may be caused by the accident; through timely response to alarm instructions, the overall safety protection capability of the system is enhanced, ensuring that the explosion-proof valve can effectively protect the surrounding environment and personnel safety in dangerous situations.

[0078] In summary, this embodiment realizes all-round monitoring and real-time response of explosion-proof valves. It not only effectively improves fault warning and emergency response capabilities, but also optimizes management and operation processes, making equipment operation safer and more efficient. In addition, through the accumulation and analysis of data, it can provide an important basis for future equipment improvements and the implementation of new solutions, thereby achieving continuous safety management and optimization.

[0079] Fig.10 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present invention is shown.

[0080] The electronic device may include a central processing unit / microprocessor / main control chip, etc. 5; a storage medium 6, coupled to the central processing unit / microprocessor / main control chip, etc. 5, and storing computer executable instructions therein, for performing the steps of each method of an embodiment of the present invention when executed by the processor.

[0081] The central processing unit / microprocessor / main control chip etc. 5 may include but is not limited to, for example, one or more processors or microprocessors etc.

[0082] The storage medium 6 may include, but is not limited to, for example, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (such as hard disk, floppy disk, solid state drive, removable disk, CD-ROM, DVD-ROM, Blu-ray disc, etc.).

[0083] In addition, the electronic device may also include (but not limited to) a data bus 7, an input / output bus / external bus / device bus 8, a display 9, and input / output devices 10 (eg, keyboard, mouse, speaker, etc.).

[0084] The central processing unit / microprocessor / main control chip etc. 5 can communicate with external devices ( 9 , 10 , etc.) through the I / O bus 8 via a wired or wireless network (not shown).

[0085] The storage medium 6 may also store at least one computer executable instruction for executing the various functions and / or method steps in the embodiments described in the present technology when executed by the central processing unit / microprocessor / main control chip, etc. 5 .

[0086] In one embodiment, the at least one computer executable instruction may also be compiled into or constitute a software product, wherein one or more computer executable instructions are executed by a processor to perform the various functions and / or method steps in the embodiments described in the present technology.

[0087] Fig.11 A schematic diagram of a computer-readable storage medium according to an embodiment of the present invention is shown.

[0088] like Fig.11As shown, instructions are stored on the non-transitory computer-readable storage medium 12, and the instructions are, for example, computer-readable instructions 11. When the computer-readable instructions 11 are executed by the processor, the various methods described above can be executed. The non-transitory computer-readable storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-transitory non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, the non-transitory computer-readable storage medium 12 can be connected to a computing device such as a computer, and then, when the computing device runs the computer-readable instructions 11 stored on the computer-readable storage medium 12, the various methods described above can be performed.

[0089] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0090] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0091] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0092] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for executing all or part of the steps of the various embodiments of the present invention through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk, etc. Various media that can store program codes.

[0093] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring and alarming explosion-proof valves based on the Internet of Things, characterized in that: The following steps are involved: Obtain the installation position of the rotating shaft of the explosion-proof wave valve, and install a data monitoring device on the rotating shaft to measure the deformation data and strain data of the rotating shaft; the data monitoring device transmits the deformation data and strain data to the Internet of Things application server through the Internet of Things interconnection interface; The deformation data and strain data are compared with the preset standard deformation data and strain data respectively; when any one of the deformation data and strain data does not meet the standard, the explosion-proof valve simulation model is started; simulation is performed according to the deformation data and strain data, if the valve door cannot be completely sealed, the deformation data and / or strain data of the rotating shaft are unqualified; otherwise, they are qualified; Receive the information that the flap door is unqualified from the IoT application server, trigger the explosion-proof wave flap alarm instruction according to the information, send the explosion-proof wave flap alarm instruction to the IoT security device, and process it according to the preset solution.

2. The explosion-proof valve monitoring and alarm method based on the Internet of Things as claimed in claim 1 is characterized in that: The process of acquiring deformation data and strain data includes the following steps: Install the monitoring equipment at the corresponding position of the rotating shaft according to the type of target data; configure the corresponding sampling channel after the monitoring equipment is installed, calibrate each monitoring equipment, compare the output with the standard value, and initialize after the comparison passes; Establish a connection between the monitoring device and the IoT interface, and transmit the deformation data and strain data to the IoT application server with the support of the data transmission protocol; After the deformation data and strain data are collected, a threshold is set for the change amplitude of each sampling point. The deformation data and / or strain data that exceeds the range will be marked as noise and removed. The continuous deformation data and / or strain data window after noise removal is averaged, and the average value of the time series is continuously calculated to eliminate the impact of fluctuations in a short period of time, and obtain preprocessed deformation data and strain data.

3. The explosion-proof valve monitoring and alarm method based on the Internet of Things as claimed in claim 2 is characterized in that: in, Type refers to strain and deformation; The process of installing at the corresponding position of the rotating axis includes: Using the relationship between stress and strain, calculate the strain energy density of the rotating axis : , where represents the stress tensor, represents the strain tensor, represents the volume to be analyzed; deformation Defined as the change in length of an object after an external force is applied: , where Indicates a position after an external force is applied Length; The original length without stress; At a certain position on the axis of rotation, the strain is calculated using the constitutive relation : , where Represents radial strain, which is used for the relative deformation of the rotating shaft in the radial direction, the ratio of the deformed length to the original length. represents the hoop strain, describing the relative deformation of the rotation axis in the hoop direction, represents the axial strain of the rotating axis, represents the hoop stress of the rotating shaft, represents the shear stress, which represents the interaction between the rotating axis in the radial and circumferential directions, It represents Young's modulus, which indicates the stiffness of the material and describes the relationship between stress and strain of the rotating shaft when it is subjected to force. It represents Poisson's ratio, which is the ratio of the strain of the rotating axis in one direction to the strain in the vertical direction. It reflects the lateral deformation behavior of the rotating axis when it is under tension or compression. Indicates that the radial strain is calculated through the relationship between the stress in the latitudinal direction and the stress in other directions. It means that the axial strain is calculated through the relationship between the axial stress and the stress in other directions. represents the calculated shear strain, showing the relationship between shear stress and Young's modulus and Poisson's ratio; Define an objective function to minimize the influence of strain energy, deformation and strain on the overall structure of the rotating shaft; the objective function is expressed as: , where represents the optimization target, represents the total damage or energy of the device, represents the strain energy density, Indicates absolute deformation, reflecting the deformation amount at a specific position. Indicates strain, reflecting the deformation characteristics of the material. , Represents the weight coefficient, which is used to adjust the influence of strain energy, deformation and strain in the overall target; During the optimization process, the following constraints are followed: Deformation Limits: , where It indicates the maximum deformation allowed by the material to avoid structural failure; Boundary conditions: Fixed end conditions: , free end condition: , randomly generate a set of solutions, each solution represents the installation location of a monitoring device, randomly generate a set of particles, the position of each particle represents the installation location of the monitoring device; select individuals for replication according to fitness; select individuals for crossover to generate new solutions; randomly mutate the newly generated solutions to introduce diversity, and update the population with the newly generated individuals; each particle updates the speed according to its own best position and the best position of the group; , where is the inertia weight, and is the learning factor, and is a random number, and are the individual best and global best positions respectively; Update particle positions: , reaching the maximum number of iterations: monitor the changes of the best solution within the set maximum number of iterations; obtain the optimal installation position, based on the minimum objective function value.

4. The explosion-proof valve monitoring and alarm method based on the Internet of Things as claimed in claim 2 is characterized in that: The process of obtaining the preprocessed deformation data and strain data includes the following steps: For each monitoring point, dynamically set the threshold of the change amplitude; perform multi-level decomposition on the signal of deformation data and / or strain data to extract high-frequency and low-frequency components; compare the high-frequency component with the set noise threshold, if the absolute value of the high-frequency component is greater than the set threshold, the data point is considered to be noise, and is removed or replaced, and the low-frequency component with noise removed and the processed high-frequency component are recombined to form a denoised signal; Introducing a sliding window of fixed length to realize group processing of deformation data and / or strain data; calculating the weighted average of each data point in the window; processing the start and end boundaries of the window; For the continuous deformation data and / or strain data that have been processed by noise, the sliding average is continuously calculated to generate a new time series to smooth the data; the moving standard deviation of each time period is calculated. If the moving standard deviation exceeds the set threshold, an alarm is issued. The alarm content includes the monitoring point identification, supervision time and abnormal data value; the data are classified and stored in the corresponding database of the Internet of Things application server, and a table is established according to the date, monitoring point and data type.

5. The explosion-proof valve monitoring and alarm method based on the Internet of Things as claimed in claim 1 is characterized in that: The process of starting the explosion-proof valve simulation model includes the following steps: The standard deformation data and standard strain data of the explosion-proof valve are preset, and the real-time deformation data and strain data obtained from the monitoring equipment are received through the Internet of Things application server; the real-time deformation data are compared with the preset standard deformation data item by item to check whether the real-time value is within the preset standard range; the same comparison operation is performed on the real-time strain data, and the comparison standard is the absolute difference of the specific values; If any of the deformation data or strain data measured in real time is greater than or less than the corresponding standard value and exceeds the set threshold range, it will be recorded as non-compliant with the standard; if any data is confirmed to be non-compliant with the standard, the explosion-proof valve simulation model will be activated to simulate the performance of the valve in the working state; the acquired real-time deformation and strain data will be input into the explosion-proof valve simulation model to evaluate the sealing effect of the valve; Analyze the results of the explosion-proof valve simulation model output. If the valve is leaking or not sealed tightly, it will be marked as unqualified. On the contrary, if the simulation results show that the valve is completely sealed, it will be recorded as qualified.

6. The explosion-proof valve monitoring and alarm method based on the Internet of Things as claimed in claim 5 is characterized in that: The process of checking whether the real-time value is within the preset standard range includes the following steps: When real-time deformation data is received, an independent thread is triggered to extract standard data. After the thread is started, a connection with the standard data repository is established. The device ID is used as the query condition, an index is established on the device ID field, and the thread constructs a query statement. To perform a query operation, the thread synchronously waits for the return result of the standard data repository; the thread receives the data returned by the database; when the query result is obtained, the thread performs data analysis, and after the analysis is completed, the thread stores the standard deformation data into the memory cache; In the main thread, read the set threshold range, each real-time deformation data, read the real-time deformation data in the memory cache one by one, and extract the standard value according to the device ID matching; calculate the absolute difference between the current real-time deformation data and the standard deformation data, and after calculating each difference, determine whether the difference exceeds the set threshold range.

7. The explosion-proof valve monitoring and alarm method based on the Internet of Things as claimed in claim 5, characterized in that: The process of simulating the performance of the valve in working condition includes the following steps: Input the deformation data and strain data that do not meet the standards into the explosion-proof wave valve simulation model, and use the deformation data and strain data as the initial conditions and boundary conditions of the explosion-proof wave valve simulation model to form a problem domain; Discretize the problem domain into small finite elements for calculation, solve the fluid and solid equations at the same time, and update the interaction force between deformation data and strain data in each iteration. During the calculation process, the interaction between fluid and solid is realized through interface coupling. After the simulation is completed, the simulation results are analyzed in depth, and the performance of the flap door under actual working conditions is predicted through the explosion-proof flap simulation model.

8. The explosion-proof valve monitoring and alarm method based on the Internet of Things as claimed in claim 1 is characterized in that: The process of triggering the explosion-proof wave valve alarm command according to the information includes the following steps: The information about the explosion-proof valve failure is received through the IoT application server, and an instant event processing mechanism is established inside the IoT application server; the failure information is parsed to identify the specific type of problem, whether it is caused by the excessive deformation data of the rotating shaft or the excessive strain data; Based on the activation signal received from the event processing mechanism, the corresponding alarm process is triggered; each unqualified situation has a corresponding alarm priority setting. If the deformation data is unqualified, the priority is higher than the strain data unqualified, and the response order is determined according to the set priority; According to the type of non-conformity, the corresponding explosion-proof wave valve alarm instruction is automatically generated, including the urgency of the response, the type of fault and the location identification; the alarm instruction contains the timestamp, equipment ID, and specific fault description information.

9. The explosion-proof valve monitoring and alarm method based on the Internet of Things as claimed in claim 8, characterized in that: The process of identifying specific problem types includes the following steps: Identify keywords related to equipment status, including over-standard, abnormal or fault, compare keywords with pre-defined fault dictionary, and locate unqualified categories; obtain fault types through the built-in mapping relationship between multiple keywords and fault types, including the association between deformation data and corresponding control methods, and the link between strain data and potential risks; Based on the surrounding information of the received keywords, the potential causes of failure are analyzed. The keyword deformation exceeds the standard and appears in the description, which is associated with the time and operation status information mentioned in the context; The parsed information is classified and marked, and the information of deformation data exceeding the standard is marked as a high-priority fault, and the information of strain data exceeding the standard is marked as a low-priority fault, and stored in the database; the classification adopts a dynamic label mechanism to automatically adjust the priority of each state according to the changes in real-time data.

10. An explosion-proof valve monitoring and alarm device based on the Internet of Things, used to implement the explosion-proof valve monitoring and alarm method based on the Internet of Things as claimed in any one of claims 1 to 9, characterized in that: include: A monitoring device module is used to install sensors on the rotating shaft of the explosion-proof valve to monitor deformation and strain data in real time and transmit the data to the IoT application server through the IoT protocol; The IoT application server module is used to receive and store the deformation data and strain data transmitted from the data monitoring equipment, conduct preliminary analysis and comparison, and determine whether the data meets the standards; The explosion-proof valve simulation model module is used to start the simulation model when the data does not meet the standards, simulate the performance of the valve under actual working conditions, and judge whether it can be completely sealed. If the simulation result shows that the valve cannot be completely sealed, the IoT application server will trigger an alarm instruction and send it to the IoT security device; The IoT safety device module is used to execute preset solutions after receiving alarm instructions, such as closing explosion-proof valves or initiating emergency measures.

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