Intelligent gas monitoring method and system based on multi-protocol conversion

Abstract

The invention relates to the technical field of gas monitoring, and provides an intelligent gas monitoring method and system based on multi-protocol conversion, and the method comprises the steps: carrying out the recognition and comparison of accumulated gas data of each gas sensor in a gas transmission channel, and obtaining two gas sensors with the multi-component gas accumulation amount difference; a gas flow model is constructed according to the real-time data of various gases passing through the first sensor and a gas transmission channel interval model, and the model reflects the flow diffusion condition among the various gases passing through the first sensor; and the corresponding leakage position is identified according to the actual gas cumulant difference condition and various gas positions of the gas flow model, so that operation and maintenance personnel can identify the gas leakage position of the gas transmission channel more efficiently, and the gas monitoring precision and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the field of gas monitoring technology, and in particular to a gas intelligent monitoring method and system based on multi-protocol conversion. Background Art

[0002] Currently, in gas monitoring of gas transmission channels, gas sensors are generally installed outside the channels at key valves or easily damaged corners to detect the industrial gas content in the air to determine whether there is a gas leak and realize gas transmission monitoring. However, this method is only accurate for gas detection at the sensor setting location, but it is difficult to achieve accurate and efficient gas monitoring in other channel areas, affecting the overall gas monitoring effect of the gas transmission channel. Summary of the Invention

[0003] The present invention provides a gas intelligent monitoring method based on multi-protocol conversion, which is used to solve the problem of low overall monitoring efficiency of gas transmission channels in the prior art.

[0004] A first aspect of the present invention provides a gas intelligent monitoring method based on multi-protocol conversion, comprising: Acquire real-time data of various types of gases from the multi-protocol conversion gas sensor, calculate the accumulation of real-time gas data from each gas sensor within a preset time length, and obtain the cumulative amount of each type of gas; identify adjacent gas sensors with different gas cumulative amounts to obtain the first gas sensor and the second gas sensor; Obtaining a gas transmission channel interval model, identifying gas flow variation characteristics within the interval based on real-time data of various types of gas in the first gas sensor, and constructing a gas flow model based on the gas transmission channel interval model; The leakage risk portion is identified in the gas flow model according to the difference in gas accumulation between the first gas sensor and the second gas sensor, and the gas transmission channel position corresponding to the leakage risk portion is sent to the maintenance personnel terminal.

[0005] Optionally, the identifying of adjacent gas sensors having different gas accumulation amounts to obtain the first gas sensor and the second gas sensor is specifically as follows: The flow time is calculated based on the length of the gas transmission channel and the gas flow velocity between each gas sensor, and the cumulative amount of various types of gas of any gas sensor is compared with the cumulative amount of various types of gas of the next gas sensor in the flow direction after the flow time. When there is a difference in gas cumulative amount greater than a preset threshold, the corresponding gas sensor is identified to obtain the first gas sensor and the second gas sensor.

[0006] Optionally, after constructing the gas flow model using the gas transmission channel interval model, the method further includes: The gas flow model is substituted into the corresponding channel segment of the preset gas flow digital twin, the gas flow digital twin is updated, and then the gas flow digital twin is visualized.

[0007] The second aspect of the present application provides a gas intelligent monitoring system based on multi-protocol conversion, comprising: The abnormal sensor identification module is used to obtain real-time data of various types of gases in the multi-protocol conversion gas sensor, calculate the accumulation of real-time gas data of each gas sensor within a preset time length, and obtain the cumulative amount of each type of gas; identify adjacent gas sensors with different gas cumulative amounts, and obtain the first gas sensor and the second gas sensor; A gas flow model building module is used to obtain a gas transmission channel interval model, identify the gas flow change characteristics within the interval based on the real-time data of various types of gas in the first gas sensor, and build a gas flow model based on the gas transmission channel interval model; The gas leakage monitoring module is used to identify the leakage risk location in the gas flow model according to the difference in gas accumulation between the first gas sensor and the second gas sensor, and send the gas transmission channel location corresponding to the leakage risk location to the maintenance personnel terminal.

[0008] Optionally, in the abnormal sensor identification module, adjacent gas sensors having different gas accumulation amounts are identified to obtain the first gas sensor and the second gas sensor, specifically: The flow time is calculated based on the length of the gas transmission channel and the gas flow velocity between each gas sensor, and the cumulative amount of various types of gas of any gas sensor is compared with the cumulative amount of various types of gas of the next gas sensor in the flow direction after the flow time. When there is a difference in gas cumulative amount greater than a preset threshold, the corresponding gas sensor is identified to obtain the first gas sensor and the second gas sensor.

[0009] Optionally, in the gas flow model construction module, after constructing the gas flow model using the gas transmission channel interval model, the module further includes: The gas flow model is substituted into the corresponding channel segment of the preset gas flow digital twin, the gas flow digital twin is updated, and then the gas flow digital twin is visualized.

[0010] A third aspect of the present application provides a gas intelligent monitoring method and device based on multi-protocol conversion, the device comprising a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the gas intelligent monitoring method based on multi-protocol conversion as described in any one of the first aspects of the present invention according to the instructions in the program code.

[0011] In a fourth aspect, the present application provides a computer-readable storage medium for storing program code, wherein the program code is used to execute a gas intelligent monitoring method based on multi-protocol conversion as described in any one of the first aspects of the present invention.

[0012] It can be seen from the above technical solution that the present invention has the following advantages: by identifying and comparing the accumulated gas data of each gas sensor in the gas transmission channel, two gas sensors with differences in the accumulated amounts of multi-component gases are obtained; a gas flow model is constructed using the real-time data of various types of gases passing through the first sensor and the gas transmission channel interval model. The model reflects the flow and diffusion conditions between the various types of gases passing through the first sensor, and then the corresponding leakage position is identified based on the actual difference in gas accumulated amounts and the positions of various types of gases in the gas flow model, so that operation and maintenance personnel can more efficiently identify the gas leakage position of the gas transmission channel and improve the accuracy and efficiency of gas monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0014] Figure 1 The figure is a flow chart of a gas intelligent monitoring method based on multi-protocol conversion; Figure 2 This is a structural diagram of a gas intelligent monitoring system based on multi-protocol conversion. DETAILED DESCRIPTION

[0015] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0016] The present invention provides a gas intelligent monitoring method based on multi-protocol conversion, which is used to solve the problem of low overall monitoring efficiency of gas transmission channels in the prior art.

[0017] See also Figure 1 , Figure 1This is a first flow chart of a gas intelligent monitoring method based on multi-protocol conversion provided by an embodiment of the present invention.

[0018] S100, acquiring real-time data of various types of gases from the multi-protocol conversion gas sensor, calculating the accumulation of the real-time gas data of each gas sensor within a preset time length, and obtaining the cumulative amount of each type of gas; identifying adjacent gas sensors with different gas cumulative amounts, and obtaining a first gas sensor and a second gas sensor; It should be noted that according to different industrial gas emission environments, gas sensors corresponding to all possible gas types are set. For example, electrochemical sensors are suitable for gases such as CO, H2S, and NO2, catalytic combustion sensors are suitable for combustible gases such as methane and propane, as well as infrared absorption, semiconductor sensors, laser spectrum sensors, etc. In the integration of multiple types of sensors, a sensor array combination algorithm must be set to eliminate cross-interference; and dynamic conversion of protocols such as Modbus, Zigbee, TCP / IP, and LoRa must be integrated to adapt to multi-protocol support for different transmission scenarios. By combining preset algorithms to improve detection speed and accuracy, the concentrations of various types of gases in the gas transmission channel can be quickly detected. In the gas transmission channel, sensors can be built-in at key positions according to the characteristics of the channel structure to detect real-time data of gas concentration inside the channel. The position of the gas sensor can generally be set at the inlet and outlet of the channel corner and the interval of the long straight channel to detect the concentration data and flow rate data of the gas passing through the setting position in real time; by accumulating and calculating the real-time data of the gas recorded over a period of time, the cumulative amount of various gases passing through the gas sensor can be calculated based on the concentration, flow rate, recording time length and the channel cross-sectional area where the gas sensor is set; the length of the gas transmission channel between adjacent sensors can be obtained according to the setting position of each gas sensor, and the time difference of the gas passing through the channel between two adjacent sensors can be obtained by using the gas flow rate, and then the corresponding relationship between the calculated gas cumulative amounts in the adjacent sensors can be identified, that is, the gas passing through the first gas sensor within a certain period of time. The accumulated gas volume of one gas sensor should also pass through the second gas sensor at the same accumulated volume after the time difference corresponding to its gas flow rate, where the first gas sensor is the sensor that the gas passes through first. After calculating the accumulated volumes of various types of gas corresponding to adjacent sensors, if there is no gas leakage in the channel, these accumulated volumes should be consistent. When adjacent gas sensors with different accumulated volumes are identified, it can be considered that there is a gas leak in the gas transmission channel between the adjacent gas sensors, and further detection is required. Because gas sensors may also have detection errors and gas may not flow at a constant flow rate during transmission, a threshold for the difference in accumulated volumes can be preset. Only when the difference in accumulated volumes exceeds the preset threshold can the corresponding first and second gas sensors be identified. S200, obtaining a gas transmission channel interval model, identifying gas flow variation characteristics within the interval based on real-time data of various types of gas in the first gas sensor, and constructing a gas flow model based on the gas transmission channel interval model; It should be noted that the gas transmission channel can be a pipeline or an exhaust channel. A gas transmission channel interval model can be constructed based on the structural size data specifications of the gas transmission channel. The interval model provides boundary conditions for the flow changes of the gas; the real-time data of various types of gases continuously detected in the first gas sensor can establish a function of gas concentration changes over time, and the gas concentration is averaged over time to obtain the emission flux. According to the cross-sectional area of ​​the gas transmission channel at the location where the first gas sensor is set, the data can be converted into gas flow rate, and the density and other physical properties of the gas can be obtained according to the type of gas, and the preset mutual diffusion coefficient can be obtained based on the gas type to perform CFD gas flow. Dynamic simulation. During the simulation process, the gas Reynolds coefficient can be calculated based on real-time data to determine whether the flow is turbulent or laminar. The CFD simulation is corrected, and the simulated multi-component gas mixed flow is used to construct the first gas flow model. Since industrial exhaust gases generally no longer react, the model reflects the process of mixing and diffusion between gases, simulating the concentration of different gases at various positions in the gas transmission channel; AI is preset to learn historical gas flow data. When CFD simulates multiple types of gas flows, AI can be combined to improve the simulation speed. The physical information neural network PINN embeds physical constraints into neural network training, so that the CFD simulation results conform to scientific laws.

[0019] S300: Identify leakage risk locations in the gas flow model based on the difference in gas accumulation between the first gas sensor and the second gas sensor, and send the gas transmission channel locations corresponding to the leakage risk locations to a maintenance personnel terminal.

[0020] In this embodiment, the gas flow model in the aforementioned steps corresponds to the flow of multi-component gas in the absence of leakage. When there is a difference in gas cumulative amounts, it indicates that there is a gas leak, and the gas leakage amount corresponds to the difference in gas cumulative amounts. When the multi-component gas is fully mixed and diffused during flow, if a pipeline leak occurs, there should be a difference in cumulative amounts according to the gas concentration ratio. In this case, the leak location cannot be identified based on the gas flow model. Therefore, the difference in gas cumulative amounts in the aforementioned step S100 should only identify the situation where there is a difference in cumulative amounts of some types of gas, that is, the difference in cumulative amounts of a certain type of gas is greater than a preset threshold, while the difference in cumulative amounts of other types of gas is less than the preset threshold. When the multi-component gas is not fully mixed and flows, the leakage location on the transmission channel is different, and the leaked gas composition is also different. For example, if two gases with large density differences exist in the gas transmission channel, although the two gases will diffuse with each other during transmission in the gas flow model, the gas with higher density will be mainly transmitted in the lower part of the gas transmission channel, and the gas with lower density will be mainly transmitted in the upper part of the gas transmission channel. When the leakage site is located in the upper part of the gas transmission channel, the difference in cumulative amount of the gas with lower density should be significant, and vice versa. When the leak is at the junction of multiple gas components, different gas components will leak due to different gas concentrations in the input gas transmission channel. That is, the difference in the difference in gas accumulation of various types of gases should be consistent with the gas ratio of a certain line along the flow direction of the boundary in the gas flow model. For example, in a certain period of time, the accumulation of X gas passing through the first gas sensor is a1 mg, and the accumulation of Y gas is b1 mg. The corresponding accumulation of X gas passing through the second gas sensor is a2 mg, and the accumulation of Y gas is b2 mg. The calculated difference in gas accumulation is a1-a2 mg for X gas and b1-b2 mg for Y gas. A1-a2 / b1-b2=1:10. Then, the gas accumulation along the gas flow can be identified on the boundary of the gas flow model. A line is found based on the direction of movement or time flow, and the ratio of gas X to gas Y on the corresponding line is also 1:10. That is, under the multi-component gas flow diffusion predicted by the gas flow model, if there is a leak at a certain point in the gas transmission channel corresponding to the line, the gas leaks according to the contact ratio of gas X to gas Y with the leak point in the gas flow model, resulting in the 1:10 difference in gas cumulative amount calculated above. Because the gas flows along the gas transmission channel, there may be a leak at any point on the line. The line position is marked as a leak risk location, and a maintenance reminder is sent to the maintenance personnel's terminal at the same time, so that the risk location can be accurately inspected and repaired, thereby improving gas monitoring accuracy and subsequent maintenance efficiency.

[0021] In this embodiment, by identifying and comparing the accumulated gas data of each gas sensor in the gas transmission channel, two gas sensors with differences in the accumulated amounts of multi-component gases are obtained; a gas flow model is constructed using the real-time data of various types of gases passing through the first sensor and the gas transmission channel interval model. The model reflects the flow and diffusion conditions between the various types of gases passing through the first sensor, and then the corresponding leakage positions are identified based on the actual differences in the accumulated amounts of gas and the positions of various types of gases in the gas flow model, so that operation and maintenance personnel can more efficiently identify the gas leakage positions in the gas transmission channel, thereby improving the accuracy and efficiency of gas monitoring.

[0022] The above is a detailed description of the first embodiment of a gas intelligent monitoring method based on multi-protocol conversion provided by this application. The following is a detailed description of the second embodiment of a gas intelligent monitoring method based on multi-protocol conversion provided by this application.

[0023] This embodiment further provides a gas intelligent monitoring method based on multi-protocol conversion. In the aforementioned step S100, the identification of adjacent gas sensors with a difference in gas accumulation amount to obtain a first gas sensor and a second gas sensor is specifically as follows: the flow time is calculated based on the length of the gas transmission channel between the gas sensors and the gas flow velocity, and the accumulation amount of each type of gas of any gas sensor is compared with the accumulation amount of each type of gas of the next gas sensor in the flow direction after the flow time. When the difference in gas accumulation amount is greater than a preset threshold, the corresponding gas sensor is identified to obtain the first gas sensor and the second gas sensor. It should be noted that the length of the gas transmission channel that the flowing gas needs to pass through between them can be obtained based on the setting position of each gas sensor, and the flow time can be calculated by dividing the gas transmission channel length by the gas flow velocity. The gas accumulation amount can be repeatedly calculated in each gas sensor according to a preset time length. After a gas sensor A calculates the gas accumulation amount once, the gas accumulation amount after the flow time is obtained in the gas sensor B next to A in the gas transmission channel. The two gas accumulation amounts obtained in this way correspond to the same batch of multi-component gases. The difference in gas accumulation amount is then compared to identify the gas sensor in the channel section with leakage risk.

[0024] Furthermore, in the aforementioned step S200, after the gas flow model is constructed using the gas transmission channel interval model, the method further includes: substituting the gas flow model into the corresponding channel segment of the preset gas flow digital twin, updating the gas flow digital twin, and then visually displaying the gas flow digital twin; it should be noted that a gas flow digital twin can be pre-constructed based on the structure of the gas transmission channel and historical gas flow data. The digital twin can provide higher performance computing and real-time simulation for CFD calculations, use GPU parallel computing and AI proxy models to accelerate computing, and solve the problems of traditional CFD. The problem of time-consuming D simulation is solved, and the Reynolds-averaged model (RANS) or large eddy simulation (LES) is applied in the gas flow digital twin to capture complex turbulent structures, combined with adaptive grid technology to improve local accuracy; real-time monitoring data is converted into simulation boundary conditions, supporting dynamic adjustment of inlet flow velocity, pressure and other parameters in response to environmental changes, further realizing data fusion and dynamic boundary setting; the real-time updated gas flow digital twin can map the gas flow conditions of each channel section, and the visual display provides a monitoring basis for gas monitoring personnel, which is conducive to the management and prediction of the flow and emission of industrial gases.

[0025] The above is a detailed description of a gas intelligent monitoring method based on multi-protocol conversion provided in the first aspect of this application. The following is a detailed description of an embodiment of a gas intelligent monitoring system based on multi-protocol conversion provided in the second aspect of this application.

[0026] See also Figure 2 , Figure 2 This embodiment provides a gas intelligent monitoring system based on multi-protocol conversion, including: Abnormal sensor identification module 10 is used to obtain real-time data of various types of gases in multi-protocol conversion gas sensors, calculate the accumulation of real-time gas data from each gas sensor over a preset time period, and obtain the cumulative amount of each type of gas; identify adjacent gas sensors with different gas accumulation amounts, and obtain the first gas sensor and the second gas sensor; multi-protocol sensors can be connected using an extended multi-protocol interface. The STM32 can expand the I / O port through the 8255A chip, or integrate wireless transmission modules compatible with different interface types: ESP8266 or HC-05 to achieve 4G / WiFi / Bluetooth transmission; the transmitted analog signal uses an instrumentation amplifier (such as AD620) to suppress common-mode interference, is converted by a 24-bit high-precision ADC (such as ADS1256), and a level conversion circuit (such as MAX485 chip) is designed to match 3.3V / 5V systems; a unified data frame format is defined at the protocol parsing layer, including fields such as sensor ID, gas type, concentration value, and timestamp, and protocol conversion firmware must be developed; The gas flow model construction module 20 is configured to obtain a gas transmission channel section model, identify a gas flow change feature in the section according to real-time data of various types of gases in the first gas sensor, and construct a gas flow model based on the gas transmission channel section model. The gas leakage monitoring module 30 is configured to identify a leakage risk position in the gas flow model according to a difference in gas accumulation amount between the first gas sensor and the second gas sensor, and send a position of the gas transmission channel corresponding to the leakage risk position to a maintenance personnel terminal.

[0027] Further, in the abnormal sensor identification module 10, adjacent gas sensors with a difference in gas accumulation amount are identified to obtain the first gas sensor and the second gas sensor, specifically as follows. The flow time is calculated according to the length of the gas transmission channel between each gas sensor and the gas flow speed, the accumulation amount of various types of gases in any gas sensor is compared with the accumulation amount of various types of gases in the next gas sensor in the flow direction after the flow time, and when there is a difference in gas accumulation amount greater than a preset threshold, the corresponding gas sensor is identified to obtain the first gas sensor and the second gas sensor.

[0028] Further, in the gas flow model construction module 20, after the gas flow model is constructed based on the gas transmission channel section model, the following steps are further included. The gas flow model is substituted into a preset gas flow digital twin corresponding channel section to update the gas flow digital twin, and the gas flow digital twin is visually displayed.

[0029] The third aspect of the present application further provides a gas intelligent monitoring method and device based on multi-protocol conversion, including a processor and a memory: the memory is used to store program code and transmit the program code to the processor; the processor is used to execute the above-mentioned gas intelligent monitoring method based on multi-protocol conversion according to the instructions in the program code.

[0030] The fourth aspect of the present application provides a computer readable storage medium, characterized in that the computer readable storage medium is used to store program code, and the program code is used to execute the above-mentioned gas intelligent monitoring method based on multi-protocol conversion.

[0031] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-mentioned device and equipment can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0032] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, 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 an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0033] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0034] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0035] If the integrated unit is implemented as 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, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0036] As described above, 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 above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can 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 gas intelligent monitoring method based on multi-protocol conversion, characterized in that include: Acquire real-time data of various types of gases from the multi-protocol conversion gas sensor, calculate the accumulation of real-time gas data from each gas sensor within a preset time length, and obtain the cumulative amount of each type of gas; identify adjacent gas sensors with different gas cumulative amounts to obtain the first gas sensor and the second gas sensor; Obtaining a gas transmission channel interval model, identifying gas flow variation characteristics within the interval based on real-time data of various types of gas in the first gas sensor, and constructing a gas flow model based on the gas transmission channel interval model; The leakage risk portion is identified in the gas flow model according to the difference in gas accumulation between the first gas sensor and the second gas sensor, and the gas transmission channel position corresponding to the leakage risk portion is sent to the maintenance personnel terminal.

2. The gas intelligent monitoring method based on multi-protocol conversion according to claim 1 is characterized in that: The step of identifying adjacent gas sensors having different gas accumulation amounts to obtain the first gas sensor and the second gas sensor is specifically as follows: The flow time is calculated based on the length of the gas transmission channel and the gas flow velocity between each gas sensor, and the cumulative amount of various types of gas of any gas sensor is compared with the cumulative amount of various types of gas of the next gas sensor in the flow direction after the flow time. When there is a difference in gas cumulative amount greater than a preset threshold, the corresponding gas sensor is identified to obtain the first gas sensor and the second gas sensor.

3. The gas intelligent monitoring method based on multi-protocol conversion according to claim 1 is characterized in that: After the gas flow model is constructed using the gas transmission channel interval model, the method further includes: The gas flow model is substituted into the corresponding channel segment of the preset gas flow digital twin, the gas flow digital twin is updated, and then the gas flow digital twin is visualized.

4. A gas intelligent monitoring system based on multi-protocol conversion, characterized in that: include: The abnormal sensor identification module is used to obtain real-time data of various types of gases in the multi-protocol conversion gas sensor, calculate the accumulation of real-time gas data of each gas sensor within a preset time length, and obtain the cumulative amount of each type of gas; identify adjacent gas sensors with different gas cumulative amounts, and obtain the first gas sensor and the second gas sensor; A gas flow model building module is used to obtain a gas transmission channel interval model, identify the gas flow change characteristics within the interval based on the real-time data of various types of gas in the first gas sensor, and build a gas flow model based on the gas transmission channel interval model; The gas leakage monitoring module is used to identify the leakage risk location in the gas flow model according to the difference in gas accumulation between the first gas sensor and the second gas sensor, and send the gas transmission channel location corresponding to the leakage risk location to the maintenance personnel terminal.

5. The gas intelligent monitoring system based on multi-protocol conversion according to claim 4 is characterized in that: In the abnormal sensor identification module, adjacent gas sensors with different gas accumulation amounts are identified to obtain the first gas sensor and the second gas sensor, specifically: The flow time is calculated based on the length of the gas transmission channel and the gas flow velocity between each gas sensor, and the cumulative amount of various types of gas of any gas sensor is compared with the cumulative amount of various types of gas of the next gas sensor in the flow direction after the flow time. When there is a difference in gas cumulative amount greater than a preset threshold, the corresponding gas sensor is identified to obtain the first gas sensor and the second gas sensor.

6. The gas intelligent monitoring system based on multi-protocol conversion according to claim 4 is characterized in that: In the gas flow model construction module, after the gas flow model is constructed using the gas transmission channel interval model, the module further includes: The gas flow model is substituted into the corresponding channel segment of the preset gas flow digital twin, the gas flow digital twin is updated, and then the gas flow digital twin is visualized.

7. A gas intelligent monitoring device based on multi-protocol conversion, characterized in that: The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the gas intelligent monitoring method based on multi-protocol conversion according to any one of claims 1 to 3 according to the instructions in the program code.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store program code, and the program code is used to execute the gas intelligent monitoring method based on multi-protocol conversion according to any one of claims 1 to 3.

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