Self-cleaning gas ultrasonic flowmeter based on intelligent monitoring

Abstract

The application discloses a self-cleaning gas ultrasonic flowmeter based on intelligent monitoring and relates to the technical field of gas flow metering equipment. The self-cleaning gas ultrasonic flowmeter comprises a flowmeter main body, a detection assembly, a filtering assembly and an embedded intelligent control module, and the intelligent control module is provided with a soft algorithm system. The detection assembly comprises an ultrasonic sensor and a gas component analyzer, and the filtering assembly comprises a first positioning plate, a second positioning plate, a Rb magnet and a PTFE hydrophobic gas-permeable membrane to form a filtering replacement mechanism. The soft algorithm system comprises dynamic calibration, multi-source data fusion, pollution grading early warning and life prediction algorithms, can receive and process detection data, dynamically adjusts detection parameters, monitors the state of the filtering assembly and outputs early warning and life results. The self-cleaning gas ultrasonic flowmeter realizes the cooperation of hardware and software through the fusion of hardware structure and soft algorithm, improves the measurement accuracy and stability, reduces the operation and maintenance cost and prolongs the service life of the equipment.

Description

Technical Field

[0001] This invention relates to the field of cleaning equipment technology, and in particular to a self-cleaning ultrasonic gas flow meter based on intelligent monitoring. Background Technology

[0002] In industrial production, natural gas transportation, and industrial gas pipeline monitoring, accurate gas flow measurement is crucial for ensuring production efficiency and rational energy allocation. Ultrasonic gas flow meters, with their advantages of wide measurement range, low pressure loss, and non-disturbing fluid flow, have become the mainstream metering equipment in this field. However, with the increasing level of industrial intelligence, traditional mechanical structure optimization has gradually reached its technological limits. Simply relying on mechanical upgrades such as improving filter protection structures and optimizing the installation of detection elements is insufficient to meet the practical application requirements of high precision and intelligence. Therefore, driving flow meters towards intelligent control through a synergistic combination of hardware and algorithms has become key to technological breakthroughs in the industry.

[0003] However, existing ultrasonic gas flow meters still face many challenges in practical applications: impurities such as liquids, dust, and oil trapped in gas delivery pipelines easily adhere to the surface of ultrasonic sensors, interfering with signal transmission, leading to decreased measurement accuracy, and exacerbating wear on the sensing elements; the lack of coordinated control between the sensing and filtering components makes it impossible to dynamically adjust detection parameters according to the characteristics of the gas medium, and the clogging and failure status of the filtering components is difficult to monitor in real time, resulting in significant maintenance delays; multi-source detection data are not system-fused, making it easy for single data deviations to affect the final measurement results. Against this backdrop, there is an urgent need to deeply integrate software algorithms with flow meter hardware, using intelligent calibration, data fusion, and status warning algorithms to address the bottlenecks in mechanical structure optimization and improve the measurement accuracy and intelligent operation and maintenance level of the equipment. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a self-cleaning ultrasonic gas flow meter based on intelligent monitoring.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a self-cleaning ultrasonic gas flow meter based on intelligent monitoring, comprising a flow meter body, detection platforms installed in the middle of both sides of the flow meter body, detection chambers provided inside the detection platforms, protective covers installed at the far ends of the two detection platforms, detection components installed in the detection chambers, and filter components installed on both sides of the flow meter body.

[0006] The flow meter body is embedded with an intelligent control module. The intelligent control module is equipped with a software-software coordinating algorithm system. The software-software algorithm system is used to receive and process all real-time data collected by the detection component, drive the detection component to dynamically adjust the detection parameters, and monitor the usage status of the filter component in real time throughout the entire cycle and output the life prediction result.

[0007] Preferably, the flow meter body has an air inlet on one side and an air outlet on the other side, and limit grooves are provided on both sides of the flow meter body, with threaded holes equally spaced around the limit grooves.

[0008] Preferably, the flowmeter body has first detection holes at equal intervals at the front and rear ends of the inner wall, which are connected to the detection chamber, and a second detection hole is formed between the two first detection holes on both sides of the front end.

[0009] Preferably, the detection component includes an ultrasonic sensor equidistantly installed in the detection chamber and adapted to the first detection hole, and a gas composition analyzer adapted to the second detection hole installed in the middle of the two detection chambers. The ultrasonic sensor and the gas composition analyzer are both electrically connected to the intelligent control module, and the real-time data collected by the detection component are transmitted to the Softcom algorithm system for processing.

[0010] Preferably, the filter assembly includes a first positioning plate disposed on one side of the flow meter body, a PTFE hydrophobic and breathable membrane installed on the other side of the first positioning plate, and a second positioning plate adapted to the first positioning plate installed on the other side of the PTFE hydrophobic and breathable membrane; positioning grooves are provided on the inner sides of both the first and second positioning plates, and neodymium magnets are installed in both positioning grooves; the first positioning plate, the second positioning plate, the neodymium magnets and the PTFE hydrophobic and breathable membrane constitute a filter replacement mechanism.

[0011] Preferably, the iSoftStone algorithm system includes a traffic detection dynamic calibration algorithm, the operation logic of which is as follows:

[0012] The intelligent control module receives real-time data on gas composition and impurity content collected by the gas composition analyzer, calls the preset gas medium-ultrasonic parameter matching database, and automatically adjusts the wavelength and transmission frequency of the ultrasonic sensor to make the detection parameters of the ultrasonic sensor match the characteristics of the gas medium currently flowing through. The adjustment process is achieved through hardware-driven software communication protocol to realize the parameter sending and taking effect without delay.

[0013] Preferably, the Softcom algorithm system also includes a multi-source detection data fusion algorithm. The operating logic of the multi-source detection data fusion algorithm is as follows: normalize the raw flow data collected by multiple ultrasonic sensors and the medium characteristic data collected by the gas composition analyzer; perform fusion analysis on the multi-source data by removing outliers and weighted averaging to obtain the detection fusion value; compare the detection fusion value with the preset detection threshold and output the gas flow detection result.

[0014] Preferably, the Softcom algorithm system includes an equipment pollution classification and early warning algorithm. The algorithm's operating logic is as follows: real-time acquisition of the signal attenuation, signal reflectivity, and flow measurement deviation values ​​of the ultrasonic sensor; comparison of each deviation value with a preset normal threshold range to determine the degree of exceedance of each parameter; triggering a level one / level two pollution early warning through the Softcom protocol based on the degree of exceedance of the data; wherein the level one early warning is a reminder of slight blockage of the filter component, and the level two early warning is a reminder of replacement of the detection element or filter component failure. The early warning signal can be displayed locally or pushed remotely through the intelligent control module.

[0015] Preferably, the Softcom algorithm system further includes a filter component lifetime prediction algorithm, the operating logic of which is as follows:

[0016] Based on the cumulative impurity content collected by the gas composition analyzer, the cumulative flow rate of the flow meter body, and the actual operating time of the equipment, combined with the rated filtration capacity of the PTFE hydrophobic and breathable membrane, a life prediction model is constructed through a multiple linear regression model to evaluate the remaining service life of the filter components in real time and obtain a life assessment value. When the life assessment value is lower than the preset life assessment threshold, a replacement reminder is triggered.

[0017] Preferably, the softcom algorithm system is configured with a hardware communication adaptation layer and an embedded data processing layer. The hardware communication adaptation layer uses the Modbus-RTU protocol to realize bidirectional data interaction between the intelligent control module and the ultrasonic sensor and gas composition analyzer. The embedded data processing layer adopts a computing architecture that adapts to the hardware operating resources of the flow meter body, and is used to synchronously complete the real-time processing of multi-source detection data, the accurate distribution of detection parameters and the dynamic calibration of flow detection, the fusion of multi-source detection data, the equipment pollution classification early warning, and the parallel operation of the filter component life prediction algorithm.

[0018] Compared with the prior art, the beneficial effects of the present invention are:

[0019] 1. This invention integrates the softcom algorithm system with hardware structure. Through algorithmic functions such as dynamic calibration and data fusion, it solves the problem of accuracy that is difficult to improve with simple mechanical improvements. It adapts to changes in gas medium and complex working conditions, and greatly improves measurement accuracy and stability.

[0020] 2. This invention uses pollution classification early warning and filter component life prediction algorithms to monitor equipment status in real time and proactively push reminders, avoiding failures and errors caused by maintenance delays. Combined with a magnetic quick-release filter structure, it simplifies replacement operations and reduces labor and maintenance costs.

[0021] 3. This invention effectively blocks impurities through a PTFE hydrophobic and breathable membrane, preventing contamination and wear of the detection elements; the algorithm dynamically adjusts the detection parameters, reducing signal interference and component loss; the dual protection mechanism extends the overall service life of the equipment and reduces the frequency of replacement and maintenance.

[0022] In summary, this invention, through the deep integration of hardware structure and software algorithms, retains the structural advantages of physical self-cleaning and quick disassembly maintenance, while comprehensively solving the pain points of traditional flow meters such as insufficient accuracy, lagging operation and maintenance, and limited lifespan through intelligent calibration, data processing, status early warning and other algorithm functions. It achieves more accurate measurement, intelligent operation and maintenance, and long-term use, significantly improving the practical value and industry adaptability of the equipment. Attached Figure Description

[0023] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0024] Figure 1 This is a schematic diagram of the overall structure proposed in this invention;

[0025] Figure 2 This is an exploded view of the overall structure proposed in this invention;

[0026] Figure 3 This is a cross-sectional view of the overall structure proposed in this invention;

[0027] Figure 4 The present invention proposes Figure 3 Enlarged diagram of part A in the middle;

[0028] Figure 5 This is a connection block diagram of the core module of the device proposed in this invention;

[0029] Figure 6 This is a flowchart of the data interaction process proposed in this invention;

[0030] Figure 7 This is a flowchart illustrating the core working principle of the present invention.

[0031] The numbers in the diagram are: 1. Detection platform; 2. Protective cover; 3. Ultrasonic sensor; 4. Gas composition analyzer; 5. First positioning plate; 6. Second positioning plate; 7. Rubidium magnet; 8. PTFE hydrophobic and breathable membrane. Detailed Implementation

[0032] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0033] See Figures 1 to 7 The present invention provides a self-cleaning ultrasonic gas flow meter based on intelligent monitoring, comprising a flow meter body, a detection platform 1 installed in the middle of both sides of the flow meter body, a detection chamber provided inside the detection platform 1, a protective cover 2 installed at the far ends of both detection platforms 1, a detection component installed in the detection chamber, and a filter component installed on both sides of the flow meter body.

[0034] An intelligent control module is embedded inside the flow meter body. This intelligent control module is bidirectionally electrically connected to the detection component through shielded wires. The intelligent control module is equipped with a soft-connection algorithm system that realizes full-link coordination between hardware and software. The soft-connection algorithm system is used to receive and process all real-time data collected by the detection component, drive the detection component to complete the dynamic adjustment of detection parameters, and at the same time monitor the usage status of the filter component in real time throughout the entire cycle and output the life prediction result.

[0035] It should be noted that the intelligent control module also integrates data interaction functions to realize the local display and remote transmission of detection data and equipment status information. Through the coordinated operation of the detection component, the filter component and the intelligent control module's soft-connection algorithm system, the integrated operation of intelligent flow meter monitoring, physical self-cleaning protection and precise algorithm control is realized.

[0036] Specifically, the flow meter body has an air inlet on one side and an air outlet on the other side, and limit grooves are opened on both sides of the flow meter body, with threaded holes equally spaced around the limit grooves.

[0037] Specifically, the front and rear ends of the inner wall of the flow meter body are respectively provided with first detection holes that connect to the detection chamber at equal intervals, and a second detection hole is provided between the two first detection holes on both sides of the front end. The first and second detection holes facilitate the subsequent detection of the gas flowing through the flow meter body by the ultrasonic sensor 3 and the gas composition analyzer 4.

[0038] Specifically, the detection component includes an ultrasonic sensor 3 that is equidistantly installed in the detection chamber and adapted to the first detection hole, and a gas composition analyzer 4 adapted to the second detection hole installed in the middle of the two detection chambers. Both the ultrasonic sensor 3 and the gas composition analyzer 4 are electrically connected to the intelligent control module, and the real-time data collected by the detection component is transmitted to the Softcom algorithm system for processing.

[0039] Specifically, the filter assembly includes a first positioning plate 5 located on one side of the flow meter body, a PTFE hydrophobic and breathable membrane 8 installed on the other side of the first positioning plate 5, and a second positioning plate 6 adapted to the first positioning plate 5 installed on the other side of the PTFE hydrophobic and breathable membrane 8; positioning grooves are provided on the inner sides of both the first positioning plate 5 and the second positioning plate 6, and neodymium magnets 7 are installed in both positioning grooves. The first positioning plate 5, the second positioning plate 6, the neodymium magnets 7 and the PTFE hydrophobic and breathable membrane 8 constitute a filter replacement mechanism.

[0040] Specifically, the iSoftStone algorithm system includes a traffic detection dynamic calibration algorithm, and the operating logic of the traffic detection dynamic calibration algorithm is as follows:

[0041] The intelligent control module receives real-time data on gas composition and impurity content collected by the gas composition analyzer 4, calls the preset gas medium-ultrasonic parameter matching database, and dynamically corrects and adjusts the wavelength and transmission frequency of the ultrasonic sensor 3 using a quantification formula. The wavelength correction formula is as follows: The formula for correcting the transmission frequency is: The corrected data is transmitted and activated without delay through the hardware driver software protocol, so that the detection parameters of the ultrasonic sensor 3 are adapted to the characteristics of the gas medium currently flowing through.

[0042] The parameters in the above formula are interpreted as follows: To correct the wavelength of the ultrasonic wave. The reference wavelength under standard operating conditions. This is the corrected ultrasonic emission frequency. The reference transmission frequency is defined as the standard operating frequency, M is the mass percentage of solid impurities in the gas, and N is the volume percentage of the non-target gas being measured. This is the correction factor for impurity content. For non-target gas components, , All data were pre-stored in the intelligent control module after being calibrated through experiments.

[0043] Specifically, the Softcom algorithm system also includes a multi-source detection data fusion algorithm, the operation logic of which is as follows:

[0044] First, the raw flow data collected by multiple ultrasonic sensors 3 and the medium characteristic data collected by the gas composition analyzer 4 are subjected to min-max normalization. Then, outliers in the normalized data are removed using the 3σ criterion. Finally, the remaining valid data are weighted and averaged to obtain the detection fusion value. The detected fusion value is compared with a preset detection threshold. If the comparison matches, the final gas flow detection result is output. The normalization formula is: Then, outliers in the normalized data are removed using the 3σ criterion. Finally, a weighted average is calculated for the valid data. The weighted average formula is as follows: The parameters in the above formula are explained as follows: Here, X represents the normalized value of the data, and X represents the original detection data. This is the historical minimum value for this type of data. The maximum historical value for this type of data is n, where n is the number of valid detection data. Let be the weighting coefficient of the i-th group of valid data. The i-th group is the normalized and restored effective flow rate data. A preset detection threshold is set, which is the reasonable flow rate range calibrated before the flow meter leaves the factory. The detection fusion value is compared with the preset detection threshold. If the detection fusion value is greater than the preset detection threshold, it indicates that the actual gas flow rate detected exceeds the measurement upper limit calibrated by the flow meter. If the detection fusion value is less than or equal to the preset detection threshold, it indicates that the actual gas flow rate detected is within the reasonable measurement range calibrated by the flow meter. The above two comparison judgment results and the detection fusion value are recorded together as the gas flow rate detection result.

[0045] Specifically, the Softcom algorithm system includes an equipment pollution classification and early warning algorithm, and the operating logic of the equipment pollution classification and early warning algorithm is as follows:

[0046] The signal attenuation, signal reflectivity, and flow measurement deviation of ultrasonic sensor 3 are collected in real time. The three types of data are first normalized using min-max normalization, and then the comprehensive pollution index is calculated using a quantification formula. The comprehensive pollution index The parameters are compared with the preset normal threshold range, and the degree of exceedance of each parameter is determined according to the numerical range of the comprehensive pollution index. Then, the corresponding level of pollution warning is triggered through the soft communication protocol.

[0047] Among them, the comprehensive pollution index The calculation formula is Where A, B, and C represent the normalized values ​​corresponding to signal attenuation, signal reflectivity, and flow measurement deviation, respectively. These represent the signal attenuation weighting coefficient, the signal reflectivity weighting coefficient, and the flow measurement deviation weighting coefficient, respectively.

[0048] Preset Level 1 Warning Threshold Level II warning threshold ;like This indicates that the equipment is in normal condition; if This triggers a Level 1 warning, specifically indicating slight blockage of the filter components; if This triggers a level two warning, specifically a replacement prompt for contaminated detection elements or malfunctioning filter components. The warning signal can be displayed locally or pushed remotely via the intelligent control module.

[0049] Specifically, the iSoftStone algorithm system also includes a filter component lifetime prediction algorithm, the operating logic of which is as follows:

[0050] Based on the cumulative impurity content collected by the gas composition analyzer 4, the cumulative flow rate of the flow meter body, and the actual operating time of the equipment, combined with the rated filtration capacity and rated baseline service life of the PTFE hydrophobic and breathable membrane 8, a life prediction model is constructed through a multiple linear regression model. The remaining service life of the filter components is calculated in real time to obtain the life assessment value. The life assessment value is compared with the preset life assessment threshold. When the life assessment value is lower than the preset life assessment threshold, a replacement reminder is automatically triggered through the soft communication protocol.

[0051] The formula for calculating lifespan prediction is as follows: ;in, This refers to the remaining service life, or lifespan assessment value, of the PTFE hydrophobic and breathable membrane 8. The flow meter accumulates the amount of impurities it retains. The cumulative gas flow rate through the main body of the flow meter. The actual continuous operating time of the equipment is represented by 'a', the cumulative impurity content regression coefficient is represented by 'b', the cumulative flow regression coefficient is represented by 'c', the actual operating time regression coefficient is represented by 'd', and the rated baseline service life of the PTFE hydrophobic and breathable membrane 8 is represented by 'd'. 'a', 'b', and 'c' are all calibrated through the accelerated aging test of the PTFE hydrophobic and breathable membrane and are pre-stored in the intelligent control module. The preset life assessment threshold is 10% of the rated baseline service life 'd'.

[0052] It should be further explained that the filter component life prediction model constructed by the multiple linear regression model in this solution uses three types of real-time monitoring data as core variables: the cumulative impurity content collected by the gas composition analyzer 4, the cumulative flow rate of the flow meter body, and the actual operating time of the equipment. It is combined with the rated filtration capacity of the PTFE hydrophobic and breathable membrane 8 as the basic performance parameter and is constructed based on the mathematical modeling method of multiple linear regression. At the same time, the regression coefficients of cumulative impurity content, cumulative flow rate, and actual operating time involved in the model are all calibrated by the accelerated aging test of the PTFE hydrophobic and breathable membrane and pre-stored in the intelligent control module as quantitative parameter support for the multiple linear regression model, and finally form a life prediction model that can calculate the remaining service life of the filter component in real time.

[0053] Specifically, the softcom algorithm system is configured with a hardware communication adaptation layer and an embedded data processing layer. The hardware communication adaptation layer adopts the Modbus-RTU protocol with communication parameters of 9600bps baud rate, 8 data bits, and 1 stop bit, enabling bidirectional data interaction between the intelligent control module and the ultrasonic sensor and gas composition analyzer. Both command issuance and feedback reception for data interaction are completed through this protocol. The embedded data processing layer adopts an embedded computing architecture, directly adapting to the hardware operating resources of the flowmeter. This layer has a built-in data processing unit and algorithm execution unit. The data processing unit is used to synchronously complete the real-time processing of multi-source detection data and the accurate issuance of detection parameters. The real-time processing of multi-source detection data includes formula-based... The system includes normalization processing and outlier removal based on the 3σ criterion. Precise transmission of detection parameters involves transferring the ultrasonic sensor's wavelength and transmission frequency parameters, adjusted by the algorithm, to the hardware. The algorithm execution unit synchronously performs parallel operations of flow detection dynamic calibration, multi-source detection data fusion, equipment pollution classification and early warning, and filter component lifespan prediction algorithms. Each algorithm is based on its corresponding quantization formula. The calculations are completed, and each algorithm independently occupies hardware resources without resource conflicts, thus achieving synchronous parallel execution of multiple algorithms.

[0054] The working principle of the self-cleaning ultrasonic gas flow meter based on intelligent monitoring in this solution is as follows:

[0055] Equipment Installation and Pre-Inspection: First, the staff visually inspects the integrity of the first positioning plate 5, second positioning plate 6, neodymium magnet 7, and PTFE hydrophobic and breathable membrane 8 of the filter assembly. The components are then assembled into a filter replacement mechanism, which is installed in the limiting grooves on both sides of the flowmeter body and secured with bolts through threaded holes. Next, the inlet and outlet ends of the flowmeter body are connected to the pipeline to be tested using bolts. Then, the protective cover 2 at the far end of the testing platform 1 is opened to check the installation status of the ultrasonic sensor 3 and gas composition analyzer 4 inside the testing chamber. After confirming their integrity, the protective cover 2 is closed, completing all preparations before equipment use.

[0056] Gas filtration and data acquisition: After the device is powered on, the gas flows in from the inlet of the flow meter body and first passes through the PTFE hydrophobic and breathable membrane 8, which blocks impurities such as liquid, dust, and oil in the gas, preventing impurities from entering the flow meter body and contaminating the detection components. The purified gas flows inside the flow meter body. At this time, the ultrasonic sensor 3 emits a detection signal through the first detection hole to collect gas flow-related data, and the gas composition analyzer 4 collects data such as gas composition and impurity content through the second detection hole. Both types of data are transmitted to the intelligent control module built into the flow meter body.

[0057] Intelligent processing and parameter control: The intelligent control module, equipped with the soft-connect algorithm system, receives real-time data collected by the detection components. It adjusts the wavelength and transmission frequency of the ultrasonic sensor 3 through a flow detection dynamic calibration algorithm to adapt the detection parameters to the current gas medium characteristics. It processes multiple sets of detection data through a multi-source detection data fusion algorithm to output accurate gas flow detection results. At the same time, it monitors the equipment pollution status in real time through an equipment pollution classification early warning algorithm and assesses the remaining service life of the filter components through a filter component life prediction algorithm. Relevant detection data and equipment status information can be displayed locally and transmitted remotely.

[0058] Early Warning and Maintenance: If the Softcom algorithm system detects excessive pollution in the equipment, it will trigger a Level 1 or Level 2 pollution warning depending on the severity of the pollution, indicating whether the filter assembly is slightly clogged and needs cleaning, or the detection element is contaminated / the filter assembly is malfunctioning and needs replacement. If the remaining service life of the filter assembly is detected to be below a preset threshold, a replacement reminder will be triggered. After receiving the warning or reminder, staff will close the pipeline valves, disassemble the filter replacement mechanism on the flow meter body, replace the filter assembly with a new one, and reinstall and secure it to restore normal equipment operation.

[0059] Equipment shutdown and maintenance: After the gas detection work is completed, turn off the power to the equipment, disconnect the connection between the flow meter body and the delivery pipeline, remove the filter components for cleaning or replacement, wipe and clean the inside of the flow meter body, detection platform 1 and detection components, and store them after maintenance to complete the entire usage process.

[0060] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A self-cleaning ultrasonic gas flow meter based on intelligent monitoring, comprising a flow meter body, detection platforms (1) installed in the middle of both sides of the flow meter body, detection chambers provided inside the detection platforms (1), and protective covers (2) installed at the far ends of the two detection platforms (1), characterized in that: The detection chamber is equipped with a detection component, and filter components are installed on both sides of the flow meter body; The flow meter body is embedded with an intelligent control module. The intelligent control module is equipped with a software algorithm system that realizes full-link collaboration between hardware and software. The software algorithm system is used to receive and process all real-time data collected by the detection component, drive the detection component to complete the dynamic adjustment of detection parameters, and monitor the usage status of the filter component in real time throughout the entire cycle and output the life prediction result. The soft-connect algorithm system also includes a multi-source detection data fusion algorithm. The operation logic of the multi-source detection data fusion algorithm is as follows: normalize the original flow data collected by multiple ultrasonic sensors (3) and the medium characteristic data collected by the gas composition analyzer (4), and perform multi-source data fusion analysis by removing outliers and weighted averaging to obtain the detection fusion value. The detection fusion value is compared with the preset detection threshold to output the gas flow detection result. The iSoftStone algorithm system also includes a filter component lifetime prediction algorithm, the operation logic of which is as follows: Based on the cumulative impurity content collected by the gas composition analyzer (4), the cumulative flow rate of the main body of the flow meter, and the actual running time of the equipment, combined with the rated filtration capacity of the PTFE hydrophobic and breathable membrane (8), a life prediction model is constructed through a multiple linear regression model to evaluate the remaining service life of the filter components in real time and obtain the life assessment value. When the life assessment value is lower than the preset life assessment threshold, a replacement reminder is triggered.

2. The self-cleaning ultrasonic gas flow meter based on intelligent monitoring according to claim 1, characterized in that: The flow meter body has an air inlet on one side and an air outlet on the other side, and limit grooves are opened on both sides of the flow meter body, with threaded holes equally spaced around the limit grooves.

3. The self-cleaning ultrasonic gas flow meter based on intelligent monitoring according to claim 2, characterized in that: The flowmeter body has first detection holes at equal intervals at the front and rear ends, which are connected to the detection chamber. A second detection hole is provided between the two first detection holes on both sides of the front end.

4. The self-cleaning ultrasonic gas flow meter based on intelligent monitoring according to claim 3, characterized in that: The detection component includes an ultrasonic sensor (3) that is equidistantly installed in the detection chamber and adapted to the first detection hole, and a gas composition analyzer (4) adapted to the second detection hole is installed in the middle of the two detection chambers. The ultrasonic sensor (3) and the gas composition analyzer (4) are electrically connected to the intelligent control module, and the real-time data collected by the detection component are transmitted to the Softcom algorithm system for processing.

5. A self-cleaning ultrasonic gas flow meter based on intelligent monitoring according to claim 1, characterized in that: The filter assembly includes a first positioning plate (5) on one side of the flow meter body, a PTFE hydrophobic and breathable membrane (8) installed on the other side of the first positioning plate (5), and a second positioning plate (6) adapted to the first positioning plate (5) installed on the other side of the PTFE hydrophobic and breathable membrane (8); both the first positioning plate (5) and the second positioning plate (6) have positioning grooves on their inner sides, and both positioning grooves are equipped with neodymium magnets (7). The first positioning plate (5), the second positioning plate (6), the neodymium magnets (7) and the PTFE hydrophobic and breathable membrane (8) constitute a filter replacement mechanism.

6. A self-cleaning ultrasonic gas flow meter based on intelligent monitoring according to claim 1, characterized in that: The iSoftStone algorithm system includes a dynamic calibration algorithm for traffic detection. The operating logic of the dynamic calibration algorithm for traffic detection is as follows: The intelligent control module receives real-time data on gas composition and impurity content collected by the gas composition analyzer (4), calls the preset gas medium-ultrasonic parameter matching database, and automatically adjusts the wavelength and emission frequency of the ultrasonic sensor (3) so that the detection parameters of the ultrasonic sensor (3) are adapted to the characteristics of the gas medium currently flowing through. The adjustment process is achieved by the hardware-driven soft communication protocol to realize the parameter delivery and effect without delay.

7. A self-cleaning ultrasonic gas flow meter based on intelligent monitoring according to claim 1, characterized in that: The Softcom algorithm system includes a device pollution classification early warning algorithm. The operation logic of the algorithm is as follows: real-time acquisition of the signal attenuation, signal reflectivity and flow measurement deviation of the ultrasonic sensor (3), comparison of each deviation value with the preset normal threshold range, and judgment of the degree of exceedance of each parameter; triggering a first-level / second-level pollution early warning through the Softcom protocol according to the degree of exceedance of the data; the first-level early warning is a prompt for slight blockage of the filter component, and the second-level early warning is a prompt for replacement of the detection element pollution or filter component failure. The early warning signal can be displayed locally or pushed remotely through the intelligent control module.

8. A self-cleaning ultrasonic gas flow meter based on intelligent monitoring according to claim 1, characterized in that: The softcom algorithm system is configured with a hardware communication adaptation layer and an embedded data processing layer. The hardware communication adaptation layer uses the Modbus-RTU protocol to realize bidirectional data interaction between the intelligent control module and the ultrasonic sensor and gas composition analyzer. The embedded data processing layer adopts a computing architecture to adapt to the hardware operating resources of the flow meter body, and is used to synchronously complete the real-time processing of multi-source detection data, accurate distribution of detection parameters and dynamic calibration of flow detection, multi-source detection data fusion, equipment pollution classification early warning, and parallel operation of filter component life prediction algorithm.

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