Wireless communication intelligent gas meter controller and control method thereof

Abstract

The invention relates to the technical field of gas meter controller testing, in particular to a wireless communication intelligent gas meter controller and a control method thereof. Comprising a microprocessor, an electrochemical corrosion monitoring module, a soil resistivity acquisition device, a stray current monitoring unit, a wireless communication unit, a metering unit and an anti-corrosion regulation and control unit, the signal output ends of the electrochemical corrosion monitoring module, the soil resistivity acquisition device and the stray current monitoring unit are respectively and electrically connected with the signal input end of the microprocessor; the control output end of the microprocessor is respectively and electrically connected with the control input ends of the wireless communication unit, the metering unit and the anti-corrosion regulation and control unit; the electrochemical corrosion monitoring module is used for collecting hydrogen sulfide concentration, relative humidity and environment temperature parameters, converting analog signals into digital signals and transmitting the digital signals to the microprocessor. The system can cope with the coupling influence of multiple environment factors in a cooperative mode.

Description

Technical Field

[0001] The present invention relates to the technical field of gas meter controller testing, and in particular to a wireless communication intelligent gas meter controller and a control method thereof. Background Art

[0002] In the practical application of wireless communication smart gas meters, complex environments such as underground pipe corridors and humid, low-lying areas pose significant challenges to the stable operation of the controller. In existing technologies, the controller's communication, metering, and anti-corrosion systems often use independent control logic: the wireless communication module transmits data according to a fixed power strategy, the metering module performs metering in a constant mode, and the anti-corrosion module triggers protection based solely on a single humidity parameter. This independent control mode is unable to cope with the coupled effects of environmental parameters—when dynamic changes in soil resistivity cause signal attenuation, the communication module struggles to adaptively adjust power; when stray currents interfere with metering, the metering module lacks targeted anti-interference switching; and when high humidity and corrosive gases jointly accelerate circuit corrosion, the anti-corrosion module's protection strength cannot accurately match the risk level. The combination of these problems can easily lead to communication interruptions, metering deviations, and shortened equipment lifespan, seriously impacting the reliability of gas metering and management.

[0003] Based on the above problems, there is an urgent need for a control solution that can synergistically cope with the coupled effects of multiple environmental factors. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and propose a wireless communication intelligent gas meter controller, which includes a microprocessor, an electrochemical corrosion monitoring module, a soil resistivity acquisition device, a stray current monitoring unit, a wireless communication unit, a metering unit and an anti-corrosion control unit; The signal output ends of the electrochemical corrosion monitoring module, the soil resistivity acquisition device, and the stray current monitoring unit are respectively electrically connected to the signal input ends of the microprocessor, and the control output ends of the microprocessor are respectively electrically connected to the control input ends of the wireless communication unit, the metering unit, and the anti-corrosion control unit; The electrochemical corrosion monitoring module is used to collect hydrogen sulfide concentration, relative humidity and ambient temperature parameters and convert the analog signals into digital signals and transmit them to the microprocessor. The soil resistivity acquisition device is used to collect soil resistivity and the distance parameters between the gas meter and the receiving base station and transmit them to the microprocessor. The stray current monitoring unit is used to collect stray current density and soil resistivity change parameters and transmit them to the microprocessor. Based on the above-collected calculation circuit corrosion risk coefficient, soil signal attenuation correction coefficient and coordinated control parameters, the microprocessor outputs a power adjustment signal to the wireless communication unit, an anti-interference mode switching signal to the metering unit, and a heating intensity control signal to the anti-corrosion control unit based on the coordinated control parameters to achieve dynamic control of environmental adaptability.

[0005] Preferably, the electrochemical corrosion monitoring module includes a hydrogen sulfide sensor component, a temperature and humidity sensor component and a signal conditioning circuit. The output end of the hydrogen sulfide sensor component and the output end of the temperature and humidity sensor component are respectively electrically connected to the input end of the signal conditioning circuit, and the output end of the signal conditioning circuit is electrically connected to the signal input end of the microprocessor; the hydrogen sulfide sensor component converts the hydrogen sulfide concentration into an electrical signal based on the electrochemical principle, and the temperature and humidity sensor component converts the relative humidity and ambient temperature into electrical signals. The signal conditioning circuit amplifies the input signal through an operational amplifier, and transmits it to the microprocessor after filtering out the high-frequency interference signal through an RC filter circuit.

[0006] Further preferably, the soil resistivity acquisition device includes a resistivity detection component, a distance detection component and a data conversion circuit, the output end of the resistivity detection component and the output end of the distance detection component are respectively electrically connected to the input end of the data conversion circuit, and the output end of the data conversion circuit is electrically connected to the signal input end of the microprocessor; the resistivity detection component obtains a soil resistivity signal by injecting a constant current into the soil and detecting the inter-electrode voltage, the distance detection component obtains a distance signal by laser ranging, and the data conversion circuit converts the analog signal into a digital signal through an analog-to-digital converter and transmits the digital signal to the microprocessor.

[0007] Further preferably, the stray current monitoring unit includes a current sensing component, a resistivity change detection component and a signal processing circuit, the output end of the current sensing component and the output end of the resistivity change detection component are electrically connected to the input end of the signal processing circuit respectively, and the output end of the signal processing circuit is electrically connected to the signal input end of the microprocessor; the current sensing component detects the stray current density signal based on the Hall effect, the resistivity change detection component obtains the resistivity change signal by continuously collecting the soil resistivity and calculating the change, the signal processing circuit suppresses the power frequency interference through a low-pass filter, and adjusts the signal to a microprocessor compatible level through a level conversion circuit before transmitting it to the microprocessor.

[0008] Further preferably, the process of the microprocessor calculating the circuit corrosion risk coefficient is: Based on the parameters collected by the electrochemical corrosion monitoring module and the stray current monitoring unit, the formula is: ; Calculate the circuit corrosion risk factor ; in, is the concentration of hydrogen sulfide; is the relative humidity; is the ambient temperature; Stray current density.

[0009] Further preferably, the process of the microprocessor calculating the soil signal attenuation correction coefficient is: Based on the parameters collected by the soil resistivity acquisition device and the circuit corrosion risk coefficient, the formula is: ; Calculate the soil signal attenuation correction coefficient ;in, is the soil resistivity; is the circuit corrosion risk factor; is the distance between the gas meter and the receiving base station; is the relative humidity.

[0010] Further preferably, the process of the microprocessor calculating the coordinated control parameters is: based on the circuit corrosion risk coefficient, the soil signal attenuation correction coefficient and the parameters collected by the stray current monitoring unit, through the formula: ; Calculate the coordinated control parameters ;in, Regulate power based on the benchmark; is the soil signal attenuation correction factor; is the circuit corrosion risk factor; is the stray current density; It is the change of soil resistivity in the past 10 minutes.

[0011] Further preferably, the method comprises the following steps: S1. Parameter Collection: The electrochemical corrosion monitoring module collects hydrogen sulfide concentration, relative humidity, and ambient temperature and transmits them to the microprocessor. The soil resistivity acquisition device collects soil resistivity and the distance between the gas meter and the receiving base station and transmits them to the microprocessor. The stray current monitoring unit collects stray current density and the change in soil resistivity over the past 10 minutes and transmits them to the microprocessor. S2. Coefficient calculation: The microprocessor calculates the circuit corrosion risk coefficient based on the hydrogen sulfide concentration, relative humidity, ambient temperature, and stray current density collected in S1. It then calculates the soil signal attenuation correction coefficient based on the soil resistivity, distance, and relative humidity. Finally, it calculates the coordinated control parameters based on the stray current density and soil resistivity change. S3. Dynamic control: When the collaborative control parameter is greater than the first threshold, the microprocessor controls the wireless communication unit to increase the transmission power, the metering unit to switch to the anti-stray current mode, and the anti-corrosion control unit to start heating; when the collaborative control parameter is less than the second threshold, the microprocessor controls the wireless communication unit to reduce the transmission power, the metering unit to switch to the normal mode, and the anti-corrosion control unit to stop heating, wherein the first threshold is greater than the second threshold.

[0012] Further preferably, the implementation process of the anti-stray current mode in step S3 is as follows: after the metering unit receives the mode switching signal, it activates the built-in differential amplifier circuit and the power frequency trap circuit, the differential amplifier circuit suppresses common-mode interference of the metering signal, and the power frequency trap circuit filters the 50 Hz power frequency interference signal. At the same time, the metering unit increases the sampling frequency to reduce the metering error introduced by stray current coupling.

[0013] Further preferably, the anti-corrosion control unit adjusts the operating duty cycle of the heating component according to the heating intensity control signal, and the operating duty cycle is positively correlated with the circuit corrosion risk coefficient; when the ambient temperature exceeds the preset value, the anti-corrosion control unit automatically reduces the operating duty cycle to avoid overheating, and when the ambient temperature is lower than the preset value, the operating duty cycle is restored to match the circuit corrosion risk coefficient.

[0014] Technical Effect: This invention achieves multi-parameter coordinated control by electrically connecting a microprocessor with multiple modules, including an electrochemical corrosion monitoring module and a soil resistivity acquisition device. This innovative feature addresses the problem in prior art where controllers, due to independent control of each system, are unable to cope with the effects of coupled environmental parameters. This feature dynamically adapts to complex environments, avoids communication interruptions, metering deviations, and shortened equipment lifespan, and improves the reliability of gas metering and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is the block diagram of the wireless communication smart gas meter controller for this application; Figure 2 This is a flow chart of the control method of the wireless communication smart gas meter controller in this application. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] Traditional wireless communication smart gas meter controllers face significant technical shortcomings in complex environments such as underground pipe corridors and humid, low-lying areas. First, the combined effects of high humidity and corrosive gases accelerate oxidation of circuit contacts, making communication interfaces susceptible to poor contact. Second, dynamic changes in soil resistivity disrupt wireless signal attenuation, making traditional fixed-power regulation solutions difficult to adapt. Third, stray currents within pipe corridors interfere with metering circuits, reducing metering accuracy. These intertwined issues cannot be fundamentally addressed by single approaches like conventional corrosion protection and power control, requiring coordinated control of multiple parameters.

[0018] Based on this, see Figure 1This embodiment provides a wireless communication intelligent gas meter controller, including a microprocessor, an electrochemical corrosion monitoring module, a soil resistivity acquisition device, a stray current monitoring unit, a wireless communication unit, a metering unit, and an anti-corrosion control unit; the signal output ends of the electrochemical corrosion monitoring module, the soil resistivity acquisition device, and the stray current monitoring unit are respectively electrically connected to the signal input ends of the microprocessor, and the control output ends of the microprocessor are respectively electrically connected to the control input ends of the wireless communication unit, the metering unit, and the anti-corrosion control unit; the electrochemical corrosion monitoring module is used to collect hydrogen sulfide concentration, relative humidity, and ambient temperature parameters and convert the analog signals into digital signals and transmit them to the microprocessor; the soil resistivity acquisition device is used to collect soil resistivity and the distance parameters between the gas meter and the receiving base station and transmit them to the microprocessor; the stray current monitoring unit is used to collect stray current density and soil resistivity change parameters and transmit them to the microprocessor; the microprocessor calculates a circuit corrosion risk coefficient, a soil signal attenuation correction coefficient, and a coordinated control parameter based on the above parameters, and outputs a power adjustment signal to the wireless communication unit, an anti-interference mode switching signal to the metering unit, and a heating intensity control signal to the anti-corrosion control unit based on the coordinated control parameters to achieve dynamic control of environmental adaptability.

[0019] This solution overcomes the limitations of traditional single-parameter monitoring by constructing a closed-loop control system through the collaborative construction of multiple modules. The electrochemical corrosion monitoring module captures corrosion-related parameters in real time, the soil resistivity acquisition device provides data on the signal propagation environment, and the stray current monitoring unit obtains targeted information on interference sources. The microprocessor serves as the core for multi-parameter coupled calculation and dynamic control. This architecture can simultaneously address three major issues: corrosion, signal attenuation, and stray current. The modules are not simply superimposed, but rather form a synergistic effect through data interaction, providing support for stable operation in complex environments.

[0020] Traditional electrochemical corrosion monitoring relies heavily on a single humidity parameter, failing to reflect the combined effects of hydrogen sulfide concentration, temperature, and stray current. This can lead to biased corrosion risk assessments, resulting in delayed activation of anti-corrosion measures and excessive energy consumption. Furthermore, the monitoring module lacks direct data linkage with subsequent control steps, making precise protection difficult to achieve.

[0021] Based on this, the electrochemical corrosion monitoring module includes a hydrogen sulfide sensor assembly, a temperature and humidity sensor assembly, and a signal conditioning circuit. The outputs of the hydrogen sulfide sensor assembly and the temperature and humidity sensor assembly are respectively electrically connected to the input of the signal conditioning circuit, which is in turn electrically connected to the signal input of a microprocessor. The hydrogen sulfide sensor assembly converts hydrogen sulfide concentration into an electrical signal based on electrochemical principles, while the temperature and humidity sensor assembly converts relative humidity and ambient temperature into electrical signals. The signal conditioning circuit amplifies the input signal using an operational amplifier, filters out high-frequency interference signals using an RC filter circuit, and transmits it to the microprocessor. This solution integrates multiple sensor components to achieve comprehensive acquisition of corrosion-related parameters. The hydrogen sulfide sensor assembly specifically captures the effects of corrosive gases, while the temperature and humidity sensor assembly provides basic environmental parameters. The three work together to avoid the one-sidedness of a single parameter.

[0022] The design of the signal conditioning circuit ensures the effective extraction of weak signals, amplification processing enhances signal strength, and filtering processing removes environmental noise, providing high-quality data for subsequent corrosion risk factor calculations.

[0023] This modular design not only solves the problem of insufficient traditional monitoring dimensions, but also reduces the data processing pressure of the microprocessor through signal preprocessing at the hardware level, making corrosion risk assessment more timely and accurate, and providing a reliable basis for anti-corrosion regulation.

[0024] Traditional soil resistivity acquisition often uses a fixed method, making it difficult to balance accuracy and environmental adaptability. Furthermore, distance parameter acquisition and resistivity acquisition are independent of each other, making it impossible to provide relevant data for signal attenuation analysis. Furthermore, direct analog signal transmission is susceptible to interference, leading to increased errors in subsequent calculations.

[0025] Based on this, the soil resistivity acquisition device includes a resistivity detection component, a distance detection component and a data conversion circuit. The output end of the resistivity detection component and the output end of the distance detection component are respectively electrically connected to the input end of the data conversion circuit, and the output end of the data conversion circuit is electrically connected to the signal input end of the microprocessor; the resistivity detection component obtains a soil resistivity signal by injecting a constant current into the soil and detecting the inter-electrode voltage, the distance detection component obtains a distance signal by laser ranging, and the data conversion circuit converts the analog signal into a digital signal through an analog-to-digital converter and transmits it to the microprocessor.

[0026] This solution integrates resistivity and distance parameter acquisition. The resistivity detection component's constant current injection method ensures stable soil conductivity measurement, avoiding the impact of soil heterogeneity on traditional methods. The distance detection component's laser ranging method provides the spatial relationship between the gas meter and base station, providing critical data for signal propagation path analysis. The data conversion circuit's analog-to-digital conversion process digitizes the analog signal, reducing interference during transmission and ensuring parameter integrity. This design addresses the issues of dispersed parameter acquisition and poor data correlation in traditional systems, enabling soil-related parameters to be directly used in signal attenuation correction factor calculations, providing precise environmental information for wireless communication power regulation.

[0027] Traditional stray current monitoring relies on simple current detection, which fails to simultaneously capture changes in soil resistivity, a key parameter. This results in an incomplete assessment of the impact of stray current on metering. Furthermore, power frequency interference in the monitoring signal is not effectively processed, making it easily mixed with valid signals and leading to subsequent misjudgments.

[0028] Based on this, the stray current monitoring unit includes a current sensing component, a resistivity change detection component and a signal processing circuit. The output end of the current sensing component and the output end of the resistivity change detection component are electrically connected to the input end of the signal processing circuit respectively, and the output end of the signal processing circuit is electrically connected to the signal input end of the microprocessor; the current sensing component detects the stray current density signal based on the Hall effect, and the resistivity change detection component obtains the resistivity change signal by continuously collecting the soil resistivity and calculating the change amount. The signal processing circuit suppresses power frequency interference through a low-pass filter, and adjusts the signal to a microprocessor compatible level through a level conversion circuit and then transmits it to the microprocessor.

[0029] This solution achieves comprehensive monitoring of stray currents and related parameters through the collaborative work of two components. The Hall effect principle of the current sensing component ensures the safety and accuracy of non-contact measurement, avoiding the wear and tear problems of traditional contact measurement. The resistivity change detection component captures the dynamic changes in soil conductivity, supplementing the analysis of stray current propagation paths. The low-pass filtering of the signal processing circuit specifically removes power frequency interference, and the level conversion ensures the compatibility of the signal with the microprocessor, improving data reliability. This design solves the problem of traditional monitoring focusing only on the current itself and ignoring environmental factors. It enables more precise use of stray current-related parameters for coordinated control and reduces its interference with measurement.

[0030] Traditional circuit corrosion risk assessment lacks a quantitative calculation model and relies heavily on empirical judgment, which cannot reflect the coupled effects of hydrogen sulfide concentration, humidity, temperature, and stray current. This results in a lack of scientific basis for the initiation and intensity control of anti-corrosion measures. Based on this, the process of calculating the circuit corrosion risk coefficient by the microprocessor is as follows: Based on the parameters collected by the electrochemical corrosion monitoring module and the stray current monitoring unit, the formula ; Calculate the circuit corrosion risk factor ;in, is the concentration of hydrogen sulfide; is the relative humidity; is the ambient temperature; is the stray current density.

[0031] This solution achieves accurate assessment of corrosion risk by establishing a quantitative model with multi-parameter coupling.

[0032] In the formula, the hydrogen sulfide concentration parameter reflects the direct impact of corrosive gases, the relative humidity exponential term reflects its nonlinear influence characteristics, the temperature exponential function reflects the thermal acceleration effect, and the stray current term quantifies the promotion of electrochemical corrosion. These parameters form a coupling effect through a multiplication relationship, avoiding the limitations of single-parameter evaluation. This calculation method solves the subjectivity of traditional empirical judgments, transforming corrosion risk from a qualitative description to a quantitative value. It provides a clear basis for controlling the heating intensity of the anti-corrosion control unit and achieves precise anti-corrosion measures, avoiding both insufficient protection when the corrosion risk is too high and excessive energy consumption when the risk is low.

[0033] In the formula Represents the concentration of hydrogen sulfide. As a corrosive gas, the concentration of hydrogen sulfide in the environment where the gas meter is located is a key factor affecting circuit corrosion. Divide it by , is to normalize the hydrogen sulfide concentration value and highlight its contribution to the corrosion risk.

[0034] For example, when the hydrogen sulfide concentration is hour, Compared with the case of lower hydrogen sulfide concentration, the value of this part increases, reflecting the aggravating effect of increased hydrogen sulfide concentration on the circuit corrosion risk.

[0035] Compared with the case of lower hydrogen sulfide concentration, the value of this part increases, reflecting the aggravating effect of increased hydrogen sulfide concentration on the circuit corrosion risk.

[0036] Here the relative humidity is divided by After The exponential form of power operation can more accurately reflect the nonlinear relationship between relative humidity and corrosion risk. In reality, as humidity increases, the corrosion risk does not increase linearly, but accelerates. The power setting is based on research and analysis of a large amount of experimental data and circuit corrosion conditions in actual environments to accurately quantify the impact of relative humidity on corrosion risk.

[0037] For example, when the relative humidity is hour, Compared with the case of low relative humidity, the values ​​of this part are significantly increased, highlighting the effect of increased relative humidity on the corrosion risk.

[0038] It is the ambient temperature. Temperature has an important influence on the rate of chemical reactions. During the circuit corrosion process, temperature changes will affect the speed of the corrosion reaction.

[0039] In the formula This item reflects the effect of temperature. As the reference temperature, when the ambient temperature is higher than hour, is a positive value, the exponential term is greater than , indicating that temperature increase will accelerate the corrosion reaction and increase the risk of circuit corrosion; when the ambient temperature is lower than hour, is a negative value, the exponential term is less than , but it will still have an impact on the corrosion risk, but it will relatively slow down the corrosion rate. For example, when the ambient temperature is hour, , indicating that the temperature rises , which will increase the contribution of this part to the corrosion risk.

[0040] is the stray current density. The stray current will accelerate the corrosion of the circuit through electrochemical action. This part reflects the influence of stray current density. When the stray current density is When , which indicates the baseline state when there is no stray current influence; as the stray current density increases, The value of increases, multiplied by Then add , the overall value increases, reflecting that the increase in stray current density will increase the risk of circuit corrosion.

[0041] For example, when the stray current density is , showing the effect of stray current on the corrosion risk of circuits.

[0042] This formula comprehensively considers four factors closely related to circuit corrosion: hydrogen sulfide concentration, relative humidity, ambient temperature, and stray current density. Through a reasonable combination of mathematical operations, it comprehensively and accurately quantifies the circuit corrosion risk factor.

[0043] Traditional soil signal attenuation assessments are often based on fixed path loss models, considering only single factors such as soil resistivity and distance. They fail to account for the indirect effects of corrosive environments on the signal propagation medium, leading to biased attenuation corrections. Furthermore, the impact of relative humidity on signal attenuation is often overlooked, further reducing correction accuracy and making wireless communication power regulation inaccurate.

[0044] Based on this, the microprocessor calculates the soil signal attenuation correction coefficient in the following process: Based on the parameters collected by the soil resistivity acquisition device and the circuit corrosion risk coefficient, the formula is: ; Calculate the soil signal attenuation correction coefficient ;in, is the soil resistivity; is the circuit corrosion risk factor; is the distance between the gas meter and the receiving base station; is the relative humidity.

[0045] This solution achieves precise correction of signal attenuation through a multi-parameter coupling model. The negative exponential term of soil resistivity in the formula reflects its nonlinear influence on signal attenuation. The additional term of the circuit corrosion risk coefficient quantifies the indirect effect of the corrosive environment on the propagation medium. The logarithmic term of distance reflects the spatial attenuation law, and the linear term of relative humidity supplements the influence of ambient humidity.

[0046] The parameters are multiplied to form a coordinated correction, avoiding the limitations of single-factor evaluation. This calculation method solves the problem of traditional models ignoring the coupling of multiple factors, transforming attenuation correction from empirical estimation to quantitative calculation. This provides a scientific basis for wireless communication power regulation, ensuring communication signal stability in complex environments while avoiding unnecessary power consumption.

[0047] Traditional wireless communication smart gas meter controllers often set control parameters based on a single factor, failing to comprehensively consider the combined effects of signal attenuation, circuit corrosion, and stray currents. This results in a lack of comprehensive control strategies. Furthermore, control parameters are weakly correlated with actual environmental changes, making dynamic adaptation difficult, impacting communication stability, metering accuracy, and device lifespan.

[0048] In the formula Represents the concentration of hydrogen sulfide in ppm. As a corrosive gas, hydrogen sulfide concentration in the gas meter environment is a key factor affecting circuit corrosion.

[0049] Divide it by , is to normalize the hydrogen sulfide concentration value and highlight its contribution to the corrosion risk. For example, when the hydrogen sulfide concentration is hour, Compared with the case of lower hydrogen sulfide concentration, the value of this part increases, reflecting the aggravating effect of increased hydrogen sulfide concentration on the circuit corrosion risk.

[0050] Relative humidity has a significant impact on circuit corrosion. When the humidity is high, water film is easily formed on the circuit surface, which accelerates the corrosion process. After The power operation and exponential form can more accurately reflect the nonlinear relationship between relative humidity and corrosion risk.

[0051] Because in reality, as humidity increases, the corrosion risk does not increase in a simple linear manner, but increases at an accelerated rate. The power setting is based on a large amount of experimental data and the research and analysis of circuit corrosion in actual environments to accurately quantify the impact of relative humidity on corrosion risk. For example, when the relative humidity is hour, Compared with the case of low relative humidity, the values ​​of this part are significantly increased, highlighting the effect of increased relative humidity on the corrosion risk.

[0052] It is the ambient temperature. Temperature has an important influence on the rate of chemical reactions. During the circuit corrosion process, changes in temperature will affect the speed of the corrosion reaction.

[0053] In the formula This item reflects the effect of temperature. As the reference temperature, when the ambient temperature is higher than hour, is a positive value, the exponential term is greater than , indicating that temperature increase will accelerate the corrosion reaction and increase the risk of circuit corrosion; when the ambient temperature is lower than hour, is a negative value, the exponential term is less than , but it will still have an impact on the corrosion risk, but it will relatively slow down the corrosion rate.

[0054] For example, when the ambient temperature is hour, , indicating that the temperature rises , which will increase the contribution of this part to the corrosion risk. is the stray current density. The stray current will accelerate the corrosion of the circuit through electrochemical action. This part reflects the influence of stray current density.

[0055] When the stray current density is When , which indicates the baseline state when there is no stray current influence; as the stray current density increases, The value of increases, multiplied by Then add , the overall value increases, reflecting that the increase in stray current density will increase the risk of circuit corrosion. For example, when the stray current density is hour, , demonstrating the effect of stray current on circuit corrosion risk. This formula comprehensively considers four factors closely related to circuit corrosion: hydrogen sulfide concentration, relative humidity, ambient temperature, and stray current density. Through a rational combination of mathematical operations, it comprehensively and accurately quantifies the circuit corrosion risk factor. Using this formula, technicians in the relevant technical field can accurately calculate the circuit corrosion risk factor based on the actual monitored parameter values, providing a scientific basis for taking appropriate anti-corrosion measures.

[0056] Based on this, the process of the microprocessor calculating the coordinated control parameters is: based on the circuit corrosion risk coefficient, the soil signal attenuation correction coefficient and the parameters collected by the stray current monitoring unit, through the formula: ; Calculate the coordinated control parameters ;in, Regulate power based on the benchmark; is the soil signal attenuation correction factor; is the circuit corrosion risk factor; is the stray current density; It is the change of soil resistivity in the past 10 minutes.

[0057] In the formula Represents the concentration of hydrogen sulfide. As a corrosive gas, the concentration of hydrogen sulfide in the environment where the gas meter is located is a key factor affecting circuit corrosion. Divide it by , is to normalize the hydrogen sulfide concentration value and highlight its contribution to the corrosion risk.

[0058] For example, when the hydrogen sulfide concentration is hour, Compared with the case of lower hydrogen sulfide concentration, the value of this part increases, reflecting the aggravating effect of increased hydrogen sulfide concentration on the circuit corrosion risk.

[0059] Relative humidity has a significant impact on circuit corrosion. When the humidity is high, water film is easily formed on the circuit surface, which accelerates the corrosion process. After The power operation and exponential form can more accurately reflect the nonlinear relationship between relative humidity and corrosion risk.

[0060] Because in reality, as humidity increases, the corrosion risk does not increase in a simple linear manner, but increases at an accelerated rate. The power setting is based on research and analysis of a large amount of experimental data and circuit corrosion conditions in actual environments to accurately quantify the impact of relative humidity on corrosion risk.

[0061] For example, when the relative humidity is hour, Compared with the case of low relative humidity, the values ​​of this part are significantly increased, highlighting the effect of increased relative humidity on the corrosion risk.

[0062] Is the ambient temperature. Temperature has an important influence on the chemical reaction rate. In the circuit corrosion process, temperature changes will affect the speed of the corrosion reaction. This item reflects the effect of temperature.

[0063] by As the reference temperature, when the ambient temperature is higher than hour, is a positive value, the exponential term is greater than , indicating that temperature increase will accelerate the corrosion reaction and increase the risk of circuit corrosion; when the ambient temperature is lower than hour, is a negative value, the exponential term is less than , but it will still have an impact on the corrosion risk, but it will relatively slow down the corrosion rate.

[0064] For example, when the ambient temperature is hour, , indicating that the temperature rises , which will increase the contribution of this part to the corrosion risk. is the stray current density. Stray current will accelerate the corrosion of the circuit through electrochemical action.

[0065] in the formula This part reflects the influence of stray current density. When the stray current density is When , which indicates the baseline state when there is no stray current influence; as the stray current density increases, The value of increases, multiplied by Then add , the overall value increases, reflecting that the increase in stray current density will increase the risk of circuit corrosion.

[0066] For example, when the stray current density is hour, , showing the effect of stray current on the corrosion risk of circuits.

[0067] This formula comprehensively considers four factors closely related to circuit corrosion: hydrogen sulfide concentration, relative humidity, ambient temperature, and stray current density. Through a reasonable combination of mathematical operations, it comprehensively and accurately quantifies the circuit corrosion risk factor.

[0068] This solution constructs a collaborative control model through multi-dimensional parameter coupling. The soil signal attenuation correction coefficient term in the formula is used to adapt to the wireless communication environment, the exponential term of the circuit corrosion risk coefficient quantifies the anti-corrosion requirements, the stray current term compensates for metering interference, and the soil resistivity change term responds to dynamic environmental changes.

[0069] Each parameter is comprehensively controlled through a multiplication relationship, forming a progressive influence logic. This calculation method solves the problem of traditional single control parameters, transforming control from independent decision-making to collaborative optimization, providing a quantitative basis for subsequent dynamic control, ensuring the coordinated operation of communication, measurement, and anti-corrosion systems, and adapting to the dynamic changes of complex environments.

[0070] Traditional wireless communication smart gas meter controllers often use fixed control processes, with weak correlation between parameter acquisition, calculation, and control. This makes it impossible to achieve real-time adaptation to environmental changes and control strategies. Furthermore, unclear control trigger conditions make it difficult to flexibly adjust to dynamic changes in environmental parameters, affecting the system's adaptability and stability.

[0071] Based on this, see Figure 2 The control method of the wireless communication intelligent gas meter controller includes the following steps: S1. Parameter acquisition: the electrochemical corrosion monitoring module acquires hydrogen sulfide concentration, relative humidity and ambient temperature and transmits them to the microprocessor, the soil resistivity acquisition device acquires soil resistivity and the distance between the gas meter and the receiving base station and transmits them to the microprocessor, and the stray current monitoring unit acquires stray current density and the change in soil resistivity in the past 10 minutes and transmits them to the microprocessor; S2. Coefficient calculation: the microprocessor calculates the circuit corrosion risk coefficient based on the hydrogen sulfide concentration, relative humidity, ambient temperature and stray current density acquired in S1, and then calculates the soil signal attenuation correction coefficient based on the soil resistivity, distance and relative humidity, and finally calculates the coordinated control parameter based on the stray current density and the change in soil resistivity; S3. Dynamic control: when the coordinated control parameter is greater than a first threshold, the microprocessor controls the wireless communication unit to increase the transmission power, the metering unit to switch to the anti-stray current mode, and the anti-corrosion control unit to start heating; when the coordinated control parameter is less than a second threshold, the microprocessor controls the wireless communication unit to reduce the transmission power, the metering unit to switch to the normal mode, and the anti-corrosion control unit to stop heating, wherein the first threshold is greater than the second threshold.

[0072] This solution achieves dynamic adaptation of environmental parameters to system operations by constructing a closed-loop control logic for data collection, calculation, and regulation. The parameter collection phase ensures comprehensive acquisition of multi-dimensional data, the coefficient calculation phase achieves quantitative conversion of parameters through a progressive model, and the dynamic regulation phase achieves precise switching of strategies based on threshold logic. A close logical connection is formed between each step, with parameter collection providing the basis for calculation, and the calculation results providing the basis for regulation. This approach solves the problem of weak correlation in traditional control processes, enabling the system to adjust its operating status in real time according to environmental changes. While ensuring stable communication and accurate measurement, it also extends the life of the equipment and adapts to the diverse needs of complex environments.

[0073] Traditional metering units' anti-interference modes often rely on fixed filtering, which fails to specifically suppress interference introduced by stray currents. Furthermore, their fixed sampling frequency makes it difficult to balance metering accuracy with data processing pressure. Furthermore, the lack of a clear technical path for enabling and operating anti-interference modes results in poor interference suppression, impacting metering accuracy.

[0074] Based on this, the control method implements the stray current resistance mode as follows: After receiving the mode switching signal, the metering unit activates the built-in differential amplifier circuit and power frequency trap circuit. The differential amplifier circuit suppresses common-mode interference of the metering signal, and the power frequency trap circuit filters the 50Hz power frequency interference signal. Simultaneously, the metering unit increases the sampling frequency to reduce the metering error introduced by stray current coupling. This solution achieves stray current resistance through the synergy of hardware circuits and sampling strategies. The differential amplifier circuit specifically suppresses common-mode interference, reducing the error introduced by stray current through spatial coupling; the power frequency trap circuit specifically filters out 50Hz power frequency interference, avoiding the influence of stray currents from the power grid. The increased sampling frequency improves metering accuracy by increasing the amount of data, reducing accidental errors.

[0075] The various technical approaches form a collaborative anti-interference logic, with the hardware circuit suppressing specific interference and the sampling strategy improving data reliability. This implementation addresses the lack of specificity in traditional anti-interference methods, enabling the metering unit to effectively resist stray current interference, ensuring the accuracy of metering data and providing reliable support for gas metering while avoiding the cost increases associated with excessive hardware performance improvements.

[0076] Traditional anti-corrosion control units often use a fixed power approach for heating control, failing to adjust heating intensity based on dynamic changes in corrosion risk. This results in either insufficient heating, ineffective corrosion prevention, or excessive heating, resulting in energy waste. Furthermore, heating control fails to consider the impact of ambient temperature, making overheating a risk, impacting equipment safety and lifespan.

[0077] Based on this, the heating control logic of the anti-corrosion control unit in the control method is: the anti-corrosion control unit adjusts the working duty cycle of the heating component according to the heating intensity control signal, and the working duty cycle is positively correlated with the circuit corrosion risk coefficient; when the ambient temperature exceeds the preset value, the anti-corrosion control unit automatically reduces the working duty cycle to avoid overheating, and when the ambient temperature is lower than the preset value, the working duty cycle is restored to match the circuit corrosion risk coefficient.

[0078] This solution achieves precise control of anti-corrosion heating through dynamic duty cycle adjustment. The positive correlation between duty cycle and corrosion risk ensures that heating intensity matches anti-corrosion requirements, while feedback adjustment of ambient temperature prevents overheating risks. These two factors work together to form a demand-driven, safety-backed control logic, ensuring effective anti-corrosion while balancing energy consumption and safety. This control logic addresses the rigid nature of traditional heating control, shifting anti-corrosion regulation from passive protection to active adaptation. This effectively delays circuit corrosion while reducing energy consumption, extending equipment life, and adapting to anti-corrosion requirements in diverse environmental conditions.

[0079] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A wireless communication smart gas meter controller, characterized in that: It includes a microprocessor, an electrochemical corrosion monitoring module, a soil resistivity acquisition device, a stray current monitoring unit, a wireless communication unit, a metering unit and an anti-corrosion control unit; The signal output ends of the electrochemical corrosion monitoring module, the soil resistivity acquisition device, and the stray current monitoring unit are respectively electrically connected to the signal input ends of the microprocessor, and the control output ends of the microprocessor are respectively electrically connected to the control input ends of the wireless communication unit, the metering unit, and the anti-corrosion control unit; The electrochemical corrosion monitoring module is used to collect hydrogen sulfide concentration, relative humidity and ambient temperature parameters and convert the analog signals into digital signals and transmit them to the microprocessor. The soil resistivity acquisition device is used to collect soil resistivity and the distance parameters between the gas meter and the receiving base station and transmit them to the microprocessor. The stray current monitoring unit is used to collect stray current density and soil resistivity change parameters and transmit them to the microprocessor. Based on the above-collected calculation circuit corrosion risk coefficient, soil signal attenuation correction coefficient and coordinated control parameters, the microprocessor outputs a power adjustment signal to the wireless communication unit, an anti-interference mode switching signal to the metering unit, and a heating intensity control signal to the anti-corrosion control unit based on the coordinated control parameters to achieve dynamic control of environmental adaptability.

2. The wireless communication intelligent gas meter controller according to claim 1, characterized in that: The electrochemical corrosion monitoring module includes a hydrogen sulfide sensor component, a temperature and humidity sensor component and a signal conditioning circuit. The output end of the hydrogen sulfide sensor component and the output end of the temperature and humidity sensor component are respectively electrically connected to the input end of the signal conditioning circuit, and the output end of the signal conditioning circuit is electrically connected to the signal input end of the microprocessor; the hydrogen sulfide sensor component converts the hydrogen sulfide concentration into an electrical signal based on the electrochemical principle, and the temperature and humidity sensor component converts the relative humidity and ambient temperature into electrical signals. The signal conditioning circuit amplifies the input signal through an operational amplifier, filters out the high-frequency interference signal through an RC filter circuit, and then transmits it to the microprocessor.

3. The wireless communication intelligent gas meter controller according to claim 1, characterized in that: The soil resistivity acquisition device includes a resistivity detection component, a distance detection component, and a data conversion circuit. The output end of the resistivity detection component and the output end of the distance detection component are respectively electrically connected to the input end of the data conversion circuit, and the output end of the data conversion circuit is electrically connected to the signal input end of the microprocessor. The resistivity detection component obtains a soil resistivity signal by injecting a constant current into the soil and detecting the inter-electrode voltage. The distance detection component obtains a distance signal by laser ranging. The data conversion circuit converts the analog signal into a digital signal through an analog-to-digital converter and transmits the digital signal to the microprocessor.

4. The wireless communication intelligent gas meter controller according to claim 1, characterized in that: The stray current monitoring unit includes a current sensing component, a resistivity change detection component and a signal processing circuit. The output end of the current sensing component and the output end of the resistivity change detection component are respectively electrically connected to the input end of the signal processing circuit, and the output end of the signal processing circuit is electrically connected to the signal input end of the microprocessor. The current sensing component detects stray current density signals based on the Hall effect. The resistivity change detection component obtains a resistivity change signal by continuously collecting soil resistivity and calculating the change. The signal processing circuit suppresses power frequency interference through a low-pass filter, adjusts the signal to a microprocessor-compatible level through a level conversion circuit, and then transmits it to the microprocessor.

5. The wireless communication intelligent gas meter controller according to claim 1, characterized in that: The process of the microprocessor calculating the circuit corrosion risk coefficient is as follows: Based on the parameters collected by the electrochemical corrosion monitoring module and the stray current monitoring unit, the formula is: ； Calculate the circuit corrosion risk factor ; in, is the concentration of hydrogen sulfide; is the relative humidity; is the ambient temperature; is the stray current density.

6. The wireless communication intelligent gas meter controller according to claim 1, characterized in that: The process of the microprocessor calculating the soil signal attenuation correction coefficient is as follows: Based on the parameters collected by the soil resistivity acquisition device and the circuit corrosion risk coefficient, the formula is: ； Calculate the soil signal attenuation correction coefficient ;in, is the soil resistivity; is the circuit corrosion risk factor; is the distance between the gas meter and the receiving base station; is the relative humidity.

7. The wireless communication intelligent gas meter controller according to claim 1, characterized in that: The process of calculating the coordinated control parameters by the microprocessor is as follows: based on the circuit corrosion risk coefficient, the soil signal attenuation correction coefficient and the parameters collected by the stray current monitoring unit, the formula: ； Calculate the coordinated control parameters ;in, Regulate power based on the benchmark; is the soil signal attenuation correction factor; is the circuit corrosion risk factor; is the stray current density; It is the change of soil resistivity in the past 10 minutes.

8. The control method of the wireless communication intelligent gas meter controller according to claim 1, characterized in that: The following steps are involved: S1. Parameter Collection: The electrochemical corrosion monitoring module collects hydrogen sulfide concentration, relative humidity, and ambient temperature and transmits them to the microprocessor. The soil resistivity acquisition device collects soil resistivity and the distance between the gas meter and the receiving base station and transmits them to the microprocessor. The stray current monitoring unit collects stray current density and the change in soil resistivity over the past 10 minutes and transmits them to the microprocessor. S2. Coefficient calculation: The microprocessor calculates the circuit corrosion risk coefficient based on the hydrogen sulfide concentration, relative humidity, ambient temperature, and stray current density collected in S1. It then calculates the soil signal attenuation correction coefficient based on the soil resistivity, distance, and relative humidity. Finally, it calculates the coordinated control parameters based on the stray current density and soil resistivity change. S3. Dynamic control: When the collaborative control parameter is greater than the first threshold, the microprocessor controls the wireless communication unit to increase the transmission power, the metering unit to switch to the anti-stray current mode, and the anti-corrosion control unit to start heating; when the collaborative control parameter is less than the second threshold, the microprocessor controls the wireless communication unit to reduce the transmission power, the metering unit to switch to the normal mode, and the anti-corrosion control unit to stop heating, wherein the first threshold is greater than the second threshold.

9. The control method of the wireless communication intelligent gas meter controller according to claim 8, characterized in that: The implementation process of the anti-stray current mode in step S3 is as follows: after the metering unit receives the mode switching signal, it activates the built-in differential amplifier circuit and the power frequency trap circuit. The differential amplifier circuit suppresses common-mode interference of the metering signal, and the power frequency trap circuit filters the 50 Hz power frequency interference signal. At the same time, the metering unit increases the sampling frequency to reduce the metering error introduced by stray current coupling.

10. The control method of the wireless communication intelligent gas meter controller according to claim 8, characterized in that: The heating control logic of the anti-corrosion control unit in step S3 is as follows: the anti-corrosion control unit adjusts the working duty cycle of the heating component according to the heating intensity control signal, and the working duty cycle is positively correlated with the circuit corrosion risk coefficient; When the ambient temperature exceeds the preset value, the anti-corrosion control unit automatically reduces the working duty cycle to avoid overheating. When the ambient temperature is lower than the preset value, the working duty cycle is restored to match the circuit corrosion risk factor.

Images