A diaphragm gas meter and system based on wireless energy transmission

Abstract

This invention discloses a diaphragm gas meter and system based on wireless power transmission, relating to the field of gas metering equipment technology. It includes a gas meter body with a built-in power receiving module and an external power supply device connected externally. Furthermore, a wireless power supply system for the diaphragm gas meter is deployed internally within the gas meter body. This invention completely eliminates the need for traditional batteries, eliminating the need for regular battery replacements, significantly reducing maintenance costs and manpower. It is particularly suitable for locations with concealed or inaccessible installations, improving the convenience of gas meter use. The invention employs a double insulation structure: an insulating outer shell and insulating isolation layer for the external power supply device, and an insulating isolation layer for the built-in power receiving module. This ensures safe isolation between the internal circuitry of the gas meter and the external power source, effectively preventing safety hazards such as electrical sparks. It meets the usage requirements of industrial sites with strict explosion-proof requirements, ensuring the safety of personnel and equipment.

Description

Technical Field

[0001] This invention relates to the field of gas metering equipment technology, specifically to a diaphragm gas meter and system based on wireless energy transmission. Background Technology

[0002] As a common gas metering device, diaphragm gas meters require a stable power supply for their internal electronic components, such as sensors, displays, and data transmission modules. Currently, diaphragm gas meters are mainly powered by traditional batteries or external wired power sources. Traditional battery power supply has many drawbacks. First, batteries have limited power and need to be replaced regularly, which not only increases maintenance costs and manpower, but also makes battery replacement difficult in some concealed or hard-to-reach locations. Second, improper disposal of used batteries can pollute the environment, which is inconsistent with the concept of sustainable development. While external wired power supply can provide stable power, it requires laying cables, which is difficult and costly to install. Furthermore, in some industrial sites with strict explosion-proof requirements, the presence of cables may pose safety hazards, such as electric sparks causing gas explosions, making it unsuitable for use in special environments.

[0003] With the development of wireless power transmission technology, although some devices have begun to try to use this technology for power supply, existing wireless power supply solutions have problems such as low energy transmission efficiency and insufficient safety isolation performance when applied to diaphragm gas meters. Specifically, in scenarios where multiple gas meters are deployed in clusters, the wireless power supply system suffers from magnetic field coupling interference between adjacent transmitting coils, which can easily lead to significant voltage fluctuations at each receiving end and a decrease in metering consistency. Furthermore, multiple gas meters in the same area may experience response delays in energy management chips due to data transmission timing conflicts, further affecting the real-time performance of power supply and collaborative management efficiency. To address these issues, a diaphragm gas meter and system based on wireless power transmission is proposed. Summary of the Invention

[0004] To solve the above-mentioned technical problems, the present invention is implemented through the following technical solution: First aspect, a diaphragm gas meter based on wireless energy transmission includes a gas meter body, the gas meter body is equipped with a built-in power receiving module, the gas meter body is externally connected to an external power supply device, and the gas meter body is internally deployed with a diaphragm gas meter wireless power supply system.

[0005] The external power supply device includes a first insulating shell, inside which a transmitting coil, an external power supply, and a driving circuit are installed. The first insulating shell is a high-strength polytetrafluoroethylene insulating shell, and an insulating rubber layer of not less than 5 mm thick is provided on the outer wall of the first insulating shell as the first insulating isolation layer. The transmitting coil is made of high-permeability enameled wire and has a flat circular structure. It is installed on the outside of the gas meter body near the edge. The transmitting coil is connected to an external power supply (such as a DC power supply after step-down rectification of the mains power). Its function is to convert electrical energy into an alternating magnetic field. The driving circuit is connected to the transmitting coil and the external power supply and is used to generate a high-frequency alternating current to drive the transmitting coil to generate an alternating magnetic field.

[0006] The built-in power receiving module includes a detachable second insulating shell installed inside the gas meter body. A second insulating layer is installed on the inner wall of the second insulating shell. Inside the second insulating shell are a rectifier and filter circuit, an energy management chip, and a receiving coil. The receiving coil corresponds to the transmitting coil, is wound with high-permeability enameled wire, and is installed inside the gas meter body opposite to the transmitting coil. The receiving coil receives the alternating magnetic field generated by the transmitting coil through electromagnetic induction and converts it into electrical energy. The size and parameters of the receiving coil are matched with those of the transmitting coil to improve energy reception efficiency. The rectifier and filter circuit is connected to the receiving coil and converts the alternating current output by the receiving coil into a stable DC voltage. The rectifier and filter circuit uses a combination of a bridge rectifier circuit and a capacitor filter circuit to effectively remove noise and output a stable DC power supply to power the electronic components inside the gas meter. The energy management chip connects the rectifier and filter circuit and the electronic components inside the gas meter for monitoring and managing electrical energy.

[0007] Secondly, a wireless power supply system for a diaphragm gas meter based on wireless energy transmission is provided, which is applied to the aforementioned diaphragm gas meter based on wireless energy transmission, and includes a power supply management controller, wherein the power supply management controller is communicatively connected to the following modules:

[0008] The directional beamforming control module, based on an improved adaptive beamforming algorithm and a dynamic magnetic field strength compensation model, monitors and adjusts the phase and power of multiple transmitting coils in a multi-gas meter cluster deployment scenario in real time, performs directional energy transmission and voltage stability control, ensures consistent voltage at each receiving end, and improves power supply accuracy and anti-interference capability.

[0009] The energy scheduling decision module introduces a time-division multiplexing-based dynamic priority scheduling algorithm and energy state prediction model. Based on the real-time power consumption and load changes of each diaphragm gas meter in the multi-gas meter cluster, it dynamically allocates communication windows and power supply strategies to optimize system response speed and collaborative management efficiency.

[0010] The collaborative transmission optimization module is used to combine the communication window and power supply strategy allocated to each diaphragm gas meter, dynamically select the optimal energy transmission path according to the real-time location and load changes of each meter, and combine the output frequency (100kHz) adjustment of the high-frequency inverter circuit to perform real-time optimization of the many-to-many energy transmission path, ensuring efficient and stable energy transmission within a distance of ≤5cm, adaptively selecting the best transmission link and tuning the frequency to maximize the overall efficiency of many-to-many parallel transmission.

[0011] The status integration monitoring module is used to collect voltage, current and power data of each meter in real time through the NFC communication module. Combined with the energy management chip, it performs anomaly diagnosis and early warning to ensure the safe and reliable operation of the power supply system. It realizes real-time visualization of system status, intelligent early warning and remote collaborative control, builds a closed loop of system health perception and early warning, and realizes intelligent operation and maintenance from monitoring to management.

[0012] Preferably, the directional shaping control module includes a phase coordination unit and a power compensation unit;

[0013] The phase coordination unit is used to drive multiple transmitting coils in a multi-gas meter cluster deployment scenario using a high-frequency inverter circuit, collect voltage feedback from each receiving coil in real time, and dynamically adjust the phase and frequency of the transmitted signal in combination with an improved adaptive beamforming algorithm to reduce magnetic field coupling interference between adjacent coils, achieve directional energy focusing, effectively suppress magnetic field crosstalk between multiple transmitting sources, and improve the energy density and transmission accuracy of the target area.

[0014] The power compensation unit is used to combine the stable DC output from the DC adapter and construct a dynamic magnetic field strength compensation model using a dynamic magnetic field strength compensation algorithm. Based on the real-time load and distance changes of each diaphragm gas meter, it calculates the instantaneous drive power compensation required for each transmitting coil to automatically adjust the transmitting power, ensuring that the receiving end voltage is stable within the 12V / 2A output range, dynamically offsetting transmission attenuation and load fluctuations, and ensuring that the receiving voltage of each meter is highly consistent and stable.

[0015] Preferably, the phase coordination unit performs the following steps:

[0016] By using a high-frequency inverter circuit to drive multiple transmitting coils in a multi-gas meter cluster deployment scenario, the voltage feedback signals of each receiving coil are collected in real time, and the initial phase and frequency status of the transmitting signal are monitored synchronously to achieve accurate synchronous capture and correlation of the initial status, thus establishing a reliable data benchmark for subsequent dynamic control.

[0017] Based on the voltage feedback of the receiving coil, an improved adaptive beamforming algorithm is adopted to dynamically calculate the required phase offset and frequency adjustment value of each transmitting coil in order to suppress magnetic field coupling interference between adjacent transmitting coils. By actively suppressing magnetic field coupling interference between adjacent coils through the algorithm, the purity of spatial energy distribution is significantly improved.

[0018] Based on the calculated phase and frequency adjustment parameters, the output signal of the high-frequency inverter circuit is adjusted in real time to synchronize the phase of multiple transmitting coils and directional focus the energy beam. This ensures that the magnetic field strength distribution at the location of each receiving coil is uniform and stable, forming a dynamically focused directional energy beam, effectively guaranteeing the uniformity and stability of energy supply at each receiving end.

[0019] Preferably, the power compensation unit performs the following steps:

[0020] It receives a stable DC power supply from the DC adapter and acquires the voltage and current data of the receiving coil of each diaphragm gas meter in real time, as well as the real-time transmission distance information between each diaphragm gas meter and its corresponding transmitting coil. It establishes a complete data acquisition benchmark to ensure the accuracy and synchronization of the compensation calculation source.

[0021] A dynamic magnetic field strength compensation model is constructed using a dynamic magnetic field strength compensation algorithm. Based on the dynamic magnetic field strength compensation model, combined with the receiver voltage data, transmission distance and real-time load changes, the instantaneous drive power compensation required for each transmitting coil is calculated to offset the voltage deviation caused by distance changes or load fluctuations, thereby achieving accurate dynamic compensation based on the physical model and suppressing voltage fluctuations from the source.

[0022] Based on the power compensation calculation results, the driving power of the corresponding transmitting coil is dynamically adjusted to ensure that the output voltage of each receiving end is stably maintained within the rated output range of 12V / 2A within each transmission distance ≤5cm, thus ensuring power supply consistency, forming a closed-loop power control, and ensuring the consistency and stability of the output voltage under multi-load scenarios.

[0023] Preferably, the energy scheduling decision module includes a communication window allocation unit and a power supply strategy pre-adjustment unit;

[0024] The communication window allocation unit is used to perform information interaction between multiple meters through the NFC communication module (rate 106kbps), and to use a time-division multiplexing-based dynamic priority scheduling algorithm to allocate independent communication time sequences to each diaphragm gas meter, thereby avoiding data transmission conflicts, improving communication efficiency and collaborative management capabilities, realizing orderly reuse of channel resources, completely eliminating data collisions, and ensuring smooth and reliable cluster communication.

[0025] The power supply strategy pre-adjustment unit is used to predict the power consumption trend of each diaphragm gas meter based on the independent communication timing allocated to each diaphragm gas meter, combined with the real-time power monitoring data and load change data of each diaphragm gas meter output by the energy management chip, and using an energy state prediction model. It dynamically adjusts the power supply priority and power output to ensure timely response to critical tasks and continuous and stable power supply. Based on the prediction, it performs forward-looking resource scheduling, shifting from passive response to proactive protection and improving the real-time performance of power supply.

[0026] Preferably, the communication window allocation unit performs the following steps:

[0027] By using the NFC communication module at a communication rate of 106kbps, the system periodically collects the identification, real-time power status, and data queue length information of each diaphragm gas meter in the multi-gas meter cluster. This allows for a comprehensive acquisition of the real-time status of each meter, providing a data foundation for precise scheduling and ensuring that scheduling decisions are based on solid evidence.

[0028] Based on the collected information, a time-division multiplexing-based dynamic priority scheduling algorithm is adopted to allocate appropriate communication time windows to each gas meter. The dynamic priority score is dynamically calculated according to the urgency of electricity consumption and the importance of data. Through dynamic priority calculation, it is ensured that emergency tasks and critical data are transmitted first, thereby optimizing the system resource utilization efficiency.

[0029] The allocated communication timing table is broadcast to each diaphragm gas meter via the NFC communication module, enabling each diaphragm gas meter to complete data transmission in an orderly manner within the specified time window. This avoids communication conflicts between multiple meters, improves cluster communication efficiency, and completely avoids channel conflicts through timing broadcasting and strict time slot control, significantly improving the reliability and efficiency of concurrent communication between multiple meters.

[0030] Preferably, the power supply strategy pre-adjustment unit performs the following steps:

[0031] Based on the communication window allocation, the energy management chip acquires the power data, historical load curves and current working mode information of each diaphragm gas meter in real time, realizing the accurate synchronous acquisition of multi-dimensional status information and providing a comprehensive input basis for the prediction model;

[0032] The acquired electricity data, historical load curves, and current working mode information are input into a pre-built energy state prediction model to predict the electricity consumption trend and peak load periods of each gas meter in the future. Based on time series analysis and pattern matching, accurate prediction of future load changes in the cluster is achieved.

[0033] Based on the prediction results, the power supply management controller dynamically adjusts the power supply priority and power allocation strategy for each diaphragm gas meter, pre-allocates high power supply weight to diaphragm gas meters that are about to enter a high load or low power state, ensures the timeliness and continuity of the system's power supply response, and realizes resource pre-allocation through a feedforward control mechanism, significantly improving the real-time response and continuity guarantee capability of the power supply system.

[0034] Preferably, the cooperative transmission optimization module performs the following steps:

[0035] Real-time acquisition of the location information, current load status, and relative distance and alignment angle between the corresponding transmitting and receiving coils of each diaphragm gas meter in a multi-gas meter cluster is used to form a basic sequence list and build a dynamic global view, providing a reliable data foundation for real-time path optimization and supporting rapid decision-making.

[0036] Based on relevant information in the basic sequence table and combined with the preset transmission efficiency model, the optimal energy transmission path of each gas meter at the current moment is dynamically calculated and selected. Priority is given to selecting coil pairs that are close, well aligned and matched to the load for energy transmission. The best path is intelligently selected to maximize the transmission efficiency of a single point, reduce energy waste and improve the overall energy efficiency of the cluster.

[0037] Based on the selected optimal energy transmission path, the output frequency of the high-frequency inverter circuit corresponding to the transmitting coil is dynamically adjusted to the optimal resonance point near 100kHz, and the working timing of multiple transmitting coils is coordinated to achieve high efficiency and stability of multi-to-multi energy transmission within a distance of ≤5cm. Through precise frequency tuning and coordinated timing, multiple links can work in parallel and stably, significantly enhancing system capacity and robustness.

[0038] Preferably, the status integration monitoring module performs the following steps:

[0039] By using the NFC communication module to periodically poll or trigger events, the system collects real-time voltage, current, and battery power data from the receiver of each diaphragm gas meter, as well as chip temperature and operating status flags reported by the energy management chip, to ensure that the system can obtain multi-dimensional operating status data of each node in the cluster in real time and completely.

[0040] The collected data is compared and analyzed with preset safety thresholds and normal operating ranges. The diagnostic logic built into the energy management chip is used to identify potential fault types, including voltage abnormality, current over-limit and power shortage, so as to achieve accurate abnormality identification and automatic classification of fault types.

[0041] When an abnormal state is diagnosed or a threshold is predicted to be exceeded, corresponding early warning information is generated and uploaded to the power management controller via the NFC communication module. At the same time, remote command issuance is supported, realizing a closed loop of real-time status visualization, intelligent early warning and collaborative control of the power supply system. This builds a closed-loop management system from proactive early warning to remote control, improving operation and maintenance response efficiency and system reliability.

[0042] This invention provides a diaphragm gas meter and system based on wireless energy transmission. It has the following beneficial effects:

[0043] (I) This diaphragm gas meter and system based on wireless energy transmission completely eliminates the need for traditional batteries, eliminating the need for regular battery replacements, greatly reducing maintenance costs and manpower input. It is especially suitable for installation locations that are concealed or difficult to access, improving the convenience of using the gas meter. It also adopts a double insulation structure, with an insulating shell and insulating isolation layer for the external power supply device, as well as an insulating isolation layer for the built-in power receiving module, ensuring safe isolation between the internal circuit of the gas meter and the external power supply, effectively preventing the generation of safety hazards such as electric sparks, meeting the usage requirements of industrial sites with strict explosion-proof requirements, and ensuring the safety of personnel and equipment.

[0044] (II) The diaphragm gas meter and system based on wireless energy transmission, based on an improved adaptive beamforming algorithm and a dynamic magnetic field strength compensation model, monitors and dynamically adjusts the phase and power of multiple transmitting coils in real time, realizes directional energy transmission and voltage stability control, significantly suppresses magnetic field coupling interference between adjacent transmitting coils, avoids voltage fluctuations at the receiving end caused by spatial magnetic field crossing, and ensures that the receiving end voltage of each diaphragm gas meter remains highly consistent in scenarios with dense deployment of multiple meters, thereby improving metering accuracy and the anti-interference capability of the power supply system.

[0045] (III) The diaphragm gas meter and system based on wireless energy transmission, combined with a time-division multiplexing-based dynamic priority scheduling algorithm and energy state prediction model, dynamically allocates communication windows and pre-adjusts power supply strategies according to the real-time power consumption, load status and task urgency of each diaphragm gas meter, effectively avoids data transmission conflicts between multiple meters, optimizes system response speed and resource utilization efficiency, realizes collaborative management of communication and power supply, and significantly improves the overall operating efficiency and reliability of the system under complex working conditions.

[0046] (iv) The diaphragm gas meter and system based on wireless energy transmission dynamically selects the optimal energy transmission path according to the real-time location, load status and coil alignment of each gas meter, and combines the frequency tuning of the high-frequency inverter circuit to realize real-time optimization of the multi-to-multi energy transmission link. This enables the system to adaptively adjust the transmission path and operating parameters within a limited transmission distance, maintain efficient and stable energy transmission, and ensure continuous and reliable power supply for all diaphragm gas meters in the cluster. Attached Figure Description

[0047] Figure 1 This is a schematic diagram illustrating the working process of a wireless power supply system for a diaphragm gas meter based on wireless energy transmission according to the present invention.

[0048] Figure 2 This is a data flow diagram of a wireless power supply system for a diaphragm gas meter based on wireless energy transmission, according to the present invention.

[0049] Figure 3 This is a schematic diagram of the external structure of a diaphragm gas meter based on wireless energy transmission according to the present invention.

[0050] In the diagram: 1. Gas meter body; 2. Built-in power receiving module; 3. External power supply device. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Example 1, please refer to Figures 1 to 3 The present invention provides a technical solution: a diaphragm gas meter based on wireless energy transmission, including a gas meter body 1, an internally installed power receiving module 2, an external power supply device 3 connected to the outside of the gas meter body 1, and a diaphragm gas meter wireless power supply system deployed inside the gas meter body 1, realizing the separation of internal and external modules, fundamentally eliminating the risk of sparks that may be introduced by wired power supply, and meeting explosion-proof requirements.

[0053] The external power supply device 3 includes a first insulating shell, inside which a transmitting coil, an external power supply, and a drive circuit are installed. The first insulating shell is a high-strength polytetrafluoroethylene (PTFE) insulating shell, and an insulating rubber layer of not less than 5 mm thick is provided on the outer wall of the first insulating shell as the first insulating isolation layer. The transmitting coil is made of high-permeability enameled wire and has a flat circular structure. It is installed on the outside of the gas meter body 1 near the edge. The transmitting coil is connected to an external power supply (such as DC power after step-down rectification of mains power), and its function is to convert electrical energy into an alternating magnetic field. The drive circuit connects the transmitting coil and the external power supply and is used to generate a high-frequency alternating current to drive the transmitting coil to generate an alternating magnetic field. The drive circuit adopts high-frequency switching power supply technology and can automatically adjust the power consumption according to the gas meter. The output power is adjusted to ensure stable power supply and improve energy transmission efficiency. In addition, the drive circuit has overcurrent and overvoltage protection functions. When the circuit is abnormal, it can automatically cut off the power supply to ensure system safety. The transmitting coil and drive circuit are encapsulated in the first insulating shell. The first insulating shell is made of high-strength, high-temperature resistant engineering plastic material with good insulation performance. The protection level of the shell reaches IP67 or above, which can effectively prevent dust, water vapor and other substances from entering the internal circuit, and at the same time play a mechanical protection role. A first insulating isolation layer is set between the first insulating shell and the gas meter body 1 shell to further enhance the insulation performance between the external power supply and the internal circuit of the gas meter. All high voltage and drive circuits are sealed in the insulating shell to achieve safe isolation from dangerous areas and facilitate maintenance.

[0054] The built-in power receiving module 2 includes a second insulating shell that is detachably installed inside the gas meter body 1. A second insulating layer is installed on the inner wall of the second insulating shell. Inside the second insulating shell are a rectifier and filter circuit, an energy management chip, and a receiving coil. The receiving coil corresponds to the transmitting coil and is wound with high-permeability enameled wire. It is installed inside the gas meter body 1 opposite to the transmitting coil. The receiving coil receives the alternating magnetic field generated by the transmitting coil through electromagnetic induction and converts it into electrical energy. The size and parameters of the receiving coil are matched with those of the transmitting coil to improve energy reception efficiency. The rectifier and filter circuit is connected to the receiving coil and converts the alternating current output by the receiving coil into a stable DC voltage. The rectifier and filter circuit uses a bridge rectifier. The combination of rectifier and capacitor filter circuits effectively removes noise and outputs a stable DC power supply to power the internal electronic components of the gas meter. The energy management chip connects the rectifier filter circuit and the internal electronic components of the gas meter to monitor and manage electrical energy. The energy management chip can automatically adjust the output voltage and current according to the power demand of different working states of the gas meter (such as normal metering, data transmission, etc.) to achieve reasonable distribution of electrical energy. At the same time, the energy management chip also has a low power warning function. When the received power is insufficient, it will send a signal in time to remind users or maintenance personnel to check the working status of the external wireless charging module. The second insulating shell and the insulating layer form a double layer of protection to ensure that the internal circuit is completely isolated from the gas environment, ensuring high safety.

[0055] The dual protective structure of the first insulating shell and the first insulating isolation layer, as well as the second insulating shell and the second insulating isolation layer, can not only effectively prevent the influence of the external environment on the circuit, but also ensure that the internal circuit of the gas meter is completely isolated from the external power supply. The protection level reaches IP67 or above, which meets the requirements of industrial sites with strict explosion-proof requirements and solves the safety hazards of traditional power supply methods in special environments.

[0056] Example 2, as Figures 1 to 3 As shown, based on Embodiment 1, the present invention also provides a wireless power supply system for a diaphragm gas meter based on wireless energy transmission, which is applied to the aforementioned diaphragm gas meter based on wireless energy transmission. The system includes a power management controller, which is communicatively connected to the following modules:

[0057] The directional beamforming control module, based on an improved adaptive beamforming algorithm and a dynamic magnetic field strength compensation model, monitors and adjusts the phase and power of multiple transmitting coils in a multi-gas meter cluster deployment scenario in real time to perform directional energy transmission and voltage stability control, ensuring consistent voltage at each receiving end, improving power supply accuracy and anti-interference capability. The directional beamforming control module includes a phase coordination unit and a power compensation unit.

[0058] The phase coordination unit is used to drive multiple transmitting coils in a multi-gas meter cluster deployment scenario using a high-frequency inverter circuit. It collects the voltage feedback from each receiving coil in real time and dynamically adjusts the phase and frequency of the transmitted signal using an improved adaptive beamforming algorithm. This reduces magnetic field coupling interference between adjacent coils, achieves directional energy focusing, effectively suppresses magnetic field crosstalk between multiple transmitters, and improves the energy density and transmission accuracy in the target area. The unit also uses a high-frequency inverter circuit to drive multiple transmitting coils in a multi-gas meter cluster deployment scenario, collects the voltage feedback signals from each receiving coil in real time, and synchronously monitors the initial phase and frequency state of the transmitted signal. This achieves accurate synchronous capture and correlation of the initial state, providing a basis for subsequent dynamic adjustment. To establish a reliable data benchmark, based on the voltage feedback of the receiving coil, an improved adaptive beamforming algorithm is adopted to dynamically calculate the required phase offset and frequency adjustment value of each transmitting coil in order to suppress magnetic field coupling interference between adjacent transmitting coils. By actively suppressing magnetic field coupling interference between adjacent coils through the algorithm, the purity of spatial energy distribution is significantly improved. According to the calculated phase and frequency adjustment parameters, the output signal of the high-frequency inverter circuit is adjusted in real time to perform phase synchronization and energy beam directional focusing of multiple transmitting coils, ensuring that the magnetic field strength distribution at the location of each receiving coil is uniform and stable, forming a dynamically focused directional energy beam, effectively ensuring the uniformity and stability of energy supply at each receiving end;

[0059] The specific operation is as follows: After the system is powered on, the high-frequency inverter circuit simultaneously drives all transmitting coils in the cluster to enter the initial working state. The voltage feedback signals of each receiving coil are acquired in real time by the analog-to-digital converter and transmitted to the signal processing unit of the power management controller. At the same time, the initial phase and reference frequency information of each transmitting channel are synchronously recorded by the embedded monitoring circuit to form an initial state matrix. This monitoring process is continuously performed at a sampling frequency of not less than 1kHz to ensure the timeliness and synchronization of the feedback signals and state information. The system compares the acquired multi-channel voltage data with the preset reference voltage in real time and associates it with the initial state of the corresponding transmitting channel. Based on the real-time acquired receiving voltage feedback matrix, a preset improved adaptive beamforming algorithm is called for online calculation. The objective function is to minimize the voltage fluctuation of each receiving end, and the phase and frequency of the transmitting coil are used as adjustable variables. Combined with the electromagnetic coupling interference model, the voltage feedback of each transmitting coil is dynamically calculated. The required real-time phase offset and frequency adjustment values ​​are calculated using an iterative optimization method, which rapidly converges to the optimal solution through gradient descent. At the algorithm level, mutual interference caused by magnetic field coupling of adjacent coils is actively canceled. After each calculation cycle, an instruction set containing specific control parameters for each transmitting channel is generated. Based on the instruction set generated by the algorithm, the driving signals of each high-frequency inverter circuit are precisely adjusted by a digital signal processor. In specific implementation, by changing the phase control word and frequency control word of the direct digital frequency synthesizer, independent phase and frequency fine-tuning of the excitation signal of each transmitting coil is achieved. The driving signals of all transmitting coils are updated synchronously according to the instructions, completing phase alignment and beamforming operations. This closed-loop control process runs continuously, enabling the combined magnetic field generated by multiple transmitting coils to be dynamically focused on the location of each target receiving coil, thereby forming a uniform and stable magnetic field strength distribution in space, effectively ensuring a stable energy supply to each gas meter receiver.

[0060] The power compensation unit, combined with the stable DC output from the DC adapter, employs a dynamic magnetic field strength compensation algorithm to construct a dynamic magnetic field strength compensation model. Based on the real-time load and distance changes of each diaphragm gas meter, it calculates the instantaneous drive power compensation required for each transmitting coil to automatically adjust the transmitting power, ensuring the receiving voltage remains stable within the 12V / 2A output range. This dynamically offsets transmission attenuation and load fluctuations, ensuring high consistency and stability of the receiving voltage across all meters. It receives stable DC power from the DC adapter and acquires real-time voltage and current data from the receiving coils of each diaphragm gas meter, as well as real-time transmission distance information between each diaphragm gas meter and its corresponding transmitting coil. This establishes a complete data acquisition benchmark, ensuring the accuracy of the compensation calculation source. To ensure consistency and synchronization, a dynamic magnetic field strength compensation model is constructed using a dynamic magnetic field strength compensation algorithm. Based on this model, combined with receiver voltage data, transmission distance, and real-time load changes, the instantaneous drive power compensation required for each transmitting coil is calculated to offset voltage deviations caused by distance changes or load fluctuations. This achieves precise dynamic compensation based on a physical model, suppressing voltage fluctuations at the source. According to the power compensation calculation results, the drive power of the corresponding transmitting coil is dynamically adjusted to ensure that the output voltage of each receiver is stably maintained within the rated output range of 12V / 2A within each transmission distance ≤5cm, ensuring power supply consistency and forming a closed-loop power control to ensure the consistency and stability of the output voltage under multi-load scenarios.

[0061] The specific work is as follows: After the system starts up and enters steady-state operation, the power compensation unit first establishes a communication link with all DC adapters in the system, continuously receiving the precisely regulated DC power supply status parameters output by them as a reference source for power regulation. Simultaneously, through high-precision sensing circuits deployed in the built-in power receiving module of each diaphragm gas meter, it synchronously collects the instantaneous DC voltage and current values ​​at the output of each receiving coil in real time at a sampling rate of no less than 100 times per second, forming a data stream reflecting the real-time power receiving status of each terminal. Furthermore, based on a preset installation location coordinate database, it continuously acquires the real-time spatial distance information between each gas meter and its corresponding transmitting coil. Then, it synchronously uploads the reference DC power supply status, the real-time electrical parameters of each receiving terminal, and the real-time energy transmission distance to the data processing core of the power management controller, performing timestamp alignment and filtering preprocessing to obtain an accurate, real-time, and synchronous input dataset. Based on the real-time acquired input dataset, the power compensation unit calls its internal... A fixed magnetic field strength dynamic compensation algorithm is constructed and run to create an online, real-time updated magnetic field strength dynamic compensation model. The core physical basis of this model is a mathematical model describing the relationship between the electromagnetic induction coupling strength at close and medium distances and the distance, the relative position of the coils, and the load impedance. The measured voltage data at the receiving end is compared with the system's set 12V rated target value in real time to calculate the current voltage deviation. At the same time, the voltage deviation value is comprehensively analyzed in conjunction with the real-time transmission distance information and the load change rate reflected by the receiving end current. Based on the built-in coupling coefficient-distance-load multi-dimensional lookup table, the equivalent magnetic field strength attenuation caused by distance changes or load fluctuations that leads to the current voltage deviation is dynamically calculated. Finally, the equivalent magnetic field strength attenuation is combined with the known efficiency parameters of the transmitting coils to reversely calculate the instantaneous drive power compensation required for each transmitting coil to offset the effect and bring the receiving end voltage back to the target value. This calculation process is completed iteratively within each control cycle, generating a precise power adjustment instruction set for each transmitting coil in the cluster.

[0062] The expression for calculating the instantaneous drive power compensation is as follows:

[0063] ;

[0064] ;

[0065] ;

[0066] In the formula: This is the instantaneous drive power compensation amount, which is the additional drive power required for the transmitting coil to bring the receiving voltage back to the target value of 12V. This is the equivalent magnetic field strength attenuation, used to quantify the loss of magnetic field strength due to changes in transmission conditions. This represents the theoretical receiving power required by the receiver to achieve a 12V voltage under the current load. This is the efficiency parameter of the transmitting coil, which is the overall efficiency of converting electrical energy into magnetic field energy; The system's rated target voltage is set to 12V; This refers to the real-time load current at the receiving end. This represents a function mapping relationship based on a multidimensional lookup table and interpolation algorithm, with the inputs being the coupling coefficient, voltage deviation, and load change rate; The coupling coefficient represents the transmission distance. relative angle with coil The function is used to describe the electromagnetic coupling efficiency between the transmitting coil and the receiving coil; This indicates the real-time transmission distance, which is the straight-line distance in space between the transmitting coil and the receiving coil; This indicates the relative position angle of the coils, that is, the angle between the normal directions of the transmitting coil and the receiving coil; This represents the voltage deviation value, i.e., the actual measured voltage at the receiving end. With the system set at a rated target voltage of 12V The difference between them; To represent the rate of change of load current, i.e., the receiving end current. The rate of change over time reflects the dynamic fluctuations of the load;

[0067] Based on the calculated power adjustment instruction set, the power compensation unit performs precise closed-loop adjustment of the front-end power drive module of the corresponding high-frequency inverter circuit through a high-bandwidth digital control interface. Specifically, by adjusting the duty cycle of the pulse width modulation signal in the drive module, the DC bus voltage of the high-frequency inverter circuit input to each target transmitting coil is dynamically controlled, thereby achieving rapid control of the magnetic field energy radiated by the transmitting coil. Through continuous operation of the monitoring-calculation-adjustment closed-loop control process, it is ensured that within the designed maximum transmission distance of 5 cm, regardless of the relative position or working state of each gas meter, the DC voltage and current at the output end of the receiving coil after rectification and filtering can be stably maintained within the rated output range of 12V and 2A, ensuring the consistency, stability and reliability of the power supply voltage of all gas meters in the cluster.

[0068] The energy scheduling decision module introduces a time-division multiplexing-based dynamic priority scheduling algorithm and energy state prediction model. Based on the real-time power consumption and load changes of each diaphragm gas meter in the multi-gas meter cluster, it dynamically allocates communication windows and power supply strategies to optimize system response speed and collaborative management efficiency. The energy scheduling decision module includes a communication window allocation unit and a power supply strategy pre-adjustment unit.

[0069] The communication window allocation unit is used for information exchange between multiple gas meters via an NFC communication module (106kbps rate). It employs a time-division multiplexing-based dynamic priority scheduling algorithm to allocate independent communication time sequences to each diaphragm gas meter, avoiding data transmission conflicts, improving communication efficiency and collaborative management capabilities, achieving orderly reuse of channel resources, completely eliminating data collisions, and ensuring smooth and reliable cluster communication. Through the NFC communication module at a communication rate of 106kbps, it periodically collects the identification, real-time power status, and data queue length information of each diaphragm gas meter in the multi-meter cluster. This allows for comprehensive acquisition of the real-time status of each meter, providing a data foundation for accurate scheduling and ensuring effective scheduling decisions. Based on the collected information, a time-division multiplexing-based dynamic priority scheduling algorithm is adopted to allocate appropriate communication time windows to each gas meter. The dynamic priority score is dynamically calculated according to the urgency of electricity consumption and the importance of data. Through dynamic priority calculation, urgent tasks and critical data are given priority for transmission, optimizing the efficiency of system resource utilization. The allocated communication time sequence table is broadcast to each diaphragm gas meter through the NFC communication module, so that each diaphragm gas meter can complete data transmission in an orderly manner within the specified time window, avoiding communication conflicts between multiple meters and improving the efficiency of cluster communication. With the help of time-series broadcasting and strict time slot control, channel conflicts are completely avoided, significantly improving the reliability and efficiency of concurrent communication between multiple meters.

[0070] The specific work involves the following steps: After the system starts and a communication link is established, the power management controller periodically broadcasts query commands to all diaphragm gas meters in the cluster via the NFC communication module at a standard communication rate of 106kbps. Upon receiving the command, each diaphragm gas meter encapsulates its own identification code, real-time power status data monitored by the energy management chip, and the length of the data queue to be transmitted in the communication buffer into a data packet according to a preset communication protocol. All information is then transmitted back to the power management controller via the NFC link. This periodic collection mechanism occurs at fixed time intervals, ensuring that the controller can continuously and accurately grasp the real-time communication needs and energy status of the entire cluster. The entire information interaction process strictly adheres to the near-field communication handshake and verification protocol to guarantee the reliability and integrity of data transmission. After collecting information from each node in the cluster, a time-division multiplexing-based dynamic priority scheduling algorithm is immediately executed. The algorithm dynamically calculates the current communication priority of each diaphragm gas meter according to preset rules. The priority is generated based on two key factors: first, the urgency of the battery charge, which is proportional to the reciprocal of the remaining battery charge or the degree to which it falls below a safety threshold; and second, the importance of the data, determined by the length of the data queue to be transmitted and the preset weight of the data type. A dynamic priority score is derived by combining these two factors, and all gas meters awaiting communication are sorted accordingly. Then, based on the sorting results, in the next communication cycle, a limited total time resource is divided into several non-overlapping time slots as communication time windows, and these windows are allocated to the corresponding diaphragm gas meters in descending order of priority, ensuring that high-priority tasks receive communication resources first.

[0071] The expression for calculating the dynamic priority score is as follows:

[0072] ;

[0073] ;

[0074] ;

[0075] In the formula: Indicates the first The dynamic priority score of each diaphragm gas meter; the higher the score, the higher the communication priority of the gas meter in the current communication cycle. The weighting coefficient represents the power urgency factor, used to adjust the proportion of power urgency in the overall priority calculation; Indicates the first The lower the battery level of a diaphragm gas meter, the lower the battery level. The higher the value, the greater the urgency. This is the real-time remaining battery power of the gas meter; The preset battery power safety threshold for the system; The weighting coefficients of the data importance factor are used to adjust the proportion of data importance in the overall priority calculation; Indicates the first The importance value of data for each diaphragm gas meter is determined by the length of the queue or the type of data. The larger the value; This is the current length of the queue of data to be transmitted in the communication buffer of the gas meter; The preset weight values ​​are assigned to the data types to be transmitted in the queue. After completing the communication window allocation calculation, the power management controller encodes the generated communication timing table. This communication timing table clearly includes the precise start time and window duration for each diaphragm gas meter to communicate in the next communication cycle. The power management controller broadcasts this communication timing table to all diaphragm gas meters in the cluster via the NFC communication module. After receiving the broadcast information, each diaphragm gas meter parses the communication time slot corresponding to its own identification code. During the waiting period, the diaphragm gas meter is in a low-power listening state. Only when the system clock reaches its allocated time window will the corresponding diaphragm gas meter activate its communication circuit and start sending the data packets in its queue. After its own time window ends, regardless of whether the data transmission is completely completed, the diaphragm gas meter immediately stops sending and re-enters the waiting state, completely avoiding channel conflicts and data collisions caused by multiple gas meters initiating communication at the same time, and significantly improving the overall communication efficiency and reliability of the cluster.

[0076] The power supply strategy pre-adjustment unit, based on the independent communication timing allocated to each diaphragm gas meter, combines real-time power monitoring data and load change data from each diaphragm gas meter output by the energy management chip. Using an energy state prediction model, it predicts the power consumption trend of each diaphragm gas meter and dynamically adjusts power supply priority and power output to ensure timely response to critical tasks and continuous and stable power supply. Based on prediction, it performs forward-looking resource scheduling, shifting from passive response to proactive protection and improving power supply real-time performance. Building upon the communication window allocation, it acquires real-time power data, historical load curves, and current operating mode information from each diaphragm gas meter through the energy management chip, achieving accurate and synchronous collection of multi-dimensional state information to support the prediction model. It provides a comprehensive input foundation, inputting acquired power data, historical load curves, and current operating mode information into a pre-built energy state prediction model to predict the power consumption trend and peak load periods of each gas meter in the future. Based on time series analysis and pattern matching, it achieves accurate prediction of future load changes in the cluster. According to the prediction results, it dynamically adjusts the power supply priority and power allocation strategy of each diaphragm gas meter by the power supply management controller, pre-allocates high power supply weight to diaphragm gas meters that are about to enter a high load or low power state, and ensures the timeliness and continuity of the system's power supply response. Through the feedforward control mechanism, it realizes resource pre-allocation, significantly improving the real-time response and continuity guarantee capability of the power supply system.

[0077] The specific work involves the following steps: After completing the communication window allocation, the power management controller immediately sends a data request command to the energy management chip inside each diaphragm gas meter via the established NFC communication link. The energy management chip, as the core of the diaphragm gas meter's internal power monitoring, collects and stores multiple key status parameters in real time. The power management controller first obtains the real-time power data of each meter, which accurately reflects the current energy reserve level as a percentage of remaining battery power. Simultaneously, the power management controller retrieves historical load curve data recorded in the energy management chip. This curve depicts the current consumption changes of the gas meter over the past cycle in a time series format, including typical load characteristics under different operating conditions such as normal operation and centralized data transmission. Furthermore, the power management controller synchronously reads the current operating mode identifier reported by the gas meter's microcontroller. All multi-dimensional and time-series status information is encapsulated into a structured data packet and reliably transmitted back to the central processing unit of the power management controller via the NFC communication link, forming a complete, synchronous, and highly timely status data packet. After receiving the real-time status data packets from each diaphragm gas meter, the power management controller inputs them into the energy state prediction model pre-installed in the controller firmware for calculation. This energy state prediction model is a software module based on time series analysis and machine learning algorithms. Its core function is to predict the future power consumption behavior of a single gas meter in the short time domain. The energy state prediction model smooths and filters the input real-time power data to eliminate noise, compares it with historical load curves and performs pattern matching to identify periodic or event-triggered load change patterns. At the same time, the energy state prediction model combines the current working mode information, calls the typical power consumption parameters associated with the mode, integrates the three types of information, and calculates the expected power consumption trend of the gas meter in the next configurable time window (the next 5 minutes) by running the built-in prediction algorithm (autoregressive integral moving average model). It also identifies the specific time periods when peak loads may occur and the estimated power demand increment. This process is performed in parallel on all online gas meters, and finally generates a cluster power demand prediction report.Based on the cluster power demand forecast report output by the energy state prediction model, the power management controller dynamically performs pre-adjustment of power supply priority and power allocation strategies. Utilizing the dynamic power supply priority list of all gas meters maintained within the cluster power demand forecast report, the power supply priority of gas meters judged to be about to enter a high-load operation period or whose power reserves are close to the warning threshold is increased, thereby obtaining higher weight in system resource allocation. At the power allocation level, the power management controller links the adjusted dynamic power supply priority list with the phase coordination unit and power compensation unit. Specifically, the power management controller pre-sends power increase commands or reserves higher power margins to the transmitting coil channels corresponding to the gas meters marked as high priority. When the predicted high-load period actually arrives, resource allocation is completed in advance, providing sufficient power support without waiting for feedback delay. Through the predictive-pre-adjustment feedforward control mechanism, the transformation from passive response to proactive protection is realized, ensuring the continuity, stability, and real-time performance of the power supply of diaphragm gas meters in the event of sudden load changes or power shortages, ensuring the continuous and reliable operation of the entire metering cluster.

[0078] The collaborative transmission optimization module is used to combine the communication window and power supply strategy allocated to each diaphragm gas meter, dynamically select the optimal energy transmission path according to the real-time location and load changes of each meter, and combine the output frequency (100kHz) adjustment of the high-frequency inverter circuit to perform real-time optimization of the many-to-many energy transmission path, ensuring efficient and stable energy transmission within a distance of ≤5cm, adaptively selecting the best transmission link and tuning the frequency to maximize the overall efficiency of many-to-many parallel transmission.

[0079] The status integration monitoring module is used to collect voltage, current and power data of each meter in real time through the NFC communication module. Combined with the energy management chip, it performs anomaly diagnosis and early warning to ensure the safe and reliable operation of the power supply system. It realizes real-time visualization of system status, intelligent early warning and remote collaborative control, builds a closed loop of system health perception and early warning, and realizes intelligent operation and maintenance from monitoring to management.

[0080] Example 3, as Figures 1 to 3As shown, based on embodiments 1-2, the present invention provides a technical solution: the collaborative transmission optimization module performs the following steps: real-time acquisition of the location information, current load status, and relative distance and alignment angle between the corresponding transmitting coil and receiving coil of each diaphragm gas meter in the multi-gas meter cluster, forming a basic sequence list, constructing a dynamic global view, providing a reliable data foundation for real-time path optimization, supporting rapid decision-making, based on the relevant information in the basic sequence list, combined with the preset transmission efficiency model, dynamically calculating and selecting the optimal energy transmission path for each gas meter at the current moment, prioritizing the selection of coil pairs that are close in distance, well aligned, and have matching loads for energy transmission, intelligently selecting the best path, maximizing single-point transmission efficiency, reducing energy waste, and improving the overall energy efficiency of the cluster, dynamically adjusting the output frequency of the high-frequency inverter circuit to which the corresponding transmitting coil belongs to the optimal resonance point near 100kHz according to the selected optimal energy transmission path, and coordinating the working timing of multiple transmitting coils to achieve high efficiency and stability of multi-to-multi energy transmission within a distance of ≤5cm, and achieving parallel and stable operation of multiple links through precise frequency tuning and collaborative timing, significantly enhancing system capacity and robustness;

[0081] The specific tasks are as follows: After system startup, the primary task of the collaborative transmission optimization module is to acquire and establish a real-time panoramic view of the physical and electrical status of the entire multi-gas meter cluster. Through integrated position sensors (magnetic encoders), it continuously acquires the three-dimensional relative position between each diaphragm gas meter and its corresponding transmitting coil, accurately calculating the spatial linear distance and normal alignment angle between them. Simultaneously, through the established communication link, it reads the current load status of each diaphragm gas meter in real time from its energy management chip, including instantaneous operating current and power consumption mode. It synchronously collects and timestamps distance information, angle information, and load status data, associating them according to the diaphragm gas meter's identification identifier to form a dynamically updated basic sequence list, which is then used as a database. The data is stored in the memory of the power management controller. The entire data acquisition process is executed periodically at a frequency of no less than 10Hz to ensure that the system can respond promptly to minor changes in the gas meter position or step changes in the load. After obtaining the real-time updated basic sequence list, the collaborative transmission optimization module calls its internal preset transmission efficiency model for online calculation. This model is a mathematical model based on electromagnetic coupling theory. Its input variables include the distance between coils, the relative angle, and the load impedance at the receiving end. The output is the estimated end-to-end energy transmission efficiency. The real-time parameters of each transmitting coil-receiving coil pair in the basic sequence list are substituted into the transmission efficiency model to calculate the current theoretical efficiency value of all possible transmission paths in parallel. Subsequently, a dynamic path selection algorithm is executed: The core objective is to find the most efficient transmitter for each receiving coil (i.e., diaphragm gas meter) at the current moment. Among multiple paths with similar efficiency, priority is given to coil pairs with shorter physical distances, better coil axial alignment, and a better match between load conditions and transmitter power output capabilities. This process is iterated within each control cycle, ultimately outputting an optimal energy transmission path mapping table. This achieves adaptive and refined energy delivery at the system level. Based on the instructions in the optimal energy transmission path mapping table, the collaborative transmission optimization module then precisely adjusts the parameters and timing of the relevant high-frequency inverter circuits and transmitting coils. For each selected transmitting coil, a frequency fine-tuning command is sent to its corresponding high-frequency inverter circuit via a digital signal interface, driving... Its output frequency is precisely adjusted to near the optimal resonant point centered at 100kHz to maximize the electromagnetic coupling efficiency and power transmission capability under this specific transmission path. At the same time, a multi-transmitter coil working timing coordination strategy is implemented to solve the potential magnetic field cross-interference problem caused by multiple transmitter coils working simultaneously. This strategy allocates appropriate excitation pulse sequences that are staggered or synchronously enhanced in time to each activated transmitter coil based on the path mapping table and load priority, ensuring that a coordinated and complementary magnetic field distribution is formed in space. Through the coordinated adjustment of frequency and timing, the efficient parallel operation of multiple point-to-point energy transmission links is stably maintained within a transmission distance of no more than 5 cm, thereby optimizing the overall energy transmission stability and efficiency of the cluster.

[0082] The status integration monitoring module performs the following steps: Real-time voltage, current, and battery power data from the receivers of each diaphragm gas meter are collected via NFC communication module periodic polling or event triggering, along with chip temperature and operating status flags reported by the energy management chip. This ensures the system obtains multi-dimensional operating status data from each node in the cluster in real time and completely. The collected data is compared and analyzed with preset safety thresholds and normal operating ranges. The diagnostic logic built into the energy management chip identifies potential fault types, including voltage anomalies, current overruns, and insufficient power, achieving accurate anomaly identification and automated fault type classification. When an abnormal state is diagnosed or a threshold is predicted to be exceeded, corresponding warning information is generated and uploaded to the power supply management controller via the NFC communication module. Remote command issuance is also supported, enabling real-time status visualization, intelligent early warning, and collaborative control of the power supply system in a closed loop. This constructs a closed-loop management system from proactive early warning to remote control, improving operational response efficiency and system reliability.

[0083] The specific work involves the following: After the system enters a stable operation phase, the status integration monitoring module continuously monitors the internal electrical status and key component operating conditions of each diaphragm gas meter through the established near-field communication link. Two complementary data acquisition mechanisms are employed: first, timed polling based on fixed time intervals to obtain system baseline operating parameters; second, real-time acquisition triggered by specific events to respond to sudden changes in status. The specific data collected includes the real-time DC voltage and load current values ​​output from the diaphragm gas meter's receiving coil after rectification and filtering, the remaining percentage of the built-in battery's charge, and the operating temperature and internal status flag register contents of the energy management chip itself. All data is encapsulated according to a preset communication protocol and transmitted efficiently and reliably back to the power management controller via an NFC communication module at a rate of 106kbps within a dedicated communication window allocated to each diaphragm gas meter. This ensures that the power management controller receives a comprehensive data package covering the entire cluster with high timeliness and consistency. After receiving the comprehensive data package uploaded by each node, the power management controller calls its built-in diagnostic analysis engine for processing. The core of this engine is a preset safe operating threshold library and a normal operating range parameter table. For various parameters such as voltage, current, power, and temperature, clearly defined normal ranges, warning thresholds, and fault thresholds are provided. The diagnostic logic will... Real-time voltage and current data are compared with rated values ​​to identify any anomalies such as overvoltage, undervoltage, overcurrent, or insufficient output power. Simultaneously, battery power is compared with preset power thresholds at various levels to determine its reserve status. Temperature data reported by the energy management chip is used to assess the chip's own and the local environment's heat dissipation. Status flags directly reflect the self-test results of the chip's internal logic. Through systematic cross-comparison and correlation analysis of multi-dimensional measured data with corresponding thresholds, the diagnostic engine accurately identifies various potential fault types such as abnormal voltage fluctuations at the receiving end, continuous overload operation, impending battery depletion, and chip overheating, and performs preliminary classification. During diagnostic analysis... When the engine identifies any parameter that exceeds the normal range, reaches the warning threshold, or has triggered a fault condition, the status integrated monitoring module immediately initiates the warning and response process, generating structured warning information. This information includes at least the identification of the abnormal gas meter, the specific type of abnormality, the degree of deviation from the threshold, and the timestamp of occurrence. This warning information is first uploaded to the power supply management controller via the NFC communication module. After comprehensively considering the status of the entire system, the power supply management controller can perform two types of collaborative control: one is to issue remote commands to the status monitoring module or energy scheduling decision module to adjust the data reporting frequency of abnormal nodes, start a more detailed diagnostic program, or temporarily modify their power supply priority and communication window allocation.Secondly, key early warning information is integrated into the system status interface, enabling real-time visual monitoring of the power supply health status of all gas meters. Through a complete closed loop of data collection → analysis and diagnosis → early warning reporting → command response, this achieves a leap from passive monitoring to proactive early warning, and from local diagnosis to remote collaboration, thereby improving the initiative, intelligence, and overall reliability of the power supply system management.

[0084] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0085] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A diaphragm gas meter based on wireless energy transmission, comprising a gas meter body (1), characterized in that: The gas meter body (1) is equipped with a built-in power receiving module (2), the gas meter body (1) is connected to an external power supply device (3), and the gas meter body (1) is equipped with a diaphragm gas meter wireless power supply system. The external power supply device (3) includes a first insulating shell, inside which a transmitting coil, an external power supply and a driving circuit are installed. The first insulating shell is a high-strength polytetrafluoroethylene insulating shell, and an insulating rubber layer of not less than 5 mm thick is provided on the outer wall of the first insulating shell as the first insulating isolation layer. The transmitting coil is installed on the outside of the gas meter body (1) near the edge. The transmitting coil is connected to the external power supply to convert electrical energy into an alternating magnetic field. The driving circuit is connected to the transmitting coil and the external power supply to generate a high-frequency alternating current and drive the transmitting coil to generate an alternating magnetic field. The built-in power receiving module (2) includes a second insulating shell that is detachably installed inside the gas meter body (1). A second insulating isolation layer is installed on the inner wall of the second insulating shell. A rectifier filter circuit, an energy management chip, and a receiving coil are arranged inside the second insulating shell. The receiving coil corresponds to the transmitting coil and is installed inside the gas meter body (1) at a position opposite to the transmitting coil. The receiving coil receives the alternating magnetic field generated by the transmitting coil through the principle of electromagnetic induction and converts it into electrical energy. The size and parameters of the receiving coil are matched with those of the transmitting coil. The rectifier filter circuit is connected to the receiving coil and converts the alternating current output by the receiving coil into DC voltage. The energy management chip is connected to the rectifier filter circuit and the electronic components inside the gas meter.

2. A wireless power supply system for a diaphragm gas meter based on wireless power transmission, applied to the diaphragm gas meter based on wireless power transmission as described in claim 1, characterized in that, It includes a power management controller, which is communicatively connected to the following modules: The directional beamforming control module, based on an improved adaptive beamforming algorithm and a dynamic magnetic field strength compensation model, monitors and adjusts the phase and power of multiple transmitting coils in a multi-gas meter cluster deployment scenario in real time to perform directional energy transmission and voltage stability control. The energy scheduling decision module introduces a time-division multiplexing-based dynamic priority scheduling algorithm and energy state prediction model. Based on the real-time power consumption and load changes of each diaphragm gas meter in the multi-gas meter cluster, it dynamically allocates communication windows and power supply strategies. The collaborative transmission optimization module is used to combine the communication windows and power supply strategies allocated to each diaphragm gas meter, dynamically select the optimal energy transmission path based on the real-time location and load changes of each meter, and perform real-time optimization of the many-to-many energy transmission path by combining the output frequency adjustment of the high-frequency inverter circuit. The status integrated monitoring module is used to collect voltage, current and power data of each meter in real time through the NFC communication module, and to perform anomaly diagnosis and early warning in conjunction with the energy management chip.

3. The wireless power supply system for a diaphragm gas meter based on wireless energy transmission according to claim 2, characterized in that: The directional shaping control module includes a phase coordination unit and a power compensation unit; The phase coordination unit is used to drive multiple transmitting coils in a multi-gas meter cluster deployment scenario using a high-frequency inverter circuit, collect voltage feedback from each receiving coil in real time, and dynamically adjust the phase and frequency of the transmitted signal in combination with an improved adaptive beamforming algorithm. The power compensation unit is used to combine the stable DC output from the DC adapter and construct a dynamic magnetic field strength compensation model using a dynamic magnetic field strength compensation algorithm. Based on the real-time load and distance changes of each diaphragm gas meter, it calculates the instantaneous drive power compensation required for each transmitting coil to automatically adjust the transmitting power.

4. A wireless power supply system for a diaphragm gas meter based on wireless energy transmission according to claim 3, characterized in that: The phase coordination unit performs the following steps: A high-frequency inverter circuit is used to drive multiple transmitting coils in a multi-gas meter cluster deployment scenario, and the voltage feedback signals of each receiving coil are collected in real time, while the initial phase and frequency status of the transmitted signal are monitored synchronously. Based on the voltage feedback of the receiving coil, an improved adaptive beamforming algorithm is used to dynamically calculate the required phase offset and frequency adjustment value of each transmitting coil in order to suppress magnetic field coupling interference between adjacent transmitting coils; Based on the calculated phase and frequency adjustment parameters, the output signal of the high-frequency inverter circuit is adjusted in real time to achieve phase synchronization of multiple transmitting coils and directional focusing of the energy beam.

5. A wireless power supply system for a diaphragm gas meter based on wireless energy transmission according to claim 3, characterized in that: The power compensation unit performs the following steps: It receives a stable DC power supply from the DC adapter and acquires the voltage and current data of the receiving coil of each diaphragm gas meter in real time, as well as the real-time transmission distance information between each diaphragm gas meter and its corresponding transmitting coil. A dynamic magnetic field strength compensation model is constructed using a dynamic magnetic field strength compensation algorithm. Based on the dynamic magnetic field strength compensation model, combined with receiver voltage data, transmission distance and real-time load changes, the instantaneous drive power compensation required for each transmitting coil is calculated. Based on the power compensation calculation results, the driving power of the corresponding transmitting coil is dynamically adjusted to ensure that the output voltage of each receiving end is stably maintained within the rated output range of 12V / 2A within each transmission distance ≤5cm.

6. A wireless power supply system for a diaphragm gas meter based on wireless energy transmission according to claim 3, characterized in that: The energy scheduling decision module includes a communication window allocation unit and a power supply strategy pre-adjustment unit; The communication window allocation unit is used to perform information interaction between multiple meters through the NFC communication module, and to allocate independent communication timing to each diaphragm gas meter using a time-division multiplexing-based dynamic priority scheduling algorithm. The power supply strategy pre-adjustment unit is used to predict the power consumption trend of each diaphragm gas meter based on the independent communication timing allocated to each diaphragm gas meter, combined with the real-time power monitoring data and load change data of each diaphragm gas meter output by the energy management chip, and to dynamically adjust the power supply priority and power output by using an energy state prediction model.

7. A wireless power supply system for a diaphragm gas meter based on wireless energy transmission according to claim 6, characterized in that: The communication window allocation unit performs the following steps: The system periodically collects the identification information, real-time power status, and data queue length of each diaphragm gas meter in the multi-gas meter cluster using an NFC communication module at a communication rate of 106kbps. Based on the collected information, a time-division multiplexing-based dynamic priority scheduling algorithm is adopted to allocate appropriate communication time windows to each gas meter. The dynamic priority score is dynamically calculated based on the urgency of electricity consumption and the importance of data. The assigned communication timing table is broadcast to each diaphragm gas meter via the NFC communication module, enabling each diaphragm gas meter to complete data transmission in an orderly manner within the specified time window.

8. A wireless power supply system for a diaphragm gas meter based on wireless energy transmission according to claim 6, characterized in that: The power supply strategy pre-tuning unit performs the following steps: Based on the communication window allocation, the energy management chip is used to obtain the power data, historical load curves and current working mode information of each diaphragm gas meter in real time; The acquired electricity data, historical load curves, and current operating mode information are input into a pre-built energy state prediction model to predict the electricity consumption trend and peak load periods of each gas meter in the future. Based on the prediction results, the power supply management controller dynamically adjusts the power supply priority and power allocation strategy for each diaphragm gas meter, and pre-allocates high power supply weight to diaphragm gas meters that are about to enter a high load or low power state.

9. A wireless power supply system for a diaphragm gas meter based on wireless energy transmission according to claim 6, characterized in that: The cooperative transmission optimization module performs the following steps: Real-time acquisition of the location information, current load status, and relative distance and alignment angle between the corresponding transmitting and receiving coils of each diaphragm gas meter in a multi-gas meter cluster, forming a basic sequence list; Based on the relevant information in the basic sequence table and combined with the preset transmission efficiency model, the optimal energy transmission path for each gas meter at the current moment is dynamically calculated and selected. Based on the selected optimal energy transmission path, the output frequency of the high-frequency inverter circuit corresponding to the transmitting coil is dynamically adjusted to the optimal resonance point near 100kHz, and the working timing of multiple transmitting coils is coordinated.

10. A wireless power supply system for a diaphragm gas meter based on wireless energy transmission according to claim 9, characterized in that: The status integration monitoring module performs the following steps: The system collects real-time voltage, current, and battery power data from the receiver of each diaphragm gas meter via NFC communication module periodic polling or event triggering, as well as chip temperature and operating status flags reported by the energy management chip. The collected data is compared and analyzed with preset safety thresholds and normal operating ranges. The diagnostic logic built into the energy management chip is used to identify potential fault types, including abnormal voltage, excessive current and insufficient power. When an abnormal state is diagnosed or a threshold is predicted to be exceeded, a corresponding early warning message is generated and uploaded to the power management controller via the NFC communication module. At the same time, remote command issuance is supported, realizing a closed loop of real-time status visualization, intelligent early warning and collaborative control of the power supply system.

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