A method for secure transmission of gas meter information based on response function compensation

Abstract

This invention relates to the field of data transmission technology and discloses a method for secure transmission of gas meter information based on response function compensation. The method includes: collecting multi-source data from the gas meter and network signal quality information; dynamically adjusting data transmission parameters according to the real-time network environment and physical conditions using a response function compensation algorithm; automatically allocating dynamic retransmission time slots for retransmission when data transmission fails within a static time slot; setting a retransmission count threshold; compensating for packet loss caused by channel errors or collisions using a retransmission response function; employing a multi-layer encryption mechanism to encrypt the transmitted data; intelligently selecting the optimal transmission channel based on network conditions; using adaptive hybrid communication via short-range star-flash communication or wide-area Wi-Fi transmission; and establishing a multi-dimensional environmental parameter response compensation mechanism based on a signal transmission model; transmitting the encrypted data to a monitoring and management platform through a secure channel; thereby achieving security early warning decisions, enabling dynamic and accurate transmission and proactive security protection.

Description

Technical Field

[0001] This invention relates to the field of data transmission technology, and more specifically, to a method for secure transmission of gas meter information based on response function compensation. Background Technology

[0002] As a crucial technology in modern energy management, the secure transmission of gas meter information, particularly its capabilities in secure transmission, dynamic compensation, and intelligent monitoring within complex network environments, directly impacts data transmission efficiency and system security. Traditional gas meter data transmission practices primarily rely on simple encryption or plaintext transmission methods, single communication protocols, or fixed-parameter transmission mechanisms. These approaches fail to provide dynamic and accurate responses and compensation to changes in the network environment. They struggle to uncover the spatiotemporal correlations between multi-dimensional environmental parameters and the evolution patterns of transmission quality. Furthermore, the secure transmission mechanism lacks multi-layered encryption protection, hindering precise encryption of data at different security levels and dynamic selection of transmission channels. More critically, existing technologies have failed to establish a complete closed-loop control process encompassing data acquisition, response compensation, encrypted transmission, and secure monitoring. This disconnect between environmental perception and transmission optimization severely limits the adaptability and reliability of data transmission strategies. Consequently, gas meter data transmission has long been relegated to a passive response state, hindering the paradigm shift from fixed transmission to adaptive secure transmission.

[0003] Therefore, how to compensate the response function through multi-source data collected by the multimodal sensing module, and further dynamically adjust and optimize the transmission parameters, while simultaneously implementing multi-layer encryption protection and intelligent channel selection for the transmitted data, and ensuring secure transmission and real-time monitoring of the encrypted data to achieve security early warning decisions, and thus achieve dynamic and accurate transmission and proactive security protection, has become an urgent technical problem to be solved. Summary of the Invention

[0004] This invention provides a gas meter information security transmission method based on response function compensation, which solves the problem in the prior art that it is difficult to perform response function compensation on multi-source data collected by multimodal sensing modules, and further dynamically adjusts and optimizes transmission parameters. At the same time, it performs multi-layer encryption protection and intelligent channel selection on the transmitted data, and performs secure transmission and real-time monitoring of encrypted data to realize security early warning decision-making, thereby solving the technical problem of dynamic and accurate transmission and proactive safety protection.

[0005] This invention provides a method for secure transmission of gas meter information based on response function compensation, comprising:

[0006] Configure sensor calibration parameters and distribute encryption keys to initialize the system operating environment, obtaining the initialization configuration results;

[0007] Based on the initial configuration results, the gas meter's airflow speed, ambient temperature, pressure fluctuations, and communication signal strength information are obtained, and the collected data is used as input parameters for response function compensation.

[0008] Based on the obtained data transmission status of the gas meter and the transmission environment conditions, the data transmission configuration is dynamically optimized using the response function compensation mechanism to obtain the compensation result;

[0009] Among them, the retransmission window allocation based on the adaptive time slot scheduling protocol is combined with the response function compensation mechanism; when data transmission fails or is lost in the transmission window within the static time slot, the backup retransmission time slot window is automatically activated for supplementary transmission, and a threshold for the number of retransmission attempts is set; the retransmission response function compensates for packet loss caused by channel error or collision.

[0010] Based on the compensation results and transmission quality optimization results, a multi-layer encryption mechanism is used to encrypt the transmitted data, and the encryption strength is dynamically adjusted according to the network security situation; and the best transmission channel is intelligently selected according to the network conditions.

[0011] Encrypted data is transmitted to the monitoring and management platform using the selected optimal transmission channel.

[0012] Furthermore, based on the compensation results and the transmission quality optimization results, a multi-layer encryption mechanism is adopted to encrypt the transmitted data, and the encryption strength is dynamically adjusted according to the network security situation.

[0013] Based on the encrypted data transmission, the system intelligently selects the best transmission channel according to network conditions and performs adaptive hybrid communication through Star Flash short-range communication or Wi-Fi wide-area transmission.

[0014] Based on the signal transmission channel, a response compensation mechanism for multi-dimensional environmental parameters such as obstacles, weather factors, and multipath fading is established to achieve intelligent switching of the optimal transmission channel;

[0015] Utilizing the selected optimal transmission channel, encrypted data is transmitted to the monitoring and management platform, and a response compensation mechanism is continuously applied during the transmission process to ensure data integrity.

[0016] After receiving encrypted data, the monitoring and management platform decrypts and verifies the received data based on the pre-distributed encryption key, obtains the decrypted data, and sends the reception status back to the sending end.

[0017] Based on the decryption verification results, the decrypted data is monitored and analyzed in real time. Anomalies are detected and warnings are issued. Corresponding warning security execution strategies are provided, including stopping data transmission, triggering alarm mechanisms, or sending abnormal information to a remote monitoring center.

[0018] Furthermore, configuring sensor calibration parameters and distributing encryption keys to initialize the system operating environment includes:

[0019] Set the initial response function compensation algorithm parameters and encryption algorithm parameters as the basic configuration for data transmission optimization and secure encryption;

[0020] A digital model of abnormal gas usage is established based on parameters to proactively detect abnormal gas theft and to work in conjunction with encryption algorithm parameters for anomaly monitoring.

[0021] Based on anomaly monitoring and combined with an identity recognition mechanism, the gas meter terminal device is registered for identity and configured for security authentication to obtain the device identity authentication result, ensuring that devices that need to be verified are connected to the gas meter;

[0022] Based on device authentication results and response function compensation algorithms, a transmission channel quality assessment benchmark and response compensation strategy library are established to provide a basis for decision-making on dynamically optimizing data transmission configuration.

[0023] Furthermore, at least two different types of sensors are configured to collect gas meter flow, temperature, pressure data and network signal quality information;

[0024] Based on the sensor configuration results, a signal transmission model is established to respond to obstacle occlusion, weather factors, and changes in multipath fading environment, serving as the basic configuration for data transmission optimization.

[0025] Based on the signal transmission model and sensor configuration, the time-domain drift and frequency-domain noise of the acquired information data are compensated through the response function mechanism. The Kalman filter algorithm is used to fuse the multi-source sensor data to eliminate environmental interference and equipment errors during the data acquisition process.

[0026] Based on the fused sensor data and signal transmission model, a multidimensional environmental supplementary parameter matrix is ​​constructed, which includes obstacle attenuation coefficient, weather influence weight, and multipath fading compensation factor. The multidimensional environmental supplementary parameter matrix is ​​used as the input parameter of the response function compensation mechanism.

[0027] Furthermore, based on the data transmission status and environmental conditions of the gas meter, and combined with the retransmission window allocation and response function compensation mechanism of the adaptive time slot scheduling protocol, the data transmission parameters are adjusted to optimize the efficiency and security of data transmission.

[0028] When data transmission fails or is lost within a static time slot transmission window, a backup retransmission time slot window is automatically activated for supplementary transmission, and a threshold for the number of retransmission attempts is set; packet loss caused by channel errors or collisions is compensated through a retransmission response function;

[0029] Dynamically adjust the transmit power, modulation method, and time slot allocation strategy to compensate for communication blind spots in star networking, establish a time slot conflict detection and avoidance mechanism, and ensure the coordinated operation of retransmission windows and normal transmission windows;

[0030] Establish a transmission quality prediction model, combine it with a response function compensation algorithm to make compensation adjustments in advance, predict potential transmission failure scenarios and pre-configure backup retransmission strategies;

[0031] An adaptive learning mechanism is set up to optimize the compensation strategy based on historical transmission data and retransmission success rate, dynamically adjust the retransmission number threshold and time slot window allocation parameters, and achieve a balance between transmission efficiency and reliability.

[0032] Furthermore, the transmission parameters of the collected gas meter information are received, and the transmission parameters are dynamically compensated based on the response function compensation.

[0033] Based on the compensated transmission parameters, determine the appropriate encryption level and communication channel identification information;

[0034] The basic data of the gas meter is encrypted in layers, and the encrypted data is merged with the channel identification information generated by the communication channel identification before being sent to the receiving end;

[0035] Obtain the encrypted confirmation information returned by the receiving end when the channel identifier verification is successful, as well as the first verification data generated by fusing and encrypting the basic data and channel identifier information;

[0036] The first verification data and encrypted confirmation information are decrypted using the aforementioned encryption level, and the basic data is obtained by parsing.

[0037] When the basic data verification is successful, the confirmation information and channel identification information are merged, and the second verification data is generated by encrypting it using the encryption level mentioned above; the second verification data is then sent to the receiving end.

[0038] Obtain the security negotiation information generated by fusing and encrypting the basic data and security key, sent by the receiving end when the confirmation information verification is successful;

[0039] The security negotiation information is decrypted using the aforementioned encryption level, and the security key is obtained by parsing it.

[0040] Secure key is used for the secure transmission and real-time monitoring of gas meter data.

[0041] Furthermore, the multi-layered encryption mechanism employs multiple encryption protections, including SM4 algorithm encryption, chaotic encryption, and digital signature;

[0042] Establish a key storage and management mechanism to ensure key security;

[0043] Establish a dynamic key upgrade response mechanism. When a key attack or risk is detected, the system will send a new encryption key to the gas meter terminal in real time through a secure upgrade channel, which can be OTA (Over-The-Air) technology or a secure wired interface, to compensate for and fix potential security vulnerabilities.

[0044] The encryption strength adaptive adjustment function can be set to select an appropriate encryption level based on the data sensitivity.

[0045] Furthermore, the transmission parameters of the collected gas meter information are received, and dynamic compensation is performed on the transmission parameters based on the response function compensation, including:

[0046] Real-time acquisition of basic data such as flow rate, pressure, and temperature of the gas meter, as well as transmission quality parameters such as signal strength, transmission delay, and packet loss rate;

[0047] Establish a transmission parameter evaluation model to comprehensively analyze the impact of current network conditions and environmental factors on data transmission;

[0048] A response function compensation algorithm is used to dynamically adjust key parameters such as transmission power, modulation scheme, and coding rate.

[0049] Set parameter compensation thresholds and response times to ensure the real-time performance and effectiveness of the compensation mechanism;

[0050] Establish a compensation effect feedback mechanism to continuously optimize the compensation strategy based on the transmission quality after compensation.

[0051] Furthermore, based on the compensated transmission parameters, the appropriate encryption level and communication channel identification information are determined, including:

[0052] Based on the transmission quality assessment results, the encryption methods with different strengths, such as SM4 algorithm, chaotic encryption, or digital signature, are dynamically selected.

[0053] Based on data sensitivity and network security conditions, the encryption level is adaptively adjusted to achieve a balance between security and transmission efficiency;

[0054] Generate unique channel identification information for each communication session, including timestamp, device ID, and session key elements;

[0055] Establish a mapping relationship between encryption levels and channel identifiers to ensure that encryption strength matches the channel security level;

[0056] Configure a dynamic update mechanism for encryption parameters to periodically refresh the encryption key and channel identifier.

[0057] Furthermore, it also includes:

[0058] The analysis results are compared with pre-defined anomaly models to identify potential transmission risks;

[0059] The system uses big data analytics models to digitally identify abnormal gas usage, automatically generate work orders, and dispatch them to field staff.

[0060] Establish multi-dimensional data correlation analysis to improve the accuracy of anomaly detection;

[0061] Set up an intelligent early warning and response mechanism to automatically trigger the corresponding processing procedures based on the level of abnormality.

[0062] The adaptive time slot scheduling protocol adopts the SADR-TDMA protocol, which combines the determinism of static time slots with the flexibility of dynamic retransmission. It compensates for the uncertainty of the wireless environment through response functions, establishes a time slot conflict detection and avoidance mechanism, and sets up an adaptive optimization function to dynamically adjust protocol parameters according to network conditions.

[0063] The signal transmission model comprehensively considers the impact of obstacle obstruction, weather factors, and multipath fading environmental parameters on signal propagation. It establishes a multi-dimensional environmental response compensation mechanism, dynamically adjusts transmission power and frequency to optimize signal coverage and transmission stability, establishes a signal quality prediction algorithm, adjusts transmission parameters in advance, and sets up an environmental adaptive learning mechanism to continuously optimize transmission model parameters.

[0064] The beneficial effects of this invention are as follows: This invention establishes a complete system startup baseline system by setting sensor calibration, encryption key distribution, and system parameter initialization. Sensor calibration ensures high accuracy and consistency of data acquisition; encryption key distribution adopts a secure key exchange protocol to ensure the confidentiality and integrity of key transmission; system parameter initialization establishes a basic parameter library for response function compensation, providing a scientific basis for subsequent dynamic adjustments. This mechanism shortens the system startup time to within a set time. Utilizing a multimodal sensing module to simultaneously collect flow, temperature, and pressure data from the gas meter, as well as network signal quality information, achieves diversified and complementary data sources. Through sensor data fusion algorithms, multimodal data provides rich environmental feature information for response function compensation, making the compensation strategy more accurate and effective. The dynamic retransmission time slot allocation mechanism based on the SADR-TDMA protocol effectively solves the limitations of traditional fixed time slot allocation. When static time slot transmission fails, the system automatically allocates dynamic retransmission time slots, setting a retransmission threshold of 3 times, and compensating for packet loss caused by channel errors through the retransmission response function. A three-layer encryption architecture using SM4 algorithm encryption, chaotic encryption, and digital signatures constructs a defense-in-depth security system. The SM4 algorithm provides core data protection, chaotic encryption enhances anti-interference capabilities, and digital signatures ensure the trustworthiness of data sources. This mechanism effectively prevents data eavesdropping, tampering, and replay attacks, improving security and increasing the difficulty of key cracking. Intelligent switching between StarScan short-range communication and Wi-Fi wide-area transmission optimizes communication coverage. StarScan communication has strong penetration in close-range, high-interference environments, while Wi-Fi offers wide coverage in long-range, low-interference environments. A multi-dimensional environmental parameter response compensation mechanism based on a signal transmission model dynamically adjusts transmission power and frequency. Encrypted data is transmitted to the monitoring and management platform via a secure channel, establishing an end-to-end secure transmission link. Two-way authentication and session key negotiation prevent unauthorized device access and man-in-the-middle attacks. The monitoring and management platform uses MAC authentication codes and CRC cyclic redundancy check technology for dual verification of received data. Decryption speed is improved; if verification fails, a retransmission mechanism is automatically triggered to ensure data integrity and authenticity. Real-time monitoring and analysis of decrypted data establishes a multi-dimensional anomaly detection model. By leveraging big data analytics, abnormal gas usage is digitally identified, improving anomaly detection accuracy and keeping the false alarm rate below ±3%. The intelligent early warning response time is reduced to less than one second, automatically generating work orders and dispatching them to field personnel. This invention achieves a technological leap in gas meter data transmission from passive protection to proactive adaptation through the organic combination of a response function compensation mechanism and multi-layer encryption technology. The overall system response time is improved, and environmental adaptability is significantly enhanced. This invention achieves improvements in key indicators such as safety, reliability, real-time performance, and environmental adaptability, providing reliable technical support for the large-scale application of smart gas meters. Attached Figure Description

[0065] Figure 1 This is a schematic flowchart of a gas meter information security transmission method based on response function compensation provided in an embodiment of the present invention. Detailed Implementation

[0066] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples.

[0067] At least one embodiment of the present invention discloses a method for secure transmission of gas meter information based on response function compensation, comprising the following steps:

[0068] Step 1: Configure sensor calibration parameters and distribute encryption keys to initialize the system operating environment and obtain the initialization configuration results;

[0069] Step 2: Based on the initial configuration results, obtain the gas meter's airflow speed, ambient temperature, pressure fluctuations, and communication signal strength information, and use the collected data as input parameters for response function compensation;

[0070] Step 3: Based on the obtained gas meter data transmission status and transmission environment conditions, dynamically optimize the data transmission configuration using a response function compensation mechanism to obtain the compensation result; wherein, the retransmission window allocation based on the adaptive time slot scheduling protocol is combined with the response function compensation mechanism; when data transmission fails or is lost in the transmission window within the static time slot, the backup retransmission time slot window is automatically activated for supplementary transmission, and a threshold for the number of retransmission attempts is set; packet loss caused by channel error or collision is compensated through the retransmission response function;

[0071] Step 4: Based on the compensation results and transmission quality optimization results, a multi-layer encryption mechanism is used to encrypt the transmitted data. The encryption strength is dynamically adjusted according to the network security situation. The best transmission channel is intelligently selected based on network conditions.

[0072] Step 5: Transmit encrypted data to the monitoring and management platform using the selected optimal transmission channel.

[0073] Example 1

[0074] Sensor calibration parameters refer to the configuration parameters used to correct sensor measurement deviations. Specifically, this can be achieved by fitting calibration curves using the least squares method to ensure the accuracy of multi-source data acquisition. Adaptive time slot scheduling protocol refers to a communication protocol that dynamically adjusts time slot allocation based on network load. Specifically, this can be implemented using the SADR-TDMA protocol, coordinating normal transmission windows and retransmission windows through a collision detection mechanism. Response function compensation mechanism refers to an algorithm that dynamically corrects transmission parameters based on changes in environmental parameters. Specifically, this can be implemented using a fusion algorithm of Kalman filtering and neural networks to eliminate the effects of channel interference. Multi-layer encryption mechanism refers to a protection system that combines multiple encryption algorithms. Specifically, this can be achieved by using the SM4 algorithm in conjunction with chaotic encryption, automatically switching encryption strength according to the network threat level. Optimal transmission channel selection refers to a dynamic routing strategy based on signal quality assessment. Specifically, this can be implemented using a fuzzy logic decision algorithm, intelligently switching between short-range satellite communication and wide-area Wi-Fi transmission.

[0075] Specifically, during the system initialization phase, a unified data acquisition benchmark and security foundation are established by calibrating multiple types of sensors and distributing encryption keys. During operation, physical environment parameters and communication quality parameters are simultaneously acquired to construct a multi-dimensional input vector-driven response function compensation model. When a data transmission anomaly is detected, the adaptive protocol automatically activates backup time slots for a limited number of retransmissions, while simultaneously correcting channel parameters through a compensation algorithm. The compensated transmission parameters trigger the encryption strength adjustment module, which selects a combination of encryption algorithms based on real-time network security assessment results. The channel selection module comprehensively evaluates signal strength, transmission delay, and packet loss rate, selecting the optimal communication path according to preset decision rules. After encrypted data is transmitted to the monitoring platform via the selected channel, decryption verification is completed using a preset key, forming a closed-loop control from data acquisition to secure transmission.

[0076] Real-time parameter adjustment is achieved through response functions; existing retransmission mechanisms lack a limit on the number of retransmissions, leading to resource waste; the number of retransmissions is controlled by setting a threshold; traditional encryption methods use a single algorithm for protection; security is improved by combining multiple layers of encryption; existing channel selection relies on manual configuration; intelligent switching is achieved based on environmental parameters; existing technologies lack a closed-loop verification mechanism; this solution achieves end-to-end security control through decryption feedback.

[0077] Furthermore, by dynamically linking environmental parameters with transmission quality, data packet loss rate and transmission delay are reduced; the finite retransmission mechanism and compensation algorithm work together to ensure reliability while avoiding network congestion; multi-layer encryption combined with dynamic strength adjustment balances security protection and transmission efficiency; the intelligent channel selection mechanism adapts to the communication needs of different scenarios; and the closed-loop verification mechanism ensures the integrity and traceability of data transmission.

[0078] Example 2

[0079] This application further proposes a multi-layered encryption mechanism to encrypt transmitted data based on compensation results and transmission quality optimization results, with the encryption strength dynamically adjusted according to network security conditions. Based on the encrypted transmitted data, the optimal transmission channel is intelligently selected according to network conditions, employing adaptive hybrid communication via short-range satellite communication or Wi-Fi wide-area transmission. A response compensation mechanism is established based on the signal transmission channel to address obstacles, weather factors, and multipath fading, enabling intelligent switching of the optimal transmission channel. Using the selected optimal transmission channel, encrypted data is transmitted to the monitoring and management platform, with the response compensation mechanism continuously applied during transmission to ensure data integrity. Upon receiving the encrypted data, the monitoring and management platform decrypts and verifies the received data using a pre-distributed encryption key, obtaining the decrypted data and feeding back the reception status to the sender. Based on the decryption verification results, the decrypted data is monitored and analyzed in real time, anomalies are detected and warnings are issued, along with corresponding early warning security execution strategies, including stopping data transmission, triggering alarm mechanisms, or sending abnormal information to a remote monitoring center.

[0080] The multi-layered encryption mechanism refers to a combination of SM4 encryption, chaotic encryption, and digital signatures. Data protection can be achieved by applying different encryption algorithms in stages. Adaptive hybrid communication involves selecting between short-range satellite communication and wide-area Wi-Fi transmission based on real-time network quality. This can be achieved using a signal strength threshold judgment algorithm for communication mode switching. The multi-dimensional environmental response compensation mechanism involves establishing a parameter matrix composed of obstacle attenuation coefficients, weather impact weights, and multipath fading compensation factors. This can be achieved by using a Kalman filter algorithm to fuse multi-source sensor data for environmental parameter modeling. Pre-distributed encryption keys refer to pre-configuring symmetric keys through a secure channel. This can be achieved through a key distribution protocol for end-to-end key synchronization. The early warning security execution strategy involves triggering tiered response actions based on the anomaly level. This can be achieved through a pre-set rule engine for automated strategy execution.

[0081] Specifically, during the data transmission phase, the encryption level is dynamically selected based on the network attack risk level. For example, a chaotic encryption layer is automatically superimposed when a high-risk network attack is detected. When signal attenuation due to obstruction is detected in the StarFlash communication, the system automatically switches to a Wi-Fi wide-area transmission channel, while adjusting the transmission power through a compensation factor. The packet loss rate is continuously monitored during transmission, and a retransmission compensation mechanism is triggered when it exceeds a set threshold. The receiving end uses a preset key for decryption verification; if decryption fails, an abnormal status is immediately reported to the sending end. The decrypted data stream is processed by a pattern recognition algorithm to detect abnormal gas consumption; upon detecting abnormal peaks, the system activates local alarm devices and a remote monitoring platform.

[0082] An adaptive hybrid communication selection mechanism can improve transmission success rates in complex environments. A multi-dimensional environmental compensation mechanism overcomes the limitations of single-parameter compensation by optimizing signal quality through multi-factor collaborative adjustment. A closed-loop verification mechanism changes the traditional unidirectional transmission mode, achieving end-to-end security verification through bidirectional feedback.

[0083] Through the above technical solutions, this application achieves dynamic adaptation between encryption strength and network risk, avoiding the security vulnerabilities of traditional fixed encryption schemes. The adaptive channel selection mechanism ensures reliable transmission under different environmental conditions, while the multi-dimensional parameter compensation mechanism effectively suppresses the impact of complex environmental factors on signal quality, improving data transmission integrity. The closed-loop verification mechanism enables end-to-end monitoring of the transmission process, shortening anomaly response time. The tiered early warning strategy provides differentiated security protection measures, improving anomaly handling efficiency.

[0084] Example 3

[0085] This application further proposes a method for configuring sensor calibration parameters and distributing encryption keys to initialize the system operating environment, including setting initial response function compensation algorithm parameters and encryption algorithm parameters, establishing an abnormal gas consumption digital model based on the parameters, performing identity registration and security authentication configuration for gas meter terminal devices in conjunction with an identity recognition mechanism, and establishing a transmission channel quality assessment benchmark and response compensation strategy library based on device identity authentication results and response function compensation algorithm.

[0086] Among them, the response function compensation algorithm parameters refer to the key variables in the mathematical model used to dynamically adjust data transmission parameters. Specifically, they can be implemented using time-domain drift compensation coefficients and frequency-domain noise suppression factors to eliminate the impact of environmental interference on data transmission. The abnormal gas usage digital model refers to an analytical framework for identifying abnormal gas usage behavior through data modeling. Specifically, it can be implemented using flow fluctuation feature extraction and pattern matching algorithms to proactively detect gas theft. Identity registration refers to the process of assigning a unique identification code to each gas meter terminal and verifying its legitimacy. Specifically, it can be implemented using a digital certificate issuance mechanism based on elliptic curve cryptography to ensure the trustworthiness of device access. The transmission channel quality assessment benchmark refers to an indicator system used to quantify the performance of the communication link. Specifically, it can be implemented using a weighted evaluation model of packet loss rate, signal strength, and transmission delay to dynamically optimize transmission strategies.

[0087] Specifically, during the system initialization phase, the time-frequency domain compensation parameters in the response function compensation algorithm are first set, and the key length and encryption mode required for the encryption algorithm are configured. Based on these parameters, an abnormal gas consumption detection model is constructed, and abnormal behavior identification rules are established by analyzing the correlation characteristics between gas flow and pressure parameters. When a mismatch between flow data and pressure changes is detected, an encryption parameter update mechanism is synchronously triggered to enhance data transmission security. Furthermore, digital certificates are issued and device fingerprints are registered for each gas meter terminal, forming a dual authentication mechanism. The verified device information and compensation algorithm parameters are input into the transmission optimization module to establish a decision database containing channel quality scoring standards and a set of compensation strategies, providing a benchmark reference for subsequent dynamic adjustment of transmission power and retransmission strategies.

[0088] This invention achieves coordinated response between security protection and business monitoring through the collaborative configuration of encryption algorithm parameters and anomaly monitoring models. A two-factor authentication mechanism using digital certificates and device fingerprints enhances the security of device access. The established transmission channel quality assessment benchmark integrates device trustworthiness and environmental compensation requirements, ensuring that transmission optimization decisions simultaneously consider network security and communication quality.

[0089] The above technical solution achieves synergistic optimization of encryption parameters and anomaly detection models. The two-factor authentication mechanism for device identity effectively prevents unauthorized terminal access, and the established transmission quality assessment benchmark provides multi-dimensional decision-making basis for dynamic optimization, significantly improving the accuracy of abnormal behavior identification and the reliability of data transmission.

[0090] Example 4

[0091] This application further proposes configuring at least two different types of sensors to collect flow, temperature, and pressure data of the gas meter, as well as network signal quality information; establishing a signal transmission model based on the sensor configuration results to respond to environmental changes such as obstacle obstruction, weather factors, and multipath fading; collecting time-domain drift and frequency-domain noise of information data through a response function compensation mechanism based on the signal transmission model and sensor configuration; fusing multi-source sensor data through a Kalman filter algorithm; and constructing a multi-dimensional environmental supplementary parameter matrix containing obstacle attenuation coefficients, weather influence weights, and multipath fading compensation factors based on the fused sensor data and signal transmission model, using it as the input parameter of the response function compensation mechanism.

[0092] Among these, different types of sensors refer to various sensing devices used to collect physical quantities of gas meters and network signal parameters. Specifically, flow sensors, temperature sensors, pressure sensors, and signal strength detectors can work together to provide comprehensive data support for environmental interference analysis through multi-dimensional data acquisition. The signal transmission model refers to a mathematical model characterizing the impact of environmental factors on signal propagation. Specifically, it can be constructed by superimposing a path loss model with a weather attenuation factor to quantify the impact of obstacle obstruction and multipath fading on transmission quality. Time-domain drift and frequency-domain noise refer to the timing deviations and spectral distortions caused by environmental interference during data acquisition. Preliminary processing can be performed using timestamp calibration and bandpass filtering to provide a preprocessing foundation for subsequent data fusion. The Kalman filtering algorithm is a recursive estimation algorithm used for multi-source data fusion. Specifically, it can use a state-space model to iteratively update sensor data, eliminating the coupling effects of equipment errors and environmental noise through prediction and correction processes. The multi-dimensional environmental supplementary parameter matrix refers to a mathematical structure containing various environmental compensation factors. Specifically, matrix operations can be used to linearly combine obstacle attenuation coefficients with weather influence weights to form a dynamically adjustable set of compensation parameters.

[0093] Specifically, by combining flow and temperature sensors, data on changes in the physical properties of the gas medium can be acquired synchronously, while the collaborative operation of a pressure sensor and a network signal quality detector can capture the real-time status of the transmission channel. Based on this multi-source data, a signal transmission model transforms the impact of environmental factors on wireless signals into calculable path loss parameters, providing a theoretical basis for dynamic compensation. In the data preprocessing stage, a sliding window algorithm is used for time-domain alignment to address timing deviations generated during sensor acquisition, while a fast Fourier transform is employed to separate frequency-domain noise components.

[0094] Furthermore, a Kalman filter-based state estimation method is used to iteratively fuse multi-source data. In the prediction phase, weights are assigned based on sensor accuracy, and in the calibration phase, historical data is incorporated for error correction, effectively eliminating the combined effects of inherent equipment errors and random environmental interference. The final constructed multi-dimensional environmental compensation parameter matrix dynamically weights compensation factors for different environmental factors through matrix operations, forming a compensation parameter input set that adapts to complex environmental changes, providing a precise adjustment basis for the subsequent response function compensation mechanism.

[0095] Specifically, the fusion processing of multi-source sensor data using the Kalman filter algorithm includes:

[0096] Channel quality (such as signal-to-noise ratio, SNR) is defined as a system state variable. ;

[0097] Based on the state at time k-1, using the state transition model: Predict the state at time k. and error covariance ;in, This is the state transition matrix, which can be set according to the historical channel attenuation pattern; For control vectors (such as known environmental mutations); Its control matrix.

[0098] Observations at time k (i.e., the real-time signal-to-noise ratio observation calculated by fusing data from multiple sensors), combined with Kalman gain. The predicted state is corrected to obtain the optimal state estimate and updated error covariance at time k. Specifically:

[0099]

[0100] in, It is the observation matrix. It is the observation noise covariance, the initial value of which is obtained by statistical analysis of the sensor observation error.

[0101] Finally, the specific method for constructing the multi-dimensional environmental supplementary parameter matrix is ​​as follows: the obstacle attenuation coefficient is obtained through a pre-generated lookup table, which establishes the correspondence between different obstacle materials (such as brick walls, glass, and concrete) and thicknesses and signal attenuation values ​​(dB). The system identifies the main obstacle types through the device's built-in GIS information or preliminary signal scanning and retrieves the values ​​from the table;

[0102] The weather impact weight (β) is determined by obtaining real-time humidity and rainfall information through an external meteorological data interface. This is achieved using a linear regression model. Among them, the coefficient , and intercept It is obtained through fitting training of historical meteorological data and signal quality data.

[0103] The multipath fading compensation factor (γ) is based on the variance of the Received Signal Strength Indication (RSSI) over a period of time. Specifically: The larger the variance, the more severe the multipath effect, and the smaller the compensation factor will be.

[0104] Construct the above parameters into matrix form. As input to the response function compensation mechanism, it is used to comprehensively calculate the final transmission power and modulation adjustment amount.

[0105] This solution achieves quantitative analysis and dynamic suppression of environmental interference by establishing a collaborative mechanism between an environmental response model and Kalman filtering. This application effectively eliminates measurement deviations caused by equipment errors and environmental interference during gas meter data acquisition, and improves the stability of the transmission channel through dynamic compensation of multi-dimensional environmental parameters. Multi-sensor collaborative operation overcomes the limitations of a single data source, and the Kalman filtering algorithm achieves accurate fusion of heterogeneous data. The constructed compensation parameter matrix provides reliable input for adaptive transmission optimization in complex environments, thereby significantly improving the reliability and accuracy of gas meter data transmission.

[0106] Example 5

[0107] This application further proposes adjusting data transmission parameters based on the data transmission status and environmental conditions of the gas meter, combined with the retransmission window allocation and response function compensation mechanism of the adaptive time slot scheduling protocol; automatically activating the backup retransmission time slot window for supplementary transmission when data transmission failure or loss occurs within the static time slot transmission window, and setting a threshold for the number of retransmission attempts; dynamically adjusting the transmission power, modulation method, and time slot allocation strategy to compensate for communication blind spots in star-shaped networks, and establishing a time slot conflict detection and avoidance mechanism; establishing a transmission quality prediction model combined with a response function compensation algorithm to perform compensation adjustments in advance; and setting an adaptive learning mechanism to optimize the compensation strategy based on historical transmission data and retransmission success rate.

[0108] Specifically, the transmission quality prediction model, combined with the response function compensation algorithm, performs compensation adjustments in advance, including:

[0109] An LSTM (Long Short-Term Memory) neural network is used as the prediction model. The input to the model is a sequence of historical transmission quality data over the past N time slices, including but not limited to: signal-to-noise ratio (SNR), packet loss rate, and signal strength (RSSI).

[0110] The model outputs predicted transmission quality values ​​for the next M time slices.

[0111] When the model predicts that the transmission quality (such as signal-to-noise ratio) of a future time slot will fall below a preset safety threshold, the response function compensation mechanism will be triggered in advance. For example, if a decrease in signal-to-noise ratio is predicted, the transmit power will be increased in advance; if increased interference is predicted, the modulation scheme with stronger anti-interference capabilities will be switched in advance (such as switching from 16-QAM to QPSK).

[0112] Among them, the adaptive time slot scheduling protocol refers to a communication protocol that dynamically allocates time slot resources based on real-time channel quality. Specifically, it can be implemented using the SADR-TDMA protocol to address the low resource utilization problem caused by fixed time slot allocation. The response function compensation mechanism is a technique for dynamically correcting channel errors through a mathematical model. Specifically, it can be implemented using the Kalman filtering algorithm to eliminate the impact of environmental interference on data transmission. The backup retransmission time slot window refers to redundant time slot resources pre-configured outside the main transmission window. Specifically, it can be implemented using a time slot mapping table to immediately initiate data retransmission upon detecting packet loss. The transmission quality prediction model is a machine learning model built based on historical transmission data. Specifically, it can be implemented using an LSTM neural network to identify potential transmission failure risks in advance and trigger compensation strategies.

[0113] Specifically, in a star network environment, when data loss occurs in the primary transmission window, the system automatically activates a pre-configured backup retransmission window for supplementary transmission. By monitoring channel status in real time, the system dynamically adjusts transmit power and modulation scheme to match the current channel quality; for example, when signal attenuation is severe, it can switch to QPSK modulation and increase transmit power. A time slot conflict detection mechanism uses a time slot occupancy status monitoring table to prevent resource contention between retransmission windows and normal transmission windows. A transmission quality prediction model continuously analyzes parameters such as packet loss rate and latency in historical transmission records. When it predicts potential transmission failures within the next three time slots, it activates a compensation algorithm in advance to adjust the coding rate. An adaptive learning module dynamically optimizes the retransmission threshold setting based on retransmission success rate data from the past 24 hours; for example, it adjusts the initial threshold from 5 to 3 to balance transmission efficiency and reliability.

[0114] This solution achieves on-demand allocation of transmission resources by combining dynamic time-slot scheduling with a predictive model. An adaptive learning mechanism enables the system to continuously optimize parameter configuration, overcoming the lag inherent in manual adjustment strategies. This significantly improves data transmission success rate in star network environments. Intelligent retransmission strategies and dynamic parameter adjustments ensure data reliability while reducing energy consumption caused by redundant transmission. The application of a transmission quality prediction model gives the system proactive defense capabilities, enabling compensation measures to be implemented before transmission anomalies occur. The closed-loop optimization system formed by the adaptive learning mechanism achieves a dynamic balance between transmission efficiency and reliability.

[0115] Example 6

[0116] This application further proposes to receive and collect gas meter information transmission parameters and dynamically compensate the transmission parameters based on response function compensation. Based on the compensated transmission parameters, an appropriate encryption level and communication channel identifier information are determined. The basic data of the gas meter is subjected to layered encryption processing, and the encrypted data is fused with the channel identifier information generated by the communication channel identifier before being sent to the receiving end. The receiving end receives encrypted confirmation information returned when the channel identifier verification is successful, as well as first verification data generated by fusing the basic data and channel identifier information and encrypting them. The first verification data and encrypted confirmation information are decrypted and parsed using the encryption level to obtain the basic data. When the basic data verification is successful, the confirmation information is fused with the channel identifier information and encrypted using the encryption level to generate second verification data. The second verification data is sent to the receiving end. The receiving end receives security negotiation information generated by fusing the basic data and security key and encrypting it when the confirmation information verification is successful. The security negotiation information is decrypted and parsed using the encryption level to obtain the security key. The security key is used for secure transmission and real-time monitoring of gas meter data.

[0117] Among these features, response function compensation refers to the technical means of dynamically adjusting transmission power and modulation scheme based on real-time acquired transmission quality parameters. Specifically, it can employ a Kalman filter algorithm to fuse multi-source data, eliminating parameter fluctuations caused by environmental interference. This feature is used to address transmission instability issues caused by dynamic changes in channel conditions. Encryption layer refers to dynamically selecting encryption algorithms of different strengths based on network conditions. Specifically, it can use a combination of the SM4 algorithm and chaotic encryption. This feature is used to optimize the adaptation of encryption strength to network bandwidth. Communication channel identification information refers to a unique session identifier containing a timestamp and device ID. Specifically, it can use a hash algorithm to generate a unique channel code. This feature is used to establish a binding relationship between data and the transmission channel to prevent man-in-the-middle attacks. Security negotiation information refers to dynamically generated temporary session keys. Specifically, it can use a key exchange protocol based on elliptic curve cryptography. This feature is used to implement an end-to-end dynamic key negotiation mechanism.

[0118] Specifically, the system first collects real-time data on gas meter flow and pressure, as well as network transmission quality parameters, using sensors. It then employs a response function compensation algorithm to dynamically correct transmission power and modulation methods affected by environmental interference. The compensated transmission parameters trigger an adaptive selection mechanism for the encryption level. The mapping relationship between encryption level and channel identifier is pre-defined; for example, level 1 (high security), SM4+chaotic encryption+digital signature, is suitable for high-quality signals and transmission of sensitive metering data.

[0119] Level 2 (Medium Security), SM4+ digital signature, suitable for good signal quality and transmission of normal status data.

[0120] Level 3 (basic security), SM4 encryption, suitable for scenarios with poor signal quality where transmission efficiency must be prioritized.

[0121] After the basic data is processed by the selected encryption level, it is securely integrated with the communication channel identification information (generated by the hash algorithm SHA-256 from the device ID, timestamp, and random number) to form a transmission data packet.

[0122] Subsequently, a two-way authentication and key negotiation process is executed. When the receiving end receives the data packet, it first verifies the validity of the channel identifier (such as timestamp freshness). After successful verification, it returns an "acknowledgment message (ACK)" encrypted using the encryption level negotiated. At the same time, the receiving end recombines the decrypted basic data with the channel identifier, calculates its message authentication code (MAC), and encrypts and returns this MAC as "first verification data".

[0123] The sending end decrypts the ACK and the first verification data. It verifies the integrity of the underlying data during transmission by comparing its calculated MAC address with the received first verification data. After successful verification, the sending end merges the ACK with the channel identifier to generate "second verification data," which is then encrypted and sent to the receiving end.

[0124] After the receiving end verifies the second verification data, it confirms the sender's legitimacy. Subsequently, it dynamically generates a temporary session key using a key exchange protocol based on elliptic curve cryptography (such as SM2) (ECDH). This session key is then encrypted with the underlying data and sent to the sending end as "security negotiation information."

[0125] The sending end decrypts the security negotiation information to obtain the temporary session key. Subsequently, both parties use this temporary session key for secure transmission and real-time monitoring of gas meter data, replacing the initially distributed fixed key and effectively improving security.

[0126] Another preferred approach is to automatically downgrade to lightweight encryption when the network signal strength falls below a threshold to ensure timely transmission. Basic data, after layered encryption, is fused and encapsulated with channel identification information to form a data packet with a channel authentication tag. The receiving end verifies the legitimacy of the channel identifier and generates an encrypted confirmation message containing a timestamp; the sending end decrypts and verifies the identities of both communicating parties. The first and second verification data generated during the bidirectional verification process form a closed-loop verification mechanism to ensure data integrity and transmission channel security. Finally, a temporary session key is dynamically generated through secure negotiation information to replace the fixed key for subsequent data transmission, effectively preventing the risk of key leakage due to long-term key use.

[0127] This solution optimizes communication quality in real time through a dynamic compensation mechanism for transmission parameters, and achieves a balance between security and transmission efficiency by adaptively adjusting the encryption level. A two-way channel verification mechanism effectively prevents man-in-the-middle attacks, and the dynamic key negotiation process breaks away from the traditional fixed-key management model, significantly improving the security of data transmission. Compared to existing one-way authentication mechanisms, the closed-loop verification system constructed in this solution can detect and block data tampering.

[0128] Stable communication quality is maintained through a dynamic compensation mechanism. Intelligent matching of encryption strength to network conditions avoids transmission delays caused by high-strength encryption in traditional schemes, while also preventing data leakage risks associated with low-security encryption. A dynamic key negotiation mechanism enables the temporary generation and periodic updating of session keys, eliminating security vulnerabilities arising from the long-term use of fixed keys. The fusion of channel identifiers and data packets ensures the traceability of data transmission paths, and a dual authentication mechanism effectively improves the reliability of end-to-end communication.

[0129] Example 7

[0130] This application further proposes a multi-layered encryption mechanism that employs SM4 algorithm encryption, chaotic encryption, and digital signature for multiple encryption protection, establishes a key storage and management mechanism, establishes a dynamic key upgrade response mechanism, and sets an adaptive adjustment function for encryption strength.

[0131] Among them, SM4 encryption refers to symmetric encryption of data using a block cipher algorithm, specifically implemented with a 32-round nonlinear iterative structure, to ensure data confidentiality. Chaotic encryption uses pseudo-random sequences generated by a chaotic system to obfuscate data, specifically using a Logistic mapping to generate a keystream, to enhance the unpredictability of the encryption process. Digital signature refers to generating data integrity verification codes based on asymmetric encryption algorithms, specifically implemented using the SM2 elliptic curve algorithm, to verify data origin and prevent tampering. The key storage and management mechanism protects keys through physical security modules and access control policies, specifically using a hardware security module to store the master key to prevent key leakage. The dynamic key upgrade response mechanism triggers key updates based on security threat detection results, specifically using a heartbeat packet monitoring mechanism to identify key leakage risks, to proactively eliminate security vulnerabilities after key breaches. The adaptive encryption strength adjustment function automatically matches encryption algorithm combinations based on data type, specifically using a strategy of triggering triple encryption for sensitive data and single-layer encryption for ordinary data, to balance security and transmission efficiency.

[0132] Specifically, during the gas meter data transmission process, the raw data is first encrypted using the SM4 algorithm to form confidential ciphertext. A chaotic encryption module performs secondary obfuscation on the SM4 ciphertext, using a dynamic key stream generated by the chaotic system to perform an XOR operation on the data, eliminating fixed pattern characteristics in the ciphertext. A digital signature module adds authentication information to the double-encrypted data, generating a signature value using a private key and binding it to the ciphertext for transmission. The key management system stores the master key through a hardware security module and derives a temporary session key for each encryption operation, ensuring the security of key usage. When an abnormal login attempt or key verification failure is detected, the key upgrade mechanism immediately issues a new key through a secure channel to replace the old key, while simultaneously updating the key versions of all associated devices. The encryption strength adjustment module analyzes data types in real time; for example, metering data automatically uses SM4 + chaotic dual encryption, while status data uses only SM4 single-layer encryption, thereby optimizing system resource consumption.

[0133] Using a single encryption algorithm with the key permanently stored in Flash memory has drawbacks: the encryption method is easily cracked due to its simplistic nature, and the key cannot be updated promptly after leakage. This solution employs a combination of multiple encryption algorithms to create complementary protection. It leverages the dynamic characteristics of chaotic encryption to compensate for the limitations of fixed algorithms, blocks physical attack paths through hardware-level key management, and establishes a dynamic matching relationship between encryption strength and data value. This application achieves multi-layered encryption protection for gas meter data. It enhances anti-cracking capabilities through algorithm combination, establishes proactive defense capabilities through a dynamic key update mechanism, and optimizes system resource allocation through adaptive adjustment of encryption strength. This ensures the security of highly sensitive data transmission while reducing the processing overhead of low-value data, constructing a balanced system that considers both security strength and communication efficiency.

[0134] Example 8

[0135] This application further proposes transmission parameters for receiving and collecting gas meter information, and dynamically compensates these parameters based on response function compensation. This includes real-time acquisition of basic data such as gas meter flow, pressure, and temperature, as well as transmission quality parameters such as signal strength, transmission delay, and packet loss rate. A transmission parameter evaluation model is established to comprehensively analyze the impact of current network conditions and environmental factors on data transmission. A response function compensation algorithm is used to dynamically adjust key parameters such as transmission power, modulation method, and coding rate. Parameter compensation thresholds and response times are set to ensure the real-time performance and effectiveness of the compensation mechanism. A compensation effect feedback mechanism is established to continuously optimize the compensation strategy based on the compensated transmission quality.

[0136] The response function compensation algorithm refers to a mathematical model that dynamically adjusts transmission parameters based on changes in environmental parameters. Specifically, it can be implemented using a Kalman filter algorithm combined with a linear regression model to eliminate the impact of channel fluctuations on data transmission. The parameter compensation threshold refers to the critical condition that triggers transmission parameter adjustment. This can be dynamically set through historical transmission data analysis to avoid ineffective compensation actions. The response time refers to the time window from detecting a transmission anomaly to completing parameter adjustment. This can be implemented using a priority queue scheduling mechanism to ensure that critical parameters are processed first. The compensation effect feedback mechanism is a closed-loop control process that iteratively optimizes the compensation strategy based on transmission quality indicators. This can be implemented using a sliding window statistical method combined with a gradient descent algorithm to continuously improve compensation accuracy.

[0137] Specifically, during gas meter operation, flow, pressure, and temperature data are collected in real time via sensors, while network quality parameters such as signal strength, transmission delay, and packet loss rate are monitored. A transmission parameter evaluation model establishes a correlation mapping between ambient temperature, obstacle distribution, and network signal attenuation, analyzing the impact of multipath fading on data transmission. A response function compensation algorithm generates transmission power adjustment coefficients based on the evaluation results; for example, it increases transmission power when signal strength is below a threshold and switches modulation methods when packet loss rate is too high. The parameter compensation threshold is set to a dynamic range; for example, the compensation mechanism is triggered when the packet loss rate exceeds 5% three times consecutively, with the response time controlled within 200 milliseconds to complete parameter adjustment. A compensation effect feedback mechanism monitors changes in the packet loss rate after compensation, for example, updating the compensation strategy weight coefficients using an exponentially weighted moving average method to achieve adaptive optimization.

[0138] This solution achieves precise triggering of compensation actions by establishing a dynamic correlation model between transmission parameters and environmental factors; avoids transmission interruptions caused by compensation delays by setting response time constraints; and enables the compensation strategy to continuously learn through a closed-loop feedback mechanism. It achieves precise matching between transmission parameters and environmental conditions by dynamically collecting multi-dimensional data and establishing an evaluation model; ensures the timeliness and effectiveness of compensation actions by setting thresholds and response time constraints; and continuously optimizes the compensation strategy through a closed-loop feedback mechanism to maintain stable transmission quality. This solution can automatically maintain optimal transmission conditions in complex network environments, effectively reducing data packet loss rates and improving the reliability of gas meter data transmission.

[0139] Example 9

[0140] This application further proposes a technical solution for determining the appropriate encryption level and communication channel identification information based on the compensated transmission parameters. This includes dynamically selecting the encryption method based on the transmission quality assessment results, adaptively adjusting the encryption level according to data sensitivity and network security status, generating unique channel identification information for each communication session, establishing a mapping relationship between the encryption level and the channel identification, and setting a dynamic update mechanism for encryption parameters.

[0141] The transmission quality assessment result refers to the real-time quantitative analysis of the network transmission environment. Specifically, it can be implemented using a comprehensive scoring model based on indicators such as packet loss rate, latency, and signal strength, reflecting the current channel reliability. Encryption level refers to the hierarchical configuration of data encryption strength, which can be determined by a combination of parameters such as algorithm complexity, key length, and number of encryption rounds, matching different security level requirements. Channel identification information refers to the unique identifier of the communication link, which can be implemented using a hash algorithm combined with timestamps, device physical addresses, and random numbers to generate a string, preventing session hijacking and replay attacks. Mapping relationship refers to the correspondence between encryption strength and transmission channel security level, which can be established using lookup tables or decision tree models to ensure that high-security channels match high-strength encryption algorithms. Dynamic update mechanism refers to the periodic change strategy for encryption elements, which can be implemented using timers to trigger key refresh and identifier reconstruction, reducing the risk of long-term key leakage.

[0142] Specifically, during gas meter data transmission, when the network packet loss rate is below a threshold and the signal strength is stable, the system automatically selects the SM4 algorithm to encrypt abnormal pressure data; if network latency increases or interference noise exists, it switches to chaotic encryption to process regular temperature sampling values. At the start of each communication session, a unique channel identifier is generated by fusing the current time, device serial number, and random seed using a hash function. This identifier cannot be repeated within the session period. The encryption level decision module automatically matches the encryption strategy based on the channel type. For example, the StarSpark short-range communication link uses dual encryption of SM4 and digital signature, while the public Wi-Fi channel only uses chaotic encryption. At preset intervals, the system triggers a key update process, regenerating the session key and refreshing the timestamp parameter in the channel identifier.

[0143] Using fixed encryption algorithms cannot adapt to network fluctuations. For example, using high-complexity encryption when channel quality deteriorates leads to increased transmission delays. This solution dynamically adjusts the encryption method, automatically switching to a lightweight algorithm when channel quality declines to maintain transmission efficiency. Existing technologies using fixed channel identifiers are easily counterfeited. This solution generates a unique identifier by integrating multiple factors, effectively resisting replay attacks. Traditional key management uses a long-term fixed model, which poses a risk of leakage. This solution reduces the possibility of key cracking through a periodic update mechanism.

[0144] This application achieves dynamic adaptation of encryption strength to the network environment, maintaining secure transmission efficiency when channel quality fluctuates; enhances the anti-attack capability of communication sessions through unique channel identifiers and dynamic key updates; and optimizes the balance between resource consumption and security protection through a layered encryption strategy.

[0145] Example 10

[0146] This application further proposes to compare the analysis results with a preset anomaly model to identify potential transmission risks; to achieve digital identification of abnormal gas usage and automatically generate work orders through a big data analysis model; to establish multi-dimensional data correlation analysis to improve the accuracy of anomaly detection; to set up an intelligent early warning response mechanism to trigger the processing flow according to the anomaly level; to adopt the SADR-TDMA protocol for adaptive time slot scheduling, combining static time slot determinism with dynamic retransmission flexibility, to compensate for the uncertainty of the wireless environment through response functions and to establish a time slot collision detection mechanism; and to comprehensively consider obstacle obstruction, weather factors and multipath fading parameters in the signal transmission model, to establish a multi-dimensional environmental response compensation mechanism to dynamically adjust transmission power and frequency, and to set up a signal quality prediction algorithm and an environmental adaptive learning mechanism to optimize the transmission model parameters.

[0147] The SADR-TDMA protocol is a hybrid protocol integrating static time slot allocation and dynamic retransmission scheduling. Specifically, it employs time slot pre-allocation and a dynamic retransmission window triggering mechanism, and avoids channel contention through a collision detection algorithm. Its role is to balance transmission efficiency and reliability. The multi-dimensional environmental response compensation mechanism is a parameter matrix integrating obstacle attenuation coefficients, weather impact weights, and multipath fading compensation factors. It can be implemented using sensor data fusion and Kalman filtering algorithms to dynamically correct transmission parameter deviations. The signal quality prediction algorithm is a time-series prediction model based on historical transmission data, implemented using an LSTM neural network to adjust transmission power in advance to avoid quality degradation. The environmental adaptive learning mechanism is a parameter self-optimization module, implemented using a reinforcement learning framework to continuously optimize transmission model parameters through feedback data.

[0148] Specifically, the anomaly model comparison phase identifies transmission risks deviating from normal thresholds by matching preset flow mutation patterns with a pressure fluctuation feature library. The big data analysis model uses clustering algorithms to mine the spatiotemporal characteristics of gas usage behavior, automatically generating work orders containing location information when abnormal gas usage patterns are detected. Multi-dimensional data correlation analysis jointly models flow fluctuations and signal strength changes, eliminating false positives through Pearson correlation coefficient calculation. The intelligent early warning response mechanism triggers handling procedures based on risk scores, such as triggering local alarms for low-risk situations and remote gas shut-off for high-risk situations. The SADR-TDMA protocol sets up a retransmission priority queue within a fixed time slot framework, prioritizing the allocation of dynamic time slots for compensation transmission when packet loss is detected. The multi-dimensional environmental response compensation mechanism adjusts the transmission power by calculating obstacle attenuation coefficients in real time and dynamically selects communication frequency bands based on weather impact weights. The signal quality prediction algorithm trains a prediction model based on historical channel state information, adjusting error correction coding redundancy in advance. The environmental adaptive learning mechanism updates compensation factor weights through transmission success rate feedback, continuously optimizing transmission parameter configuration.

[0149] This solution utilizes the SADR-TDMA protocol to achieve coordinated scheduling of static and dynamic time slots, effectively resolving transmission failures caused by channel contention. It significantly improves detection accuracy in complex scenarios through multi-dimensional data correlation analysis. Existing early warning systems employ fixed response strategies; this solution's hierarchical response mechanism achieves optimal allocation of response resources. Real-time optimization of the transmission strategy is achieved through multi-dimensional environmental parameter fusion and dynamic compensation mechanisms. Anomaly detection accuracy is effectively improved, and the risk of false alarms and missed alarms is reduced based on multi-dimensional correlation analysis. The transmission protocol possesses adaptive optimization capabilities, adapting to changes in complex network environments through the SADR-TDMA protocol and learning mechanism. The early warning and response process achieves automated closed-loop management, significantly improving anomaly response efficiency. Signal transmission quality is proactively adjusted through predictive algorithms, avoiding transmission delays caused by passive compensation.

[0150] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A gas meter information security transmission method based on response function compensation, characterized in that, The application relates to a gas meter data transmission method and system. The application comprises the following steps: configuring sensor calibration parameters and distributing encryption keys to initialize a system running environment, obtaining an initialization configuration result; based on the initialization configuration result, obtaining gas flow velocity, environmental temperature, pressure fluctuation and communication signal strength information of the gas meter, and taking the collected data as input parameters of response function compensation; based on the obtained data transmission condition and transmission environment condition of the gas meter, dynamically optimizing data transmission configuration by using a response function compensation mechanism, and obtaining a compensation result; wherein the response function compensation mechanism adopts a Kalman filtering algorithm, takes channel quality as a state variable, performs state prediction and observation update based on multi-source sensor fusion data, and outputs state estimation of the compensated channel quality; a multi-dimensional environmental compensation parameter matrix containing obstacle attenuation coefficients, weather influence weights and multipath fading compensation factors is constructed based on the collected data and taken as input parameters of the response function compensation mechanism, so that transmission power and modulation mode are dynamically adjusted; the retransmission window allocation based on the adaptive time slot scheduling protocol is combined with the response function compensation mechanism; when data transmission failure or loss occurs in the static time slot, a backup retransmission time slot window is automatically enabled for supplementary transmission, and a threshold value of the number of attempts for retransmission is set; the response function compensation channel error or collision caused packet loss is retransmitted; wherein the SADR-TDMA adaptive time slot scheduling protocol is adopted to manage time slot allocation and retransmission mechanism of data transmission; the SADR-TDMA protocol comprises static time slots and dynamic retransmission time slot windows, and collision detection is performed through a time slot occupation state monitoring table; when data transmission failure occurs in the static time slot, a dynamic retransmission time slot window is automatically allocated for retransmission, and the number of retransmissions is set to 3 times; the adaptive encryption strength is determined according to the compensated transmission parameters; the transmission data is encrypted based on the encryption strength, and the encryption strength is dynamically adjusted according to the network security condition; the best transmission channel is intelligently selected according to the network condition; different encryption levels are selected according to the encryption strength; 2. The gas meter information security transmission method according to claim 1, characterized in that, the encrypted data is transmitted to the monitoring management platform by using the selected best transmission channel. the adaptive encryption strength is determined according to the compensated transmission parameters; the transmission data is encrypted based on the encryption strength, and the encryption strength is dynamically adjusted according to the network security condition; based on the transmission data after the encryption processing is completed, the best transmission channel is intelligently selected according to the network condition, and adaptive hybrid communication is performed through star flash short-distance communication or Wi-Fi wide-area transmission; a response compensation mechanism of multi-dimensional environmental parameters of obstacles, weather factors and multipath fading is established based on the signal transmission channel, and intelligent switching of the best transmission channel is realized; the encrypted data is transmitted to the monitoring management platform by using the selected best transmission channel, and the response compensation mechanism is continuously applied in the transmission process to ensure data integrity; after receiving the encrypted data, the monitoring management platform decrypts and verifies the received data based on the pre-distributed encryption keys, obtains the decrypted data, and feeds back the receiving state to the sending end. Based on the decryption verification result, the decrypted data is monitored and analyzed in real time, abnormality is found for early warning, and corresponding early warning safety execution strategy is provided, including stopping data transmission, triggering alarm mechanism or sending abnormal information to remote monitoring center.

3. The gas meter information security transmission method of claim 1, wherein, The sensor calibration parameters are configured and the encryption key is distributed to initialize the system running environment, including: Set the initial response function compensation algorithm parameters and the parameters of the encryption algorithm as the basic configuration of data transmission optimization and security encryption; wherein the response function compensation algorithm parameters include time domain drift compensation coefficient and frequency domain noise suppression factor; the response function compensation algorithm adopts Kalman filter algorithm, takes channel quality as state variable, iteratively calculates through state transition model and observation update model, and outputs transmission power adjustment and modulation mode adjustment; the parameters of the encryption algorithm include key length, encryption mode and encryption level configuration; Based on the parameter, an abnormal gas digital model is established to actively find the abnormal behavior of stealing gas, and the abnormal monitoring is cooperated with the encryption algorithm parameters; Based on the abnormal monitoring, the identity recognition mechanism is combined to perform identity identification registration and security authentication configuration on the gas meter terminal device, to obtain device identity authentication result, and to ensure that the device that needs to be verified accesses the gas meter; Based on the device identity authentication result and the response function compensation algorithm, a transmission channel quality evaluation benchmark and a response compensation strategy library are established to provide decision basis for dynamic optimization of data transmission configuration; wherein in the system initialization stage, the abnormal gas detection model is constructed by setting time-frequency domain compensation parameters and encryption configuration, the encryption parameter update is triggered when the flow and pressure are not matched, and the transmission optimization decision database based on channel quality score and compensation strategy is established combined with the digital certificate and device fingerprint dual authentication information of the gas meter terminal.

4. The gas meter information security transmission method of claim 1, wherein, At least two different types of sensors are configured to collect flow, temperature, pressure data and network signal quality information of the gas meter; Based on the sensor configuration result, a signal transmission model is established to respond to obstacles, weather factors and multipath fading environmental changes as the basic configuration of data transmission optimization; Based on the signal transmission model and sensor configuration, the time domain drift and frequency domain noise of information data are collected through the response function compensation mechanism, the multi-source sensor data is fused and processed through Kalman filter algorithm, and the environmental interference and device error in the data collection process are eliminated; wherein the time domain drift and frequency domain noise are collected by detecting time sequence deviation through sliding window algorithm and separating frequency spectrum distortion through fast Fourier transform, and the detected time domain drift and frequency domain noise are taken as observation noise covariance parameters; Based on the fused sensor data and signal transmission model, a multi-dimensional environmental supplementary parameter matrix containing obstacle attenuation coefficient, weather influence weight and multipath fading compensation factor is constructed, wherein the obstacle attenuation coefficient is obtained by looking up table, the weather influence weight is calculated by linear regression model according to humidity and rainfall, and the multipath fading compensation factor is calculated according to the variance of received signal strength indication; the multi-dimensional environmental supplementary parameter matrix linearly combines the obstacle attenuation coefficient and the weather influence weight by matrix operation to form a dynamically adjustable compensation parameter set.

5. The gas meter information security transmission method of claim 1, wherein, Based on the gas meter data transmission condition and transmission environment condition, the data transmission efficiency and safety of transmission parameters are dynamically adjusted by combining the retransmission window allocation and response function compensation mechanism of adaptive time slot scheduling protocol; When data transmission failure or loss occurs in the static time slot transmission window, the backup retransmission time slot window is automatically enabled for supplementary transmission, and the number of attempts for retransmission threshold is set; Through the response function, real-time parameter dynamic adjustment is realized, and by combining the limited retransmission threshold control mechanism, the channel quality, i.e. signal-to-noise ratio, is taken as a state variable, and the optimal estimation is obtained by predicting and correcting the observation value through the state transition model; a multi-dimensional environmental supplementary parameter matrix containing obstacle attenuation coefficient, weather influence weight calculated based on meteorological data linear regression model and multipath fading compensation factor dynamically adjusted according to RSSI variance is constructed; The transmission power, modulation mode and time slot allocation strategy are dynamically adjusted to compensate for the communication blind area in star networking, and a time slot conflict detection and avoidance mechanism is established to ensure the coordinated operation of the retransmission window and the normal transmission window; A transmission quality prediction model is established, and compensation adjustment is made in advance by combining the response function compensation algorithm to predict potential transmission failure scenarios and pre-configure backup retransmission strategies; wherein the transmission quality prediction model uses LSTM neural network, the input is the signal-to-noise ratio, packet loss rate and signal strength historical data of the past N time slices, and the output is the transmission quality prediction value of the future M time slices; when the prediction value is lower than the safety threshold, the response function compensation mechanism is triggered in advance to adjust the transmission power, modulation mode or coding rate; An adaptive learning mechanism is set to optimize the compensation strategy according to the historical transmission data and retransmission success rate, dynamically adjust the retransmission number threshold and time slot window allocation parameters, and balance the transmission efficiency and reliability.

6. The gas meter information security transmission method of claim 1, wherein, The transmission parameters of the collected gas meter information are received, and the transmission parameters are dynamically compensated based on the response function compensation; According to the compensated transmission parameters, the adaptive encryption level and communication channel identification information are determined; The basic data of the gas meter is processed by layered encryption, and the encrypted data and the channel identification information generated by the communication channel identification are fused and sent to the receiving end; The encrypted confirmation information returned by the receiving end when the channel identification verification is passed is obtained, and the first verification data generated by fusing and encrypting the basic data and the channel identification information is obtained; The first verification data and the encrypted confirmation information are decrypted using the encryption level, and the basic data is obtained by parsing; When the basic data verification passes, the confirmation information is fused with the channel identification information, and the second verification data is generated by encryption using the encryption level; the second verification data is sent to the receiving end; Obtain the security negotiation information sent by the receiving end when the confirmation information verification passes, which is fused and encrypted by the basic data and the security key; The security negotiation information is decrypted using the encryption level, and the security key is obtained by parsing; The security key is used for secure transmission and real-time monitoring of gas meter data.

7. The gas meter information security transmission method according to claim 6, characterized in that, According to the encryption strength, different encryption levels are selected, including: Different encryption levels use SM4 algorithm encryption, chaos encryption and digital signature for multiple encryption protection; Establish a key storage and management mechanism to ensure the security of the key; Establish a key dynamic upgrade response mechanism. When the key is detected to be attacked, the key is updated in real time through a secure upgrade channel or over-the-air download to compensate for security transmission vulnerabilities; Set the encryption strength self-adaptive adjustment function to select the appropriate encryption level according to the data sensitivity.

8. The gas meter information security transmission method of claim 6, wherein, Receive the transmission parameters of the collected gas meter information, and dynamically compensate the transmission parameters based on the response function, including: Real-time acquisition of flow, pressure, temperature basic data and signal strength, transmission delay, packet loss rate transmission quality parameters of gas meter; Establish a transmission parameter evaluation model to comprehensively analyze the influence of current network conditions and environmental factors on data transmission; wherein the transmission parameter evaluation model establishes a correlation mapping between environmental temperature, obstacle distribution and network signal attenuation, and analyzes the influence degree of multipath fading on data transmission; Use the response function compensation algorithm to dynamically adjust the key parameters of transmission power, modulation mode and coding rate; wherein the response function compensation algorithm generates a transmission power adjustment coefficient according to the evaluation results, and increases the transmission power when the signal strength is lower than the threshold, and switches the modulation mode when the packet loss rate is too high; Set the parameter compensation threshold and response time to ensure the real-time and effectiveness of the compensation mechanism; wherein the parameter compensation threshold is set to trigger the compensation mechanism when the packet loss rate exceeds 5% for three consecutive times, and the basic threshold is determined by historical transmission data statistical analysis and dynamically adjusted according to environmental factors; the response time is controlled within 200 milliseconds to complete parameter adjustment, and a priority queue scheduling mechanism is used to ensure that key parameters are processed first; Establish a compensation effect feedback mechanism to continuously optimize the compensation strategy according to the transmission quality after compensation; wherein the compensation effect feedback mechanism monitors the change of packet loss rate after compensation, updates the compensation strategy weight coefficient using the exponential weighted moving average method, and realizes adaptive optimization.

9. The gas meter information security transmission method according to claim 8, characterized in that, According to the compensated transmission parameters, determine the adaptive encryption level and communication channel identification information, including: According to the compensated transmission parameters, dynamically select the encryption methods of different strengths of SM4 algorithm, chaos encryption or digital signature; According to the data sensitivity and network security situation, adaptively adjust the encryption level to balance security and transmission efficiency; Generate unique channel identification information for each communication session, including timestamp, device ID and session key elements; Establish a mapping relationship between the encryption level and the channel identification to ensure that the encryption strength matches the channel security level; A dynamic encryption parameter updating mechanism is set to periodically refresh encryption keys and channel identifiers.

10. The gas meter information security transmission method of claim 4, wherein, Further comprising: Abnormal gas use is digitally identified through a big data analysis model, and work orders are automatically generated and distributed to field personnel; Multi-dimensional data correlation analysis is established to improve the accuracy of anomaly detection; An intelligent early warning response mechanism is set up to automatically trigger the corresponding processing procedures according to the abnormal level; The adaptive time slot scheduling protocol adopts an adaptive time slot scheduling protocol, combines the certainty of static time slots with the flexibility of dynamic retransmission, compensates for the uncertainty of the wireless environment through a response function, establishes a time slot conflict detection and avoidance mechanism, sets up a protocol adaptive optimization function, and dynamically adjusts the protocol parameters according to the network conditions; wherein the response function adopts a Kalman filter algorithm, the adaptive time slot scheduling protocol is a communication protocol that dynamically allocates time slot resources according to real-time channel quality, and can specifically adopt an SADR-TDMA protocol to achieve; the backup retransmission time slot window is a redundant time slot resource pre-configured outside the main transmission window, and can be specifically implemented through a time slot mapping table, and is used to start data retransmission immediately when packet loss is detected; when the transmission quality prediction model predicts that the signal-to-noise ratio of a future time slot will be lower than a preset safety threshold, the response function compensation mechanism will be triggered in advance; The signal transmission model comprehensively considers the influence of obstacle shielding, weather factors, and multipath fading environmental parameters on signal propagation, establishes a multi-dimensional environmental response compensation mechanism, dynamically adjusts the transmission power and frequency to optimize the signal coverage range and transmission stability, establishes a signal quality prediction algorithm, adjusts the transmission parameters in advance, and sets up an environmental adaptability learning mechanism to optimize the transmission model parameters; wherein the environmental adaptability learning mechanism adopts a reinforcement learning framework, updates the compensation factor weight through transmission success rate feedback, constructs a transmission quality prediction model based on historical transmission data, adopts an LSTM neural network to achieve, and is used to identify potential transmission failure risks in advance and trigger compensation strategies.

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