Gas leakage detection system based on 5G and Beidou satellite

Abstract

The invention relates to the technical field of leakage detection, and discloses a gas leakage detection system based on 5G and Beidou satellites. Comprising a multi-modal sensor module, a data acquisition and processing module, a 5G communication and hybrid network module, a multi-source fusion positioning module, a data storage and analysis module, an alarm and control module, a user interface and monitoring platform module and a power supply and energy efficiency management module. The multi-mode sensor module is used for integrating multiple gas detection technologies, can detect the concentration and components of multiple gases at the same time, and has self-repairing and self-diagnosis functions. By integrating spectrum detection, chemical induction and electrochemical sensor technologies, multiple gas types can be detected at the same time, the possibility of false alarm and missing alarm is effectively reduced through an intelligent fusion algorithm, and compared with a traditional single sensor system, the sensitivity and precision of gas leakage detection are remarkably improved through multi-modal fusion.

Description

Technical Field

[0001] The present invention relates to the technical field of leakage detection, and specifically to a gas leakage detection system based on 5G and Beidou satellite. Background Art

[0002] With the advancement of industrialization, gas leak detection plays an important role in the fields of petrochemical, natural gas transportation, pharmaceuticals and mining. Traditional gas leak detection systems rely on a single type of sensor, which can detect a specific type of gas and is easily interfered with in complex environments, resulting in false alarms or missed alarms. In addition, traditional sensor systems lack adaptive capabilities and cannot cope with dynamic changes in environmental conditions, which reduces the accuracy of the detection system in long-term use or requires frequent maintenance, increasing maintenance costs and reducing system reliability.

[0003] The existing gas leak detection system adopts a centralized data processing architecture, which transmits sensor data to a central server for processing and analysis. The centralized processing architecture faces high data transmission delays and the real-time response capability of the system is limited.

[0004] In terms of positioning technology, the existing gas leak detection system relies on a single GPS or Beidou satellite positioning technology. Its satellite positioning has a good effect in an open environment, but in a closed, indoor or underground environment, the positioning accuracy is reduced due to signal obstruction;

[0005] At the same time, the existing gas leak detection system lacks intuitive support for on-site operators. Traditional alarm and information transmission methods rely on text or sound prompts. Operators are unable to quickly locate leaks and handle them effectively in complex on-site environments, resulting in inefficient emergency response and increased difficulty and risk in accident handling.

[0006] Therefore, those skilled in the art provide a gas leakage detection system based on 5G and Beidou satellite to solve the problems raised in the above background technology. Summary of the invention

[0007] In view of the shortcomings of the prior art, the present invention provides a gas leakage detection system based on 5G and Beidou satellite to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a gas leakage detection system based on 5G and Beidou satellite, including a multimodal sensor module, a data acquisition and processing module, a 5G communication and hybrid network module, a multi-source fusion positioning module, a data storage and analysis module, an alarm and control module, a user interface and monitoring platform module, and a power supply and energy efficiency management module;

[0009] The multimodal sensor module is used to integrate multiple gas detection technologies, can detect the concentration and composition of multiple gases at the same time, and has self-repair and self-diagnosis functions;

[0010] The data acquisition and processing module is responsible for preliminary data processing and analysis near the sensor nodes, using machine learning algorithms for dynamic data processing and having real-time collaborative processing capabilities;

[0011] The 5G communication and hybrid network module performs high-priority, low-latency data transmission based on the 5G network, combines low-power wide area network technology, automatically switches the communication mode when 5G coverage is insufficient, and dynamically adjusts the communication strategy according to network load and environmental conditions;

[0012] The multi-source fusion positioning module is used to integrate multiple positioning technologies and use reality technology to provide users with intuitive positioning information and navigation guidance;

[0013] The data storage and analysis module is used to store multimodal data collected by sensors, and use distributed computing and big data analysis technology to conduct in-depth analysis of historical data and real-time data to identify potential leakage risks and trends;

[0014] The alarm and control module combines AI technology to make intelligent judgments based on real-time data and historical patterns, triggering precise alarms and automated control measures;

[0015] The user interface and monitoring platform module displays real-time data, location and analysis reports to users through a graphical user interface or mobile device, and combines AR technology to provide real-time environmental information and leakage point guidance to on-site personnel;

[0016] The power supply and energy efficiency management module is equipped with multiple power supply modes and automatically optimizes power distribution and use through the energy efficiency management system.

[0017] Preferably, the multimodal sensor module comprises a gas detection sensor unit, a self-calibration unit, and a self-repairing unit; the gas detection sensor unit integrates a plurality of sensors for detecting different types of gas leaks; the self-calibration unit is used to automatically calibrate the sensitivity and accuracy of the sensor; the self-repairing unit automatically repairs or switches to a spare sensor when a sensor failure is detected;

[0018] The data acquisition and processing module includes an edge computing unit, a dynamic data processing unit, and a collaborative processing unit; the edge computing unit performs preliminary data processing and analysis near the sensor node; the dynamic data processing unit uses a machine learning algorithm to perform real-time data analysis and dynamically adjust the data processing process; the collaborative processing unit realizes data collaborative processing of multiple sensor nodes;

[0019] The 5G communication and hybrid network module includes a 5G communication unit, a wide area network switching unit, and an adaptive network unit; the 5G communication unit uses the 5G network for high-speed, low-latency data transmission; the wide area network switching unit automatically switches to a low-power wide area network when the 5G signal coverage is insufficient or the signal is limited; the adaptive network unit dynamically adjusts the communication strategy according to the real-time network load and environmental conditions.

[0020] Preferably, the multi-source fusion positioning module includes a Beidou satellite positioning unit, a UWB positioning unit, an inertial navigation unit, and a fusion positioning engine unit; the Beidou satellite positioning unit is used for preliminary positioning of the leakage point; the UWB positioning unit achieves centimeter-level positioning accuracy; the inertial navigation unit provides short-term precise positioning through the inertial navigation system in the absence of external signals; the fusion positioning engine unit fuses multiple positioning data to generate comprehensive location data;

[0021] The data storage and analysis module includes a data storage unit, a distributed computing unit, and a risk assessment and prediction unit; the data storage unit is used for multimodal data and historical data collected by sensors; the distributed computing unit uses distributed computing technology to process and analyze massive data, and supports real-time and offline data analysis; the risk assessment and prediction unit is used to analyze historical data and real-time data, assess leakage risks, and predict potential leakage trends;

[0022] The alarm and control module includes an intelligent alarm unit, an automatic control unit, and a remote control unit; the intelligent alarm unit intelligently triggers an alarm according to real-time data and historical data patterns; the automatic control unit automatically executes predetermined control measures after detecting a gas leak; the remote control unit allows remote control and monitoring of the system.

[0023] Preferably, the gas detection sensor unit uses Bayesian fusion of signals from different sensors;

[0024] Assume that sensors S1, S2, ..., S n The detected gas concentrations are x1, x2, …, x n , the fused estimate It is expressed as:

[0025]

[0026] Among them, w i is the weight of each sensor signal, and the weight is calculated by the following formula:

[0027]

[0028] in, It is sensor Si The measurement noise variance of

[0029] The self-calibration unit uses a recursive least square method to calibrate the parameters of the sensor;

[0030] Assume that the relationship between the sensor output y(t) and input u(t) is:

[0031] y(t)=θ1u(t)+θ2u(t-1)+…+θ n u(t-n+1)+∈(t),

[0032] Among them, θ i The calibration parameters, ∈(t) measure the noise, and the RLS algorithm is used to estimate the parameters θ,

[0033] θ(t)=θ(t-1)+K(t)[y(t)-φ T (t)θ(t-1)],

[0034] Among them, φ(t)=[u(t),u(t-1),…,u(t-n+1)] T is the input vector, K(t) is the gain matrix, and its calculation formula is:

[0035]

[0036] Among them, λ is the forgetting factor.

[0037] Preferably, in the dynamic data processing unit, an adaptive filtering algorithm is used to update the data processing model in real time to cope with dynamic changes in the environment;

[0038] Assume that the input signal is x(t), the target output signal is d(t), and the output of the adaptive filter is y(t). The goal of adaptive filtering is to minimize the error:

[0039] e(t)=d(t)-y(t).

[0040] The update formula of the minimum mean square error algorithm is:

[0041] y(t)=w T (txt),

[0042] w(t+1)=w(t)+μe(t)x(t),

[0043] Where w(t) is the weight vector of the filter, x(t) is the input vector, and μ is the learning rate;

[0044] The collaborative processing unit uses an extended Kalman filter for state estimation of nonlinear systems;

[0045] Assume that the state model of the system is:

[0046] x k+1 =f(x k ,u k )+w k ,

[0047] z k =h(x k )+v k ,

[0048] Among them, x k is the state vector, u k is the control input, z k is the observed value, w k and v k is process noise and observation noise, the steps of EKF are as follows:

[0049] Status prediction:

[0050]

[0051] Among them, F k is the state transfer matrix, Q k is the process noise covariance matrix;

[0052] renew:

[0053]

[0054] P k|k =(IK k H k ) k|k-1 ,

[0055] Among them, H k The observation matrix, R k Observation noise covariance matrix, K k Kalman gain matrix.

[0056] Preferably, the 5G communication unit assumes that there are N users and the channel gain of each user is g i , the power allocated to user i is p i , the signal-to-noise ratio is γ i , whose expression is:

[0057]

[0058] Among them, σ 2 is the noise power;

[0059] The optimization problem of resource allocation is expressed as:

[0060]

[0061] The constraints are:

[0062]

[0063] p i ≥0,

[0064] Among them, P max is the total power constraint;

[0065] In the adaptive network unit, assume that there are N traffic requests in the network, and the bandwidth requirement of each request is b i , the available bandwidth is B total , the bandwidth allocation problem is expressed as:

[0066]

[0067] Among them, U i (b i ) is the utility function of the i-th traffic request, and the utility function is a logarithmic function:

[0068] U i (b i )=log(b i ),

[0069] The constraints are:

[0070]

[0071] In actual networks, adaptive flow control is achieved by adjusting the window size or transmission rate. Assuming the window size is w(t) and the feedback signal of the network is f(t), the formula is:

[0072] w(t+1)=w(t)+α·f(t),

[0073] Among them, α is the adjustment coefficient.

[0074] Preferably, in the inertial navigation unit, the position, velocity and attitude are calculated by using the acceleration and angular velocity measured by the accelerometer and the gyroscope;

[0075] Assuming the acceleration is a(τ) and the angular velocity is v(τ), the velocity v(t) and position r(t) are obtained by integration:

[0076]

[0077] The angular velocity v(τ) is used to update the attitude matrix R(t):

[0078]

[0079] Among them, the attitude matrix R(t) represents the transformation relationship between the inertial coordinate system and the navigation coordinate system.

[0080] Preferably, in the distributed computing unit, the distributed algorithm is a distributed computing model for large-scale data processing, which is used for data analysis and processing;

[0081] Mapping function:

[0082] Map(k1,v1)→list(k2,v2),

[0083] Among them, k1, v1 is the input key-value pair, and the output is the new key-value pair k2, v2;

[0084] Reduction function:

[0085] Reduce(k2,list(v2))→list(v3),

[0086] The reduction function merges the values ​​v2 with the same key k2 and outputs a new value v3;

[0087] Suppose you want to count the number of times each word appears in a large-scale data set. The mapping function maps each word to a key-value pair (word, 1), and the reduction function is responsible for summing the values ​​of the same key:

[0088] Reduce(word,[1,1,1,…])→(word,count),

[0089] In this way, large-scale data can be processed efficiently in distributed systems.

[0090] Preferably, the automatic control unit adopts a fuzzy logic control algorithm to realize automatic control of complex systems. The fuzzy logic system processes control problems through fuzzification, rule reasoning and defuzzification steps;

[0091] Fuzzification: Assume that the membership function of input variables x1 and x2 is μ Ai (x i ), the fuzzified input is:

[0092] μ A (x1)=Fuzzy(x1),μ B (x2) = Fuzzy(x2),

[0093] Rule reasoning: Let the fuzzy rule R j is "If x1 is A and x2 is B, then y is C j ", the rule reasoning result is:

[0094]

[0095] Defuzzification: Calculate the output y using the centroid method:

[0096]

[0097] The defuzzified y is used to drive the control system.

[0098] Preferably, the remote control unit should use an adaptive flow control algorithm to adjust the data transmission rate in the case of network instability or delay to ensure reliable transmission of control commands;

[0099] TCP-based adaptive flow control: Assuming the congestion window size is w(t) and the feedback signal is f(t), the adaptive adjustment formula of the congestion window is:

[0100]

[0101] Among them, α and β are control parameters, and C is the network bandwidth capacity;

[0102] Remote control based on adaptive service quality: The remote control system must ensure the service quality. Assume that the transmission delay of the remote control signal is D(t) and the expected value of the delay is D target , then the adaptive service quality control algorithm adjusts the transmission parameters through the following formula:

[0103] D(t+1)=D(t)+γ·(D(t)-D target ),

[0104] Among them, γ is the adjustment coefficient.

[0105] The present invention provides a gas leakage detection system based on 5G and Beidou satellite. It has the following beneficial effects:

[0106] 1. The present invention can detect multiple gas types simultaneously by integrating spectral detection, chemical sensing and electrochemical sensor technologies, and effectively reduce the possibility of false alarms and missed alarms through intelligent fusion algorithms. Compared with traditional single sensor systems, multimodal fusion significantly improves the sensitivity and accuracy of gas leak detection. In addition, self-calibration and self-repair algorithms significantly improve the reliability of the system, reduce maintenance costs, and improve the automation level of the system compared with the existing technology.

[0107] 2. Through the combination of edge computing units and dynamic data processing units, the system can process data in real time near sensor nodes and respond quickly in abnormal situations. Compared with traditional centralized data processing systems, distributed edge computing reduces data transmission delays and improves the real-time response capability of the system. In addition, the introduction of multi-sensor collaborative processing algorithms enables the system to perform efficient collaborative analysis between multi-point and multi-source sensor data, significantly improving the comprehensive analysis capabilities of complex gas leakage scenarios.

[0108] 3. The present invention uses a multi-source fusion positioning engine of Beidou satellite, UWB positioning and inertial navigation. The system can provide high-precision positioning in different environments. In complex scenes such as indoors and underground, the combination of UWB and inertial navigation effectively makes up for the defects of satellite positioning. Compared with traditional single positioning technology, its multi-source fusion positioning solution has significant improvement in accuracy and environmental adaptability. In addition, the introduction of AR technology enables on-site operators to see the precise location and environmental information of the leakage point in real time on mobile devices, which significantly improves the efficiency and accuracy of emergency response. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] Figure 1 is a system diagram of the present invention;

[0110] Figure 2 is a schematic diagram of a multimodal sensor module of the present invention;

[0111] Figure 3 It is a schematic diagram of the data acquisition and processing module of the present invention;

[0112] Figure 4 This is a schematic diagram of the 5G communication and hybrid network module of the present invention;

[0113] Figure 5 It is a schematic diagram of a multi-source fusion positioning module of the present invention;

[0114] Figure 6 A schematic diagram of a data storage and analysis module of the present invention;

[0115] Figure 7 It is a schematic diagram of the alarm and control module of the present invention. DETAILED DESCRIPTION

[0116] In order to make the technical personnel in the technical field understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in combination with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a partial embodiment of the present invention, not a complete embodiment. Based on the embodiment of the present invention, other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0117] The present invention is described in detail below in conjunction with the accompanying drawings:

[0118] Example:

[0119] Please see attached Figure 1 - Attachment Figure 7The embodiment of the present invention provides a gas leakage detection system based on 5G and Beidou satellite, including a multimodal sensor module, a data acquisition and processing module, a 5G communication and hybrid network module, a multi-source fusion positioning module, a data storage and analysis module, an alarm and control module, a user interface and monitoring platform module, and a power supply and energy efficiency management module; the multimodal sensor module is used to integrate multiple gas detection technologies, can simultaneously detect the concentration and composition of multiple gases, and has self-repair and self-diagnosis functions; the data acquisition and processing module is responsible for performing preliminary data processing and analysis near the sensor node, using a machine learning algorithm for dynamic data processing, and has real-time collaborative processing capabilities; the 5G communication and hybrid network module performs high-priority, low-latency data transmission based on the 5G network, and combines low-power wide area network technology to automatically switch the communication mode when 5G coverage is insufficient, according to the network The communication strategy is dynamically adjusted according to network load and environmental conditions; the multi-source fusion positioning module is used to integrate multiple positioning technologies and use reality technology to provide users with intuitive positioning information and navigation guidance; the data storage and analysis module is used to store multimodal data collected by sensors, and use distributed computing and big data analysis technology to conduct in-depth analysis of historical data and real-time data to identify potential leakage risks and trends; the alarm and control module combines AI technology to make intelligent judgments based on real-time data and historical patterns, triggering accurate alarms and automated control measures; the user interface and monitoring platform module displays real-time data, location and analysis reports to users through a graphical user interface or mobile device, and combines AR technology to provide real-time environmental information and leakage point guidance to on-site personnel; the power supply and energy efficiency management module is equipped with a variety of power supply methods, and automatically optimizes power distribution and use through the energy efficiency management system.

[0120] The benefits of the multimodal sensor module are that it can detect the concentration and composition of multiple gases at the same time, greatly improving the sensitivity and detection range of gas leak detection, reducing maintenance costs and downtime, and improving the reliability of the system; the benefits of the data acquisition and processing module are that it reduces the delay in data transmission, improves the real-time response capability of the system, and effectively improves the system's comprehensive analysis capabilities for complex gas leakage scenarios; the benefits of the 5G communication and hybrid network module ensure that key data can be transmitted quickly and safely, greatly reducing the response time of the system; the benefits of the multi-source fusion positioning module can effectively make up for the shortcomings of a single positioning technology and improve the efficiency and accuracy of emergency handling; the benefits of the data storage and analysis module ensure the integrity and availability of data, provide data support for preventive maintenance and early warning, help users take timely countermeasures, and reduce accident risks; the benefits of the alarm and control module reduce the possibility of false alarms and missed alarms, improve the effectiveness of emergency response, reduce the impact of accidents, and improve the automation level and safety of the system; the benefits of the user interface and monitoring platform module enhance the efficiency and effectiveness of emergency handling; the benefits of the power supply and energy efficiency management module ensure that the system continues to operate stably in various environments, improve the energy utilization efficiency of the system, extend the operating time of the equipment, and reduce the overall operating cost.

[0121] The multimodal sensor module includes a gas detection sensor unit, a self-calibration unit, and a self-repairing unit; the gas detection sensor unit integrates multiple sensors for detecting different types of gas leaks; the self-calibration unit is used to automatically calibrate the sensitivity and accuracy of the sensor; the self-repairing unit automatically repairs or switches to a backup sensor when a sensor failure is detected; the data acquisition and processing module includes an edge computing unit, a dynamic data processing unit, and a collaborative processing unit; the edge computing unit performs preliminary data processing and analysis near the sensor node; the dynamic data processing unit uses a machine learning algorithm to perform real-time data analysis and dynamically adjusts the data processing process; the collaborative processing unit realizes data collaborative processing of multiple sensor nodes; the 5G communication and hybrid network module includes a 5G communication unit, a wide area network switching unit, and an adaptive network unit; the 5G communication unit uses the 5G network for high-speed, low-latency data transmission; the wide area network switching unit automatically switches to a low-power wide area network when the 5G signal coverage is insufficient or the signal is limited; the adaptive network unit dynamically adjusts the communication strategy according to the real-time network load and environmental conditions; the multi-source fusion positioning module includes Beidou satellite positioning Unit, UWB positioning unit, inertial navigation unit, fusion positioning engine unit; Beidou satellite positioning unit is used for preliminary positioning of leakage points; UWB positioning unit achieves centimeter-level positioning accuracy; inertial navigation unit provides short-term precise positioning through inertial navigation system in the absence of external signals; fusion positioning engine unit fuses multiple positioning data to generate comprehensive location data; data storage and analysis module includes data storage unit, distributed computing unit, risk assessment and prediction unit; data storage unit is used for multimodal data and historical data collected by sensors; distributed computing unit uses distributed computing technology to process and analyze massive data, and supports real-time and offline data analysis; risk assessment and prediction unit is used to analyze historical data and real-time data, assess leakage risks and predict potential leakage trends; alarm and control module includes intelligent alarm unit, automatic control unit, remote control unit; intelligent alarm unit intelligently triggers alarm according to real-time data and historical data patterns; automatic control unit automatically executes predetermined control measures after detecting gas leakage; remote control unit allows remote control and monitoring of the system.

[0122] The benefits of the gas detection sensor unit are to improve the coverage and sensitivity of detection, effectively reduce false alarms and missed alarms caused by environmental interference, and improve detection accuracy and reliability; the benefits of the self-calibration unit are to reduce the reliance on manual adjustment, reduce maintenance frequency and cost, and extend the service life of the sensor; the benefits of the self-repair unit are to reduce system downtime caused by sensor failure and provide continuous monitoring;

[0123] The benefits of the edge computing unit are to reduce the delay of data transmission, improve the real-time response capability of the system, and improve the processing efficiency and scalability of the overall system; the benefits of the dynamic data processing unit are to improve the accuracy and intelligence level of data processing; the benefits of the collaborative processing unit are to realize the collaborative processing of data from multiple sensor nodes, enhance the system's analytical capabilities when facing multi-point leakage or complex scenarios, and reduce the impact of single-point failures on the overall system;

[0124] 5G communication unit benefits: reduce the risk of data loss and delay; WAN switching unit benefits: reduce the risk of communication interruption due to poor signal; adaptive network unit benefits: improve the communication efficiency and adaptability of the system, and improve the overall performance of the system; Beidou satellite positioning unit benefits: achieve preliminary positioning of the leakage point, and enhance the practicality of the system in various geographical environments; UWB positioning unit benefits: effectively make up for the defects of satellite positioning and maintain accurate positioning; inertial navigation unit benefits: provide short-term accurate positioning, ensure the continuity of the system, and improve the positioning reliability in the case of signal loss or shielding; fusion positioning engine unit benefits: improve the overall positioning accuracy and environmental adaptability, and reduce the limitations of a single technology;

[0125] The data storage unit can store and manage data for a long time, providing support for trend analysis and historical data backtracking; the distributed computing unit supports real-time and offline data analysis, improving data processing efficiency and system response speed; the risk assessment and prediction unit helps users take preventive measures in advance to reduce the accident rate;

[0126] The benefits of intelligent alarm units are to reduce false alarms and missed alarms, improve the accuracy of system alarms, and ensure on-site safety; the benefits of automatic control units are to reduce the impact of leakage accidents, reduce dependence on manual intervention, reduce human errors in emergency operations, and improve system safety; the benefits of remote control units are to improve the convenience of system management and quickly respond to emergencies.

[0127] The gas detection sensing unit uses Bayesian fusion of signals from different sensors;

[0128] Assume that sensors S1, S2, ..., S n The detected gas concentrations are x1, x2, …, x n , the fused estimate It is expressed as:

[0129]

[0130] Among them, w i is the weight of each sensor signal, and the weight is calculated by the following formula:

[0131]

[0132] in, It is sensor S i The measurement noise variance of

[0133] The self-calibration unit uses the recursive least square method to calibrate the sensor parameters;

[0134] Assume that the relationship between the sensor output y(t) and input u(t) is:

[0135] y(t)=θ1u(t)+θ2u(t-1)+…+θ n u(t-n+1)+∈(t),

[0136] Among them, θ i The calibration parameters, ∈(t) measure the noise, and the RLS algorithm is used to estimate the parameters θ,

[0137] θ(t)=θ(t-1)+K(t)[y(t)-φ T (t)θ(t-1)],

[0138] Among them, φ(t)=[u(t),u(t-1),…,u(t-n+1)] T is the input vector, K(t) is the gain matrix, and its calculation formula is:

[0139]

[0140] Among them, λ is the forgetting factor.

[0141] In a gas leak detection system, algorithms work together to achieve efficient and accurate gas detection and sensor calibration:

[0142] Bayesian fusion algorithm: It integrates data from different sensors by weighted averaging to provide more reliable gas concentration estimation, reduce the error impact of a single sensor, and improve detection accuracy and system robustness;

[0143] Recursive least squares algorithm: used for sensor self-calibration. By updating parameters in real time, it ensures the accuracy and stability of the sensor during long-term use. The self-calibration function reduces maintenance requirements and improves the automation level of the system.

[0144] The combination of algorithms enhances the system's detection and adaptive capabilities, allowing the gas leak detection system to work continuously and efficiently in a changing environment, ensuring safety and reliability.

[0145] In the dynamic data processing unit, the adaptive filtering algorithm is used to update the data processing model in real time to cope with the dynamic changes in the environment;

[0146] Assume that the input signal is x(t), the target output signal is d(t), and the output of the adaptive filter is y(t). The goal of adaptive filtering is to minimize the error:

[0147] e(t)=d(t)-y(t).

[0148] The update formula of the minimum mean square error algorithm is:

[0149] y(t)=w T (txt),

[0150] w(t+1)=w(t)+μe(t)x(t),

[0151] Where w(t) is the weight vector of the filter, x(t) is the input vector, and μ is the learning rate;

[0152] Extended Kalman filter is used for state estimation of nonlinear systems in the collaborative processing unit;

[0153] Assume that the state model of the system is:

[0154] x k+1 =f(x k ,u k )+w k ,

[0155] z k =h(x k )+v k ,

[0156] Among them, x k is the state vector, u k is the control input, z k is the observed value, w k and v k is process noise and observation noise, the steps of EKF are as follows:

[0157] Status prediction:

[0158]

[0159]

[0160] Among them, F k is the state transfer matrix, Q k is the process noise covariance matrix;

[0161] renew:

[0162]

[0163] P k|k =(IK k Hk ) k|k-1 ,

[0164] Among them, H k The observation matrix, R k Observation noise covariance matrix, K k Kalman gain matrix.

[0165] In the dynamic data processing and co-processing unit of the gas leak detection system, the algorithm implements the following functions:

[0166] Adaptive filtering algorithm: Through the minimum mean square error algorithm, the adaptive filter can adjust its weight in real time to minimize the error between the output and the target signal. Its adaptive ability enables the filter to dynamically respond to environmental changes and ensure the accuracy and robustness of signal processing;

[0167] Extended Kalman filter algorithm: used for state estimation of nonlinear systems. Kalman filter provides the best estimate of the system state as it evolves over time through prediction and update steps. Its ability is particularly important for processing complex, nonlinear dynamic systems, especially in multi-sensor fusion scenarios, to improve the accuracy and reliability of state estimation.

[0168] In general, the combined effect of the algorithms enables the system to maintain efficient and accurate signal processing and state estimation capabilities in dynamic and complex environments. The adaptive filtering algorithm ensures the real-time and accuracy of signal processing, while the extended Kalman filter enhances the collaborative processing capabilities of the multi-sensor system, ensuring the overall robustness and reliability of the system.

[0169] In a 5G communication unit, assume that there are N users and the channel gain of each user is g i , the power allocated to user i is p i , the signal-to-noise ratio is γ i , whose expression is:

[0170]

[0171] Among them, σ 2 is the noise power;

[0172] The optimization problem of resource allocation is expressed as:

[0173]

[0174] The constraints are:

[0175]

[0176] p i ≥0,

[0177] Among them, P maxis the total power constraint;

[0178] In the adaptive network unit, assume that there are N traffic requests in the network and the bandwidth requirement of each request is b i , the available bandwidth is B total , the bandwidth allocation problem is expressed as:

[0179]

[0180] Among them, U i (b i ) is the utility function of the i-th traffic request, and the utility function is a logarithmic function:

[0181] U i (b i )=log(b i ),

[0182] The constraints are:

[0183]

[0184] In actual networks, adaptive flow control is achieved by adjusting the window size or transmission rate. Assuming the window size is w(t) and the feedback signal of the network is f(t), the formula is:

[0185] w(t+1)=w(t)+α·f(t),

[0186] Among them, α is the adjustment coefficient.

[0187] In the 5G communication unit and the adaptive network unit, the algorithm formula implements the following functions:

[0188] 5G communication unit - resource allocation optimization algorithm: Through signal-to-noise ratio calculation and power allocation optimization, the system can maximize the total throughput of the network and improve communication efficiency under the premise of meeting the total power constraint. Its formula ensures the optimal power allocation under limited resources, improves fairness among users and overall system performance;

[0189] Adaptive network unit - bandwidth allocation and adaptive flow control algorithm: The bandwidth allocation algorithm optimizes the utility function and reasonably allocates limited bandwidth resources to ensure that different traffic requests receive appropriate bandwidth allocation, thereby improving network utility. The adaptive flow control algorithm dynamically adjusts the transmission window size and flexibly adjusts the transmission rate according to network feedback to maintain network stability and efficiency.

[0190] The algorithms work together to ensure the efficiency, reliability and adaptability of the communication and network parts of the gas leak detection system. In 5G and complex network environments, the system can reasonably allocate resources, dynamically respond to environmental changes, ensure the speed and stability of data transmission, and improve the robustness of the overall system and user experience.

[0191] In the inertial navigation unit, the position, velocity and attitude are calculated by the acceleration and angular velocity measured by the accelerometer and gyroscope;

[0192] Assuming the acceleration is a(τ) and the angular velocity is v(τ), the velocity v(t) and position r(t) are obtained by integration:

[0193]

[0194] The angular velocity v(τ) is used to update the attitude matrix R(t):

[0195]

[0196] Among them, the attitude matrix R(t) represents the transformation relationship between the inertial coordinate system and the navigation coordinate system.

[0197] The algorithm formula works together to form the inertial navigation system's ability to continuously infer the object's position, velocity, and attitude. Through real-time data input from accelerometers and gyroscopes, the system can dynamically calculate the object's motion trajectory and attitude changes. The inertial navigation system has the problem of cumulative errors, but through precise calculation of the formula, the system can provide high-precision position and attitude estimates in a short period of time, ensuring effective navigation in the absence of external navigation signals.

[0198] In the distributed computing unit, the distributed algorithm is a distributed computing model for large-scale data processing, which is used for data analysis and processing;

[0199] Mapping function:

[0200] Map(k1,v1)→list(k2,v2),

[0201] Among them, k1, v1 is the input key-value pair, and the output is the new key-value pair k2, v2;

[0202] Reduction function:

[0203] Reduce(k2,list(v2))→list(v3),

[0204] The reduction function merges the values ​​v2 with the same key k2 and outputs a new value v3;

[0205] Suppose you want to count the number of times each word appears in a large-scale data set. The mapping function maps each word to a key-value pair (word, 1), and the reduction function is responsible for summing the values ​​of the same key:

[0206] Reduce(word,[1,1,1,…])→(word,count),

[0207] In this way, large-scale data can be processed efficiently in distributed systems.

[0208] In the distributed computing unit, mapping functions and reduction functions are the core components for processing large-scale data. Through these functions, data is segmented, distributed, and processed in parallel, which significantly improves computing efficiency.

[0209] Mapping function: splits the input data into multiple key-value pairs, which is suitable for distributed processing architecture. The process can decompose complex data processing tasks into smaller independent subtasks, each of which is executed independently on different nodes, maximizing the advantages of parallel processing;

[0210] Reduction function: Aggregates the results generated by the mapping function to generate the final calculation output. The reduction function ensures that in a distributed system, even if the data is distributed on multiple nodes for processing, the final result is still correct and consistent;

[0211] By combining mapping and reduction functions, distributed computing units can efficiently process large-scale data and quickly count the frequency of words in the data set. This method is particularly suitable for application scenarios that need to process massive amounts of data. Overall, the distributed computing model of mapping and reduction can significantly improve the system's processing power, scalability, and efficiency.

[0212] The automatic control unit uses fuzzy logic control algorithm to realize the automatic control of complex systems. The fuzzy logic system handles the control problem through fuzzification, rule reasoning and defuzzification steps;

[0213] Fuzzification: Assume that the membership function of input variables x1 and x2 is μ Ai (x i ), the fuzzified input is:

[0214] μ A (x1)=Fuzzy(x1),μ B (x2) = Fuzzy(x2),

[0215] Rule reasoning: Let the fuzzy rule R j is "If x1 is A and x2 is B, then y is C j ", the rule reasoning result is:

[0216]

[0217] Defuzzification: Calculate the output y using the centroid method:

[0218]

[0219] The defuzzified y is used to drive the control system.

[0220] Fuzzy logic control algorithm realizes automatic control of complex systems through fuzzification, rule reasoning, and defuzzification steps:

[0221] Fuzzification: Converting precise input data into membership values ​​in fuzzy sets allows the system to handle uncertainty and fuzzy information. This process enables the control system to cope with complex and nonlinear input situations.

[0222] Rule reasoning: Based on predefined fuzzy rules, the membership of input variables is inferred and calculated to generate fuzzy output, allowing the system to apply complex logical reasoning and make control decisions by considering the combination of multiple conditions;

[0223] Defuzzification: convert fuzzy output into specific control commands, and obtain accurate output values ​​by calculating the centroid position of the membership function. This process ensures that the control output of the system is directly applied by the actuator to achieve automated control.

[0224] The integration of steps enables the fuzzy logic control algorithm to effectively handle complex systems with uncertainty, fuzziness or nonlinear characteristics. Compared with traditional control methods, fuzzy logic control is more flexible and can perform effective control without a precise mathematical model.

[0225] When the network is unstable or there is a delay, the remote control unit should use an adaptive flow control algorithm to adjust the data transmission rate to ensure reliable transmission of control commands;

[0226] TCP-based adaptive flow control: Assuming the congestion window size is w(t) and the feedback signal is f(t), the adaptive adjustment formula of the congestion window is:

[0227]

[0228] Among them, α and β are control parameters, and C is the network bandwidth capacity;

[0229] Remote control based on adaptive service quality: The remote control system must ensure the service quality. Assume that the transmission delay of the remote control signal is D(t) and the expected value of the delay is D target , then the adaptive service quality control algorithm adjusts the transmission parameters through the following formula:

[0230] D(t+1)=D(t)+γ·(D(t)-Dtarget ),

[0231] Among them, γ is the adjustment coefficient.

[0232] TCP-based adaptive flow control: By dynamically adjusting the congestion window size, the system can respond to network congestion in real time. When the network is in good condition, the transmission rate is increased; when the network is congested, the transmission rate is reduced. The process ensures the efficiency and stability of data transmission in the case of unstable network.

[0233] Remote control based on adaptive quality of service: By adjusting the transmission delay, the system can adaptively adjust the transmission parameters to meet the predetermined quality of service goals when facing different network delays. This algorithm is particularly suitable for use in remote control and can ensure that control commands can still be transmitted in a timely and accurate manner when network conditions change;

[0234] Through the combination of adaptive control algorithms, the remote control unit can effectively cope with uncertainties and fluctuations in the network, ensuring the reliability and real-time performance of remote control operations. The algorithm enables the system to find a balance between high bandwidth utilization and low latency, adapting to the needs under different network conditions and improving the overall performance and user experience of the remote control system.

[0235] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A gas leak detection system based on 5G and Beidou satellite, characterized in that: It includes multimodal sensor module, data acquisition and processing module, 5G communication and hybrid network module, multi-source fusion positioning module, data storage and analysis module, alarm and control module, user interface and monitoring platform module, power supply and energy efficiency management module; The multimodal sensor module is used to integrate multiple gas detection technologies, can detect the concentration and composition of multiple gases at the same time, and has self-repair and self-diagnosis functions; The data acquisition and processing module is responsible for preliminary data processing and analysis near the sensor nodes, using machine learning algorithms for dynamic data processing and having real-time collaborative processing capabilities; The 5G communication and hybrid network module performs high-priority, low-latency data transmission based on the 5G network, combines low-power wide area network technology, automatically switches the communication mode when 5G coverage is insufficient, and dynamically adjusts the communication strategy according to network load and environmental conditions; The multi-source fusion positioning module is used to integrate multiple positioning technologies and use reality technology to provide users with intuitive positioning information and navigation guidance; The data storage and analysis module is used to store multimodal data collected by sensors, and use distributed computing and big data analysis technology to conduct in-depth analysis of historical data and real-time data to identify potential leakage risks and trends; The alarm and control module combines AI technology to make intelligent judgments based on real-time data and historical patterns, triggering precise alarms and automated control measures; The user interface and monitoring platform module displays real-time data, location and analysis reports to users through a graphical user interface or mobile device, and combines AR technology to provide real-time environmental information and leakage point guidance to on-site personnel; The power supply and energy efficiency management module is equipped with multiple power supply modes and automatically optimizes power distribution and use through the energy efficiency management system.

2. A gas leak detection system based on 5G and Beidou satellite according to claim 1, characterized in that: The multimodal sensor module includes a gas detection sensor unit, a self-calibration unit, and a self-repair unit; the gas detection sensor unit integrates multiple sensors for detecting different types of gas leaks; the self-calibration unit is used to automatically calibrate the sensitivity and accuracy of the sensor; the self-repair unit automatically repairs or switches to a spare sensor when a sensor failure is detected; The data acquisition and processing module includes an edge computing unit, a dynamic data processing unit, and a collaborative processing unit; the edge computing unit performs preliminary data processing and analysis near the sensor node; the dynamic data processing unit uses a machine learning algorithm to perform real-time data analysis and dynamically adjust the data processing process; the collaborative processing unit realizes data collaborative processing of multiple sensor nodes; The 5G communication and hybrid network module includes a 5G communication unit, a wide area network switching unit, and an adaptive network unit; the 5G communication unit uses the 5G network for high-speed, low-latency data transmission; the wide area network switching unit automatically switches to a low-power wide area network when the 5G signal coverage is insufficient or the signal is limited; the adaptive network unit dynamically adjusts the communication strategy according to the real-time network load and environmental conditions.

3. A gas leak detection system based on 5G and BeiDou satellite according to claim 1, characterized in that: The multi-source fusion positioning module includes a Beidou satellite positioning unit, a UWB positioning unit, an inertial navigation unit, and a fusion positioning engine unit; the Beidou satellite positioning unit is used for preliminary positioning of the leakage point; the UWB positioning unit achieves centimeter-level positioning accuracy; the inertial navigation unit provides short-term precise positioning through the inertial navigation system in the absence of external signals; the fusion positioning engine unit fuses multiple positioning data to generate comprehensive location data; The data storage and analysis module includes a data storage unit, a distributed computing unit, and a risk assessment and prediction unit; the data storage unit is used for multimodal data and historical data collected by sensors; the distributed computing unit uses distributed computing technology to process and analyze massive data, and supports real-time and offline data analysis; the risk assessment and prediction unit is used to analyze historical data and real-time data, assess leakage risks, and predict potential leakage trends; The alarm and control module includes an intelligent alarm unit, an automatic control unit, and a remote control unit; the intelligent alarm unit intelligently triggers an alarm according to real-time data and historical data patterns; the automatic control unit automatically executes predetermined control measures after detecting a gas leak; the remote control unit allows remote control and monitoring of the system.

4. A gas leak detection system based on 5G and Beidou satellite according to claim 2, characterized in that: The gas detection sensor unit uses Bayesian fusion of signals from different sensors; Assume that sensors S1, S2, ..., S n The detected gas concentrations are x1, x2, ..., x n , the fused estimate It is expressed as: Among them, w i is the weight of each sensor signal, and the weight is calculated by the following formula: in, It is sensor S i The measurement noise variance of The self-calibration unit uses a recursive least square method to calibrate the parameters of the sensor; Assume that the relationship between the sensor output y(t) and input u(t) is: y(t)=θ1u(t)+θ2u(t-1)+…+θ n u(t-n+1)+∈(t), Among them, θ i The calibration parameters, ∈(t) measure the noise, and the RLS algorithm is used to estimate the parameters θ, θ(t)=θ(t-1)+K(t)[y(t)-φ T (t)θ(t-1)], Among them, φ(t)=[u(t), u(t-1),..., u(t-n+1)] T is the input vector, K(t) is the gain matrix, and its calculation formula is: Among them, λ is the forgetting factor.

5. A gas leak detection system based on 5G and Beidou satellite according to claim 2, characterized in that: In the dynamic data processing unit, an adaptive filtering algorithm is used to update the data processing model in real time to cope with dynamic changes in the environment; Assume that the input signal is x(t), the target output signal is d(t), and the output of the adaptive filter is y(t). The goal of adaptive filtering is to minimize the error: e(t)=d(t)-y(t). The update formula of the minimum mean square error algorithm is: y(t)=w T (t)x(t), w(t+1)=w(t)+μe(t)x(t), Where w(t) is the weight vector of the filter, x(t) is the input vector, and μ is the learning rate; The collaborative processing unit uses an extended Kalman filter for state estimation of nonlinear systems; Assume that the state model of the system is: x k+1 =f(x k ,u k )+w k , z k =h(x k )+V k , Among them, x k is the state vector, u k is the control input, z k is the observed value, w k and v k is process noise and observation noise, the steps of EKF are as follows: Status prediction: Among them, F k is the state transfer matrix, Q k is the process noise covariance matrix; renew: P k|k =(I-K k H k )P k|k-1 , Among them, H k The observation matrix, R k Observation noise covariance matrix, K k Kalman gain matrix.

6. A gas leakage detection system based on 5G and Beidou satellite according to claim 2, characterized in that: The 5G communication unit assumes that there are N users and the channel gain of each user is g i , the power allocated to user i is p i , the signal-to-noise ratio is Y i , whose expression is: Among them, σ 2 is the noise power; The optimization problem of resource allocation is expressed as: The constraints are: p i ≥0， Among them, P max is the total power constraint; In the adaptive network unit, assume that there are N traffic requests in the network, and the bandwidth requirement of each request is b i , the available bandwidth is B total , the bandwidth allocation problem is expressed as: Among them, U i (b i ) is the utility function of the i-th traffic request, and the utility function is a logarithmic function: U i (b i )=log(b i ), The constraints are: In actual networks, adaptive flow control is achieved by adjusting the window size or transmission rate. Assuming the window size is w(t) and the feedback signal of the network is f(t), the formula is: w(t+1)=w(t)+α·f(t), Among them, α is the adjustment coefficient.

7. A gas leakage detection system based on 5G and Beidou satellite according to claim 3, characterized in that: In the inertial navigation unit, the position, velocity and attitude are calculated by using the acceleration and angular velocity measured by the accelerometer and the gyroscope; Assuming the acceleration is a(τ) and the angular velocity is v(τ), the velocity v(t) and position r(t) are obtained by integration: The angular velocity v(τ) is used to update the attitude matrix R(t): Among them, the attitude matrix R(t) represents the transformation relationship between the inertial coordinate system and the navigation coordinate system.

8. The gas leakage detection system based on 5G and Beidou satellite according to claim 3, characterized in that: In the distributed computing unit, the distributed algorithm is a distributed computing model for large-scale data processing, which is used for data analysis and processing; Mapping function: Map(k1, v1)→list(k2, V2), Among them, k1, v1 are the input key-value pairs, and the output is the new key-value pair k2, v2; Reduction function: Reduce(k2, list(v2))→list(V3), The reduction function merges the values ​​v2 with the same key k2 and outputs a new value v3; Suppose you want to count the number of times each word appears in a large-scale data set. The mapping function maps each word to a key-value pair (word, 1), and the reduction function is responsible for summing the values ​​of the same key: Reduce(word,[1,1,1,...])→(word,count), In this way, large-scale data can be processed efficiently in distributed systems.

9. The gas leakage detection system based on 5G and Beidou satellite according to claim 3, characterized in that: The automatic control unit adopts fuzzy logic control algorithm to realize automatic control of complex systems. Fuzzy logic system handles control problems through fuzzification, rule reasoning and defuzzification steps: Fuzzification: Assume that the membership function of input variables x1 and x2 is The fuzzified input is: μ A (x1)=Fuzzy(x1),μ B (x2)=Fuzzy(x2), Rule reasoning: Let the fuzzy rule R j is "If x1 is A and x2 is B, then y is C j ", the rule reasoning result is: Defuzzification: Calculate the output y using the centroid method: The defuzzified y is used to drive the control system.

10. A gas leakage detection system based on 5G and Beidou satellite according to claim 3, characterized in that: When the network is unstable or delayed, the remote control unit shall use an adaptive flow control algorithm to adjust the data transmission rate to ensure reliable transmission of control commands; TCP-based adaptive flow control: Assuming the congestion window size is w(t) and the feedback signal is f(t), the adaptive adjustment formula of the congestion window is: Among them, α and β are control parameters, and C is the network bandwidth capacity; Remote control based on adaptive service quality: The remote control system must ensure the service quality. Assume that the transmission delay of the remote control signal is D(t) and the expected value of the delay is D target , then the adaptive service quality control algorithm adjusts the transmission parameters through the following formula: D(t+1)=D(t)+γ·(D(t)-D target ), Among them, γ is the adjustment coefficient.

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