A remote intelligent seismograph system based on star network communication and a working method thereof

Abstract

The application discloses a remote intelligent seismograph system based on star network communication and a working method thereof, and belongs to the field of seismic exploration. The remote intelligent seismograph system comprises a node seismograph module, the node seismograph module is in bidirectional communication with a remote management module through a star network communication unit, and the node seismograph module, the star network communication unit and the remote management module are electrically connected with a multi-source energy module. The node seismograph module is used for collecting seismic wave vibration signals and pre-processing the collected seismic wave vibration signals. The star network communication unit is used for receiving the pre-processed seismic wave vibration signals and uploading the signals to the remote management module. The remote management module is used for receiving the seismic wave vibration signals and outputting a three-dimensional fault model and a health monitoring report. The remote intelligent seismograph system based on star network communication and the working method thereof realize remote downloading and remote monitoring of data, and significantly improve the efficiency and reliability of seismic data collection and management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of seismic exploration, and particularly relates to a remote intelligent seismograph system based on star network communication and a working method thereof. BACKGROUND

[0002] In the field of seismic exploration, seismic node seismographs are widely used for collecting and analyzing seismic wave signals. However, the traditional seismic node seismograph has the following problems, which limit its efficiency and reliability:

[0003] 1. Outdated data acquisition method: When the traditional seismic node seismograph completes the collection task or reaches the upper limit of data storage, it needs to be manually recovered and the seismic wave data is downloaded or parameters are written through wired transmission. This method not only relies on frequent manual operation, increasing the workload and maintenance cost, but also may cause data loss or interruption due to untimely maintenance, seriously affecting the operation efficiency and data integrity.

[0004] 2. Signal problem: In remote areas, traditional network transmission methods such as 4G often face the problem of unstable network or even no signal, making real-time data transmission unreliable. This further exacerbates the difficulty of data acquisition, especially in emergency situations, which may cause important information to be delayed or missing.

[0005] 3. Power supply difficulty: The existing seismic node seismograph relies on battery power supply, and needs to be manually recovered for charging after the battery is depleted. Especially in remote areas or unattended environments, the lack of reliable power supply makes it difficult to guarantee the continuity and stability of device operation. Even by increasing external power supply devices such as generators, the complexity and maintenance cost of the system will be significantly increased.

[0006] 4. High dependence on manual intervention: The existing technology highly depends on manual intervention, including device parameter adjustment, data download and power supply maintenance. This dependence not only increases the labor cost and operation difficulty, but also reduces the efficiency and safety of maintenance work in remote and harsh environments. Especially in these special environments, the work risk of maintenance personnel is high and the efficiency is low, which affects the overall operation effect.

[0007] In summary, the traditional seismic node seismograph has obvious shortcomings in data acquisition, signal transmission, power supply guarantee and manual dependence. SUMMARY

[0008] The purpose of the present application is to provide a remote intelligent seismograph system based on star network communication and a working method thereof, which solves the above technical problems.

[0009] To achieve the above objectives, the present invention provides a remote intelligent seismograph system based on star network communication, comprising a node seismograph module, the node seismograph module bidirectionally communicating with a remote management module via a star network communication unit, and the node seismograph module, the star network communication unit, and the remote management module are all electrically connected to a multi-source energy module;

[0010] Among them, the node seismometer module includes a connected three-axis MEMS seismic wave sensor, which is connected to the edge intelligent preprocessing unit in sequence through the amplification and filtering unit and the analog-to-digital conversion unit, and is used to use the three-axis MEMS seismic wave sensor to collect seismic wave vibration signals, and use the edge intelligent preprocessing unit to preprocess the collected seismic wave vibration signals;

[0011] The star network communication unit includes a modem, a communication protocol processing unit, and a phased array antenna connected in sequence. The phased array antenna is a three-mode link structure composed of an FSO communication unit, an adaptive radio frequency unit, and a BeiDou RDSS link. It is used to receive pre-processed seismic wave vibration signals and upload them to the remote management module, or receive instructions from the remote management module and transmit them to the node seismograph module;

[0012] The remote management module is used to receive seismic wave vibration signals and remotely monitor and manage the node seismometer modules to output three-dimensional fault models and health monitoring reports.

[0013] Preferably, the multi-source energy module includes a solar power generation unit and a vertical axis wind power generation unit, and both the solar power generation unit and the vertical axis wind power generation unit are electrically connected to the lithium iron phosphate battery pack via the battery management unit.

[0014] Preferably, the three-axis MEMS seismic wave sensor integrates a seismic wave sensor, an amplifying and filtering unit, an analog-to-digital conversion unit and a microprocessor that are electrically connected in sequence. The microprocessor is also respectively connected to the edge intelligent preprocessing unit and the communication unit. At the same time, the edge intelligent preprocessing unit is connected to the communication unit, and the communication unit communicates with the star network communication unit to realize the use of the three-axis MEMS seismic wave sensor to collect seismic wave vibration signals, and transmit them to the amplifying and filtering unit for filtering and signal amplification, and then transmit them to the microprocessor after analog-to-digital conversion by the analog-to-digital converter, and after data compression based on wavelet transform by the edge intelligent preprocessing unit, the collected seismic data is uploaded using the communication unit.

[0015] Preferably, the uplink of the FSO communication unit adopts 1550nm wavelength laser modulation and combines the data compressed by wavelength-mode division multiplexing modulation with a transmission rate of ≥20Gbps and a bit error probability of ≤5×10 -9 ;

[0016] The adaptive radio frequency unit operates in the 1.4GHz-5.8GHz frequency band, supports OFDM modulation and polarization diversity, and has a transmission rate of ≥50Mbps when the rain attenuation is ≤30dB;

[0017] The RDSS link integrates BeiDou-3 B1I+B2a dual-frequency reception, and the end-to-end delay in emergency mode is ≤2.5s.

[0018] A method for operating a remote intelligent seismograph system based on star network communication comprises the following steps:

[0019] S1, a three-axis MEMS seismic wave sensor collects vibration signals at a set sampling rate. After amplification, filtering, and analog-to-digital conversion, the edge intelligent pre-processing unit performs wavelet denoising and data compression to generate a feature data set;

[0020] S2. Determine a communication transmission link, and upload the feature data set to a remote management module based on the determined communication transmission link;

[0021] S3. Based on the feature data set, a three-dimensional fault model is generated using the remote management module and output.

[0022] Preferably, in step S1, the specific steps of performing wavelet denoising and data compression are as follows:

[0023] The first step is to use the Daubechies9 orthogonal wavelet basis to perform discrete wavelet decomposition on the digital signal after analog-to-digital conversion to obtain the approximate coefficients and detail coefficients of each layer. The decomposition formula is as follows:

[0024] A j+1 [k]=∑ n h[n]×A j [2k-n];

[0025] D j+1 [k]=∑ n g[n]×A j [2k-n];

[0026] Where A j+1 and A j Represent the approximate coefficients of the j+1th layer and the jth layer respectively; n represents the number of decomposition layers; h[n] represents the low-pass filter; D j+1 represents the detail coefficient of the j+1th layer; [2k-n] represents the downsampling operation;

[0027] The second step is threshold denoising, and setting the threshold λ of the jth layer j for:

[0028]

[0029] Where, σ jN represents the noise standard deviation of the detail coefficient of the jth layer; j Indicates the length of the detail coefficient of the jth layer;

[0030] Step 3: Based on threshold λ j Corrected wavelet coefficient W j,k :

[0031]

[0032] Where, represents the modified wavelet coefficient;

[0033] The fourth step is to quantize the wavelet coefficients using a hierarchical compression strategy, where 5% of the maximum amplitude coefficients are retained for high-frequency noise and 95% of the amplitude coefficients are retained for low-frequency noise;

[0034] Step 5: Perform Huffman coding on the retained coefficients to achieve compression.

[0035] Preferably, step S2 specifically includes the following steps:

[0036] S21. Filter the link candidate set, and the filtering conditions are:

[0037]

[0038] Where B candidate represents the candidate link bandwidth; S data represents the size of the feature dataset; Δt max Indicates the maximum allowed transmission time;

[0039] S22. Use signal-to-noise ratio (SNR) and bit error rate (BER) to evaluate channel quality:

[0040]

[0041] Where, P signal Indicates signal power; P noise Represents noise power; N error Indicates the number of bit errors; N total Indicates the complete transmission code;

[0042] S23. Calculate the transmission performance scores of the FSO communication unit, adaptive radio frequency unit, and BeiDou RDSS link based on the comprehensive weighted scoring model:

[0043]

[0044] Where w1, w2 and w3 represent the weights of bandwidth, reliability and delay respectively; B max Indicates the maximum bandwidth of the candidate link; Latency indicates the link transmission delay;

[0045] S24. Sort the transmission performance scores of the FSO communication unit, the adaptive radio frequency unit, and the Beidou RDSS link, and take the link with the largest transmission performance score as the optimal link;

[0046] S25, modulating the received seismic wave vibration signal using the modem, encrypting and packaging the modulated signal using the communication protocol processing unit, and uploading it to the remote management module using the determined optimal link;

[0047] Alternatively, the remote management module command is received using the optimal link, and after demodulation by the modem, the modulated signal is encrypted and packaged by the communication protocol processing unit and then transmitted to the node seismograph module.

[0048] Preferably, step S3 specifically includes the following steps:

[0049] S31. Normalize the original signal in the received feature data set in the time-frequency domain:

[0050]

[0051] in,

[0052]

[0053] Where, χ norm represents the standardized data; x represents the original signal; μ represents the signal mean; σ represents the standard deviation; N represents the total number of samples; x i represents the i-th data sample;

[0054] S32. Extract multi-scale features of faults using the MultiRes-Unet3D neural network:

[0055]

[0056] in,

[0057]

[0058] Where, L weighted represents the weighted loss function; C represents the total number of categories; W c represents the weight of category c; y c represents the true label; p c represents the probability that the prediction is true; f c Represents the frequency of category c in the feature dataset; ∈ represents a constant;

[0059] S33. Generate fault point cloud based on stereo vision method and calculate depth information using multi-view seismic data:

[0060]

[0061] in,

[0062] d = argmin d ∑ p∈Ω ∣I left (p)-I right (p+d)∣ 2 ;

[0063] Where Z is the depth of the fault point; B is the binocular baseline distance; f is the focal length; d is the parallax; I left (p) and I right (p+d) represents the intensity value of the left view and the right view at point p respectively; Ω represents the disparity search range;

[0064] S34, using the iterative closest point algorithm to align the fault point cloud, where the objective function E of the iterative closest point algorithm is ICP as follows:

[0065]

[0066] Among them, p i represents the source point cloud; q i Represents the target point cloud; T represents the rigid body transformation matrix; I represents the number of points in the fault point cloud;

[0067] S35. Convert the optimized point cloud into a gridded surface model to obtain a three-dimensional fault model.

[0068] Therefore, the present invention adopts the above-mentioned remote intelligent seismograph system based on star network communication and its working method, which has the following beneficial effects:

[0069] 1. Real-time remote monitoring and management: (1) Immediacy: The StarNet communication unit enables the node seismometer module to communicate with the remote management module in real time, ensuring the instant transmission of seismic wave vibration signals, which greatly shortens the time from data acquisition to processing and improves the speed of emergency response; (2) Remote control: The remote management module not only receives seismic wave vibration signals, but also performs remote parameter configuration and status monitoring of the node seismometer module, reducing the need for on-site maintenance and lowering operation and maintenance costs. At the same time, users do not need to go to the site to recover equipment or manually download data, which significantly reduces the need for manual intervention and related workload, and avoids data lag and loss caused by manual intervention;

[0070] 2. High reliability and stability: (1) Star network communication: Using phased array antenna and modem to realize stable data transmission, realizing real-time transmission of seismic data in global range, especially suitable for remote areas such as mountainous areas, even in the environment without network coverage, it can also upload data stably, greatly improving the timeliness and accuracy of seismic monitoring, compared with traditional network methods such as 4G, it has higher reliability and anti-interference ability; (2) Power supply guarantee: Multi-source energy module ensures that the device can work continuously in remote areas lacking of power supply, and the design of battery management unit and energy storage battery makes the system can maintain stable power supply under different light conditions, avoiding data interruption caused by power shortage;

[0071] 3. Intelligence and automation: (1) Automatic pre-processing: The built-in amplification and filtering unit and analog-to-digital conversion unit in the node seismograph module can automatically complete signal filtering, amplification and conversion, reducing the burden of subsequent data analysis; (2) Intelligent analysis: The data processing unit in the remote management module not only can store and analyze the received seismic wave vibration signals, but also can generate health monitoring reports to help users quickly understand the seismic activity situation;

[0072] 4. Multi-dimensional information feedback: In addition to seismic wave vibration signals, the system can also real-time feedback the running state information of the device (such as power, storage space and communication quality), helping managers to find potential problems in time and take measures to prevent the device from losing, damaging or running failure, resulting in monitoring interruption;

[0073] 5. Multi-source energy module is adopted to realize energy self-sufficiency, without frequent battery charging. Even in the case of insufficient light, the energy storage battery can ensure the continuous operation of the device, reducing the maintenance frequency and operation cost, so as to realize long-term stable seismic monitoring.

[0074] The technical solutions of the present application will be further described in detail below through the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0075] Figure 1 The structure block diagram of the present application, a remote intelligent seismograph system based on star network communication. DETAILED DESCRIPTION

[0076] In order to make the purposes, technical solutions and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention and are not intended to limit the embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, where the same or similar numbers throughout represent the same or similar elements or elements with the same or similar functions.

[0077] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or devices.

[0078] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0079] like Figure 1 As shown, a remote intelligent seismograph system based on star network communication includes a node seismograph module, which communicates bidirectionally with a remote management module via a star network communication unit, and the node seismograph module, the star network communication unit and the remote management module are all electrically connected to a multi-source energy module; wherein, the node seismograph module includes a connected three-axis MEMS seismic wave sensor, which is connected to an edge intelligent preprocessing unit via an amplification and filtering unit and an analog-to-digital conversion unit in sequence, and is used to collect seismic wave vibration signals using the three-axis MEMS seismic wave sensor, and use the edge intelligent preprocessing unit to process the seismic wave vibration signals. The collected seismic wave vibration signals are preprocessed; the star network communication unit includes a modem, a communication protocol processing unit and a phased array antenna connected in sequence. The phased array antenna is a three-mode link structure composed of an FSO communication unit, an adaptive radio frequency unit and a Beidou RDSS link. It is used to receive the preprocessed seismic wave vibration signals and upload them to the remote management module, or transmit them to the node seismograph module after receiving the instructions of the remote management module; the remote management module is used to receive the seismic wave vibration signals and remotely monitor and manage the node seismograph modules at the same time to output a three-dimensional fault model and a health monitoring report.

[0080] The multi-source energy module includes a solar power generation unit and a vertical axis wind power generation unit, and both the solar power generation unit and the vertical axis wind power generation unit are electrically connected to the lithium iron phosphate battery pack through the battery management unit.

[0081] The three-axis MEMS seismic wave sensor integrates a seismic wave sensor, an amplifying and filtering unit, an analog-to-digital conversion unit and a microprocessor that are electrically connected in sequence. The microprocessor is also connected to the edge intelligent preprocessing unit and the communication unit respectively. At the same time, the edge intelligent preprocessing unit is connected to the communication unit, and the communication unit communicates with the star network communication unit to realize the use of the three-axis MEMS seismic wave sensor to collect seismic wave vibration signals, and transmit them to the amplifying and filtering unit for filtering and signal amplification. After analog-to-digital conversion by the analog-to-digital converter, it is transmitted to the microprocessor, and after data compression based on wavelet transform by the edge intelligent preprocessing unit, the collected seismic data is uploaded using the communication unit.

[0082] The uplink of the FSO communication unit uses 1550nm wavelength laser modulation and combines the data compressed by wavelength-mode division multiplexing modulation. The transmission rate is ≥20Gbps and the bit error probability is ≤5×10 -9 The adaptive radio frequency unit operates in the 1.4GHz-5.8GHz frequency band, supports OFDM modulation and polarization diversity, and the transmission rate is ≥50Mbps when the rain attenuation is ≤30dB; the RDSS link integrates BeiDou-3 B1I+B2a dual-frequency reception, and the end-to-end delay in emergency mode is ≤2.5s.

[0083] It should be noted that the above-mentioned electronic components are mature products on the market. This embodiment only needs to purchase them and connect them according to the instructions. No improvement is made to them, so their circuit connection structure and principles will not be described in detail here.

[0084] A method for operating a remote intelligent seismograph system based on star network communication comprises the following steps:

[0085] S1, a three-axis MEMS seismic wave sensor collects vibration signals at a set sampling rate. After amplification, filtering, and analog-to-digital conversion, the edge intelligent pre-processing unit performs wavelet denoising and data compression to generate a feature data set;

[0086] In step S1, the specific steps of performing wavelet denoising and data compression are as follows:

[0087] The first step is to use the Daubechies9 orthogonal wavelet basis to perform discrete wavelet decomposition on the digital signal after analog-to-digital conversion to obtain the approximate coefficients and detail coefficients of each layer. The decomposition formula is as follows:

[0088] A j+1 [k]=∑ n h[n]×A j [2k-n];

[0089] D j+1 [k]=∑ n g[n]×A j [2k-n];

[0090] Where A j+1 and A j Represent the approximate coefficients of the j+1th layer and the jth layer respectively; n represents the number of decomposition layers; h[n] represents the low-pass filter; D j+1 represents the detail coefficient of the j+1th layer; [2k-n] represents the downsampling operation;

[0091] The second step is threshold denoising, and setting the threshold λ of the jth layer j for:

[0092]

[0093] Where, σ j N represents the noise standard deviation of the detail coefficient of the jth layer; j Indicates the length of the detail coefficient of the jth layer;

[0094] Step 3: Based on threshold λ j Corrected wavelet coefficient W j,k :

[0095]

[0096] Where, represents the modified wavelet coefficient;

[0097] The fourth step is to quantize the wavelet coefficients using a hierarchical compression strategy, where 5% of the maximum amplitude coefficients are retained for high-frequency noise and 95% of the amplitude coefficients are retained for low-frequency noise;

[0098] Step 5: Perform Huffman coding on the retained coefficients to achieve compression.

[0099] S2. Determine a communication transmission link, and upload the feature data set to a remote management module based on the determined communication transmission link;

[0100] Step S2 specifically includes the following steps:

[0101] S21. Filter the link candidate set, and the filtering conditions are:

[0102]

[0103] Where B candidate represents the candidate link bandwidth; S data represents the size of the feature dataset; Δt max Indicates the maximum allowed transmission time;

[0104] S22. Use signal-to-noise ratio (SNR) and bit error rate (BER) to evaluate channel quality:

[0105]

[0106] Where, P signal Indicates signal power; P noise Represents noise power; N error Indicates the number of bit errors; N total Indicates the complete transmission code;

[0107] S23. Calculate the transmission performance scores of the FSO communication unit, adaptive radio frequency unit, and BeiDou RDSS link based on the comprehensive weighted scoring model:

[0108]

[0109] Where w1, w2 and w3 represent the weights of bandwidth, reliability and delay respectively; B max Indicates the maximum bandwidth of the candidate link; Latency indicates the link transmission delay;

[0110] S24. Sort the transmission performance scores of the FSO communication unit, the adaptive radio frequency unit, and the Beidou RDSS link, and take the link with the largest transmission performance score as the optimal link;

[0111] S25, modulating the received seismic wave vibration signal using the modem, encrypting and packaging the modulated signal using the communication protocol processing unit, and uploading it to the remote management module using the determined optimal link;

[0112] Alternatively, the remote management module command is received using the optimal link, and after demodulation by the modem, the modulated signal is encrypted and packaged by the communication protocol processing unit and then transmitted to the node seismograph module.

[0113] S3. Based on the feature data set, a three-dimensional fault model is generated using the remote management module and output.

[0114] Step S3 specifically includes the following steps:

[0115] S31. Normalize the original signal in the received feature data set in the time-frequency domain:

[0116]

[0117] in,

[0118]

[0119] Where, χ norm represents the standardized data; x represents the original signal; μ represents the signal mean; σ represents the standard deviation; N represents the total number of samples; x i represents the i-th data sample;

[0120] S32. Extract multi-scale features of faults using the MultiRes-Unet3D neural network:

[0121]

[0122] in,

[0123]

[0124] Where, L weighted represents the weighted loss function; C represents the total number of categories; W c represents the weight of category c; y c represents the true label; p c represents the probability that the prediction is true; f c Represents the frequency of category c in the feature dataset; ∈ represents a constant;

[0125] S33. Generate fault point cloud based on stereo vision method and calculate depth information using multi-view seismic data:

[0126]

[0127] in,

[0128] d = argmin d ∑ p∈Ω ∣I left (p)-I right (p+d)∣ 2 ;

[0129] Where Z is the depth of the fault point; B is the binocular baseline distance; f is the focal length; d is the parallax; I left (p) and I right (p+d) represents the intensity value of the left view and the right view at point p respectively; Ω represents the disparity search range;

[0130] S34, using the iterative closest point algorithm to align the fault point cloud, where the objective function E of the iterative closest point algorithm is ICP as follows:

[0131]

[0132] Among them, p i represents the source point cloud; q i Represents the target point cloud; T represents the rigid body transformation matrix; I represents the number of points in the fault point cloud;

[0133] S35. Convert the optimized point cloud into a gridded surface model to obtain a three-dimensional fault model.

[0134] Example

[0135] Step 1: Seismic wave data acquisition and preprocessing:

[0136] When conducting exploration work in an earthquake-prone area, node seismograph modules are deployed at multiple key locations (the node seismograph modules include three-axis MEMS seismic wave sensors arranged in a multi-point array, which can accurately capture seismic wave vibration signals from different directions). When the multi-point array three-axis MEMS seismic wave sensors detect seismic waves and the vibration signal intensity reaches the preset threshold, the built-in microprocessor immediately starts the acquisition process and transmits the signal to the amplification and filtering unit.

[0137] Adaptive gain control (AGC) technology is used in the signal amplification process. For example, when a weak seismic wave signal is detected, the system automatically increases the amplification factor to ensure that the signal can be presented clearly. When encountering a strong seismic signal, the amplification factor is adjusted appropriately to prevent signal overload and distortion. In the filtering stage, an adaptive Kalman filter algorithm is used in combination with a bandpass filter. The Kalman filter dynamically corrects the measurement signal, effectively eliminating environmental noise and irrelevant interference. At the same time, the bandpass filter is used to allow seismic wave signals in the main frequency range to pass smoothly, filtering out low-frequency and high-frequency noise before transmitting them to the analog-to-digital conversion unit.

[0138] The analog-to-digital conversion unit uses a 24-bit high-precision successive approximation register (SAR) analog-to-digital converter to accurately convert analog signals into digital signals. The converted digital signals are further processed by the edge intelligent pre-processing unit, including noise reduction and data correction. Using a built-in data compression algorithm, the data volume is reduced and stored in a high-speed solid-state storage unit.

[0139] Step 2: Determine the transmission link and upload the data to the StarNet communication unit:

[0140] The preprocessed data is transmitted from the edge intelligent preprocessing unit to the communication unit, and the communication unit uses LoRaWAN wireless communication technology to transmit the data to the star network communication unit.

[0141] The StarNet communication unit's modem performs QPSK modulation on data and applies forward error correction (FEC). During a test in a mountainous area, despite signal interference, FEC maintained data transmission accuracy exceeding 98%, effectively ensuring data integrity. The communication protocol processing unit encrypts the modulated data using the TLS / SSL encryption protocol and then packages it according to a specific protocol format.

[0142] The phased array antenna's automatic pointing function ensures stable data transmission to the remote management module via a low-orbit satellite network. During testing in remote areas, the antenna quickly locked onto satellite signals, enabling stable data upload even in complex terrain and adverse weather conditions.

[0143] Step 3: Remote Management Module Receiving and Processing

[0144] The remote management module receives earthquake data via StarNet satellites, demodulates it using a modem, and decrypts it via the communication protocol processing unit. The decrypted data is then analyzed to calculate parameters such as signal strength, frequency, and earthquake source direction, ultimately outputting a three-dimensional fault model. During one earthquake monitoring event, the system quickly and accurately calculated the earthquake source direction, providing a crucial reference for subsequent rescue efforts. Simultaneously, health monitoring reports are generated to provide real-time updates on the device's operating status, triggering alerts if any abnormalities, such as low battery levels, are detected.

[0145] The remote management module's parameter configuration unit also allows real-time adjustment of operating parameters such as the sampling rate and gain of node seismometer modules. When testing in areas with varying seismic intensity, the system automatically adjusts parameters based on actual conditions to ensure optimal data collection. It also monitors all modules' power, storage space, and communication quality in real time to ensure stable operation.

[0146] Step 4: Data storage and backup

[0147] The remote management module stores all received earthquake data in a distributed storage system and performs redundant backup (during long-term operation, multiple data recovery tests have proven that this storage backup mechanism can effectively avoid data loss and ensure data security and integrity).

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for operating a remote intelligent seismograph system based on star network communication, the remote intelligent seismograph system based on star network communication comprising a node seismograph module, the node seismograph module bidirectionally communicating with a remote management module via a star network communication unit, and the node seismograph module, the star network communication unit, and the remote management module are all electrically connected to a multi-source energy module; in, The node seismometer module includes a connected three-axis MEMS seismic wave sensor, which is connected to the edge intelligent preprocessing unit via an amplification and filtering unit and an analog-to-digital conversion unit in sequence, and is used to collect seismic wave vibration signals using the three-axis MEMS seismic wave sensor, and preprocess the collected seismic wave vibration signals using the edge intelligent preprocessing unit; The star network communication unit includes a modem, a communication protocol processing unit, and a phased array antenna connected in sequence. The phased array antenna is a three-mode link structure composed of an FSO communication unit, an adaptive radio frequency unit, and a BeiDou RDSS link. It is used to receive pre-processed seismic wave vibration signals and upload them to the remote management module, or receive instructions from the remote management module and transmit them to the node seismograph module; Remote management module, used to receive seismic wave vibration signals and remotely monitor and manage node seismometer modules to output three-dimensional fault models and health monitoring reports; The method is characterized in that: the working method comprises the following steps: S1, a three-axis MEMS seismic wave sensor collects vibration signals at a set sampling rate. After amplification, filtering, and analog-to-digital conversion, the edge intelligent pre-processing unit performs wavelet denoising and data compression to generate a feature data set; In step S1, the specific steps of performing wavelet denoising and data compression are as follows: The first step is to use the Daubechies9 orthogonal wavelet basis to perform discrete wavelet decomposition on the digital signal after analog-to-digital conversion to obtain the approximate coefficients and detail coefficients of each layer. The decomposition formula is as follows: ； ； Where, and Respectively represent Layer and Layer approximation coefficient; Indicates the number of decomposition levels; represents a low-pass filter; Indicates the Layer detail coefficient; represents the downsampling operation; The second step is threshold denoising, and setting the Layer threshold for: ； Where, Indicates the Noise standard deviation of layer detail coefficients; Indicates the The length of the layer detail coefficient; Step 3: Threshold-based Corrected wavelet coefficients : ； Where, represents the modified wavelet coefficient; The fourth step is to quantize the wavelet coefficients using a hierarchical compression strategy, where 5% of the maximum amplitude coefficients are retained for high-frequency noise and 95% of the amplitude coefficients are retained for low-frequency noise. Step 5: Huffman code the retained coefficients to achieve compression; S2. Determine a communication transmission link, and upload the feature data set to a remote management module based on the determined communication transmission link; S3. Based on the feature data set, a three-dimensional fault model is generated using the remote management module and output.

2. The method of claim 1 , wherein: Step S2 specifically includes the following steps: S21. Filter the link candidate set, and the filtering conditions are: ； Where, represents the candidate link bandwidth; Indicates the size of the feature dataset; Indicates the maximum allowed transmission time; S22. Using signal-to-noise ratio and bit error rate Evaluate channel quality: ； ； Where, Indicates signal power; represents the noise power; Indicates the number of bit errors; Indicates the complete transmission code; S23. Calculate the transmission performance scores of the FSO communication unit, adaptive radio frequency unit, and BeiDou RDSS link based on the comprehensive weighted scoring model. ‌ : ； Where, 、 and Represent the weights of bandwidth, reliability, and delay respectively; Indicates the maximum bandwidth of the candidate link; Indicates link transmission delay; S24: Transmission performance scores of the FSO communication unit, adaptive radio frequency unit, and BeiDou RDSS link Sort and take the largest transmission performance score The corresponding link is taken as the optimal link; S25, modulating the received seismic wave vibration signal using the modem, encrypting and packaging the modulated signal using the communication protocol processing unit, and uploading it to the remote management module using the determined optimal link; Alternatively, the remote management module command is received using the optimal link, and after demodulation by the modem, the modulated signal is encrypted and packaged by the communication protocol processing unit and then transmitted to the node seismograph module.

3. The operating method of a remote intelligent seismograph system based on star network communication according to claim 1, characterized in that: Step S3 specifically includes the following steps: S31. Perform time-frequency domain normalization on the original signal in the received feature data set: ； in, ； ； Where, Represents the standardized data; represents the original signal; represents the signal mean; represents the standard deviation; represents the total number of samples; Indicates the data samples; S32. Extract multi-scale features of faults using the MultiRes-Unet3D neural network: ； in, ； Where, represents the weighted loss function; Indicates the total number of categories; Representation category The weight of represents the true label; represents the probability that the prediction is true; Representation category Frequency of occurrence in the feature dataset; represents a constant; S33. Generate fault point cloud based on stereo vision method and calculate depth information using multi-view seismic data: ； in, ； Where, Indicates the depth of the fault point; Indicates the binocular baseline distance; Indicates focal length; Indicates parallax; and Indicates that the left view and the right view are at point The intensity value at Indicates the disparity search range; S34. Use the iterative closest point algorithm to align the fault point cloud, where the objective function of the iterative closest point algorithm is as follows: ； in, represents the source point cloud; Represents the target point cloud; Represents the rigid body transformation matrix; The number of points representing the fault point cloud; S35. Convert the optimized point cloud into a gridded surface model to obtain a three-dimensional fault model.

4. The method of claim 1 , wherein: The multi-source energy module includes a solar power generation unit and a vertical axis wind power generation unit, and both the solar power generation unit and the vertical axis wind power generation unit are electrically connected to the lithium iron phosphate battery pack through the battery management unit.

5. The operating method of a remote intelligent seismograph system based on star network communication according to claim 1, characterized in that: The three-axis MEMS seismic wave sensor integrates a seismic wave sensor, an amplifying and filtering unit, an analog-to-digital conversion unit and a microprocessor that are electrically connected in sequence. The microprocessor is also connected to the edge intelligent preprocessing unit and the communication unit respectively. At the same time, the edge intelligent preprocessing unit is connected to the communication unit, and the communication unit communicates with the star network communication unit to realize the use of the three-axis MEMS seismic wave sensor to collect seismic wave vibration signals, and transmit them to the amplifying and filtering unit for filtering and signal amplification. After analog-to-digital conversion by the analog-to-digital converter, it is transmitted to the microprocessor, and after data compression based on wavelet transform by the edge intelligent preprocessing unit, the collected seismic data is uploaded using the communication unit.

6. The operating method of a remote intelligent seismograph system based on star network communication according to claim 5, characterized in that: The uplink of the FSO communication unit uses ‌1550nm wavelength laser modulation and combines the data compressed by wavelength-mode division multiplexing modulation with a transmission rate of ≥20Gbps and a bit error probability of ≤5×10 -9 ; The adaptive radio frequency unit operates in the 1.4GHz-5.8GHz frequency band, supports OFDM modulation and polarization diversity, and has a transmission rate of ≥50Mbps when the rain attenuation is ≤30dB; The RDSS link integrates BeiDou-3 B1I+B2a dual-frequency reception, and the end-to-end delay in emergency mode is ≤2.5s.

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