A system and method for integrating perimeter security with subsurface monitoring
By integrating perimeter security and underground monitoring systems and utilizing sensor and data processing technologies, the problems of traditional systems being unable to detect underground threats and the discrete nature of geotechnical engineering monitoring have been solved, enabling continuous, economical, and efficient monitoring of surface and underground conditions.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BAFANG SEISMIC INC
- Filing Date
- 2026-05-06
- Publication Date
- 2026-06-02
AI Technical Summary
In existing technologies, traditional perimeter security systems cannot effectively detect underground threats, such as illegal excavation or foundation changes, and geotechnical engineering monitoring methods are difficult to achieve continuous dynamic monitoring of the underground structural status of large areas.
An integrated perimeter security and underground monitoring system is adopted, which collects vibration data through multiple sensors, performs time synchronization and data processing, detects perimeter intrusion events and calculates the relative changes in seismic velocity, and uses machine learning models and environmental noise interferometry to monitor the condition of underground machinery.
It enables simultaneous and continuous monitoring of surface activities and underground conditions, improving the economic efficiency and environmental adaptability of the monitoring scheme. It can work effectively in complex environments and detect underground activities that traditional security systems cannot cover.
Smart Images
Figure CN122135476A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of engineering safety monitoring and intelligent security technology, specifically to a system and method that integrates perimeter security and underground monitoring. Background Technology
[0002] In residential, commercial, and infrastructure environments, risks such as unauthorized intrusion, soil instability, moisture infiltration, and ground subsidence exist. Therefore, security protection and structural health monitoring are two long-standing challenges in these environments.
[0003] In existing technologies, on the one hand, traditional perimeter security systems, such as optical cameras and infrared detectors, are easily affected by environmental factors such as lighting, inclement weather, and physical obstructions, and cannot effectively detect threats from underground, such as illegal excavation or foundation changes. On the other hand, traditional geotechnical engineering monitoring methods, such as borehole sampling and the deployment of inclinometers, often produce discrete, static point data, making it difficult to achieve continuous dynamic monitoring of the underground structural conditions (such as soil density and moisture content) over a large area.
[0004] Therefore, how to provide a technical solution that can simultaneously and continuously monitor surface activity and underground machinery conditions with reliable monitoring performance has become an urgent technical problem to be solved. Summary of the Invention
[0005] In view of this, in order to solve the above-mentioned technical problems, the present invention provides a system and method that integrates perimeter security and underground monitoring.
[0006] The present invention adopts the following technical solution:
[0007] In a first aspect, the present invention provides a system integrating perimeter security and underground monitoring, comprising: a data acquisition module, a time synchronization module, a data processing module, and an alarm module; The data acquisition module includes multiple sensors, which are deployed at different locations in the monitoring area. The sensors are used to collect vibration data at the corresponding locations and send it to the time synchronization module. The time synchronization module is used to synchronize the vibration data sent by each of the sensors in time and then send it to the data processing module. The data processing module is used to detect perimeter intrusion events and calculate the relative change of earthquake velocity based on the vibration data; the data processing module is also used to send a corresponding alarm command to the alarm module when a perimeter intrusion event is detected or when the relative change of earthquake velocity is determined to meet a preset condition. The alarm module is used to execute the corresponding alarm action according to the alarm command.
[0008] Optionally, the sensor is a vibration sensor or a seismic sensor.
[0009] Optionally, the data processing module detects perimeter intrusion events based on the vibration data, specifically including: The data processing module filters and extracts features from the vibration data to obtain target features; The data processing module inputs the target features into a preset machine learning model to obtain the event classification results output by the machine learning model. When there is a perimeter intrusion event, the event classification results include the event type and probability of the perimeter intrusion event. When there is no perimeter intrusion event, the event classification results include no event.
[0010] Optionally, this system may also include: a display module; The data processing module is also used for: When a perimeter intrusion event occurs, the spatial location of the perimeter intrusion event is located based on a preset positioning technology and the vibration data from the relevant sensors. A spatial intrusion probability map is generated based on the event type and probability of the perimeter intrusion event and the spatial location. The display module is controlled to display the space intrusion probability map.
[0011] Optionally, the data processing module calculates the relative change in earthquake velocity based on the vibration data, specifically including: The data processing module extracts continuous environmental seismic noise from the vibration data collected within the most recent preset time period; The data processing module preprocesses the continuous environmental seismic noise from each of the sensors and then segments the preprocessed continuous environmental seismic noise into time windows. For each time window, the data processing module performs cross-correlation analysis on the continuous environmental seismic noise between each sensor pair within the time window to obtain the cross-correlation function; The data processing module superimposes the cross-correlation functions of multiple time windows for each sensor pair to obtain an enhanced impulse response function, which approximately represents the Green's function of seismic wave propagation between the sensor pairs. The data processing module compares the impulse response function with a reference impulse response function corresponding to the impulse response function for a preset reference time period, measures the time travel offset between the two, and calculates the time travel disturbance. The data processing module calculates the relative change of earthquake velocity based on the relationship between travel time disturbance and the relative change of earthquake velocity.
[0012] Optionally, the data processing module is further configured to: Based on a preset detection algorithm, the arrival event of seismic P-waves is detected according to the vibration data; When an earthquake P-wave arrival event is detected, an early earthquake warning command is generated; The earthquake early warning command is sent to the alarm module so that the alarm module performs a preset earthquake early warning action.
[0013] Optionally, the preset detection algorithm is a short-time / long-time average energy ratio detection algorithm, a frequency domain classification algorithm, or a multi-site consistency check algorithm.
[0014] Optionally, the time synchronization module synchronizes the vibration data sent by each of the sensors in time, specifically including: The time synchronization module acquires a time reference, which is derived from a satellite positioning system calibration clock or a network precision time protocol. The time synchronization module assigns a unified timestamp to the vibration data sent by each sensor based on the time reference. The time synchronization module aligns the vibration data in chronological order based on the timestamp.
[0015] Optionally, the data processing module includes at least one of an edge device, a local server, and a cloud server.
[0016] Secondly, the present invention provides a method for integrating perimeter security and underground monitoring, applied to the system for integrating perimeter security and underground monitoring as described above, wherein the method for integrating perimeter security and underground monitoring includes: The data acquisition module collects vibration data at corresponding locations using sensors deployed at different locations within the monitoring area and sends it to the time synchronization module; The time synchronization module synchronizes the vibration data sent by each sensor in time and then sends it to the data processing module. The data processing module detects perimeter intrusion events and calculates the relative changes in earthquake velocity based on the vibration data; when the data processing module detects a perimeter intrusion event or determines that the relative changes in earthquake velocity meet preset conditions, it sends a corresponding alarm command to the alarm module. The alarm module executes the corresponding alarm action according to the alarm command.
[0017] This invention employs the above technical solution, utilizing a dual data processing mode (detecting perimeter intrusion events and determining relative changes in seismic velocity) through a single data acquisition module and a data processing module. This enables the invention to simultaneously and continuously monitor surface activity and underground mechanical conditions, avoiding the enormous costs of deploying multiple independent systems for different purposes, and significantly improving the economy and comprehensiveness of the monitoring solution. Secondly, as a sensor used to collect vibration data, its data acquisition relies on ground mechanical vibrations unaffected by light or visual obstruction. This allows the invention to operate effectively in complex environments such as darkness, dense fog, and vegetation obstruction, and to detect underground activities that traditional security systems cannot cover. It exhibits strong environmental adaptability and reliable monitoring performance. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 This is a schematic diagram of the structure of a system integrating perimeter security and underground monitoring provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a sensor deployment location provided in an embodiment of the present invention; Figure 3 This is the embodiment of the invention corresponding to Figure 2 A schematic diagram of an interconnected sensor network; Figure 4 This is a schematic diagram of an intrusion detection and alarm provided in an embodiment of the present invention; Figure 5 This is an orthogonal slice view of a three-dimensional underground velocity model provided in an embodiment of the present invention; Figure 6 This is a schematic diagram illustrating the relative change of earthquake velocity provided in an embodiment of the present invention; Figure 7 This is a flowchart illustrating a method for integrating perimeter security and underground monitoring provided in an embodiment of the present invention. Detailed Implementation
[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.
[0021] Figure 1 This is a schematic diagram of a system integrating perimeter security and underground monitoring provided in an embodiment of the present invention. Figure 1 As shown, this system includes: a data acquisition module 11, a time synchronization module 12, a data processing module 13, and an alarm module 14.
[0022] The data acquisition module 11 includes multiple sensors, which are deployed at different locations within the monitoring area. These locations can be on the surface, underground, or below ground level. The multiple sensors together form an interconnected sensor network. Figure 2 This is a schematic diagram of a sensor deployment location provided in an embodiment of the present invention. Figure 3 This is the embodiment of the invention corresponding to Figure 2 A schematic diagram of an interconnected sensor network. (See diagram below.) Figure 2 As shown, eight sensors are deployed around a detached house, specifically approximately 0.5 meters below the surface in the surrounding yard. Figure 3 As shown, the eight sensors can be regarded as multiple interconnected sensor nodes, which are roughly evenly distributed along the perimeter of the residence, thus forming a closed monitoring loop, namely an interconnected sensor network.
[0023] The sensor is used to collect vibration data at the corresponding location and send it to the time synchronization module. The sensor can be a vibration sensor or a seismic sensor. In a specific example, the sensor can be a seismograph, a MEMS (Micro-Electro-Mechanical Systems) accelerometer, a broadband seismograph, or a piezoelectric vibration sensor.
[0024] The sensors can be battery-powered or wired-powered and support interconnection between nodes to form a mesh or bus network. The number, spacing, and installation depth of the sensors can vary depending on the monitoring target. For example, eight sensors with a spacing of about 5–25 meters and a burial depth of about 0.5 meters can be deployed around a residential structure, while along pipelines or border areas, the number of sensors can be greater, the spacing can be larger, and the burial depth can be deeper.
[0025] In another specific example, the data acquisition module 11 can also use a DAS (Distributed Acoustic Sensing) fiber optic system to achieve continuous distributed vibration monitoring by laying sensing fibers along the monitoring area.
[0026] The time synchronization module 12 is used to synchronize the vibration data sent by each sensor before sending it to the data processing module. The time synchronization module 12 can have a built-in satellite positioning system calibration clock, network precise time protocol, or other high-precision synchronization mechanism. The satellite positioning system calibration clock can be a GNSS (Global Navigation Satellite System) calibration clock or a GPS (Global Positioning System) calibration clock used alone.
[0027] Based on this, the time synchronization module synchronizes the vibration data sent by each sensor, which may include: (1) the time synchronization module obtains a time reference, which is derived from the calibration clock of the satellite positioning system or the network precision time protocol. (2) the time synchronization module adds a unified timestamp to the vibration data sent by each sensor based on the time reference. (3) the time synchronization module aligns the vibration data according to the time sequence based on the timestamp.
[0028] This process ensures precise temporal alignment of vibration data from different sensors, enabling joint analysis of vibration data collected by multiple sensors and thus improving detection accuracy and source localization capabilities. Understandably, this is crucial for subsequent algorithms that require the joint analysis of data from multiple sensors.
[0029] It should be noted that the present invention can use wired or wireless networks to transmit data. For example, data can be transmitted between the data acquisition module 11 and the time synchronization module 12, between the time synchronization module 12 and the data processing module 13, and between the data processing module 13 and the alarm module 14 using wired or wireless networks. Wired networks include Ethernet, and wireless networks include Wi-Fi, cellular networks, mesh networks, and LPWAN (Low-Power Wide-Area Network).
[0030] Data processing module 13 can perform data analysis through one or more of its data processing units. Data processing module 13 may include at least one of edge devices, local servers, and cloud servers. Data processing module 13 can support low-latency real-time alert pipelines, as well as high-throughput long-term interferometry analysis and machine learning model training pipelines.
[0031] The data processing module 13 is used to detect perimeter intrusion events and calculate the relative change of earthquake velocity based on vibration data; the data processing module is also used to send a corresponding alarm command to the alarm module when a perimeter intrusion event is detected or when the relative change of earthquake velocity is determined to meet preset conditions.
[0032] On the one hand, the data processing module 13 can be configured to analyze transient vibration signals to detect intrusion events related to human, vehicle, animal or mechanical activities. Figure 4 This is a schematic diagram of an intrusion detection and alarm provided by an embodiment of the present invention. Figure 4 As shown, human activities, animal activities, and vehicle movement all generate unique vibration signals. Therefore, intrusion events related to human, vehicle, animal, or mechanical activities can be detected by analyzing these vibration signals, and corresponding preset alarm actions can be executed when an intrusion event is detected.
[0033] Based on this, in this embodiment of the invention, the data processing module detects perimeter intrusion events according to vibration data, specifically including: (1) The data processing module filters and extracts features from the vibration data to obtain the target features.
[0034] Specifically, in the process of filtering vibration data, bandpass filters can be used to process the vibration data to retain frequency band components related to intrusion behaviors such as human activity, animal activity, vehicle movement, and excavation, while suppressing high-frequency environmental noise and extremely low-frequency ground pulsations. Furthermore, target characteristics can include signal amplitude characteristics (such as peak value and root mean square), frequency content (such as spectral centroid and subband energy), time-domain waveform characteristics (such as zero-crossing rate and waveform duration), and spatial coherence between sensor pairs (such as cross-correlation peak value or coherence coefficient).
[0035] (2) The data processing module inputs the target features into the preset machine learning model and obtains the event classification results output by the machine learning model. When there is a perimeter intrusion event, the event classification results include the event type and probability of the perimeter intrusion event. When there is no perimeter intrusion event, the event classification results include no event.
[0036] Specifically, the machine learning model can be a neural network model, decision tree, hidden Markov model, k-nearest neighbor classifier, or ensemble learning model. Among these, the neural network model can be a convolutional neural network model, a recurrent neural network model, or a Transformer architecture. The probability output by the machine learning model can be a confidence score.
[0037] It should be noted that the machine learning model used in this invention is pre-trained, and its training method is existing technology, which will not be elaborated here. The machine learning model can incorporate physical constraints derived from seismic wave propagation theory, such as travel time relationships and dispersion characteristics. This physical information-driven learning method improves the model's robustness and can be transferred and used in different sensor deployments.
[0038] In this embodiment of the invention, the integrated perimeter security and underground monitoring system of the present invention may further include: a display module.
[0039] The data processing module is also used for: (1) When a perimeter intrusion event occurs, the spatial location of the perimeter intrusion event is located based on the preset positioning technology and the vibration data of the relevant sensors.
[0040] Specifically, the preset positioning technology can be time difference of arrival analysis, beamforming methods, or learning spatial mapping networks.
[0041] (2) Generate a spatial intrusion probability map based on the event type, probability, and spatial location of the perimeter intrusion event.
[0042] (3) Control the display module to display the spatial intrusion probability map to visualize the activities in the monitoring area.
[0043] On the other hand, the data processing module 13 can also be configured to analyze continuous environmental seismic noise to monitor underground machinery conditions. This process follows the principle of environmental noise interferometry. The core of environmental noise interferometry is that by performing cross-correlation calculations on continuous environmental seismic noise recorded over a long period by two sensors, the impulse response between the two sensors can be empirically reconstructed. This impulse response approximately represents the Green's function of seismic wave propagation between the sensor pairs. Using the reconstructed impulse response, the near-surface seismic velocity characteristics can be estimated, and the temporal variation of the seismic velocity characteristics can be quantified without the need for an active seismic source.
[0044] Based on this, in this embodiment of the invention, the data processing module calculates the relative change in earthquake velocity according to the vibration data, which specifically may include: (1) The data processing module extracts continuous environmental seismic noise from vibration data collected within the most recent preset time period. The most recent preset time period can be the most recent 24 hours.
[0045] (2) The data processing module preprocesses the continuous environmental seismic noise of each sensor and divides the preprocessed continuous environmental seismic noise into time windows.
[0046] Specifically, preprocessing of continuous environmental seismic noise can include bandpass filtering (e.g., filtering out high-frequency anthropogenic interference while retaining low-frequency components sensitive to underground structures, such as 0.1-1 Hz), spectral whitening, and time-domain normalization to enhance the effectiveness and stability of the background noise. Subsequently, the processed long-term data can be divided into multiple shorter time windows, such as 30 minutes each.
[0047] (3) For each time window, the data processing module performs cross-correlation analysis on the continuous environmental seismic noise between each sensor pair within the time window to obtain the cross-correlation function.
[0048] (4) For each sensor pair, the data processing module superimposes (linearly superimposes) the cross-correlation functions of multiple time windows to obtain an enhanced impulse response function. The impulse response function approximately represents the Green's function of seismic wave propagation between the sensor pairs.
[0049] Specifically, for each sensor pair, linearly superimposing the cross-correlation functions obtained from all time windows can significantly suppress incoherent random noise while highlighting the coherent signal propagating stably between the two sensors. The resulting stable waveform (i.e., the enhanced impulse response function) can be physically approximated as the seismic record received at sensor B when a pulse source is applied at sensor A; that is, the empirical Green's function or impulse response function. This function contains rich information about the subsurface medium traversed by the seismic wave as it propagates from sensor A to sensor B.
[0050] (5) The data processing module compares the impulse response function with the reference impulse response function corresponding to the impulse response function in the preset reference time period, measures the time offset between the two, and calculates the time disturbance.
[0051] Among them, the time travel disturbance can be calculated by cross-correlation, spectrum analysis, waveform stretching or phase shift.
[0052] It should be noted that the reference impulse response function is calculated using continuous environmental seismic noise within a preset reference time period. The calculation process is described above for the impulse response function and will not be repeated here. Furthermore, the impulse response function and its corresponding reference impulse response function correspond to the same pair of locations, for example, sensor A and sensor B. The preset reference time period can be the previous most recent preset time period or a time period specified by the user.
[0053] (6) The data processing module calculates the relative change of seismic velocity based on the relationship between travel time disturbance and the relative change of seismic velocity. The relative change of seismic velocity can reflect the time change of underground conditions.
[0054] Furthermore, the data processing module can estimate the average propagation speed of a wave between two points by measuring the arrival time of a specific wave phase (such as a surface wave) in the impulse response function and combining this with the known sensor spacing. When a sufficient number of sensor pairs are available, it is even possible to construct a three-dimensional subsurface velocity model using inversion techniques such as tomography. Figure 5 This is an orthogonal slice view of a three-dimensional underground velocity model provided in an embodiment of the present invention. For example... Figure 5 As shown in the model, different colors or gray values can represent different seismic wave velocities, thereby identifying high-velocity areas with relatively dense structures and low-velocity anomaly areas that may contain loose, water-bearing, or cavitary structures.
[0055] In addition, the data processing module can also analyze the dispersion characteristics of the impulse response function, such as extracting the dispersion curve of surface waves, to invert the velocity stratification structure of the subsurface medium or identify low-velocity anomaly regions.
[0056] The data processing module is also used to send corresponding alarm commands to the alarm module when a perimeter intrusion event is detected or when the relative change in earthquake velocity meets preset conditions. In a specific example, if the data processing module detects a perimeter intrusion event, it sends a first alarm command to the alarm module, causing the alarm module to perform a first alarm action, such as pushing an alarm notification to the homeowner's mobile application indicating "human activity detected in the yard," and simultaneously activating response devices, such as turning on the yard lights and triggering the audible and visual alarms.
[0057] If the data processing module determines that the relative change in earthquake velocity meets a preset condition, it sends a second alarm command to the alarm module, causing the alarm module to perform a second alarm action, such as sending a warning message to the homeowner. The preset condition can be set as follows: the relative change in earthquake velocity along the path measured by any sensor within the monitoring area is lower than a preset value for three consecutive days; the preset value can be -0.5%.
[0058] Figure 6 This is a schematic diagram illustrating the relative change of earthquake velocity provided in an embodiment of the present invention. For example... Figure 6 As shown, (a) illustrates stable subsurface conditions, (b) shows moderate velocity changes, and (c) shows a significant velocity decrease. When the relative change in seismic velocity meets preset conditions, the monitoring results will change from (a) to (b), and finally to (c). The relative change in seismic velocity can sensitively reflect changes in subsurface conditions, including: changes in soil stiffness, water saturation or groundwater movement, soil consolidation or softening, formation of underground cavities or subsidence, excavation or tunnel construction activities, and unstable structural foundations.
[0059] In addition, the data processing module can also use machine learning anomaly detection algorithms to identify whether the relative changes in earthquake velocity meet preset conditions.
[0060] The alarm module 14 is used to execute the corresponding alarm action according to the alarm command.
[0061] It should be noted that the response action of the alarm module 14 is configurable and can operate in manual, automatic or timed mode.
[0062] This invention employs the above technical solution, utilizing a dual data processing mode (detecting perimeter intrusion events and determining relative changes in seismic velocity) through a single data acquisition module and a data processing module. This enables the invention to simultaneously and continuously monitor surface activity and underground mechanical conditions, avoiding the enormous costs of deploying multiple independent systems for different purposes, and significantly improving the economy and comprehensiveness of the monitoring solution. Secondly, as a sensor used to collect vibration data, its data acquisition relies on ground mechanical vibrations unaffected by light or visual obstruction. This allows the invention to operate effectively in complex environments such as darkness, dense fog, and vegetation obstruction, and to detect underground activities that traditional security systems cannot cover. It exhibits strong environmental adaptability and reliable monitoring performance.
[0063] In this embodiment of the invention, the data processing module can also be used for: (1) Based on the preset detection algorithm, the arrival event of the seismic P-wave is detected according to the vibration data. The preset detection algorithm can be a short-time / long-time average energy ratio detection algorithm, a frequency domain classification algorithm, or a multi-site consistency check algorithm.
[0064] (2) When an earthquake P-wave arrival event is detected, an early earthquake warning command is generated. Specifically, the data processing module can determine that an earthquake P-wave arrival event has been detected when vibration data from multiple sensors all indicate the arrival of earthquake P-waves (P-waves), in order to rule out false detections by a single sensor that may be caused by strong local interference (such as falling heavy objects).
[0065] (3) Send the earthquake early warning command to the alarm module so that the alarm module can perform the preset earthquake early warning actions. The earthquake early warning actions include activating the monitoring camera and lighting system, issuing emergency evacuation notices through the public broadcast system and mobile phone push, automatically cutting off gas pipeline valves and unnecessary power supply, and controlling the elevator to stop at the nearest floor and open the door, etc.
[0066] It should be noted that, under normal circumstances, the data acquisition module continuously collects vibration data at a first sampling rate. When the data processing module detects an increase in vibration energy or other abnormalities, it controls the data acquisition module to continuously collect vibration data at a second sampling rate or record at a higher resolution to capture high-fidelity waveforms. Furthermore, archived records can be processed periodically to calculate seismic velocity trends and spatial seismic velocity maps.
[0067] Furthermore, this invention can also employ artificial intelligence models to improve detection performance and interpret complex vibration modes. Based on this, examples of AI-based mapping representations include: Time-to-space mapping: Converting sensor time-series inputs into a spatial intrusion probability map.
[0068] Time-to-velocity mapping: converting vibration data into a velocity domain representation of a moving object.
[0069] Anomaly detection model: Identifies deviations from baseline environmental noise characteristics.
[0070] The training dataset may include labeled field recordings, simulated scenarios, and synthetic data generated based on physical models.
[0071] It should be noted that this system is suitable for deployments requiring long-term continuous monitoring, including residential, commercial, industrial facilities, data centers, infrastructure, and urban monitoring networks. The system architecture is scalable, allowing deployment from single residential properties to large infrastructure networks or city-scale monitoring systems. The following are examples of deployment scenarios for this system: Residential: Sensors around the residence detect intrusion events and monitor soil stability, water seepage, or ground subsidence around the building foundation.
[0072] Commercial and industrial facilities: Distributed sensor arrays monitor the safety of large campuses and detect underground changes that could affect the stability of infrastructure.
[0073] Infrastructure: Installations along pipelines, substations, or transport corridors can detect unauthorized excavation and monitor ground stability along infrastructure routes.
[0074] Urban monitoring network: Deploying large-scale, low-cost sensor networks in urban areas enables scalable monitoring of ground conditions and safety-related activities.
[0075] In this embodiment of the invention, the sensor can be fused with a camera and an environmental sensor. The camera collects video data of the surrounding environment, and the environmental sensor collects environmental data of the surrounding environment. The vibration data, video data, and environmental data are then fused and analyzed. Furthermore, during data transmission, the invention employs an encrypted communication channel and a tamper detection mechanism to improve data transmission security. Moreover, the threshold involved in this invention can be adaptively adjusted using machine learning.
[0076] Based on a general inventive concept, the present invention also provides a method for integrating perimeter security and underground monitoring, which is applied to the integrated perimeter security and underground monitoring system described above. Figure 7 This is a flowchart illustrating a method for integrating perimeter security and underground monitoring provided by an embodiment of the present invention. Figure 7 As shown, this integrated method for perimeter security and underground monitoring includes: Step 701: The data acquisition module collects vibration data at corresponding locations through its sensors deployed at different locations in the monitoring area and sends it to the time synchronization module.
[0077] Step 702: The time synchronization module synchronizes the vibration data sent by each sensor and then sends it to the data processing module.
[0078] Step 703: The data processing module detects perimeter intrusion events and calculates the relative change of earthquake velocity based on the vibration data; when the data processing module detects a perimeter intrusion event or determines that the relative change of earthquake velocity meets the preset conditions, it sends a corresponding alarm command to the alarm module.
[0079] Step 704: The alarm module executes the corresponding alarm action according to the alarm command.
[0080] It should be noted that the specific implementation of this method has been described in detail in the aforementioned system embodiments, and will not be elaborated here.
[0081] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.
[0082] It should be noted that in the description of this invention, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means at least two.
[0083] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.
[0084] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.
[0085] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.
[0086] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.
[0087] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.
[0088] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.
[0089] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.
Claims
1. A system integrating perimeter security and underground monitoring, characterized in that, include: Data acquisition module, time synchronization module, data processing module, and alarm module; The data acquisition module includes multiple sensors, which are deployed at different locations in the monitoring area. The sensors are used to collect vibration data at the corresponding locations and send it to the time synchronization module. The time synchronization module is used to synchronize the vibration data sent by each of the sensors in time and then send it to the data processing module. The data processing module is used to detect perimeter intrusion events and calculate the relative change of earthquake velocity based on the vibration data; the data processing module is also used to send a corresponding alarm command to the alarm module when a perimeter intrusion event is detected or when the relative change of earthquake velocity is determined to meet a preset condition. The alarm module is used to execute the corresponding alarm action according to the alarm command.
2. The integrated perimeter security and underground monitoring system according to claim 1, characterized in that, The sensor is a vibration sensor or an earthquake sensor.
3. The integrated perimeter security and underground monitoring system according to claim 1, characterized in that, The data processing module detects perimeter intrusion events based on the vibration data, specifically including: The data processing module filters and extracts features from the vibration data to obtain target features; The data processing module inputs the target features into a preset machine learning model to obtain the event classification results output by the machine learning model. When there is a perimeter intrusion event, the event classification results include the event type and probability of the perimeter intrusion event. When there is no perimeter intrusion event, the event classification results include no event.
4. The integrated perimeter security and underground monitoring system according to claim 3, characterized in that, Also includes: Display module; The data processing module is also used for: When a perimeter intrusion event occurs, the spatial location of the perimeter intrusion event is located based on a preset positioning technology and the vibration data from the relevant sensors. A spatial intrusion probability map is generated based on the event type and probability of the perimeter intrusion event and the spatial location. The display module is controlled to display the space intrusion probability map.
5. The integrated perimeter security and underground monitoring system according to claim 1, characterized in that, The data processing module calculates the relative change in earthquake velocity based on the vibration data, specifically including: The data processing module extracts continuous environmental seismic noise from the vibration data collected within the most recent preset time period; The data processing module preprocesses the continuous environmental seismic noise from each of the sensors and then segments the preprocessed continuous environmental seismic noise into time windows. For each time window, the data processing module performs cross-correlation analysis on the continuous environmental seismic noise between each sensor pair within the time window to obtain the cross-correlation function; The data processing module superimposes the cross-correlation functions of multiple time windows for each sensor pair to obtain an enhanced impulse response function, which approximately represents the Green's function of seismic wave propagation between the sensor pairs. The data processing module compares the impulse response function with a reference impulse response function corresponding to the impulse response function for a preset reference time period, measures the time travel offset between the two, and calculates the time travel disturbance. The data processing module calculates the relative change of earthquake velocity based on the relationship between travel time disturbance and the relative change of earthquake velocity.
6. The integrated perimeter security and underground monitoring system according to claim 1, characterized in that, The data processing module is also used for: Based on a preset detection algorithm, the arrival event of seismic P-waves is detected according to the vibration data; When an earthquake P-wave arrival event is detected, an early earthquake warning command is generated; The earthquake early warning command is sent to the alarm module so that the alarm module performs a preset earthquake early warning action.
7. The integrated perimeter security and underground monitoring system according to claim 6, characterized in that, The preset detection algorithm is a short-time / long-time average energy ratio detection algorithm, a frequency domain classification algorithm, or a multi-site consistency check algorithm.
8. The integrated perimeter security and underground monitoring system according to claim 1, characterized in that, The time synchronization module synchronizes the vibration data sent by each sensor in time, specifically including: The time synchronization module acquires a time reference, which is derived from a satellite positioning system calibration clock or a network precision time protocol. The time synchronization module assigns a unified timestamp to the vibration data sent by each sensor based on the time reference. The time synchronization module aligns the vibration data in chronological order based on the timestamp.
9. The integrated perimeter security and underground monitoring system according to claim 1, characterized in that, The data processing module includes at least one of an edge device, a local server, and a cloud server.
10. A method integrating perimeter security and underground monitoring, characterized in that, The method for integrating perimeter security and underground monitoring, applicable to any one of claims 1 to 9, comprises: The data acquisition module collects vibration data at corresponding locations using sensors deployed at different locations within the monitoring area and sends it to the time synchronization module. The time synchronization module synchronizes the vibration data sent by each sensor in time and then sends it to the data processing module. The data processing module detects perimeter intrusion events and calculates the relative changes in earthquake velocity based on the vibration data; when the data processing module detects a perimeter intrusion event or determines that the relative changes in earthquake velocity meet preset conditions, it sends a corresponding alarm command to the alarm module. The alarm module executes the corresponding alarm action according to the alarm command.