Electronic fence-based anti-intrusion early warning method and system for field camp

Abstract

The invention discloses an anti-intrusion early warning method for a field camp based on an electronic fence, and the method comprises the steps: arranging the electronic fence, enabling a current between electrode columns to change when an object approaches or touches the electronic fence, enabling a signal collection and transmission module to transmit a change signal to a data analysis and processing module, and enabling the data analysis and processing module to carry out the early warning. The data analyzing and processing module firstly filters the signals to remove noise interference, then extracts and analyzes the features of the processed signals through a preset algorithm, and compares the features of the processed signals with the features of current signals under normal conditions; and if the intrusion behavior is judged, the data analysis processing module sends an early warning instruction to the early warning module, and the early warning module sends early warning information to associated mobile equipment through sound-light alarm. The system has the beneficial effects of efficient and comprehensive intrusion monitoring, adaptability to complex environments, reliable system performance, accurate signal processing and analysis, safe and reliable early warning instruction transmission, guarantee of smooth information interaction, improvement of management efficiency and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of security warning, and particularly to an anti-intrusion warning method and system for a field camp based on an electronic fence. Background Art

[0002] In outdoor activities such as outdoor exploration, outdoor camping, and field scientific research, the security of field camps is of crucial importance. The existing security protection means for field camps are relatively limited. The traditional manual patrol method not only consumes a lot of manpower, but also has patrol blind spots and time intervals, and cannot monitor the surrounding situation of the camp in real time and comprehensively. Some simple warning devices such as simple infrared induction devices are greatly affected by environmental factors and are prone to false alarms or missed alarms in complex outdoor environments. For example, in areas with a lot of vegetation, the slightest movement of the wind and grass may cause the infrared induction device to frequently give false alarms; in bad weather such as heavy rain and sandstorms, its induction performance will drop significantly, thus failing to effectively play the warning role. Therefore, there is an urgent need for a method and system that can adapt to complex outdoor environments and accurately and timely warn of intrusion behaviors.

[0003] Therefore, those skilled in the art are committed to providing an anti-intrusion warning method and system for a field camp based on an electronic fence that can solve the above problems. Summary of the Invention

[0004] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is to provide an anti-intrusion warning method and system for a field camp based on an electronic fence that can solve the above problems.

[0005] To achieve the above object, the present invention provides an anti-intrusion warning method for a field camp based on an electronic fence, including the following steps:

[0006] Arrange an electronic fence. When an object approaches or touches the electronic fence, the current between the electrode columns will change. The signal acquisition and transmission module transmits this changed signal to the data analysis and processing module. The data analysis and processing module first filters the signal to remove noise interference, and then extracts and analyzes the features of the processed signal through a preset algorithm, and compares them with the current signal features under normal conditions; if it is determined to be an intrusion behavior, the data analysis and processing module sends a warning instruction to the warning module, and the warning module gives an audible and visual alarm and sends a warning message to the associated mobile device.

[0007] Preferably, the arrangement of the electronic fence specifically includes

[0008] Perimeter survey of the camp. Before installing the electric fence, conduct a survey of the surrounding environment of the field camp. The survey includes topography, geomorphology, vegetation distribution, and road conditions to determine the layout route of the electric fence and ensure that it can fully cover the surrounding area of the camp.

[0009] Topographic obstacle identification and avoidance. For mountainous or complex terrain areas, avoid installing the electric fence at the bottom of valleys and ensure that the installation of the electric fence does not damage the hillside.

[0010] Vegetation obstacle identification and avoidance. During the layout process, identify whether it is possible to avoid areas with dense vegetation such as tall trees and shrubs. If it is unavoidable, increase the density of electrode posts or adjust the height of electrode posts.

[0011] Hole digging. Dig holes at the selected locations. The depth of the holes is 1 - 1.5 meters, and the diameter of the holes is 0.3 - 0.5 meters.

[0012] Foundation treatment. Level the bottom of the holes, remove debris and stones in the holes. If the soil conditions are poor, a layer of gravel or sand can be laid in the holes to improve the stability and conductivity of the foundation.

[0013] Electrode post positioning. Place the electrode posts into the holes, ensure the accurate position of the electrode posts, and use a level and a plumb line to calibrate the electrode posts to ensure that the electrode posts are perpendicular to the ground.

[0014] Fix the electrode posts. After the electrode posts are installed in place, use fixing materials to fix the electrode posts.

[0015] Connect the electrode posts. Connect the connecting cables between adjacent electrode posts. The connecting cables are made of waterproof, corrosion-resistant, and wear-resistant materials. When connecting, ensure that the joints of the connecting cables are firm and reliable to avoid problems such as poor contact.

[0016] Underground laying. When excavating the cable trench, the depth of the cable trench is 0.5 - 0.8 meters, and the width of the cable trench is 0.3 - 0.5 meters. After the cables are laid in the cable trench, cover them with a layer of soil or sand and gravel.

[0017] System debugging and detection, including power-on testing, signal detection, and function testing.

[0018] Preferably, for power-on testing, after the installation of the electric fence is completed, conduct a power-on test to check the on-off situation of the current between the electrode posts and ensure that the entire electric fence system can work normally.

[0019] Signal detection: Use signal detection equipment to detect the signal strength and stability parameters of the electric fence system. The detection points are evenly distributed in the surrounding area of the electric fence to ensure that the changes in the signal can be fully detected.

[0020] The functional test includes intrusion simulation test and equipment fault detection

[0021] The intrusion simulation test includes

[0022] Performance test of the electronic fence. By using a current monitoring device, monitor the on-off situation of the current between the electrode posts of the electronic fence. During the simulated intrusion process, observe the changes in the current signal, including sudden interruption of the current, significant decrease in the current value, etc. When a simulated intrusion behavior occurs, the on-off of the current should conform to the preset logical relationship.

[0023] Low-voltage pulse current characteristic test. Use a current measuring instrument to measure the parameters of the low-voltage pulse current of the electronic fence, including pulse frequency, pulse amplitude, pulse width, etc. During the simulated intrusion process, observe the changes in these parameters. At the same time, the actual test should meet the following standards: the pulse frequency should remain stable with an error within ±10%; the pulse amplitude should remain consistent within the preset range with an error within ±20%; the pulse width should meet the preset requirements with an error within ±1 ms;

[0024] Signal acquisition and transmission module test. Use a signal generator to generate an analog signal similar to the current change of the electronic fence and input it into the signal acquisition and transmission module. Compare the signal data collected by the signal acquisition and transmission module with the input analog signal data to check whether they are consistent; the error between the collected data and the input analog signal data should be within ±5%;

[0025] Signal transmission stability test. Set signal receiving points at different distances to measure the attenuation of the signal during transmission. Gradually increase the transmission distance until the signal quality drops to the extent that it cannot be normally recognized; set an electromagnetic interference source near the electronic fence to observe whether the signal transmission is interfered; use a wireless signal interference device to interfere with the wireless transmission signal to observe whether the signal transmission is interrupted or an error occurs; under the condition of meeting the signal quality requirements, the signal transmission distance should be not less than 500 meters; under the condition of being affected by electromagnetic interference and wireless interference, the bit error rate should be lower than 1%;

[0026] Data processing accuracy test. Use known intrusion signal feature samples to test the feature extraction algorithm of the data analysis and processing module. Check whether the module can accurately extract the features of the intrusion signal, compare the extracted features with the preset intrusion judgment threshold, and judge whether it is an intrusion behavior; through multiple tests, count the accuracy rate of intrusion judgment; the accuracy rate of feature extraction should be not less than 90%; the accuracy rate of intrusion judgment should be not less than 80%;

[0027] Processing speed test: Measure the time taken by the data analysis and processing module to process an intrusion signal once. Based on the measured processing time, evaluate whether the processing ability of the module meets the requirements of actual applications. The time taken to process an intrusion signal once should not exceed 100 ms;

[0028] Alarm device function test: Check whether parameters such as the brightness, color, and flashing frequency of the LED warning light meet the design requirements. Use equipment such as a photometer and an oscilloscope to measure the optical and electrical properties of the LED warning light; Test the volume, pitch, and sounding time of the buzzer; Use a sound level meter and an audio analyzer to measure the acoustic performance of the buzzer;

[0029] Alarm response time test: The time interval from when the data analysis and processing module issues an alarm command to when the audible and visual alarm device starts to alarm should not exceed 5 s. The audible and visual alarm device should be able to continuously alarm, and the alarm duration should not be less than 30 s;

[0030] Mobile device reception test: Send warning messages to multiple mobile devices, check whether the mobile devices can accurately receive the messages, and use a mass text messaging platform or the reception test software of the mobile device for testing. Check whether the content of the warning messages received by the mobile devices is complete, including information such as the intrusion location, time, and intrusion type;

[0031] Network connection test: In different network environments, test the network connection stability between the mobile device and the warning server. Use network speed measurement software and network monitoring tools for testing. Check whether there are situations such as loss and error of warning messages during network transmission. Evaluate the accuracy of data transmission by comparing the sent and received message content; The reception accuracy rate of the mobile device should not be less than 95%, and the integrity rate of the message content received by the mobile device should not be less than 90%. In the case of a poor network environment, the network connection between the mobile device and the warning server should be able to remain stable, and the interruption time should not exceed 10 s.

[0032] Preferably, the processed signal is subjected to feature extraction and analysis through a preset algorithm, where the preset algorithm includes a feature extraction algorithm

[0033] Time-domain feature extraction: Calculate the peak current value of the signal. When an object approaches or touches the electronic fence, the peak current will change significantly. By setting a threshold, if the peak current exceeds the threshold, it indicates an intrusion behavior; Detect the rising edge and falling edge characteristics of the current signal. An intrusion behavior can cause changes in the current signal. By analyzing the slope and time parameters of the rising edge and falling edge, information is obtained;

[0034] Frequency domain feature extraction: Perform Fourier transform on the filtered current signal to convert the time-domain signal into a frequency-domain signal, analyze the spectral distribution of the signal, and pay attention to the changes in specific frequency components; calculate the power spectral density of the signal, and judge whether there is an abnormality by observing the changes in the power spectral density. The intrusion object affects the electric field of the electronic fence, resulting in a change in the power spectral density of the current signal.

[0035] Preferably, the processed signal is subjected to feature extraction and analysis through a preset algorithm, where the preset algorithm includes a data analysis algorithm.

[0036] Pattern recognition algorithm: SVM is used to classify the current signal features into normal and intrusion categories. By training the SVM model with known normal and intrusion signal feature samples, it learns the feature patterns of normal and intrusion signals. In actual detection, input the signal features to be analyzed into the trained SVM model, and the model outputs the classification result. If the SVM model outputs the intrusion category, it is judged as an intrusion behavior.

[0037] Decision tree algorithm: Construct a decision tree model to make classification decisions based on different attributes of the current signal features. Each node of the decision tree represents a feature attribute. Through testing and judging the feature attributes, the signal features are gradually classified into different branches, and finally it is determined whether it is an intrusion behavior.

[0038] Statistical analysis algorithm: Calculate the mean and variance of the signal within a certain time window. Under normal circumstances, the mean and variance of the current signal should remain relatively stable. When there is an intrusion behavior, the mean and variance change significantly.

[0039] Preferably, the comparison with the current signal features under normal circumstances includes a data preparation stage, a feature extraction and analysis stage, a judgment and decision stage, and a result output stage.

[0040] Preferably, in the data preparation stage, during the system installation and debugging stage, collect the current signal data between the electrode columns under normal circumstances, and extract the feature parameters from the normal signal data as the template for comparison with abnormal signals. These feature parameters include the average value, standard deviation, peak value, frequency components, and waveform features of the current.

[0041] In the feature extraction and analysis stage, in the processed signal, extract the time-domain features, including the rise time, fall time, and duration of the signal; convert the signal to the frequency domain and analyze the frequency components and energy distribution of the signal; compare the extracted signal feature parameters with the normal signal feature template one by one to check whether each feature parameter is within the normal range; not only pay attention to the numerical value of a single feature parameter, but also analyze the change trend of the feature parameter. If the change trend of the signal feature does not match the normal situation, it indicates an intrusion behavior.

[0042] In the judgment and decision-making stage, calculate the Euclidean distance between the processed signal features and the normal signal feature template. The smaller the Euclidean distance, the closer the signal is to the normal situation; the larger the distance, the more likely it is an intrusion behavior. Calculate the correlation coefficient between the signal features and the normal signal feature template. The closer the correlation coefficient is to 1, the stronger the correlation between the two, and the more likely the signal is normal; the closer the correlation coefficient is to 0 or -1, the weaker the correlation between the two, indicating an intrusion behavior. Set the threshold for similarity calculation and judge it as an intrusion behavior.

[0043] In the result output stage, make a warning decision based on the result of the comparison and judgment. If it is judged as an intrusion behavior, trigger the warning mechanism; if it is judged as a normal situation, continue to monitor the signal; record the judgment result and relevant signal feature data for subsequent query and analysis.

[0044] Preferably, the data analysis and processing module first performs filtering processing on the signal, including the following steps

[0045] 1) Select a filtering algorithm

[0046] Select a suitable filtering algorithm according to the characteristics of the signal and the characteristics of the noise. Specifically, for signals mainly with high-frequency noise, select a high-pass filtering algorithm; for signals mainly with low-frequency noise, select a low-pass filtering algorithm; for signals containing both high-frequency and low-frequency noise, select a band-pass filtering algorithm.

[0047] Specific algorithms include

[0048] Adaptive filtering algorithm: This algorithm can automatically adjust the filtering parameters according to the real-time changes of the signal to adapt to different signal environments;

[0049] Wavelet filtering algorithm: It has the ability of multi-resolution analysis, can decompose the signal into different frequency scales, better remove noise and retain the detailed information of the signal;

[0050] 2) Filtering process

[0051] Data input: Input the current change signal transmitted by the signal acquisition and transmission module into the filtering module;

[0052] Filtering operation

[0053] Filtering parameter setting: According to the selected filtering algorithm, set the corresponding filtering parameters, such as the type, order, and cut-off frequency of the filter;

[0054] Filtering calculation: Perform filtering calculation on the input signal. According to the principle of the filtering algorithm, screen and process the frequency components of the signal, remove the noise components, and retain the useful information of the signal;

[0055] Filtering effect evaluation

[0056] Real-time monitoring: During the filtering process, the filtering effect is monitored in real time. By monitoring indicators such as the spectrum and signal-to-noise ratio of the filtered signal, the filtering parameters are adjusted in a timely manner;

[0057] The result evaluation includes

[0058] Signal quality evaluation: Check whether the filtered signal meets the requirements of subsequent processing, such as signal clarity and accuracy. If the signal quality does not meet the requirements, the filtering parameters should be readjusted and the filtering process should be carried out;

[0059] Noise removal effect evaluation: Evaluate the noise removal effect of the filtering algorithm. By comparing the noise levels of the signal before and after filtering, judge the effectiveness of the filtering algorithm. If the noise removal effect is not ideal, other suitable filtering algorithms should be selected or the filtering parameters should be adjusted.

[0060] Preferably, the data analysis and processing module sends a warning instruction to the warning module, including the following steps:

[0061] Step 1: Confirm the intrusion behavior determination

[0062] Feature matching review: When the data analysis and processing module initially determines that there is an intrusion behavior, it will compare the extracted abnormal signal features with the normal signal feature library again. It will not only compare the numerical values of the key feature parameters, but also analyze the correlation and change trend between the feature parameters to ensure the accuracy of the judgment. If it is found that the current peak value increases abnormally and the frequency component also shows a specific pattern change, and both combinations meet the preset intrusion feature pattern, then it will enter the next step;

[0063] Multi-algorithm cross-validation: Use multiple data analysis algorithms to cross-validate the intrusion judgment results. Only when multiple algorithms come to the conclusion that there is an intrusion behavior is the intrusion behavior determined to be established;

[0064] False judgment exclusion mechanism: Combine the current environmental factors and the system operation status for comprehensive analysis to exclude false judgments caused by environmental interference or system problems. For example, in thunderstorm weather, if the detected signal anomaly may be caused by lightning electromagnetic interference, the system will automatically identify and mark it. If there is no further change in the signal that conforms to the intrusion characteristics, it will not be determined as an intrusion;

[0065] Step 2: Instruction generation

[0066] Information integration: Once it is confirmed that there is an intrusion behavior, the data analysis and processing module will integrate the detailed information related to the intrusion event; in addition to the basic intrusion time and approximate location, it will also include the specific feature parameters of the abnormal signal and the possible intrusion type;

[0067] Instruction format standardization: Generate warning instructions in a specific format recognizable by the warning module. This format will include a unique instruction number for convenient subsequent tracking and management; it will also encode the integrated information.

[0068] Priority setting: Set the priority for warning instructions according to the severity and urgency of intrusion behaviors. If it is judged as a deliberately human intrusion that may pose a major threat to the camp security, set it as the highest priority; if it is an intrusion caused by small animals touching, set it as a lower priority.

[0069] Step 3: Instruction encryption and security verification

[0070] Data encryption: To ensure the security and confidentiality of warning instructions during transmission, use an encryption algorithm to encrypt the instructions. The encryption key will be updated regularly, and a session key will be dynamically generated before each transmission to increase the difficulty of cracking.

[0071] Identity verification: Before sending a warning instruction, the data analysis and processing module will perform identity verification with the warning module. By exchanging pre-set digital certificates and verification codes, ensure the legality of the identities of both communication parties.

[0072] Integrity check: Add a data integrity check code to the encrypted instruction. After receiving the instruction, the warning module will perform an integrity check on the instruction according to this check code. If it is found that the check fails, it will request to re-send the instruction.

[0073] Step 4: Instruction transmission

[0074] Communication protocol selection: Select an appropriate communication protocol for instruction transmission according to the actual application scenario and network environment.

[0075] Transmission retry mechanism: To prevent instructions from being lost or having errors during transmission, set up a transmission retry mechanism. If the confirmation feedback information from the warning module is not received within the specified time, the data analysis and processing module will automatically re-send the warning instruction.

[0076] Transmission monitoring: During the instruction transmission process, monitor the transmission status in real time. If transmission anomalies are found, adjust the transmission parameters or switch the communication method in a timely manner to ensure that the instruction can reach the warning module smoothly.

[0077] Step 5: Receiving confirmation and feedback

[0078] Warning module receiving and parsing: After receiving the encrypted warning instruction, the warning module first performs identity verification and integrity check. If the verification passes, it will decrypt the instruction using the corresponding decryption key and parse out the intrusion information contained therein according to the preset format.

[0079] Confirmation feedback sending: After successfully receiving and parsing the warning instruction, the warning module will immediately send a confirmation feedback message to the data analysis and processing module. The feedback message will include content such as the instruction number, receiving time, processing status, etc., indicating that the instruction has been successfully received;

[0080] Abnormal handling feedback: If an abnormality occurs during the process of the warning module receiving or parsing the instruction, it will promptly send an abnormal handling feedback message to the data analysis and processing module.

[0081] An anti-intrusion warning system for a field camp based on an electronic fence, including

[0082] A data analysis and processing module, which receives the current change signal transmitted by the signal acquisition and transmission module, conducts information interaction with the warning module, and sends a warning instruction to the warning module according to the judgment result;

[0083] A warning module, which receives the warning instruction sent by the data analysis and processing module and issues a warning by means of sound and light alarm and sending a warning message to the associated mobile device;

[0084] A mobile device, as the receiving terminal of the warning message, receives the warning message sent by the warning module, including information such as the intrusion location, time, intrusion type, etc., to help the management personnel understand the situation in a timely manner;

[0085] A warning server, which conducts interaction with the mobile device and the data analysis and processing module, is responsible for storing and managing warning information, and at the same time manages and monitors the network connection between the mobile device and the warning module.

[0086] The beneficial effects of the present invention are:

[0087] 1) Efficient and comprehensive intrusion monitoring: By arranging the electronic fence, the surrounding conditions of the camp can be monitored in real time and accurately. When an object approaches or touches the electronic fence, the current change signal between the electrode columns will be quickly collected and transmitted. After filtering processing, feature extraction and analysis, and comparison with the characteristics of the normal current signal, the intrusion behavior can be accurately judged. Compared with traditional manual patrols and simple warning devices, it can discover intrusions more comprehensively and timely, eliminate the blind spots and time intervals of patrols, and overcome the influence of environmental factors on the warning effect.

[0088] 2) Adapt to complex environments: In the process of arranging the electronic fence, the complex field environment is fully considered. The terrain, landform, vegetation distribution and road conditions around the camp are surveyed, and the layout route is reasonably planned to effectively avoid terrain and vegetation obstacles. For special environments such as mountainous areas and areas with dense vegetation, measures such as increasing the density of electrode columns and adjusting the height are taken to ensure the stable operation of the electronic fence and enable it to work reliably in various field environments.

[0089] 3) The system has reliable performance: System debugging and detection cover aspects such as power-on testing, signal detection, and function testing. In function testing, strict tests are conducted on the performance of the electronic fence, the characteristics of low-voltage pulse current, the functions of each module, etc. Clear standards are set, such as the signal transmission distance is not less than 500 meters, the bit error rate is lower than 1%, the accuracy of feature extraction is not less than 90%, the accuracy of intrusion judgment is not less than 80%, etc., to ensure the stable and reliable performance of the system and reduce false alarms and missed alarms.

[0090] 4) Accurate signal processing and analysis: A variety of filtering algorithms are adopted, and appropriate algorithms are selected according to the characteristics of signals and noise, such as adaptive filtering algorithms and wavelet filtering algorithms, which can effectively remove noise and retain signal details. The preset feature extraction and data analysis algorithms are rich and diverse, extracting signal features from multiple perspectives in the time domain and frequency domain, and combining algorithms such as pattern recognition, decision tree, and statistical analysis to accurately judge intrusion behaviors and improve detection accuracy.

[0091] 5) Secure and reliable warning instruction transmission: When the data analysis and processing module sends a warning instruction to the warning module, there is a strict process of judgment confirmation, instruction generation, encryption and security verification, transmission, and reception confirmation feedback. By feature matching review, multi-algorithm cross-verification, and combining environmental factors to eliminate false judgments, the judgment accuracy is ensured; instruction encryption, identity verification, and integrity check ensure the secure transmission of instructions; selecting an appropriate communication protocol, setting a transmission retry mechanism, and monitoring the transmission status ensure the reliable transmission of instructions to the warning module and issue warnings in a timely and accurate manner.

[0092] 6) Convenient information reception and management: The warning system includes mobile devices and a warning server. The mobile device, as the receiving terminal, receives warning information, enabling management personnel to promptly grasp key information such as the intrusion location, time, and type, facilitating a rapid response. The warning server is responsible for storing and managing warning information, monitoring the network connection between the mobile device and the warning module, ensuring smooth information interaction, and enhancing management efficiency and other beneficial effects. Brief Description of the Drawings

[0093] Figure 1 It is a schematic flow diagram of an anti-intrusion warning method for a field camp based on an electronic fence. Detailed Embodiment

[0094] The present invention will be further described below in conjunction with the drawings and embodiments:

[0095] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, the terms "first", "second", "third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0096] In the description of the present invention, it should be noted that unless otherwise clearly specified and limited, the terms "installation", "setting", "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0097] As Figure 1 shown, an anti-intrusion warning method for a field camp based on an electronic fence includes the following steps:

[0098] S100, arranging an electronic fence;

[0099] S200, when an object approaches or touches the electronic fence, the current between the electrode columns will change.

[0100] S300, the signal acquisition and transmission module transmits this changed signal to the data analysis and processing module. The data analysis and processing module first filters the signal to remove noise interference.

[0101] S400, then extracts and analyzes the features of the processed signal through a preset algorithm, and compares them with the current signal features under normal conditions.

[0102] S500, if it is determined as an intrusion behavior, the data analysis and processing module sends a warning instruction to the warning module, and the warning module gives an audible and visual alarm and sends a warning message to the associated mobile device.

[0103] In the present invention, the arrangement of the electronic fence specifically includes site selection planning

[0104] Camp perimeter survey: Before arranging the electronic fence, conduct a detailed survey of the surrounding environment of the field camp, including terrain, landform, vegetation distribution, surrounding roads, etc. Determine the best layout route of the electronic fence to ensure that it can fully cover the surrounding area of the camp while avoiding excessive obstacles from the terrain and vegetation.

[0105] Obstacle recognition and avoidance include terrain obstacles: For mountainous areas or regions with complex terrain, special attention should be paid to terrain obstacles such as valleys and slopes. Avoid arranging the electric fence at the bottom of the valley because the signal may be blocked and reflected by the valley, affecting the signal transmission quality. At the same time, consider the slope and stability of the slope to ensure that the installation of the electric fence will not damage the slope.

[0106] Vegetation obstacles: Dense vegetation may affect the signal propagation and detection effect of the electric fence. During the arrangement process, try to avoid areas with dense vegetation such as tall trees and bushes. If it is unavoidable, the density of the electrode posts can be appropriately increased or the height of the electrode posts can be adjusted to ensure the normal operation of the electric fence.

[0107] The installation of electrode posts includes foundation construction

[0108] Hole excavation: Excavate holes at the selected location according to the design requirements. The depth and diameter of the holes should be determined according to the specifications of the electrode posts and the soil conditions. Generally, the depth is 1 - 1.5 meters and the diameter is 0.3 - 0.5 meters. During the excavation process, pay attention to the regular shape of the holes to avoid safety accidents such as cave-ins.

[0109] Foundation treatment: Level the bottom of the hole and remove the sundries and stones in the hole. If the soil conditions are poor, a layer of gravel or sand can be laid in the hole to improve the stability and conductivity of the foundation.

[0110] Electrode post positioning: Slowly place the electrode post into the hole to ensure the accurate position of the electrode post. Use tools such as a level and a plumb line to calibrate the electrode post to ensure that the electrode post is perpendicular to the ground.

[0111] Fixing the electrode post: After the electrode post is installed in place, use concrete or other fixing materials to fix the electrode post. The choice of fixing materials should be determined according to the local geological conditions and climate environment to ensure that the electrode post will not shift or tilt during long-term use.

[0112] Connecting the electrode posts: Connect the connecting cables between adjacent electrode posts. The connecting cables are selected from materials with waterproof, corrosion-resistant, and wear-resistant properties to ensure normal operation in harsh environments. When connecting, ensure that the joints of the connecting cables are firm and reliable to avoid problems such as poor contact.

[0113] Cable laying includes cable selection: Select appropriate cables according to the length of the electric fence and the signal transmission requirements. The cables should have good conductivity, insulation, and anti-interference capabilities. Common cable types include copper core cables, optical fibers, etc.

[0114] The cable laying method includes underground laying and overhead laying according to actual needs

[0115] Underground laying: For most areas, underground laying is an ideal choice. When excavating the cable trench, attention should be paid to the requirements of the trench depth and width. The trench depth is generally 0.5 - 0.8, and the trench width is 0.3 - 0.5 meters. After the cable is laid in the trench, a layer of soil or sand and gravel should be covered to protect the cable from external factors.

[0116] Overhead laying: In some special areas, such as above rivers, roads, etc., overhead laying may be required. When laying overhead, it is necessary to ensure that the cable is firmly fixed to avoid the cable being affected by natural factors such as wind, sun, and rain. At the same time, attention should be paid to the safe distance between the cable and other objects to avoid collisions and interference.

[0117] System commissioning and testing

[0118] Power-on test: After the installation of the electronic fence is completed, a power-on test is carried out. Check the current on / off situation between the electrode posts to ensure that the entire electronic fence system can work properly.

[0119] Signal detection: Use professional signal detection equipment to detect parameters such as the signal strength and stability of the electronic fence system. The detection points should be evenly distributed in the surrounding area of the electronic fence to ensure that the signal changes can be comprehensively detected.

[0120] Function test includes intrusion simulation test: By simulating intrusion behaviors, the warning function of the electronic fence system is tested. The test content includes aspects such as the emission time, accuracy, and reliability of the warning signal. Specifically, the performance test of the electronic fence

[0121] Current on / off test includes test method: Through a current monitoring device, the current on / off situation between the electrode posts of the electronic fence is monitored in real time. During the simulated intrusion process, observe the changes in the current signal, including the sudden interruption of the current and the significant decrease in the current value, etc.

[0122] Test standard: When a simulated intrusion behavior occurs, the current on / off should conform to the preset logical relationship. For example, when the intrusion object touches the electronic fence, the current should be interrupted instantly or reduced to a certain threshold or below, and after the intrusion object leaves, the current should return to normal.

[0123] Low-voltage pulse current characteristic test includes test method: Use professional current measuring instruments to measure the parameters of the low-voltage pulse current of the electronic fence, including pulse frequency, pulse amplitude, pulse width, etc. During the simulated intrusion process, observe the changes in these parameters.

[0124] The test standards include pulse frequency stability: The pulse frequency should remain stable with an error within ±10%. For example, if the preset pulse frequency is 5 Hz, during the simulated intrusion, the measured pulse frequency should be between 4.5 Hz and 5.5 Hz.

[0125] Pulse amplitude consistency: The pulse amplitude should remain consistent within the preset range with an error within ±20%. For example, if the preset pulse amplitude is 10 V, during the simulated intrusion, the measured pulse amplitude should be between 8 V and 12 V.

[0126] Pulse width accuracy: The pulse width should meet the preset requirements with an error within ±1 ms. For example, if the preset pulse width is 1 ms, during the simulated intrusion, the measured pulse width should be between 0.9 ms and 1.1 ms.

[0127] The test of the signal acquisition and transmission module includes the test of signal acquisition accuracy. The test method is

[0128] Analog signal input: Use a signal generator to generate an analog signal similar to the current change of the electronic fence and input it into the signal acquisition and transmission module.

[0129] Data acquisition comparison: Compare the signal data collected by the signal acquisition and transmission module with the input analog signal data to check whether they are consistent.

[0130] The test standards include the data error range: The error between the collected data and the input analog signal data should be within ±5%. For example, if the current value of the analog signal is 10 mA, the collected data should be between 9.5 mA and 10.5 mA.

[0131] The test of signal transmission stability includes the transmission distance test: Set signal receiving points at different distances and measure the attenuation of the signal during transmission. Gradually increase the transmission distance until the signal quality deteriorates to the extent that it cannot be recognized normally.

[0132] The interference test includes electromagnetic interference: Set electromagnetic interference sources such as welding machines and transformers near the electronic fence and observe whether the signal transmission is interfered.

[0133] Wireless interference: Use a wireless signal interference device to interfere with the wireless transmission signal and observe whether the signal transmission is interrupted or in error.

[0134] The test standards include the transmission distance requirement: Under the condition of meeting the signal quality requirements, the signal transmission distance should be not less than 500 meters.

[0135] Anti-interference ability: Under the conditions of electromagnetic interference and wireless interference, the signal transmission should remain stable and the bit error rate should be lower than 1%.

[0136] The test of the data analysis and processing module includes the test of data processing accuracy and feature extraction accuracy: Use known intrusion signal feature samples to test the feature extraction algorithm of the data analysis and processing module. Check whether the module can accurately extract the features of the intrusion signal.

[0137] Intrusion judgment accuracy: Compare the extracted features with the preset intrusion judgment threshold to determine whether it is an intrusion behavior. Through multiple tests, count the accuracy rate of intrusion judgment.

[0138] The test criteria include the feature extraction accuracy rate: The feature extraction accuracy rate should be not less than 90%. For example, if there are 100 known intrusion signal feature samples, the number of samples for which the module correctly extracts the features should be not less than 90.

[0139] Intrusion judgment accuracy rate: The intrusion judgment accuracy rate should be not less than 80%. For example, if 100 intrusion simulation tests are carried out, the number of times the module correctly judges as an intrusion should be not less than 80.

[0140] The processing speed test includes processing time measurement: Use professional test software to measure the time taken by the data analysis and processing module to process an intrusion signal once.

[0141] Processing capacity evaluation: Based on the measured processing time, evaluate whether the processing capacity of the module meets the requirements of actual applications. For example, for a signal acquisition frequency of 5 times per second, the module should be able to complete signal processing and intrusion judgment within the specified time.

[0142] The test criteria include the upper limit of processing time: The time taken to process an intrusion signal once should not exceed 100 ms.

[0143] Early warning module test, audible and visual alarm test, alarm device function test

[0144] LED warning light: Check whether parameters such as the brightness, color, and flashing frequency of the LED warning light meet the design requirements. Use equipment such as a photometer and an oscilloscope to measure the optical and electrical properties of the LED warning light.

[0145] Buzzer: Test parameters such as the volume, tone, and sounding time of the buzzer. Use equipment such as a sound level meter and an audio analyzer to measure the acoustic performance of the buzzer.

[0146] The alarm response time test includes the overall response time: The time interval from when the data analysis and processing module issues an alarm command to when the audible and visual alarm device starts to alarm should not exceed 5 s.

[0147] Alarm duration: The audible and visual alarm device should be able to continuously alarm, and the alarm duration should be not less than 30 s.

[0148] The information notification test includes the mobile device reception test, and the reception accuracy: Send early warning information to multiple mobile devices and check whether the mobile devices can receive the information accurately. Use a mass text messaging platform or the reception test software of the mobile device to conduct the test.

[0149] The integrity of the information content: Check whether the content of the early warning information received by the mobile device is complete, including information such as the intrusion location, time, and intrusion type.

[0150] The network connection stability test includes the network connection test: Test the network connection stability between the mobile device and the early warning server under different network environments. Use network speed measurement software and network monitoring tools to conduct the test.

[0151] The accuracy of data transmission: Check whether there are situations such as loss or error of the early warning information during network transmission. Evaluate the accuracy of data transmission by comparing the sent and received information content.

[0152] The test criteria include the reception accuracy rate: The reception accuracy rate of the mobile device should not be lower than 95%.

[0153] The information content integrity rate: The information content integrity rate received by the mobile device should not be lower than 90%.

[0154] The network connection stability: In the case of a poor network environment, the network connection between the mobile device and the early warning server should be able to remain stable, and the interruption time should not exceed 10s.

[0155] Equipment fault detection: Conduct fault detection on each device in the electronic fence system, including electrode posts, connecting cables, signal acquisition and transmission modules, data analysis and processing modules, early warning modules, etc. Check whether the equipment is working properly and whether there are faults or damages.

[0156] Through the above detailed layout steps, the effective layout of the electronic fence in the field camp can be ensured, providing a reliable guarantee for the safety protection of the camp.

[0157] In the present invention, the processed signal is subjected to feature extraction and analysis through a preset algorithm, and the preset algorithm includes a feature extraction algorithm (time-domain feature extraction)

[0158] Peak detection: Calculate the peak current value of the signal within a certain time window. When an object approaches or touches the electronic fence, the peak current will change significantly. By setting a threshold, if the peak current exceeds the threshold, it may indicate an intrusion behavior. For example, set the threshold of the peak current to 1.5 times the normal peak current. When the detected peak current reaches or exceeds this threshold, trigger further analysis.

[0159] Rising edge and falling edge detection: Detect the rising edge and falling edge characteristics of the current signal. Invasive behavior may cause rapid changes in the current signal. By analyzing parameters such as the slope and time of the rising edge and falling edge, useful information can be obtained. For example, if the slope of the rising edge or falling edge exceeds a certain threshold, or the time interval between the rising edge and the falling edge is abnormal, it may indicate an intrusion.

[0160] Frequency domain feature extraction includes Fourier transform: Perform Fourier transform on the filtered current signal to convert the time-domain signal into a frequency-domain signal. Analyze the spectral distribution of the signal and pay attention to the changes in specific frequency components. For example, if there is obvious energy concentration in a certain frequency range, it may be related to invasive behavior.

[0161] Power spectral density analysis: Calculate the power spectral density of the signal and judge whether there is an abnormality by observing the changes in the power spectral density. Invasive objects may affect the electric field of the electronic fence, resulting in changes in the power spectral density of the current signal.

[0162] Data analysis algorithms include pattern recognition algorithms

[0163] Support Vector Machine (SVM): SVM is a commonly used classification algorithm that can be used to classify the current signal characteristics into normal and invasive categories. By training the SVM model with known normal and invasive signal characteristic samples, it learns the characteristic patterns of normal and invasive signals. In actual detection, input the signal characteristics to be analyzed into the trained SVM model, and the model outputs the classification result. For example, if the SVM model outputs an invasive category, it is judged as an invasive behavior.

[0164] Decision tree algorithm: Construct a decision tree model and make classification decisions according to different attributes of the current signal characteristics. Each node of the decision tree represents a characteristic attribute. Through the test and judgment of the characteristic attributes, the signal characteristics are gradually classified into different branches, and finally it is determined whether it is an invasive behavior.

[0165] Statistical analysis algorithms include mean and variance analysis: Calculate the mean and variance of the signal within a certain time window. Under normal circumstances, the mean and variance of the current signal should remain relatively stable. When there is an invasive behavior, the mean and variance may change significantly. For example, if the mean suddenly increases or the variance increases significantly, it may indicate an abnormality.

[0166] Correlation analysis: Analyze the correlation between the current signal characteristics. If the correlation between certain characteristics changes during an invasive behavior, it may indicate an intrusion. For example, the correlation between the frequency and amplitude of the current signal is stable under normal circumstances, but may show abnormal changes during an intrusion.

[0167] The feature comparison algorithm includes template matching: establishing a feature template for the current signal under normal conditions, and matching the processed signal features with the template. If the matching degree is lower than a certain threshold, it is judged as an intrusion behavior. The template can be parameters such as the average value and standard deviation obtained by statistical analysis of a large number of normal signal features, or typical feature patterns learned through machine learning algorithms.

[0168] Dynamic threshold comparison: Dynamically adjust the threshold for judging intrusion according to the historical data and statistical characteristics of the signal. For example, when there is an obvious difference between the change trend of the signal and the historical data, automatically adjust the threshold to improve the accuracy of intrusion detection.

[0169] These algorithms can be used alone or in combination, and are selected and optimized according to specific application requirements and signal characteristics to achieve effective analysis of the electronic fence signal and accurate judgment of intrusion behavior.

[0170] The comparison with the current signal characteristics under normal conditions includes a data preparation stage, a feature extraction and analysis stage, a judgment and decision-making stage, and a result output stage. Specifically:

[0171] The data preparation stage includes establishing a normal signal database: During the system installation and debugging stage, collect a large amount of current signal data between the electrode columns under normal conditions. These data should cover different environmental conditions, time points, and scenarios without intrusion activities. For example, collect data under different weather conditions (sunny, rainy, foggy, etc.), different time periods (daytime, night, early morning, etc.), and when people are normally active in the camp.

[0172] Feature extraction template definition: Extract representative feature parameters from the normal signal data as templates for comparison with abnormal signals. These feature parameters can include the average value, standard deviation, peak value, frequency components, waveform characteristics, etc. of the current. For example, for the average value of the current, calculate the average value of the current signal over a period of time and set a reasonable range as the normal range.

[0173] The feature extraction and analysis stage includes signal feature extraction

[0174] Time-domain feature extraction: In the processed signal, extract time-domain features such as the rise time, fall time, and duration of the signal. These features can reflect the change speed and stability of the signal. For example, if the rise time of the signal is significantly shortened or the fall time is abnormally extended, it may mean that there is an abnormal situation.

[0175] Frequency domain feature extraction: Using methods such as Fourier transform, the signal is converted to the frequency domain to analyze the frequency components and energy distribution of the signal. For example, paying attention to the energy changes within a specific frequency range, if there is an obvious energy peak at a certain frequency, it may be related to intrusion behavior.

[0176] Feature comparison and analysis includes feature parameter comparison: The extracted signal feature parameters are compared one by one with the normal signal feature template. Check whether each feature parameter is within the normal range. For example, for the average current value, if the measured value exceeds ±10% of the normal range, there may be an anomaly.

[0177] Feature trend analysis: Not only pay attention to the values of individual feature parameters, but also analyze the change trends of feature parameters. If the change trend of the signal features does not match the normal situation, for example, the average current value suddenly rises and continues to increase, it may indicate an intrusion behavior.

[0178] The judgment and decision-making stage includes similarity calculation

[0179] Euclidean distance calculation: Calculate the Euclidean distance between the processed signal features and the normal signal feature template. The smaller the Euclidean distance, the closer the signal is to the normal situation; the larger the distance, the more likely it is an intrusion behavior. For example, for two vectors A=(a 1 ,a 2 …,a n ) and B=(b 1 ,b 2 …,b n ), the Euclidean distance formula is

[0180]

[0181] where d represents the Euclidean distance between vector A and vector B, and this distance measures the degree of difference between the two vectors in the feature space. In an intrusion detection system, it reflects the size of the difference between the currently detected signal features and the normal signal feature template.

[0182] n represents the dimension of the vector, that is, the number of feature parameters. For example, if we extract multiple feature parameters such as the average current value, current standard deviation, and frequency from the signal, then the total number of these feature parameters is n;

[0183] ai represents the i-th element in vector A, corresponding to the value of a certain feature parameter in the signal features.

[0184] b i represents the i th element in vector B, corresponding to the value of the feature parameter at the same position in the normal signal feature template.

[0185] Suppose we extract two feature parameters from the signal, namely the average current and the standard deviation of the current, i.e., n = 2. There is a detected signal feature vector A = (a 1 , a 2 ), where a 1 is the average current detected currently, and a 2 is the standard deviation of the current detected currently; the normal signal feature template vector B = (b 1 , b 2 ), b 1 is the average current under normal conditions, and b 2 is the standard deviation of the current under normal conditions.

[0186] Calculate the Euclidean distance according to the formula The larger the value obtained, the greater the difference between the currently detected signal features and the normal signal feature template, and the more likely there is an intrusion behavior; on the contrary, the smaller the distance, the closer the signal is to the normal situation.

[0187] In the present invention, the data analysis and processing module first performs filtering processing on the signal, including the following steps

[0188] Select a filtering algorithm, including considerations: select a suitable filtering algorithm according to the characteristics of the signal and the characteristics of the noise. For example, for a signal dominated by high-frequency noise, a high-pass filtering algorithm can be selected; for a signal dominated by low-frequency noise, a low-pass filtering algorithm can be selected; for a signal containing both high-frequency and low-frequency noise, a band-pass filtering algorithm can be selected.

[0189] Specific algorithms include the adaptive filtering algorithm: This algorithm can automatically adjust the filtering parameters according to the real-time changes of the signal to adapt to different signal environments. It can effectively remove noise while retaining the useful information of the signal. In the present invention, the adaptive filtering algorithm can automatically adjust the coefficients of the filter according to the characteristics of the electronic fence signal and the statistical characteristics of the noise, so as to effectively remove the noise.

[0190] Wavelet filtering algorithm: It has the ability of multi-resolution analysis, and can decompose the signal into different frequency scales, so as to better remove noise and retain the detailed information of the signal. In the present invention, the wavelet filtering algorithm can perform multi-scale decomposition on the electronic fence signal, and perform filtering processing on the signals of different scales respectively, so as to effectively remove the noise and effectively extract the signal features.

[0191] The filtering process includes data input: input the current change signal transmitted by the signal acquisition and transmission module into the filtering module.

[0192] The filtering operation includes filtering parameter setting: According to the selected filtering algorithm, set the corresponding filtering parameters, such as the type, order, cut-off frequency, etc. of the filter. The setting of these parameters should be adjusted according to the characteristics of the signal and the characteristics of the noise to ensure the optimization of the filtering effect.

[0193] Filtering calculation: Perform filtering calculation on the input signal. According to the principle of the filtering algorithm, screen and process the frequency components of the signal, remove the noise components, and retain the useful information of the signal.

[0194] The filtering effect evaluation includes real-time monitoring: During the filtering process, monitor the filtering effect in real time. By monitoring indicators such as the spectrum and signal-to-noise ratio of the filtered signal, adjust the filtering parameters in a timely manner to ensure the stability and reliability of the filtering effect.

[0195] The result evaluation includes signal quality evaluation: Check whether the filtered signal meets the requirements of subsequent processing, such as the clarity and accuracy of the signal. If the signal quality does not meet the requirements, the filtering parameters should be readjusted for filtering processing.

[0196] Noise removal effect evaluation: Evaluate the noise removal effect of the filtering algorithm. By comparing the noise levels of the signal before and after filtering, judge the effectiveness of the filtering algorithm. If the noise removal effect is not ideal, select other suitable filtering algorithms or adjust the filtering parameters.

[0197] Differences from the prior art

[0198] Comprehensive filtering ability: In the prior art, a single filtering algorithm is used to process signals, while the present invention uses a combination of an adaptive filtering algorithm and a wavelet filtering algorithm, which can better adapt to different signal environments and noise characteristics, and improve the stability and reliability of the filtering effect.

[0199] Real-time monitoring and adjustment: In the prior art, the setting of filtering parameters is usually fixed and cannot be adjusted according to the real-time changes of the signal. While the present invention can automatically adjust the filtering parameters according to the changes of the signal by real-time monitoring the filtering effect, so as to better remove noise and retain the useful information of the signal.

[0200] Multi-index evaluation: In the prior art, the evaluation of the filtering effect usually only focuses on certain specific indicators of the signal, such as signal-to-noise ratio, distortion degree, etc. While the present invention comprehensively evaluates multiple indicators such as the quality of the signal and the noise removal effect to comprehensively evaluate the filtering effect and ensure the accuracy and effectiveness of the filtering process.

[0201] The data analysis and processing module sends a warning instruction to the warning module, which includes the following steps:

[0202] Step 1: Confirm the determination of intrusion behavior

[0203] Feature matching review: After the data analysis and processing module preliminarily determines the existence of an intrusion behavior, it will compare the extracted abnormal signal features with the normal signal feature library again. It not only compares the numerical values of the key feature parameters, but also analyzes the correlation and change trend between the feature parameters to ensure the accuracy of the judgment. For example, if it is found that the current peak value increases abnormally and the frequency component also shows a specific pattern change, and both meet the preset intrusion feature pattern when combined, it will enter the next step.

[0204] Multi-algorithm cross-verification: Multiple data analysis algorithms are used to cross-verify the intrusion judgment results. In addition to the mainly used machine learning algorithms (such as support vector machines), decision tree algorithms, Bayesian classification algorithms, etc. will also be used for secondary judgment. Only when multiple algorithms all conclude that there is an intrusion behavior is the intrusion behavior determined to be established.

[0205] False judgment exclusion mechanism: Comprehensive analysis is carried out in combination with the current environmental factors (such as weather conditions, surrounding electromagnetic interference conditions, etc.) and the system operation status (such as whether there is a fault warning for the equipment, etc.) to exclude false judgments caused by environmental interference or system problems. For example, in thunderstorm weather, if the detected signal anomaly may be caused by lightning electromagnetic interference, the system will automatically identify and mark it. If there is no further change in the signal that conforms to the intrusion characteristics, it will not be determined as an intrusion.

[0206] Step 2: Instruction generation

[0207] Information integration: Once the existence of an intrusion behavior is confirmed, the data analysis and processing module will integrate the detailed information related to the intrusion event. In addition to the basic intrusion time and approximate location, it will also include the specific feature parameters of the abnormal signal (such as the change amplitude of the current peak value, frequency offset, etc.), and the possible intrusion types (such as human climbing over, animal touching, etc., obtained through in-depth analysis of the signal characteristics).

[0208] Instruction format standardization: Generate a warning instruction according to a specific format that the warning module can recognize. This format will include a unique instruction number for convenient subsequent tracking and management; it will also encode the integrated information. For example, different intrusion types will be represented by specific codes to reduce the data transmission volume and improve the transmission efficiency.

[0209] Priority setting: Set the priority for the warning instruction according to the severity and urgency of the intrusion behavior. For example, if it is judged to be a deliberate human intrusion and may pose a major threat to the camp security, it is set as the highest priority; if it is an intrusion caused by a small animal touching, it is set as a lower priority.

[0210] Step 3: Instruction encryption and security verification

[0211] Data Encryption: To ensure the security and confidentiality of early warning instructions during transmission, advanced encryption algorithms (such as AES encryption algorithm) are used to encrypt the instructions. The encryption key is updated regularly, and a session key is dynamically generated before each transmission to increase the difficulty of cracking.

[0212] Identity Authentication: Before sending early warning instructions, the data analysis and processing module will perform identity authentication with the early warning module. By exchanging pre-set digital certificates and verification codes, it ensures the legal identities of both communication parties and prevents the instructions from being maliciously intercepted or tampered with.

[0213] Integrity Check: A data integrity check code (such as CRC check code) is added to the encrypted instructions. After receiving the instructions, the early warning module will perform an integrity check on the instructions based on this check code. If it is found that the check fails, the instructions will be requested to be resent.

[0214] Step Four: Instruction Transmission

[0215] Communication Protocol Selection: Select a suitable communication protocol for instruction transmission according to the actual application scenario and network environment. If there is a good wireless communication signal in the wild, the 4G / 5G network communication protocol is preferred to ensure the transmission speed and stability; if the wireless signal is poor, it can be switched to a low-power wide area network (LPWAN) communication protocol (such as LoRaWAN) to ensure the reliable transmission of instructions.

[0216] Transmission Retry Mechanism: To prevent instructions from being lost or in error during transmission, a transmission retry mechanism is set. If the confirmation feedback information from the early warning module is not received within the specified time, the data analysis and processing module will automatically resend the early warning instructions. The number of retries can be set according to the actual situation, usually 3 - 5 times.

[0217] Transmission Monitoring: During the instruction transmission process, the transmission status is monitored in real time, including indicators such as signal strength, transmission rate, and packet loss rate. If transmission anomalies are found, such as a sudden weakening of the signal or a too high packet loss rate, the transmission parameters will be adjusted in a timely manner or the communication method will be switched to ensure that the instructions can reach the early warning module smoothly.

[0218] Step Five: Receiving Confirmation and Feedback

[0219] Early Warning Module Receiving and Parsing: After receiving the encrypted early warning instructions, the early warning module first performs identity authentication and integrity check. If the verification passes, it will decrypt the instructions using the corresponding decryption key and parse out the intrusion information contained therein according to the preset format.

[0220] Confirmation Feedback Sending: After successfully receiving and parsing the early warning instructions, the early warning module will immediately send a confirmation feedback message to the data analysis and processing module. The feedback message will contain content such as instruction number, receiving time, and processing status, indicating that the instructions have been successfully received.

[0221] Exception handling feedback: If an exception occurs during the process of the warning module receiving or parsing an instruction (such as decryption failure, format error, etc.), it will promptly send exception handling feedback information to the data analysis and processing module, explaining the specific exception situation and the problem location, so that the data analysis and processing module can perform corresponding processing and adjustments.

[0222] An anti-intrusion warning system for a field camp based on an electronic fence, including a data analysis and processing module that includes receiving signals: receiving the current change signals transmitted by the signal acquisition and transmission module.

[0223] Interacting with other modules: Interacting with the warning module, and sending a warning instruction to the warning module according to the judgment result.

[0224] Filtering processing: Filtering the received signals to remove noise interference and improve the quality and accuracy of the signals.

[0225] Feature extraction and analysis: Extracting the features of the signals through a preset algorithm, and comparing them with the features of the current signals under normal conditions to determine whether there is an intrusion behavior.

[0226] Decision-making judgment: Making a judgment decision on the intrusion behavior according to the results of feature extraction and analysis, and sending corresponding instructions to the warning module.

[0227] The warning module includes receiving instructions: Receiving the warning instructions sent by the data analysis and processing module.

[0228] Performing warning operations: Sending out warnings by means of acoustic and optical alarms and sending warning messages to associated mobile devices.

[0229] Acoustic and optical alarm: When receiving a warning instruction, start the acoustic and optical alarm devices, such as high-brightness LED warning lights and high-decibel buzzers, to send an intuitive alarm signal to the on-site personnel to attract attention.

[0230] Information notification: By sending warning messages to associated mobile devices, such as text messages, APP push notifications, etc., notify the camp management personnel and relevant personnel in a timely manner so that they can take corresponding countermeasures.

[0231] Other relevant parts also include mobile devices: As the receiving terminal of warning messages, receiving the warning messages sent by the warning module, including information such as the intrusion location, time, intrusion type, etc., to help the management personnel understand the situation in a timely manner.

[0232] Warning server: Interacting with mobile devices and the data analysis and processing module, responsible for storing and managing warning information, and at the same time managing and monitoring the network connection between mobile devices and the warning module.

[0233] The hardware part includes an electronic fence device, which consists of multiple electrode columns. The electrode columns are arranged at intervals around the field camp. It is installed through steps such as pit excavation, foundation treatment, positioning, fixing, and connection, including laying cables underground.

[0234] Function: When an object approaches or touches, the current between the electrode columns will change. It is the basic sensing component of the entire warning system.

[0235] Signal acquisition and transmission module, connection relationship: Connected to the electronic fence device, receiving the current change signal between the electrode columns and transmitting it to the data analysis and processing module.

[0236] Signal acquisition: Real-time acquisition of the current change signal between the electrode columns, including parameters such as current intensity and voltage.

[0237] Signal transmission: Ensure that the collected signal can be accurately and stably transmitted to the data analysis and processing module to provide data support for subsequent analysis and processing.

[0238] The above has described in detail the preferred specific embodiments of the present invention. It should be understood that those of ordinary skill in the art can make many modifications and changes based on the concept of the present invention without creative labor. Therefore, all technical solutions that can be obtained by those skilled in the art in the technical field of the present invention through logical analysis, reasoning, or limited experiments based on the concept of the present invention on the basis of the prior art should be within the protection scope determined by the claims.

Claims

1. An anti-intrusion early warning method for a wild camp based on an electronic fence, characterized in that: The following steps are involved: Arrange an electronic fence. When an object approaches or touches the electronic fence, the current between the electrode columns will change. The signal acquisition and transmission module transmits this change signal to the data analysis and processing module. The data analysis and processing module first filters the signal to remove noise interference, and then extracts and analyzes the features of the processed signal through a preset algorithm and compares it with the current signal features under normal conditions; If it is determined to be an intrusion, the data analysis and processing module sends a warning instruction to the warning module, and the warning module sends warning information to the associated mobile devices through sound and light alarms.

2. The anti-intrusion early warning method for outdoor camps based on electronic fences as claimed in claim 1 is characterized in that: The arrangement of the electronic fence specifically includes Survey the surroundings of the campsite before deploying the electronic fence. The survey includes topography, landforms, vegetation distribution and road conditions to determine the layout route of the electronic fence and ensure that the surrounding area of ​​the campsite is fully covered. Terrain obstacle identification and avoidance: For mountainous areas or areas with complex terrain, avoid placing electronic fences at the bottom of valleys, and ensure that the installation of electronic fences does not cause damage to the hillsides; Vegetation obstacle identification and avoidance: During the layout process, identify whether dense vegetation areas such as tall trees and bushes can be avoided. If it cannot be avoided, increase the density of electrode columns or adjust the height of electrode columns; Pit excavation: digging pits at selected locations with a depth of 1-1.5 meters and a diameter of 0.3-0.5 meters; Foundation treatment: level the bottom of the pit and remove debris and stones in the pit. If the soil conditions are poor, lay a layer of gravel or sand in the pit to improve the stability and conductivity of the foundation; Put the electrode column in place, put it into the pit, make sure the position of the electrode column is accurate, use a level and plumb line to calibrate the electrode column, and make sure the electrode column is perpendicular to the ground; Fix the electrode column. After the electrode column is installed in place, fix the electrode column with fixing materials; Connect the electrode columns and connect the connecting cables between adjacent electrode columns. The connecting cables are made of waterproof, corrosion-resistant and wear-resistant materials. When connecting, ensure that the connectors of the connecting cables are firm and reliable to avoid problems such as poor contact. Underground laying: when digging a cable trench, the trench depth is 0.5-0.8 meters, the trench width is 0.3-0.5 meters, and after the cables are laid in the trench, they are covered with a layer of soil or sand; System debugging and testing, including power-on test, signal detection and functional test.

3. The anti-intrusion early warning method for outdoor camps based on electronic fences as claimed in claim 2 is characterized in that: Power-on test: After the electronic fence is installed, a power-on test is performed to check the current between the electrode columns to ensure that the entire electronic fence system can work normally; Signal detection: Use signal detection equipment to detect the signal strength and stability parameters of the electronic fence system. The detection points are evenly distributed in the surrounding area of ​​the electronic fence to ensure that the signal changes can be fully detected; Functional testing includes intrusion simulation testing and device fault detection The intrusion simulation test includes The electronic fence performance test uses current monitoring equipment to monitor the current on / off status between the electronic fence electrode columns. During the simulated intrusion process, the changes in the current signal are observed, including sudden interruption of the current, obvious drop in the current value, etc. When the simulated intrusion behavior occurs, the current on / off should conform to the preset logical relationship. Low-voltage pulse current characteristic test, using current measuring instruments to measure the parameters of the low-voltage pulse current of the electronic fence, including pulse frequency, pulse amplitude, pulse width, etc. During the simulated intrusion process, the changes of these parameters are observed. At the same time, the actual test meets the following standards: the pulse frequency remains stable, with an error within ±10%; the pulse amplitude should remain consistent within the preset range, with an error within ±20%; the pulse width should meet the preset requirements, with an error within ±1ms; Signal acquisition and transmission module test: use a signal generator to generate an analog signal similar to the current change of the electronic fence, input it into the signal acquisition and transmission module, compare the signal data collected by the signal acquisition and transmission module with the input analog signal data, and check whether the two are consistent; the error between the collected data and the input analog signal data should be within ±5%; Signal transmission stability test: set up signal receiving points at different distances, measure the attenuation of the signal during transmission, and gradually increase the transmission distance until the signal quality drops to the point where it cannot be recognized normally; set up an electromagnetic interference source near the electronic fence to observe whether the signal transmission is interfered with; use wireless signal interference equipment to interfere with the wireless transmission signal to observe whether the signal transmission is interrupted or errors occur; if the signal quality requirements are met, the signal transmission distance should be no less than 500 meters; if subject to electromagnetic interference and wireless interference, the bit error rate should be less than 1%; Data processing accuracy test: Use known intrusion signal feature samples to test the feature extraction algorithm of the data analysis and processing module to check whether the module can accurately extract the features of the intrusion signal, compare the extracted features with the preset intrusion judgment threshold, and determine whether it is an intrusion behavior; after multiple tests, the accuracy of intrusion judgment is statistically calculated; the accuracy of feature extraction should not be less than 90%; the accuracy of intrusion judgment should not be less than 80%; Processing speed test: measure the time it takes for the data analysis and processing module to process an intrusion signal. Based on the measured processing time, evaluate whether the processing capacity of the module meets the requirements of actual applications. The time it takes to process an intrusion signal should not exceed 100ms. Alarm equipment function test, check whether the brightness, color, flashing frequency and other parameters of the LED warning light meet the design requirements, use photometers and oscilloscopes and other equipment to measure the optical and electrical properties of the LED warning light; test the volume, tone, and sounding time of the buzzer; use sound level meters and audio analyzers to measure the acoustic performance of the buzzer; Alarm response time test: the time interval from the alarm command issued by the data analysis and processing module to the start of the alarm of the sound and light alarm device should not exceed 5s. The sound and light alarm device should be able to continuously alarm, and the alarm duration should not be less than 30s; Mobile device reception test: send warning information to multiple mobile devices to check whether the mobile devices can accurately receive the information. Use SMS group messaging platform or mobile device reception test software to test whether the warning information received by the mobile device is complete, including intrusion location, time, intrusion type and other information; Network connection test: Test the stability of the network connection between the mobile device and the warning server in different network environments. Use network speed test software and network monitoring tools to test whether the warning information is lost or erroneous during network transmission. Evaluate the accuracy of data transmission by comparing the content of sent and received information. The reception accuracy of mobile devices should not be less than 95%, and the completeness of the information content received by the mobile devices should not be less than 90%. In the case of poor network environment, the network connection between the mobile device and the warning server should be able to remain stable, and the interruption time should not exceed 10s.

4. The anti-intrusion early warning method for outdoor camps based on electronic fences as claimed in claim 3 is characterized in that: The processed signal is subjected to feature extraction and analysis by a preset algorithm, wherein the preset algorithm includes a feature extraction algorithm. Time domain feature extraction, calculation of signal peak current value. When an object approaches or touches the electronic fence, the current peak value will change significantly. By setting a threshold, if the peak current exceeds the threshold, it indicates that there is an intrusion behavior. Detect the rising and falling edge characteristics of the current signal. Intrusion behavior can cause changes in the current signal. By analyzing the slope and time parameters of the rising and falling edges, information can be obtained. Frequency domain feature extraction, Fourier transform of the filtered current signal, convert the time domain signal into a frequency domain signal, analyze the spectral distribution of the signal, and pay attention to the changes in specific frequency components; calculate the power spectral density of the signal, and judge whether there is an abnormality by observing the changes in the power spectral density. The intruding object affects the electric field of the electronic fence, thereby causing the power spectral density of the current signal to change.

5. The anti-intrusion early warning method for outdoor camps based on electronic fences as claimed in claim 4 is characterized in that: The processed signal is subjected to feature extraction and analysis by a preset algorithm, wherein the preset algorithm includes a data analysis algorithm. Pattern recognition algorithm, SVM is used to classify the current signal features into two categories: normal and intrusion. By training the SVM model, using known normal and intrusion signal feature samples, it learns the feature patterns of normal and intrusion signals. In actual detection, the signal features to be analyzed are input into the trained SVM model, and the model outputs the classification results. If the SVM model outputs an intrusion category, it is judged as an intrusion behavior; Decision tree algorithm, build a decision tree model, and make classification decisions based on the different attributes of the current signal characteristics. Each node of the decision tree represents a characteristic attribute. By testing and judging the characteristic attributes, the signal characteristics are gradually classified into different branches, and finally determine whether it is an intrusion behavior; The statistical analysis algorithm calculates the mean and variance of the signal within a certain time window. Under normal circumstances, the mean and variance of the current signal should remain relatively stable. When there is an intrusion, the mean and variance change significantly.

6. The anti-intrusion early warning method for outdoor camps based on electronic fences as claimed in claim 5 is characterized in that: The comparison with the current signal characteristics under normal conditions includes a data preparation stage, a feature extraction and analysis stage, a judgment and decision-making stage, and a result output stage.

7. The anti-intrusion early warning method for outdoor camps based on electronic fences as claimed in claim 6 is characterized in that: In the data preparation stage, during the system installation and debugging stage, the current signal data between the electrode columns under normal conditions are collected, and characteristic parameters are extracted from the normal signal data as a template for comparison with the abnormal signal. These characteristic parameters include the average value, standard deviation, peak value, frequency component and waveform characteristics of the current; In the feature extraction and analysis stage, the time domain features are extracted from the processed signal, including the rise time, fall time, and duration of the signal; the signal is converted to the frequency domain, and the frequency components and energy distribution of the signal are analyzed; the extracted signal feature parameters are compared with the normal signal feature template one by one to check whether each feature parameter is within the normal range; not only the value of a single feature parameter is paid attention to, but also the change trend of the feature parameter needs to be analyzed. If the change trend of the signal feature is inconsistent with the normal situation, it indicates an intrusion behavior; In the judgment and decision stage, the Euclidean distance between the processed signal feature and the normal signal feature template is calculated. The smaller the Euclidean distance, the closer the signal is to the normal situation; the larger the distance, the more likely it is an intrusion behavior. The correlation coefficient between the signal feature and the normal signal feature template is calculated. The closer the correlation coefficient is to 1, the stronger the correlation between the two is, and the more likely the signal is normal. The closer the correlation coefficient is to 0 or -1, the weaker the correlation between the two, indicating that there is intrusion behavior; set the threshold for similarity calculation to determine intrusion behavior; In the result output stage, a warning decision is made based on the comparison and judgment results. If it is judged to be an intrusion behavior, the warning mechanism is triggered; If it is judged to be normal, continue to monitor the signal; record the judgment result and related signal characteristic data for subsequent query and analysis.

8. The anti-intrusion early warning method for outdoor camps based on electronic fences as claimed in claim 7 is characterized in that: The data analysis and processing module first performs filtering processing on the signal, including the following steps 1) Select filtering algorithm Select the appropriate filtering algorithm according to the characteristics of the signal and the characteristics of the noise. Specifically, for signals with mainly high-frequency noise, select the high-pass filtering algorithm; for signals with mainly low-frequency noise, select the low-pass filtering algorithm; for signals containing both high-frequency and low-frequency noise, select the band-pass filtering algorithm; The specific algorithms include Adaptive filtering algorithm: This algorithm can automatically adjust the filtering parameters according to the real-time changes of the signal to adapt to different signal environments; Wavelet filtering algorithm: It has the ability of multi-resolution analysis and can decompose the signal into different frequency scales to better remove noise and retain the signal details; 2) Filtering process Data input: input the current change signal transmitted by the signal acquisition and transmission module into the filtering module; Filtering Operation Filter parameter setting: according to the selected filtering algorithm, set the corresponding filtering parameters, such as filter type, order, and cutoff frequency; Filtering calculation: Filter the input signal. According to the principle of filtering algorithm, filter and process the frequency components of the signal, remove the noise components and retain the useful information of the signal. Filtering effect evaluation Real-time monitoring: During the filtering process, the filtering effect is monitored in real time, and the filtering parameters can be adjusted in time by monitoring the signal spectrum, signal-to-noise ratio and other indicators after filtering; Outcome assessment includes Signal quality assessment: Check whether the filtered signal meets the requirements of subsequent processing, such as signal clarity and accuracy. If the signal quality does not meet the requirements, the filtering parameters should be readjusted and filtering should be performed; Noise removal effect evaluation: Evaluate the noise removal effect of the filtering algorithm. By comparing the noise levels of the signal before and after filtering, the effectiveness of the filtering algorithm can be judged. If the noise removal effect is not ideal, other appropriate filtering algorithms should be selected or the filtering parameters should be adjusted.

9. The anti-intrusion early warning method for outdoor camps based on electronic fences as claimed in claim 8, characterized in that: The data analysis and processing module sends an early warning instruction to the early warning module, including the following steps: Step 1: Confirmation of intrusion behavior Feature matching review: When the data analysis and processing module initially determines that there is an intrusion, it will compare the extracted abnormal signal features with the normal signal feature library again, not only comparing the values ​​of key feature parameters, but also analyzing the correlation and change trend between feature parameters to ensure the accuracy of the judgment. If it is found that the current peak value increases abnormally and the frequency component also changes in a specific pattern, and the combination of the two meets the preset intrusion feature pattern, it will proceed to the next step; Multi-algorithm cross-validation: Multiple data analysis algorithms are used to cross-validate the intrusion judgment results. Only when multiple algorithms conclude that there is an intrusion, the intrusion behavior is considered to be established. False positive elimination mechanism: Comprehensive analysis is performed based on the current environmental factors and system operation status to eliminate false positives caused by environmental interference or system problems. For example, in thunderstorm weather, if the detected signal abnormality may be caused by electromagnetic interference from lightning, the system will automatically identify and mark it. If the subsequent signal does not have further changes that meet the intrusion characteristics, it will not be judged as an intrusion. Step 2: Instruction generation Information integration: Once the intrusion behavior is confirmed, the data analysis and processing module will integrate detailed information related to the intrusion event; in addition to the basic intrusion time and approximate location, it will also include specific characteristic parameters of abnormal signals and possible intrusion types; Standardization of instruction format: Generate warning instructions in a specific format that can be recognized by the warning module. The format will include a unique instruction number to facilitate subsequent tracking and management; the integrated information will also be encoded; Priority setting: Set the priority for the warning command according to the severity and urgency of the intrusion behavior. If it is judged to be a deliberate human intrusion and may pose a major threat to the safety of the camp, it will be set to the highest priority; if it is an intrusion caused by a small animal, it will be set to a lower priority; Step 3: Command encryption and security verification Data encryption: To ensure the security and confidentiality of early warning instructions during transmission, encryption algorithm instructions are used for encryption processing. The encryption key will be updated regularly, and a session key will be dynamically generated before each transmission to increase the difficulty of cracking; Identity verification: Before sending the warning instruction, the data analysis and processing module will conduct identity verification with the warning module, and ensure the legitimacy of the identities of both parties in the communication by exchanging pre-set digital certificates and verification codes; Integrity check: Add a data integrity check code to the encrypted command. After receiving the command, the early warning module will perform an integrity check on the command based on the check code. If the check fails, the command will be required to be resent. Step 4: Command transmission Communication protocol selection: Select the appropriate communication protocol for command transmission based on the actual application scenario and network environment; Transmission retry mechanism: To prevent the loss or error of instructions during transmission, a transmission retry mechanism is set. If the confirmation feedback information from the early warning module is not received within the specified time, the data analysis and processing module will automatically resend the early warning instruction; Transmission monitoring: During the instruction transmission process, the transmission status is monitored in real time. If any transmission abnormality is found, the transmission parameters will be adjusted or the communication mode will be switched in time to ensure that the instruction can reach the early warning module smoothly; Step 5: Receive confirmation and feedback Receiving and parsing the warning module: After receiving the encrypted warning instruction, the warning module first performs identity authentication and integrity verification. If the verification passes, the instruction is decrypted using the corresponding decryption key, and the intrusion information contained therein is parsed according to the preset format; Confirmation feedback sending: After successfully receiving and parsing the warning instruction, the early warning module will immediately send confirmation feedback information to the data analysis and processing module. The feedback information will contain the instruction number, receiving time, processing status, etc., indicating that the instruction has been successfully received; Exception handling feedback: If an exception occurs in the process of receiving or parsing instructions, the early warning module will promptly send exception handling feedback information to the data analysis and processing module.

10. An anti-intrusion warning system for outdoor camps based on electronic fences, characterized in that: include The data analysis and processing module receives the current change signal from the signal acquisition and transmission module, exchanges information with the early warning module, and sends an early warning instruction to the early warning module according to the judgment result; The early warning module receives the early warning instruction sent by the data analysis and processing module, and issues an early warning by means of sound and light alarms and sending early warning information to associated mobile devices; Mobile devices, as receiving terminals for early warning information, receive early warning information sent by the early warning module, including intrusion location, time, intrusion type and other information, to help managers understand the situation in a timely manner; The early warning server interacts with the mobile device and the data analysis and processing module, is responsible for storing and managing the early warning information, and manages and monitors the network connection between the mobile device and the early warning module.

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