Intrusion mode identification method and system based on distributed optical fiber sensing

Abstract

The invention relates to the technical field of optical fiber sensing, and provides an intrusion mode identification method and system based on distributed optical fiber sensing, and the method comprises the steps: obtaining the current background vibration information of a preset sensing point location of an optical fiber sensing line of a park and the soil environment information of the preset sensing point location; determining current vibration power spectral density deviation information of the preset sensing point location according to the current background vibration information of the preset sensing point location; when a potential intrusion vibration signal from the preset sensing point location is received, performing compensation processing on the potential intrusion vibration signal according to the current vibration power spectral density deviation information and the soil environment information of the preset sensing point location; and performing intrusion mode identification based on the compensated potential intrusion vibration signal. The accuracy of intrusion identification can be improved.

Description

Technical Field

[0001] This application relates to the field of fiber optic sensing technology, and in particular to an intrusion pattern recognition method and system based on distributed fiber optic sensing. Background Technology

[0002] With the increasing demand for security and protection in society, especially in key areas such as large warehousing and logistics parks or border security, traditional security methods often fall short of the requirements for all-weather, wide-range, and high-precision monitoring. Distributed fiber optic sensing technology, due to its unique advantages such as long distance, passive operation, and resistance to electromagnetic interference, is widely used in perimeter security. It continuously collects physical signals such as vibrations and sound waves in the environment by laying sensing fibers to identify various potential physical intrusion behaviors. However, in actual deployment and long-term operation, complex environmental factors and construction conditions often pose severe challenges to the intrusion pattern recognition of such systems.

[0003] In large warehousing and logistics parks, a security system based on distributed fiber optic sensing is typically deployed to ensure perimeter security. This system continuously monitors micro-vibrations and acoustic signals in the environment via sensing fibers laid along the park's boundary, aiming to identify and differentiate various potential physical intrusion behaviors. However, in actual park construction, to balance construction efficiency and cost, the laying of sensing fibers in some areas has not fully adhered to strict standard backfilling specifications. Specifically, in some fiber optic sections, the construction team may not have used the specified high-standard, uniformly compacted sand or gravel as backfill material, but instead used existing ordinary soil within the park for simple covering. This ordinary soil often contains irregular gravel pieces and may even contain a small amount of construction waste, and it was not adequately compacted layer by layer during the backfilling process. This non-standard backfilling method results in significant local heterogeneity in the physical properties of the medium surrounding the fiber optics.

[0004] Over time, daily operations within the park, particularly the frequent passage of heavy forklifts and large freight vehicles, have continuously compacted the ground in these non-standard backfilled areas. This long-term mechanical stress, combined with the soil's inherent settlement characteristics, further alters the soil structure above and around the optical fiber. Specifically, the soil gradually compacts under vehicle loads, and irregular gravel or construction debris embedded within it may come into direct contact with the outer sheath of the sensing optical fiber during soil settlement, creating localized stress concentration areas at these contact points. This stress concentration is not uniformly distributed but appears discretely at specific locations on the optical fiber, causing subtle and complex changes in the fiber's micro-vibration response characteristics at these points. For example, at these stress concentration points, the fiber's ability to transmit external vibrations may be locally distorted, manifesting as attenuation or enhancement of certain frequency components, or abnormally increased or decreased sensitivity to specific vibration modes. Consequently, the signal acquisition by the optical fiber in these areas is no longer linear and uniform.

[0005] This localized alteration in fiber optic response characteristics presents a significant challenge for identification. In these affected areas, even normal daily activities within the park, such as security personnel patrolling, small electric transport vehicles passing by, or slight swaying of fences caused by wind, can generate vibration signals that, when passing through these distorted locations, become highly similar in frequency range or amplitude envelope to the signal characteristics of a completely different, distant, minor intrusion. This signal "camouflage" makes it difficult for the system to accurately extract discriminative features from the raw vibration data that clearly distinguish normal activity from intrusion. Existing security systems in the park primarily rely on preset vibration signal energy thresholds and relatively simple frequency domain feature matching for pattern recognition. Faced with the highly similarity between normal activity signals and intrusion signals in key feature dimensions in the aforementioned scenario, traditional methods prove inadequate. The system's inability to effectively distinguish these subtle but crucial differences leads to a significant increase in false alarm rates at several fixed locations affected by non-standard installations. Summary of the Invention

[0006] This application provides an intrusion pattern recognition method and system based on distributed optical fiber sensing, which can improve the accuracy of intrusion recognition.

[0007] To achieve the above objectives, this application adopts the following technical solution: In a first aspect, this application discloses an intrusion pattern recognition method based on distributed optical fiber sensing, comprising: acquiring current background vibration information and soil environment information of preset sensing points of optical fiber sensing lines in a park; determining current vibration power spectral density deviation information of preset sensing points based on current background vibration information of preset sensing points; when a potential intrusion vibration signal is received from a preset sensing point, compensating the potential intrusion vibration signal based on the current vibration power spectral density deviation information and soil environment information of the preset sensing point; and performing intrusion pattern recognition based on the compensated potential intrusion vibration signal.

[0008] This technical solution effectively addresses the technical problem of decreased accuracy and increased false alarm rate in existing distributed fiber optic sensing security systems under complex environmental factors and non-standard construction conditions, significantly improving the system's ability to identify real intrusion behaviors and reducing the false alarm rate.

[0009] Furthermore, acquiring the current background vibration information of preset sensing points in the fiber optic sensing line includes: determining whether the current time is within a preset idle time period; if the current time is within the preset idle time period, determining whether the park's video alarm system has recorded an alarm within a first preset time period before the current time; if the park's video alarm system has not recorded an alarm within the first preset time period before the current time, collecting vibration sensing data of the preset sensing points, and using the vibration sensing data as the current background vibration information of the preset sensing points.

[0010] Through this technical solution, this application can ensure that the background vibration information is acquired in a "pure" state with minimal activity and interference in the park, thereby providing a more accurate benchmark for subsequent calculation of vibration power spectral density deviation information, avoiding misjudging normal activities as background noise, and improving the reliability of background information acquisition.

[0011] Based on this, the current vibration power spectral density deviation information of the preset sensing point is determined according to the current background vibration information of the preset sensing point, including: obtaining the ideal power spectral density of the preset sensing point under ideal conditions; determining the current power spectral density of the current background vibration information of the preset sensing point according to the current background vibration information of the preset sensing point; and determining the current vibration power spectral density deviation information of the preset sensing point according to the ideal power spectral density and the current power spectral density.

[0012] Through this technical solution, this application can quantify the difference between the current environmental vibration and the ideal state, providing key calibration parameters for subsequent signal compensation, enabling the system to adapt to changes in the fiber optic laying environment and improving the accuracy of identifying potential intrusion signals.

[0013] Furthermore, the current vibration power spectral density deviation information of the preset sensing point is determined based on the ideal power spectral density and the current power spectral density, including: using the ratio of the current power spectral density to the ideal power spectral density as the initial vibration power spectral density deviation information; determining the current energy value of the current background vibration information of the preset sensing point in the preset frequency band based on the current background vibration information of the preset sensing point; and determining the current vibration power spectral density deviation information based on the current energy value and the initial vibration power spectral density deviation information.

[0014] Through this technical solution, this application can more precisely characterize the deviation of vibration power spectral density. By introducing energy values ​​to correct the initial deviation, the deviation information not only reflects the change in the spectrum shape, but also takes into account the influence of vibration intensity, thereby improving the accuracy of the deviation information and the effectiveness of the compensation process.

[0015] In some preferred embodiments, determining the current vibration power spectral density deviation information based on the current energy value and the initial vibration power spectral density deviation information includes: obtaining a first preset correspondence; the first preset correspondence includes a one-to-one correspondence between multiple energy value ranges and multiple first adjustment coefficients; using the first adjustment coefficient corresponding to the energy value range in the first preset correspondence as the target first adjustment coefficient; and using the product of the initial vibration power spectral density deviation information and the target first adjustment coefficient as the current vibration power spectral density deviation information.

[0016] Through this technical solution, this application can dynamically adjust the vibration power spectral density deviation information according to the actual vibration energy level, making the calculation of the deviation information more flexible and accurate, and further improving the system's adaptability to environmental changes under different vibration intensities.

[0017] As a technical improvement, the potential intrusion vibration signal is compensated based on the current vibration power spectral density deviation information and soil environment information of the preset sensing points. This includes: obtaining historical vibration power spectral density deviation information within a second preset time period before the current moment; smoothing the historical vibration power spectral density deviation information and the current vibration power spectral density deviation information to obtain the target vibration power spectral density deviation information; and compensating the potential intrusion vibration signal based on the target vibration power spectral density deviation information and soil environment information.

[0018] Through this technical solution, this application can effectively filter out instantaneous noise and abnormal fluctuations by introducing historical deviation information and performing smoothing processing, making the target deviation information used for compensation more stable and reliable, thereby improving the robustness of the compensation processing and the accuracy of intrusion pattern recognition.

[0019] To improve the scheme, the potential intrusion vibration signal is compensated based on the target vibration power spectral density deviation information and the soil environment information, including: determining the original power spectral density of the potential intrusion vibration signal; using the product of the original power spectral density and the target vibration power spectral density deviation information as the first power spectral density of the potential intrusion vibration signal; and compensating the first power spectral density of the potential intrusion vibration signal based on the soil environment information to obtain the second power spectral density of the potential intrusion vibration signal.

[0020] Through this technical solution, this application can compensate for the impact of environmental factors on the signal in stages. First, the spectral characteristics of the signal are corrected by the vibration power spectral density deviation information, and then further fine-tuned by combining soil environmental information, so that the compensated signal is closer to the characteristics of the real intrusion signal and the accuracy of identification is improved.

[0021] As a further improvement, the first power spectral density of the potential intrusion vibration signal is compensated based on soil environmental information to obtain the second power spectral density of the potential intrusion vibration signal, including: determining an environmental adjustment coefficient based on soil environmental information; and using the product of the first power spectral density of the potential intrusion vibration signal and the environmental adjustment coefficient as the second power spectral density of the potential intrusion vibration signal.

[0022] Through this technical solution, this application can quantify the impact of soil environment on signal propagation into an environmental adjustment coefficient, which can be directly applied to the compensation of signal power spectral density, making the compensation process more intuitive and efficient, and further improving the system's adaptability to different soil environments.

[0023] Based on the above, this application further proposes that the soil environmental information includes soil moisture content and soil temperature, and that the environmental adjustment coefficient is determined based on the soil environmental information, including: obtaining a second preset correspondence; the second preset correspondence includes a one-to-one correspondence between multiple first information and multiple second adjustment coefficients; the first information includes a soil moisture content range and a soil temperature range; the first information corresponding to soil moisture content and soil temperature in the second preset correspondence is used as the target first information; and the second adjustment coefficient corresponding to the target first information in the second preset correspondence is used as the environmental adjustment coefficient.

[0024] Through this technical solution, this application can establish a correspondence between soil moisture content and soil temperature and environmental adjustment coefficients, thereby achieving precise quantification and compensation for the impact on the soil environment. This enables the system to dynamically adjust the compensation strategy based on real-time changes in the soil environment, further improving the accuracy of intrusion pattern recognition and environmental adaptability.

[0025] Secondly, this application also discloses an intrusion pattern recognition system based on distributed optical fiber sensing, comprising: an acquisition device and a processing device; the acquisition device is used to acquire the current background vibration information and soil environment information of preset sensing points of the optical fiber sensing line in the park; the processing device is used to determine the current vibration power spectral density deviation information of the preset sensing points based on the current background vibration information of the preset sensing points; the processing device is used to compensate the potential intrusion vibration signal based on the current vibration power spectral density deviation information and soil environment information of the preset sensing points when a potential intrusion vibration signal is received from the preset sensing points; the processing device is used to perform intrusion pattern recognition based on the compensated potential intrusion vibration signal.

[0026] Beneficial effects This application provides an intrusion pattern recognition method based on distributed optical fiber sensing. It acquires current background vibration information and soil environment information at preset sensing points along the optical fiber sensing lines in a park, and compensates for potential intrusion vibration signals based on this information. Finally, it identifies intrusion patterns based on the compensated signals. This method effectively solves the problems of high false alarm rates and low recognition accuracy caused by factors such as non-standard optical fiber laying, complex and variable soil environments, and interference from daily operations in existing technologies. Specifically, by dynamically acquiring background vibration and soil environment information, this application can assess the environmental impact on specific points along the optical fiber sensing lines in real time and compensate for potential intrusion signals accordingly. This eliminates the distortion effect of environmental factors on signal characteristics, allowing the compensated signals to more accurately reflect real intrusion behavior. Compared with existing methods that rely on simple thresholds and frequency domain feature matching, this application significantly improves the system's ability to distinguish between normal activities and intrusion behavior through refined signal compensation, thereby reducing the false alarm rate and improving the accuracy and reliability of intrusion pattern recognition. This provides a more efficient and stable security solution for critical areas such as large warehousing and logistics parks or border security. Attached Figure Description

[0027] Figure 1 A flowchart illustrating an intrusion pattern recognition method based on distributed optical fiber sensing provided in this application; Figure 2 A flowchart illustrating another intrusion pattern recognition method based on distributed optical fiber sensing provided in this application; Figure 3 A flowchart illustrating another intrusion pattern recognition method based on distributed optical fiber sensing provided in this application; Figure 4 This is a schematic diagram of an intrusion pattern recognition system based on distributed optical fiber sensing, provided in this application. Detailed Implementation

[0028] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0029] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0030] Traditional perimeter security systems, when deployed in critical areas such as large warehousing and logistics parks or border security zones, suffer from localized distortions in their response characteristics to external vibrations due to the complex environments in which fiber optic cables are laid. Factors such as uneven backfill material, soil subsidence, and vehicle traffic cause these distortions. This distortion makes vibration signals generated by normal activity highly similar in characteristics to potential intrusion signals, leading to a significant increase in the system's false alarm rate and making it difficult to meet the requirements of 24 / 7, wide-range, and high-precision monitoring.

[0031] In this regard, such as Figure 1 As shown, this application proposes an intrusion pattern recognition method based on distributed optical fiber sensing, including: S101. Obtain the current background vibration information and soil environment information of the preset sensing points of the fiber optic sensing lines in the park.

[0032] S102. Determine the current vibration power spectral density deviation information of the preset sensing point based on the current background vibration information of the preset sensing point.

[0033] S103. When a potential intrusion vibration signal is received from a preset sensing point, the potential intrusion vibration signal is compensated based on the current vibration power spectral density deviation information and soil environment information of the preset sensing point.

[0034] S104. Intrusion pattern recognition is performed based on the compensated potential intrusion vibration signal.

[0035] This application compensates for potential intrusion vibration signals by introducing background vibration information and soil environment information, effectively eliminating the interference of environmental factors on the sensing signals and significantly improving the accuracy of intrusion pattern recognition. This overcomes the problems of high false alarm rate and insufficient recognition accuracy caused by environmental complexity in the prior art.

[0036] To better understand the technical solution proposed in this application, some key terms involved will be explained first.

[0037] "Preset sensor locations" refer to specific locations or areas pre-defined or divided within the fiber optic sensing line for collecting vibration signals. These locations can be physical markers or logically divided fiber optic segments, and their purpose is to provide refined area monitoring capabilities.

[0038] "Current background vibration information" refers to vibration data collected at preset sensor locations caused by environmental factors (such as wind, vehicle traffic, daily operations, etc.) when no intrusion occurs. This information reflects the normal vibration characteristics of that location within a specific time period.

[0039] "Soil environmental information" refers to the physicochemical properties of the soil surrounding the preset sensing points, such as soil moisture content, soil temperature, soil density, and soil type. This information has a significant impact on the propagation and attenuation of vibration signals.

[0040] "Current vibration power spectral density deviation information" refers to the degree of deviation of the vibration power spectral density of a preset sensing point from the ideal or reference state under the current background vibration conditions. This information quantifies the impact of environmental factors on the fiber optic sensing response characteristics.

[0041] "Potential intrusion vibration signals" refer to vibration signals received by the system that may be caused by intrusion. Before processing, these signals may be affected by environmental noise and are difficult to use directly for accurate identification.

[0042] "Compensation processing" refers to the process of adjusting and correcting potential intrusion vibration signals based on current vibration power spectral density deviation information and soil environmental information. The aim is to eliminate or reduce the influence of environmental factors on the signal, making it closer to the characteristics of the actual intrusion signal.

[0043] "Intrusion pattern recognition" refers to the process of determining whether intrusion behavior exists based on compensated vibration signals, using specific algorithms and models, and further identifying the type of intrusion (e.g., walking, running, digging, climbing, etc.).

[0044] This application provides an intrusion pattern recognition method based on distributed optical fiber sensing, the core of which lies in improving the recognition accuracy of intrusion signals by quantifying and compensating for environmental factors.

[0045] Specifically, this method first acquires the current background vibration information and soil environment information of preset sensing points along the fiber optic sensing lines in the park. The current background vibration information can be acquired in various ways. For example, it can be manually measured at the preset sensing points using specialized vibration sensors within a specific time period, recording the vibration data over a period of time and using this as the current background vibration information. Alternatively, it can be obtained through system integration, where vibration data continuously collected by the fiber optic sensing system in non-alarm mode is preliminarily filtered and processed before being used as the current background vibration information. Soil environment information can be acquired in real time by deploying soil sensors near the preset sensing points. For example, soil moisture sensors and soil temperature sensors can be deployed to obtain soil moisture and temperature data. These sensors can periodically or continuously transmit data to the central processing unit.

[0046] After acquiring the current background vibration information, the current vibration power spectral density deviation information at the preset sensing point is determined based on this information. For example, an ideal vibration power spectral density can be preset as a benchmark, which can be obtained by measurement in a laboratory environment or under conditions of minimal environmental interference during the initial stage of fiber optic installation. Then, the current background vibration information is subjected to a Fourier transform to obtain its current power spectral density. By comparing the current power spectral density with the ideal power spectral density, the current vibration power spectral density deviation information can be calculated. This deviation information reflects the degree of influence of current environmental factors on the fiber optic sensing response characteristics.

[0047] When the system receives a potential intrusion vibration signal from a preset sensing point, the signal needs to be compensated. This compensation is based on previously determined current vibration power spectral density deviation information and soil environmental information. For example, a compensation model can be designed that takes the original power spectral density of the potential intrusion vibration signal as input and combines the current vibration power spectral density deviation information and soil environmental information as adjustment parameters. The compensation model can be a linear or nonlinear function, and its goal is to eliminate or reduce the influence of environmental factors on the potential intrusion vibration signal. For example, if the current vibration power spectral density deviation information shows that the signal in a certain frequency band is enhanced by environmental factors, then the compensation process will attenuate the potential intrusion vibration signal in that frequency band accordingly. Similarly, if changes in soil moisture content or temperature affect the propagation characteristics of the vibration signal, the compensation model will also adjust the signal according to the soil environmental information.

[0048] Finally, intrusion pattern recognition is performed based on the compensated potential intrusion vibration signal. The compensated signal has a higher signal-to-noise ratio and more accurate features, thus allowing for analysis using various mature pattern recognition algorithms. For example, machine learning algorithms such as Support Vector Machine (SVM), neural networks, and decision trees can be used to extract features and classify the compensated signal. By learning from a large number of known intrusion pattern samples, these algorithms can accurately distinguish between normal activity signals and real intrusion signals, and identify the specific intrusion type.

[0049] The core innovation of the intrusion pattern recognition method based on distributed fiber optic sensing proposed in this application lies in the introduction of a dynamic compensation mechanism for environmental factors. Traditional security systems, when facing complex park environments, often rely directly on raw vibration signals for intrusion pattern recognition. This method is highly susceptible to environmental noise interference in scenarios with irregular fiber optic cable laying, variable soil conditions, and frequent daily operations, leading to a high false alarm rate and difficulty in guaranteeing recognition accuracy. For example, when heavy vehicles pass through the park or the soil moisture content changes significantly, the response characteristics of the fiber optic sensor change, blurring the features of vibration signals generated by normal activities from intrusion signals, thus making it impossible for the system to accurately distinguish them.

[0050] This application quantifies the impact of environmental factors by acquiring current background vibration and soil environment information at preset sensing points and determining the current vibration power spectral density deviation. Furthermore, when a potential intrusion vibration signal is received, this quantified information is used to compensate the signal. This compensation effectively eliminates or reduces the interference of environmental noise on the potential intrusion vibration signal, allowing the compensated signal to more accurately reflect the characteristics of the intrusion behavior. For example, if the background vibration information shows high noise in a certain frequency band, the compensation process will correspondingly reduce the weight of that frequency band; if the soil environment information indicates that high soil moisture content leads to signal attenuation, the compensation process will enhance the signal to compensate for the attenuation.

[0051] Compared with existing technologies, the advantages of this application are as follows: First, by dynamically acquiring and utilizing current background vibration information, the system can perceive and adapt to changes in environmental noise in real time, avoiding false alarms caused by environmental changes. Second, by combining soil environmental information to compensate for the signal, the accuracy and comprehensiveness of the compensation are further improved, making the system's recognition performance more stable under different soil conditions. Finally, intrusion pattern recognition based on the compensated high-quality signal significantly improves the accuracy and robustness of the recognition, effectively reducing the risk of false alarms and missed alarms. Therefore, the method of this application can better adapt to complex and ever-changing practical application scenarios, providing more reliable and efficient security protection for key areas such as large-scale warehousing and logistics parks.

[0052] like Figure 2 As shown, this application further proposes a step for obtaining the current background vibration information of a preset sensing point in an optical fiber sensing line, including: S201. Determine whether the current time is within the preset idle time period.

[0053] S202. If the current time is within a preset idle time period, determine whether the video alarm system in the park has recorded an alarm within the first preset time period before the current time.

[0054] S203. If the video alarm system in the park has not recorded any alarms within the first preset time period before the current time, collect vibration sensing data from the preset sensing points and use the vibration sensing data as the current background vibration information of the preset sensing points.

[0055] Specifically, the preset idle time period refers to a specific time interval within the park where expected activity levels are low and environmental vibration interference is minimal, such as at night or during non-working hours. The purpose of setting a preset idle time period is to collect background vibration information in a relatively quiet environment, reducing the impact of daily activities on background data. The first preset duration refers to a continuous period preceding the current moment, used to check for alarm records in the video alarm system. By determining whether the park's video alarm system has alarm records within the first preset duration preceding the current moment, actual intrusion events that may occur during the potential background information collection period can be effectively ruled out, ensuring that the collected vibration data truly reflects background vibration under non-intrusion conditions. In practical applications, if both conditions are met—that is, the current moment falls within the preset idle time period and the video alarm system has no alarm records within the first preset duration—then the current environment is considered to be in a stable and non-intrusion-free background state. In this case, vibration sensing data from the preset sensor points is collected and used as the current background vibration information for those preset sensor points.

[0056] The proposed solution ensures that the acquired background vibration information is collected under conditions of relatively stable park environment and no known intrusion events by introducing the judgment of preset idle time periods and verification of alarm records from the video alarm system. It is precisely because potential interference and intrusion signals are eliminated that the acquired background vibration information is purer and more accurate, providing a reliable benchmark for subsequent determination of vibration power spectral density deviation information and compensation processing for potential intrusion vibration signals.

[0057] The above technical solution effectively avoids misclassifying interference- or intrusion-affected vibration data as background information, significantly improving the accuracy and reliability of current background vibration information. This allows for more precise calculation of vibration power spectral density deviation and more effective compensation processing of potential intrusion vibration signals, thereby enhancing the accuracy and robustness of the entire intrusion pattern recognition method and reducing the risk of false alarms and false negatives.

[0058] In some preferred embodiments, it is assumed that the preset idle time period for a certain park is set to 0:00 to 5:00 AM daily, and the first preset duration is set to 30 minutes. When the system needs to obtain the current background vibration information of the preset sensing points, it first determines whether the current time is between 0:00 and 5:00 AM. If the current time is 2:30 AM, the first condition is met. Next, the system queries whether the video alarm system has any alarm records between 2:00 and 2:30 AM (i.e., within the first preset duration before the current time). If the video alarm system does not trigger any alarms during this period, the system will activate the fiber optic sensing line to collect vibration sensing data from the preset sensing points. The collected vibration sensing data will be confirmed as the current background vibration information. Conversely, if the current time is not within the preset idle time period, or if the video alarm system has alarm records within the first preset duration, the system will abandon the collection of background information at the current time, or wait for the next time that meets the conditions to collect the data, to ensure the purity of the background information.

[0059] Specifically, determining the current vibration power spectral density deviation information of the preset sensing point based on the current background vibration information of the preset sensing point may include the following steps.

[0060] like Figure 3 As shown, according to the above-mentioned intrusion pattern recognition method based on distributed optical fiber sensing, the current vibration power spectral density deviation information of the preset sensing point is determined based on the current background vibration information of the preset sensing point, including: S301. Obtain the ideal power spectral density of the preset sensing point under ideal conditions.

[0061] S302. Determine the current power spectral density of the current background vibration information of the preset sensing point based on the current background vibration information of the preset sensing point.

[0062] S303. Determine the current vibration power spectral density deviation information of the preset sensing point based on the ideal power spectral density and the current power spectral density.

[0063] The ideal power spectral density of a preset sensing point under ideal conditions refers to the power spectral density that represents the inherent vibration characteristics of the preset sensing point, obtained through long-term monitoring and data analysis under ideal operating conditions where the fiber optic sensing lines in the park are stable and interference-free. This ideal power spectral density can be pre-calibrated and stored as a benchmark for subsequent deviation calculations. For example, in the early stages of park construction or after sufficient calibration, under conditions without any known interference sources, long-term vibration data is collected from the preset sensing point, and spectral analysis such as Fourier transform is performed on these data. The average or statistical value is then taken as the ideal power spectral density.

[0064] Furthermore, based on the current background vibration information at the preset sensing points, the current power spectral density of the current background vibration information at the preset sensing points is determined. Specifically, this involves using signal processing techniques, such as Fast Fourier Transform (FFT), wavelet transform, or adaptive spectral estimation, to perform spectral analysis on the real-time acquired current background vibration information, thereby obtaining its energy distribution at different frequencies. This step aims to capture the background vibration characteristics of the current environment in real time.

[0065] Therefore, the deviation information of the current vibration power spectral density at the preset sensing point is determined based on the ideal power spectral density and the current power spectral density. The purpose is to quantify the difference between the current environmental vibration and the ideal environmental vibration. This deviation information can be a ratio, a difference, or other mathematical quantity that reflects the degree of deviation. For example, the ratio or logarithmic difference between the current power spectral density and the ideal power spectral density can be used as the initial deviation information. This deviation information will be used for subsequent compensation processing of potential intrusion vibration signals.

[0066] The proposed solution introduces an ideal power spectral density as a benchmark and calculates the current power spectral density of the background vibration information in real time. This allows for the precise quantification of the impact of current environmental factors (such as wind, rain, and geological disturbances) on the vibration signal of the fiber optic sensing line. This method, based on comparing the ideal state with the current state, enables the system to identify and isolate changes in background noise, ensuring that subsequent compensation processing for potential intrusion vibration signals is more accurately targeted at actual intrusion events rather than fluctuations in environmental noise.

[0067] The above technical solution provides a more refined and accurate method for determining background vibration power spectral density deviation information. By establishing an ideal benchmark and comparing it in real time, this method effectively avoids errors that may arise from judging based solely on single background vibration information, thereby improving the accuracy of background noise modeling. This provides a reliable basis for subsequent compensation processing of potential intrusion vibration signals, thus enhancing the robustness and recognition accuracy of the entire intrusion pattern recognition system.

[0068] This application further proposes a method for determining the current vibration power spectral density deviation information of the aforementioned preset sensing point, which includes: The ratio of the current power spectral density to the ideal power spectral density is used as the initial vibration power spectral density deviation information; based on the current background vibration information of the preset sensing point, the current energy value of the current background vibration information of the preset sensing point in the preset frequency band is determined; based on the current energy value and the initial vibration power spectral density deviation information, the current vibration power spectral density deviation information is determined.

[0069] Specifically, the initial vibration power spectral density deviation information can be understood as the preliminary ratio of the current power spectral density to the ideal power spectral density, aiming to provide a basic measure of deviation. The preset frequency band refers to one or more frequency ranges pre-defined in a fiber optic sensing system based on actual application scenarios and experience. For example, it can be set as a critical frequency range where intrusion vibration signals may occur, or a frequency range where background noise energy is significant, aiming to focus on specific frequency components that have a significant impact on intrusion pattern recognition. In practical applications, the current energy value specifically refers to the energy level of the current background vibration information within the preset frequency band. For example, it can be obtained by integrating or calculating the root mean square of the vibration signal within that frequency band, aiming to quantify the activity level of background vibration within that frequency band.

[0070] The proposed solution first calculates the ratio of the current power spectral density to the ideal power spectral density as initial vibration power spectral density deviation information, providing a preliminary deviation benchmark. Based on this, further, by determining the current energy value of the background vibration information at a preset sensing point within a preset frequency band, the intensity information of the background vibration within a specific frequency range can be obtained. Because the distribution and intensity of background vibration energy in different frequency bands significantly affect the power spectral density of the vibration signal, simply relying on the power spectral density ratio may not fully reflect the complexity of the actual environment. Therefore, by combining the current energy value with the initial vibration power spectral density deviation information, the initial deviation information can be corrected or weighted, thereby enabling the finally determined current vibration power spectral density deviation information to more accurately reflect the true vibration characteristics under the current environment, especially when the background noise energy fluctuates significantly, providing a more robust deviation assessment.

[0071] By employing the aforementioned technical solution, when determining the current vibration power spectral density deviation information at preset sensing points, not only is the difference in power spectral density between the ideal state and the current state considered, but the current background vibration energy value within the preset frequency band is also introduced. Therefore, the determined current vibration power spectral density deviation information can more accurately reflect the background vibration characteristics in the actual environment, especially when background noise energy changes, providing a more adaptive and accurate deviation assessment. This significantly improves the accuracy of subsequent compensation processing of potential intrusion vibration signals, thereby enhancing the reliability and recognition accuracy of the entire intrusion pattern recognition method.

[0072] This application further proposes a method for determining the current vibration power spectral density deviation information based on the current energy value and the initial vibration power spectral density deviation information, including: Obtain a first preset correspondence; the first preset correspondence includes a one-to-one correspondence between multiple energy value ranges and multiple first adjustment coefficients; take the first adjustment coefficient corresponding to the energy value range of the current energy value in the first preset correspondence as the target first adjustment coefficient; take the product of the initial vibration power spectral density deviation information and the target first adjustment coefficient as the vibration power spectral density deviation information.

[0073] Specifically, the first preset correspondence refers to a pre-established data structure or lookup table that describes the relationship between different energy value ranges and their corresponding adjustment coefficients. This correspondence aims to provide a suitable adjustment factor based on the energy level of the current background vibration information, in order to more accurately correct the initial vibration power spectral density deviation information. Multiple energy value ranges can divide the energy intensity of the vibration signal into different levels, such as low energy range, medium energy range, and high energy range. Each energy value range corresponds to a specific first adjustment coefficient, which quantifies the degree of correction to the initial vibration power spectral density deviation information within that energy range.

[0074] Specifically, using the first adjustment coefficient corresponding to the energy value range of the current energy value as the target first adjustment coefficient means that the system searches for the range to which the energy value belongs in a first preset correspondence based on the energy value of the current background vibration information collected in real time, and extracts the adjustment coefficient associated with that range. For example, if the current energy value falls within the "medium energy range", then the first adjustment coefficient corresponding to the "medium energy range" will be selected as the target first adjustment coefficient.

[0075] In practical applications, the product of the initial vibration power spectral density deviation information and the target first adjustment coefficient is used as the vibration power spectral density deviation information. The purpose is to refine the initially calculated initial vibration power spectral density deviation information by introducing an adjustment factor adapted to the current energy level. This product operation effectively incorporates the influence of the energy level into the deviation information calculation, allowing the final vibration power spectral density deviation information to more accurately reflect the actual vibration characteristics under the current environment.

[0076] The solution in this application introduces a first preset correspondence to segment and quantify the complex relationship between energy values ​​and power spectral density deviations. When the current energy value of the current background vibration information is obtained, the system can find a matching target first adjustment coefficient from the first preset correspondence based on the specific energy value range in which the energy value falls. It is precisely because of this dynamic adjustment mechanism based on energy value range that the initial vibration power spectral density deviation information can be adaptively corrected according to the current actual vibration energy level. For example, in a low-energy environment, the impact of environmental noise on the signal may be relatively small, and the adjustment coefficient may be close to 1 or slightly less than 1; while in a high-energy environment, environmental noise or background vibration may have a more significant impact on the signal, and the adjustment coefficient may need to be larger or smaller to accurately compensate. By multiplying the initial vibration power spectral density deviation information by this adaptive target first adjustment coefficient, the deviation caused by environmental changes and energy level differences can be effectively corrected, thereby obtaining more accurate current vibration power spectral density deviation information.

[0077] The above technical solution enables more precise and adaptive adjustment of the initial vibration power spectral density deviation information based on the energy level of the current background vibration information. This dynamic adjustment mechanism based on energy range effectively solves the inaccuracy problem that may be caused by simple adjustments in traditional methods, significantly improving the calculation accuracy of vibration power spectral density deviation information. This provides a more reliable basis for subsequent compensation processing of potential intrusion vibration signals, thereby improving the accuracy and robustness of intrusion pattern recognition.

[0078] In some embodiments described above in this application, when compensating for potential intrusion vibration signals, the current vibration power spectral density deviation information and soil environmental information are directly used for compensation. However, in practical applications, the current vibration power spectral density deviation information may be affected by instantaneous environmental noise or short-term fluctuations, resulting in unstable instantaneous values. If directly used for compensation processing, it may introduce errors, affecting the accuracy of compensation and the reliability of subsequent intrusion pattern recognition.

[0079] In response, this application further proposes an optimization scheme that introduces historical data and performs smoothing to obtain more stable and representative vibration power spectral density deviation information, thereby improving the accuracy and robustness of the compensation process.

[0080] Specifically, according to the aforementioned intrusion pattern recognition method based on distributed optical fiber sensing, the potential intrusion vibration signal is compensated based on the current vibration power spectral density deviation information and soil environment information at preset sensing points, including: Obtain historical vibration power spectral density deviation information within a second preset time period prior to the current moment; smooth the historical vibration power spectral density deviation information and the current vibration power spectral density deviation information to obtain target vibration power spectral density deviation information; and perform compensation processing on potential intrusion vibration signals based on the target vibration power spectral density deviation information and soil environment information.

[0081] Specifically, "acquiring historical vibration power spectral density deviation information within a second preset time period prior to the current moment" means that the system continuously records and stores the vibration power spectral density deviation information of preset sensing points at different times. When compensation processing of potential intrusion vibration signals is required, the system will retrieve and obtain all historical vibration power spectral density deviation information within a preset time period (i.e., the second preset time period) prior to the current moment from the stored historical data. The second preset time period can be flexibly configured according to the actual application scenario and the requirements for real-time performance and accuracy of compensation; for example, it can be set to several seconds, several minutes, or longer.

[0082] "Smoothing historical and current vibration power spectral density deviation information to obtain target vibration power spectral density deviation information" refers to fusing the acquired historical and current vibration power spectral density deviation information and smoothing it using a specific algorithm. The purpose of smoothing is to eliminate random noise and instantaneous fluctuations in the data, making the obtained vibration power spectral density deviation information more representative and stable. For example, smoothing can employ various statistical or signal processing methods such as moving average, exponential smoothing, and Kalman filtering. Through smoothing, a more stable and accurate target vibration power spectral density deviation information reflecting the current background vibration characteristics can be obtained.

[0083] "Compensation processing of potential intrusion vibration signals based on target vibration power spectral density deviation information and soil environmental information" refers to combining the smoothed target vibration power spectral density deviation information with the soil environmental information of preset sensing points to compensate for the received potential intrusion vibration signals. The compensation processing aims to eliminate or reduce the influence of background vibration and environmental factors on potential intrusion vibration signals, making the characteristics of the intrusion signals more prominent and facilitating accurate subsequent identification.

[0084] The proposed solution incorporates historical vibration power spectral density deviation information within a second preset time period prior to the current moment, and combines this with the current vibration power spectral density deviation information for smoothing. This effectively filters out the influence of instantaneous noise and short-term fluctuations on the vibration power spectral density deviation information. The smoothing process makes the obtained target vibration power spectral density deviation information more statistically significant and stable, and more accurately reflects the true background vibration characteristics under the current environment. Therefore, when using this target vibration power spectral density deviation information to compensate for potential intrusion vibration signals, the accuracy and robustness of the compensation are significantly improved, avoiding miscompensation or undercompensation caused by instantaneous data fluctuations.

[0085] Through the above technical solution, this application can effectively overcome the problems of instantaneous fluctuations and noise interference that may exist when using only the current vibration power spectral density deviation information for compensation. The smoothed target vibration power spectral density deviation information is more stable and reliable, making the compensation processing of potential intrusion vibration signals more accurate, thereby significantly improving the accuracy of intrusion pattern recognition and the system's anti-interference ability, and reducing the false alarm rate and false negative rate.

[0086] In some preferred embodiments, it is assumed that the second preset duration is set to the past 5 minutes. The system continuously records the current vibration power spectral density deviation information every second. When a potential intrusion vibration signal is received, the system first obtains all vibration power spectral density deviation information from the historical records for the past 5 minutes and combines it with the vibration power spectral density deviation information at the current moment. Subsequently, a moving average method can be used to smooth these data, for example, calculating the average of the past 10 data points as the target vibration power spectral density deviation information. Specifically, if the vibration power spectral density deviation information at the current moment is D_current, and the deviation information recorded per second in the past 5 minutes is D_1, D_2, ..., D_300, then the target vibration power spectral density deviation information D_target can be calculated as (D_current + D_1 + ... + D_N) / (N + 1), where N is the number of historical data points within the smoothing window. Finally, this smoothed D_target and soil environmental information are used to compensate for the potential intrusion vibration signal.

[0087] Specifically, the steps for compensating for potential intrusion vibration signals described above can be further refined.

[0088] According to the aforementioned intrusion pattern recognition method based on distributed optical fiber sensing, the potential intrusion vibration signal is compensated based on the target vibration power spectral density deviation information and soil environment information, including: The original power spectral density of the potential intrusion vibration signal is determined; the product of the deviation information between the original power spectral density and the target vibration power spectral density is taken as the first power spectral density of the potential intrusion vibration signal; the first power spectral density of the potential intrusion vibration signal is compensated according to the soil environment information to obtain the second power spectral density of the potential intrusion vibration signal.

[0089] Determining the original power spectral density of the potential intrusion vibration signal involves performing a spectral analysis on the signal upon receiving it from a preset sensing point. For example, signal processing methods such as Fast Fourier Transform (FFT) can transform the potential intrusion vibration signal from the time domain to the frequency domain, thereby obtaining its energy distribution at different frequencies, i.e., the original power spectral density. This step aims to acquire the initial spectral characteristics of the potential intrusion vibration signal, providing fundamental data for subsequent compensation processing.

[0090] The product of the original power spectral density and the deviation information of the target vibration power spectral density is used as the first power spectral density of the potential intrusion vibration signal. This can be understood as using pre-determined environmental background vibration deviation information to perform preliminary correction on the original spectrum of the potential intrusion vibration signal. The target vibration power spectral density deviation information reflects the change in the spectral characteristics of the current environmental background vibration relative to the ideal state. By multiplying it by the original power spectral density, the influence of environmental background noise on the potential intrusion vibration signal can be initially eliminated or reduced, resulting in a pre-compensated spectrum, i.e., the first power spectral density.

[0091] The second power spectral density of the potential intrusion vibration signal is obtained by compensating for the first power spectral density based on soil environmental information. This involves further considering the influence of soil environmental factors on the propagation and attenuation of the vibration signal, building upon the initial compensation. Environmental information such as soil moisture content, temperature, and density alters the propagation characteristics of the vibration signal; therefore, the first power spectral density needs to be finely adjusted based on this information to more accurately reproduce the true characteristics of the potential intrusion vibration signal, ultimately yielding the second power spectral density. This step aims to make the signal closer to its true performance in an interference-free environment through the correction of environmental parameters.

[0092] The proposed solution first determines the original power spectral density of the potential intrusion vibration signal to obtain its initial characteristics in the frequency domain. Then, it uses the target vibration power spectral density deviation information to perform preliminary correction on the original power spectral density. This deviation information reflects the influence of current environmental background vibration on the signal. Through multiplication, the interference of background noise can be effectively reduced, resulting in a preliminarily compensated first power spectral density. Based on this, soil environmental information is further introduced to compensate for the first power spectral density. This is because soil environment (such as moisture content and temperature) has a significant impact on the propagation and attenuation of the vibration signal. By adjusting based on soil environmental information, the true spectral characteristics of the potential intrusion vibration signal can be more accurately restored, thus obtaining a second power spectral density. This series of compensation processes aims to eliminate the interference of environmental factors on the potential intrusion vibration signal to the greatest extent possible, making it closer to the real intrusion vibration signal and providing a cleaner and more accurate data foundation for subsequent intrusion pattern recognition.

[0093] Through the above technical solution, the compensation process for potential intrusion vibration signals is refined into multiple steps, making the compensation process more accurate and systematic. First, preliminary correction is performed using background vibration deviation information, effectively suppressing environmental noise interference. Second, secondary compensation is performed by introducing soil environmental information, further considering the influence of the signal propagation medium's characteristics, enabling the compensated signal to more accurately reflect the actual intrusion event. This significantly improves the accuracy and reliability of intrusion pattern recognition, reduces false alarm and false negative rates, and thus enhances the performance of the entire distributed fiber optic sensing system.

[0094] Specifically, the second power spectral density of the potential intrusion vibration signal is obtained by compensating for the first power spectral density based on soil environmental information, including: The environmental adjustment coefficient is determined based on soil environmental information; the product of the first power spectral density of the potential intrusion vibration signal and the environmental adjustment coefficient is taken as the second power spectral density of the potential intrusion vibration signal.

[0095] The environmental adjustment coefficient is a parameter that quantifies the influence of soil on vibration signal propagation based on soil environmental information, such as soil type, humidity, and temperature. This coefficient aims to reflect the attenuation or enhancement characteristics of vibration signals under different soil environments. By determining the environmental adjustment coefficient, the influence of the soil environment on the first power spectral density of potential intrusion vibration signals can be assessed more accurately. Using the product of the first power spectral density of the potential intrusion vibration signal and the environmental adjustment coefficient as the second power spectral density of the potential intrusion vibration signal means that the first power spectral density has been directly adjusted based on soil environmental information, thus obtaining a vibration signal power spectral density closer to reality.

[0096] This application's solution achieves quantitative compensation for the impact of soil environmental information by introducing an environmental adjustment coefficient and multiplying it by the first power spectral density of the potential intrusion vibration signal. Specifically, changes in soil environmental information, such as variations in soil moisture or temperature, affect the propagation characteristics of vibration signals in the soil. The environmental adjustment coefficient is designed to reflect the degree of influence of these environmental changes on the signal power spectral density. By multiplying the first power spectral density by this environmental adjustment coefficient, signal distortion caused by changes in the soil environment can be effectively corrected, allowing the compensated second power spectral density to more accurately represent the actual intrusion vibration signal intensity, thereby providing a more reliable data foundation for subsequent intrusion pattern recognition.

[0097] The above technical solution enables refined compensation of the first power spectral density of potential intrusion vibration signals based on soil environmental information. Compared to schemes that only mention compensation based on soil environmental information, this application provides a specific and quantifiable compensation mechanism by introducing an environmental adjustment coefficient and performing multiplication operations, thereby improving the accuracy and reliability of the compensation. This more effectively eliminates the interference of soil environmental factors on the identification of intrusion vibration signals, making subsequent intrusion pattern recognition more accurate and reducing the risk of false alarms and missed alarms.

[0098] Specifically, the aforementioned soil environmental information includes soil moisture content and soil temperature, and the steps for determining the environmental adjustment coefficient based on the soil environmental information can be further refined as follows.

[0099] Soil environmental information includes soil moisture content and soil temperature. Environmental adjustment coefficients are determined based on this information, including: Obtain a second preset correspondence; the second preset correspondence includes a one-to-one correspondence between multiple first information and multiple second adjustment coefficients; the first information includes the soil moisture content range and the soil temperature range; the first information corresponding to the soil moisture content and soil temperature in the second preset correspondence is taken as the target first information; the second adjustment coefficient corresponding to the target first information in the second preset correspondence is taken as the environmental adjustment coefficient.

[0100] Specifically, soil environmental information refers to environmental factors that affect the propagation and attenuation characteristics of vibration signals in fiber optic sensing lines. In this application, it specifically refers to soil moisture content and soil temperature. Soil moisture content refers to the amount of water in the soil, usually expressed as a percentage; soil temperature refers to the actual temperature of the soil. Both of these parameters have a significant impact on the propagation characteristics of vibration signals in the soil.

[0101] The second pre-defined correspondence can be understood as a pre-established lookup table, database, or mathematical model, the purpose of which is to map specific soil environmental conditions (i.e., the combination of soil moisture content and soil temperature) to corresponding environmental adjustment coefficients. This correspondence can be obtained through preliminary experiments, historical data analysis, or simulation to ensure its accuracy and reliability.

[0102] The first piece of information is an entry in the second preset correspondence, specifically including a soil moisture content range and a soil temperature range. For example, the first piece of information can be represented as a combination of conditions: "soil moisture content between 10% and 20% and soil temperature between 15℃ and 25℃". When the actual measured soil moisture content and soil temperature fall within the range defined by a certain piece of first information, that first piece of information is determined as the target first piece of information.

[0103] The second adjustment coefficient is a value that corresponds one-to-one with the first information. It is used to quantify the degree to which the first power spectral density of the potential intrusion vibration signal is compensated under specific soil environmental conditions. This coefficient can be a multiplicative factor used to adjust the amplitude or power spectral density of the signal to eliminate or reduce the influence of environmental factors on the sensing signal.

[0104] The reason why the scheme in this application can determine the environmental adjustment coefficient based on soil environmental information is that soil moisture content and temperature are key factors affecting the propagation speed, attenuation, and energy loss of vibration waves in the soil. Different soil moisture contents and temperatures will cause changes in the intensity and spectral characteristics of the vibration signal received by the fiber optic sensing line. For example, when the soil moisture content is high, the soil density and elastic modulus will change, thus affecting the propagation of the vibration signal; changes in soil temperature will also cause changes in the physical properties of the soil medium, thereby affecting the signal attenuation.

[0105] By establishing a second preset correspondence, different soil moisture content ranges and soil temperature ranges are associated with corresponding second adjustment coefficients. This allows the system to accurately locate the corresponding environmental adjustment coefficients based on real-time soil environmental information. These environmental adjustment coefficients are used to compensate for the first power spectral density of potential intrusion vibration signals, effectively correcting signal distortion caused by changes in the soil environment and ensuring the accuracy of subsequent intrusion pattern recognition.

[0106] Through the above technical solution, this application can more precisely consider the impact of soil environmental factors on fiber optic sensing signals, thereby improving the accuracy of determining the environmental adjustment coefficient. Specifically, by using soil moisture content and soil temperature as key parameters and employing a pre-defined second pre-defined correspondence, accurate modeling and compensation of signal propagation characteristics under different soil environmental conditions can be achieved. This effectively eliminates or reduces signal fluctuations caused by changes in the soil environment, significantly reducing false alarm and false negative rates, making intrusion pattern recognition based on compensated potential intrusion vibration signals more reliable and accurate.

[0107] This application also discloses an intrusion pattern recognition system based on distributed optical fiber sensing, comprising: an acquisition device and a processing device; the acquisition device is used to acquire the current background vibration information and soil environment information of preset sensing points of the optical fiber sensing line in the park; the processing device is used to determine the current vibration power spectral density deviation information of the preset sensing points based on the current background vibration information of the preset sensing points; the processing device is used to compensate the potential intrusion vibration signal based on the current vibration power spectral density deviation information and soil environment information of the preset sensing points when a potential intrusion vibration signal is received from the preset sensing points; the processing device is used to perform intrusion pattern recognition based on the compensated potential intrusion vibration signal.

[0108] The intrusion pattern recognition system based on distributed fiber optic sensing proposed in this application aims to solve the technical problems of high false alarm rates and insufficient recognition accuracy caused by environmental noise interference in traditional security systems under complex environments (such as non-standard fiber optic cable laying, variable soil conditions, and frequent daily operations). This system achieves dynamic perception, quantification, and compensation of environmental factors through structured acquisition and processing devices, thereby significantly improving the accuracy and robustness of intrusion pattern recognition. The acquisition device is responsible for collecting key environmental data in real time, while the processing device intelligently compensates for potential intrusion signals based on this data, ultimately achieving accurate intrusion pattern recognition. This systematic design ensures that normal activities can be effectively distinguished from actual intrusion behavior under various complex operating conditions, providing reliable technical support for park security.

[0109] The above embodiments have already described the steps of acquiring the current background vibration information and soil environment information of the preset sensing points of the fiber optic sensing lines in the park; determining the current vibration power spectral density deviation information of the preset sensing points based on the current background vibration information; when a potential intrusion vibration signal is received from the preset sensing point, compensating the potential intrusion vibration signal based on the current vibration power spectral density deviation information and soil environment information of the preset sensing point; and performing intrusion pattern recognition based on the compensated potential intrusion vibration signal, etc., which will not be repeated here. It should be emphasized that the system of this application implements the above methods through specific hardware or software modules.

[0110] Specifically, the acquisition device can be understood as a hardware module responsible for data acquisition. In one implementation, the acquisition device may include distributed fiber optic sensing equipment, such as a fiber optic sensor host based on Rayleigh or Brillouin scattering principles, capable of continuously acquiring vibration signals along fiber optic lines. Furthermore, the acquisition device can integrate environmental sensors, such as soil moisture sensors and soil temperature sensors, which are deployed near preset sensing points to acquire real-time soil environmental information. In another implementation, the acquisition device can be a standalone, programmable data acquisition unit that connects to the fiber optic sensor host and environmental sensors via an interface, responsible for synchronous data acquisition, preliminary formatting, and transmission. For example, this data acquisition unit can be an embedded system with a built-in analog-to-digital converter (ADC) and communication module, capable of converting analog vibration signals and environmental parameters into digital signals and transmitting them to a processing device via wired or wireless means.

[0111] The processing device can be understood as a computing unit responsible for data analysis, compensation, and identification. In one implementation, the processing device can be a central server equipped with a high-performance processor, large-capacity memory, and network interface, capable of receiving data from multiple acquisition devices and running complex algorithms for processing. For example, this server can be deployed in the control center of a campus, communicating with the acquisition devices via Ethernet or fiber optic networks. In another implementation, the processing device can be an edge computing device deployed close to the acquisition devices, such as at the convergence point of sensing optical fibers or within a regional control box. This edge computing device has sufficient computing power and storage space to perform real-time preprocessing and partial compensation of data in a local area, thereby reducing the computational burden on the central server and lowering data transmission latency. For example, this edge computing device can be an industrial PC or a dedicated embedded AI inference board with a built-in digital signal processor (DSP) or field-programmable gate array (FPGA) for accelerating the calculation of the power spectral density of vibration signals and the execution of compensation algorithms. The processing device may also include a database module for storing ideal power spectral density, a first preset correspondence, a second preset correspondence, and an intrusion pattern recognition model.

[0112] The system of this application achieves dynamic perception and compensation of environmental factors through the coordinated operation of an acquisition device and a processing device. The acquisition device is responsible for converting vibrations and environmental parameters of the physical world into processable digital signals, while the processing device uses these digital signals and a sophisticated algorithm model to correct potential intrusion vibration signals, enabling them to more accurately reflect real intrusion behavior. Therefore, the system can effectively cope with complex and ever-changing environmental challenges and provide high-precision intrusion pattern recognition capabilities.

[0113] The core innovation of the intrusion pattern recognition system based on distributed fiber optic sensing proposed in this application lies in its structured acquisition and processing devices, which enable a dynamic compensation mechanism for environmental factors, thereby significantly improving the accuracy of intrusion pattern recognition. Traditional security systems often lack the ability to finely perceive and compensate for environmental factors in complex campus environments, causing their pattern recognition modules to directly process raw signals interfered with by environmental noise, resulting in high false alarm and false negative rates. For example, in scenarios with non-standard fiber optic cable laying, variable soil conditions, and frequent daily operational activities, existing systems struggle to effectively distinguish between normal activities and genuine intrusion behavior.

[0114] Compared with existing technologies, the advantages of this application's system are as follows: First, the acquisition device can comprehensively and in real-time collect current background vibration information and soil environmental information, providing an accurate data foundation for subsequent compensation processing. Second, the processing device can dynamically determine the vibration power spectral density deviation and compensate for potential intrusion vibration signals based on this environmental information, effectively eliminating the interference of environmental noise on the signal. This systematic compensation processing results in a higher signal-to-noise ratio and more accurate features in the signal input to the intrusion pattern recognition module, thereby significantly improving the accuracy and robustness of the recognition. Therefore, the system of this application can better adapt to complex and ever-changing practical application scenarios, providing more reliable and efficient security protection for key areas such as large-scale warehousing and logistics parks, and effectively reducing the risk of false alarms and missed alarms.

[0115] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. An intrusion pattern recognition method based on distributed optical fiber sensing, characterized in that, include: Obtain the current background vibration information and soil environment information of the preset sensing points of the fiber optic sensing lines in the park. The current vibration power spectral density deviation information of the preset sensing point is determined based on the current background vibration information of the preset sensing point. When a potential intrusion vibration signal is received from a preset sensing point, the potential intrusion vibration signal is compensated based on the current vibration power spectral density deviation information of the preset sensing point and the soil environment information. Intrusion pattern recognition based on compensated potential intrusion vibration signals.

2. The intrusion pattern recognition method based on distributed optical fiber sensing according to claim 1, characterized in that, Acquire the current background vibration information of preset sensing points in the fiber optic sensing line, including: Determine whether the current time is within a preset idle time period; If the current time is within a preset idle time period, determine whether the video alarm system of the park has recorded an alarm within the first preset time period before the current time; If the video alarm system of the park has not recorded any alarms within the first preset time period before the current moment, vibration sensing data of preset sensing points will be collected and used as the current background vibration information of the preset sensing points.

3. The intrusion pattern recognition method based on distributed optical fiber sensing according to claim 1, characterized in that, Determining the current vibration power spectral density deviation information of the preset sensing point based on the current background vibration information of the preset sensing point includes: Obtain the ideal power spectral density of the preset sensing point under ideal conditions; Based on the current background vibration information of the preset sensing point, determine the current power spectral density of the current background vibration information of the preset sensing point. The current vibration power spectral density deviation information of the preset sensing point is determined based on the ideal power spectral density and the current power spectral density.

4. The intrusion pattern recognition method based on distributed optical fiber sensing according to claim 3, characterized in that, The current vibration power spectral density deviation information of the preset sensing point is determined based on the ideal power spectral density and the current power spectral density, including: The ratio of the current power spectral density to the ideal power spectral density is used as the initial vibration power spectral density deviation information; Based on the current background vibration information of the preset sensing point, determine the current energy value of the current background vibration information of the preset sensing point in the preset frequency band. The current vibration power spectral density deviation information is determined based on the current energy value and the initial vibration power spectral density deviation information.

5. The intrusion pattern recognition method based on distributed optical fiber sensing according to claim 4, characterized in that, The current vibration power spectral density deviation information is determined based on the current energy value and the initial vibration power spectral density deviation information, including: Obtain a first preset correspondence; the first preset correspondence includes a one-to-one correspondence between multiple energy value ranges and multiple first adjustment coefficients; The first adjustment coefficient corresponding to the energy value range of the current energy value in the first preset correspondence is taken as the target first adjustment coefficient; The product of the initial vibration power spectral density deviation information and the target first adjustment coefficient is used as the current vibration power spectral density deviation information.

6. The intrusion pattern recognition method based on distributed optical fiber sensing according to claim 1, characterized in that, Based on the current vibration power spectral density deviation information of the preset sensing points and the soil environment information, compensation processing is performed on the potential intrusion vibration signal, including: Obtain historical vibration power spectral density deviation information within a second preset time period prior to the current moment; The historical vibration power spectral density deviation information and the current vibration power spectral density deviation information are smoothed to obtain the target vibration power spectral density deviation information. The potential intrusion vibration signal is compensated based on the target vibration power spectral density deviation information and the soil environment information.

7. The intrusion pattern recognition method based on distributed optical fiber sensing according to claim 6, characterized in that, Based on the target vibration power spectral density deviation information and the soil environmental information, the potential intrusion vibration signal is compensated, including: Determine the original power spectral density of the potential intrusion vibration signal; The product of the original power spectral density and the deviation information of the target vibration power spectral density is used as the first power spectral density of the potential intrusion vibration signal. The first power spectral density of the potential intrusion vibration signal is compensated based on the soil environmental information to obtain the second power spectral density of the potential intrusion vibration signal.

8. The intrusion pattern recognition method based on distributed optical fiber sensing according to claim 7, characterized in that, The first power spectral density of the potential intrusion vibration signal is compensated based on the soil environmental information to obtain the second power spectral density of the potential intrusion vibration signal, including: The environmental adjustment coefficient is determined based on the soil environmental information. The product of the first power spectral density of the potential intrusion vibration signal and the environmental adjustment coefficient is taken as the second power spectral density of the potential intrusion vibration signal.

9. The intrusion pattern recognition method based on distributed optical fiber sensing according to claim 8, characterized in that, The soil environmental information includes soil moisture content and soil temperature. An environmental adjustment coefficient is determined based on this soil environmental information, including: Obtain a second preset correspondence; the second preset correspondence includes a one-to-one correspondence between multiple first pieces of information and multiple second adjustment coefficients; the first pieces of information include soil moisture content range and soil temperature range; The first information corresponding to the soil moisture content and the soil temperature in the second preset correspondence is taken as the target first information; The second adjustment coefficient corresponding to the target first information in the second preset correspondence is used as the environmental adjustment coefficient.

10. An intrusion pattern recognition system based on distributed optical fiber sensing, characterized in that, include: Acquisition device and processing device; The acquisition device is used to acquire the current background vibration information and soil environment information of the preset sensing points of the fiber optic sensing lines in the park. The processing device is used to determine the current vibration power spectral density deviation information of the preset sensing point based on the current background vibration information of the preset sensing point. The processing device is used to compensate the potential intrusion vibration signal based on the current vibration power spectral density deviation information of the preset sensing point and the soil environment information when it receives a potential intrusion vibration signal from a preset sensing point. The processing device is used to perform intrusion pattern recognition based on the compensated potential intrusion vibration signal.

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