CN117496650B - Distributed optical fiber intrusion early warning method and system based on environment embedding - Google Patents
Distributed optical fiber intrusion early warning method and system based on environment embedding Download PDFInfo
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Abstract
The invention discloses a distributed optical fiber intrusion early warning method and system based on environment embedding, comprising the following steps: acquiring reverse Rayleigh scattering light signal data in a detection optical fiber; preprocessing the optical signal data to obtain a daily defense area alarm sequence; dividing an alarm sequence, introducing an embedded vector, and embedding the embedded vector into the alarm sequence; inputting the alarm sequence added with the embedded vector into a first neural network to obtain an environment characteristic vector; performing first intrusion alarm probability calculation based on the environmental feature vector and the time feature vector; performing second intrusion alarm probability calculation based on the environmental feature vector, the time feature vector and the first intrusion alarm probability, and performing intrusion early warning based on the calculation result; according to the invention, the full-space distribution of the intrusion event is modeled by fusing the environmental feature vector and the time feature vector, so that the accuracy of the distributed optical fiber early warning is improved.
Description
Technical Field
The invention relates to the technical field of pipeline monitoring, in particular to a distributed optical fiber intrusion early warning method and system based on environment embedding.
Background
In recent years, distributed optical fiber intrusion early warning systems have attracted extensive attention in the fields of perimeter security, pipeline monitoring and the like. The pipeline is one of the most important transportation modes of oil gas, but different from single scene environments in the fields of security protection, high-speed rails and the like, the environments along the long-distance pipeline are complex and changeable, including plain, mountain areas, lakes and the like. Different geological conditions have different effects on the propagation of vibrations, which presents a great challenge for the external perception of the distributed optical fiber sensing system.
However, the prior art mostly does not consider the influence of the spatial environment information on intrusion recognition. The natural and artificial environment surrounding the defense area has an important influence on the modal distribution of the vibration waveform. For example, in a guard area near a farmland, the average amplitude of the waveform is about 50, whereas a guard area near a high speed may have an average amplitude of 2000. The same model can not realize effective intrusion detection in farmland and nearby high speed, and the usability and practicability of the model are severely limited. Therefore, the application of the distributed optical fiber intrusion early warning technology to long-distance pipelines needs to overcome the influence of environmental factors.
For example, chinese patent CN201810475691.9 discloses an on-line monitoring system for fluid delivery pipes based on optical fiber sensing, which links the on-line detection of pipes, the management of pipe information and the inspection of pipes together, and after the optical fiber sensing pipe detection subsystem detects that the pipe has an abnormality, sends alarm information to the pipe information management subsystem, the pipe information management subsystem generates an inspection task and sends the task to the pipe inspection subsystem, the pipe inspection subsystem takes measures in time to process the abnormal pipe, and returns the execution result of the task to the pipe information management subsystem; however, the system still does not overcome the influence of the application of the optical fiber intrusion early warning technology on the long-distance pipeline on environmental factors.
Disclosure of Invention
The invention mainly solves the problem of low early warning accuracy caused by the influence of environmental factors in the optical fiber intrusion early warning technology in the prior art; the distributed optical fiber intrusion early warning method and system based on environment embedding are provided, the pipeline intrusion detection precision is improved, and the early warning accuracy is high.
The technical problems of the invention are mainly solved by the following technical proposal: a distributed optical fiber intrusion early warning method based on environment embedding comprises the following steps:
acquiring reverse Rayleigh scattering light signal data in a detection optical fiber;
preprocessing the optical signal data to obtain a daily defense area alarm sequence;
dividing an alarm sequence, introducing an embedded vector, and embedding the embedded vector into the alarm sequence;
inputting the alarm sequence added with the embedded vector into a first neural network to obtain an environment characteristic vector;
performing first intrusion alarm probability calculation based on the environmental feature vector and the time feature vector;
and performing second intrusion alarm probability calculation based on the environmental feature vector, the time feature vector and the first intrusion alarm probability, and performing intrusion early warning based on the calculation result.
Preferably, the method for preprocessing the optical signal data comprises the following steps:
performing discrete wavelet transformation on the initial optical signal data to obtain wavelet coefficients;
modifying wavelet coefficients by threshold rules;
wavelet reconstruction is performed using an inverse discrete wavelet transform based on the modified wavelet coefficients.
Preferably, the alarm sequence is divided by using a window K.
Preferably, the embedded vector is determined according to the environmental weight coefficient and the alarm sequence of the current day.
In another aspect, the present invention further provides an environment-embedded distributed optical fiber intrusion early warning system, including: the optical signal transmitting unit modulates the light source and transmits pulse light to the circulator; the circulator is used for transmitting the pulse light to the detection optical fiber and obtaining the reverse Rayleigh scattering light signal data of the detection optical fiber; the data acquisition module acquires reverse Rayleigh scattering light signal data of the circulator and transmits the data to the processing module; the processing module is used for preprocessing the optical signal data to obtain a daily defense area alarm sequence; dividing an alarm sequence, introducing an embedded vector, and embedding the embedded vector into the alarm sequence; inputting the alarm sequence added with the embedded vector into a first neural network to obtain an environment characteristic vector; performing first intrusion alarm probability calculation based on the environmental feature vector and the time feature vector; and performing second intrusion alarm probability calculation based on the environmental feature vector, the time feature vector and the first intrusion alarm probability, and performing intrusion early warning based on the calculation result.
Preferably, the optical signal transmitting unit includes: a narrow linewidth laser producing a continuous light source; an acousto-optic modulator modulating continuous light of the narrow linewidth laser into pulse light; and the optical fiber amplifier is used for amplifying the pulse light transmitted by the acousto-optic modulator.
Preferably, the processing module includes: a preprocessing unit for preprocessing the optical signal data; an environmental feature vector generation unit generating a plurality of environmental feature vectors using the preprocessed optical signal data; the optical fiber intrusion early warning unit is used for calculating the probability of the first intrusion alarm based on the environmental feature vector and the time feature vector; and performing second intrusion alarm probability calculation based on the environmental feature vector, the time feature vector and the first intrusion alarm probability, and performing intrusion early warning based on the calculation result.
Preferably, the optical fiber intrusion early warning unit includes: the time feature module stores time feature vectors; the environment feature module stores environment feature vectors; the space-time feature judging module is used for carrying out first intrusion alarm probability calculation based on the XGBoost model according to the input time feature vector and the environment feature vector; and the fusion module takes the time feature vector, the environment feature vector and the first intrusion alarm probability as input, adopts an MLP network model to calculate the second intrusion alarm probability, and obtains an intrusion early warning result.
The beneficial effects of the invention are as follows: by introducing an embedded vector into an alarm sequence signal, obtaining an environment vector similar to an actual environment after model training, fully excavating environment information around a defending area, effectively characterizing the environment vector, modeling the whole space distribution of an intrusion event by fusing an environment feature vector and a time feature vector, designing an integrated learning model based on environment embedding and time sequence features aiming at random noise and environment interference, greatly improving the generalization capability and practicability of the model, and improving the accuracy of distributed optical fiber early warning.
Drawings
Fig. 1 is a flow chart of a distributed optical fiber intrusion early warning method according to an embodiment of the invention.
FIG. 2 is a schematic diagram of a fiber optic data intercept segment according to an embodiment of the present invention.
Fig. 3 is a waveform diagram of a wavelet high frequency coefficient according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of signals before denoising according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of a denoised signal according to an embodiment of the present invention.
Fig. 6 is a schematic diagram of training loss variation of an MLP network model according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, further detailed description of the technical solutions in the embodiments of the present invention will be given by the following examples with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Examples: as shown in FIG. 1, the distributed optical fiber intrusion early warning method specifically comprises the following steps:
s1: acquiring reverse Rayleigh scattering light signal data in a detection optical fiber; the acquisition frequency of the data of the reverse Rayleigh scattered light signal is 2kHz.
S2: preprocessing the optical signal data to obtain a daily defense area alarm sequence; firstly, all signals are subjected to noise reduction, and because random noise such as system noise, environmental interference and the like exists in the distributed optical fiber sensing system, the waveform data is required to be subjected to noise reduction so as to improve the signal to noise ratio of the system.
The method for preprocessing the optical signal data comprises the following steps:
s21: performing discrete wavelet transformation on the initial optical signal data to obtain wavelet coefficients;
s22: modifying wavelet coefficients by threshold rules; the invention is realized by adopting a soft threshold value;
s23: wavelet reconstruction is performed using an inverse discrete wavelet transform based on the modified wavelet coefficients.
Specifically, as shown in fig. 2-5, 6000 continuous values are intercepted from real-time data of the optical fiber for explanation, the real-time data of the optical fiber contains a large amount of noise, and it can be seen that the waveform is not smooth and has a lot of clutters, and the wavelet transformation denoising is performed first.
The data is subjected to wavelet decomposition, five layers of wavelet decomposition are performed by using 8-order Symlet wavelet, and the decomposition high-frequency coefficient result is shown in figure 3. After five levels of wavelet are obtained, the high frequency coefficients need to be thresholded. The soft threshold is obtained by subtracting the threshold value when the wavelet threshold value is larger than the threshold value, and setting the wavelet to 0 when the wavelet threshold value is smaller than the threshold value, as shown in the following formula:
wherein,is wavelet coefficient +.>Is a threshold value. The signal obtained through the soft threshold function has better continuity and is not easy to generate large fluctuation. />Selecting a VisuShrink threshold, and calculating by the following formula
Wherein N is the number of wavelet layers, ，/>the value of (2) is set to an intermediate value of the absolute value of the first layer wavelet decomposition coefficient. Finally, the reconstruction is performed through inverse discrete wavelet transform to obtain a noise-reduced signal, as shown in fig. 5. By usingThe noise reduction effect was evaluated by signal-to-noise ratio (SNR) and calculated as follows:
for maximum amplitude of the effective vibration signal, +.>Is the maximum amplitude of the background noise. The calculated SNR was 7.27db.
The natural and artificial environment surrounding the defense area has a significant impact on the waveform distribution of the event. For example, the a defence area is adjacent to a highway and traffic flow is large. The vibration amplitude of the optical fiber is generally maintained at a high level of 2000. In contrast, the B guard zone is immediately adjacent to the farmland and the surrounding environment is relatively quiet. The vibration amplitude of the optical fiber is generally about 50. For a vibration segment with an a-defense area amplitude of about 2000, a dangerous intrusion event cannot be accurately identified. While for a vibration segment with an amplitude of about 2000 for the B defense area, an intrusion event is considered to have occurred. It can be seen that environmental characteristics are important factors in determining intrusion detection accuracy.
S3: dividing an alarm sequence, introducing an embedded vector, and embedding the embedded vector into the alarm sequence; embedding is a distributed representation method that converts raw input data into a linear combination of a series of features. The method solves the problem of oversized representation dimensions and provides very efficient representation capabilities. Embedding has a wide range of applications in natural language processing, for representing basic characters and words. Embedding techniques based on single thermal coding train entity embedding vectors with meaningful sequence prediction tasks. Compared with Shan Re coding, the embedded vector has the characteristics of flexible dimension setting and high feature abstraction. In a distributed fiber optic system, it is theoretically possible to set a class-based Shan Re code for each defense area. For example, categories such as expressways, highways, farmlands, factories, and the like may be set. The single thermal code is then determined from the surrounding of the particular defensive area. However, this method of extracting environmental features is inefficient and has limited accuracy for the following reasons:
(1) The surrounding environment of a plurality of defense areas is investigated, and a large amount of manpower and material resources are consumed;
(2) The optimal category setting cannot be determined;
(3) A prejudice to humans is introduced.
Therefore, the invention provides an environment embedding technology, and reasonable and efficient environment representation vectors are set for specific defense areas. The specific implementation steps are as follows:
an alarm sequence for a distributed fiber optic monitoring system having n defense areas may be expressed as a determination of an alarm based on the day of the defense area. Assuming that a t-day alarm sequence result is provided, a k window segmentation alarm sequence is set, and an embedded vector is determined according to an environment weight coefficient and the alarm sequence of the same day, a weight coefficient matrix of an environment embedded layer can be expressed as follows:
wherein the t-th time step embeds the vectorCan be expressed as +.>Embedding the whole alarm sequence to obtain an embedding matrix +.>The context embedding vector, in which each row corresponds to a time step, is expressed as:
s4: inputting the alarm sequence added with the embedded vector into a first neural network to obtain an environment characteristic vector; will add the embedded vectorRNN neural network training is carried out by utilizing the defense areas at two sides of the sequence to predict the middle defense area, and training a three-layer neural network to obtain the environmental feature vector of the defense area, wherein the environmental embedding vector and the environmental feature vector are respectively mentioned, the environmental embedding vector is the environmental feature vector before the neural network training, a three-layer RNN cyclic neural network model is introduced, and the hidden state of the model is->The update rule may be expressed as:
wherein,is a weight matrix from hidden state to hidden state, < +.>Is a weight matrix of environment embedded vectors to hidden states, < >>Is a sigmoid activation function.
The cyclic neural network (Recurrent Neural Network, RNN) is a type of cyclic neural network which takes sequence data as input, performs recursion (recovery) in the evolution direction of the sequence, and all nodes (cyclic units) are connected in a chained mode, wherein the output of the cyclic neural network can be one-to-one single output or one-to-one single output, and can also be output according to a sequence corresponding sequence.
Further, the output of the RNN may be used to measure the similarity of the alarm sequences, expressed as:
wherein,、/>output values of hidden layers, which are RNN, ">The similarity measurement value can be used for comparing the similarity between alarm sequences on different days to extract the similarity characteristics of the alarm sequences.
The aim of the whole process of training the environmental feature vector by utilizing the RNN network model is to enable the learned environmental feature vector to well express the similarity between alarm sequences by adjusting the parameters of an embedded layer and the model. In the training process, the loss function is minimized through a back propagation algorithm, model parameters are optimized, and the model parameters mainly comprise the input of a model, a weight matrix and the like.
The method comprises the steps of selecting an alarm sequence according to actual observation, wherein a defending area which is frequently alarmed at about 8 a.m. is positioned near a highway or a highway and other areas with large traffic flow. The environment feature vector can span the ground surface positioning features such as roadsides, high-speed edges and the like, so that the high-level abstract features of large traffic flow are deeply excavated, and the high-level abstract features have stronger characterization and generalization capabilities.
S5: performing first intrusion alarm probability calculation based on the environmental feature vector and the time feature vector; the time feature vector acquisition method comprises the following steps: and (3) calculating and combining characteristic values of the windowed waveforms to obtain a time characteristic vector, and extracting high-level time characteristics by adopting a time characteristic module, wherein the extracted high-level time characteristics comprise peak values, minimum values, energy, average values, variances, peak-to-peak values, root mean square, standard deviations, peak factors, skewness factors, waveform factors, pulse factors and margin factors.
And storing a plurality of time feature vectors in the time feature module, selecting the time feature vectors through the environment feature vectors, and similarly, summarizing and storing the environment feature vectors in the environment feature module, selecting the environment feature vectors through the time feature vectors, and obtaining a probability value through an n-layer XGBoost model by combining the time sequence feature vectors and the environment embedded vectors as input by the space-time feature judging module. The introduction of the module can significantly improve the final recognition accuracy. In order to avoid random noise interference, the invention takes n=10, and the overall output can be expressed as:
wherein,indicating the output of the i-th layer. The objective function is expressed as:
where n is the number of samples,alarm prediction result representing sample i at time t, < >>Is the alarm prediction value given by the model of step t-1, < >>Is the predicted value of the new model that needs to be added in step t,/->Representing regularization terms. The first intrusion alert probability is derived based on the output of this module.
S6: and performing second intrusion alarm probability calculation based on the environmental feature vector, the time feature vector and the first intrusion alarm probability, and performing intrusion early warning based on the calculation result. If the value of the second intrusion alarm probability exceeds the early warning threshold, performing intrusion early warning prompt, otherwise, returning to the step S5 to reselect the environment feature vector and the time feature vector to perform new intrusion alarm probability calculation until all intrusion alarm probabilities of all environment feature vectors and all time feature vectors are calculated.
The fusion module receives the outputs of the first three modules as input variablesFeature fusion is performed through a multi-layer neural network. The MLP network model can be expressed as:
wherein H represents a hidden layer, O represents an output layer,representing the input variable +.>；/>，/>，/>，Respectively representing network layer parameters,/-, and>representing a classifier; />Representing the activation function, since the alarm is judged to be a two-class problem, using the sigmoid function as the activation function, the result can be mapped to the (0, 1) interval, expressed as:
finally, model training is carried out through the optical fiber alarm data set, and the model training process is shown in fig. 6.
The invention also provides a distributed optical fiber intrusion early warning system based on environment embedding, which comprises an optical signal transmitting unit, a circulator, a data acquisition module and a processing module, wherein the optical signal transmitting unit is connected with the circulator, the circulator is connected with an optical fiber, the data acquisition module is connected with the circulator, the processing module is connected with the data acquisition module, and the optical signal transmitting unit modulates a light source and then transmits pulse light to the circulator; the circulator transmits the pulse light to the detection optical fiber and obtains reverse Rayleigh scattering light signal data of the detection optical fiber; the data acquisition module acquires reverse Rayleigh scattering light signal data of the circulator and transmits the data to the processing module; the processing module is used for preprocessing the optical signal data to obtain a daily defense area alarm sequence; the alarm sequence is segmented, an embedded vector is introduced, the embedded vector is embedded into the alarm sequence, the alarm sequence added with the embedded vector is input into a first neural network to obtain an environment feature vector, first intrusion alarm probability calculation is conducted based on the environment feature vector and the time feature vector, second intrusion alarm probability calculation is conducted based on the environment feature vector, the time feature vector and the first intrusion alarm probability, and intrusion early warning is conducted based on a calculation result.
The optical signal transmitting unit includes: the device comprises a narrow linewidth laser, an acousto-optic modulator and an optical fiber amplifier, wherein the narrow linewidth laser is connected with the acousto-optic modulator, and the acousto-optic modulator is connected with the optical fiber amplifier, and the narrow linewidth laser generates a continuous light source; the acousto-optic modulator modulates continuous light of the narrow linewidth laser into pulse light; the optical fiber amplifier amplifies the pulse light transmitted by the acousto-optic modulator.
The distributed optical fiber sensing technology adopted by the invention is based on a phase sensitive optical time domain reflectometer (ϕ -OTDR) technology. The method is to detect the intensity of the reverse Rayleigh scattered light in the optical fiber and classify the vibration sources according to the difference of interference waveforms. The sensing optical fibers are distributed along the oil-gas pipe network in a large area, and each sensing unit is arranged as a defense area. Other components are integrated into the chassis. The laser source with narrow linewidth is switched into pulse light by the modulator and then sent into the sensing optical fiber through the circulator. The Rayleigh back scattering light output from the other end of the circulator is received by the photoelectric detector and is transmitted to the processing module through the data acquisition card.
The distributed optical fiber vibration sensing system has the advantages of high precision, high reliability, high efficiency and the like. The vibration source can effectively detect the pipeline vibration signal and position the vibration source. When the sensing optical fiber is disturbed, the optical phase of the disturbed position changes due to the elastic optical effect. Therefore, the phase of the back-scattered light at the corresponding position changes, and the internal pulse width of the interference light intensity of the scattered light also changes accordingly. The main parameters of the system are that the central wavelength of the narrow linewidth laser is 1550nm, the linewidth is 3kHz, the total length of the distributed optical fiber is 48km, the signal loss is 0.27dB/km, and the data acquisition frequency is 2kHz.
The processing module comprises: the device comprises a preprocessing unit, an environment feature vector generating unit and an optical fiber intrusion early warning unit; the preprocessing unit is used for preprocessing the optical signal data; the environment feature vector generating unit generates a plurality of environment feature vectors by using the preprocessed optical signal data; the optical fiber intrusion early warning unit calculates the probability of the first intrusion alarm based on the environmental feature vector and the time feature vector; and performing second intrusion alarm probability calculation based on the environmental feature vector, the time feature vector and the first intrusion alarm probability, and performing intrusion early warning based on the calculation result.
The optical fiber intrusion early warning unit comprises: the system comprises a time feature module, an environment feature module, a space-time feature judging module and a fusion module, wherein the time feature module and the environment feature module are connected with the space-time feature judging module, and the time feature module, the environment feature module and the space-time feature judging module are connected with the fusion module; the time feature module stores a time feature vector; the environment feature module stores environment feature vectors; the space-time feature judging module carries out first intrusion alarm probability calculation based on the XGBoost model according to the input time feature vector and the environment feature vector; the fusion module takes the time feature vector, the environment feature vector and the first intrusion alarm probability as input, adopts an MLP network model to calculate the second intrusion alarm probability, and obtains an intrusion early warning result.
According to the invention, the embedded vector is introduced into the alarm sequence signal, the environment vector similar to the realization environment is obtained after model training, the environment information around the defending area is fully mined and effectively characterized, the full space distribution of the intrusion event is modeled by fusing the environment feature vector and the time feature vector, and the integrated learning model based on the environment embedding and the time sequence feature is designed aiming at random noise and environment interference, so that the generalization capability and the practicability of the model are greatly improved, and the accuracy of the distributed optical fiber early warning is improved.
The above-described embodiment is only a preferred embodiment of the present invention, and is not limited in any way, and other variations and modifications may be made without departing from the technical aspects set forth in the claims.
Claims (7)
1. The distributed optical fiber intrusion early warning method based on environment embedding is characterized by comprising the following steps of:
acquiring reverse Rayleigh scattering light signal data in a detection optical fiber;
preprocessing the optical signal data to obtain a daily defense area alarm sequence;
dividing an alarm sequence, introducing an embedded vector, determining the embedded vector according to an environment weight coefficient and an alarm sequence of the current day, and embedding the embedded vector into the alarm sequence;
inputting the alarm sequence added with the embedded vector into a first neural network to obtain an environment characteristic vector;
performing first intrusion alarm probability calculation based on the environmental feature vector and the time feature vector;
and performing second intrusion alarm probability calculation based on the environmental feature vector, the time feature vector and the first intrusion alarm probability, and performing intrusion early warning based on the calculation result.
2. The method for environmental embedding-based distributed fiber intrusion alert of claim 1, wherein,
the method for preprocessing the optical signal data comprises the following steps:
performing discrete wavelet transformation on the initial optical signal data to obtain wavelet coefficients;
modifying wavelet coefficients by threshold rules;
wavelet reconstruction is performed using an inverse discrete wavelet transform based on the modified wavelet coefficients.
3. The method for environmental embedding-based distributed fiber intrusion alert of claim 1, wherein,
the alarm sequence is divided by a window K.
4. The utility model provides a distributed optical fiber intrusion early warning system based on environment embedding which characterized in that includes:
the optical signal transmitting unit modulates the light source and transmits pulse light to the circulator;
the circulator is used for transmitting the pulse light to the detection optical fiber and obtaining the reverse Rayleigh scattering light signal data of the detection optical fiber;
the data acquisition module acquires reverse Rayleigh scattering light signal data of the circulator and transmits the data to the processing module;
the processing module is used for preprocessing the optical signal data to obtain a daily defense area alarm sequence; dividing an alarm sequence, introducing an embedded vector, determining the embedded vector according to an environment weight coefficient and an alarm sequence of the current day, and embedding the embedded vector into the alarm sequence; inputting the alarm sequence added with the embedded vector into a first neural network to obtain an environment characteristic vector; performing first intrusion alarm probability calculation based on the environmental feature vector and the time feature vector; and performing second intrusion alarm probability calculation based on the environmental feature vector, the time feature vector and the first intrusion alarm probability, and performing intrusion early warning based on the calculation result.
5. The distributed optical fiber intrusion alert system based on environmental embedding of claim 4, wherein,
the optical signal transmitting unit includes:
a narrow linewidth laser producing a continuous light source;
an acousto-optic modulator modulating continuous light of the narrow linewidth laser into pulse light;
and the optical fiber amplifier is used for amplifying the pulse light transmitted by the acousto-optic modulator.
6. The distributed optical fiber intrusion alert system based on environmental embedding of claim 4 or 5, wherein,
the processing module comprises:
a preprocessing unit for preprocessing the optical signal data;
an environmental feature vector generation unit generating a plurality of environmental feature vectors using the preprocessed optical signal data;
the optical fiber intrusion early warning unit is used for calculating the probability of the first intrusion alarm based on the environmental feature vector and the time feature vector; and performing second intrusion alarm probability calculation based on the environmental feature vector, the time feature vector and the first intrusion alarm probability, and performing intrusion early warning based on the calculation result.
7. The distributed fiber intrusion alert system based on environmental embedding of claim 6, wherein,
the optical fiber intrusion early warning unit comprises:
the time feature module stores time feature vectors;
the environment feature module stores environment feature vectors;
the space-time feature judging module is used for carrying out first intrusion alarm probability calculation based on the XGBoost model according to the input time feature vector and the environment feature vector;
and the fusion module takes the time feature vector, the environment feature vector and the first intrusion alarm probability as input, adopts an MLP network model to calculate the second intrusion alarm probability, and obtains an intrusion early warning result.
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