CN118091331A - Cable fault sensing method and system - Google Patents
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Abstract
The invention discloses a cable fault sensing method and a system, wherein the method comprises the following steps: collecting fault monitoring data information of a cable circuit; transmitting the cable line fault monitoring data information by utilizing an NB-IoT technology, including modulating and demodulating the cable line fault monitoring data information by utilizing a QPSK technology; constructing a cable line fault detection model based on deep learning; extracting a connection weight and a threshold value from a cable line fault detection model based on deep learning, constructing a cable line fault detection deep learning model library, updating the cable line fault detection deep learning model library into a cable line fault monitoring system, and transmitting collected field data into the cable line fault monitoring system based on NB-IoT for online monitoring of cable line faults. The invention has the characteristics of wide monitoring range, low communication energy consumption, strong perceptibility and the like, and can comprehensively improve the perception level of cable line faults.
Description
Technical Field
The invention relates to the technical field of electrical equipment, in particular to a cable fault sensing method and system.
Background
Cable line fault monitoring is of great significance to the operation of the power system. Firstly, for safety, potential cable line problems can be found in time through monitoring, potential safety hazards such as fire and electric shock are avoided, and therefore overall safety of a power system is improved. Secondly, fault monitoring helps to improve reliability of the power system, and reduces power failure time of the cable line by timely finding and repairing faults. In addition, monitoring can also improve maintenance efficiency, reduce associated costs, and reduce emergency maintenance requirements by predictively maintaining problems that are discovered and repaired before the failure is exacerbated. The service lives of the cable lines and related equipment can be prolonged by timely processing faults, and the equipment aging process is slowed down. Finally, the fault monitoring can prevent the problems of voltage fluctuation, harmonic waves and the like caused by cable line faults, and ensure the stable operation of the power system.
At present, many researches on a cable fault monitoring method are carried out, but the following disadvantages still exist: first, the limited real-time performance and sensitivity, the conventional cable fault monitoring system has a certain limitation in terms of real-time performance and fault sensitivity, and may not be capable of detecting a weak fault signal immediately. The complexity of the sensor data, the environment of the cabling, is then complex, and the sensor data may be affected by a variety of factors, including temperature, humidity, electromagnetic interference, etc., thereby affecting the accuracy of the monitoring. Second, the high cost and complex maintenance, which is typically required for large scale equipment deployment, brings about high equipment costs and complex maintenance requirements, especially in large power systems, where the costs and complexity are more pronounced. Finally, it is difficult to distinguish between normal operation and potential faults, some changes in the normal operation may be misinterpreted as faults, resulting in false alarms, or the actual potential fault signals may be masked.
Disclosure of Invention
In order to solve the above problems, the present invention provides a cable fault sensing method and system capable of improving the capability of monitoring cable faults.
In order to achieve the above object, the present invention is realized by the following technical scheme:
the invention relates to a cable fault sensing method, which comprises the following operations:
Collecting fault monitoring data information of a cable line, wherein the fault monitoring data information comprises current data, voltage data, temperature data, humidity data, resistance data, vibration data, electric field data and magnetic field data of the cable;
Transmitting the cable line fault monitoring data information by utilizing an NB-IoT technology, including modulating and demodulating the cable line fault monitoring data information by utilizing a QPSK technology;
Constructing a cable line fault detection model based on deep learning;
extracting a connection weight and a threshold value from a cable line fault detection model based on deep learning, constructing a cable line fault detection deep learning model library, updating the cable line fault detection deep learning model library into a cable line fault monitoring system, and transmitting collected field data into the cable line fault monitoring system based on NB-IoT for online monitoring of cable line faults.
The invention further improves that: the collected cable line fault monitoring data information expression is as follows:
（1）；
In the method, in the process of the invention, For the cable line fault monitoring data at the moment t,/>For the cabling current data at time t,For the voltage data of the cable line at the moment t,/>For the temperature data of the cable line at the moment t,/>For the humidity data of the cable circuit at the moment t,/>For the resistance data of the cable line at the moment t,/>For the vibration data of the cable line at the moment t,/>For the electric field data of the cable line at the moment t,/>And the magnetic field data of the cable line at the time t.
The invention further improves that: the transmission of the cable line fault monitoring data information by using the NB-IoT technology comprises the modulation and demodulation of the cable line fault monitoring data information by using the QPSK technology, and specifically comprises the following operations: modulating the collected cable line fault monitoring data information, wherein the NB-IoT transmits the modulated cable line fault monitoring data information, a receiving end of the NB-IoT demodulates the received cable line fault monitoring data information, the following formulas (2) - (6) are cable line electric field data NB-IoT transmission processing procedures, and the formula (7) is an expression of the cable line fault monitoring data information obtained through NB-IoT transmission:
（2）；
（3）；
（4）；
（5）；
（6）；
（7）；
Wherein: for the modulated electric field data of the cable line at the t moment,/> For the electric field data of the cable line at the moment t,/>For the electric field signal frequency of the cable line at the moment t,/>For the electric field phase angle of the cable line at the moment t,/>For the electric field data of the cable line at the t moment received by the receiving end,/>For the noise experienced in NB-IoT transmission signals,/>、The sine component and the cosine component after the low-pass filtering processing are respectively, the lowpass { } function represents low-pass filtering and is used for extracting baseband signal components in the function, the arg () function represents finding the largest sine component and cosine component, and the/>For cable line fault monitoring data after NB-IoT transmission,/>For the cable line current data transmitted over NB-IoT,For cabling voltage data after NB-IoT transmission,/>For cabling temperature data after NB-IoT transmission,/>For cabling humidity data after NB-IoT transmission,/>For cabling resistance data after NB-IoT transmission,/>For cabling vibration data after NB-IoT transmission,/>For cable line electric field data transmitted through NB-IoT,/>Is the cabling magnetic field data after being transmitted through the NB-IoT.
The invention further improves that: the construction of the cable line fault detection model based on deep learning specifically comprises the following operations:
Marking the received cable line fault monitoring data to form a training sample of the deep learning model; training a sample by using a deep learning model, wherein the training comprises forward propagation and reverse propagation training processes, the prediction error of the deep learning model is minimized by adjusting the weight and the bias of a neural network, the performance of the deep learning model is estimated by using an independent test data set, and the final training forms a cable line fault detection model based on deep learning;
the cable line fault detection model based on deep learning comprises 8 inputs and 2 outputs, wherein the 8 inputs are respectively: cable line current data transmitted over NB-IoT Is a sample of the cabling voltage data/>Is a sample of the cabling temperature data/>Is a sample of the cabling humidity data/>Is a sample of the cabling resistance data/>Is a sample of the cable run vibration data/>Is a sample of the cable run electric field data/>Is a sample of the cabling magnetic field data/>The 2 outputs are respectively: /(I)To have a fault,/>To be fault-free;
The hidden layer of the deep learning model has n neurons, and each neuron in the hidden layer The outputs of (2) are calculated by weighting and through an activation function, and the expressions are shown in (8) - (9):
（8）；
（9）；
Wherein: the neuron of the hidden layer of the deep learning model is j is the sequence number of the neuron, and the value range is 1-n,/> Activation function for deep learning model,/>Is a connection input/>And neurons/>Weights of/>Is neuron/>H is the output vector of the hidden layer of the deep learning model,/>The input of the deep learning model is that i is the layer number of the deep learning model;
The output of the deep learning model is:
（10）；
Wherein: for the output of deep learning model,/> To connect neurons/>And output/>Weights of/>For output/>Is included.
The invention further improves that: the expression of the cable line fault detection deep learning model library is as follows:
（11）；
Wherein: Deep learning model for kth cable line fault detection,/> For a trained deep learning model,/>And connecting weights from the input layer to the output layer of the trained deep learning model.
The invention discloses a cable fault sensing system, which comprises an acquisition module, an NB-IoT transmission module, a model construction module, a model library construction and update module and an alarm module, wherein the acquisition module is used for acquiring a cable of a cable;
The acquisition module is used for acquiring fault monitoring data information of the cable line, including current data, voltage data, temperature data, humidity data, resistance data, vibration data, electric field data and magnetic field data of the cable;
The NB-IoT transmission module is configured to transmit cable line fault monitoring data information, and includes performing modulation and demodulation of the cable line fault monitoring data information by using a QPSK technology;
The model construction module is used for constructing a cable line fault detection model based on deep learning;
The model library constructing and updating module is used for extracting a connection weight and a threshold value from a cable line fault detection model based on deep learning, constructing a cable line fault detection deep learning model library, and updating the cable line fault detection deep learning model library into a cable line fault monitoring system;
and the alarm module is used for giving an alarm when the cable line fault monitoring system detects a fault.
The invention further improves that: the collected cable line fault monitoring data information expression is as follows:
（12）；
In the method, in the process of the invention, For the cable line fault monitoring data at the moment t,/>For the cabling current data at time t,For the voltage data of the cable line at the moment t,/>For the temperature data of the cable line at the moment t,/>For the humidity data of the cable circuit at the moment t,/>For the resistance data of the cable line at the moment t,/>For the vibration data of the cable line at the moment t,/>For the electric field data of the cable line at the moment t,/>And the magnetic field data of the cable line at the time t.
The invention further improves that: the NB-IoT transmission module includes: modulating the collected cable line fault monitoring data information, wherein the NB-IoT transmits the modulated cable line fault monitoring data information, a receiving end of the NB-IoT demodulates the received cable line fault monitoring data information, the following formulas (13) - (17) are cable line electric field data NB-IoT transmission processing processes, and the formula (18) is an expression of the cable line fault monitoring data information obtained through NB-IoT transmission;
（13）；
（14）；
（15）；
（16）；
（17）；
（18）；
Wherein: for the modulated electric field data of the cable line at the t moment,/> For the electric field data of the cable line at the moment t,/>For the electric field signal frequency of the cable line at the moment t,/>For the electric field phase angle of the cable line at the moment t,/>For the electric field data of the cable line at the t moment received by the receiving end,/>For the noise experienced in NB-IoT transmission signals,/>、The sine component and the cosine component after the low-pass filtering processing are respectively, the lowpass { } function represents low-pass filtering and is used for extracting baseband signal components in the function, the arg () function represents finding the largest sine component and cosine component, and the/>For cable line fault monitoring data after NB-IoT transmission,/>For the cable line current data transmitted over NB-IoT,For cabling voltage data after NB-IoT transmission,/>For cabling temperature data after NB-IoT transmission,/>For cabling humidity data after NB-IoT transmission,/>For cabling resistance data after NB-IoT transmission,/>For cabling vibration data after NB-IoT transmission,/>For cable line electric field data transmitted through NB-IoT,/>Is the cabling magnetic field data after being transmitted through the NB-IoT.
The invention further improves that: the construction of the cable line fault detection model based on deep learning specifically comprises the following operations:
Marking the received cable line fault monitoring data to form a training sample of the deep learning model; training a sample by using a deep learning model, wherein the training comprises forward propagation and reverse propagation training processes, the prediction error of the deep learning model is minimized by adjusting the weight and the bias of a neural network, the performance of the deep learning model is estimated by using an independent test data set, and the final training forms a cable line fault detection model based on deep learning;
the cable line fault detection model based on deep learning comprises 8 inputs and 2 outputs, wherein the 8 inputs are respectively: cable line current data transmitted over NB-IoT Is a sample of the cabling voltage data/>Is a sample of the cabling temperature data/>Is a sample of the cabling humidity data/>Is a sample of the cabling resistance data/>Is a sample of the cable run vibration data/>Is a sample of the cable run electric field data/>Is a sample of the cabling magnetic field dataThe 2 outputs are respectively: /(I)To have a fault,/>To be fault-free;
The hidden layer of the deep learning model has n neurons, and each neuron in the hidden layer The outputs of (2) are weighted and calculated by an activation function, and the expressions are shown in (8) - (9):
（19）；
（20）；
Wherein: the neuron of the hidden layer of the deep learning model is j is the sequence number of the neuron, and the value range is 1-n,/> Activation function for deep learning model,/>Is a connection input/>And neurons/>Weights of/>Is neuron/>H is the output vector of the hidden layer of the deep learning model,/>The input of the deep learning model is that i is the layer number of the deep learning model;
The output of the deep learning model is:
（21）；
Wherein: for the output of deep learning model,/> To connect neurons/>And output/>Weights of/>For output/>Is included.
The invention further improves that: the expression of the cable line fault detection deep learning model library is as follows:
（22）；
Wherein: Deep learning model for kth cable line fault detection,/> For a trained deep learning model,/>And connecting weights from the input layer to the output layer of the trained deep learning model.
The beneficial effects of the invention are as follows: 1) With NB-IoT technology, wider coverage connectivity can be provided while reducing the power consumption of the device, enabling the monitoring system to be deployed over a larger range and run for a longer period of time.
2) The invention supports remote monitoring and management, so that a user can monitor the state of the cable line at any time and any place and timely acquire fault information.
3) Real-time decision making and predictive maintenance are combined with NB-IoT and deep learning, the system can achieve real-time decision making and predictive maintenance, discover and respond to potential problems in time, reduce outage time, and improve reliability of the power system.
Drawings
FIG. 1 is a flow chart of a method in an embodiment of the invention;
Fig. 2 is a schematic diagram of a cable line fault detection model based on deep learning in an embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. 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. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
As shown in fig. 1, a cable fault sensing method of the present embodiment includes the following operations:
And step 1, providing a cable line fault monitoring data acquisition technology. The collected cable line fault monitoring number information comprises current data, voltage data, temperature data, humidity data, resistance data, vibration data, electric field data and magnetic field data of the cable. Current data, abnormal current waveforms in the cable lines, may suggest potential faults, such as short circuits or overloads. Voltage data, which may evaluate the electrical performance of the cabling, may indicate that the cable is problematic due to unstable voltages or voltage fluctuations. Temperature data, abnormal temperature rise may indicate cable overload or other problems. Humidity data, in a humid environment, humidity monitoring is important to prevent insulation problems and corrosion. Resistance data, resistance changes in the cable, including potential short circuits or ground problems, can be detected. Vibration data, mainly detecting abnormal vibrations on the cable lines, may be caused by mechanical problems or external disturbances. Electric and magnetic field data, electric and magnetic field sensors can be used to monitor electric and magnetic field changes in the vicinity of the cable to help identify cable faults.
Step 2, propose the cable line information transmission technology based on NB-IoT. The cable distribution range is wide due to the low probability of cable lines failing. Therefore, the present embodiment adopts NB-IoT wide area low power technology to transmit the cable line fault monitoring data information, and uses QPSK (Quadrature PHASE SHIFT KEYING) technology to modulate and demodulate the cable line fault monitoring data information: QPSK modulation modulates signals mainly on two orthogonal carriers, converts digital data into analog signals, and in modulation, each symbol represents two bits, and has higher data transmission rate; demodulation of QPSK involves a phase demodulator that compares the received signal with a known phase to recover the original binary data. The embodiment utilizes the NB-IoT technology to periodically send the cable line fault monitoring data, reduces the communication energy consumption and the cost, and realizes the effective transmission of the data of the cable line in a large range.
And step 3, providing a cable line fault detection model based on deep learning. Marking the received cable line fault monitoring data according to the presence or absence of faults to form a training sample of a deep learning model; training samples by using a deep learning model, including forward propagation and reverse propagation training processes, minimizing prediction errors of the deep learning model by adjusting weights and biases of a neural network, evaluating performance of the deep learning model by using an independent test data set, ensuring that the deep learning model can accurately classify normal conditions and various fault conditions, and finally training to form a cable line fault detection model based on deep learning. The data under the environments such as temperature and humidity, electromagnetic fields and the like are not used, a cable line fault detection deep learning model library under different environments can be obtained, and effective detection of cable line faults under different environments is realized.
And 4, providing a cable line fault monitoring model. And in the online monitoring stage of the cable line fault, updating the cable line fault detection deep learning model library into a cable line fault monitoring system. And the collected field data is transmitted to a monitoring system based on NB-IoT, the monitoring system inputs the data into a fault detection deep learning model library for analysis, and once a fault is found, an alarm is sent out to realize the online monitoring of the fault of each cable line.
In step1, the information expression of the collected cable line fault monitoring data is as follows:
（1）；
In the method, in the process of the invention, For the cable line fault monitoring data at the moment t,/>For the cabling current data at time t,For the voltage data of the cable line at the moment t,/>For the temperature data of the cable line at the moment t,/>For the humidity data of the cable circuit at the moment t,/>For the resistance data of the cable line at the moment t,/>For the vibration data of the cable line at the moment t,/>For the electric field data of the cable line at the moment t,/>And the magnetic field data of the cable line at the time t.
In step 2, the proposed NB-IoT-based cable line information transmission technology is shown in formulas (2) - (6), and mainly includes the processes of QPSK modulation and demodulation of cable line data. Taking cable line electric field data as an example, taking (2) as a modulation process function of the cable line electric field data, and collecting the cable line electric field dataModulating, wherein the modulated electric field data is that. NB-IoT transmission of modulated electric field data/>The noise suffered in the transmission process is/>Is/>As shown in formula (3). The NB-IoT receiver demodulates the received electric field information as shown in equations (4) - (6). First, the received signal/>The method comprises the steps of dividing the filter into a sine component and a cosine component, and carrying out low-pass filtering treatment, wherein the treated sine component and cosine component are respectively/>、/>. Finally, the demodulated signal is mapped back to the original binary data according to equation (6). According to the principle, other information in the formula (1) is processed, and finally information transmitted through NB-IoT is shown in the formula (7).
（2）；
（3）；
（4）；
（5）；
（6）；
（7）；
Wherein: for the modulated electric field data of the cable line at the t moment,/> For the electric field data of the cable line at the moment t,/>For the electric field signal frequency of the cable line at the moment t,/>For the electric field phase angle of the cable line at the moment t,/>For the electric field data of the cable line at the t moment received by the receiving end,/>For the noise experienced in NB-IoT transmission signals,/>、The lowpass { } function represents low-pass filtering for extracting baseband signal components in the function, and the arg () function represents finding the largest sine and cosine components,/>, respectivelyFor cable line fault monitoring data after NB-IoT transmission,/>For cabling current data after transmission over NB-IoT,/>For cabling voltage data after NB-IoT transmission,/>For cabling temperature data after NB-IoT transmission,/>For cabling humidity data after NB-IoT transmission,/>For cabling resistance data after NB-IoT transmission,/>For cabling vibration data after NB-IoT transmission,/>For cable line electric field data transmitted through NB-IoT,/>Is the cabling magnetic field data after being transmitted through the NB-IoT.
In step 3, a cable line fault detection model based on deep learning is shown in fig. 2, and cable line fault characteristics under different environments are extracted through deep learning of fault samples of the cable line, so that accurate identification of cable faults under different environments is realized.
The cable line fault detection model based on deep learning comprises 8 inputs and 2 outputs, wherein the 8 inputs are respectively: cable line current data transmitted over NB-IoTIs a sample of the cabling voltage data/>Is a sample of the cabling temperature data/>Is a sample of the cabling humidity data/>Is a sample of the cabling resistance data/>Is a sample of the cable run vibration data/>Is a sample of the cable run electric field data/>Is a sample of the cabling magnetic field data/>The 2 outputs are respectively: /(I)To have a fault,/>To be fault-free;
The hidden layer of the deep learning model has n neurons, and each neuron in the hidden layer The outputs of (2) are calculated by weighting and through an activation function, and the expressions are shown in (8) - (9):
（8）；
（9）；
Wherein: the neuron of the hidden layer of the deep learning model is j is the sequence number of the neuron, and the value range is 1-n,/> Activation function for deep learning model,/>Is a connection input/>And neurons/>Weights of/>Is neuron/>H is the output vector of the hidden layer of the deep learning model,/>The input of the deep learning model is that i is the layer number of the deep learning model;
The output of the deep learning model is:
（10）；
Wherein: for the output of deep learning model,/> To connect neurons/>And output/>Weights of/>For output/>Is included.
In step 4, extracting a connection weight and a threshold value from a cable line fault detection model based on deep learning, and constructing a cable line fault detection deep learning model library, wherein the expression of the cable line fault detection deep learning model library is as follows:
（11）；
Wherein: Deep learning model for kth cable line fault detection,/> For a trained deep learning model,/>And connecting weights from the input layer to the output layer of the trained deep learning model.
And updating the cable line fault detection deep learning model library to periodically update the cable line fault detection deep learning model library to a cable line fault monitoring system, analyzing cable line data based on NB-IoT online transmission, and realizing effective monitoring of cable line faults in different environments.
The cable line fault monitoring model based on NB-IoT and deep learning constructed by the invention has low communication power consumption and wide monitoring range, and can be suitable for effectively monitoring cable line faults in an unused environment.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus, systems, computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
While the foregoing is directed to embodiments of the present invention, other and further details of the invention may be had by the present invention, it should be understood that the foregoing description is merely illustrative of the present invention and that no limitations are intended to the scope of the invention, except insofar as modifications, equivalents, improvements or modifications are within the spirit and principles of the invention.
Claims (10)
1. A cable fault sensing method is characterized in that: comprising the following operations:
Collecting fault monitoring data information of a cable line, wherein the fault monitoring data information comprises current data, voltage data, temperature data, humidity data, resistance data, vibration data, electric field data and magnetic field data of the cable;
Transmitting the cable line fault monitoring data information by utilizing an NB-IoT technology, including modulating and demodulating the cable line fault monitoring data information by utilizing a QPSK technology;
Constructing a cable line fault detection model based on deep learning;
extracting a connection weight and a threshold value from a cable line fault detection model based on deep learning, constructing a cable line fault detection deep learning model library, updating the cable line fault detection deep learning model library into a cable line fault monitoring system, and transmitting collected field data into the cable line fault monitoring system based on NB-IoT for online monitoring of cable line faults.
2. A method of cable fault sensing according to claim 1, wherein: the collected cable line fault monitoring data information expression is as follows:
（1）；
In the method, in the process of the invention, For the cable line fault monitoring data at the moment t,/>For the current data of the cable line at the moment t,/>For the voltage data of the cable line at the moment t,/>For the temperature data of the cable line at the moment t,/>For the humidity data of the cable circuit at the moment t,/>For the resistance data of the cable line at the moment t,/>For the vibration data of the cable line at the moment t,/>For the electric field data of the cable line at the moment t,/>And the magnetic field data of the cable line at the time t.
3. A method of cable fault sensing according to claim 1, wherein: the transmission of the cable line fault monitoring data information by using the NB-IoT technology comprises the modulation and demodulation of the cable line fault monitoring data information by using the QPSK technology, and specifically comprises the following operations: modulating the collected cable line fault monitoring data information, wherein the NB-IoT transmits the modulated cable line fault monitoring data information, a receiving end of the NB-IoT demodulates the received cable line fault monitoring data information, the following formulas (2) - (6) are cable line electric field data NB-IoT transmission processing procedures, and the formula (7) is cable line fault monitoring data information obtained through NB-IoT transmission:
（2）；
（3）；
（4）；
（5）；
（6）；
（7）；
Wherein: for the modulated electric field data of the cable line at the t moment,/> For the cable line electric field data at time t,For the electric field signal frequency of the cable line at the moment t,/>For the electric field phase angle of the cable line at the moment t,/>For the electric field data of the cable line at the t moment received by the receiving end,/>For the noise experienced in NB-IoT transmission signals,/>、/>The sine component and the cosine component after the low-pass filtering processing are respectively, the lowpass { } function represents low-pass filtering and is used for extracting baseband signal components in the function, the arg () function represents finding the largest sine component and cosine component, and the/>For cable line fault monitoring data after NB-IoT transmission,/>For cabling current data after transmission over NB-IoT,/>For cabling voltage data after NB-IoT transmission,/>For the cabling temperature data after transmission over NB-IoT,For cabling humidity data after NB-IoT transmission,/>For cabling resistance data after NB-IoT transmission,/>For cabling vibration data after NB-IoT transmission,/>For cable line electric field data transmitted through NB-IoT,/>Is the cabling magnetic field data after being transmitted through the NB-IoT.
4. A method of cable fault sensing according to claim 1, wherein: the construction of the cable line fault detection model based on deep learning specifically comprises the following operations:
Marking the received cable line fault monitoring data to form a training sample of the deep learning model; training a sample by using a deep learning model, wherein the training comprises forward propagation and reverse propagation training processes, the prediction error of the deep learning model is minimized by adjusting the weight and the bias of a neural network, the performance of the deep learning model is estimated by using an independent test data set, and the final training forms a cable line fault detection model based on deep learning;
the cable line fault detection model based on deep learning comprises 8 inputs and 2 outputs, wherein the 8 inputs are respectively: cable line current data transmitted over NB-IoT Is a sample of the cabling voltage data/>Is a sample of the cabling temperature data/>Is a sample of the cabling humidity data/>Is a sample of the cabling resistance data/>Is a sample of the cable run vibration data/>Is a sample of the cable run electric field data/>Is a sample of the cabling magnetic field data/>The 2 outputs are respectively: /(I)To have a fault,/>To be fault-free;
The hidden layer of the deep learning model is provided with n neurons, and the neurons in the hidden layer The outputs of (2) are weighted and calculated by an activation function, and the expressions are shown in (8) - (9):
（8）；
（9）；
Wherein: the neuron of the hidden layer of the deep learning model is j is the sequence number of the neuron, and the value range is 1-n,/> Activation function for deep learning model,/>Is a connection input/>And neurons/>Weights of/>Is neuron/>H is the output vector of the hidden layer of the deep learning model,/>The input of the deep learning model is that i is the layer number of the deep learning model;
The output of the deep learning model is:
（10）；
Wherein: for the output of deep learning model,/> To connect neurons/>And output/>Weights of/>For output/>Is included.
5. A method of cable fault sensing as defined in claim 4, wherein: the expression of the cable line fault detection deep learning model library is as follows:
（11）；
Wherein: Deep learning model for kth cable line fault detection,/> In order to train the deep learning model well,And connecting weights from the input layer to the output layer of the trained deep learning model.
6. The utility model provides a cable fault perception system which characterized in that: the system comprises an acquisition module, an NB-IoT transmission module, a model construction module, a model library construction and update module and an alarm module;
The acquisition module is used for acquiring fault monitoring data information of the cable line, including current data, voltage data, temperature data, humidity data, resistance data, vibration data, electric field data and magnetic field data of the cable;
The NB-IoT transmission module is configured to transmit cable line fault monitoring data information, and includes performing modulation and demodulation of the cable line fault monitoring data information by using a QPSK technology;
The model construction module is used for constructing a cable line fault detection model based on deep learning;
The model library constructing and updating module is used for extracting a connection weight and a threshold value from a cable line fault detection model based on deep learning, constructing a cable line fault detection deep learning model library, and updating the cable line fault detection deep learning model library into a cable line fault monitoring system;
and the alarm module is used for giving an alarm when the cable line fault monitoring system detects a fault.
7. A cable fault sensing system according to claim 6, wherein: the collected cable line fault monitoring data information expression is as follows:
（12）；
In the method, in the process of the invention, For the cable line fault monitoring data at the moment t,/>For the current data of the cable line at the moment t,/>For the voltage data of the cable line at the moment t,/>For the temperature data of the cable line at the moment t,/>For the humidity data of the cable circuit at the moment t,/>For the resistance data of the cable line at the moment t,/>For the vibration data of the cable line at the moment t,/>For the electric field data of the cable line at the moment t,/>And the magnetic field data of the cable line at the time t.
8. A cable fault sensing system according to claim 6, wherein: the NB-IoT transmission module includes operations to: modulating the collected cable line fault monitoring data information, transmitting the modulated cable line fault monitoring data information by using an NB-IoT technology, demodulating the received cable line fault monitoring data information by using a receiving end of the NB-IoT technology, wherein the following formulas (13) - (17) are cable line electric field data NB-IoT transmission processing procedures, and the formula (18) is cable line fault monitoring data information obtained through NB-IoT transmission:
（13）；
（14）；
（15）；
（16）；
（17）；
（18）；
Wherein: for the modulated electric field data of the cable line at the t moment,/> For the cable line electric field data at time t,For the electric field signal frequency of the cable line at the moment t,/>For the electric field phase angle of the cable line at the moment t,/>For the electric field data of the cable line at the t moment received by the receiving end,/>For the noise experienced in NB-IoT transmission signals,/>、/>The sine component and the cosine component after the low-pass filtering processing are respectively, the lowpass { } function represents low-pass filtering and is used for extracting baseband signal components in the function, the arg () function represents finding the largest sine component and cosine component, and the/>For cable line fault monitoring data after NB-IoT transmission,/>For cabling current data after transmission over NB-IoT,/>For cabling voltage data after NB-IoT transmission,/>For the cabling temperature data after transmission over NB-IoT,For cabling humidity data after NB-IoT transmission,/>For cabling resistance data after NB-IoT transmission,/>For cabling vibration data after NB-IoT transmission,/>For cable line electric field data transmitted through NB-IoT,/>Is the cabling magnetic field data after being transmitted through the NB-IoT.
9. A cable fault sensing system according to claim 6, wherein: the construction of the cable line fault detection model based on deep learning specifically comprises the following operations:
Marking the received cable line fault monitoring data to form a training sample of the deep learning model; training a sample by using a deep learning model, wherein the training comprises forward propagation and reverse propagation training processes, the prediction error of the deep learning model is minimized by adjusting the weight and the bias of a neural network, the performance of the deep learning model is estimated by using an independent test data set, and the final training forms a cable line fault detection model based on deep learning;
the cable line fault detection model based on deep learning comprises 8 inputs and 2 outputs, wherein the 8 inputs are respectively: cable line current data transmitted over NB-IoT Is a sample of the cabling voltage data/>Is a sample of the cabling temperature data/>Is a sample of the cabling humidity data/>Is a sample of the cabling resistance data/>Is a sample of the cable run vibration data/>Is a sample of the cable run electric field data/>Is a sample of the cabling magnetic field data/>The 2 outputs are respectively: /(I)To have a fault,/>To be fault-free;
The hidden layer of the deep learning model is provided with n neurons, and the neurons in the hidden layer The outputs of (2) are weighted and calculated by an activation function, and the expressions are shown in (8) - (9):
（19）；
（20）；
Wherein: the neuron of the hidden layer of the deep learning model is j is the sequence number of the neuron, and the value range is 1-n,/> Activation function for deep learning model,/>Is a connection input/>And neurons/>Weights of/>Is neuron/>H is the output vector of the hidden layer of the deep learning model,/>The input of the deep learning model is that i is the layer number of the deep learning model;
The output of the deep learning model is:
（21）；
Wherein: for the output of deep learning model,/> To connect neurons/>And output/>Weights of/>For outputtingIs included.
10. A cable fault sensing system according to claim 9, wherein: the expression of the cable line fault detection deep learning model library is as follows:
（22）；
Wherein: Deep learning model for kth cable line fault detection,/> In order to train the deep learning model well,And connecting weights from the input layer to the output layer of the trained deep learning model.
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