Cable external damage early warning and monitoring method and device, electronic equipment and storage medium

Abstract

The invention provides a cable external damage early warning and monitoring method and device, electronic equipment and a storage medium, relates to the technical field of cable monitoring, is applied to a cable external damage early warning and monitoring system, and the cable external damage early warning and monitoring system comprises a monitoring unit, a cloud platform and an early warning platform. The monitoring unit comprises a communication module, a processor module and a sensor composed of a positioning module, a three-axis vibration module, a three-axis inclination angle module and a sound acquisition module. By analyzing the surrounding environment condition of the cable through multiple features and multiple dimensions, early warning signals of different levels are output in time, early warning is carried out more accurately and timely, the cable is prevented from being damaged by construction machinery, and the probability of cable damage is remarkably reduced.

Description

Technical Field

[0001] The present application relates to the technical field of cable monitoring, and in particular to a cable external damage early warning monitoring method, device, electronic equipment and storage medium. Background Art

[0002] It is extremely important to prevent underground cables from being damaged. When planning the cable laying, areas with frequent construction, unstable geology, and areas that are prone to external damage should be avoided as much as possible. Obvious cable signs should be set up on the ground, such as cable piles and signboards. A strict inspection system should be established to inspect the cable lines regularly. At present, intelligent ground nails, fiber optic sensors, and image recognition are mainly used to warn against external damage of cables. Among them, the use of intelligent ground nails is prone to missed reports and false alarms. The use of fiber optic sensors has high installation and maintenance costs on the one hand, and because the optical fiber is laid above the cable, the cable may be damaged at the same time as the optical fiber is damaged. The image recognition method is easily affected by weather and environmental factors, which reduces the accuracy of recognition.

[0003] The above measures have high manpower and material costs and the actual effect of preventing external damage is not ideal. They are increasingly unable to meet the growing external damage pressure. In addition, some equipment can only reflect the results after the cable is damaged, which cannot meet the needs of accurate early warning of cable protection from external damage. Summary of the invention

[0004] In view of this, the purpose of this application is to provide a cable external damage early warning monitoring method, which can accurately identify the external damage behavior of buried cables by combining multiple features, and adopt a graded early warning mechanism to reflect the severity of potential destructive behavior.

[0005] In the first aspect, an embodiment of the present application provides a cable external damage early warning monitoring method, which is applied to a cable external damage early warning monitoring system, wherein the cable external damage early warning monitoring system includes a monitoring unit, a cloud platform and an early warning platform, wherein the monitoring unit includes a communication module, a processor module, and a sensor composed of a positioning module, a three-axis vibration module, a three-axis tilt module and a sound collection module, wherein a plurality of the monitoring units are deployed at intervals above the cable that is protected from external force damage; the method comprises the following steps:

[0006] The three-axis vibration module, sound collection module, three-axis inclination module, and positioning module of the sensor collect vibration signals, sound signals, inclination signals, and positioning and timing signals around the cable in real time, and the collected vibration signals and sound signals are analyzed based on the neural network model built into the processor module to obtain a judgment result on whether the cable has external damage behavior;

[0007] Based on the communication module, the judgment result and the data collected by the sensor are sent to the cloud platform; if the cable is broken, a third-level warning is sent to the warning platform through the cloud platform, and based on the positioning signal and the inclination signal, it is judged whether the monitoring unit has a posture change, and the attention mode is triggered to increase the data collection frequency of the sensor, and the distance between the construction position and the cable is determined in the attention mode;

[0008] A second-level warning or a first-level warning is sent to the warning platform according to whether the monitoring unit changes its posture and the distance between the construction position and the cable.

[0009] In some embodiments, the neural network model built into the processor module is obtained by:

[0010] Acquire a vibration sound data set, and randomly divide the vibration sound data set into a vibration sound training data set and a vibration sound verification data set according to a preset ratio; wherein the vibration sound data set includes a plurality of historical vibration signals and sound signals around the cable collected by the sensor, and whether the cable corresponding to the vibration signal and the sound signal has external damage behavior;

[0011] Training the constructed deep convolutional neural network model framework based on the vibration sound training data set to obtain an initial neural network model;

[0012] Inputting the vibration signal and the sound signal in the vibration and sound verification data set into the initial neural network model, and performing probability accuracy verification on the initial neural network model;

[0013] If the probability accuracy of the initial neural network model meets the preset requirements, the initial neural network model is used as the neural network model.

[0014] In some embodiments, if it is determined based on the positioning timing signal and the inclination signal that the monitoring unit has undergone a posture change, or the cable is within a set first distance range from the construction location, a first-level warning is sent to the warning platform via the cloud platform; if the cable is within a set second distance range from the construction location, a second-level warning is sent to the warning platform via the cloud platform.

[0015] In some embodiments, the distance between the construction location and the cable is determined in the following manner, including the following steps:

[0016] Selecting a reference sensor and an arbitrary sensor from the plurality of sensors;

[0017] Calculating the distance d1 from the vibration source to the reference sensor according to the time difference when the reference sensor receives the vibration signal, and calculating the distance d2 from the sound source to the reference sensor according to the time difference when the reference sensor receives the sound signal;

[0018] The distance d from the vibration source to the arbitrary sensor is determined according to the distance d1, the distance d2, and the time difference between the reference sensor and the arbitrary sensor. zi and the distance d from the sound source to the arbitrary sensor si ;

[0019] According to the distance d1, the distance d2, the distance d zi , the distance d si , the positions of the reference sensor and the arbitrary sensor, determining a first distance from a vibration source to a straight line connecting the reference sensor and the arbitrary sensor, and determining a second distance from a sound source to a straight line connecting the reference sensor and the arbitrary sensor;

[0020] The distance between the cable and the construction location is determined based on the correlation coefficient between the vibration signal and the sound signal, and the first distance and the second distance.

[0021] In some embodiments, if the correlation coefficient between the vibration signal and the sound signal is equal to a set threshold, the average of the first distance and the second distance is used as the distance between the cable and the construction location; if the correlation coefficient between the vibration signal and the sound signal is less than the set threshold, the first distance is used as the distance between the cable and the construction location.

[0022] In some embodiments, the similarity coefficient between the vibration signal and the sound signal is calculated by the following formula:

[0023]

[0024] Where A={a1,a2,...,a n} represents the vibration signal; B={b1,b2,...,b n} represents a sound signal; Cov(A,B) represents the covariance value between the vibration signal and the sound signal, and is the average value of the vibration signal, is the average value of the sound signal; σ a represents the standard deviation of the vibration signal, and σ b represents the standard deviation of the sound signal, and

[0025] In some embodiments, the monitoring unit further includes a solar power supply module, and if the cable is within a set second distance range from the construction location, sending a secondary warning to the warning platform through the cloud platform includes the following steps:

[0026] If the distance between the cable and the construction position is within a set second distance range, obtaining the output voltage of the solar power supply module, and determining whether the output voltage is abnormal;

[0027] If there is no abnormality, a second-level warning is sent to the warning platform through the cloud platform; if there is an abnormality, a first-level warning is sent to the warning platform through the cloud platform.

[0028] In a second aspect, an embodiment of the present application provides a cable external damage early warning monitoring device, wherein the cable external damage early warning monitoring system includes a monitoring unit, a cloud platform and an early warning platform, wherein the monitoring unit includes a communication module, a processor module, and a sensor composed of a positioning module, a three-axis vibration module, a three-axis tilt module and a sound collection module, wherein a plurality of the monitoring units are deployed at intervals above the cable that is protected from external force damage; the device includes:

[0029] A data acquisition module, which is used to collect vibration signals, sound signals, inclination signals, and positioning and timing signals around the cable in real time based on the three-axis vibration module, sound acquisition module, three-axis inclination module, and positioning module of the sensor, and analyze the collected vibration signals and sound signals based on the neural network model built into the processor module to obtain a judgment result on whether the cable has external damage behavior;

[0030] A data transmission module, used to send the judgment result and the data collected by the sensor to a cloud platform based on the communication module; wherein, if the cable is broken, a third-level warning is sent to the warning platform through the cloud platform, and based on the positioning signal and the inclination signal, it is judged whether the monitoring unit has a posture change, and the attention mode is triggered to increase the data collection frequency of the sensor, and the distance between the construction position and the cable is determined in the attention mode;

[0031] The analysis and warning module is used to send a second-level warning or a first-level warning to the warning platform according to whether the monitoring unit has a posture change and the distance between the construction position and the cable.

[0032] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory through the bus, and when the machine-readable instructions are executed by the processor, the steps of a cable external damage early warning monitoring method described in any one of the first aspects are performed.

[0033] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of a cable external damage early warning monitoring method described in any one of the above-mentioned first aspects are executed.

[0034] The present application describes a cable external damage early warning monitoring method, device, electronic device and storage medium. The three-axis vibration module, sound collection module, three-axis inclination module and positioning module of the sensor collect vibration signals, sound signals, inclination signals, and positioning and timing signals around the cable in real time, and analyze the collected vibration signals and sound signals based on the neural network model built into the processor module to obtain a judgment result of whether the cable has external damage; based on the communication module, the judgment result and the data collected by the sensor are sent to the cloud platform; if the cable has external damage, a third-level warning is sent to the early warning platform through the cloud platform, and based on the positioning signal and the inclination signal, it is judged whether the monitoring unit has a posture change, and the attention mode is triggered to increase the data collection frequency of the sensor, and the distance between the construction position and the cable is determined in the attention mode; a second-level warning or a first-level warning is sent to the early warning platform according to whether the monitoring unit has a posture change and the distance between the construction position and the cable. Therefore, comprehensive analysis of multiple feature information can be performed to improve the accuracy of judgment and reduce false alarms that may be caused by single feature analysis. In addition, a graded early warning mechanism can reflect the severity of potential destructive behavior, allowing maintenance personnel or relevant agencies to respond in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 A flow chart of the cable external damage early warning monitoring method according to an embodiment of the present application is shown;

[0037] Figure 2 A schematic diagram of the structure of the cable external damage early warning monitoring system according to an embodiment of the present application is shown;

[0038] Figure 3 A schematic diagram of the structure of a monitoring unit according to an embodiment of the present application is shown;

[0039] Figure 4A schematic diagram showing a structure of determining the distance between the cable and the construction location based on the vibration signal and the sound signal in an embodiment of the present application;

[0040] Figure 5 A schematic diagram of the structure of the cable external damage early warning monitoring device according to an embodiment of the present application is shown;

[0041] Figure 6 A structural block diagram of an electronic device described in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0042] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of explanation and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn in real proportion. The flowchart used in this application shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can be implemented out of sequence, and the steps without logical context can be reversed in order or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart under the guidance of the content of the present application, or remove one or more operations from the flowchart.

[0043] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0044] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0045] In view of the technical problems raised by the background technology, the present application provides a cable external damage early warning monitoring method, device, electronic equipment and storage medium, which can accurately identify the external damage behavior of buried cables by combining multiple features, and adopt a graded early warning mechanism to reflect the severity of potential destructive behavior and respond in a timely manner.

[0046] See the instruction manual Figure 1The present application provides a cable external damage early warning monitoring method, which is applied to a cable external damage early warning monitoring system. The cable external damage early warning monitoring system includes a monitoring unit, a cloud platform and an early warning platform. The monitoring unit includes a communication module, a processor module, and a sensor composed of a positioning module, a three-axis vibration module, a three-axis tilt module and a sound collection module. A plurality of the monitoring units are deployed at intervals above the cable that is protected from external force damage. The method includes the following steps:

[0047] S1, based on the three-axis vibration module, sound collection module, three-axis inclination module, and positioning module of the sensor, the vibration signal, sound signal, inclination signal, and positioning and timing signal around the cable are collected in real time, and the collected vibration signal and sound signal are analyzed based on the neural network model built into the processor module to obtain a judgment result on whether the cable has external damage behavior;

[0048] S2. Sending the judgment result and the data collected by the sensor to the cloud platform based on the communication module; wherein, if the cable is broken, a level 3 warning is sent to the warning platform through the cloud platform, and based on the positioning signal and the inclination signal, it is judged whether the monitoring unit has a posture change, and the attention mode is triggered to increase the data collection frequency of the sensor, and the distance between the construction position and the cable is determined in the attention mode;

[0049] S3. Sending a second-level warning or a first-level warning to the warning platform according to whether the monitoring unit changes its posture and the distance between the construction position and the cable.

[0050] In order to clearly understand the technical solution of the embodiment of the present invention, an exemplary description of the application scenario can be given first. Figure 2 And the instruction manual Figure 3In the present application, the cable external damage warning monitoring system includes a monitoring unit, a cloud platform and an early warning platform, wherein the monitoring unit is deployed above the cable that needs to be protected from external force damage, and one can be installed every 50 meters, and rapid installation and deployment and device replacement can be achieved through a prefabricated cement base, thereby covering the key areas along the cable; specifically, the monitoring unit includes a solar power supply module, a communication module, a processor module, and a sensor composed of a positioning module, a three-axis vibration module, a three-axis tilt module and a sound collection module; wherein the solar power supply module adopts low-power processing technology to power the monitoring unit to ensure long-term stable operation without routine maintenance; the processor module is responsible for collecting data from the three-axis vibration module, the three-axis tilt module, the positioning module, and the sound collection module, and performing analysis and calculation processing; the three-axis vibration module is used to collect three-axis vibration information of the surrounding environment, including vibration amplitude, speed, acceleration, etc.; the three-axis tilt module is used to collect the posture of the monitoring unit; the positioning module is used to provide the monitoring unit with high-precision positioning and timing data; the sound collection module is used to perceive the sound information emitted by the external damage tool. The Neural-network Processing Unit (NPU) inside the processor module runs the artificial intelligence model, performs model training, extracts features and preprocesses the raw data, and accurately identifies external destructive behaviors such as impact drills, hook machines, and excavators. The processor module then operates the NB (Narrow Band Internet of Things) communication module to upload the preliminary judgment results and raw data to the cloud platform; the cloud platform uses differential positioning technology to further determine the precise location of the construction activity, evaluates its relationship with the cable protection area, triggers different levels of warnings and feeds back to the on-site warning platform, and the staff takes necessary preventive or emergency measures to eliminate risks.

[0051] Specifically, in step S1, according to the cable direction and terrain characteristics, high-risk areas that are vulnerable to damage are selected, and monitoring units are evenly deployed to ensure that all potential risk areas are covered. Then, sensors installed along the cable collect vibration signals, sound signals, inclination signals, and positioning and timing signals around the cable in real time, and the NPU built into the processor module pre-processes the collected vibration signals and sound signals, including data cleaning, feature extraction, etc., and then analyzes the vibration signals and sound signals based on the trained neural network model to identify abnormal patterns. Then, the communication module uploads the judgment results and the original data collected by the sensor to the cloud platform.

[0052] Among them, the neural network model built into the processor module is obtained in the following way: obtaining a vibration and sound data set, and randomly dividing the vibration and sound data set into a vibration and sound training data set and a vibration and sound verification data set according to a preset ratio; wherein the vibration and sound data set includes multiple historical vibration signals and sound signals around the cable collected by the sensor, and whether the cable corresponding to the vibration signal and the sound signal has external damage behavior; based on the vibration and sound training data set, the constructed deep convolutional neural network model framework is trained to obtain an initial neural network model; the vibration signal and sound signal in the vibration and sound verification data set are input into the initial neural network model, and the probability accuracy of the initial neural network model is verified; if the probability accuracy of the initial neural network model meets the preset requirements, the initial neural network model is used as the neural network model.

[0053] In one embodiment, the preset ratio can be 7:3, and the tensorfow system (a symbolic mathematical system based on data flow programming) is used as the backend of the deep convolutional neural network model framework for learning and training. The specific implementation process should be a technical means well known to those skilled in the art, and will not be described in detail here. This use of a convolutional neural network to train a model based on historical data and known examples of vandalism, through which the model learns the difference between normal environmental noise and vandalism, and how to identify patterns of vandalism from multi-feature data, can more accurately distinguish between normal environmental noise and signals of real vandalism, reducing the possibility of false alarms.

[0054] In step S2, if the processor module detects a cable abnormality, the cloud platform will immediately trigger the third-level warning of the hierarchical warning mechanism and feedback to the on-site warning platform to remind relevant personnel to pay attention to possible risks. At the same time as the third-level warning is triggered, the three-axis inclination module and the positioning module begin to monitor whether the posture of the monitoring unit has changed. The system will also enter the attention mode and increase the data collection frequency of each module to capture more details.

[0055] In step S3, it should be noted that, preferably, only when the processor module detects an abnormality, the judgment result of the cable's external rupture behavior and the data collected by the sensor are sent to the cloud platform based on the communication module, thereby reducing the energy consumption of data transmission between the communication module and the cloud platform. Among them, the positioning signal collected by the comprehensive positioning module and the inclination signal of the three-axis inclination module are used to judge whether the posture of the monitoring unit has changed. The three-axis inclination module uses a MEMS accelerometer and a gyroscope to collect the posture of the monitoring unit and the inclination angle in three-dimensional space, and outputs the inclination angle relative to the earth's gravity. Then, if it is detected that the position or inclination angle of the monitoring unit has changed or exceeds the set change threshold, the first-level warning of the graded warning mechanism is directly triggered.

[0056] In addition, in the attention mode, it should be noted that the acquisition frequencies of the three-axis vibration module, sound collection module, three-axis tilt module, and positioning module in the sensor will all increase, but the acquisition frequencies of the three-axis vibration module, sound collection module, and positioning module will increase more than the acquisition frequency of the three-axis tilt module. For example, the vibration frequency is 100kHz, the sampling frequency of the three-axis vibration module is 200kHz under normal circumstances, and the sampling frequency in the attention mode is 1MHz; the sound frequency is 48kHz, the sampling frequency of the sound collection module is 96kHz under normal circumstances, and the sampling frequency in the attention mode is 480kHz; the positioning module collects data once every 30s under normal circumstances, and once every 10s in the attention mode; the three-axis tilt module collects data once every 30s under normal circumstances, and once every 15s in the attention mode. Among them, the three-axis vibration module is based on piezoelectric sensor technology, which is used to collect three-axis vibration information of the surrounding environment, including vibration amplitude, speed, acceleration, etc., and measure the vibration acceleration of the object on two orthogonal axes. When the object vibrates, the inertial force sensed by the sensor will be converted into an electrical signal output; the sound collection module converts the sound wave into an electrical signal, and then converts it into a digital signal through an analog-to-digital converter, continuously monitors the ambient sound, uses a digital filter to denoise the signal, extracts features from the signal, and captures abnormal noise. Then, the cloud platform analyzes the high-frequency collected vibration signals and sound signals, quickly determines the distance between the cable and the construction location, and triggers different levels of warnings according to the proximity of the construction location to the cable.

[0057] When calculating the distance between the cable and the construction location, firstly, a reference sensor and an arbitrary sensor are selected from the multiple deployed sensors; the distance d1 from the vibration source to the reference sensor is calculated according to the time difference when the reference sensor receives the vibration signal, and the distance d2 from the sound source to the reference sensor is calculated according to the time difference when the reference sensor receives the sound signal; the distance d2 from the vibration source to the arbitrary sensor is determined according to the distance d1, the distance d2, and the time difference between the reference sensor and the arbitrary sensor. zi and the distance d from the sound source to the arbitrary sensor si According to the distance d1, the distance d2, the distance d zi , the distance d si , the positions of the reference sensor and the arbitrary sensor, determining a first distance from the vibration source to a straight line connecting the reference sensor and the arbitrary sensor, and determining a second distance from the sound source to a straight line connecting the reference sensor and the arbitrary sensor; determining the distance between the cable and the construction location based on the correlation coefficient between the vibration signal and the sound signal, and the first distance and the second distance.

[0058] In one embodiment, the reference description is attached Figure 4 , assume that there are n sensors deployed along the cable and a vibration source P at an unknown location z (x z ,y z ) and a sound source P at an unknown location s (x s ,y s ), the position of each sensor i is (x zi ,y zi )、(x si ,y si ), i = 1, 2, 3..., n., the wave speed is v (the propagation speed in concrete is 3000 m / s), the sound speed is c (the propagation speed in air is 350 m / s), and the time when the vibration wave and the sound wave arrive at any sensor i (determined based on the timing signal obtained by the positioning module) are t zi ,t si , the time when they reach the reference sensor is t1 and t2 respectively, then the time difference between any sensor i and the reference sensor is Δt zi =t zi -t1, Δt si =t si -t2; the distance from the vibration source to sensor i is d zi , the distance from the sound source to sensor i is d si , the distance from the vibration source to the reference sensor is d1, the distance from the sound source to the reference sensor is d2, d x It is the straight-line distance between the reference sensor and any sensor (determined based on the positioning signal obtained by the positioning module). The calculation formula is as follows:

[0059] d zi =vt zi =v(t zi -t1)+d1

[0060] d si =ct si =c(t si -t2)+d2

[0061] d1=v×t1

[0062] d2=c×t2

[0063] Using Heron's formula and the equal area method, we can find d:

[0064]

[0065] The above formula can be used to calculate the first distance d from the vibration source to the straight line connecting the reference sensor and the arbitrary sensor. A, and the second distance d from the sound source to the straight line between the reference sensor device and any sensor B .

[0066] Further, after calculating the first distance d A and the second distance d B After that, the correlation coefficient between the vibration signal and the sound signal needs to be calculated. The correlation coefficient statistic is used to evaluate the correlation between the sound signal and the vibration signal. In one embodiment, if the absolute value of the correlation coefficient is greater than or equal to 0.7 and the closer it is to 1, the stronger the correlation is, which indicates that the sound and vibration may be caused by the same event. The information can be further fused to take the first distance d A and the second distance d B The average value of the correlation coefficient is taken as the distance between the cable and the construction location; if the absolute value of the correlation coefficient is less than 0.7, it means that the correlation is weak. In this case, the first distance d calculated based on the vibration A As the distance between the cable and the construction location.

[0067] In the embodiment of the present application, let A = {a1, a2, ..., a n} represents the vibration signal; B={b1,b2,...,b n} represents the sound signal, and the similarity coefficient between the vibration signal and the sound signal is calculated by the following formula:

[0068]

[0069] Wherein, Cov(A, B) represents the cosquare value between the vibration signal and the sound signal, and is the average value of the vibration signal, is the average value of the sound signal; σ a represents the standard deviation of the vibration signal, and σ b represents the standard deviation of the sound signal, and

[0070] If the calculated distance between the cable and the construction location is within the set first distance range, a first-level warning is sent to the warning platform through the cloud platform; if the calculated distance between the cable and the construction location is within the set second distance range, a second-level warning is sent to the warning platform through the cloud platform. For example, if the construction location is within 5 meters from the cable protection area, the first-level warning of the graded warning mechanism is immediately triggered; if the construction location is within 5-10 meters from the cable protection area, the second-level warning of the graded warning mechanism is immediately triggered.

[0071] The above-mentioned graded warning mechanism enables the system to issue different levels of warnings according to the severity of potential destructive behaviors, allowing maintenance personnel or relevant agencies to respond in advance, rather than passively responding after the damage occurs. Early intervention can prevent small-scale damage from turning into large-scale damage, thereby reducing maintenance costs and service interruption time; and resources (such as manpower and material resources) can be reasonably allocated according to risk levels, giving priority to high-risk areas, improving work efficiency, and ensuring that resources are optimally configured where they are most needed.

[0072] In other embodiments, if it is determined that the distance between the cable and the construction location is within a set second distance range, the output voltage of the solar power supply module is obtained, and it is determined whether the output voltage is abnormal; if there is no abnormality, a second-level warning is sent to the warning platform through the cloud platform; if there is an abnormality, a first-level warning is sent to the warning platform through the cloud platform. Among them, when the solar power supply module is directly exposed to sunlight, its output voltage is higher; when there is an obstruction blocking the sunlight, the output voltage will decrease, and the output voltage of the solar power supply module is monitored to determine whether there is an obstruction, that is, whether a construction vehicle is parked near the monitoring unit.

[0073] It can be seen that the cable external damage early warning monitoring method provided by the present application analyzes the surrounding environment of the cable through multi-feature and multi-dimensional analysis, timely outputs early warning signals of different levels, provides early warnings more accurately and timely, prevents construction machinery from damaging the cable, and significantly reduces the probability of cable damage.

[0074] Based on the same inventive concept, a cable external damage early warning monitoring device is also provided in the embodiment of the present application. Since the principle of solving the problem by the device in the embodiment of the present application is similar to the above-mentioned cable external damage early warning monitoring method in the embodiment of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0075] As the instruction manual Figure 5 As shown, the embodiment of the present application also provides a cable external damage early warning monitoring device, which is applied to the cable external damage early warning monitoring system. The cable external damage early warning monitoring system includes a monitoring unit, a cloud platform and an early warning platform. The monitoring unit includes a communication module, a processor module, and a sensor composed of a positioning module, a three-axis vibration module, a three-axis tilt module and a sound collection module, wherein a plurality of the monitoring units are deployed at intervals above the cable that is protected from external force damage; the device includes:

[0076] The data acquisition module 501 is used to collect vibration signals, sound signals, inclination signals, and positioning and timing signals around the cable in real time based on the three-axis vibration module, sound acquisition module, three-axis inclination module, and positioning module of the sensor, and analyze the collected vibration signals and sound signals based on the neural network model built into the processor module to obtain a judgment result on whether the cable has external damage behavior;

[0077] The data transmission module 502 is used to send the judgment result and the data collected by the sensor to the cloud platform based on the communication module; if the cable is broken, a third-level warning is sent to the warning platform through the cloud platform, and based on the positioning signal and the inclination signal, it is judged whether the monitoring unit has a posture change, and the attention mode is triggered to increase the data collection frequency of the sensor, and the distance between the construction position and the cable is determined in the attention mode;

[0078] The analysis and warning module 503 is used to send a secondary warning or a primary warning to the warning platform according to whether the monitoring unit has a posture change and the distance between the construction location and the cable; wherein, if it is determined based on the positioning signal and the inclination signal that the monitoring unit has a posture change, or the cable is within a set first distance range from the construction location, a primary warning is sent to the warning platform via the cloud platform; if the cable is within a set second distance range from the construction location, a secondary warning is sent to the warning platform via the cloud platform.

[0079] In some embodiments, the analysis and early warning module 503 determines the distance between the cable and the construction location based on the vibration signal and the sound signal, including: selecting a reference sensor and an arbitrary sensor from a plurality of the sensors; calculating the distance d1 from the vibration source to the reference sensor according to the time difference when the reference sensor receives the vibration signal, and calculating the distance d2 from the sound source to the reference sensor according to the time difference when the reference sensor receives the sound signal; determining the distance d1 from the vibration source to the arbitrary sensor according to the distance d2, the distance d2, and the time difference between the reference sensor and the arbitrary sensor. zi and the distance d from the sound source to the arbitrary sensor si According to the distance d1, the distance d2, the distance d zi , the distance d si, the positions of the reference sensor and the arbitrary sensor, determining a first distance from the vibration source to a straight line connecting the reference sensor and the arbitrary sensor, and determining a second distance from the sound source to a straight line connecting the reference sensor and the arbitrary sensor; determining the distance between the cable and the construction location based on the correlation coefficient between the vibration signal and the sound signal, and the first distance and the second distance; wherein, if the correlation coefficient between the vibration signal and the sound signal is greater than a set threshold, taking the average of the first distance and the second distance as the distance between the cable and the construction location; if the correlation coefficient between the vibration signal and the sound signal is less than a set threshold, taking the first distance as the distance between the cable and the construction location.

[0080] In some embodiments, the analysis and warning module 503 calculates the similarity coefficient between the vibration signal and the sound signal by the following formula:

[0081]

[0082] Where A={a1,a2,...,a n} represents the vibration signal; B={b1,b2,...,b n} represents a sound signal; Cov(A,B) represents the covariance value between the vibration signal and the sound signal, and is the average value of the vibration signal, is the average value of the sound signal; σ a represents the standard deviation of the vibration signal, and σ b represents the standard deviation of the sound signal, and

[0083] In some embodiments, the monitoring unit also includes a solar power supply module. If the cable is within a set second distance range from the construction location, the analysis and early warning module 503 obtains the output voltage of the solar power supply module and determines whether the output voltage is abnormal; if there is no abnormality, a second-level early warning is sent to the early warning platform through the cloud platform; if there is an abnormality, a first-level early warning is sent to the early warning platform through the cloud platform.

[0084] The present application discloses a cable external damage early warning monitoring device, which collects vibration signals, sound signals, inclination signals and positioning timing signals around the cable in real time based on the three-axis vibration module, sound collection module, three-axis inclination module and positioning module of the sensor through a data acquisition module, and analyzes the collected vibration signals and sound signals based on the neural network model built into the processor module to obtain a judgment result of whether the cable has external damage; sends the judgment result and the data collected by the sensor to a cloud platform based on the communication module through a data transmission module; wherein, if the cable has external damage, a third-level warning is sent to the early warning platform through the cloud platform, and based on the positioning timing signal and the inclination signal, it is judged whether the monitoring unit has a posture change, and a focus mode is triggered to increase the data collection frequency of the sensor, and the distance between the construction position and the cable is determined in the focus mode; a second-level warning or a first-level warning is sent to the early warning platform according to whether the monitoring unit has a posture change and the distance between the construction position and the cable through the analysis and early warning module. Therefore, comprehensive analysis of multiple feature information can be performed to improve the accuracy of judgment and reduce false alarms that may be caused by single feature analysis. In addition, a graded early warning mechanism can reflect the severity of potential destructive behavior, allowing maintenance personnel or relevant agencies to respond in advance.

[0085] Based on the same concept of the present invention, the specification is attached Figure 6 As shown, the structure of an electronic device 600 provided in an embodiment of the present application includes: at least one processor 601, at least one network interface 604 or other user interface 603, a memory 605, and at least one communication bus 602. The communication bus 602 is used to realize the connection and communication between these components. The electronic device 600 optionally includes a user interface 603, including a display (for example, a touch screen, LCD, CRT, holographic imaging (Holographic) or projection (Projector), etc.), a keyboard or a pointing device (for example, a mouse, a trackball (trackball), a touch pad or a touch screen, etc.).

[0086] The memory 605 may include a read-only memory and a random access memory, and provides instructions and data to the processor 601. A portion of the memory 605 may also include a non-volatile random access memory (NVRAM).

[0087] In some implementations, the memory 605 stores the following elements, executable modules or data structures, or a subset thereof, or an extended set thereof:

[0088] Operating system 6051, including various system programs for implementing various basic services and processing hardware-based tasks;

[0089] The application module 6052 includes various application programs, such as a launcher, a media player, a browser, etc., which are used to implement various application services.

[0090] In an embodiment of the present application, by calling the program or instructions stored in the memory 605, the processor 601 is used to execute steps in a cable external damage early warning monitoring method, which can accurately identify the external damage behavior of the buried cable by combining multiple features, and adopt a graded early warning mechanism to reflect the severity of potential destructive behavior.

[0091] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the cable external damage early warning monitoring method are executed.

[0092] Specifically, the storage medium can be a general storage medium, such as a mobile disk, a hard disk, etc. When the computer program on the storage medium is run, the above-mentioned cable external damage early warning monitoring method can be executed.

[0093] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0094] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0095] In addition, each functional unit in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0096] If the function is implemented in the form of a business function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0097] Finally, it should be noted that the above embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the aforementioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the aforementioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application. They should all be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A cable external damage early warning monitoring method, characterized in that: Applied to a cable external damage early warning monitoring system, the cable external damage early warning monitoring system comprises a monitoring unit, a cloud platform and an early warning platform, the monitoring unit comprises a communication module, a processor module, and a sensor composed of a positioning module, a three-axis vibration module, a three-axis tilt module and a sound collection module, wherein a plurality of the monitoring units are deployed at intervals above the cable that is protected from external force damage; the method comprises the following steps: The three-axis vibration module, sound collection module, three-axis inclination module, and positioning module of the sensor collect vibration signals, sound signals, inclination signals, and positioning and timing signals around the cable in real time, and the collected vibration signals and sound signals are analyzed based on the neural network model built into the processor module to obtain a judgment result on whether the cable has external damage behavior; Based on the communication module, the judgment result and the data collected by the sensor are sent to the cloud platform; if the cable is broken, a third-level warning is sent to the warning platform through the cloud platform, and based on the positioning signal and the inclination signal, it is judged whether the monitoring unit has a posture change, and the attention mode is triggered to increase the data collection frequency of the sensor, and the distance between the construction position and the cable is determined in the attention mode; A second-level warning or a first-level warning is sent to the warning platform according to whether the monitoring unit changes its posture and the distance between the construction position and the cable.

2. According to claim 1, a cable external damage early warning monitoring method is characterized in that: The neural network model built into the processor module is obtained in the following manner: Acquire a vibration sound data set, and randomly divide the vibration sound data set into a vibration sound training data set and a vibration sound verification data set according to a preset ratio; wherein the vibration sound data set includes a plurality of historical vibration signals and sound signals around the cable collected by the sensor, and whether the cable corresponding to the vibration signal and the sound signal has external damage behavior; Training the constructed deep convolutional neural network model framework based on the vibration sound training data set to obtain an initial neural network model; Inputting the vibration signal and the sound signal in the vibration and sound verification data set into the initial neural network model, and performing probability accuracy verification on the initial neural network model; If the probability accuracy of the initial neural network model meets the preset requirements, the initial neural network model is used as the neural network model.

3. According to claim 1, a cable external damage early warning monitoring method is characterized in that: in, If it is determined based on the positioning signal and the inclination signal that the monitoring unit has changed its posture, or the cable is within a set first distance range from the construction location, a first-level warning is sent to the warning platform via the cloud platform; if the cable is within a set second distance range from the construction location, a second-level warning is sent to the warning platform via the cloud platform.

4. A cable external damage early warning monitoring method according to claim 3, characterized in that: The distance between the construction location and the cable is determined in the following manner, including the following steps: Selecting a reference sensor and an arbitrary sensor from the plurality of sensors; Calculating the distance d1 from the vibration source to the reference sensor according to the time difference when the reference sensor receives the vibration signal, and calculating the distance d2 from the sound source to the reference sensor according to the time difference when the reference sensor receives the sound signal; The distance d from the vibration source to the arbitrary sensor is determined according to the distance d1, the distance d2, and the time difference between the reference sensor and the arbitrary sensor. zi and the distance d from the sound source to the arbitrary sensor si ; According to the distance d1, the distance d2, the distance d zi , the distance d si , the positions of the reference sensor and the arbitrary sensor, determining a first distance from a vibration source to a straight line connecting the reference sensor and the arbitrary sensor, and determining a second distance from a sound source to a straight line connecting the reference sensor and the arbitrary sensor; The distance between the cable and the construction location is determined based on the correlation coefficient between the vibration signal and the sound signal, and the first distance and the second distance.

5. A cable external damage early warning monitoring method according to claim 4, characterized in that: in, If the correlation coefficient between the vibration signal and the sound signal is greater than or equal to a set threshold, the average of the first distance and the second distance is used as the distance between the cable and the construction location; if the correlation coefficient between the vibration signal and the sound signal is less than the set threshold, the first distance is used as the distance between the cable and the construction location.

6. A cable external damage early warning monitoring method according to claim 5, characterized in that: in, The similarity coefficient between the vibration signal and the sound signal is calculated by the following formula: Where A={a1,a2,...,a n } represents the vibration signal; B={b1,b2,...,b n } represents a sound signal; Cov(A,B) represents the covariance value between the vibration signal and the sound signal, and is the average value of the vibration signal, is the average value of the sound signal; σ a represents the standard deviation of the vibration signal, and σ b represents the standard deviation of the sound signal, and 7. A cable external damage early warning monitoring method according to claim 3, characterized in that: The monitoring unit further includes a solar power supply module. If the cable is within a set second distance range from the construction location, a secondary warning is sent to the warning platform via the cloud platform, including the following steps: If the distance between the cable and the construction position is within a set second distance range, obtaining the output voltage of the solar power supply module, and determining whether the output voltage is abnormal; If there is no abnormality, a second-level warning is sent to the warning platform through the cloud platform; if there is an abnormality, a first-level warning is sent to the warning platform through the cloud platform.

8. A cable external damage early warning monitoring device, characterized in that: Applied to the cable damage warning monitoring system, the cable damage warning monitoring system includes a monitoring unit, a cloud platform and an early warning platform, the monitoring unit includes a communication module, a processor module, and a sensor composed of a positioning module, a three-axis vibration module, a three-axis tilt module and a sound collection module, wherein a plurality of the monitoring units are deployed at intervals above the cable to prevent external force damage; the device includes: A data acquisition module, which is used to collect vibration signals, sound signals, inclination signals, and positioning and timing signals around the cable in real time based on the three-axis vibration module, sound acquisition module, three-axis inclination module, and positioning module of the sensor, and analyze the collected vibration signals and sound signals based on the neural network model built into the processor module to obtain a judgment result on whether the cable has external damage behavior; A data transmission module, used to send the judgment result and the data collected by the sensor to a cloud platform based on the communication module; wherein, if the cable is broken, a third-level warning is sent to the warning platform through the cloud platform, and based on the positioning signal and the inclination signal, it is judged whether the monitoring unit has a posture change, and the attention mode is triggered to increase the data collection frequency of the sensor, and the distance between the construction position and the cable is determined in the attention mode; The analysis and warning module is used to send a second-level warning or a first-level warning to the warning platform according to whether the monitoring unit has a posture change and the distance between the construction position and the cable.

9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of a cable external damage early warning monitoring method as described in any one of claims 1 to 7 are performed.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the cable external damage early warning monitoring method as claimed in any one of claims 1 to 7 are executed.

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