Road cable height monitoring system

Abstract

The invention discloses a road cable height monitoring system which comprises a cable height monitoring terminal and a data management platform. The monitoring terminal comprises an ultrasonic distance sensor which is used for measuring the distance between the terminal and the road-crossing cable and is provided with an anti-interference algorithm; the gyroscope sensor is used for recording attitude information when the attitude of the terminal is not correct due to strong wind or collision; the vibration sensor is used for detecting vibration data generated when the terminal is collided by external force; the processor is used for processing and transmitting the collected data; the data is transmitted to the data management platform through the communication module; and the data management platform is used for carrying out visualization and intelligent analysis on the obtained monitoring data, carrying out multi-level alarm and adaptive alarm threshold setting, carrying out intelligent order sending inspection and providing predictive maintenance suggestions. According to the invention, accurate measurement and real-time monitoring of cable height data are realized. And through the intelligent management function of the platform, the automation level of monitoring and maintenance is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of road cable height monitoring, and more particularly to a road cable height monitoring system. Background Art

[0002] With the acceleration of urbanization, urban infrastructure construction is becoming increasingly perfect, especially the expansion of power supply networks and the huge communication networks. However, the safety of cross-road cables has gradually become an important issue in urban management. Traditional cable height monitoring methods rely on manual inspections, which are inefficient and prone to missed inspections. In recent years, although some automatic monitoring equipment has begun to be put into use, they often have problems such as low measurement accuracy, inability to feedback data in real time, and weak anti-interference ability. Therefore, it is particularly necessary to develop an efficient, accurate and reliable cable height monitoring system. Summary of the invention

[0003] In view of this, the present invention provides a road cable height monitoring system, aiming to solve the problems of insufficient automation, data accuracy and intelligence of cable height monitoring equipment in the prior art.

[0004] In order to achieve the above object, the present invention adopts the following technical solution:

[0005] An embodiment of the present invention provides a road cable height monitoring system, including: a cable height monitoring terminal and a data management platform;

[0006] Wherein, the monitoring terminal comprises: a processor and an ultrasonic distance sensor, a gyroscope sensor, a vibration sensor, a communication module and a power supply module respectively connected to the processor;

[0007] The ultrasonic distance sensor is used to measure the distance between the terminal and the cross-cable, is configured with an anti-interference algorithm, and transmits the measured distance data to the processor;

[0008] The gyro sensor is used to record the posture information when the terminal is in an incorrect posture due to strong wind or collision, and transmit the posture information to the processor;

[0009] The vibration sensor is used to detect vibration data generated by an external force impacting the terminal and transmit the vibration data to the processor;

[0010] The processor is used for data collection, and processes and transmits the collected data based on the configured correction unit and alarm unit;

[0011] The communication module is used to transmit the monitoring data generated by the processor to the data management platform in real time;

[0012] The power supply module adopts a solar self-powered design and has a low-battery alarm function to provide power support to the monitoring terminal;

[0013] The data management platform is used to visualize and intelligently analyze the acquired monitoring data, set multi-level alarms and adaptive alarm thresholds, and intelligently dispatch inspections and provide predictive maintenance recommendations.

[0014] Furthermore, the ultrasonic distance sensor is configured with an anti-interference algorithm as follows:

[0015] D filtered =α·D current +(1-α)·D previous (1)

[0016] Among them, D filtered is the distance data after filtering, α is the filtering factor, the filtering strength is adjusted according to the external environment and is set between 0 and 1; D current is the distance data obtained in the current measurement cycle, that is, the cable distance data detected by the sensor at the current time point; D previous It is the distance data obtained in the previous measurement cycle, that is, the cable distance data recorded by the sensor at the previous time point.

[0017] Furthermore, the correction unit is used to calculate according to the attitude information of the gyro sensor and the measured distance data of the ultrasonic distance sensor to obtain the vertical height of the measured cable; the formula is as follows:

[0018] H=D filtered ×sin(θ) (2)

[0019] Wherein, θ is the terminal tilt angle, that is, the angle with the horizontal plane; H is the measurement height.

[0020] Furthermore, the alarm unit is used to generate scratch warning information based on the collected collision data and send out an alarm when the vibration sensor monitoring terminal is collided with a vehicle or a pedestrian; and transmit the information to the data management platform.

[0021] Furthermore, the processor adopts a SOC chip and further includes:

[0022] The adaptive energy-saving unit is used to put the monitoring terminal into sleep mode when there is no operation for a long time; or to issue a low-battery alarm message when the power of the power supply module is detected to be lower than the threshold.

[0023] Furthermore, the communication module is a 4G communication module.

[0024] Furthermore, the data management platform includes:

[0025] Visualization and intelligent analysis unit, used to dynamically display the height distribution, location distribution and alarm status of road cables in real time, and provide visual displays including bar graphs, heat maps and trend graphs;

[0026] The alarm setting unit is used to automatically set different alarm levels according to the criticality, location or frequency of use of different cables, and dynamically adjust the alarm threshold according to actual environmental changes;

[0027] Intelligent maintenance dispatch unit, which is used to automatically generate maintenance tasks and intelligently assign them to appropriate maintenance personnel or teams based on the alarm level, fault type and resource allocation when abnormal height data is monitored or a fault alarm is received;

[0028] The predictive maintenance suggestion unit provides predictive maintenance suggestions based on historical data and environmental information.

[0029] Furthermore, the data management platform further includes:

[0030] The intelligent power saving and wake-up management unit is used to monitor the power status of each monitoring terminal in real time. When the terminal power is low, the maintenance personnel will replace the battery; when the monitoring terminal is in a low-flow monitoring area, a remote command is issued to enter the power saving mode;

[0031] Equipment status monitoring and remote diagnosis unit, used to monitor the operating status of all monitoring terminals in real time, automatically generate reports when abnormalities occur and remind technicians to perform maintenance;

[0032] Environmental and seasonal adaptation unit, used to automatically adjust the terminal's measurement mode according to seasonal changes, including: adjusting the measurement frequency and sensitivity, and multiple confirmation of alarm data in the rainy season;

[0033] The big data analysis and report generation unit is used to generate monthly, quarterly or annual reports based on the cable height, alarm frequency and maintenance records collected through regular analysis.

[0034] Furthermore, the visualization and intelligent analysis unit is also used to adopt an adaptive variational recursive network to predict the cable height change trend in real time and identify potential anomalies;

[0035] Specifically include:

[0036] 1) Treat the cable height data as a multivariate time series input and combine it with seasonal characteristics and environmental data for dynamic modeling;

[0037] 2) Using adaptive variational recurrent networks, the structure and weights of the network are adaptively adjusted through the Bayesian inference mechanism;

[0038] 3) Based on historical height data, predict future changes in cable height, generate predicted height values ​​for the next few hours or days, and identify potential abnormal heights in advance.

[0039] It can be seen from the above technical solutions that compared with the prior art, the present invention has the following technical advantages:

[0040] The system of the present invention integrates ultrasonic sensors, gyroscopes, vibration sensors, etc., transmits data to the data management platform through the communication module, and can realize visualization and intelligent analysis of the acquired monitoring data, multi-level alarm and adaptive alarm threshold setting, as well as intelligent dispatch inspection and provide predictive maintenance suggestions. The invention realizes accurate measurement and real-time monitoring of cable height data, and maintains long-term operation in low-power mode, which can effectively reduce operation and maintenance costs, improve monitoring efficiency, and has great market application prospects.

[0041] In addition, the present invention achieves high-precision, low-power consumption and automated management of road cable height monitoring through innovative posture correction algorithms, anti-interference design, intelligent alarm management and other technical means, which is in line with the national development direction of intelligent manufacturing and has significant market potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0043] Figure 1 This is a usage scenario diagram of the road cable height monitoring system provided by the present invention.

[0044] Figure 2a This is a structural block diagram of the cable height monitoring terminal provided by the present invention.

[0045] Figure 2b A circuit diagram of some modules of the cable height monitoring terminal provided by the present invention.

[0046] Figure 3 This is a functional block diagram of the data management platform provided by the present invention.

[0047] Figure 4 This is an additional functional block diagram of the data management platform provided by the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Reference Figure 1 As shown, an embodiment of the present invention discloses a road cable height monitoring system, including a cable height monitoring terminal 1 and a data management platform 2.

[0050] Among them, the cable height monitoring terminal 1 is used to be installed near the cable above the road. It is responsible for collecting cable height data, posture information, collision data, etc., and uploading the monitoring data to the data management platform 2 through the communication module; the data management platform 2 is used to perform intelligent analysis and management of the monitoring data, and provide dynamic visualization display, alarm setting, intelligent dispatching and predictive maintenance recommendations and other functions.

[0051] Reference Figure 2a As shown, the cable height monitoring terminal is composed of the following components: a processor, and an ultrasonic distance sensor, a gyroscope sensor, a vibration sensor, a magnetic field sensor, a communication module and a power supply module respectively connected to the processor.

[0052] The processor uses SOC chip to realize data collection, correction, alarm and other functions; the ultrasonic distance sensor is used to measure the distance between the terminal and the cable, and is filtered through the anti-interference algorithm; the gyroscope sensor detects the change of equipment posture and is used for posture correction. When the terminal encounters strong wind and changes its measurement posture, it can use the gyroscope sensor to obtain its own posture and calculate the vertical height, and has anti-interference ability; the vibration sensor is used to detect collision and generate an alarm signal. For example, when the terminal is hit by a vehicle or pedestrian, the vibration sensor collects collision data and generates a scratch alarm signal; the magnetic field sensor can be used as an auxiliary gyroscope sensor to improve data accuracy and system stability; the communication module uses a 4G communication module to transmit the monitoring data to the data management platform; the power supply module adopts a solar self-powered design to ensure the long-term operation of the system.

[0053] The system integrates multiple sensors and data management platforms. Through the collaborative work of hardware and platform, the system can automatically collect cable height information and transmit, process and analyze data. Through the intelligent management function of the platform, the automation level of monitoring and maintenance is improved and manual intervention is reduced.

[0054] Reference Figure 2bAs shown, U1 needs to be connected to a 3.3V power supply (3V3) and a ground wire (GND). The 1 and 2 pins of the ultrasonic detection module (U2) are connected to the A9 and A10 pins of the microcontroller U1; the level conversion module U5 (TXS0108) can use the UART interface to connect the TX and RX pins of U1 to the A1 and A2 pins of U5. For 4G transmission module U4 (4G) communication, the UART interface can be used to connect the TX and RX pins of U1 to the TX and RX pins of U1.

[0055] The single-chip microcomputer is connected to each module through different interfaces to realize functions such as data collection, processing, and transmission. The ultrasonic detection module and other sensor detection modules are connected to the single-chip microcomputer through analog input and digital input. The level conversion module is used to realize the communication between the single-chip microcomputer and the 4G transmission module. The 4G transmission module is connected to the host computer operating system through wireless communication.

[0056] In one embodiment, the principle of the ultrasonic sensor for measuring distance is to calculate the distance from the cable to the monitoring terminal by transmitting and receiving ultrasonic waves. The specific steps are as follows:

[0057] 1. Ultrasonic emission: The transmitter in the sensor emits an ultrasonic signal of a certain frequency, which propagates in the air.

[0058] 2. Ultrasonic reflection: Ultrasonic waves will be reflected back after encountering obstacles such as cables and return to the sensor's receiver.

[0059] 3. Receiving and calculating the time difference: The sensor's receiver receives the reflected ultrasonic signal and measures the time difference from transmission to reception. This time difference is usually expressed as t.

[0060] Calculate the distance: Use the formula d = v × t / 2 to calculate the distance between the cable and the sensor.

[0061] v is the propagation speed of ultrasound in the air (usually about 343 meters per second, the specific speed is affected by the air temperature and humidity), and t is the round-trip time of ultrasound.

[0062] Height calculation: If the sensor is installed on the ground and directly below the lower position of the cable, the measured distance d is the height of the cable. However, in practice, the monitoring terminal is often not directly below the cable, and there are many interference factors.

[0063] Therefore, using an anti-interference algorithm, in order to obtain stable altitude data, the system will continuously measure altitude data multiple times, and use a filtering algorithm to eliminate the influence of environmental interference (such as strong winds, pedestrians, vehicles, etc.) to ensure that the data is accurate and reliable.

[0064] The ultrasonic distance sensor is equipped with an anti-interference algorithm as follows:

[0065] D filtered =α·D current +(1-α)·D previous (1)

[0066] Among them, D filtered is the distance data after filtering, α is the filtering factor, the filtering strength is adjusted according to the external environment and is set between 0 and 1; D current is the distance data obtained in the current measurement cycle, that is, the cable distance data detected by the sensor at the current time point; D previous is the distance data obtained in the previous measurement cycle, that is, the cable distance data recorded by the sensor at the previous time point. The anti-interference algorithm effectively reduces the impact of environmental noise on distance measurement.

[0067] Of course, the value of the filter factor α can be adjusted according to different situations of environmental noise to adapt to different interference conditions. Below are several typical scenarios and examples of how to adjust α:

[0068] 1). Low noise environment

[0069] In an environment without obvious interference factors, such as an open road without strong wind, no vehicles or pedestrians passing by, and no dense reflections from buildings, α can be set higher, such as 0.8-0.9, so that the system pays more attention to the current measurement value D current .

[0070] Under low-interference conditions, current measurements are usually more reliable and can quickly reflect real-time data without relying too much on historical data.

[0071] Example: When measuring cable height on an open road in the suburbs, where there is little interference, setting α = 0.9 will make the system more sensitive and respond quickly to actual height changes.

[0072] 2). Moderate noise environment

[0073] In scenarios with certain interference factors, such as wind force, but the wind speed is relatively stable, or vehicles occasionally pass by nearby, but the impact is small. α can be set to a medium level, such as 0.5-0.7, taking into account both current measurements and historical data.

[0074] In a medium noise environment, directly relying on the current measurement value may cause certain errors. A medium filtering factor can balance real-time performance and data stability.

[0075] Example: When measuring cable height on a non-busy road in an urban area, setting α = 0.6 will effectively smooth the data, making the system response moderate and not affected by transient noise.

[0076] 3). High noise environment

[0077] In environments with large interference, such as areas with strong changes in wind speed, dense traffic, or areas with a large number of high-rise buildings reflecting signals, α can be set lower, such as 0.1-0.3, so that the system relies more on previously measured data to enhance data stability.

[0078] In a high-noise environment, the current measurement value may fluctuate greatly, and direct use may cause errors. After reducing α, the system relies more on historical data, which helps smooth out noise interference.

[0079] Example: When measuring cable height in a busy city center, setting α = 0.2 can help eliminate temporary interference caused by passing vehicles and provide more stable measurement results.

[0080] By flexibly adjusting the filter factor α, the system can achieve stability and smoothness of measurement data in different environments.

[0081] Furthermore, the correction unit of the processor calculates the vertical height of the cable based on the distance data of the ultrasonic distance sensor and the attitude information of the gyroscope sensor. The specific calculation formula is as follows:

[0082] H=D filtered ×sin(θ) (2)

[0083] Where θ is the terminal tilt angle, that is, the angle with the horizontal plane; H is the corrected measurement height.

[0084] If the device tilts due to strong winds or collisions, the gyroscope sensor will record the tilt angle and transmit the angle data to the processor. The processor calculates the vertical height value based on the angle and distance data to ensure that the height data remains accurate even in a tilted state.

[0085] In specific implementation, for example, the cable height monitoring terminal reads the height sensor data G and the gyroscope sensor data A once within a time period T, and automatically calculates the vertical height data SG of the cable from the ground based on the G and A data. If SG is less than the set height BG, the G and A data are read multiple times at intervals of T1ms for N consecutive times. If the N times are all lower than BG, an alarm is triggered. Otherwise, wait for T and collect again.

[0086] In addition, the processor also includes: an alarm unit and an adaptive energy-saving unit;

[0087] The alarm unit is used to generate scratch warning information based on the collected collision data and send out an alarm when the vibration sensor monitoring terminal is hit by a vehicle or a pedestrian; and transmit the information to the data management platform.

[0088] When the vibration sensor detects an external force collision (for example, a vehicle or pedestrian touches the monitoring terminal), it will immediately collect vibration data and generate collision warning information. For example, when a collision perception signal is obtained, the height sensor data G and gyroscope sensor data A are read once, and the vertical height data SG of the cable from the ground is automatically calculated based on the G and A data, and the warning data is reported.

[0089] The warning information includes data such as collision intensity and timestamp. The processor will package the information and upload it to the data management platform through the communication module to remind the operation and maintenance personnel to check the safety status of the cables and equipment.

[0090] The adaptive energy-saving unit is used to enter the sleep mode when the monitoring terminal has no operation for a long time; or to send a low-battery alarm message when the power of the power supply module is detected to be lower than the threshold. For example, if the voltage value V is read once within the time period T, if the voltage is lower than BV, and if the voltage alarm mark is false, the low voltage alarm data is reported, and the voltage alarm mark is set to TRUE after the report is successful.

[0091] Low power consumption design of adaptive energy-saving unit: In order to save electricity, the monitoring terminal is equipped with an adaptive energy-saving unit. When the system detects that the monitoring terminal has no operation or data collection requirements within the set time, the adaptive energy-saving unit will put the monitoring terminal into sleep mode to extend the life of the solar charging battery. At the same time, when the power supply module detects that the power is lower than the set threshold, the terminal will issue a low power alarm message, which will be uploaded to the data management platform through the communication module to prompt the operation and maintenance personnel to replace the battery as soon as possible.

[0092] In one embodiment, the monitoring terminal communicates with the data management platform through a 4G communication module. The main advantages are higher data transmission rate, low latency, wider network coverage, flexible device interconnection capability and higher reliability. The 4G communication module is suitable for high-frequency and large data volume transmission scenarios. It can maintain a stable connection under various environmental conditions and support more complex equipment and application integration. For the cable monitoring system, the 4G communication module can provide stronger real-time performance, larger-scale data processing capabilities and higher system scalability. The module realizes real-time and stable data transmission, ensures that data is successfully uploaded to the data management platform, and facilitates remote monitoring and analysis.

[0093] In one embodiment, the magnetic field sensor is a sensor that can detect and measure the strength, direction and change of the magnetic field, and is generally used in applications such as position detection, direction sensing, object recognition, etc. In the cable height monitoring system, the magnetic field sensor can be used as an auxiliary sensor to improve data accuracy and system stability.

[0094] 1. Used for position and posture correction

[0095] The gyroscope sensor in the monitoring terminal is used to detect the device's attitude and tilt angle, but in some cases (such as limited device installation location, external vibration) it may affect the measurement accuracy. By adding a magnetic field sensor, the attitude information can be further corrected.

[0096] Working principle: The magnetic field sensor can sense the direction of the geomagnetic field and work with the gyroscope sensor to form a three-axis attitude reference system. Even if the device is tilted or in an unstable environment, the monitoring terminal can correct the attitude through the geomagnetic direction. This multi-sensor fusion method can improve the measurement accuracy of the cable height, especially when the device has a slight tilt or vibration, the data output is automatically adjusted through the geomagnetic field direction to ensure the stability of the height measurement.

[0097] 2. Detection of abnormal magnetic field changes

[0098] The magnetic field sensor can detect the magnetic field environment around the device in real time and identify abnormal magnetic field changes. For example, when there is a strong source of electromagnetic interference nearby (such as high-power equipment, vehicles, etc.), the system will receive an interference warning.

[0099] Working principle: When the magnetic field sensor detects a significant change in the magnetic field, the system can automatically determine whether there is an interference source in the area, and use the signal to specially mark or filter the measurement data in the area. This design helps to reduce the impact of interference sources on height measurement data and avoid false alarms or data deviations due to electromagnetic interference. At the same time, this function can provide operation and maintenance personnel with interference information in the cable area for further protection of cable safety.

[0100] 3. Data compensation in failure state

[0101] If some sensor modules (such as ultrasonic sensors or gyroscope sensors) are in failure, the magnetic field sensor can be used as a backup sensor to provide basic data reference.

[0102] Working principle: When the ultrasonic sensor fails to work properly, the system can obtain the approximate change trend of the cable position through the magnetic field sensor and make predictive compensation for the height based on historical data.

[0103] This design improves the robustness and fault tolerance of the system, allowing the system to provide basic cable status monitoring through magnetic field information even in the event of partial sensor failure, thereby improving data continuity and system reliability.

[0104] In one embodiment, referring to Figure 3 The following is a detailed diagram of the functions of the data management platform. It not only has the basic functions of data management and alarm, but also can intelligently and automatically optimize the monitoring and operation and maintenance processes:

[0105] 1. Visualization and intelligent analysis unit

[0106] The data management platform can dynamically display the height distribution, location distribution and alarm status of road cables in real time, and provide a variety of views including bar charts, heat maps, trend charts, etc., to facilitate management personnel to intuitively monitor the system operation status.

[0107] Based on historical monitoring data, machine learning algorithms are used to analyze data trends, such as predicting the height change patterns of cable locations, identifying potential risk points, and providing a basis for advance maintenance.

[0108] Among them, the machine learning algorithm can adopt an adaptive time series prediction model, namely, Adaptive Variational Recurrent Network (AVRN), which is used to predict the cable height change trend in real time and identify potential anomalies.

[0109] The specific steps include:

[0110] 1) The cable height data is considered as a multivariate time series input, and dynamic modeling is performed by combining seasonal characteristics and environmental data (such as temperature, humidity, wind speed, etc.).

[0111] 2) Use adaptive variational recursive networks to adaptively adjust the network structure and weights through the Bayesian reasoning mechanism, so as to better adapt to the short-term fluctuations and long-term changes of height data.

[0112] 3) When the prediction period is short, the system will automatically reduce the model complexity to keep the computational cost and latency low; when data for a longer period is available, the system will enable more complex structures to improve prediction accuracy.

[0113] The AVRN model can accurately predict future changes in cable height in a changing environment, generate predicted height values ​​for the next few hours or days, identify potential abnormal heights in advance, and prevent safety hazards.

[0114] The adaptive time series forecasting model combines adaptivity and Bayesian reasoning mechanisms to cope with high data volatility and long-term and short-term trends. The following is the reasoning process for the adaptive variational recurrent network:

[0115] a. Input features

[0116] Multivariate time series input: The model input includes cable height data, sensor attitude, environmental factors (such as temperature, humidity, wind speed), etc. The multivariate features of these data can capture the impact of different factors on cable height.

[0117] For environmental data and posture data, periodic features such as seasonality and day and night are added to enhance the prediction accuracy of the model in different time periods.

[0118] b. Variational recursive structure

[0119] Variational Autoencoder (VAE) structure: VAE captures the potential representation of time series, allowing the model to handle the complex nonlinear characteristics of highly complex data.

[0120] Recurrent Neural Network (RNN) layer: The potential representation of the VAE output is input into the RNN layer, and LSTM or GRU units are used for sequence modeling. The RNN layer captures the long-term and short-term dependency characteristics of the data through time recursion.

[0121] In this embodiment, in order to adapt to the dynamically changing time series, AVRN adds an adaptive weight adjustment mechanism to the VAE structure, which can automatically increase the representation ability of the features when the data fluctuates greatly.

[0122] c. Bayesian Reasoning Module

[0123] Adaptive Bayesian Update: AVRN uses Bayesian reasoning to adjust the weights of the prediction model in real time. Whenever new data arrives, the model will dynamically update the parameter distribution, allowing the model to adapt to real-time changes in data.

[0124] Through Bayesian regularization, AVRN can control the complexity of the model. For relatively stable data segments, the model will adaptively reduce the complexity; for drastically fluctuating data segments, the model will increase the complexity to fit more accurately.

[0125] d. Adaptive adjustment mechanism

[0126] Error-based dynamic learning rate adjustment: AVRN introduces a dynamic learning rate adjustment mechanism based on prediction error. When the prediction error of the model is large, the learning rate is automatically increased to accelerate the model's adaptation to new data; when the error is small, the learning rate is reduced to prevent overfitting.

[0127] AVRN adjusts the time window length according to data changes. For example, when there is a short-term high volatility, the system will shrink the time window to focus on the current trend; in a more stable stage, the time window will automatically expand to capture long-term trends.

[0128] e. Exception handling and self-correction

[0129] AVRN embeds anomaly detection into the model structure through variational Bayesian methods. When some data deviates from the normal distribution, the model will identify it as a potential anomaly and mark it as "noise data", which will not significantly affect the prediction results.

[0130] Anomaly detection feedback loop: The model’s anomaly detection results are fed back to the input layer, which stabilizes the model’s time series forecasts by “ignoring” or “smoothing” outliers.

[0131] f. Multi-step prediction and adaptive update

[0132] AVRN uses a rolling multi-step forecasting method, which uses the forecast value of the previous time step to predict the next time step, and then recursively until the preset time span is reached. This can be used to generate highly predicted values ​​for the next few hours or days.

[0133] Every time new data arrives, AVRN fine-tunes the weights during lightweight training. This online update mechanism ensures that the model is always up to date and adapts to changes in the environment.

[0134] g. Adaptive output uncertainty estimation

[0135] The output of AVRN not only contains the height prediction value, but also provides uncertainty intervals (i.e., confidence intervals) to evaluate the reliability of the prediction using Bayesian distribution. This uncertainty estimate is crucial for subsequent alarm priority judgment and maintenance decisions.

[0136] When the uncertainty is high, the system will mark the prediction as "low confidence" and recommend further data collection; when the uncertainty is low, the system can be directly used for subsequent alarm judgment and maintenance suggestion generation.

[0137] Through the adaptive variational recurrent network, AVRN can accurately predict the cable height in an uncertain environment, automatically adjust the model structure and parameters, and provide a confidence interval for the prediction, enabling the system to make intelligent decisions based on the changing trend of the data and the uncertainty of the prediction. This innovative algorithm is particularly suitable for scenarios with data volatility, abnormality and real-time requirements in cable height monitoring.

[0138] 2. Alarm setting unit

[0139] Based on the criticality, location or frequency of use of different cables, the platform automatically sets different alarm levels (such as low, high, and emergency alarms) and dynamically adjusts the alarm thresholds based on actual environmental changes.

[0140] For example, the height threshold is automatically lowered on busy roads or in bad weather to increase sensitivity; in low-risk areas, the system appropriately relaxes the threshold to reduce unnecessary alarms and maintenance frequency and improve management efficiency.

[0141] 3. Intelligent maintenance dispatch unit

[0142] When abnormal height data is monitored or an alarm is received, the platform will automatically generate maintenance tasks based on the alarm level, fault type and resource allocation, and intelligently assign them to appropriate maintenance personnel or teams.

[0143] For example, through geo-location-based optimization algorithms, the platform can prioritize dispatching the nearest maintenance personnel to handle tasks, saving manual management and allocation time and improving maintenance efficiency.

[0144] 4. Predictive maintenance recommendation unit

[0145] The platform provides predictive maintenance recommendations based on historical data and environmental information. For example, data modeling can predict that certain cables may become highly abnormal over a period of time due to aging or climate change.

[0146] Predictive maintenance can not only reduce the probability of sudden failures, but also optimize the use of maintenance resources, making operations management more efficient and intelligent.

[0147] This unit uses a self-learning meta-learning network (SL-MLN) to automatically generate personalized maintenance suggestions based on the equipment's operating status, alarm data, maintenance records, etc.

[0148] Using the meta-learning framework, the system can quickly generate maintenance recommendations even with a small amount of data. When the system encounters a new situation (such as environmental disturbances that have never occurred or changes in equipment characteristics), SL-MLN fine-tunes with a small amount of new data, quickly adapts to the changes, and gives the best maintenance measures. The model continuously self-learns, gradually optimizes and personalizes maintenance recommendations, and improves the system's response efficiency and maintenance quality.

[0149] When new types of faults occur, the system can quickly learn and generate adaptive maintenance plans. For example, if it detects that the height fluctuation of cables in a certain area is increasing, SL-MLN will generate early inspection suggestions to avoid excessive wear and tear of equipment.

[0150] 5. Intelligent power saving and wake-up management unit

[0151] The platform monitors the power status of each terminal in real time. When it detects that the terminal power is low, the platform can notify maintenance personnel to replace the battery. When the terminal is in a low-flow monitoring area, the platform will remotely issue a command to enter power-saving mode to extend the battery life of the device.

[0152] By centrally managing the power status and mode switching of the terminal, the service life of the device can be extended, and the collection and uploading of key data can be maintained while saving power.

[0153] 6. Equipment status monitoring and remote diagnosis unit

[0154] The platform can monitor the operating status of all monitoring terminals (including ultrasonic sensors, gyroscopes, vibration sensors, etc.) in real time, automatically generate reports when equipment fails, and remind technicians to perform maintenance.

[0155] Through remote diagnosis, the platform analyzes the real-time data and historical working status of the terminal to preliminarily determine the cause of the fault so that technicians can prepare the necessary materials and tools in advance and improve maintenance efficiency.

[0156] 7. Environmental and seasonal adaptation unit

[0157] The platform can automatically adjust the terminal's measurement mode according to seasonal changes. For example, it can adjust the measurement frequency and sensitivity in winter when the temperature is low and the sensor may be affected, or perform multiple confirmations on alarm data in the rainy season to avoid errors caused by humidity and temperature fluctuations.

[0158] Automatically adapt to different environmental factors and seasonal characteristics, reduce the risk of manual adjustments and system false alarms, and make the system more intelligent and stable.

[0159] 8. Big data analysis and report generation unit

[0160] The platform regularly analyzes the collected cable height, alarm frequency and maintenance records, and generates monthly, quarterly or annual reports to help users understand the overall status and trends of road cables.

[0161] With the help of big data technology, the platform can discover potential failure modes and patterns, automatically generate structured reports and charts, and help management make more forward-looking decisions.

[0162] 9. Multi-platform linkage and information sharing unit

[0163] The platform can also be linked with other municipal management platforms (such as road lighting management, traffic signal monitoring systems, etc.) to achieve data sharing and collaborative management.

[0164] By integrating data sources from different systems, the platform can provide comprehensive information, such as road conditions, surrounding facilities, etc., providing more comprehensive data support for cable height monitoring and maintenance.

[0165] The data management platform receives the height data and alarm data uploaded by the terminal in real time, and can also manage the GPS position and span information of the cable cross-road, display the cable and obstacle information in real time on the electronic map, and automatically dispatch orders according to the preset cable ownership intelligent maintenance dispatch unit. For example, it can also push hidden danger work order information through WeChat applet or the provincial company's intelligent network management platform. It can specify the area cable monitoring equipment to collect height assessment data, provide height risk analysis and optimization suggestions, and support hidden danger inspection lists.

[0166] Reference Figure 4 The data management platform also has the following functions:

[0167] 1. Cross-Cable Resource Management

[0168] 1) Basic information maintenance unit for cross-line cables

[0169] Manage the basic information of cross-road cables, including the district and county where the cross-road cables are located, maintenance team, address, GPS coordinates, lateral span, initial elevation information, cross-road cable installation method, installation time, number of cables, cable type, whether it is a trunk, whether it is shared, sharing party (China Mobile, China Telecom, China Unicom), last inspection time and other information.

[0170] Provides the functions of adding, modifying, deleting and querying cross-route cables;

[0171] Provide basic cable information query by district, maintenance team, elevation, trunk or not, and sharing party;

[0172] Provides the function of exporting cross-route cable list information to EXCEL;

[0173] Provides the function of importing cross-route cable information into EXCEL;

[0174] 2) Cross-cable parameter management unit

[0175] The cross-line cable parameters include the configuration of cross-line cable installation method, sharing party, cable category and other parameters.

[0176] Supports administrators to customize parameters.

[0177] 3) Management unit of maintenance team of cross-line cables

[0178] Manage the basic information of the maintenance team of the cross-line cable, including team ID number, team name, person in charge, contact number, district and county, etc.;

[0179] Provides the functions of adding, modifying, deleting and querying cross-line cable teams;

[0180] Provide the moving in and out of the team to which the cross-route cable belongs, with a simple interface and easy operation;

[0181] Provides the function of exporting cross-line cable maintenance team information to EXCEL;

[0182] Provides the function of importing EXCEL files of cross-line cable maintenance teams;

[0183] Provides batch import function for maintenance team information of cross-line cables;

[0184] The cross-road cable maintenance team information should be consistent with the personnel information in the provincial NOC system to prepare for the subsequent dispatch of obstacle work orders.

[0185] 4) Cross-cable monitoring equipment management unit

[0186] Manage the basic information of the span cable monitoring device, including device ID, IMSI, device model, device software version number, and span cable ID to which the device belongs.

[0187] Provides the functions of adding, modifying, deleting and querying cross-cable monitoring equipment;

[0188] Provides the function of exporting cross-line cable equipment information to EXCEL;

[0189] Provides the function of importing cross-line cable equipment information into EXCEL;

[0190] Provides batch import function of cross-cables belonging to cross-cable equipment;

[0191] 2. Cable Height Monitoring Hall

[0192] 1) Cross-cable overview unit

[0193] Provides an overview of cross-line cables, including the total number of current cross-line cables, the total number of monitoring devices, the number of 24-hour online devices, the number of alarms in 72 hours, the number of alarms to be handled within 72 hours, and the number of alarms handled within 72 hours.

[0194] Provides a graph of the number of various alarms in the past 30 days;

[0195] Provides a graph of the number of various alarms in the past 12 months;

[0196] Provide various alarm processing and pending processing pie charts;

[0197] Provide pie charts showing the proportion of various alarms;

[0198] Provide a list of duplicate alarms;

[0199] 2) Cross-cable alarm display unit

[0200] Display voltage alarm list and handling status;

[0201] Display the list of tilt reports and the status of their disposal;

[0202] Display the low voltage alarm list and handling status;

[0203] Display the insufficient altitude warning list and handling status;

[0204] Display the scratch alarm list and processing status;

[0205] Display the current working status, working mode (monitoring, evaluation), alarm status, and heartbeat status time of the height monitoring equipment;

[0206] 3) Pre-processing of cross-cable alarm

[0207] Provide voltage alarm handling function;

[0208] Provides display tilt alarm handling function;

[0209] Provide low voltage alarm handling function;

[0210] Provide the function of handling the insufficient display height warning;

[0211] Provides display scratch alarm handling function.

[0212] This data management platform can not only significantly improve the automation and intelligence level of the system, but also effectively optimize the operation and maintenance management process, improve the efficiency and stability of the entire system, and meet the management needs of modern smart cities.

[0213] A road cable height monitoring system provided by the present invention can accurately, efficiently and intelligently realize the height monitoring and maintenance management of road cables, significantly reduce manual intervention and maintenance costs, and improve the safety management level of road cables.

[0214] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0215] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A road cable height monitoring system, characterized in that: include: Cable height monitoring terminal and data management platform; Wherein, the monitoring terminal comprises: a processor and an ultrasonic distance sensor, a gyroscope sensor, a vibration sensor, a communication module and a power supply module respectively connected to the processor; The ultrasonic distance sensor is used to measure the distance between the terminal and the cross-cable, is configured with an anti-interference algorithm, and transmits the measured distance data to the processor; The gyro sensor is used to record the posture information when the terminal is in an incorrect posture due to strong wind or collision, and transmit the posture information to the processor; The vibration sensor is used to detect vibration data generated by an external force impacting the terminal and transmit the vibration data to the processor; The processor is used for data collection, and processes and transmits the collected data based on the configured correction unit and alarm unit; The communication module is used to transmit the monitoring data generated by the processor to the data management platform in real time; The power supply module adopts a solar self-powered design and has a low-battery alarm function to provide power support to the monitoring terminal; The data management platform is used to visualize and intelligently analyze the acquired monitoring data, set multi-level alarms and adaptive alarm thresholds, and intelligently dispatch inspections and provide predictive maintenance recommendations.

2. A road cable height monitoring system according to claim 1, characterized in that: The ultrasonic distance sensor is configured with an anti-interference algorithm as follows: D filtered =α·D current +(1-a)·D previous (1) Among them, D filtered is the distance data after filtering, α is the filtering factor, the filtering strength is adjusted according to the external environment and is set between 0 and 1; D current is the distance data obtained in the current measurement cycle, that is, the cable distance data detected by the sensor at the current time point; D previous It is the distance data obtained in the previous measurement cycle, that is, the cable distance data recorded by the sensor at the previous time point.

3. A road cable height monitoring system according to claim 2, characterized in that: The correction unit is used to calculate according to the attitude information of the gyro sensor and the measured distance data of the ultrasonic distance sensor to obtain the vertical height of the measured cable; the formula is as follows: H=D filtered ×sin(θ) (2) Wherein, θ is the terminal tilt angle, that is, the angle with the horizontal plane; H is the measurement height.

4. A road cable height monitoring system according to claim 1, characterized in that: The alarm unit is used to generate scratch warning information based on the collected collision data and issue an alarm when the vibration sensor monitoring terminal is collided with a vehicle or a pedestrian; And transmit it to the data management platform.

5. A road cable height monitoring system according to claim 1, characterized in that: The processor adopts a SOC chip and also includes: The adaptive energy-saving unit is used to put the monitoring terminal into sleep mode when there is no operation for a long time; or to issue a low-battery alarm message when the power of the power supply module is detected to be lower than the threshold.

6. A road cable height monitoring system according to claim 1, characterized in that: The communication module is a 4G communication module.

7. A road cable height monitoring system according to claim 1, characterized in that: The data management platform comprises: Visualization and intelligent analysis unit, used to dynamically display the height distribution, location distribution and alarm status of road cables in real time, and provide visual displays including bar graphs, heat maps and trend graphs; The alarm setting unit is used to automatically set different alarm levels according to the criticality, location or frequency of use of different cables, and dynamically adjust the alarm threshold according to actual environmental changes; Intelligent maintenance dispatch unit, which is used to automatically generate maintenance tasks and intelligently assign them to appropriate maintenance personnel or teams based on the alarm level, fault type and resource allocation when abnormal height data is monitored or a fault alarm is received; The predictive maintenance suggestion unit provides predictive maintenance suggestions based on historical data and environmental information.

8. A road cable height monitoring system according to claim 7, characterized in that: The data management platform further includes: The intelligent power saving and wake-up management unit is used to monitor the power status of each monitoring terminal in real time. When the terminal power is low, the maintenance personnel will replace the battery; when the monitoring terminal is in a low-flow monitoring area, a remote command is issued to enter the power saving mode; Equipment status monitoring and remote diagnosis unit, used to monitor the operating status of all monitoring terminals in real time, automatically generate reports when abnormalities occur and remind technicians to perform maintenance; Environmental and seasonal adaptation unit, used to automatically adjust the terminal's measurement mode according to seasonal changes, including: adjusting the measurement frequency and sensitivity, and multiple confirmation of alarm data in the rainy season; The big data analysis and report generation unit is used to generate monthly, quarterly or annual reports based on the cable height, alarm frequency and maintenance records collected through regular analysis.

9. A road cable height monitoring system according to claim 7, characterized in that: The visualization and intelligent analysis unit is also used to use an adaptive variational recursive network to predict the cable height change trend in real time and identify potential anomalies; Specifically include: 1) Treat the cable height data as a multivariate time series input and combine it with seasonal characteristics and environmental data for dynamic modeling; 2) Using adaptive variational recurrent networks, the structure and weights of the network are adaptively adjusted through the Bayesian inference mechanism; 3) Based on historical height data, predict future changes in cable height, generate predicted height values ​​for the next few hours or days, and identify potential abnormal heights in advance.

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