Automatic alarm method and system for inclination of telegraph pole

Abstract

The invention discloses an automatic alarm method and system for pole inclination, and relates to the technical field of pole inclination detection, pole state data and environmental data are acquired by constructing a multi-modal sensor array, and the data acquisition frequency is optimized based on a dynamic period adjustment mechanism, so that the pole inclination is accurately detected. The method comprises the following steps of: constructing a telegraph pole ad hoc network: realizing interaction among telegraph poles by routing nodes, common nodes and virtual transmission channels, and judging whether the telegraph poles are inclined or not in a cloud server by utilizing a dynamic threshold value; setting of a dynamic threshold is combined with an environment compensation model and a vibration correction item, so that the judgment accuracy is improved; and corresponding alarms, including preliminary alarms, serious alarms and emergency alarms, are sent to an administrator based on the multi-level alarm threshold values, and early warning is carried out to surrounding people through acousto-optic equipment under serious conditions. According to the method, the intelligence and response efficiency of pole inclination monitoring are remarkably improved through a gating mechanism and dynamic threshold judgment.

Description

Technical Field

[0001] The present invention relates to the technical field of pole inclination detection, and particularly relates to an automatic alarm method and system for pole inclination. Background Art

[0002] As an important carrier of power, communication and other infrastructure, poles are exposed to complex environments for a long time and are easily affected by factors such as wind loads, soil settlement, mechanical vibrations and human collisions, resulting in inclination or even collapse, which in turn causes power outages, communication failures or public safety accidents; Existing pole inclination monitoring technologies mainly rely on a single sensor for data collection, and it is difficult to comprehensively reflect the comprehensive state of the pole. For example, traditional methods usually only monitor the static inclination angle, while ignoring the dynamic effects of vibration spectra and environmental parameters on the stability of the pole; in addition, the decision-making mechanism with fixed thresholds has poor adaptability in the face of complex environmental changes and is prone to false alarms or missed alarms due to meteorological interference; in terms of network architecture, most systems use wired transmission or centralized wireless networks, which have problems of high deployment costs and poor scalability. Especially in remote areas or large-scale pole groups, communication delays and node failures may cause data interruptions, seriously affecting the real-time monitoring. At the same time, when collecting multi-source data, the mixed storage of high-frequency vibration data and low-frequency environmental parameters leads to storage waste, and high-priority alarms in narrowband channels are easily blocked by low-priority data, resulting in the situation where pole inclination cannot be detected in time; Therefore, it is of great significance to design an intelligent method for automatic alarm of pole inclination. Summary of the Invention

[0003] The purpose of the present invention is to provide an automatic alarm method and system for pole inclination to solve the deficiencies in the background art.

[0004] To achieve the above purpose, the present invention provides the following technical solution: An automatic alarm method for pole inclination, comprising: Construct a multi-modal sensor array corresponding to the pole, and use the multi-modal sensor array to collect sensor data including pole state data and environmental data; Construct a pole self-organizing network, the self-organizing network includes a routing node and multiple ordinary nodes, construct a time window in the communication module for pole state data interaction, and transmit the sensor data to the cloud server based on the self-organizing network, and construct a dynamic threshold in the cloud server to determine whether the pole is inclined; Set multi-level alarms based on the dynamic threshold, and send corresponding alarms to the administrator for the inclined pole.

[0005] In a preferred embodiment, the step of constructing a multi-modal sensor array corresponding to the pole and using the multi-modal sensor array to collect sensor data including pole state data and environmental data is: The multimodal sensor array includes an accelerometer, a gyroscope, a piezoelectric vibration sensor, and an environmental sensor, and a pan-tilt unit is configured for the multimodal sensor array for dynamic balancing; The line pole status data and environmental data are collected by using the multimodal sensor array based on a dynamic period adjustment mechanism; Among them, the status data of the line pole includes the line pole ID, three-dimensional tilt angle, tilt rate, and vibration spectrum, and the environmental data includes meteorological parameters and soil parameters.

[0006] In a preferred embodiment, the dynamic period adjustment mechanism is as follows: Define an initial collection period , and collect the line pole status data and environmental data within a preset number of periods as a threshold data set , and obtain the mean value of the threshold data set within a preset period as the reference data: Among them represents the reference data, represents the status data and environmental data set in the th period; Collect the set of line pole status data and environmental data within a preset number of periods after collection, and obtain the data mean value : Among them represents the data mean value, represents the status data and environmental data set in the th period; Preset an error threshold , calculate the error between the data mean value and the reference data : If then extend the collection period based on a preset increment, if then do not adjust the collection period.

[0007] In a preferred embodiment, for the step of constructing a line pole self-organizing network, the self-organizing network includes a routing node and multiple ordinary nodes, and a time window is constructed in the communication module for the interaction of line pole status data: For the utility poles in the preset area, select the utility poles in the central area to configure routing nodes including communication modules and sensors, and configure the remaining utility poles with ordinary nodes including communication modules and sensors. The routing nodes and ordinary nodes are connected through a virtual transmission channel, and the routing nodes, ordinary nodes, and virtual transmission channel are combined to form a self-organizing network of utility poles; Construct time windows for the communication modules corresponding to the routing nodes and ordinary nodes to store and transmit sensor data; Connect to the cloud server through the routing node to transmit the sensor data to the cloud server, and judge whether the utility pole is tilted based on the dynamic threshold.

[0008] In a preferred embodiment, the step of constructing the time window for storing and transmitting sensor data is as follows: Define a sensor data set based on the sensor type , construct a grid-style storage space Store the sensor data set, and each sensor data is correspondingly stored in an independent grid unit , set a door for each independent grid unit , and control the output of the sensor data based on the gating mechanism; Assign an initial transmission time slot to each door through the gating mechanism. The value of the initial transmission time slot is based on the priority of the sensor data, expressed as: where, represents the initial transmission time slot, is the priority of the sensor data, is the normalization constant, is the adjustable parameter; Connect the doors of the independent grid units corresponding to the routing nodes of the same sensor data type to the independent grid units corresponding to the ordinary nodes one-to-one through a virtual channel, and set a preset number of detection points on the virtual channel. The detection points are used to identify the priority of the sensor data, generate a borrowing application, map the cache space, and transmit the data; For any moment , the sensor data and the reference data The data deviation is: where, is the data deviation. When , mark as quasi-fault data and update the priority to: where, Is the updated priority, Is the highest priority, Is the preset floating threshold; All kinds of sensor data are transmitted through the gate in the virtual channel. When passing through the detection point, the detection point on the corresponding virtual channel will detect and record the priority of the corresponding sensor data. If it is detected as quasi-fault data and the virtual channel where it is located is blocked, the detection point will send a channel borrowing application to the virtual channel where the sensor data with the lowest priority is located, and use the virtual channel that receives the channel borrowing application as the emergency channel; For the emergency channel, the emergency channel will stop transmitting its own sensor data, activate the cache space mapped by the detection point based on the channel borrowing application, transmit its own sensor data to the cache space, and transmit the quasi-fault data through the detection point of the emergency channel to the adjacent virtual channel for transmission.

[0009] In a preferred embodiment, the step of judging the pole inclination based on the dynamic threshold is as follows: The routing node acquires the sensor data of the adjacent ordinary node and itself , and transmits it to the cloud server, where Represents the inclination data, Represents the vibration data, Respectively represent temperature, wind speed and soil looseness; Construct an environmental compensation model to correct the inclination data: Among them, Is the corrected inclination, Is the true inclination data, and the coefficient is estimated using the multiple linear regression algorithm ; Preset a fixed threshold, and at the same time introduce a dynamic threshold adjustment rule to correct the fixed threshold: Among them, Represents the environmental correction term, Represents the vibration correction term, and Is an adjustable parameter; When Then it is determined that the pole is inclined.

[0010] In a preferred embodiment, the step of setting multi-level alarms based on the dynamic threshold and sending corresponding alarms to the administrator for the inclined pole is as follows: Set multi-level alarm thresholds including the first-level early warning threshold , the second-level early warning threshold And the third-level early warning threshold ; When If it reaches the first-level warning, the cloud server sends a preliminary alarm to the administrator, reminding the administrator to strengthen the monitoring of the poles that trigger the first-level warning line; When If it reaches the second-level warning, the cloud server sends a serious alarm to the administrator, suggesting that the administrator quickly carry out repairs; When If it reaches the third-level warning, the pole that triggers the third-level warning issues a warning to the surrounding people through sound and light alarms, and the cloud server sends an emergency alarm to the administrator, suggesting that the administrator deactivate the relevant power line and organize a technical team to carry out on-site repair or reinforcement.

[0011] The present invention also provides an automatic alarm system for pole inclination, including: Sensor module: A multi-modal sensor array is constructed corresponding to the pole, and sensor data including pole state data and environmental data is collected by using the multi-modal sensor array; Inclination judgment module: Connected to the sensor module, a self-organizing network for poles is constructed. The self-organizing network includes a routing node and multiple ordinary nodes. A time window is constructed in the communication module for the interaction of pole state data. Based on the self-organizing network, the sensor data is transmitted to the cloud server, and a dynamic threshold is constructed in the cloud server to judge whether the pole is inclined; Warning module: Connected to the inclination judgment module, multiple levels of alarms are set based on the dynamic threshold, and corresponding alarms are sent to the administrator for the inclined poles.

[0012] In the above technical solution, the technical effects and advantages provided by the present invention: 1. By adopting a multi-modal sensor array, the present invention integrates various sensors, such as accelerometers, gyroscopes, piezoelectric vibration sensors and environmental sensors, which can comprehensively monitor the state and environmental conditions of the pole from different dimensions. This enables the system to collect real-time dynamic data of the pole. Through the fusion of multi-modal data, the system can provide more accurate state interpretation, effectively reducing the monitoring blind area caused by the failure of a single sensor. Combined with the dynamic cycle adjustment mechanism, the system can adaptively adjust the acquisition frequency according to data stability, extend the cycle and reduce the amount of redundant data in the steady state; restore high-frequency acquisition during abnormal fluctuations to ensure the integrity of key data; 2. The present invention provides high flexibility and reliability for the communication and data transmission of the system by constructing a self-organizing network of utility poles. The self-organizing network uses an architecture with one routing node and multiple ordinary nodes, enabling each sensor node to communicate with each other and share data. At the same time, a time window combined with a gating mechanism is constructed in the communication module to control data output by controlling data output, ensuring the priority transmission of key data, improving the response ability and fault handling efficiency, and reducing the risk of data loss. This solution enhances the real-time monitoring ability and reliability of the system, ensures the safe and stable operation of power and communication infrastructure under various environmental conditions, and provides a solid foundation for the implementation of the intelligent monitoring system. 3. The present invention effectively improves the sensitivity and accuracy of tilt determination and reduces the incidence of false alarms and missed alarms by constructing a dynamic threshold and environmental compensation mechanism. Traditional monitoring systems mostly rely on static thresholds, which are often ineffective in dealing with different environmental conditions and situation changes. The dynamic threshold mechanism analyzes real-time data through algorithms and intelligently adjusts the alarm threshold to ensure a more accurate judgment of the pole state. Combining with a multi-level alarm system significantly improves the efficiency of accident response. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0014] Figure 1 It is a flowchart of the method of the present invention.

[0015] Figure 2 It is a system block diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0017] Example 1, please refer to Figure 1 As shown, a method for automatically alarming the tilt of a utility pole in this embodiment includes: S1. Construct a multi-modal sensor array corresponding to the utility pole, and use the multi-modal sensor array to collect sensor data including utility pole state data and environmental data; S2. Build a self-organizing network of utility poles. The self-organizing network includes a routing node and multiple ordinary nodes. Build a time window in the communication module for the interaction of utility pole status data. Transmit the sensor data to the cloud server based on the self-organizing network. Build a dynamic threshold in the cloud server to determine whether the utility pole is tilted. S3. Set multiple levels of alarms based on the dynamic threshold, and send corresponding alarms to the administrator for the tilted utility poles. As described in the above steps S1 - S3, utility poles, as important carriers of power, communication and other infrastructure, are long-term exposed to complex environments and are vulnerable to factors such as wind loads, soil settlement, mechanical vibrations and human collisions, resulting in tilting or even collapse, and further causing power outages, communication failures or public safety accidents. Existing utility pole tilt monitoring technologies mainly rely on a single sensor for data collection, and it is difficult to comprehensively reflect the comprehensive state of utility poles. For example, traditional methods usually only monitor static inclination angles, while ignoring the dynamic effects of vibration spectra and environmental parameters on the stability of utility poles. In addition, the fixed-threshold determination mechanism has poor adaptability in the face of complex environmental changes and is prone to false alarms or missed alarms due to meteorological interference. In terms of network architecture, most systems use wired transmission or centralized wireless networks, which have problems such as high deployment costs and poor scalability. Especially in remote areas or large-scale utility pole groups, communication delays and node failures may cause data interruptions, seriously affecting the real-time monitoring. At the same time, when collecting multi-source data, the mixed storage of high-frequency vibration data and low-frequency environmental parameters leads to storage waste, and high-priority alarms in narrowband channels are easily blocked by low-priority data, resulting in the situation where the tilting of utility poles cannot be detected in time. By adopting a multi-modal sensor array, the present invention integrates multiple sensors, such as accelerometers, gyroscopes, piezoelectric vibration sensors, and environmental sensors, which can comprehensively monitor the status and environmental conditions of utility poles from different dimensions. This enables the system to collect dynamic data of utility poles in real time. Through the fusion of multi-modal data, the system can provide more accurate status interpretation, effectively reducing the monitoring blind spots caused by single-sensor failures. Combined with the dynamic cycle adjustment mechanism, the system can adaptively adjust the acquisition frequency according to data stability, extending the cycle and reducing the amount of redundant data in the steady state; restoring high-frequency acquisition during abnormal fluctuations to ensure the integrity of key data; providing high flexibility and reliability for the communication and data transmission of the system by constructing a self-organizing network for utility poles. The self-organizing network uses an architecture with one routing node and multiple ordinary nodes, enabling each sensor node to communicate with each other and share data. At the same time, a time window combined with a gating mechanism is constructed in the communication module to control data output, ensuring the priority transmission of key data, improving the response ability and fault handling efficiency, and reducing the risk of data loss. This solution enhances the real-time monitoring ability and reliability of the system, ensuring the safe and stable operation of power and communication infrastructure under various environmental conditions, and providing a solid foundation for the implementation of intelligent monitoring systems; by constructing a dynamic threshold and environmental compensation mechanism, the sensitivity and accuracy of tilt determination are effectively improved, and the incidence of false alarms and missed alarms is reduced. Traditional monitoring systems mostly rely on static thresholds, which are often powerless in the face of different environmental conditions and situation changes. The dynamic threshold mechanism analyzes real-time data through algorithms and intelligently adjusts the alarm threshold to ensure a more accurate judgment of the status of utility poles. Combined with a multi-level alarm system, the efficiency of accident response is significantly improved.

[0018] In one embodiment, the steps S1 of constructing a multi-modal sensor array for the corresponding utility pole and collecting sensor data including utility pole status data and environmental data by using the multi-modal sensor array include: S11. The multi-modal sensor array includes an accelerometer, a gyroscope, a piezoelectric vibration sensor, and an environmental sensor, and a pan-tilt unit is configured for the multi-modal sensor array for dynamic leveling; S12. Using the multi-modal sensor array to collect utility pole status data and environmental data based on the dynamic cycle adjustment mechanism; S13. The status data of the utility pole includes the utility pole ID, three-dimensional tilt angle, tilt rate, and vibration spectrum, and the environmental data includes meteorological parameters and soil parameters; As described in the above steps S11 - S13, an accelerometer is used to monitor the vibration and tilt of the pole, a gyroscope provides the angular velocity information of the pole to assist in detecting changes in direction, a piezoelectric vibration sensor is used to capture minute vibration signals to evaluate the health status of the pole, and an environmental sensor is used to detect meteorological data including the temperature, humidity, and wind speed at the location of the pole. A pan - tilt head is configured for the multi - modal sensor array, and a pan - tilt head with high - precision attitude control and fast response capabilities is selected, supporting multi - axis rotation, such as a three - axis pan - tilt head, to ensure that the pan - tilt head has sufficient load - bearing capacity to carry the weight of the multi - modal sensor array, which is used to ensure that the sensors can adapt to dynamic leveling in different environments, providing more stable and accurate data collection. Determine the installation position and direction of the multi - modal sensor array to maximize data collection efficiency, adjust the position of the pan - tilt head to ensure that all sensors are in the best working state. After installation, the multi - modal sensor array collects sensor data based on a dynamic cycle adjustment mechanism.

[0019] In one embodiment, the dynamic cycle adjustment mechanism S13 includes: S131. Define an initial collection period , and collect the pole status data and environmental data within a preset number of periods as a threshold data set , and calculate the mean value of the threshold data set within a preset period as the reference data: S132. Where represents the reference data, and represents the set of status data and environmental data in the th period; S133. Collect the set of pole status data and environmental data within a preset number of periods after that, and calculate the data mean value : : S134. Where represents the data mean value, and represents the set of status data and environmental data in the th period; S135. A preset error threshold , calculate the error between the data mean value and the reference data : S136. If then extend the collection period based on a preset increment. If Then the acquisition period is not adjusted; As described in the above steps S2 - S24, the dynamic period adjustment mechanism aims to optimize the data acquisition frequency of sensors according to the changes in real - time data, so as to improve the data validity and save resources. Before starting the monitoring, an initial acquisition period needs to be set, which can usually be selected based on experience or existing data. For example, it can be set to 1 hour or 2 hours. After multiple initial acquisition periods, the data obtained during these initial acquisition periods are used as a threshold data set, and the mean value of the threshold data set is calculated as the reference data. The reference data is mainly used to determine whether the acquisition period needs to be adjusted. After obtaining the reference data, a certain number of initial acquisition periods are preset as, and the mean value of the sensor data in a certain number of initial acquisition periods is calculated as the comparison data. By presetting an error threshold, the error between the comparison data and the reference data is calculated. If the error is less than the error threshold, the acquisition period is extended; if the error is greater than the error threshold, the acquisition period is not adjusted. The idea is that if the error is greater than the preset error threshold, it means the current state exists, so the acquisition period is not changed, and the change of the state is continuously monitored based on the optimal initial acquisition period. If the error is less than or equal to the preset error threshold, it means the current state change is small. At this time, the acquisition period is extended according to the preset increment, so as to enable the long - time operation of the multi - modal sensor array and reduce the generation of redundant data when the on - line pole state changes little. Further, priorities are set for various sensor data, and different initial acquisition periods are set for various sensor data based on the priorities. For important state data, such as the tilt angle, more frequent acquisition may be required, while environmental data such as meteorological parameters can be relatively reduced. This dynamic period adjustment mechanism effectively optimizes the acquisition frequency through the setting of the initial acquisition period, the calculation of the mean value of real - time data, the evaluation of the error threshold, and the implementation of the adjustment strategy, ensuring the timely acquisition of key data under different environmental conditions.

[0020] In one embodiment, the step S2 of constructing the pole self - organizing network, where the self - organizing network includes a routing node and multiple ordinary nodes, and constructing a time window in the communication module for the interaction of pole state data includes: S21. For the poles in the preset area, select the poles in the central area to configure the routing node including the communication module and the sensor, and configure the remaining poles as ordinary nodes including the communication module and the sensor. The routing node and the ordinary nodes are connected through a virtual transmission channel, and the routing node, the ordinary nodes, and the virtual transmission channel are combined to form the pole self - organizing network; S22. Corresponding to the communication modules of the routing node and the ordinary nodes, construct a time window for storing and transmitting sensor data; S23. Connect the routing node to the cloud server to transmit the sensor data to the cloud server, and judge whether the pole is tilted based on the dynamic threshold; As described in the above steps S21 - S23, select the utility pole at the center of the preset area as the routing node, which is responsible for data aggregation and forwarding. The remaining utility poles are used as ordinary nodes, which are responsible for collecting the sensor data of the utility poles and transmitting it to the routing node. A virtual transmission channel is established between the routing node and the ordinary nodes using a 4G module for data transmission. The routing node, ordinary nodes, and virtual transmission channel form a self-organizing network of utility poles, and a star topology structure can be adopted. The routing node radiates as the center, and the ordinary nodes surround it to ensure signal stability and facilitate management and maintenance. A time window is constructed between the routing node and the ordinary nodes for storing and transmitting sensor data regularly. The ordinary nodes transmit the collected sensor data to the cloud server via the routing node. In the cloud server, it is determined whether the utility pole is tilted based on a dynamic threshold.

[0021] In one embodiment, step S22 of the construction time window for storing and transmitting sensor data includes: S221. Define a sensor data set based on the sensor type , construct a grid - type storage space to store the sensor data set, and each sensor data is stored in an independent grid unit correspondingly . Set a door for each independent grid unit , and control the output of the sensor data based on a gating mechanism; S222. Assign an initial transmission time slot to each door through the gating mechanism. The value of the initial transmission time slot is based on the priority of the sensor data, expressed as: S223. Among them, represents the initial transmission time slot, is the priority of the sensor data, is a normalization constant, is an adjustable parameter; S224. Connect the doors of the independent grid units corresponding to the routing nodes of the same sensor data type to the independent grid units corresponding to the ordinary nodes one - to - one through a virtual channel, and set a preset number of detection points on the virtual channel. The detection points are used to identify the priority of the sensor data, generate a borrowing application, map the cache space, and transmit data; S225. For any moment , the data deviation between the sensor data and the reference data is: S226. Among them, is the data deviation. When When, the tag is the pseudo-fault data, and update the priority to: S227. Among them, is the updated priority, is the highest priority, is the preset floating threshold; S228. Various sensor data are transmitted in the virtual channel through the gate. When passing through the detection point, the detection point on the corresponding virtual channel will detect and record the priority of the corresponding sensor data. If it is detected as pseudo-fault data and the virtual channel where it is located is blocked, the detection point will send a channel borrowing application to the virtual channel where the sensor data with the lowest priority is located, and use the virtual channel that receives the channel borrowing application as the emergency channel; S229. For the emergency channel, the emergency channel will stop transmitting its own sensor data, activate the buffer space mapped by the detection point based on the channel borrowing application, transmit its own sensor data to the buffer space, and transmit the pseudo-fault data through the detection point of the emergency channel to the adjacent virtual channel; As described in the above steps S221 - S229, it aims to optimize the data flow through the grid storage space and the gating mechanism. At the same time, through dynamic priority adjustment and channel borrowing mechanism, improve the efficiency and reliability of data transmission, and construct a grid storage space to ensure that the data of each sensor is stored independently, which is convenient for management and extraction. Each independent grid unit is responsible for storing the data set of one sensor, which is convenient for subsequent data reading and processing. Among them , a gating is set for each grid unit, and the output of sensor data within the unit is controlled through a gating mechanism. The gating mechanism is as follows: an initial transmission time slot is assigned to each gate, and a formula for the initial transmission time slot is defined. The idea is to dynamically adjust the opening and closing time of the gate according to the priorities of various sensors. According to the formula, the higher the priority of the sensor data, the higher the frequency of the gate opening and closing, which means that the sensor data can be transmitted more times within a certain period. Because in the actual process, there are data with little change in the pole state and environmental data. If this data is transmitted synchronously with other data, it will cause waste of bandwidth resources, increase the transmission pressure of routing nodes, and increase the computing amount of cloud servers; when two poles communicate with each other, two opposite grid-like storage spaces are formed. The gates of each independent grid unit are connected one-to-one with the same type of gates of the opposite independent grid unit through virtual channels. At the same time, a preset number of detection points are set on the virtual channels. The detection point is a database space with the functions of identification, transmission, and mapping cache space. The detection point stores dynamically updated sensor priority data, and creates a bandwidth monitoring table to record the bandwidth usage of the virtual channel in real time, and generates a borrowing application including information such as required bandwidth and priority based on an event trigger mechanism. The event is that the virtual channel is blocked and the sensor data is quasi-fault data. It has a cache management module that dynamically allocates cache space according to data priority and size; during the transmission of various sensor data, the priorities of various sensor data are regulated. By comparing various sensor data with reference data through a preset floating threshold to determine whether priority adjustment is required. If the difference between a certain sensor data and the basic data is greater than the floating threshold, the sensor data is marked as quasi-fault data and its priority is raised to the highest. When the quasi-fault data passes through the gate and through the detection point, and the virtual channel where the quasi-fault data is located is blocked, a borrowing application is sent to the virtual channel where the sensor data with the lowest priority is located through the detection point, and the quasi-fault data is transmitted to the emergency channel through the detection point. The quasi-fault data is transmitted through the emergency channel. The blockage determination is based on the bandwidth utilization rate of the virtual channel. By calculating the bandwidth utilization rate of the virtual channel, when the bandwidth utilization rate reaches 85%, the virtual channel is regarded as a blocked state. The original transmission data in the emergency channel is stored in the cache space mapped by the detection point, and the virtual channel is cleared for the quasi-fault data. In the actual process, the gate of the virtual channel where the sensor data with the lowest priority is located has the longest opening and closing time, and the data volume in its channel is generally small. As an emergency channel, it can optimize the utilization rate of bandwidth resources and can quickly transmit quasi-fault data, laying a foundation for the inclination determination of the pole.

[0022] In one embodiment, the step S23 of determining the pole inclination based on the dynamic threshold includes: S231. The routing node obtains the sensor data of adjacent ordinary nodes and itself , transmitted to the cloud server, where represents the inclination angle data, represents the vibration data, respectively represent temperature, wind speed, and soil looseness; S232. Construct an environmental compensation model to correct the inclination angle data: S233. Among them, is the corrected inclination angle, is the true inclination angle data, and the coefficients are estimated using the multiple linear regression algorithm ; S234. Preset a fixed threshold, and at the same time introduce a dynamic threshold adjustment rule to correct the fixed threshold: S235. Among them, represents the environmental correction term, represents the vibration correction term, and is an adjustable parameter; S236. When then it is determined that the pole is tilted; As described in the above steps S231 - S236, by obtaining sensor data and judging the inclination of the pole through the dynamic threshold, compensating and dynamically adjusting using environmental factors and vibration data, the accuracy and reliability of inclination judgment are improved. The ordinary nodes forward the collected sensor data to the cloud server via the routing nodes. In the cloud server, an environmental compensation model is constructed to correct the inclination data, and the true inclination data measured by the sensor is corrected using environmental factors to obtain the corrected inclination. The coefficients in its formula can be estimated using the multiple linear regression algorithm. These coefficients represent the influence of different environmental factors on the inclination data. On the other hand, the fixed threshold is dynamically corrected based on the environment and vibration conditions to obtain the dynamic threshold, which includes an environmental correction term and a vibration correction term. The adjustable parameters are set based on experience or can be analyzed using a neural network model to obtain the optimal parameters. Further, an anomaly detection mechanism can be established to summarize the sensor data of the inclined poles in the area, analyze the dynamic changes of the sensor data, and identify potential abnormal patterns, such as sudden sharp changes in the inclination angle, large - scale pole inclination due to land subsidence, and slow inclination due to damaged pole materials. The design process is to establish a data warehouse on the cloud server, regularly archive the sensor data, perform time - series analysis on the collected sensor data, identify the trends and periodic changes of the sensor data, analyze the dynamic changes of the sensor data, and perform sensor data analysis including sudden inclination detection: define the sudden inclination threshold, and the threshold is given a time attribute, such as 5° / 1min. If the corrected inclination exceeds the sudden inclination threshold, it is determined as a sudden inclination; large - scale inclination: check the corrected inclinations of multiple poles. If a group of poles simultaneously show inclinations beyond the normal range, it is regarded as a large - scale inclination and the environmental data is analyzed to obtain the cause of the pole inclination; slow inclination detection: perform a long - term trend analysis on the corrected inclination of the pole, identify the growth trend, set a sliding window, calculate the average value and standard deviation through statistical calculation of the data within the window, preset the inclination threshold. If the average inclination continuously increases and exceeds the inclination threshold, it is determined as a slow inclination.

[0023] In one embodiment, step S3 of setting multi - level alarms based on the dynamic threshold and sending corresponding alarms to the administrator for the inclined poles includes: S31. Set multi - level alarm thresholds including a first - level early - warning threshold , a second - level early - warning threshold and a third - level early - warning threshold ; S32. When then the first - level early - warning is reached, and the cloud server sends a preliminary alarm to the administrator to remind the administrator to strengthen the monitoring of the poles that trigger the first - level early - warning; S33. When Then it reaches the secondary warning level, and the cloud server sends a serious alert to the administrator, suggesting that the administrator quickly carry out repairs; S34. When Then it reaches the tertiary warning level. The pole that generates the tertiary warning issues a warning to the surrounding people through acoustic and optical alarms. The cloud server sends an emergency alert to the administrator, suggesting that the administrator deactivate the relevant power line and organize a technical team to conduct on-site repair or reinforcement; As described in the above steps S31 - S34, by setting a multi-level alarm mechanism, corresponding alarms are sent to the administrator and the surrounding people for the tilted pole. First, set the multi-level alarm thresholds, including the primary warning threshold for indicating slight tilt and requiring enhanced monitoring; the secondary warning threshold for showing moderate tilt and requiring quick repair; the tertiary warning threshold indicating severe tilt and requiring immediate deactivation of the power line and organizing on-site repair. Further, establish an alarm confirmation mechanism to ensure that the administrator promptly confirms and takes corresponding handling measures after receiving the alarm. If no reply is received within a certain time, the cloud server will send a reminder again until a positive confirmation is received. At the same time, regularly evaluate the working effect of the alarm system, collect usage feedback, and adjust key parameters, such as the adjustment range, alarm trigger, etc., to adapt to different actual application scenarios.

[0024] Reference Figure 2 , the present invention also proposes an automatic alarm system for pole tilt, including: Sensor module: Construct a multi-modal sensor array corresponding to the pole, and use the multi-modal sensor array to collect sensor data including pole state data and environmental data; Tilt judgment module: Connected to the sensor module, construct a self-organizing network for the pole. The self-organizing network includes a routing node and multiple ordinary nodes. Build a time window in the communication module for pole state data interaction, transmit the sensor data to the cloud server based on the self-organizing network, and construct a dynamic threshold in the cloud server to judge whether the pole is tilted; Warning module: Connected to the tilt judgment module, set multi-level alarms based on the dynamic threshold, and send corresponding alarms to the administrator for the tilted pole.

[0025] As described above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. An automatic alarm method for pole tilt, characterized in that: A multimodal sensor array is constructed corresponding to the utility pole, and sensor data including utility pole status data and environmental data are collected by using the multimodal sensor array; Construct a self-organizing network of power poles. The self-organizing network includes a routing node and multiple common nodes. In the communication module, a time window is constructed for the exchange of power pole status data. Based on the self-organizing network, the sensor data is transmitted to the cloud server. In the cloud server, a dynamic threshold is constructed to determine whether the power pole is tilted. Set up multi-level alarms based on dynamic thresholds to alert managers to leaning poles.

2. The automatic alarm method for pole tilt according to claim 1, characterized in that: The steps of constructing a multimodal sensor array corresponding to the utility pole and collecting sensor data including utility pole status data and environmental data using the multimodal sensor array are as follows: The multimodal sensor array includes an accelerometer, a gyroscope, a piezoelectric vibration sensor, and an environmental sensor, and a gimbal is configured for the multimodal sensor array for dynamic balancing; Using a multi-modal sensor array to collect pole status data and environmental data based on a dynamic period adjustment mechanism; The status data of the poles include pole ID, three-dimensional tilt angle, tilt rate and vibration spectrum, and the environmental data include meteorological parameters and soil parameters.

3. The automatic alarm method for pole tilt according to claim 1, characterized in that: The dynamic period adjustment mechanism is: Define the initial collection period , collect preset number of cycles The pole status data and environmental data in the , find the threshold data set The average value within the preset period is used as the benchmark data: in Represents the benchmark data, Indicates A collection of status data and environmental data within a cycle; After a preset number of cycles of acquisition A collection of internal pole status data and environmental data , and find the mean of the data : in represents the data mean, Indicates A collection of status data and environmental data within a cycle; Preset error threshold , calculate the mean of the data With benchmark data Error : like The collection period is extended based on the preset increment. The collection period is not adjusted.

4. The automatic alarm method for pole tilt according to claim 1, characterized in that: The step of constructing a self-organizing network of power poles, wherein the self-organizing network includes a routing node and a plurality of common nodes, and constructing a time window in a communication module for exchanging power pole status data is as follows: For the poles in the preset area, the poles in the central area are selected to configure routing nodes including communication modules and sensors, and the remaining poles are configured with common nodes including communication modules and sensors. The routing nodes and the common nodes are connected through virtual transmission channels, and the routing nodes, the common nodes and the virtual transmission channels are combined to form a pole self-organizing network; The communication modules corresponding to the routing nodes and ordinary nodes build time windows for storing and transmitting sensor data; The sensor data is transmitted to the cloud server through the routing node connection, and the tilt of the pole is determined based on the dynamic threshold.

5. The automatic alarm method for pole tilt according to claim 4, characterized in that: The steps of constructing a time window for storing and transmitting sensor data are: Define sensor data sets based on sensor type , build a grid storage space Store sensor data sets, each sensor data Corresponding storage in independent grid unit , set doors for each independent grille unit ,control the output of sensor data based on the gating mechanism; The gating mechanism assigns an initial transmission time slot to each gate. The value of the initial transmission time slot is based on the priority of the sensor data and is expressed as: in, Represents the initial transmission time slot, for Prioritization of sensor data, is the normalization constant, is an adjustable parameter; The gates of the independent grid units corresponding to the routing nodes of the same sensor data type are connected one-to-one with the independent grid units corresponding to the common nodes through virtual channels, and a preset number of detection points are set on the virtual channels. The detection points are used to identify the priority of sensor data, generate borrowing applications, map cache space, and transmit data; For any time , sensor data With benchmark data The data deviation is: in, is the data deviation, when When, mark The fault data is simulated and the priority is updated as follows: in, is the updated priority, The highest priority is is the preset floating threshold; All kinds of sensor data are transmitted in the virtual channel through the gate. When passing through the detection point, the detection point on the corresponding virtual channel will detect and record the priority of the corresponding sensor data. If it is detected as simulated fault data and the virtual channel is blocked, the detection point will send a channel borrowing request to the virtual channel where the sensor data with the lowest priority is located, and the virtual channel that receives the channel borrowing request will be used as an emergency channel. For the emergency channel, the emergency channel will stop transmitting its own sensor data, map out the cache space based on the activation detection point of the channel borrowing application, transmit its own sensor data to the cache space, and transmit the simulated fault data into the adjacent virtual channel through the emergency channel detection point for transmission.

6. The automatic alarm method for pole tilt according to claim 1, characterized in that: The step of judging the inclination of the pole based on the dynamic threshold is: Routing nodes obtain sensor data from nearby ordinary nodes and themselves , transmitted to the cloud server, where Represents the inclination data, Represents vibration data, represent temperature, wind speed and soil looseness respectively; Construct an environmental compensation model to correct the inclination data: in, To correct the caster angle, is the actual inclination data, and the coefficients are estimated using the multivariate linear regression algorithm ; Preset fixed thresholds and introduce dynamic threshold adjustment rules to correct fixed thresholds: in, represents the environmental correction term, represents the vibration correction term, and is an adjustable parameter; when When , it is determined that the pole is tilted.

7. The automatic alarm method for pole tilt according to claim 1, characterized in that: The steps of setting a multi-level alarm based on a dynamic threshold and issuing a corresponding alarm to an administrator for a tilted pole are: Set multiple levels of alarm thresholds including the first level warning threshold , Second level warning threshold and the third-level warning threshold ; when If the warning reaches the first level, the cloud server sends a preliminary alarm to the administrator, reminding the administrator to strengthen the monitoring of the poles that generate the first level warning; when If the fault reaches the second-level warning level, the cloud server will send a serious alert to the administrator and recommend that the administrator quickly perform maintenance. when If the warning reaches level three, the power pole that generates the level three warning will send out warnings to the surrounding people through sound and light alarms, and the cloud server will send an emergency alert to the administrator, suggesting that the administrator disable the relevant power lines and organize a technical team to carry out on-site repairs or reinforcements.

8. An automatic alarm system for a utility pole tilt, used to implement an automatic alarm method for a utility pole tilt as claimed in any one of claims 1 to 7, characterized in that: Sensor module: a multi-modal sensor array is constructed corresponding to the utility pole, and sensor data including utility pole status data and environmental data are collected by using the multi-modal sensor array; Tilt judgment module: connected with the sensor module, constructing a self-organizing network of the pole, which includes a routing node and multiple common nodes. A time window is constructed in the communication module for the interaction of pole status data. The sensor data is transmitted to the cloud server based on the self-organizing network, and a dynamic threshold is constructed in the cloud server to determine whether the pole is tilted. Early warning module: connected with the tilt judgment module, it sets multi-level alarms based on dynamic thresholds and sends corresponding alarms to administrators for tilted poles.

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