Tower state monitoring method and device, computer equipment and storage medium

Abstract

The application relates to a tower state monitoring method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring initial monitoring data and real-time monitoring data collected by a plurality of target sensors at the same height on a target tower; obtaining fluctuation statistical values corresponding to the target sensors based on the fluctuation of each real-time monitoring data corresponding to the same target sensor relative to the corresponding initial monitoring data; when at least one fluctuation statistical value is greater than or equal to a first preset statistical value, obtaining a target monitoring result corresponding to the target tower based on the change trend of each real-time monitoring data corresponding to the target fluctuation statistical value with the collection time; and when each fluctuation statistical value is less than the first preset statistical value, obtaining a target monitoring result corresponding to the target tower based on each monitoring statistical value. The method can improve the efficiency of tower state monitoring.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, computer equipment, storage medium, and computer program product for monitoring the condition of power poles. Background Technology

[0002] With the widespread coverage of communication networks, people have been provided with better communication services and brought great convenience to their lives. However, problems have also arisen from the expansion of communication networks. If communication equipment is damaged by natural environment or human factors and is not repaired in time, the communication line towers may experience foundation settlement, tilting, deformation or even collapse. Once a communication tower collapses, communication will be interrupted in the coverage area, directly affecting the operational safety of the communication network.

[0003] Currently, traditional tower condition monitoring relies on regular manual monitoring and maintenance, with staff analyzing the tower condition based on past monitoring experience. This method suffers from low tower condition monitoring efficiency. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, device, computer equipment, computer-readable storage medium, and computer program product for monitoring tower conditions that can improve the efficiency of tower condition monitoring, in order to address the above-mentioned technical problems.

[0005] This application provides a method for monitoring the condition of a power pole. The method includes:

[0006] Acquire initial monitoring data collected by multiple target sensors at the same height on the target tower, as well as multiple real-time monitoring data collected by each target sensor within the current monitoring period;

[0007] Based on the fluctuation of each real-time monitoring data collected by the same target sensor within the current monitoring period relative to the corresponding initial monitoring data, the fluctuation statistics of each target sensor are obtained.

[0008] When at least one fluctuation statistical value is greater than or equal to a first preset statistical value, the target monitoring result of the target tower in the current monitoring period is obtained based on the changing trend of each real-time monitoring data collected by the target sensor corresponding to the target fluctuation statistical value during the current monitoring period with the collection time; the target fluctuation statistical value is a fluctuation statistical value that is greater than or equal to the first preset statistical value.

[0009] When all fluctuation statistics are less than the first preset statistics, the monitoring statistics are calculated based on the real-time monitoring data collected by the same target sensor in the current monitoring period. Based on the monitoring statistics corresponding to each target sensor, the target monitoring result of the target tower in the current monitoring period is obtained.

[0010] This application also provides a tower condition monitoring device. The device includes:

[0011] The monitoring data acquisition module is used to acquire the initial monitoring data collected by multiple target sensors at the same height on the target tower, as well as the multiple real-time monitoring data collected by each target sensor within the current monitoring cycle.

[0012] The fluctuation statistics determination module is used to obtain the fluctuation statistics of each target sensor based on the fluctuation of each real-time monitoring data collected by the same target sensor in the current monitoring period relative to the corresponding initial monitoring data.

[0013] The first monitoring result determination module is used to obtain the target monitoring result of the target tower in the current monitoring cycle based on the changing trend of each real-time monitoring data collected by the target sensor corresponding to the target fluctuation statistical value during the current monitoring cycle, when at least one fluctuation statistical value is greater than or equal to the first preset statistical value; the target fluctuation statistical value is a fluctuation statistical value that is greater than or equal to the first preset statistical value.

[0014] The second monitoring result determination module is used to calculate the monitoring statistics based on the real-time monitoring data collected by the same target sensor in the current monitoring period when all fluctuation statistics are less than the first preset statistics. Based on the monitoring statistics corresponding to each target sensor, the target monitoring result of the target tower in the current monitoring period is obtained.

[0015] A computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described tower status monitoring method.

[0016] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described tower status monitoring method.

[0017] A computer program product includes a computer program that, when executed by a processor, implements the steps of the above-described tower status monitoring method.

[0018] The aforementioned pole / tower condition monitoring method, device, computer equipment, storage medium, and computer program product obtain fluctuation statistics for each target sensor by analyzing the fluctuations of real-time monitoring data collected by the same target sensor within the current monitoring period and relative to the corresponding initial monitoring data. When at least one fluctuation statistics value is greater than a first preset statistical value, it indicates that the real-time monitoring data collected by the corresponding target sensor has abnormal fluctuations. Furthermore, based on the changing trend of the real-time monitoring data collected by the target sensor corresponding to the target fluctuation statistics value over the acquisition time, the monitoring result corresponding to the target pole / tower is further determined, eliminating the influence of false anomalies and improving the accuracy of pole / tower condition monitoring. When all fluctuation statistics values ​​are less than the first preset statistical value, it indicates that the fluctuations of the real-time monitoring data collected by each target sensor are relatively stable and no abnormal fluctuations have occurred. Furthermore, based on the monitoring statistics corresponding to each target sensor, the monitoring result corresponding to the target pole / tower is further determined, improving the accuracy of pole / tower condition monitoring. In addition, by acquiring the real-time monitoring data and initial monitoring data collected by each target sensor and performing statistical analysis on the monitoring data to determine the pole / tower condition, the efficiency of pole / tower condition monitoring can be improved. Attached Figure Description

[0019] Figure 1 This is an application environment diagram of the tower condition monitoring method in one embodiment;

[0020] Figure 2 This is a flowchart illustrating a tower status monitoring method in one embodiment;

[0021] Figure 3 This is a flowchart illustrating the process of determining target monitoring results in one embodiment;

[0022] Figure 4 This is a flowchart illustrating the tower condition monitoring method in another embodiment;

[0023] Figure 5 This is a structural block diagram of a tower condition monitoring device in one embodiment;

[0024] Figure 6 This is an internal structural diagram of a computer device in one embodiment;

[0025] Figure 7 This is a diagram of the internal structure of a computer device in another embodiment. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0027] The tower condition monitoring method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or located on the cloud or other network servers. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can be smart TVs, smart in-vehicle devices, etc. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. Server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers. Terminal 102 and server 104 can be directly or indirectly connected via wired or wireless communication, which is not limited herein.

[0028] Both the terminal and the server can be used independently to execute the tower status monitoring method provided in the embodiments of this application.

[0029] For example, the terminal acquires initial monitoring data collected by multiple target sensors at the same height on the target tower, as well as multiple real-time monitoring data collected by each target sensor within the current monitoring period. Based on the fluctuations of each real-time monitoring data collected by the same target sensor within the current monitoring period relative to the corresponding initial monitoring data, the terminal obtains the fluctuation statistics for each target sensor. When at least one fluctuation statistics value is greater than or equal to a first preset statistics value, the terminal obtains the target monitoring result for the target tower within the current monitoring period based on the changing trend of each real-time monitoring data collected by the target sensor corresponding to the target fluctuation statistics value over the current monitoring period. The target fluctuation statistics value is a fluctuation statistics value greater than or equal to the first preset statistics value. When all fluctuation statistics values ​​are less than the first preset statistics value, the terminal calculates the monitoring statistics value based on each real-time monitoring data collected by the same target sensor within the current monitoring period, and obtains the target monitoring result for the target tower within the current monitoring period based on the monitoring statistics value corresponding to each target sensor.

[0030] The terminal and server can also be used in conjunction to execute the tower status monitoring method provided in the embodiments of this application.

[0031] For example, the terminal acquires and sends to the server initial monitoring data collected by multiple target sensors at the same height on the target tower, as well as multiple real-time monitoring data collected by each target sensor within the current monitoring period. The server, based on the fluctuations of each real-time monitoring data collected by the same target sensor within the current monitoring period relative to the corresponding initial monitoring data, obtains the fluctuation statistics for each target sensor. When at least one fluctuation statistics value is greater than or equal to a first preset statistics value, the server, based on the changing trend of each real-time monitoring data collected by the target sensor corresponding to the target fluctuation statistics value within the current monitoring period over time, obtains the target monitoring result for the target tower within the current monitoring period. The target fluctuation statistics value is a fluctuation statistics value greater than or equal to the first preset statistics value. When all fluctuation statistics values ​​are less than the first preset statistics value, the server calculates the monitoring statistics value based on each real-time monitoring data collected by the same target sensor within the current monitoring period, and obtains the target monitoring result for the target tower within the current monitoring period based on the monitoring statistics value corresponding to each target sensor. The server sends the target monitoring result to the terminal, which can then display the target monitoring result.

[0032] In one embodiment, such as Figure 2 As shown, a method for monitoring the status of power poles is provided. Taking the application of this method to a computer device as an example, the computer device can be a terminal or a server. The method can be executed independently by the terminal or server, or it can be implemented through interaction between the terminal and the server. The power pole status monitoring method includes the following steps:

[0033] Step S202: Obtain initial monitoring data collected by multiple target sensors at the same height on the target tower, as well as multiple real-time monitoring data collected by each target sensor within the current monitoring period.

[0034] In this context, "target tower" refers to the tower whose status requires monitoring. A tower is a support structure made of wood, reinforced concrete, or steel. For example, a tower could be a communication tower supporting various mobile antennas, or a power tower supporting transmission lines in an overhead power line, and so on. "Target sensor" refers to a sensor used to collect monitoring indicators such as the target tower's tilt angle, height above the ground, or soil pressure. For example, a target sensor could be a distance sensor, an angle sensor, or a pressure sensor.

[0035] The monitoring cycle refers to the time interval for monitoring the status of a pole or tower. For example, the monitoring cycle can be one hour, one day, or one week. The current monitoring cycle refers to the monitoring cycle during which pole or tower status monitoring is required to determine the target monitoring results corresponding to the target pole or tower.

[0036] Initial monitoring data refers to the monitoring data of the target tower collected by the target sensor in its initial state. For example, initial monitoring data could be the monitoring data first collected by the target sensor when it is installed on the target tower. Real-time monitoring data refers to the monitoring data of the target tower collected by the target sensor within the current monitoring period.

[0037] For example, to monitor the status of a target pole, multiple target sensors are set up at the same height on the target pole. Each target sensor collects monitoring data of the target pole, and based on this data, the status of the target pole is identified. This enables automatic monitoring of the pole status, improving the efficiency and real-time performance of monitoring. The computer equipment acquires the initial monitoring data collected by each target sensor at the same height on the target pole, and then acquires multiple real-time monitoring data collected by each target sensor within the current monitoring period.

[0038] Step S204: Based on the fluctuation of each real-time monitoring data collected by the same target sensor within the current monitoring period relative to the corresponding initial monitoring data, obtain the fluctuation statistics value corresponding to each target sensor.

[0039] Here, fluctuation refers to the difference between the real-time monitoring data collected by the target sensor and the corresponding initial monitoring data. Fluctuation statistics are statistical values ​​used to characterize the overall fluctuation of each real-time monitoring data collected by the target sensor within the current monitoring period.

[0040] For example, the computer device determines the fluctuation of each real-time monitoring data point relative to the corresponding initial monitoring data based on the differences between each real-time monitoring data point collected by the target sensor within the current monitoring period and the initial monitoring data collected by the target sensor. The fluctuation of each real-time monitoring data point collected by the same target sensor within the current monitoring period relative to the corresponding initial monitoring data is statistically analyzed to obtain the fluctuation statistics for each target sensor. For example, the average value of each fluctuation corresponding to the same target sensor is calculated as the fluctuation statistics for the target sensor; the median value of each fluctuation corresponding to the same target sensor is used as the fluctuation statistics for the target sensor; and so on.

[0041] Step S206: When at least one fluctuation statistical value is greater than or equal to the first preset statistical value, the target monitoring result of the target tower in the current monitoring period is obtained based on the changing trend of each real-time monitoring data collected by the target sensor corresponding to the target fluctuation statistical value during the current monitoring period with the collection time; the target fluctuation statistical value is a fluctuation statistical value that is greater than or equal to the first preset statistical value.

[0042] The first preset statistical value refers to the preset threshold corresponding to the fluctuation statistical value, which is used to determine whether the fluctuation statistical value corresponding to the target sensor is within the normal range. Specifically, when the fluctuation statistical value corresponding to the target sensor in the current monitoring period is less than the first preset statistical value, it indicates that the fluctuation of each real-time monitoring data collected by the target sensor is within the normal range. When the fluctuation statistical value corresponding to the target sensor in the current monitoring period is greater than or equal to the first preset statistical value, it indicates that the fluctuation of each real-time monitoring data collected by the target sensor exceeds the normal range and abnormal fluctuation has occurred.

[0043] The trend of change refers to the change of each real-time monitoring data collected by the target sensor within the current monitoring period as the collection time progresses. The trend of change can be a regular change, such as continuous increase or continuous decrease, or it can be an irregular change.

[0044] The target monitoring results refer to the monitoring results of the target tower within the current monitoring period, including but not limited to normal and abnormal situations.

[0045] For example, the computer device acquires a first preset statistical value. When the fluctuation statistical value corresponding to at least one target sensor is greater than or equal to the first preset statistical value, the fluctuation statistical value greater than or equal to the first preset statistical value is taken as the target fluctuation statistical value. The device analyzes the changing trend of each real-time monitoring data collected by the target sensor corresponding to the target fluctuation statistical value within the current monitoring period over the acquisition time. If the changing trend of the real-time monitoring data corresponding to each target fluctuation statistical value over the acquisition time is irregular, the target monitoring result of the target tower in the current monitoring period is determined to be normal. If the changing trend of the real-time monitoring data corresponding to a target fluctuation statistical value over the acquisition time is regular, the target monitoring result of the target tower in the current monitoring period is determined to be abnormal.

[0046] Step S208: When all fluctuation statistics are less than the first preset statistics, calculate the monitoring statistics based on the real-time monitoring data collected by the same target sensor in the current monitoring period, and obtain the target monitoring result of the target tower in the current monitoring period based on the monitoring statistics corresponding to each target sensor.

[0047] Among them, the monitoring statistics value refers to the statistical value used to characterize the general level of each real-time monitoring data collected by the target sensor within the current monitoring period.

[0048] For example, the computer device acquires a first preset statistical value. When all fluctuation statistical values ​​are less than the first preset statistical value, it statistically analyzes the real-time monitoring data collected by the same target sensor within the current monitoring period to obtain the monitoring statistical values ​​corresponding to each target sensor within the current monitoring period. A preset monitoring indicator value is acquired, and each monitoring statistical value is compared with the preset monitoring indicator value to determine the target monitoring result for the target tower in the current monitoring period. For example, when all monitoring statistical values ​​are less than the preset monitoring indicator value, the target monitoring result is determined to be normal; when at least one monitoring statistical value is greater than or equal to the preset monitoring indicator value, the target monitoring result is determined to be abnormal.

[0049] In the aforementioned pole / tower status monitoring method, fluctuation statistics are obtained for each target sensor based on the fluctuations of real-time monitoring data collected by the same target sensor within the current monitoring period and relative to the corresponding initial monitoring data. When at least one fluctuation statistics value is greater than a first preset statistical value, it indicates that the real-time monitoring data collected by the corresponding target sensor has abnormal fluctuations. Furthermore, based on the changing trend of the real-time monitoring data collected by the target sensor corresponding to the target fluctuation statistics value over the acquisition time, the monitoring result for the target pole / tower is further determined, eliminating the influence of false anomalies and improving the accuracy of pole / tower status monitoring. When all fluctuation statistics values ​​are less than the first preset statistical value, it indicates that the fluctuations of the real-time monitoring data collected by each target sensor are relatively stable and no abnormal fluctuations have occurred. Furthermore, based on the monitoring statistics values ​​corresponding to each target sensor, the monitoring result for the target pole / tower is further determined, improving the accuracy of pole / tower status monitoring. In addition, by acquiring the real-time monitoring data and initial monitoring data collected by each target sensor and performing statistical analysis on the monitoring data to determine the pole / tower status, the efficiency of pole / tower status monitoring can be improved.

[0050] In one embodiment, based on the fluctuations of each real-time monitoring data collected by the same target sensor within the current monitoring period relative to the corresponding initial monitoring data, fluctuation statistics for each target sensor are obtained, including:

[0051] Calculate the initial difference between each real-time monitoring data collected by the same target sensor within the current monitoring period and the corresponding initial monitoring data; correct each initial difference value to obtain the target difference value; fuse the target difference values ​​corresponding to the same target sensor to obtain the fluctuation statistics value corresponding to each target sensor.

[0052] The initial difference value refers to the difference calculated based on real-time monitoring data and corresponding initial monitoring data. Correction processing refers to adjusting the initial difference value so that the resulting target difference value accurately represents the fluctuation of real-time monitoring data relative to the corresponding initial monitoring data. The target difference value is the difference value obtained after correcting the initial difference value, which accurately represents the fluctuation of real-time monitoring data relative to the corresponding initial monitoring data.

[0053] For example, the computer device calculates the differences between each real-time monitoring data collected by the same target sensor within the current monitoring period and the initial monitoring data collected by the target sensor as initial difference values. Each initial difference value is then corrected to obtain a target difference value; for example, the absolute value of the initial difference value is taken; the square of the initial difference value is calculated as the target difference value; the sum of the initial difference value and a preset value is used as the target difference value; and so on. The target difference values ​​corresponding to the same target sensor are then fused to obtain the fluctuation statistics corresponding to each target sensor; for example, the average of the target difference values ​​corresponding to the same sensor is used as the fluctuation statistics corresponding to the target sensor; the weighted average of the target difference values ​​corresponding to the same sensor is used as the fluctuation statistics corresponding to the target sensor; and so on.

[0054] In the above embodiments, by calculating the initial difference values ​​between each real-time monitoring data collected by the same target sensor in the current monitoring period and the corresponding initial monitoring data, and fusing the target difference value obtained after correcting each initial difference value, the fluctuation statistics value corresponding to the target sensor is obtained. The fluctuation statistics value obtained in this way can accurately characterize the overall fluctuation of each real-time monitoring data collected by the target sensor in the current monitoring period, thereby improving the accuracy of the target monitoring results corresponding to the target tower determined based on the fluctuation statistics value, that is, improving the accuracy of tower status monitoring.

[0055] In one embodiment, when at least one fluctuation statistic is greater than or equal to a first preset statistic, the target monitoring result for the target tower in the current monitoring cycle is obtained based on the changing trend of each real-time monitoring data collected by the target sensor corresponding to the target fluctuation statistic over the acquisition time, including:

[0056] Calculate the difference between each real-time monitoring data collected by the target sensor within the current monitoring period and the corresponding initial monitoring data, corresponding to the target fluctuation statistics. When all the difference values ​​corresponding to any target fluctuation statistics show a non-linear trend with the collection time, the target monitoring result of the target tower in the current monitoring period is determined to be normal. When at least one of the difference values ​​corresponding to the target fluctuation statistics shows a linear trend with the collection time, the target monitoring result of the target tower in the current monitoring period is determined to be abnormal.

[0057] Linear trends refer to continuous increases or decreases. Non-linear trends refer to trends other than continuous increases and decreases; for example, the difference values ​​exhibit a discrete distribution over time. The difference value refers to the difference between the real-time monitoring data collected by the target sensor and the initial monitoring data collected by the same sensor.

[0058] For example, when the fluctuation statistics corresponding to at least one target sensor are greater than or equal to a first preset statistical value, it indicates that the fluctuation level of each real-time monitoring data collected by at least one target sensor exceeds the normal range, i.e., abnormal fluctuation has occurred. Therefore, it is necessary to further determine whether the target tower has experienced any abnormalities during the current monitoring period based on the changing trend of each real-time monitoring data collected by the target sensor corresponding to the target fluctuation statistics over the acquisition time.

[0059] The computer equipment calculates the differences between each real-time monitoring data collected by the target sensor within the current monitoring period and the corresponding initial monitoring data, corresponding to the target fluctuation statistics, to obtain various difference values. When each difference value corresponding to the target fluctuation statistics shows a non-linear trend with the acquisition time, for example, when the difference values ​​show a discrete distribution with the acquisition time, the target monitoring status of the target tower within the current monitoring period is determined to be normal. When at least one difference value corresponding to the target fluctuation statistics shows a linear trend with the acquisition time, the target monitoring status of the target tower within the current monitoring period is determined to be abnormal.

[0060] In the above embodiments, when the fluctuation statistics corresponding to the target sensor are compared with the first preset statistics, and it is determined that the fluctuation level of each real-time monitoring data collected by at least one target sensor exceeds the normal range, it indicates that abnormal fluctuations have occurred in the real-time monitoring data collected by the target sensor. Therefore, based on the changing trend of each real-time monitoring data collected by the target acquisition device corresponding to the target fluctuation statistics over the acquisition time, it is further determined whether the abnormal fluctuations in the real-time monitoring data collected by the target sensor are caused by an anomaly in the target tower.

[0061] Specifically, when the differences corresponding to the target fluctuation statistics show a linear trend with the acquisition time, it indicates that the abnormal fluctuations in the real-time monitoring data collected by the target sensor corresponding to the target fluctuation statistics are caused by an anomaly in the target tower. For example, when the target sensor is a distance sensor, if the difference between the real-time monitoring data collected by the target sensor in the current monitoring period and the initial monitoring data shows a linear trend with the acquisition time, it indicates that the ground soil layer in the area monitored by the distance sensor is continuously increasing or decreasing. When the ground soil layer is continuously increasing, it indicates that the soil in the area where the target tower is located is accumulating. When the soil surface on one side of the target tower is too high, it exerts a thrust on the other sides of the target tower, causing the tower to tilt. When the ground soil layer is continuously decreasing, it indicates that the soil in the area where the target tower is located is eroding. When the soil surface on one side of the target tower is too low, this side will be subjected to a thrust from the other sides, causing the tower to tilt.

[0062] In this way, by determining the target monitoring results corresponding to the target tower based on the changing trend of each real-time monitoring data collected by the target sensor over the collection time, the accuracy of the target monitoring results can be guaranteed, and the efficiency of tower monitoring can be improved.

[0063] In one embodiment, such as Figure 3 As shown, monitoring statistics are calculated based on real-time monitoring data collected by the same target sensor within the current monitoring period. Based on the monitoring statistics corresponding to each target sensor, the target monitoring results for the target tower in the current monitoring period are obtained, including:

[0064] Step S302: Statistically analyze the real-time monitoring data collected by the same target sensor within the current monitoring period to obtain the monitoring statistics corresponding to each target sensor.

[0065] Step S304: Merge the various monitoring statistics to obtain the merged statistics.

[0066] Step S306: When the fused statistical value is less than or equal to the second preset statistical value, the target monitoring result of the target tower in the current monitoring cycle is determined to be normal.

[0067] Step S308: When the fused statistical value is greater than the second preset statistical value, the target monitoring result of the target tower in the current monitoring cycle is determined based on the difference between the monitoring statistical value corresponding to the same target sensor and the preset monitoring data.

[0068] The fused statistical value refers to the comprehensive monitoring value of the target tower obtained by fusing the monitoring statistical values ​​corresponding to each target sensor. The second preset statistical value is a preset threshold corresponding to the fused statistical value, used to determine whether the fused statistical value is within the normal range. Specifically, when the fused statistical value of the target sensor in the current monitoring period is less than or equal to the second preset statistical value, it indicates that the target tower is in a normal state in the current monitoring period; when the fused statistical value of the target sensor in the current monitoring period is greater than the second preset statistical value, it indicates that the target tower is in an abnormal state in the current monitoring period.

[0069] Preset monitoring data refers to the standard statistical values ​​preset for the monitoring statistics corresponding to the target sensors. These values ​​represent the monitoring statistics corresponding to the target tower under normal conditions. Specifically, a set of preset monitoring data can be set for each target sensor, or a unified set of preset monitoring data can be set for all target sensors.

[0070] For example, when all fluctuation statistics are less than a first preset statistical value, it indicates that the fluctuation levels of each real-time monitoring data collected by each target sensor are within the normal range, and no abnormal fluctuations have occurred. At this point, the monitoring statistics corresponding to each target sensor are used to further determine whether the target tower has experienced any abnormalities during the current monitoring period. The computer equipment statistically analyzes the real-time monitoring data collected by the same target sensor during the current monitoring period to obtain the monitoring statistics corresponding to each target sensor. For example, the average value of each real-time monitoring data collected by the target sensor during the current monitoring period is calculated as the monitoring statistics corresponding to the target sensor. Then, the various monitoring statistics are fused to obtain a fused statistical value. For example, the average value of each monitoring statistics is used as the fused statistical value; the average of the maximum and minimum values ​​among the various monitoring statistics is taken to obtain the fused statistical value; and so on.

[0071] A second preset statistical value is obtained. When the fused statistical value is less than or equal to the second preset statistical value, the target monitoring result for the target tower within the current monitoring period is determined to be normal. When the fused statistical value is greater than the second preset statistical value, the monitoring status of the target tower within the current monitoring period is further analyzed based on the differences between the monitoring statistical values ​​corresponding to each target sensor and the corresponding preset monitoring data. For example, when the differences between the monitoring statistical values ​​corresponding to each target sensor and the corresponding preset monitoring data are all greater than the preset value, the target monitoring result for the target tower is determined to be abnormal, and the target monitoring result for the other cases is normal; when the differences between the monitoring statistical values ​​corresponding to more than half of the target sensors and the corresponding preset monitoring data are greater than the preset value, the target monitoring result for the target tower is determined to be abnormal, and the target monitoring result for the other cases is normal; and so on.

[0072] In the above embodiments, when the fluctuation statistics corresponding to the target sensor are compared with the first preset statistics, and it is determined that each fluctuation statistics is less than the first preset statistics, it indicates that the fluctuation level of each real-time monitoring data collected by each target sensor is within the normal range and no abnormal fluctuation has occurred. At this point, further calculation of the monitoring statistics corresponding to each target sensor, based on the monitoring statistics that characterize the general level of each real-time monitoring data collected by the target sensor within the current monitoring period, further determines whether the target tower has experienced any abnormalities within the current monitoring period, thereby improving the accuracy of tower status monitoring.

[0073] In one embodiment, the target monitoring result for the target tower in the current monitoring cycle is determined based on the difference between the monitoring statistics corresponding to the same target sensor and preset monitoring data, including:

[0074] When the difference between the monitoring statistics corresponding to each target sensor and the corresponding preset monitoring data is less than the preset value, the target monitoring result of the target tower in the current monitoring cycle is determined to be normal; when the difference between the monitoring statistics corresponding to at least one target sensor and the corresponding preset monitoring data is greater than or equal to the preset value, the target monitoring result of the target tower in the current monitoring cycle is determined to be abnormal.

[0075] The preset value refers to a pre-defined value used to determine whether the monitored statistical value is within the normal range. For example, the preset value can be set to 0.

[0076] For example, when the fused statistical value is greater than the second preset statistical value, i.e., when the fused statistical value exceeds the normal range, the monitoring status of the target tower in the current monitoring cycle is further analyzed based on the differences between the monitoring statistical values ​​corresponding to each target sensor and the corresponding preset monitoring data. The computer equipment calculates the difference between the monitoring statistical values ​​corresponding to each target sensor and the corresponding preset monitoring data. When the differences between the monitoring statistical values ​​corresponding to each target sensor and the corresponding preset monitoring data are all less than the preset value, the target monitoring result of the target tower in the current monitoring cycle is determined to be normal. If the difference between the monitoring statistical value corresponding to at least one target sensor and the corresponding preset monitoring data is greater than or equal to the preset value, it indicates that the monitoring statistical value corresponding to at least one target sensor exceeds the normal range, and the target monitoring result of the target tower in the current monitoring cycle is determined to be abnormal.

[0077] In the above embodiments, when the fused statistical value exceeds the normal range, the monitoring status of the target tower in the current monitoring cycle is further analyzed based on the differences between the monitoring statistical values ​​corresponding to each target sensor and the corresponding preset monitoring data. When the differences between the monitoring statistical values ​​corresponding to each target sensor and the corresponding preset monitoring data are all less than the preset value, the target monitoring result is determined to be normal. When the difference between the monitoring statistical value corresponding to at least one target sensor and the corresponding preset monitoring data is greater than or equal to the preset value, that is, when the monitoring statistical value corresponding to at least one target sensor exceeds the normal range, the target monitoring result of the target tower in the current monitoring cycle is determined to be abnormal. Thus, analyzing the actual state of the target tower based on the differences between the monitoring statistical values ​​corresponding to each target sensor and the corresponding preset monitoring data can improve the accuracy of tower status monitoring.

[0078] In one embodiment, the target sensor is any one of a distance sensor, an angle sensor, and a weight sensor.

[0079] Among them, a distance sensor is a sensor used to monitor the distance between the target tower and the ground. An angle sensor is a sensor that can measure angles and is used to monitor the tower offset corresponding to the target tower; tower offset refers to the distance the top of the tower deviates from the center line of the tower. A weight sensor is a sensor that can measure weight and is used to monitor the soil pressure around the tower's erection surface.

[0080] For example, multiple target sensors are arranged in a circular array at the same height of the target tower. Each target sensor collects monitoring data in different directions at the same height of the target tower. When all target sensors are distance sensors, the monitoring data collected by the target sensors is the distance between the target sensor and the ground. Based on the monitoring data collected by each target sensor, it is possible to analyze the soil accumulation, foundation settlement, or soil loss that may exist on the ground corresponding to each target sensor, thereby determining the state of the target tower. When all target sensors are angle sensors, the monitoring data collected by the target sensors is the tower offset corresponding to the target tower. Based on the monitoring data collected by each target sensor, it is possible to analyze the tower offset in different directions, thereby determining the state of the target tower. When all target sensors are weight sensors, each target sensor is set around the tower erection surface to collect the soil pressure around the tower erection surface. Based on the collected soil pressure around the tower erection surface, the state of the target tower is determined.

[0081] In one embodiment, multiple sensors can be installed on the target tower. The state of the target tower can be analyzed based on the monitoring data collected by the same sensor to obtain the initial monitoring results corresponding to each sensor. The initial monitoring results obtained from the monitoring data collected by each sensor are fused together to analyze the target monitoring results corresponding to the target tower. This enables the analysis of the tower state from different angles and improves the accuracy of tower state monitoring.

[0082] In the above embodiments, the target sensor can be any one of a distance sensor, an angle sensor, and a weight sensor. Monitoring data collected by any one of these sensors can be used to monitor the status of the target tower, which can effectively improve the universality of tower status monitoring.

[0083] In one specific embodiment, with the widespread coverage of communication networks, people have been provided with higher-quality communication services, bringing great convenience to their lives. However, the expansion of communication networks has also brought problems. If communication equipment is damaged by natural environmental factors or human factors and is not repaired in time, communication towers may experience foundation settlement, tilting, deformation, or even collapse. Once a communication tower collapses, communication will be interrupted within the coverage area, directly affecting the operational safety of the communication network. To address these safety hazards, it is necessary to monitor the tilt angle, tilt distance, and sway trajectory of the towers, and to monitor the deformation status of the towers in real time. Currently, the market mainly uses manual tower monitoring and maintenance, which is not only inefficient but also cannot monitor the tower status in real time.

[0084] The pole / tower condition monitoring method of this application can be applied to a pole / tower deformation monitoring system. The system includes a deformation monitoring module, a deformation diagnosis module, a deformation early warning module, and a server. The deformation monitoring module includes a dynamic monitoring unit, which in turn includes four distance sensors arranged in a circular array around the pole / tower at the same height to monitor the distance between the pole / tower and the ground. The deformation monitoring module, deformation diagnosis module, and deformation early warning module are electrically connected to the server. Figure 4 As shown, the tower condition monitoring method includes the following steps:

[0085] 1. Monitor the data during the operation of the tower to obtain the tower monitoring data.

[0086] The dynamic monitoring unit in the tower deformation system marks the initial distance monitored by the distance sensors as initial monitoring data, thus obtaining the initial tower distance monitoring data. It also marks the real-time distance monitored by the distance sensors as real-time monitoring data, thus obtaining the real-time tower distance monitoring data. The dynamic monitoring unit transmits the initial and real-time monitoring data collected by the four distance sensors to the server in the tower deformation system, respectively. The server stores the received initial and real-time monitoring data.

[0087] 2. Process the tower deformation data through tower monitoring to obtain tower anomaly signals.

[0088] The deformation diagnosis module in the tower deformation system marks the received initial monitoring data as H. ij Where i represents the initial monitoring record, j represents the sensor number, and the initial monitoring data collected by the four distance sensors are denoted as H. i1 H i2 H i3 H i4 The deformation diagnosis module marks the received real-time monitoring data as H. kj Where k represents the real-time monitoring record, and the value of k indicates the acquisition order of the real-time monitoring data within the acquisition period; j is the sensor number; and the real-time monitoring data of the four distance sensors are denoted as H. k1 H k2 H k3 H k4 .

[0089] The initial monitoring data and real-time monitoring data of each distance sensor are mapped one-to-one. Within a monitoring data collection period, for example, the collection period can be set to 30 days (the collection period can be manually set), the mean and variance of the difference between the real-time monitoring data collected by each distance sensor and the corresponding initial monitoring data are obtained. The actual mean of the difference between the real-time monitoring data and the corresponding initial monitoring data for each distance sensor is denoted as E. j The actual variance of the difference between the real-time monitoring data and the corresponding initial monitoring data of each distance sensor is denoted as F. j The deformation diagnosis module receives the preset mean and preset variance transmitted from the server, and marks the preset mean as E. 1j The predefined variance is F. 1j .

[0090] When any one of the four distance sensors has an actual variance F within the current acquisition period j Greater than or equal to the preset variance F 1j At that time, the first operational anomaly signal of the tower is generated. When the actual variance F of the four distance sensors within the current acquisition period...j All are less than the preset variance F 1j At that time, the actual mean value E corresponding to the four distance sensors is obtained. j The samples are labeled E1, E2, E3, and E4. The actual mean values ​​from the four distance sensors are sorted, the two middle values ​​are removed, and the mean values ​​from the remaining two distance sensors are averaged. This results in the final actual mean value, labeled E. jk When the preset mean E 1j ≥ Actual final mean E jk At that time, a normal tower operation signal is generated. When the preset average value E 1j <Actual ultimate mean E jk At that time, a second abnormal operation signal for the tower is generated.

[0091] The deformation diagnosis module in the tower deformation system sends the first and second operational abnormality signals it receives to the server.

[0092] 3. By processing abnormal signals from the towers, early warning processing for tower deformation can be completed.

[0093] The deformation early warning module in the tower deformation system receives a first operational anomaly signal and a second operational anomaly signal transmitted from the server, and issues early warnings for the first and second operational anomaly signals respectively. The specific process is as follows:

[0094] When the deformation early warning module receives the first operational anomaly signal from the server, it processes the monitoring data collected by distance sensors whose actual variance is greater than or equal to the preset variance. It obtains the actual distance collected by the distance sensor each day within the current acquisition period, and calculates the difference between the daily actual distance and the initial monitoring data. If the difference between the daily actual distance and the corresponding initial monitoring data changes linearly within a continuous period (continuously increasing or decreasing), it indicates that the ground soil layer in the area monitored by the distance sensor is continuously increasing or decreasing, generating an early warning signal. If the difference between the daily actual distance and the corresponding initial monitoring data is discretely distributed within a continuous period, it indicates that the ground soil layer in the area monitored by the distance sensor is in a normal state of erosion or accumulation, generating a normal signal. The deformation early warning module then sends the obtained early warning signal or normal signal to the server, which issues an alarm response to the received early warning signal.

[0095] When the deformation early warning module receives the second operational anomaly signal from the server, it performs difference processing on the actual average value corresponding to each of the four distance sensors and their respective preset average values. If the difference between the actual average value and the preset average value of all four distance sensors is greater than zero, it indicates that there is a risk of soil erosion or foundation settlement on the monitored ground corresponding to each of the four distance sensors, and an early warning signal is generated. If the difference between the actual average value and the preset average value of any one of the four distance sensors is less than zero, a normal signal is generated. The deformation early warning module then sends the obtained early warning signal or normal signal to the server, and the server issues an alarm response to the received early warning signal.

[0096] In the above embodiments, by identifying the deformation of the tower through a quantitative model, the real-time performance, effectiveness, and automation of early warning are improved. During the tower deformation process, by extending the monitoring cycle, the soil layers in the tower erection area are observed and processed from a longer time dimension. That is, soil layer changes are transformed from a minute visual experience into a quantitative calculation model, thus making the calculation of soil layer changes in the tower erection area more accurate. This enables periodic collection and analysis of soil layers in the tower erection area, effectively providing early warning and response to tower foundation settlement, tilting, deformation, or even collapse, effectively ensuring the safe operation of the tower.

[0097] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0098] Based on the same inventive concept, this application also provides a tower condition monitoring device for implementing the tower condition monitoring method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more tower condition monitoring device embodiments provided below can be found in the limitations of the tower condition monitoring method described above, and will not be repeated here.

[0099] In one embodiment, such as Figure 5As shown, a tower condition monitoring device is provided, including: a monitoring data acquisition module 502, a fluctuation statistics determination module 504, a first monitoring result determination module 506, and a second monitoring result determination module 508, wherein:

[0100] The monitoring data acquisition module 502 is used to acquire the initial monitoring data collected by multiple target sensors at the same height on the target tower, as well as the multiple real-time monitoring data collected by each target sensor in the current monitoring cycle.

[0101] The fluctuation statistics determination module 504 is used to obtain the fluctuation statistics of each target sensor based on the fluctuation of each real-time monitoring data collected by the same target sensor in the current monitoring period relative to the corresponding initial monitoring data.

[0102] The first monitoring result determination module 506 is used to obtain the target monitoring result of the target tower in the current monitoring period based on the changing trend of each real-time monitoring data collected by the target sensor corresponding to the target fluctuation statistical value during the current monitoring period, when at least one fluctuation statistical value is greater than or equal to the first preset statistical value; the target fluctuation statistical value is a fluctuation statistical value that is greater than or equal to the first preset statistical value.

[0103] The second monitoring result determination module 508 is used to calculate the monitoring statistics based on the real-time monitoring data collected by the same target sensor in the current monitoring period when all fluctuation statistics are less than the first preset statistics. Based on the monitoring statistics corresponding to each target sensor, the target monitoring result of the target tower in the current monitoring period is obtained.

[0104] In one embodiment, the fluctuation statistics determination module 504 is further configured to:

[0105] Calculate the initial difference between each real-time monitoring data collected by the same target sensor within the current monitoring period and the corresponding initial monitoring data; correct each initial difference value to obtain the target difference value; fuse the target difference values ​​corresponding to the same target sensor to obtain the fluctuation statistics value corresponding to each target sensor.

[0106] In one embodiment, the first monitoring result determination module 506 is further configured to:

[0107] Calculate the difference between each real-time monitoring data collected by the target sensor within the current monitoring period and the corresponding initial monitoring data, corresponding to the target fluctuation statistics. When all the difference values ​​corresponding to any target fluctuation statistics show a non-linear trend with the collection time, the target monitoring result of the target tower in the current monitoring period is determined to be normal. When at least one of the difference values ​​corresponding to the target fluctuation statistics shows a linear trend with the collection time, the target monitoring result of the target tower in the current monitoring period is determined to be abnormal.

[0108] In one embodiment, the second monitoring result determination module 508 is further configured to:

[0109] The system collects real-time monitoring data from the same target sensor within the current monitoring period to obtain the monitoring statistics for each target sensor. These statistics are then fused to obtain a fused statistical value. When the fused statistical value is less than or equal to a second preset statistical value, the target monitoring result for the target tower in the current monitoring period is determined to be normal. When the fused statistical value is greater than the second preset statistical value, the target monitoring result for the target tower in the current monitoring period is determined based on the difference between the monitoring statistics for the same target sensor and the preset monitoring data.

[0110] In one embodiment, the second monitoring result determination module 508 is further configured to:

[0111] When the difference between the monitoring statistics corresponding to each target sensor and the corresponding preset monitoring data is less than the preset value, the target monitoring result of the target tower in the current monitoring cycle is determined to be normal; when the difference between the monitoring statistics corresponding to at least one target sensor and the corresponding preset monitoring data is greater than or equal to the preset value, the target monitoring result of the target tower in the current monitoring cycle is determined to be abnormal.

[0112] In one embodiment, the target sensor is any one of a distance sensor, an angle sensor, and a weight sensor.

[0113] The aforementioned pole / tower status monitoring device obtains fluctuation statistics for each target sensor by analyzing the fluctuations of real-time monitoring data collected by the same target sensor within the current monitoring cycle and relative to the corresponding initial monitoring data. When at least one fluctuation statistics value exceeds a first preset statistical value, it indicates abnormal fluctuations in the real-time monitoring data collected by the corresponding target sensor. Based on the changing trend of the real-time monitoring data collected by the target sensor with the corresponding fluctuation statistics value over time, the monitoring result for the target pole / tower is further determined, eliminating the influence of false anomalies and improving the accuracy of pole / tower status monitoring. When all fluctuation statistics values ​​are less than the first preset statistical value, it indicates that the fluctuations in the real-time monitoring data collected by each target sensor are relatively stable and no abnormal fluctuations have occurred. Based on the monitoring statistics values ​​corresponding to each target sensor, the monitoring result for the target pole / tower is further determined, improving the accuracy of pole / tower status monitoring. Furthermore, by acquiring real-time monitoring data and initial monitoring data collected by each target sensor and performing statistical analysis on the monitoring data to determine the pole / tower status, the efficiency of pole / tower status monitoring can be improved.

[0114] Each module in the aforementioned tower condition monitoring device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0115] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores real-time monitoring data, initial monitoring data, and other data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a tower status monitoring method.

[0116] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for monitoring the status of a pole tower. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0117] Those skilled in the art will understand that Figure 6 , 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0118] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0119] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0120] In one embodiment, a computer program product or computer program is provided, the computer product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the computer device to perform the steps in the above-described method embodiments.

[0121] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data shall comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0122] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0123] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0124] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for monitoring the condition of a power pole, characterized in that, The method includes: Acquire initial monitoring data collected by multiple target sensors at the same height on the target tower, as well as multiple real-time monitoring data collected by each target sensor within the current monitoring period; Based on the fluctuation of each real-time monitoring data collected by the same target sensor within the current monitoring period relative to the corresponding initial monitoring data, the fluctuation statistics of each target sensor are obtained respectively. When at least one fluctuation statistical value is greater than or equal to a first preset statistical value, the difference between each real-time monitoring data collected by the target sensor corresponding to the target fluctuation statistical value within the current monitoring period and the corresponding initial monitoring data is calculated; when each difference value corresponding to any target fluctuation statistical value shows a non-linear trend with the collection time, the target monitoring result of the target tower in the current monitoring period is determined to be normal; when at least one difference value corresponding to a target fluctuation statistical value shows a linear trend with the collection time, the target monitoring result of the target tower in the current monitoring period is determined to be abnormal; the target fluctuation statistical value is a fluctuation statistical value that is greater than or equal to the first preset statistical value. When all fluctuation statistics are less than the first preset statistics, the monitoring statistics are calculated based on the real-time monitoring data collected by the same target sensor in the current monitoring period. Based on the monitoring statistics corresponding to each target sensor, the target monitoring result of the target tower in the current monitoring period is obtained.

2. The method according to claim 1, characterized in that, The fluctuation statistics for each target sensor are obtained based on the fluctuations of each real-time monitoring data collected by the same target sensor within the current monitoring period relative to the corresponding initial monitoring data, including: Calculate the initial difference between each real-time monitoring data collected by the same target sensor within the current monitoring period and the corresponding initial monitoring data; Each initial difference value is corrected to obtain the target difference value. By fusing the difference values ​​of various targets corresponding to the same target sensor, the fluctuation statistics values ​​corresponding to each target sensor are obtained.

3. The method according to claim 1, characterized in that, Each of the target sensors is used to collect monitoring data of the target tower in different directions at the same height.

4. The method according to claim 1, characterized in that, The process of calculating monitoring statistics based on real-time monitoring data collected by the same target sensor within the current monitoring period, and obtaining the target monitoring result of the target tower in the current monitoring period based on the monitoring statistics corresponding to each target sensor, includes: By statistically analyzing the real-time monitoring data collected by the same target sensor within the current monitoring period, the monitoring statistics corresponding to each target sensor are obtained. By integrating the various monitoring statistics, a merged statistical value is obtained. When the fused statistical value is less than or equal to the second preset statistical value, the target tower is determined to be normal in the target monitoring result corresponding to the current monitoring cycle; When the fused statistical value is greater than the second preset statistical value, the target monitoring result of the target tower in the current monitoring cycle is determined based on the difference between the monitoring statistical value corresponding to the same target sensor and the preset monitoring data.

5. The method according to claim 4, characterized in that, The determination of the target monitoring result of the target tower in the current monitoring cycle based on the difference between the monitoring statistics corresponding to the same target sensor and the preset monitoring data includes: When the difference between the monitoring statistics value corresponding to each target sensor and the corresponding preset monitoring data is less than the preset value, the target tower is determined to be normal in the current monitoring cycle. When the difference between the monitoring statistics value corresponding to at least one target sensor and the corresponding preset monitoring data is greater than or equal to the preset value, the target tower is determined to be abnormal in the target monitoring result corresponding to the current monitoring cycle.

6. The method according to any one of claims 1 to 5, characterized in that, The target sensor can be any one of a distance sensor, an angle sensor, or a weight sensor.

7. A tower condition monitoring device, characterized in that, The device includes: The monitoring data acquisition module is used to acquire the initial monitoring data collected by multiple target sensors at the same height on the target tower, as well as the multiple real-time monitoring data collected by each target sensor within the current monitoring cycle. The fluctuation statistics determination module is used to obtain the fluctuation statistics of each target sensor based on the fluctuation of each real-time monitoring data collected by the same target sensor in the current monitoring period relative to the corresponding initial monitoring data. The first monitoring result determination module is used to calculate the difference between each real-time monitoring data collected by the target sensor corresponding to the target fluctuation statistical value and the corresponding initial monitoring data in the current monitoring period when at least one fluctuation statistical value is greater than or equal to a first preset statistical value; when each difference value corresponding to any target fluctuation statistical value shows a non-linear trend with the collection time, the target monitoring result of the target tower in the current monitoring period is determined to be normal; when at least one difference value corresponding to the target fluctuation statistical value shows a linear trend with the collection time, the target monitoring result of the target tower in the current monitoring period is determined to be abnormal; the target fluctuation statistical value is a fluctuation statistical value that is greater than or equal to the first preset statistical value. The second monitoring result determination module is used to calculate the monitoring statistics based on the real-time monitoring data collected by the same target sensor in the current monitoring period when all fluctuation statistics are less than the first preset statistics. Based on the monitoring statistics corresponding to each target sensor, the target monitoring result of the target tower in the current monitoring period is obtained.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images