An artificial intelligence edge computing decision controller

Abstract

The present invention relates to the field of edge computing technology, and in particular to an artificial intelligence edge computing decision controller, including a data acquisition module, an artificial intelligence processing module, an abnormality analysis module, and a decision control module. The present invention can not only collect and analyze the key performance indicators of the bolts of the communication signal tower in real time through highly automated and intelligent monitoring capabilities, but also deeply process the data through advanced algorithms, thereby accurately identifying potential structural anomalies and risks. This real-time monitoring and early warning mechanism significantly improves the maintenance efficiency and response speed of communication signal towers, reduces communication interruptions and equipment damage caused by structural failures, ensures the stability and reliability of communication networks, reduces the need for emergency repairs through preventive maintenance, and effectively solves the problem of low equipment monitoring and maintenance efficiency due to the limitations of data collection and processing.

Description

Technical Field

[0001] The present invention relates to the field of edge computing technology, and in particular to an artificial intelligence edge computing decision controller. Background Art

[0002] With the rapid development of the Internet of Things and artificial intelligence technologies, the traditional way of processing data in cloud computing centers can no longer meet the needs of real-time and security, especially in the fields of industrial automation and smart cities. Edge intelligence, as an emerging technology to address this challenge, significantly reduces latency, improves response speed, and enhances data privacy protection by performing data processing and analysis on edge devices near the data source. However, when applying edge intelligence in extreme environments, how to ensure the stability and reliability of the system and the real-time and accuracy of the algorithm has become a major challenge facing the development of technology.

[0003] The patent document with Chinese patent application publication number CN114037094A discloses an intelligent decision support system for edge computing, including: an edge data acquisition module, which is used to collect real-time operation data of equipment; a data storage module, which is used to receive and store the real-time operation data; a knowledge module, which stores maintenance knowledge; a decision module, which is used to analyze the real-time operation data and determine whether there is a fault in the equipment. If so, maintenance knowledge is selected from the knowledge module according to the fault type, and a maintenance plan is generated based on the maintenance knowledge.

[0004] It can be seen that the real-time data analysis flexibility of the knowledge module in the system is poor, and there is a problem of insufficient timely knowledge updating; and it lacks the ability to automatically adjust and optimize monitoring parameters, and has limitations when processing large-scale or diversified data. Summary of the invention

[0005] To this end, the present invention provides an artificial intelligence edge computing decision controller to overcome the problem of low efficiency of equipment monitoring and maintenance due to limitations of data collection and processing in the prior art.

[0006] To achieve the above objectives, the present invention provides an artificial intelligence edge computing decision controller, comprising:

[0007] A data acquisition module is used to monitor the real-time temperature, real-time wind speed and real-time displacement of each bolt within a preset height range on the communication signal tower, as well as the real-time concave area and real-time friction force at the bolt contact surface;

[0008] an artificial intelligence processing module, which is connected to the data acquisition module and is used to adjust the real-time temperature, real-time displacement and real-time friction according to a preset conversion model to form a corrected temperature, a corrected displacement and a corrected friction;

[0009] an abnormality analysis module, which is connected to the data acquisition module and the artificial intelligence processing module respectively, and includes a deformation abnormality unit for determining the deformation abnormality according to the corrected temperature and the corrected displacement, an abnormality determination unit for determining a number of abnormal bolts according to all the corrected friction forces within a preset abnormal determination time under the deformation abnormality, and an element determination unit for determining the abnormal elements of a number of abnormal bolts according to the heights of all the abnormal bolts within a preset element time, the real-time wind speed, and the corrected concave area;

[0010] A decision control module, which is connected to the element determination unit and the data acquisition module respectively, includes a consistency determination unit for determining the fluctuation consistency based on all the corrected displacements and the corrected depression areas within a preset decision time period obtained based on the abnormal element, a correction unit for adjusting the preset height range according to the fluctuation consistency and a preset standard consistency range until the fluctuation consistency is within the preset standard consistency range to form a corrected height range, and an alarm unit for issuing an alarm according to the fluctuation consistency and the preset standard consistency range.

[0011] Furthermore, the abnormal deformation unit includes:

[0012] A temperature comparison subunit, used to determine a temperature anomaly result based on a comparison result between the corrected temperature and a preset standard temperature;

[0013] A displacement comparison subunit, used to determine the displacement abnormality result according to the comparison result between the corrected displacement and the preset standard displacement;

[0014] The temporary anomaly determination subunit is connected to the temperature comparison subunit and the displacement comparison subunit respectively, and is used to determine the deformation anomaly according to the temperature anomaly result and the displacement anomaly result.

[0015] Furthermore, the abnormality determination unit includes:

[0016] an abnormality calculation subunit, used to calculate the standard deviation of the corrected friction force to form a friction force standard deviation;

[0017] The abnormality determination subunit is connected to the abnormality calculation subunit and is used to determine a number of abnormal bolts according to the comparison result between the friction standard deviation and a preset friction standard deviation threshold.

[0018] Furthermore, the element determination unit includes:

[0019] A height fluctuation calculation subunit, which is used to calculate the standard deviation of the height to obtain the height distribution;

[0020] A wind speed impact determination subunit, connected to the height fluctuation calculation subunit, for determining a wind speed impact result according to a comparison result between the height distribution and a preset height standard distribution;

[0021] The element determination subunit is connected to the wind speed area determination subunit and is used to determine the abnormal element according to the real-time wind speed obtained based on the wind speed influence result and the corrected concave area.

[0022] Furthermore, the element determination subunit includes:

[0023] A wind speed gradient curve drawing subtask unit is used to draw a wind speed gradient curve according to all real-time wind speeds at a preset number of abnormal bolts in the vertical direction;

[0024] A depression gradient curve drawing subtask unit is used to draw a depression gradient curve according to all corrected depression areas at the preset number of abnormal bolts in the vertical direction;

[0025] a matching coefficient calculation subtask unit, which is connected to the wind speed gradient curve drawing subtask unit and the concave gradient curve drawing subtask unit respectively, and is used to calculate a correlation coefficient according to the wind speed gradient curve and the concave gradient curve to obtain a matching coefficient;

[0026] The element determination subtask unit is used to determine the abnormal element according to the comparison result between the matching coefficient and the preset coefficient threshold.

[0027] Furthermore, the consistency determination unit includes:

[0028] A displacement fluctuation calculation subunit, used to calculate the standard deviation of the corrected displacement to obtain the displacement fluctuation;

[0029] A concave fluctuation calculation subunit, used to calculate the standard deviation of the corrected concave area to obtain the concave fluctuation;

[0030] The consistency calculation subunit is connected to the displacement fluctuation calculation subunit and the concave fluctuation calculation subunit respectively, and is used to calculate the correlation coefficient between the displacement fluctuation and the concave fluctuation to obtain the fluctuation consistency.

[0031] Furthermore, the correction unit comprises:

[0032] A correction calculation subunit, used to correct the upper limit difference according to the maximum value of the fluctuation consistency and the preset standard consistency range;

[0033] A correction determination subunit, connected to the correction calculation subunit, for determining the maximum value of the preset height range that needs to be corrected according to the comparison result of the correction upper limit difference and the preset standard correction difference, and forming a correction upper limit determination result;

[0034] The adjusting subunit is connected to the correction determination subunit and is used to increase the maximum value of the preset altitude range according to the correction upper limit determination result to form a correction altitude range.

[0035] Furthermore, the correction calculation subunit is further used to determine the correction lower limit difference according to the minimum value of the fluctuation consistency and the preset standard consistency range;

[0036] The correction determination subunit is also used to determine the minimum value of the preset height range that needs to be corrected according to the comparison result of the correction lower limit difference and the preset standard correction difference, so as to form a correction lower limit determination result;

[0037] The adjustment subunit is further configured to reduce the minimum value of the preset altitude range according to the correction lower limit determination result to form a correction altitude range.

[0038] Furthermore, the alarm unit is used to issue an alarm for immediately inspecting the bolt according to a comparison result between the correction upper limit difference and a preset standard correction difference.

[0039] Furthermore, the alarm unit is also used to issue a regular maintenance alarm based on a comparison result between the correction lower limit difference and a preset standard correction difference.

[0040] Compared with the prior art, the beneficial effect of the present invention is that, through highly automated and intelligent monitoring capabilities, it can not only collect and analyze the key performance indicators of the bolts of the communication signal tower in real time, but also deeply process the data through advanced algorithms, so as to accurately identify potential structural anomalies and risks. This real-time monitoring and early warning mechanism significantly improves the maintenance efficiency and response speed of the communication signal tower, reduces communication interruptions and equipment damage caused by structural failures, and ensures the stability and reliability of the communication network. At the same time, the intelligent decision support function provides maintenance personnel with a scientific decision-making basis, and reduces the need for emergency repairs through preventive maintenance, extending the service life of the communication signal tower, and effectively solves the problem of low equipment monitoring and maintenance efficiency due to the limitations of data collection and processing.

[0041] Furthermore, through real-time comparison and analysis of two key parameters, temperature and displacement, potential deformation problems of bolts can be discovered earlier, so that preventive measures can be taken to avoid possible structural damage or failure.

[0042] Furthermore, timely anomaly detection helps to quickly and accurately identify abnormal conditions of bolts, thus improving the maintenance efficiency and safety of communication signal towers.

[0043] Furthermore, by calculating the height distribution, we can understand the fluctuation of the bolt position, and by evaluating the impact of wind speed, we can understand the impact of environmental factors on the bolt status. Finally, through comprehensive analysis, the abnormal factors are determined, which improves the accuracy and efficiency of bolt abnormality detection.

[0044] Furthermore, by plotting the wind speed gradient and concave gradient curves, the physical state changes of the bolts at different heights can be intuitively observed. The calculation of the matching coefficient provides a quantitative indicator for the abnormal state of the bolts, which helps to more accurately identify and locate abnormal bolts.

[0045] Furthermore, the consistency between the two is evaluated by calculating the correlation coefficient. A high fluctuation consistency means that the change trends of displacement and depression area are similar, indicating that the behavior of the bolts is as expected or affected by similar factors. On the contrary, a low fluctuation consistency may indicate that there are some abnormal or inconsistent factors affecting the behavior of the bolts, which helps to detect potential structural problems in time and improve the safety and stability of the communication signal tower.

[0046] Furthermore, through the correction process, it can be ensured that the monitoring of the bolts of the communication signal tower is more accurate and adaptable. When the fluctuation consistency of the bolt is high, it indicates that it has good stability, and the height range of monitoring can be appropriately increased to reduce unnecessary intervention and maintenance. On the contrary, if the fluctuation consistency is lower than the preset standard, the correction unit can adjust the monitoring strategy in time, increase the frequency or strictness of monitoring, and ensure the safety of the bolt.

[0047] Furthermore, the correction process allows the system to dynamically adjust the monitoring parameters to adapt to the actual bolt status, thereby improving the accuracy and efficiency of monitoring. When the bolt's fluctuation consistency is high, indicating that its status is better than expected, the system can reduce the monitoring height range to reduce unnecessary waste of monitoring resources. Conversely, if the fluctuation consistency is lower than expected, the system can increase the monitoring height range to ensure the stability and safety of the bolt.

[0048] Furthermore, by real-time monitoring of the key parameters of the bolts and timely alarms, signal tower failures or accidents caused by bolt problems can be minimized. In addition, this automated alarm system can reduce the workload and cost of manual monitoring and improve the efficiency of maintenance work.

[0049] Furthermore, by setting up an alarm unit to monitor the corrected lower limit difference of the bolt, it can be ensured that the stability and safety of the bolt receive timely attention. This alarm mechanism helps to achieve preventive maintenance, that is, maintenance before the bolt may have problems, thereby avoiding emergency failures and unexpected downtime. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a schematic diagram of the artificial intelligence edge computing decision controller of this embodiment;

[0051] Figure 2 A determination logic diagram for determining an abnormal deformation situation by the abnormal deformation unit of this embodiment;

[0052] Figure 3 A decision logic diagram for determining a number of abnormal bolts by the abnormality determination unit of this embodiment;

[0053] Figure 4 This is a decision logic diagram for determining the wind speed impact result by the factor determination unit in this embodiment. DETAILED DESCRIPTION

[0054] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0055] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0056] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0057] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0058] See also Figure 1As shown, it is a schematic diagram of the artificial intelligence edge computing decision controller of this embodiment;

[0059] Under extreme climatic conditions, the bolts of communication signal towers may be subject to drastic temperature changes, strong winds, and mechanical stress, resulting in degradation of bolt performance and even structural safety issues. In this context, this embodiment provides an artificial intelligence edge computing decision controller, including:

[0060] A data acquisition module is used to monitor the real-time temperature, real-time wind speed and real-time displacement of each bolt within a preset height range on the communication signal tower, as well as the real-time concave area and real-time friction force at the bolt contact surface;

[0061] an artificial intelligence processing module, which is connected to the data acquisition module and is used to adjust the real-time temperature, real-time displacement and real-time friction according to a preset conversion model to form a corrected temperature, a corrected displacement and a corrected friction;

[0062] an abnormality analysis module, which is connected to the data acquisition module and the artificial intelligence processing module respectively, and includes a deformation abnormality unit for determining the deformation abnormality according to the corrected temperature and the corrected displacement, an abnormality determination unit for determining a number of abnormal bolts according to all the corrected friction forces within a preset abnormal determination time under the deformation abnormality, and an element determination unit for determining the abnormal elements of a number of abnormal bolts according to the heights of all the abnormal bolts within a preset element time, the real-time wind speed, and the corrected concave area;

[0063] A decision control module, which is connected to the element determination unit and the data acquisition module respectively, includes a consistency determination unit for determining the fluctuation consistency based on all the corrected displacements and the corrected depression areas within a preset decision time period obtained based on the abnormal element, a correction unit for adjusting the preset height range according to the fluctuation consistency and a preset standard consistency range until the fluctuation consistency is within the preset standard consistency range to form a corrected height range, and an alarm unit for issuing an alarm according to the fluctuation consistency and the preset standard consistency range.

[0064] The preset height range refers to the specific height interval of bolt monitoring on the communication signal tower. It depends on the design specifications, usage environment and historical maintenance data of the communication signal tower. It is usually set to 40 meters to 60 meters, and in this embodiment it is set to 50 meters to 100 meters, which can specifically monitor the height interval most prone to problems and improve monitoring efficiency.

[0065] The preset conversion model is an algorithm or model that converts raw monitoring data into corrected data. It depends on the physical properties of the bolt, environmental factors, and historical data analysis. It is usually set to a model derived from experience and data regression analysis. In this embodiment, a machine learning model is used to adjust the data, which can adjust the data according to actual conditions and improve the accuracy and reliability of the data.

[0066] The preset abnormal determination time is the length of time required to observe the abnormal state of the bolt. It depends on the normal service life of the bolt and the historical failure data. It is usually set between 1 month and 4 months. In this embodiment, it is set to 3 months, which can issue an early warning in time before the bolt is about to fail.

[0067] The preset factor duration is the time range that needs to be considered to determine the bolt abnormality factor. It depends on the historical performance data of the bolt and the environmental change cycle. It is usually set between half a month and 2 months, determined according to the statistical cycle of the bolt performance change. In this embodiment, it is set to 1 month, which helps to capture the trend of the bolt performance change and provide a basis for further analysis.

[0068] The preset decision time is the time required from discovering an anomaly to making a decision. It depends on the response speed and processing capacity of the system. It is usually determined based on the real-time processing capacity of the system and the historical decision time, and is set between 0.5 hours and 2 hours. In this embodiment, it is set to 1 hour. It can ensure that the system can make a decision quickly after discovering an anomaly, reducing potential failure time.

[0069] The preset standard consistency range is a standard for judging whether the fluctuation consistency is within an acceptable range. It depends on the normal working range of the bolt and the historical performance fluctuation data. It is usually determined based on the historical statistical data of the bolt performance and is set to a fluctuation of ±10%. In this embodiment, it is set to a fluctuation of ±5%. It can provide a clear judgment standard for the bolt performance, which is convenient for quickly identifying and handling abnormalities.

[0070] First, the data acquisition module collects real-time data of the bolts on the communication signal tower, including temperature, wind speed, displacement, contact surface depression area and friction. Then, the artificial intelligence processing module adjusts these data using the preset conversion model to generate corrected temperature, displacement and friction data. The abnormal analysis module further analyzes these corrected data to identify abnormal deformations, abnormal bolts and abnormal elements. Finally, the decision control module determines the fluctuation consistency based on these analysis results, adjusts the height range of bolt monitoring, and issues an alarm when necessary.

[0071] Through highly automated and intelligent monitoring capabilities, it is not only possible to collect and analyze the key performance indicators of the bolts of communication signal towers in real time, but also to deeply process the data through advanced algorithms, so as to accurately identify potential structural anomalies and risks. This real-time monitoring and early warning mechanism significantly improves the maintenance efficiency and response speed of communication signal towers, reduces communication interruptions and equipment damage caused by structural failures, and ensures the stability and reliability of the communication network. At the same time, the intelligent decision support function provides maintenance personnel with a scientific basis for decision-making, reduces the need for emergency repairs through preventive maintenance, and extends the service life of communication signal towers, effectively solving the problem of low efficiency in equipment monitoring and maintenance due to limitations in data collection and processing.

[0072] Please continue reading Figure 2 As shown, it is a determination logic diagram of the abnormal deformation unit of this embodiment for determining the abnormal deformation situation;

[0073] Specifically, the abnormal deformation unit includes:

[0074] A temperature comparison subunit, used to determine a temperature abnormality result according to a comparison result between the corrected temperature and a preset standard temperature, that is, when the corrected temperature is greater than the preset standard temperature, a temperature abnormality result is determined;

[0075] A displacement comparison subunit, used to determine the displacement abnormality result according to the comparison result between the corrected displacement and the preset standard displacement, that is, when the corrected displacement is greater than the preset standard displacement, the displacement abnormality result is determined;

[0076] The temporary anomaly determination subunit is connected to the temperature comparison subunit and the displacement comparison subunit respectively, and is used to determine the deformation anomaly according to the temperature anomaly result and the displacement anomaly result.

[0077] The preset standard temperature refers to the temperature value of the bolt under normal working conditions. It depends on the thermal stability of the bolt material, the climate conditions of the signal tower location, and historical temperature data. It is generally set between 0°C and 40°C according to the bolt material and the use environment. In this embodiment, it is set to 25°C. Ensure the performance and life of the bolt at normal temperature, and detect abnormalities in time by comparing with the real-time temperature.

[0078] The preset standard displacement refers to the maximum displacement allowed by the bolt under normal working conditions. It depends on the design strength of the bolt, the elastic limit of the material and the structural stability of the signal tower. According to the bolt specifications and the design requirements of the signal tower, it is usually set within a few millimeters. In this embodiment, it is set to ±5mm. By limiting the displacement range, the bolt can be prevented from causing structural failure due to excessive deformation.

[0079] The deformation anomaly unit identifies the abnormal deformation of the bolt through three submodules. First, the temperature comparison subunit compares the real-time monitored correction temperature with the preset standard temperature. Once the correction temperature exceeds the standard temperature, it will be recorded as a temperature anomaly. Secondly, the displacement comparison subunit compares the correction displacement with the preset standard displacement, and the value exceeding the standard displacement will be marked as a displacement anomaly. Finally, the temporary anomaly determination subunit integrates the results of temperature anomaly and displacement anomaly, and determines whether the bolt has a deformation anomaly through logical judgment.

[0080] By comparing and analyzing the two key parameters of temperature and displacement in real time, potential deformation problems of bolts can be discovered earlier, so that preventive measures can be taken to avoid possible structural damage or failure.

[0081] Please continue reading Figure 3 As shown, it is a decision logic diagram of the abnormality determination unit of this embodiment for determining a number of abnormal bolts;

[0082] Specifically, the abnormality determination unit includes:

[0083] an abnormality calculation subunit, used to calculate the standard deviation of the corrected friction force to form a friction force standard deviation;

[0084] The abnormality determination subunit is connected to the abnormality calculation subunit and is used to determine a number of abnormal bolts according to the comparison result between the friction standard deviation and a preset friction standard deviation threshold.

[0085] The preset friction standard deviation threshold is the standard deviation value used to determine whether there is an abnormality in the friction of the bolt. It depends on the friction characteristics of the bolt material, historical friction data, and environmental factors. It is usually set between 0.1 and 0.5 Newton meters according to the bolt material and the use environment. In this embodiment, it is set to 0.2 Newton meters. By comparing with the friction standard deviation monitored in real time, it is possible to promptly identify whether the bolt has abnormal friction, thereby preventing potential structural problems or failures.

[0086] The abnormality determination unit calculates the standard deviation of the corrected friction force through the abnormality calculation subunit, and then the abnormality determination subunit compares the standard deviation with a preset friction force standard deviation threshold to determine whether there is an abnormal bolt.

[0087] Timely anomaly detection helps to quickly and accurately identify abnormal conditions of bolts and improve the maintenance efficiency and safety of communication signal towers.

[0088] Please continue reading Figure 4 As shown, it is a decision logic diagram of the element determination unit in this embodiment for determining the wind speed influence result;

[0089] Specifically, the element determination unit includes:

[0090] A height fluctuation calculation subunit, which is used to calculate the standard deviation of the height to obtain the height distribution;

[0091] A wind speed impact determination subunit, connected to the height fluctuation calculation subunit, for determining the wind speed impact result according to a comparison result between the height distribution and a preset height standard distribution, that is, determining the wind speed impact result when the height distribution is greater than the preset height standard distribution;

[0092] The element determination subunit is connected to the wind speed area determination subunit and is used to determine the abnormal element according to the real-time wind speed obtained based on the wind speed influence result and the corrected concave area.

[0093] The preset height standard distribution is a statistically based standard used to determine the acceptable fluctuation range of the bolt position height. It depends on the initial accuracy of the bolt installation, the stability of the signal tower structure, and historical monitoring data. It is usually set based on statistical analysis of historical data and is set within the range of ±5% to ±10% of the average height of the bolt. In this embodiment, the average installation height of the bolt is 100 meters, and the preset height standard distribution is set to ±5 meters (i.e., 95 meters to 105 meters). Reasonable preset height standard distribution helps to identify bolts that may have abnormal displacement due to wind speed, temperature changes or other environmental factors.

[0094] The element determination unit evaluates the abnormal elements of the bolts of the communication signal tower through three submodules. First, the height fluctuation calculation subunit calculates the standard deviation of the bolt height to obtain the height distribution. Then, the wind speed impact determination subunit evaluates the impact of wind speed on the bolt by comparing the height distribution with the preset standard distribution. Finally, the element determination subunit combines the wind speed impact results, the real-time wind speed, and the corrected concave area to comprehensively judge the abnormal elements of the bolt.

[0095] By calculating the height distribution, we can understand the fluctuation of the bolt position, and by evaluating the impact of wind speed, we can understand the impact of environmental factors on the bolt status. Finally, through comprehensive analysis, the abnormal factors are determined, which improves the accuracy and efficiency of bolt abnormality detection.

[0096] Specifically, the element determination subunit includes:

[0097] A wind speed gradient curve drawing subtask unit is used to draw a wind speed gradient curve according to all real-time wind speeds at a preset number of abnormal bolts in the vertical direction;

[0098] A depression gradient curve drawing subtask unit is used to draw a depression gradient curve according to all corrected depression areas at the preset number of abnormal bolts in the vertical direction;

[0099] a matching coefficient calculation subtask unit, which is connected to the wind speed gradient curve drawing subtask unit and the concave gradient curve drawing subtask unit respectively, and is used to calculate a correlation coefficient according to the wind speed gradient curve and the concave gradient curve to obtain a matching coefficient;

[0100] The element determination subtask unit is used to determine the abnormal element according to the comparison result between the matching coefficient and the preset coefficient threshold.

[0101] Among them, the correlation coefficient is based on the Pearson correlation coefficient.

[0102] The preset number is the number of abnormal bolts that need to be focused on and is pre-set in the monitoring system. It depends on the design scale of the communication signal tower, the total number of bolts, and historical fault data. It is usually set according to the scale and importance of the signal tower, and is set to 1% to 5% of the total number of bolts. In this embodiment, a signal tower has 1,000 bolts, and 50 of them are preset as abnormal bolts that need to be focused on. By setting a reasonable preset number, resources and attention can be focused on those bolts that are statistically more likely to have problems or are more important, thereby improving the efficiency and response speed of monitoring.

[0103] The preset coefficient threshold is a standard value used to determine whether the matching coefficient exceeds the normal range. It depends on the statistical analysis of historical data, the physical properties of the bolts, and environmental factors. It is usually set based on the statistical analysis of historical matching coefficient data and is set between 0.5 and 0.9. In this embodiment, it is set to 0.7 as the threshold for abnormal determination. It helps to standardize the judgment criteria for bolt abnormalities, so that the monitoring system can more automatically and objectively identify those bolts that may need maintenance.

[0104] The element determination sub-unit evaluates the abnormal elements of the bolts through a series of sub-task units. First, the wind speed gradient curve drawing sub-task unit draws the wind speed gradient curve based on the real-time wind speed data at the abnormal bolt. Next, the depression gradient curve drawing sub-task unit draws the depression gradient curve based on the corrected depression area at the abnormal bolt. Then, the matching coefficient calculation sub-task unit calculates the correlation coefficient between the two gradient curves to obtain the matching coefficient. Finally, the element determination sub-task unit compares the matching coefficient with the preset coefficient threshold to determine the abnormal elements of the bolt.

[0105] By plotting the wind speed gradient and concave gradient curves, the physical state changes of the bolts at different heights can be intuitively observed. The calculation of the matching coefficient provides a quantitative indicator for the abnormal state of the bolts, which helps to more accurately identify and locate abnormal bolts.

[0106] Specifically, the consistency determination unit includes:

[0107] A displacement fluctuation calculation subunit, used to calculate the standard deviation of the corrected displacement to obtain the displacement fluctuation;

[0108] A concave fluctuation calculation subunit, used to calculate the standard deviation of the corrected concave area to obtain the concave fluctuation;

[0109] The consistency calculation subunit is connected to the displacement fluctuation calculation subunit and the concave fluctuation calculation subunit respectively, and is used to calculate the correlation coefficient between the displacement fluctuation and the concave fluctuation to obtain the fluctuation consistency.

[0110] The consistency determination unit evaluates the fluctuation consistency of the displacement and recessed area of ​​the bolt through three subunits. First, the displacement fluctuation calculation subunit calculates the standard deviation of the corrected displacement data to obtain the displacement fluctuation. Second, the recess fluctuation calculation subunit calculates the standard deviation of the corrected recessed area to obtain the recess fluctuation. Finally, the consistency calculation subunit calculates the correlation coefficient between the displacement fluctuation and the recess fluctuation to obtain the fluctuation consistency.

[0111] The consistency between the two is evaluated by calculating the correlation coefficient. A high fluctuation consistency means that the displacement and the depression area have similar changing trends, indicating that the bolts behave as expected or are affected by similar factors. On the contrary, a low fluctuation consistency may indicate that there are some abnormal or inconsistent factors affecting the behavior of the bolts, which helps to detect potential structural problems in a timely manner and improve the safety and stability of communication signal towers.

[0112] Specifically, the correction unit includes:

[0113] A correction calculation subunit, used to correct the upper limit difference according to the maximum value of the fluctuation consistency and the preset standard consistency range;

[0114] A correction determination subunit, connected to the correction calculation subunit, for determining the maximum value of the preset height range that needs to be corrected according to the comparison result of the correction upper limit difference and the preset standard correction difference, and forming a correction upper limit determination result;

[0115] The adjustment subunit is connected to the correction determination subunit and is used to increase the maximum value of the preset altitude range according to the correction upper limit determination result and the preset altitude adjustment coefficient to form a correction altitude range.

[0116] The preset height adjustment coefficient is a coefficient used to adjust the preset height range. It determines how much the maximum value of the preset height range should be adjusted when there is a difference between the fluctuation consistency and the maximum value of the preset standard consistency range. It depends on the physical properties of the bolt, the design standards of the signal tower, and historical maintenance data. It is usually set between 0 and 1, and the specific value depends on the requirements for system sensitivity and response speed. In this embodiment, it is set to 0.1. By reasonably setting the preset height adjustment coefficient, it can ensure that the system responds appropriately to changes in the bolt state and improve the safety and stability of the communication signal tower.

[0117] The correction unit is responsible for adjusting the preset height range according to the bolt monitoring data. First, the correction calculation subunit calculates the correction upper limit difference according to the maximum value of the fluctuation consistency and the preset standard consistency range. Then, the correction determination subunit compares this correction upper limit difference with the preset standard correction difference to determine whether it is necessary to adjust the maximum value of the preset height range to form a correction upper limit determination result. Finally, the adjustment subunit increases the maximum value of the preset height range according to the correction upper limit determination result to form a new correction height range.

[0118] Through the correction process, it is possible to ensure that the monitoring of the bolts of the communication signal tower is more accurate and adaptable. When the fluctuation consistency of the bolt is high, it indicates that it is stable, and the height range of monitoring can be appropriately increased to reduce unnecessary intervention and maintenance. On the contrary, if the fluctuation consistency is lower than the preset standard, the correction unit can adjust the monitoring strategy in time, increase the frequency or strictness of monitoring, and ensure the safety of the bolt.

[0119] Specifically, the correction calculation subunit is further used to determine the correction lower limit difference according to the minimum value of the fluctuation consistency and the preset standard consistency range;

[0120] The correction determination subunit is also used to determine the minimum value of the preset height range that needs to be corrected according to the comparison result of the correction lower limit difference and the preset standard correction difference, so as to form a correction lower limit determination result;

[0121] The adjustment subunit is further configured to reduce the minimum value of the preset altitude range according to the correction lower limit determination result and the preset altitude adjustment coefficient to form a correction altitude range.

[0122] The correction calculation subunit in the correction unit is responsible for calculating the difference between the fluctuation consistency and the minimum value of the preset standard consistency range, and determining the correction lower limit difference. Then, the correction determination subunit compares this difference with the preset standard correction difference to form a determination result on whether the minimum value of the preset height range needs to be adjusted. Finally, the adjustment subunit reduces the minimum value of the preset height range according to the determination result to form a new correction height range.

[0123] The correction process allows the system to dynamically adjust the monitoring parameters to adapt to the actual bolt status, thereby improving the accuracy and efficiency of monitoring. When the bolt's fluctuation consistency is high, indicating that its status is better than expected, the system can reduce the monitoring height range to reduce unnecessary waste of monitoring resources. Conversely, if the fluctuation consistency is lower than expected, the system can increase the monitoring height range to ensure the stability and safety of the bolt.

[0124] Specifically, the alarm unit is used to issue an alarm for immediately inspecting the bolt according to the comparison result of the correction upper limit difference and the preset standard correction difference.

[0125] The alarm unit is a key part of the monitoring system. Its function is to monitor the status of the bolts in real time. When the correction upper limit difference calculated by the correction calculation subunit is compared with the preset standard correction difference, if the difference exceeds the preset standard correction difference, the alarm unit will be triggered. Once triggered, the alarm unit will issue an alarm to immediately inspect the bolts, prompting maintenance personnel to inspect and repair the bolts.

[0126] By monitoring the key parameters of bolts in real time and issuing alarms in time, signal tower failures or accidents caused by bolt problems can be minimized. In addition, this automated alarm system can reduce the workload and cost of manual monitoring and improve the efficiency of maintenance work.

[0127] In the bolt monitoring system of the communication signal tower, the maximum value of the preset height range is 100 meters, the fluctuation consistency is 0.85, and the maximum value of the preset standard consistency range is 0.9. If the preset height adjustment coefficient is 0.1, the correction calculation subunit may calculate the correction upper limit difference as 0.05 (i.e. 0.85- 0.9). If the preset standard correction difference is 0.03, then the correction upper limit difference exceeds the standard correction difference, and the alarm unit will sound an alarm. At this time, the maintenance team will be notified immediately and dispatch technicians to the site to inspect the bolts and make necessary repairs to ensure the stability and safety of the signal tower.

[0128] Specifically, the alarm unit is further used to issue a regular maintenance alarm according to a comparison result between the correction lower limit difference and a preset standard correction difference.

[0129] The alarm unit plays a vital role in the monitoring system. It not only monitors the upper limit deviation of the bolt status, but also pays attention to the lower limit deviation. When the correction lower limit difference calculated by the correction calculation subunit is compared with the preset standard correction difference, if this difference exceeds the preset range, the alarm unit will be triggered. Once triggered, the alarm unit will issue a regular maintenance alarm to remind the maintenance team to inspect and maintain the bolts within the planned maintenance cycle.

[0130] By setting up an alarm unit to monitor the lower limit difference of the bolt correction, it can ensure that the stability and safety of the bolt receive timely attention. This alarm mechanism helps to achieve preventive maintenance, that is, maintenance before the bolt may have problems, thus avoiding emergency failures and unexpected downtime.

[0131] In the bolt monitoring system of the communication signal tower, the minimum value of the preset height range is 80 meters, the fluctuation consistency is 0.75, and the minimum value of the preset standard consistency range is 0.7. If the preset height adjustment coefficient is 0.1, the correction calculation subunit

[0132] The correction lower limit difference will be calculated to be -0.05 (i.e. 0.75-0.7). If the preset standard correction difference is 0.03 and the absolute value of the correction lower limit difference exceeds this standard, the alarm unit will issue a regular maintenance alarm. At this time, the maintenance team will conduct a detailed inspection and necessary maintenance work on the bolts in the next maintenance cycle according to the alarm prompt to ensure the long-term stability of the bolts and the safe operation of the signal tower.

[0133] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0134] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An artificial intelligence edge computing decision controller, characterized in that: include: A data acquisition module is used to monitor the real-time temperature, real-time wind speed and real-time displacement of each bolt within a preset height range on the communication signal tower, as well as the real-time concave area and real-time friction force at the bolt contact surface; an artificial intelligence processing module, which is connected to the data acquisition module and is used to adjust the real-time temperature, real-time displacement and real-time friction according to a preset conversion model to form a corrected temperature, a corrected displacement and a corrected friction; an abnormality analysis module, which is connected to the data acquisition module and the artificial intelligence processing module respectively, and includes a deformation abnormality unit for determining the deformation abnormality according to the corrected temperature and the corrected displacement, an abnormality determination unit for determining a number of abnormal bolts according to all the corrected friction forces within a preset abnormal determination time under the deformation abnormality, and an element determination unit for determining the abnormal elements of a number of abnormal bolts according to the heights of all the abnormal bolts within a preset element time, the real-time wind speed, and the corrected concave area; a decision control module, which is connected to the element determination unit and the data acquisition module respectively, and includes a consistency determination unit for determining the fluctuation consistency according to all the corrected displacements and the corrected concave areas within a preset decision time period obtained based on the abnormal element, a correction unit for adjusting the preset height range according to the fluctuation consistency and a preset standard consistency range until the fluctuation consistency is within the preset standard consistency range to form a corrected height range, and an alarm unit for issuing an alarm according to the fluctuation consistency and the preset standard consistency range; The consistency determination unit comprises: A displacement fluctuation calculation subunit, used to calculate the standard deviation of the corrected displacement to obtain the displacement fluctuation; A concave fluctuation calculation subunit, used to calculate the standard deviation of the corrected concave area to obtain the concave fluctuation; The consistency calculation subunit is connected to the displacement fluctuation calculation subunit and the concave fluctuation calculation subunit respectively, and is used to calculate the correlation coefficient between the displacement fluctuation and the concave fluctuation to obtain the fluctuation consistency.

2. The artificial intelligence edge computing decision controller according to claim 1, characterized in that: The abnormal deformation unit comprises: A temperature comparison subunit, used to determine a temperature anomaly result based on a comparison result between the corrected temperature and a preset standard temperature; A displacement comparison subunit, used to determine the displacement abnormality result according to the comparison result between the corrected displacement and the preset standard displacement; The temporary anomaly determination subunit is connected to the temperature comparison subunit and the displacement comparison subunit respectively, and is used to determine the deformation anomaly according to the temperature anomaly result and the displacement anomaly result.

3. The artificial intelligence edge computing decision controller according to claim 2, characterized in that: The abnormality determination unit comprises: an abnormality calculation subunit, used to calculate the standard deviation of the corrected friction force to form a friction force standard deviation; The abnormality determination subunit is connected to the abnormality calculation subunit and is used to determine a number of abnormal bolts according to the comparison result between the friction standard deviation and a preset friction standard deviation threshold.

4. The artificial intelligence edge computing decision controller according to claim 3, characterized in that: The element determination unit comprises: A height fluctuation calculation subunit, which is used to calculate the standard deviation of the height to obtain the height distribution; A wind speed impact determination subunit, connected to the height fluctuation calculation subunit, for determining a wind speed impact result according to a comparison result between the height distribution and a preset height standard distribution; The element determination subunit is connected to the wind speed area determination subunit and is used to determine the abnormal element according to the real-time wind speed obtained based on the wind speed influence result and the corrected concave area.

5. The artificial intelligence edge computing decision controller according to claim 4, characterized in that: The element determination subunit comprises: A wind speed gradient curve drawing subtask unit is used to draw a wind speed gradient curve according to all real-time wind speeds at a preset number of abnormal bolts in the vertical direction; A depression gradient curve drawing subtask unit is used to draw a depression gradient curve according to all corrected depression areas at the preset number of abnormal bolts in the vertical direction; a matching coefficient calculation subtask unit, which is connected to the wind speed gradient curve drawing subtask unit and the concave gradient curve drawing subtask unit respectively, and is used to calculate a correlation coefficient according to the wind speed gradient curve and the concave gradient curve to obtain a matching coefficient; The element determination subtask unit is used to determine the abnormal element according to the comparison result between the matching coefficient and the preset coefficient threshold.

6. The artificial intelligence edge computing decision controller according to claim 5, characterized in that: The correction unit comprises: A correction calculation subunit, used to correct the upper limit difference according to the maximum value of the fluctuation consistency and the preset standard consistency range; A correction determination subunit, connected to the correction calculation subunit, for determining the maximum value of the preset height range that needs to be corrected according to the comparison result of the correction upper limit difference and the preset standard correction difference, and forming a correction upper limit determination result; The adjusting subunit is connected to the correction determination subunit and is used to increase the maximum value of the preset altitude range according to the correction upper limit determination result to form a correction altitude range.

7. The artificial intelligence edge computing decision controller according to claim 6, characterized in that: The correction calculation subunit is also used to determine the correction lower limit difference according to the minimum value of the fluctuation consistency and the preset standard consistency range; The correction determination subunit is also used to determine the minimum value of the preset height range that needs to be corrected according to the comparison result of the correction lower limit difference and the preset standard correction difference, so as to form a correction lower limit determination result; The adjustment subunit is further configured to reduce the minimum value of the preset altitude range according to the correction lower limit determination result to form a correction altitude range.

8. The artificial intelligence edge computing decision controller according to claim 7, characterized in that: The alarm unit is used to issue an alarm for immediately inspecting the bolt according to the comparison result of the correction upper limit difference and the preset standard correction difference.

9. The artificial intelligence edge computing decision controller according to claim 8, characterized in that: The alarm unit is also used to issue a regular maintenance alarm based on a comparison result between the correction lower limit difference and a preset standard correction difference.

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