Online monitoring method and system for cable working well

Abstract

The invention discloses an online monitoring method and system for a cable working well, and belongs to the technical field of cable working well detection.The method comprises the steps that real-time sensor data of the cable working well are obtained through sensors in the cable working well and on a well lid; performing feature extraction on the pressure data and the temperature and humidity data to obtain a pressure change trend and a temperature and humidity change range; covering detection is conducted on a well lid of the cable working well according to the pressure change trend, abnormal condition detection is conducted on the interior of the cable working well according to the temperature and humidity change range, and the real-time state of the cable working well is obtained; and predicting the future state change trend of the cable working well by combining the position data of the cable working well and the real-time sensor data, and feeding back the safety condition of the cable working well by combining the real-time state. Therefore, by implementing the method, the problems that in the prior art, the inspection efficiency of the cable working well is low, and faults of the cable working well cannot be found in time and rapid maintenance cannot be achieved can be solved.

Description

Technical Field

[0001] The present application belongs to the technical field of cable well detection, and specifically relates to an online monitoring method and system for cable wells. Background Art

[0002] As urban infrastructure continues to expand and improve, cable networks, as a crucial means of power transmission, are also expanding in scope and complexity. Increasingly, underground cable networks are becoming the primary means of ensuring a stable urban power supply. Cable wells, crucial nodes in underground cable networks, support the weight of the cables and ensure safe and convenient access for maintenance personnel to conduct inspections and operations. These wells provide the necessary space and convenience for cable laying, maintenance, and troubleshooting. Therefore, determining whether cable wells have been damaged and ensuring timely repairs are crucial.

[0003] Currently, cable manholes are routinely inspected and maintained through manual inspections. Since cable manholes are often located throughout a city, maintenance personnel must conduct individual inspections, which is time-consuming and labor-intensive. Furthermore, relying solely on manual inspections in a large-scale cable network not only fails to guarantee comprehensive coverage but is also prone to missed or incorrect inspections. Furthermore, relying solely on manual inspections cannot achieve real-time, continuous monitoring. When problems arise in cable manholes, feedback often takes a long time to arrive, severely impacting repair efforts. Summary of the Invention

[0004] This application proposes an online monitoring method and system for cable wells, which can solve the problems in the existing technology of low efficiency in inspecting cable wells and inability to timely detect cable well faults and achieve rapid maintenance.

[0005] A first aspect of the present application provides an online monitoring method for a cable well, the method comprising:

[0006] Obtain real-time sensor data of the cable well through sensors inside the cable well and on the well cover; wherein the real-time sensor data includes pressure data and temperature and humidity data;

[0007] Performing feature extraction on the pressure data and the temperature and humidity data respectively to obtain a pressure change trend and a temperature and humidity change range;

[0008] Conducting inspections on the cable well cover based on the pressure change trend, and detecting abnormal conditions inside the cable well based on the temperature and humidity change range to obtain the real-time status of the cable well.

[0009] Combining the location data of the cable well and the real-time sensor data, predicting the future state change trend of the cable well;

[0010] Feedback on the safety status of the cable pit is provided based on the real-time status and the future status change trend.

[0011] The above scheme collects data from the manhole cover and inside the cable manhole. The pressure data determines whether the manhole cover is covered during the current period. The temperature and humidity data determines whether the cable manhole is too humid or the temperature is too high or too low during the current period. These conditions can easily damage the cables in the cable manhole. Therefore, the real-time status of the cable manhole can be predicted to confirm whether the cable manhole needs to be repaired. In addition, a corresponding model is constructed based on historical data to predict the future status of the cable manhole. The main purpose is to confirm the season of the cable manhole based on its location data. It further infers whether the weather changes brought about by the season have caused the manhole cover to be covered or the interior to become humid or the temperature to rise. The future status change trend of the cable manhole is accurately predicted, providing data support for the timely maintenance of the cable manhole.

[0012] In a possible implementation method of the first aspect, feature extraction is performed on the pressure data and the temperature and humidity data to obtain a pressure change trend and a temperature and humidity change range, specifically:

[0013] Preprocessing the pressure data and the temperature and humidity data;

[0014] Dividing the pre-processed pressure data into preset time periods, and analyzing the pressure data of adjacent time periods through a preset sliding window to obtain a pressure change trend;

[0015] The mean and variance of the pre-processed temperature and humidity data are calculated to obtain the temperature and humidity variation range.

[0016] In a possible implementation method of the first aspect, preprocessing the pressure data and the temperature and humidity data is specifically performed as follows:

[0017] The pressure data and the temperature and humidity data are sequentially cleaned, duplicate data are removed, missing values ​​are filled, data format is unified, data type conversion and data normalization are performed;

[0018] The unified data format is a unified timestamp format for all data; and the data type conversion is to convert text type data into a numerical type.

[0019] The above solution improves data quality by preprocessing pressure data and temperature and humidity data, making it easier to perform subsequent mathematical operations and perform accurate cable well status detection.

[0020] In a possible implementation method of the first aspect, a cover detection is performed on a manhole cover of a cable manhole according to a pressure change trend, specifically as follows:

[0021] If the pressure change trend exceeds a preset pressure threshold, it is considered that there is a covering on the manhole cover of the cable pit; wherein the pressure threshold is obtained by calculating the average value and standard deviation of historical pressure data under normal conditions, and then adding or subtracting a number of times the standard deviation from the average value;

[0022] If the pressure change trend continues to increase within a period of time, it is considered that there is a covering on the manhole cover of the cable pit.

[0023] The above scheme sets two judgment conditions for determining whether there is a cover on the cable manhole cover; when the pressure change trend exceeds the preset pressure threshold, it means that the pressure currently borne by the manhole cover is abnormal, which is caused by the excessive weight of the cover. In order to prevent the cover from damaging the manhole cover, maintenance is required; when the pressure change trend continues to increase within a period of time, it means that the cover on the manhole cover is continuing to increase, so the cover needs to be removed in time.

[0024] In a possible implementation method of the first aspect, abnormal conditions are detected inside the cable pit based on the temperature and humidity variation range, specifically:

[0025] Calculate the average and standard deviation of historical temperature and humidity data under normal conditions and construct the normal range of temperature and humidity;

[0026] If the temperature and humidity change range exceeds the normal temperature and humidity range, it is considered that there is an abnormality inside the cable well.

[0027] The above solution shows that the temperature and humidity change range in the current period is too large, which means that extreme temperature and humidity may cause damage to the cables inside the cable well. Therefore, it is necessary to repair the abnormal conditions inside the cable well.

[0028] In a possible implementation method of the first aspect, the location data of the cable well and the real-time sensor data are combined to predict the future state change trend of the cable well, specifically:

[0029] Based on the location data, the historical sensor data is annotated with timestamps and seasonal factors to obtain a standard training set;

[0030] The time series model is trained using a standard training set, and an anomaly detection mechanism is added to the time series model to obtain a trend prediction model;

[0031] The seasonal factors in the real-time sensor data are analyzed by the trend prediction model to output the future state change trend.

[0032] The above scheme annotates the data with timestamps and seasonal factors according to the location of the cable well, providing support for considering seasonal factors when subsequent models predict the future status of the cable well.

[0033] In a possible implementation method of the first aspect, the anomaly detection mechanism is specifically:

[0034] The anomaly detection mechanism includes dynamic threshold adjustment, multi-dimensional verification and model parameter adjustment;

[0035] Among them, the dynamic adjustment of the threshold is to dynamically adjust the normal state fluctuation range of the cable manhole according to the seasonal factors of the data; the multi-dimensional verification is to take into account the impact of seasonal changes on pressure data and temperature and humidity data; the model parameter adjustment adjusts the model parameters according to the abnormal state caused by the seasonal factors.

[0036] The above solution verifies the differences between predicted data and actual data through an anomaly detection mechanism to improve the prediction accuracy of the model.

[0037] In a possible implementation method of the first aspect, real-time sensor data of the cable manhole is obtained through sensors inside the cable manhole and on the manhole cover, specifically as follows:

[0038] The temperature and humidity data are obtained by monitoring the humidity and temperature inside the cable pit using a temperature-humidity sensor installed inside the cable pit;

[0039] The pressure data is obtained by monitoring the pressure change on the manhole cover through a pressure sensor installed on the manhole cover of the cable manhole.

[0040] A second aspect of the present application provides an online monitoring system for cable wells, the system comprising: a sensor data acquisition module, a feature extraction module, a state detection module, a state trend prediction module, and an alarm module;

[0041] The sensor data acquisition module is used to obtain real-time sensor data of the cable well through sensors inside the cable well and on the well cover; wherein the real-time sensor data includes pressure data and temperature and humidity data;

[0042] The feature extraction module is used to extract features from the pressure data and the temperature and humidity data respectively to obtain the pressure change trend and the temperature and humidity change range;

[0043] The status detection module is used to detect the cover of the cable well according to the pressure change trend, and detect abnormal conditions inside the cable well according to the temperature and humidity change range to obtain the real-time status of the cable well;

[0044] The state trend prediction module is used to combine the location data of the cable pit and the real-time sensor data to predict the future state change trend of the cable pit;

[0045] The alarm module is used to feedback the safety status of the cable pit based on the real-time status and the future status change trend.

[0046] A third aspect of the present application provides a terminal device, which includes: a terminal device including a processor and a memory, the memory storing a computer program, and the processor implementing the steps of an online monitoring method for cable wells as described in any one of the embodiments of the present application when executing the computer program. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the implementation. Obviously, the drawings described below are only some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0048] Figure 1 This is a schematic diagram of a specific process of an online monitoring method for cable wells provided in one embodiment of the present application;

[0049] Figure 2 This is a structural diagram of an online monitoring system for cable wells provided in one embodiment of the present application;

[0050] Figure 3 A structural diagram of a terminal device is provided for a certain embodiment of the present application. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0052] It should be understood that the step numbers used herein are only for convenience of description and are not intended to limit the order in which the steps are to be executed.

[0053] First embodiment

[0054] As an important node for cable joints, branches and turns, once the cable manhole is damaged, it is likely to cause partial or large-scale paralysis of the power system. Therefore, it is necessary to monitor the cable manhole in real time to ensure its integrity and normal operation. Due to the acceleration of urban construction, the misoperation of mechanical equipment during the construction process may cause direct damage to the manhole cover or the cables in the manhole. For example, due to road construction and renovation, the manhole cover may be buried by soil, sand and gravel. In response to the above situation, the embodiment of the present application will detect whether there is a cover on the manhole cover and the temperature and humidity inside the cable manhole through the data collected by the sensor, to assist maintenance personnel in promptly discovering faults in the cable manhole.

[0055] like Figure 1 As shown, in order to solve the problems in the prior art of low efficiency in inspecting cable wells and inability to timely discover faults in cable wells and achieve rapid maintenance, the first embodiment of the present application provides a specific flow chart of an online monitoring method for cable wells. The online monitoring method for cable wells in this embodiment includes steps S1 to S5, which are detailed as follows:

[0056] Step S1, obtaining real-time sensor data of the cable well through sensors inside the cable well and on the well cover.

[0057] In this embodiment of the application, a pressure sensor is installed on the cable pit cover to detect subtle pressure changes in the cover to determine whether there is a covering such as a pile of soil. A temperature and humidity sensor is installed inside the cable pit to monitor the humidity and temperature inside the cable pit to prevent cable failures caused by environmental changes. In addition, a GPS device is used to accurately locate the specific location of the cable pit and assist in visualizing the location and status of the cable pit on a map, making it easier for operators to quickly find the site.

[0058] The above equipment is powered by solar cells and backup batteries. When the lighting conditions are good, the solar panels provide the main power supply, ensuring long-term stable operation of the equipment in the wild environment; when the sunlight is insufficient, the backup batteries are used for power supply.

[0059] First, a pressure sensor collects pressure data from the cable manhole cover, and a temperature-humidity sensor inside the cable manhole collects temperature and humidity data, including the temperature and humidity inside the manhole. The pressure and temperature and humidity data are then cleaned, deduplicated, missing values ​​filled, data formatted, converted, and normalized to improve data quality.

[0060] Specifically, invalid data is removed through data cleaning. For example, if there is obvious anomaly in the real-time sensor data, it is regarded as invalid data and eliminated. There may be occasional missing data in the real-time sensor data, and the missing values ​​are filled through linear interpolation, mean method or median interpolation method. The timestamps of all data are ensured to be consistent through unified data format, and data normalization is used to convert data of different dimensions to the same scale for subsequent analysis. Data type conversion is used to convert text data into numerical data for subsequent mathematical operations.

[0061] Step S2: extracting features from the pressure data and the temperature and humidity data to obtain a pressure change trend and a temperature and humidity change range.

[0062] In an embodiment of the present application, the pressure change trend and the temperature and humidity change range of the cable well in the current period are determined from the pressure data and the temperature and humidity data respectively through feature extraction.

[0063] Specifically, to determine pressure trends, we first evenly divide the pressure data into several time periods and calculate the pressure change rate. Then, we use a sliding window technique to analyze the average or maximum pressure change rate within adjacent time periods to identify pressure trends. The sliding window size is related to the time period size. We then calculate the mean and variance of the temperature and humidity data to assess the degree of temperature and humidity fluctuation and determine the temperature and humidity range.

[0064] Step S3, performing a covering inspection on the cable well cover according to the pressure variation trend, and performing an abnormality inspection inside the cable well according to the temperature and humidity variation range to obtain the real-time status of the cable well.

[0065] In this embodiment of the present application, a pressure threshold is first determined based on historical pressure readings of the cable pit cover under normal conditions. Then, data from the pressure trend is compared with the pressure threshold to determine whether a covering is currently on the cover. If the pressure trend exceeds the pressure threshold, it is determined that a covering is present on the cable pit cover.

[0066] Optionally, the embodiment of the present application calculates the average and standard deviation of historical pressure readings under normal conditions, and sets the pressure threshold to be the average value plus or minus several times the standard deviation. This pressure threshold can cover most normal situations and improve the accuracy of the prediction.

[0067] For example, if the historical pressure readings show an average pressure of 50 kPa and a standard deviation of 2 kPa, the pressure thresholds can be set to 54 kPa and 46 kPa. When the pressure change trend exceeds 54 kPa or is lower than 46 kPa, it is considered that there is a cover on the manhole cover.

[0068] Additionally, if the pressure trend continues to increase over a period of time, it can be considered that there is a covering on the cable manhole cover. This is because the accumulation of covering can be a slow process, such as seasonal rainfall causing soil accumulation. Therefore, the pressure trend does not exceed the pressure threshold, but the covering still exists.

[0069] Optionally, the time period for evaluating pressure trends can range from a few minutes to several hours, depending on the application. If the presence of cover is likely due to seasonal factors, a longer time period can be selected.

[0070] To detect abnormal conditions within a cable well based on the temperature and humidity variation range, we first construct a normal temperature and humidity range using the average and standard deviation of historical temperature and humidity data under normal conditions (the construction process is the same as the pressure threshold). If the temperature and humidity variation range exceeds the normal temperature and humidity range, it is considered an abnormality within the cable well.

[0071] Abnormal temperature and humidity inside cable wells can cause a range of problems. Excessive temperature accelerates the aging of cable insulation, causing it to become brittle and crack, reducing dielectric strength and increasing the risk of breakdown. Excessive humidity can cause moisture to penetrate the cable insulation or joints, triggering partial discharge and dendritic discharge, ultimately leading to insulation failure.

[0072] For example, considering that the environment inside the cable manhole is relatively closed and stable, assuming that under normal circumstances the average temperature of the historical temperature and humidity data is 20°C, with a standard deviation of 2°C; the average humidity is 60%, with a standard deviation of 10%; then, the normal temperature range can be set to 16°C to 24°C, and the normal humidity range is 40% to 80%.

[0073] Based on the detection results of covering and abnormal conditions inside the cable well, it can be determined whether there is covering in the cable well and whether the temperature and humidity inside are abnormal during the current period, and maintenance personnel can be notified in time to carry out inspections.

[0074] Step S4: combining the location data of the cable pit and the real-time sensor data to predict the future state change trend of the cable pit.

[0075] In addition to detecting the current status of the cable manhole, the embodiment of the present application also uses a trend prediction model to predict the status change trend of the cable manhole in the future, aiming to discover potential problems in advance so that timely measures can be taken.

[0076] First, the historical pressure data and historical temperature and humidity data of the cable well are collected and preprocessed. Then, the preprocessed historical data are annotated with timestamps and seasonal factors according to the location data of the cable well to obtain a standard training set.

[0077] Specifically, historical data is first classified and labeled so that the model can identify the impact of seasonal changes during subsequent analysis. Timestamp annotation adds a timestamp to each historical record and divides the data into different seasons (such as spring, summer, autumn, and winter) based on the timestamp. Seasonal factor annotation records environmental factors such as standard rainfall and temperature for each historical record, which may affect the status of the cable shaft.

[0078] As an improvement to the above solution, considering the impact of temperature, humidity and covering materials on the status of cable wells due to seasonal changes, an anomaly detection mechanism was added when building the model to detect the differences between the predicted results and the actual data and identify possible unexpected situations.

[0079] Optionally, the present embodiment constructs a trend prediction model based on the ARIMA model. In other embodiments, a SARIMA model may be used. The ARIMA model is a time series model that analyzes data at consecutive time points to achieve future predictions. Before using the standard training set to train the model, the standard training set is also differentiated to make it stable.

[0080] The model is trained based on a standard training set. By annotating timestamps and seasonal factors, it is ensured that the model can accurately capture the changing trends of different seasons. The model parameters are optimized through anomaly detection mechanisms, making the model's performance more stable in different seasons.

[0081] Specifically, the anomaly detection mechanism includes dynamic threshold adjustment, multi-dimensional verification, and model parameter adjustment. During model training and prediction, historical data is used to calculate the normal fluctuation range for each season. This normal fluctuation range can be used to predict future state changes in the cable manhole. Furthermore, pressure, temperature, and humidity thresholds are dynamically adjusted based on seasonal characteristics to accommodate external disturbances. For example, the pressure threshold can be appropriately relaxed during the rainy season.

[0082] Multi-dimensional verification combines data from multiple sensors to improve the accuracy of predictions. For example, if the forecast indicates a significant increase in pressure on the manhole cover over the next few days, while temperature and humidity data fluctuate within normal ranges during the same period, soil accumulation may be due to seasonal rainfall; otherwise, further investigation may be necessary to determine if there are other unforeseen issues. Furthermore, in the process of predicting future changes in the cable manhole's state, in-depth analysis is conducted incorporating environmental factors such as rainfall and temperature. For example, during the summer rainy season, if temperature and humidity data indicate a significant increase in humidity and pressure data also show anomalies, it can be inferred that soil accumulation may be due to rainfall.

[0083] Model parameter adjustment is used to update the model and thresholds in real time. New sensor data is continuously collected through predictions, and online learning algorithms such as incremental learning are used to dynamically update model parameters, allowing the model to better adapt to actual conditions. When the model predicts a possible abnormality in the cable well in the future, an alarm is automatically triggered and the relevant data is sent to maintenance personnel for confirmation. If the abnormality is confirmed to be caused by seasonal factors, the seasonal parameters in the model can be updated to improve future prediction accuracy.

[0084] The real-time sensor data is analyzed through the trained trend prediction model, and the future state change trend of the cable well is output based on the impact of seasonal factors on future state changes.

[0085] Step S5: Feedback the safety status of the cable pit based on the real-time status and the future status change trend.

[0086] In this embodiment, based on the real-time status of the cable pit and the predicted future status change trend, feedback is provided on whether the cable pit needs maintenance, allowing maintenance personnel to quickly locate and handle the situation. The relevant alarm signals can be sent via SMS, phone, email, and other methods.

[0087] The implementation of the embodiments of the present application has the following beneficial effects:

[0088] The embodiment of the present application collects data on the manhole cover and inside the cable manhole respectively, and uses pressure data to determine whether the manhole cover of the cable manhole is covered during the current period, and uses temperature and humidity data to determine whether the cable manhole is too humid or the temperature is too high / low during the current period. These situations can easily cause damage to the cables in the cable manhole, so the real-time status of the cable manhole can be predicted to confirm whether the cable manhole needs to be repaired. In addition, a corresponding model is constructed through historical data to predict the future status of the cable manhole. It is mainly based on the location data of the cable manhole to confirm the season of the cable manhole, and further infer whether the weather changes brought about by the season will cause the manhole cover to be covered or the interior to become humid, the temperature to rise, etc., accurately predicting the future status change trend of the cable manhole, and providing data support for the timely maintenance of the cable manhole.

[0089] Second embodiment

[0090] Furthermore, in order to implement the online monitoring system for cable wells corresponding to the above method embodiment and achieve corresponding functions and technical effects, Figure 2 A structural diagram of an online monitoring system for cable manholes is provided. For ease of explanation, only the parts related to this embodiment are shown. The online monitoring system for cable manholes provided in this embodiment of the application includes:

[0091] The sensor data acquisition module 201 is used to obtain real-time sensor data of the cable manhole through sensors inside the cable manhole and on the manhole cover; wherein the real-time sensor data includes pressure data and temperature and humidity data.

[0092] In this embodiment of the application, a pressure sensor is installed on the cable pit cover to detect subtle pressure changes in the cover to determine whether there is a covering such as a pile of soil. A temperature and humidity sensor is installed inside the cable pit to monitor the humidity and temperature inside the cable pit to prevent cable failures caused by environmental changes. In addition, a GPS device is used to accurately locate the specific location of the cable pit and assist in visualizing the location and status of the cable pit on a map, making it easier for operators to quickly find the site.

[0093] The above equipment is powered by solar cells and backup batteries. When the lighting conditions are good, the solar panels provide the main power supply, ensuring long-term stable operation of the equipment in the wild environment; when the sunlight is insufficient, the backup batteries are used for power supply.

[0094] First, a pressure sensor collects pressure data from the cable manhole cover, and a temperature-humidity sensor inside the cable manhole collects temperature and humidity data, including the temperature and humidity inside the manhole. The pressure and temperature and humidity data are then cleaned, deduplicated, missing values ​​filled, data formatted, converted, and normalized to improve data quality.

[0095] Specifically, invalid data is removed through data cleaning. For example, if there is obvious anomaly in the real-time sensor data, it is regarded as invalid data and eliminated. There may be occasional missing data in the real-time sensor data, and the missing values ​​are filled through linear interpolation, mean method or median interpolation method. The timestamps of all data are ensured to be consistent through unified data format, and data normalization is used to convert data of different dimensions to the same scale for subsequent analysis. Data type conversion is used to convert text data into numerical data for subsequent mathematical operations.

[0096] The feature extraction module 202 is used to extract features from the pressure data and the temperature and humidity data respectively to obtain the pressure change trend and the temperature and humidity change range.

[0097] In an embodiment of the present application, the pressure change trend and the temperature and humidity change range of the cable well in the current period are determined from the pressure data and the temperature and humidity data respectively through feature extraction.

[0098] Specifically, to determine pressure trends, we first evenly divide the pressure data into several time periods and calculate the pressure change rate. Then, we use a sliding window technique to analyze the average or maximum pressure change rate within adjacent time periods to identify pressure trends. The sliding window size is related to the time period size. We then calculate the mean and variance of the temperature and humidity data to assess the degree of temperature and humidity fluctuation and determine the temperature and humidity range.

[0099] The status detection module 203 is used to detect the cover of the cable pit according to the pressure change trend, and to detect abnormal conditions inside the cable pit according to the temperature and humidity change range to obtain the real-time status of the cable pit.

[0100] In this embodiment of the present application, a pressure threshold is first determined based on historical pressure readings of the cable pit cover under normal conditions. Then, data from the pressure trend is compared with the pressure threshold to determine whether a covering is currently on the cover. If the pressure trend exceeds the pressure threshold, it is determined that a covering is present on the cable pit cover.

[0101] Optionally, the embodiment of the present application calculates the average and standard deviation of historical pressure readings under normal conditions, and sets the pressure threshold to be the average value plus or minus several times the standard deviation. This pressure threshold can cover most normal situations and improve the accuracy of the prediction.

[0102] For example, if the historical pressure readings show an average pressure of 50 kPa and a standard deviation of 2 kPa, the pressure thresholds can be set to 54 kPa and 46 kPa. When the pressure change trend exceeds 54 kPa or is lower than 46 kPa, it is considered that there is a cover on the manhole cover.

[0103] Additionally, if the pressure trend continues to increase over a period of time, it can be considered that there is a covering on the cable manhole cover. This is because the accumulation of covering can be a slow process, such as seasonal rainfall causing soil accumulation. Therefore, the pressure trend does not exceed the pressure threshold, but the covering still exists.

[0104] Optionally, the time period for evaluating pressure trends can range from a few minutes to several hours, depending on the application. If the presence of cover is likely due to seasonal factors, a longer time period can be selected.

[0105] To detect abnormal conditions within a cable well based on the temperature and humidity variation range, we first construct a normal temperature and humidity range using the average and standard deviation of historical temperature and humidity data under normal conditions (the construction process is the same as the pressure threshold). If the temperature and humidity variation range exceeds the normal temperature and humidity range, it is considered an abnormality within the cable well.

[0106] Abnormal temperature and humidity inside cable wells can cause a range of problems. Excessive temperature accelerates the aging of cable insulation, causing it to become brittle and crack, reducing dielectric strength and increasing the risk of breakdown. Excessive humidity can cause moisture to penetrate the cable insulation or joints, triggering partial discharge and dendritic discharge, ultimately leading to insulation failure.

[0107] For example, considering that the environment inside the cable manhole is relatively closed and stable, assuming that under normal circumstances the average temperature of the historical temperature and humidity data is 20°C, with a standard deviation of 2°C; the average humidity is 60%, with a standard deviation of 10%; then, the normal temperature range can be set to 16°C to 24°C, and the normal humidity range is 40% to 80%.

[0108] Based on the detection results of covering and abnormal conditions inside the cable well, it can be determined whether there is covering in the cable well and whether the temperature and humidity inside are abnormal during the current period, and maintenance personnel can be notified in time to carry out inspections.

[0109] The state trend prediction module 204 is used to combine the position data of the cable pit and the real-time sensor data to predict the future state change trend of the cable pit.

[0110] In addition to detecting the current status of the cable manhole, the embodiment of the present application also uses a trend prediction model to predict the status change trend of the cable manhole in the future, aiming to discover potential problems in advance so that timely measures can be taken.

[0111] First, the historical pressure data and historical temperature and humidity data of the cable well are collected and preprocessed. Then, the preprocessed historical data are annotated with timestamps and seasonal factors according to the location data of the cable well to obtain a standard training set.

[0112] Specifically, historical data is first classified and labeled so that the model can identify the impact of seasonal changes during subsequent analysis. Timestamp annotation adds a timestamp to each historical record and divides the data into different seasons (such as spring, summer, autumn, and winter) based on the timestamp. Seasonal factor annotation records environmental factors such as standard rainfall and temperature for each historical record, which may affect the status of the cable shaft.

[0113] As an improvement to the above solution, considering the impact of temperature, humidity and covering materials on the status of cable wells due to seasonal changes, an anomaly detection mechanism was added when building the model to detect the differences between the predicted results and the actual data and identify possible unexpected situations.

[0114] Optionally, the present embodiment constructs a trend prediction model based on the ARIMA model. In other embodiments, a SARIMA model may be used. The ARIMA model is a time series model that analyzes data at consecutive time points to achieve future predictions. Before using the standard training set to train the model, the standard training set is also differentiated to make it stable.

[0115] The model is trained based on a standard training set. By annotating timestamps and seasonal factors, it is ensured that the model can accurately capture the changing trends of different seasons. The model parameters are optimized through anomaly detection mechanisms, making the model's performance more stable in different seasons.

[0116] Specifically, the anomaly detection mechanism includes dynamic threshold adjustment, multi-dimensional verification, and model parameter adjustment. During model training and prediction, historical data is used to calculate the normal fluctuation range for each season. This normal fluctuation range can be used to predict future state changes in the cable manhole. Furthermore, pressure, temperature, and humidity thresholds are dynamically adjusted based on seasonal characteristics to accommodate external disturbances. For example, the pressure threshold can be appropriately relaxed during the rainy season.

[0117] Multi-dimensional verification combines data from multiple sensors to improve the accuracy of predictions. For example, if the forecast indicates a significant increase in pressure on the manhole cover over the next few days, while temperature and humidity data fluctuate within normal ranges during the same period, soil accumulation may be due to seasonal rainfall; otherwise, further investigation may be necessary to determine if there are other unforeseen issues. Furthermore, in the process of predicting future changes in the cable manhole's state, in-depth analysis is conducted incorporating environmental factors such as rainfall and temperature. For example, during the summer rainy season, if temperature and humidity data indicate a significant increase in humidity and pressure data also show anomalies, it can be inferred that soil accumulation may be due to rainfall.

[0118] Model parameter adjustment is used to update the model and thresholds in real time. New sensor data is continuously collected through predictions, and online learning algorithms such as incremental learning are used to dynamically update model parameters, allowing the model to better adapt to actual conditions. When the model predicts a possible abnormality in the cable well in the future, an alarm is automatically triggered and the relevant data is sent to maintenance personnel for confirmation. If the abnormality is confirmed to be caused by seasonal factors, the seasonal parameters in the model can be updated to improve future prediction accuracy.

[0119] The real-time sensor data is analyzed through the trained trend prediction model, and the future state change trend of the cable well is output based on the impact of seasonal factors on future state changes.

[0120] The alarm module 205 is used to feedback the safety status of the cable pit based on the real-time status and the future status change trend.

[0121] In this embodiment, based on the real-time status of the cable pit and the predicted future status change trend, feedback is provided on whether the cable pit needs maintenance, allowing maintenance personnel to quickly locate and handle the situation. The relevant alarm signals can be sent via SMS, phone, email, and other methods.

[0122] The implementation of the embodiments of the present application has the following beneficial effects:

[0123] The embodiment of the present application collects data on the manhole cover and inside the cable manhole respectively, and uses pressure data to determine whether the manhole cover of the cable manhole is covered during the current period, and uses temperature and humidity data to determine whether the cable manhole is too humid or the temperature is too high / low during the current period. These situations can easily cause damage to the cables in the cable manhole, so the real-time status of the cable manhole can be predicted to confirm whether the cable manhole needs to be repaired. In addition, a corresponding model is constructed through historical data to predict the future status of the cable manhole. It is mainly based on the location data of the cable manhole to confirm the season of the cable manhole, and further infer whether the weather changes brought about by the season will cause the manhole cover to be covered or the interior to become humid, the temperature to rise, etc., accurately predicting the future status change trend of the cable manhole, and providing data support for the timely maintenance of the cable manhole.

[0124] Further, Figure 3 This is a structural diagram of a terminal device provided in one embodiment of the present application. Figure 3 As shown, the terminal device 3 of this embodiment includes: at least one processor 30 (in Figure 3 Only one is shown) and a memory 31 and a computer program 32 stored in the memory 31 and executable on the at least one processor. When the processor 30 executes the computer program 32, the steps of an online monitoring method for cable wells described in any one of the embodiments of the present application can be implemented.

[0125] The terminal device 3 may be a computing device such as a desktop computer, a cloud server, or a laptop computer. The computing device may include but is not limited to a processor 30 and a memory 31 . Figure 3 This is merely an example of the terminal device 3 and does not constitute a limitation on the terminal device 3 , which may include more or fewer components than those shown in the figure.

[0126] The specific embodiments described above further illustrate the purpose, technical solutions, and beneficial effects of this application. It should be understood that the above description is merely a specific embodiment of this application and is not intended to limit the scope of protection of this application. In particular, it should be noted that for those skilled in the art, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this application should be included in the scope of protection of this application.

Claims

1. An online monitoring method for cable wells, characterized in that: include: Obtain real-time sensor data of the cable well through sensors inside the cable well and on the well cover; wherein the real-time sensor data includes pressure data and temperature and humidity data; Performing feature extraction on the pressure data and the temperature and humidity data respectively to obtain a pressure change trend and a temperature and humidity change range; Conducting inspections on the cable well cover based on the pressure change trend, and detecting abnormal conditions inside the cable well based on the temperature and humidity change range to obtain the real-time status of the cable well. Combining the location data of the cable well and the real-time sensor data, predicting the future state change trend of the cable well; Feedback on the safety status of the cable pit is provided based on the real-time status and the future status change trend.

2. The online monitoring method for cable wells according to claim 1, characterized in that: The pressure data and the temperature and humidity data are respectively subjected to feature extraction to obtain the pressure change trend and the temperature and humidity change range, specifically: Preprocessing the pressure data and the temperature and humidity data; Dividing the pre-processed pressure data into preset time periods, and analyzing the pressure data of adjacent time periods through a preset sliding window to obtain a pressure change trend; The mean and variance of the pre-processed temperature and humidity data are calculated to obtain the temperature and humidity variation range.

3. The online monitoring method for cable wells according to claim 2, characterized in that: The preprocessing of the pressure data and the temperature and humidity data is specifically as follows: The pressure data and the temperature and humidity data are sequentially cleaned, duplicate data are removed, missing values ​​are filled, data format is unified, data type conversion and data normalization are performed; The unified data format is a unified timestamp format for all data; and the data type conversion is to convert text type data into a numerical type.

4. The online monitoring method for cable wells according to claim 1, characterized in that: The method of detecting the covering of the manhole cover of the cable well according to the pressure change trend is as follows: If the pressure change trend exceeds a preset pressure threshold, it is considered that there is a covering on the manhole cover of the cable pit; wherein the pressure threshold is obtained by calculating the average value and standard deviation of historical pressure data under normal conditions, and then adding or subtracting a number of times the standard deviation from the average value; If the pressure change trend continues to increase within a period of time, it is considered that there is a covering on the manhole cover of the cable pit.

5. The online monitoring method for cable wells according to claim 1, characterized in that: The abnormal condition detection inside the cable well according to the temperature and humidity variation range is specifically as follows: Calculate the average and standard deviation of historical temperature and humidity data under normal conditions and construct the normal range of temperature and humidity; If the temperature and humidity change range exceeds the normal temperature and humidity range, it is considered that there is an abnormality inside the cable well.

6. The online monitoring method for cable wells according to claim 1, characterized in that: The future state change trend of the cable well is predicted by combining the position data of the cable well and the real-time sensor data, specifically: Based on the location data, the historical sensor data is annotated with timestamps and seasonal factors to obtain a standard training set; The time series model is trained using a standard training set, and an anomaly detection mechanism is added to the time series model to obtain a trend prediction model; The seasonal factors in the real-time sensor data are analyzed by the trend prediction model to output the future state change trend.

7. The online monitoring method for cable wells according to claim 6, characterized in that: The anomaly detection mechanism is specifically: The anomaly detection mechanism includes dynamic threshold adjustment, multi-dimensional verification and model parameter adjustment; Among them, the dynamic adjustment of the threshold is to dynamically adjust the normal state fluctuation range of the cable manhole according to the seasonal factors of the data; the multi-dimensional verification is to take into account the impact of seasonal changes on pressure data and temperature and humidity data; the model parameter adjustment adjusts the model parameters according to the abnormal state caused by the seasonal factors.

8. The online monitoring method for cable wells according to claim 1, characterized in that: The real-time sensor data of the cable well is obtained through sensors inside the cable well and on the well cover, specifically: The temperature and humidity data are obtained by monitoring the humidity and temperature inside the cable pit using a temperature-humidity sensor installed inside the cable pit; The pressure data is obtained by monitoring the pressure change on the manhole cover through a pressure sensor installed on the manhole cover of the cable manhole.

9. An online monitoring system for cable wells, characterized in that: include: Sensor data acquisition module, feature extraction module, state detection module, state trend prediction module and alarm module; The sensor data acquisition module is used to obtain real-time sensor data of the cable well through sensors inside the cable well and on the well cover; wherein the real-time sensor data includes pressure data and temperature and humidity data; The feature extraction module is used to extract features from the pressure data and the temperature and humidity data respectively to obtain the pressure change trend and the temperature and humidity change range; The status detection module is used to detect the cover of the cable well according to the pressure change trend, and detect abnormal conditions inside the cable well according to the temperature and humidity change range to obtain the real-time status of the cable well; The state trend prediction module is used to combine the location data of the cable pit and the real-time sensor data to predict the future state change trend of the cable pit; The alarm module is used to feedback the safety status of the cable pit based on the real-time status and the future status change trend.

10. A terminal device, characterized in that: The system comprises a processor and a memory, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the online monitoring method for cable wells according to any one of claims 1 to 8 are implemented.

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