A method and device for monitoring smart ground stakes based on BeiDou positioning

Abstract

This application relates to the technical field of IoT monitoring, and discloses a method and device for monitoring smart ground stakes based on BeiDou positioning. The method includes: generating vibration data and attitude data based on vibration sensors and attitude sensors; calculating the average vibration value and the time-domain attitude fluctuation value; triggering the vibration component if the average vibration value is greater than the average reference value; triggering the attitude component if the time-domain attitude fluctuation value is greater than the fluctuation reference value; reading positioning data and transmitting the vibration component, attitude component, and positioning data to the cloud; recording vibration timestamps and attitude timestamps; calculating the distribution density value of the vibration timestamps, and recording vibration timestamps greater than the first density value as vibration time sets; calculating the distribution density value of the attitude timestamps, and recording attitude timestamps greater than the second density value as attitude time sets; calculating the overlap percentage of the time sets, and if the overlap percentage is greater than the reference percentage, generating abnormal monitoring data and uploading it to the cloud to improve the monitoring effect.

Description

Technical Field

[0001] This application relates to the technical field of IoT monitoring, and in particular to a smart ground stake monitoring method and device based on BeiDou positioning. Background Technology

[0002] With the acceleration of urbanization, underground cable networks have become a core infrastructure for urban energy transmission, undertaking the task of delivering critical resources such as electricity and communications, and serving as a lifeline to ensure the normal operation of urban production and life. Currently, the scale of urban underground cable laying continues to expand, covering various areas such as main urban roads, commercial centers, and residential communities, and is often laid under complex geological conditions and dense building clusters. Its operation and maintenance management is directly related to the stability and security of urban energy supply.

[0003] Traditional cable maintenance primarily relies on regular manual inspections and physical markers for management. Physical markers, often in the form of warning signs, cement posts, or spray paint, are used to mark cable laying paths and burial depth information. Manual inspections require maintenance personnel to conduct on-site checks along pre-set routes, which is labor-intensive, inefficient, and easily limited by factors such as weather, terrain, and traffic conditions. Furthermore, physical markers are prone to loss or failure due to construction damage, natural erosion, or human relocation, leading to unclear cable path information.

[0004] Traditional manual inspection and passive protection modes cannot detect external damage such as construction excavation and vehicle crushing in real time. Problems are often only discovered after cables are damaged or faults occur, which can easily lead to serious consequences such as large-scale power outages and communication interruptions, causing significant economic losses and social impact. After an anomaly occurs, manual judgment of the fault location is still required. The positioning accuracy is greatly affected by factors such as experience and environment, making it difficult to quickly and accurately locate the fault point, resulting in a delay in emergency repair response. The effectiveness of inspection and monitoring needs to be improved. Summary of the Invention

[0005] To improve the inspection and monitoring effect of underground cable networks, this application provides a smart ground nail monitoring method and device based on Beidou positioning.

[0006] Firstly, this application provides a smart ground stake monitoring method based on BeiDou positioning, employing the following technical solution:

[0007] A smart ground stake monitoring method based on BeiDou positioning includes the following steps:

[0008] Vibration and attitude data are generated according to a preset first cycle based on vibration sensors and attitude sensors installed on underground cables; the average vibration value is calculated based on a preset number of vibration data; and the time-domain attitude fluctuation value is calculated based on a preset number of attitude data.

[0009] If the average vibration value is greater than the preset average reference value, the vibration component is triggered; if the time-domain attitude fluctuation value is greater than the preset fluctuation reference value, the attitude component is triggered; based on the triggering of the vibration component or attitude component, the smart ground stake reads the positioning data based on the Beidou positioning module and uploads the vibration component, attitude component and positioning data to the cloud through temporary communication.

[0010] The cloud records positioning data, vibration timestamps of vibration components, and attitude timestamps of attitude components. Within a preset recording time period, based on the calculated distribution density value of vibration timestamps, vibration timestamps with a distribution density value greater than a preset first density value are merged and recorded as a vibration time set. Based on the calculated distribution density value of attitude timestamps, attitude timestamps with a distribution density value greater than a preset second density value are merged and recorded as an attitude time set.

[0011] Calculate the percentage of overlap between the vibration time set and the attitude time set. If the percentage of overlap is greater than the preset reference percentage, the cloud will call the smart ground stakes based on the communication connection.

[0012] The smart ground stakes generate vibration and attitude data according to a preset second cycle, where the second cycle is shorter than the first cycle. The vibration and attitude data are filtered and then fused with the vibration, attitude, and positioning data to generate abnormal monitoring data, which is then uploaded to the cloud.

[0013] By adopting the above technical solution, vibration sensors and attitude sensors dynamically generate vibration data and attitude data according to the first cycle. Based on a set number of vibration data points, the average vibration value is calculated. Based on a set number of attitude data points, the time-domain attitude fluctuation value is obtained. When the average vibration value is greater than the average reference value, the vibration component is triggered; when the time-domain attitude fluctuation value is greater than the fluctuation reference value, the attitude component is triggered. The smart ground stake is activated using an event-triggered wake-up mechanism algorithm. Positioning data is read through the BeiDou positioning module, and the vibration component, attitude component, and positioning data are directly transmitted to the cloud via temporary communication. The cloud records the positioning data, vibration timestamps, and attitude timestamps. Within the recorded time period, the distribution density value is calculated. Vibration timestamps greater than the first density value are merged into a vibration time set, and those greater than... The attitude timestamps of the second density value are merged into an attitude time set. By calculating the overlap percentage, if the value exceeds the reference percentage, the cloud calls the smart ground stake through the communication connection to generate data in a second cycle that is less than the first cycle. After filtering, the vibration data, attitude data, and positioning data are fused to generate abnormal monitoring data and uploaded. The overall process is adapted to external force damage monitoring scenarios and matches fault response requirements. It is conducive to developers quickly receiving alarm signals and dispatching after-sales personnel to handle them. Through the positioning data uploaded synchronously, the location of the accident site can be directly viewed, realizing real-time perception of external force damage to underground cables. This solves the problems of delayed fault detection and reliance on manual location judgment in the traditional mode, and improves the inspection and monitoring effect of underground cable networks.

[0014] Optionally, the steps of calculating the average vibration value based on a preset number of vibration data and calculating the time-domain attitude fluctuation value based on a preset number of attitude data further include the following sub-steps:

[0015] Remove the first number of extreme values ​​from the vibration data, calculate the average value of the remaining vibration data as the vibration average value, and adjust the proportion of the sample in the sample standard deviation according to the positive correlation of the vibration average value.

[0016] Calculate the standard deviation of a set number of attitude data samples, and use the de-normalized standard deviation as the time-domain attitude fluctuation value. The first quantity is adjusted according to the negative correlation of the time-domain attitude fluctuation value.

[0017] By adopting the above technical solution, the extreme values ​​of the first quantity in the vibration data are removed, and the sample proportion of the standard deviation of the sample is adjusted with positive correlation, which helps to maintain the objectivity of the vibration average value; the time-domain attitude fluctuation value is obtained by de-normalizing the standard deviation of the attitude data and adjusting the first quantity with negative correlation, which is suitable for complex external force influence scenarios, matches the data screening requirements, and facilitates the timely identification of vibration and attitude anomalies.

[0018] Optionally, the step of merging the vibration time set and the attitude time set may further include the following sub-steps:

[0019] Associate the corresponding timestamps based on the distribution density values ​​involved in the merged records;

[0020] Extract the associated timestamps and merge consecutive timestamps into a time period;

[0021] If there are multiple time periods, calculate the time difference between adjacent time periods;

[0022] If the time difference is less than the preset time reference difference, then the adjacent time periods are merged into one time period; otherwise, the number of timestamps between adjacent time periods is calculated. If the number of timestamps is less than the preset timestamp reference amount, then the adjacent time periods are shrunk by a preset second number of timestamps.

[0023] Record the time period as a time set.

[0024] By adopting the above technical solution, the distribution density value is associated with the timestamp, the time period and time difference are calculated, the availability of the time set is maintained, the time periods are merged when they are close to each other, and the width of the time period is narrowed when there are few timestamps between the time periods. This helps to improve the accuracy of the result calculation, adapt to complex time distribution scenarios, meet the cloud's needs for analyzing abnormal time patterns, facilitate the accurate calculation of the percentage of overlap between the vibration time set and the attitude time set, and provide matching time data for the generation of abnormal monitoring data.

[0025] Optionally, the step of merging the vibration time set and the attitude time set may further include the following sub-steps:

[0026] Calculate the percentage of adjacent time periods merged into one time period compared to the total number of time periods merged.

[0027] The second quantity is adjusted based on a positive correlation with the proportion of merged quantities.

[0028] By adopting the above technical solutions, it is easier to adapt to dynamic time distribution scenarios and improve the effectiveness of using time-related data.

[0029] Optionally, the step of filtering the vibration data and attitude data may further include the following sub-steps:

[0030] Vibration data is smoothed using a preset first window, and attitude data is smoothed using a preset second window; the lengths of both the first and second windows are less than the second period.

[0031] The frequency at which the vibration component is triggered is calculated as the vibration trigger frequency. The vibration trigger value is calculated based on the vibration trigger frequency and the preset first frequency. The length of the first window is adjusted according to the negative correlation between the vibration trigger value and the vibration trigger value.

[0032] The frequency at which attitude components are triggered is called the attitude trigger frequency. The attitude trigger value is calculated based on the attitude trigger frequency and a preset second frequency. The length of the second window is adjusted according to the positive correlation between the attitude trigger value and the attitude trigger value.

[0033] By adopting the above technical solution, through window smoothing and dynamic adjustment of window length according to trigger frequency, data stability is maintained, adapting to different triggering scenarios and matching filtering requirements, which is conducive to the fusion of vibration and attitude data and timely identification of abnormal situations.

[0034] Optionally, the step of generating abnormal monitoring data after fusing vibration data, attitude data, and positioning data may further include the following sub-steps:

[0035] The vibration data is converted into vibration frequency domain data using a frequency domain conversion algorithm, and the frequency domain fluctuation value is extracted from the vibration frequency domain data using a wave algorithm.

[0036] The attitude data is converted into frequency domain data using a frequency domain conversion algorithm, and then the frequency domain attitude fluctuation value is extracted using a fluctuation algorithm based on the attitude frequency domain data.

[0037] Calculate the fluctuation difference between the frequency domain fluctuation value and the frequency domain attitude fluctuation value. If the fluctuation difference is less than the preset fluctuation reference difference, the vibration data and attitude data are marked as normally correlated; otherwise, the vibration data and attitude data are marked as abnormally correlated.

[0038] Vibration data, attitude data, correlation data, and positioning data are packaged to generate anomaly monitoring data.

[0039] By adopting the above technical solution, through frequency domain data processing and fluctuation feature extraction, fluctuation difference comparison, the correlation between vibration and attitude data is dynamically captured, maintaining the objectivity of the correlation, adapting to the data change characteristics under the scenario of external force damage to underground cables, and matching the needs of abnormal monitoring data; it is conducive to clearly defining the data correlation type, and facilitates cloud analysis so that the packaged abnormal monitoring data can be adapted to multi-dimensional monitoring.

[0040] Optionally, the fluctuation algorithm includes the following steps:

[0041] Calculate the energy difference for the same frequency range based on the current frequency domain data and the previous frequency domain data;

[0042] The frequency bands are sorted from largest to smallest based on the energy difference. A number of pre-set temporary frequency bands are selected, and the average frequency is calculated based on the selected frequency bands. The average frequency is then used as the fluctuation value.

[0043] By adopting the above technical solution, the target frequency band is selected by sorting the energy difference and the average frequency is calculated. This maintains the rationality of the fluctuation value extraction, adapts to the dynamic changes of frequency domain data, matches the needs of fluctuation feature extraction, facilitates the clear capture of frequency fluctuation patterns, and provides matching data for the correlation labeling of vibration and attitude data.

[0044] Optionally, the method further includes the following steps:

[0045] The vibration comparison value is calculated based on the distribution density value of the vibration timestamp and the first density value, and the second cycle is adjusted according to the negative correlation of the vibration comparison value; the attitude comparison value is calculated based on the distribution density value of the attitude timestamp and the second density value, and the recording time period is adjusted according to the positive correlation of the attitude comparison value.

[0046] By adopting the above technical solution, the second cycle is adjusted by negative correlation of vibration comparison value and the recording time period is adjusted by positive correlation of attitude comparison value, so as to dynamically adapt to changes in monitoring scenario and maintain the coordination of data acquisition and recording.

[0047] Optionally, the method further includes the following steps:

[0048] Obtain the percentage of overlap of all smart pegs within a preset area;

[0049] The average of all overlapping percentages is calculated as the percentage mean. If the percentage mean is less than the preset standard percentage, the first cycle is adjusted according to the negative correlation between the percentage mean and the first percentage; where the first percentage is less than the reference percentage.

[0050] By adopting the above technical solution, the first cycle is adjusted based on the average percentage of overlapping smart ground stakes in the region, allowing the detection cycle to dynamically adapt to the regional monitoring status.

[0051] Secondly, this application provides an intelligent ground stake monitoring device based on BeiDou positioning, which adopts the following technical solution:

[0052] A smart ground stake monitoring device based on BeiDou positioning includes a processor, wherein the processor executes the steps of the smart ground stake monitoring method based on BeiDou positioning as described in any one of the above.

[0053] In summary, this application includes at least one of the following beneficial technical effects: By dynamically acquiring data from vibration and attitude sensors, combined with an event-triggered wake-up mechanism and BeiDou positioning technology, real-time perception and precise positioning of external force damage to underground cables are achieved, effectively solving the pain points of low efficiency, delayed fault detection, and reliance on human experience for location judgment in traditional manual inspections; Through optimization processing such as extreme value removal, dynamic window filtering, frequency domain feature extraction, and time set overlap analysis, the accuracy of abnormal data identification is improved. At the same time, by dynamically adjusting the first and second cycles and adaptively adapting the recording time period, the equipment operating efficiency is considered while ensuring monitoring sensitivity; Real-time linkage between the cloud and smart ground anchors allows abnormal monitoring data to be uploaded quickly, helping maintenance personnel to respond quickly and repair accurately, significantly improving the inspection and monitoring effect of underground cable networks. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating the steps of an intelligent ground stake monitoring method based on BeiDou positioning.

[0055] Figure 2 This is a sub-step diagram of merging records. Detailed Implementation

[0056] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0057] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0058] In this embodiment, the core hardware configuration of the smart ground stake includes a Beidou positioning module, a vibration sensor, an attitude sensor, and a communication module. The Beidou positioning module uses a high-precision LC29DA module, supports Beidou-3 dual-mode positioning, and has an RTK positioning accuracy of up to ±2cm, which can accurately obtain the laying location information of underground cables. The vibration sensor is a triaxial vibration sensor with a range of ±16g and a sampling rate of 1000Hz, which can sensitively capture vibration signals generated by external forces in the underground environment. The attitude sensor uses a triaxial accelerometer with a measurement accuracy of ±0.01g, which can monitor the attitude changes of the smart ground stake in real time and indirectly reflect the influence of external forces on the cable. The communication module uses an NB-IoT low-power module, which has wide coverage and low latency characteristics, with a transmission delay of ≤5 seconds, and is suitable for the signal transmission requirements of shallow underground burial scenarios. The following are the preset basic parameters in this embodiment: the first cycle is 30 seconds, the set quantity is 50 sets, the average reference value is set according to the differences in cable laying scenarios, such as 0.8g in the driveway area, 0.5g in the sidewalk area, and 0.3g in the green belt area, the fluctuation reference value is 0.3 (after de-unitization), the first density value is 10 pieces / minute, the second density value is 8 pieces / minute, the reference percentage is 60%, the second cycle is 10 seconds (less than the first cycle), the recording time period is 1 hour, the time reference difference is 3 minutes, the timestamp reference quantity is 5, the second quantity default value is 1, the first window initial length is 3 seconds, the second window initial length is 2 seconds, the first frequency is 5 times / hour, the second frequency is 4 times / hour, the fluctuation reference difference is 0.5, the temporary quantity is 5, the standard percentage is 40%, and the first percentage is 30%, which is less than the reference percentage.

[0059] This application discloses an intelligent ground stake monitoring method based on BeiDou positioning, referring to... Figure 1 and Figure 2 It includes the following steps:

[0060] Step 1: Data Acquisition and Basic Feature Calculation

[0061] The smart ground stakes are installed and deployed along the underground cable laying path: the stakes are fixed to the outside of the cable protection pipe at 50-meter intervals, ensuring that the vibration sensor and attitude sensor are in close contact with the cable body to achieve synchronous sensing of external forces. After installation, the vibration sensor and attitude sensor start data acquisition according to the preset first cycle (30 seconds), collecting 50 sets of vibration data (unit: g) and 50 sets of attitude data (including X, Y, and Z axis acceleration components, unit: g) per cycle. The collected data is temporarily stored in the local cache module of the smart ground stake to avoid data loss.

[0062] In calculating the average vibration value, to eliminate interference from abnormal data caused by accidental impacts (such as gravel impacts or temporary compaction), extreme value removal is first performed on the 50 sets of vibration data collected in each cycle: The first number of extreme values ​​(initially 5 sets, including 3 sets of maximum values ​​and 2 sets of minimum values) are removed, and then the arithmetic mean of the remaining 45 sets of valid vibration data is calculated, which is the average vibration value for that cycle. Simultaneously, the sample proportion of the standard deviation is adjusted according to the positive correlation between the magnitude of the average vibration value and the sample size: when the average vibration value is close to 80% of the average reference value, the sample proportion is set to 80%, i.e., 40 sets of data are selected to calculate the standard deviation; when the average vibration value exceeds 90% of the average reference value, the sample proportion is adjusted to 90%, i.e., 45 sets of data are selected to calculate the standard deviation. Increasing the sample size improves the reliability of the standard deviation calculation, thereby assisting in judging the stability of the vibration data. The adjusted standard deviation is used to assist in judging the stability of the vibration data. When the standard deviation exceeds 30% of the average vibration value, the first threshold for extreme value removal is further optimized to avoid interference from accidental abnormal data.

[0063] In the calculation of time-domain attitude fluctuation values, the sample standard deviation is calculated using the Bessel correction formula for 50 sets of attitude data collected in each cycle to accurately reflect the dispersion of the attitude data. Since the unit of attitude data is g, to facilitate comparison with the preset fluctuation reference value, the calculated sample standard deviation is divided by the benchmark value 1g for de-unitization, ultimately obtaining the unitless time-domain attitude fluctuation value. Simultaneously, the first quantity is adjusted based on the negative correlation between the magnitude of the time-domain attitude fluctuation value and the following: when the time-domain attitude fluctuation value is greater than 0.25, it indicates that the current attitude change is relatively gentle, and the extreme values ​​have little impact on the average vibration value, so the first quantity is reduced to 3 sets; when the time-domain attitude fluctuation value is less than 0.1, it indicates that the attitude change is drastic, and there may be many abnormal extreme values, so the first quantity is increased to 7 sets. By dynamically adjusting the number of extreme value removals, the objectivity of the dynamic average value and the sensitivity of the data response are balanced.

[0064] Step 2: Event Triggering and Data Upload

[0065] The local processor of the smart ground stake compares the calculated average vibration value and time-domain attitude fluctuation value with the preset reference value in real time. If the average vibration value is greater than the average reference value of the corresponding scene, such as 0.8g in the driveway area, the vibration component is triggered and a vibration abnormality triggering flag is generated, with 0 indicating no triggering and 1 indicating triggering. If the time-domain attitude fluctuation value is greater than the fluctuation reference value (0.3), the attitude component is triggered and an attitude abnormality triggering flag is generated, with 0 indicating no triggering and 1 indicating triggering.

[0066] When either the vibration component or the attitude component is triggered, the smart ground stake initiates an event-triggered wake-up mechanism, waking from a low-power sleep state and simultaneously activating the BeiDou positioning module to collect positioning data. The BeiDou positioning module receives signals from at least four BeiDou satellites and uses RTK differential technology to achieve high-precision positioning, acquiring positioning data including latitude and longitude (accurate to 0.00001°) and altitude (accurate to 0.1m). The positioning process takes no more than 2 seconds. Subsequently, the smart ground stake establishes a temporary communication connection via the NB-IoT communication module, packaging the vibration component (including average vibration value and trigger identifier), attitude component (including time-domain attitude fluctuation value and trigger identifier), and positioning data in JSON format and uploading them to the cloud server to ensure rapid reporting of abnormal signals.

[0067] Step 3: Cloud-based time set processing

[0068] After receiving the data uploaded by the smart ground stakes, the cloud server immediately creates a data archive indexed by the smart ground stake number, recording detailed location data, vibration timestamps corresponding to vibration components (accurate to milliseconds, representing the moment data acquisition was completed), and attitude timestamps corresponding to attitude components (recorded synchronously with the vibration timestamps). Within the preset recording period (1 hour), the cloud server processes the timestamps according to the following process to generate vibration time sets and attitude time sets:

[0069] The distribution density value of the timestamps is calculated. The distribution density value is defined as the number of timestamps within a unit of time (1 minute), i.e., distribution density value = total number of timestamps within that minute / 1 minute. For vibration timestamps, timestamps with distribution density values ​​greater than the first density value (10 times / minute) are selected; for attitude timestamps, timestamps with distribution density values ​​greater than the second density value (8 times / minute) are selected. A correlation mapping is established between the selected timestamps and their corresponding distribution density values ​​to ensure that the density attribute of each timestamp is traceable.

[0070] Extract the associated timestamps and sort them chronologically. Merge consecutive timestamps (with a time interval of less than 1 second) into a continuous time segment. For example, timestamps 14:00:01, 14:00:02, and 14:00:03 are merged into the time segment 14:00:01-14:00:03. If multiple time segments are obtained after merging, calculate the time difference between adjacent time segments: if the time difference is less than a preset time reference difference (3 minutes), merge these two adjacent time segments into a complete time segment. For example, the time difference between 14:00:01-14:00:03 and 14:02:00-14:02:05 is 1 minute and 57 seconds, which is less than 3 minutes, so they are merged into 14:00:01-14:02:05. If the time difference is greater than 3 minutes, further calculate the time difference between the two adjacent time segments. If the number of timestamps between time periods is less than the preset reference number (5), then the two adjacent time periods will be shrunk by a preset second number of timestamps (default 1). For example, if the number of timestamps between time periods 14:00:01-14:00:05 and 14:05:00-14:05:05 is 3, which is less than 5, then it will be shrunk to 14:00:02-14:00:05 and 14:05:00-14:05:04. After the time periods are shrunk, when calculating the percentage of overlap between the vibration time set and the attitude time set, the actual length of the shrunk time period should be used as the benchmark, not the original length. If there is partial overlap between the time periods after shrunk, the actual duration of the overlapping part should be included in the total overlap length to avoid misjudgment of the degree of overlap due to shrunkness.

[0071] During the above merging process, it is also necessary to calculate the merging quantity ratio, which is calculated as: Merging quantity ratio = Number of times adjacent time periods are merged into one time period / Total number of time periods before merging. The second quantity is adjusted based on a positive correlation with the merging quantity ratio: when the merging quantity ratio is 60%, the second quantity is increased from 1 to 2; when the merging quantity ratio is 30%, the second quantity remains at 1; when the merging quantity ratio exceeds 80%, the second quantity is increased to 3. By dynamically adjusting the contraction amplitude, the accuracy of the time set is improved to adapt to the dynamic changes in timestamp distribution. Finally, all processed time periods are integrated and recorded to form vibration time sets and attitude time sets, respectively.

[0072] Step 4: Overlap Analysis and Smart Peg Activation

[0073] After the cloud server generates the vibration time set and the attitude time set, it calculates the overlap percentage between the two. The overlap percentage is calculated as follows: Overlap percentage = (Total length of overlapping time period between vibration time set and attitude time set / Total length of recording time period) × 100%. The overlapping time period refers to the part where the start time and end time of the two time sets intersect. For example, the time period of 14:00:01-14:02:05 in the vibration time set and the time period of 14:01:00-14:03:00 in the attitude time set have an overlap period of 14:01:00-14:02:05, which is 1 minute and 5 seconds long.

[0074] If the calculated overlap percentage is greater than the preset reference percentage (60%), it indicates that the external force currently acting on the underground cable has simultaneously triggered continuous abnormal vibration and posture, posing a high risk of external force damage, such as construction excavation or long-term crushing by heavy vehicles. At this time, the cloud server sends a call command to the corresponding smart ground anchor via NB-IoT communication. This command includes a high-frequency acquisition start signal and filter parameter configuration information. Upon receiving the command, the smart ground anchor immediately switches to the data acquisition mode and starts high-frequency data acquisition from the vibration and posture sensors according to the preset second cycle (10 seconds) to increase the density of abnormal data acquisition and accurately capture the changing trend of external force.

[0075] Step 5: High-frequency data processing and anomaly monitoring data generation

[0076] After the smart ground stake collects vibration and attitude data in the second cycle (10 seconds), the data is filtered to remove interference from environmental noise (such as the natural settling of soil particles and the slight vibrations caused by groundwater flow). The filtering process uses a window smoothing algorithm: the vibration data is smoothed using a preset first window (initial length 3 seconds), and the attitude data is smoothed using a preset second window (initial length 2 seconds). The lengths of both the first and second windows are less than the second cycle (10 seconds) to ensure that the data in each cycle is completely filtered.

[0077] To address the static error problem in window smoothing, this method dynamically adjusts the window length by adjusting the trigger frequency: The trigger frequency of the vibration component is calculated, i.e., the number of times the vibration component is triggered within one hour. The ratio of this trigger frequency to a preset first frequency (5 times / hour) is used as the vibration trigger value. The first window length is adjusted based on the negative correlation with the vibration trigger value. When the vibration trigger value is 2 (trigger frequency 10 times / hour), the first window length is shortened from 3 seconds to 2 seconds, improving the response speed to high-frequency vibrations. When the vibration trigger value is 0.5 (trigger frequency 2.5 times / hour), the first window length is maintained at 3 seconds, enhancing the data smoothing effect. Similarly, the trigger frequency of the attitude component is calculated, and the ratio to a preset second frequency (4 times / hour) is used as the attitude trigger value. The second window length is adjusted based on the positive correlation with the attitude trigger value. When the attitude trigger value is 1.5 (trigger frequency 6 times / hour), the second window length is extended from 2 seconds to 3 seconds, improving the stability of the attitude data. When the attitude trigger value is 0.8 (trigger frequency 3.2 times / hour), the second window length is maintained at 2 seconds.

[0078] After filtering, the vibration data and attitude data are fused to generate abnormal monitoring data: Fast Fourier Transform (FFT) is used as the frequency domain conversion algorithm to convert the time-domain vibration data into vibration frequency domain data, which includes energy distribution information in the 0-100Hz frequency band, and the time-domain attitude data into attitude frequency domain data; then, the fluctuation value is extracted by the fluctuation algorithm. The specific execution process of the fluctuation algorithm is as follows: calculate the energy difference between the current frequency domain data and the previous cycle frequency domain data in the corresponding frequency band range, where the energy difference = energy of a certain frequency band in the current frequency domain - energy of the corresponding frequency band in the previous cycle; sort all frequency band ranges from largest to smallest according to the size of the energy difference, and select the top 5 frequency band ranges as feature frequency bands; calculate the average center frequency of these 5 feature frequency bands, and use this average value as the frequency domain fluctuation value (for vibration data) or the frequency domain attitude fluctuation value (for attitude data).

[0079] The fluctuation difference (absolute value) between the frequency domain fluctuation value and the frequency domain attitude fluctuation value is calculated. If the fluctuation difference is less than the preset fluctuation reference difference (e.g., 0.5), it indicates that the vibration change and attitude change show a coordinated pattern, and the vibration data and attitude data are marked as "normal correlation". If the fluctuation difference is greater than or equal to 0.5, it indicates that the vibration change and attitude change deviate abnormally, which may be caused by damage to the cable protection structure or a sudden change in the mode of external force application, and it is marked as "abnormal correlation". The filtered vibration data, attitude data, correlation label ("normal correlation" or "abnormal correlation"), and real-time positioning data collected by the Beidou positioning module (updated every 10 seconds) are packaged in JSON format to generate abnormal monitoring data, which is then uploaded to the cloud server in real time through the NB-IoT communication module.

[0080] Step 6: Dynamic parameter adjustment

[0081] To further improve the method's adaptability to different scenarios, this method also includes a multi-level dynamic parameter adjustment mechanism:

[0082] (a) Adjustment of the second cycle and recording time period

[0083] During the recording of vibration timestamps and attitude timestamps, the cloud server calculates in real time the ratio of the distribution density value of the vibration timestamps to the first density value (10 times / minute) as the vibration comparison value. The second cycle is adjusted according to the negative correlation of the vibration comparison value: when the comparison value = 1, the second cycle = 10 seconds; for every increase of 0.2 in the comparison value, the second cycle is shortened by 2 seconds, with a minimum of 5 seconds; for every decrease of 0.2 in the comparison value, the second cycle is extended by 2 seconds, with a maximum of 15 seconds.

[0084] Simultaneously, the ratio of the distribution density value of the attitude timestamp to the second density value (8 times / minute) is calculated as the attitude comparison value. The recording time period is adjusted according to the positive correlation of the attitude comparison value: when the comparison value = 1, the recording time period = 1 hour; for every increase of 0.5 in the comparison value, the recording time period is extended by 0.5 hours, with a maximum of 3 hours; for every decrease of 0.3 in the comparison value, the recording time period is shortened by 0.2 hours, with a minimum of 0.5 hours.

[0085] (II) First Cycle Adjustment

[0086] For a predefined area, such as the core urban area of ​​a city, with an area of ​​50 km² 2 The cloud server performs regional parameter calibration every 24 hours: it obtains the overlap percentage of all smart ground stakes in the region and calculates the arithmetic mean of all overlap percentages as the percentage mean; if the percentage mean is less than the preset standard percentage (40%), it indicates that there is less external interference in the region and the monitoring demand is relatively mild. At this time, the first cycle is adjusted according to the negative correlation between the percentage mean and the first percentage (30%): when the percentage mean is 30% (equal to the first percentage, the ratio is 1), the first cycle is extended from 30 seconds to 40 seconds; when the percentage mean is 20% (the ratio is about 0.67), the first cycle is extended to 50 seconds to reduce the energy consumption of the equipment by reducing the regular collection frequency; if the percentage mean is greater than or equal to 40%, the first cycle is kept unchanged at 30 seconds to ensure monitoring sensitivity.

[0087] Step 7: Data Storage and Backtracking

[0088] After receiving all uploaded data (including basic acquisition data, feature calculation data, anomaly monitoring data, parameter adjustment records, etc.), the cloud server uses a distributed storage architecture for categorized storage: basic acquisition data (raw vibration data, raw attitude data) is retained for 6 months, and anomaly monitoring data and parameter adjustment records are retained for 1 year, meeting the needs of fault tracing and algorithm optimization. Simultaneously, a three-dimensional index system of "smart ground stake number - timestamp - anomaly type" is established, with an index format such as "SN001-202406151030-anomaly association," allowing maintenance personnel to retrieve target data within 10 seconds by entering keywords through the maintenance management platform.

[0089] When a cable fails, maintenance personnel can initiate a data retrospective operation through the maintenance management platform. First, they input the approximate time range of the fault (accurate to the minute) and the geographical range of the fault area (latitude and longitude interval). Based on a three-dimensional index of "smart anchor number - timestamp - anomaly type," the platform quickly matches the smart anchors within the corresponding area and retrieves all monitoring data for that time period. The platform automatically visualizes the retrospective data, generating a "data change trend report." This report includes vibration amplitude change curves, time-domain attitude fluctuation value change curves, frequency fluctuation value change curves, and a time-series diagram of correlation markers, intuitively presenting the changing patterns of external forces before and after the fault. For example, if the report shows that vibration and attitude data changed from "normal correlation" to "abnormal correlation" within 10 minutes before the fault, and the vibration amplitude continued to rise, it can help determine that the cause of the fault may be damage to the cable protection pipe caused by construction excavation. This can then guide maintenance personnel to bring targeted repair equipment to the site, improving repair efficiency. Meanwhile, the backtracking data will also serve as a basis for parameter optimization. Based on the fault analysis results, maintenance personnel can adjust the preset parameters such as the average reference value and fluctuation reference value of the smart ground stakes in the corresponding area, so that the monitoring system can be better adapted to the actual environmental characteristics of the area.

[0090] This application adjusts the first number of extreme value removals by negatively adjusting the temporal attitude fluctuation values, and adjusts the sample proportion by positively adjusting the vibration average value. This avoids abnormal data interference caused by accidental impacts or environmental noise, while ensuring the objectivity and sensitivity of the vibration average value calculation, adapting to scenarios with different external force interference intensities, such as driveways, sidewalks, and green belts. Based on the distribution density value associated with the timestamp, the accuracy of the overlap calculation between the vibration time set and the attitude time set is improved through time period merging, contraction, and dynamic adjustment of the second number. Even in complex scenarios with discrete timestamp distribution, it can accurately identify continuous abnormal events and reduce the probability of misjudgment. To address the static error problem of window smoothing, the window length is adjusted in conjunction with the trigger frequency. In high-frequency triggering scenarios, the vibration window is shortened to improve response speed, and the attitude window is extended to enhance stability. In low-frequency triggering scenarios, the window length is maintained to ensure data smoothness, adapting to external force scenarios with different intensities and frequencies. FFT frequency domain transformation and energy difference sorting are used to extract fluctuation features. By comparing fluctuation differences, accurate marking of "normal correlation" and "abnormal correlation" is achieved, enabling maintenance personnel to quickly determine the nature of external forces, such as conventional crushing and structural damage, providing concrete data support for fault prediction. A full-dimensional parameter adjustment mechanism is constructed from the device level (second cycle, window length), single event level (recording time period) to the regional level (first cycle). While ensuring monitoring accuracy, the energy consumption of smart ground stakes is minimized, and the equipment's endurance is extended (12 months of continuous operation on a single charge), adapting to the complex and diverse laying environment and long-term maintenance needs of urban underground cable networks.

[0091] This embodiment effectively solves the problems of high labor intensity, low efficiency, delayed fault detection, and insufficient positioning accuracy associated with traditional manual inspections through the implementation of the complete steps described above. Relying on the ±2cm high-precision positioning capability of the LC29DA Beidou positioning module, combined with the high-sensitivity data acquisition of vibration and attitude sensors, and equipped with event-triggered wake-up and high-frequency data acquisition mechanisms, it achieves real-time perception, precise positioning, and dynamic monitoring of external force damage to underground cables. Through cloud-based data processing, correlation analysis, and data backtracking, it provides maintenance personnel with full-process technical support from anomaly alarms and fault location to emergency repair guidance and parameter optimization, significantly improving the operation and maintenance management level of underground cable networks and effectively ensuring the stability and security of urban energy supply.

[0092] This application also discloses an intelligent ground stake monitoring device based on BeiDou positioning, including a processor, wherein the processor executes the steps of the intelligent ground stake monitoring method based on BeiDou positioning as described in any of the above embodiments.

[0093] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for monitoring smart ground stakes based on BeiDou positioning, characterized in that, Includes the following steps: Vibration and attitude data are generated according to a preset first cycle based on vibration sensors and attitude sensors installed on underground cables; the average vibration value is calculated based on a preset number of vibration data; and the time-domain attitude fluctuation value is calculated based on a preset number of attitude data. If the average vibration value is greater than the preset average reference value, then the vibration component is triggered; If the temporal attitude fluctuation value is greater than the preset fluctuation reference value, the attitude component is triggered; Triggered by vibration or attitude components, the smart ground stake reads positioning data based on the Beidou positioning module and uploads the vibration, attitude and positioning data to the cloud via temporary communication. The cloud records positioning data, vibration timestamps of vibration components, and attitude timestamps of attitude components. Within a preset recording time period, based on the calculated distribution density value of vibration timestamps, vibration timestamps with a distribution density value greater than a preset first density value are merged and recorded as a vibration time set. Based on the calculated distribution density value of attitude timestamps, attitude timestamps with a distribution density value greater than a preset second density value are merged and recorded as an attitude time set. Calculate the percentage of overlap between the vibration time set and the attitude time set. If the percentage of overlap is greater than the preset reference percentage, the cloud will call the smart ground stakes based on the communication connection. The smart ground stakes generate vibration and attitude data according to a preset second cycle, where the second cycle is shorter than the first cycle. The vibration and attitude data are filtered and then fused with the vibration, attitude, and positioning data to generate abnormal monitoring data, which is then uploaded to the cloud.

2. The intelligent ground stake monitoring method based on BeiDou positioning according to claim 1, characterized in that, The steps of calculating the average vibration value based on a preset number of vibration data and calculating the time-domain attitude fluctuation value based on a preset number of attitude data also include the following sub-steps: Remove the first number of extreme values ​​from the vibration data, calculate the average value of the remaining vibration data as the vibration average value, and adjust the proportion of the sample in the sample standard deviation according to the positive correlation of the vibration average value. Calculate the standard deviation of a set number of attitude data samples, and use the de-normalized standard deviation as the time-domain attitude fluctuation value. The first quantity is adjusted according to the negative correlation of the time-domain attitude fluctuation value.

3. The intelligent ground stake monitoring method based on BeiDou positioning according to claim 1, characterized in that, The step of merging and generating the vibration time set and the attitude time set also includes the following sub-steps: Associate the corresponding timestamps based on the distribution density values ​​involved in the merged records; Extract the associated timestamps and merge consecutive timestamps into a time period; If there are multiple time periods, calculate the time difference between adjacent time periods; If the time difference is less than the preset time reference difference, then the adjacent time periods are merged into one time period; otherwise, the number of timestamps between adjacent time periods is calculated. If the number of timestamps is less than the preset timestamp reference amount, then the adjacent time periods are shrunk by a preset second number of timestamps. Record the time period as a time set.

4. The intelligent ground stake monitoring method based on BeiDou positioning according to claim 3, characterized in that, The step of merging and generating the vibration time set and the attitude time set also includes the following sub-steps: Calculate the percentage of adjacent time periods merged into one time period compared to the total number of time periods merged. The second quantity is adjusted based on a positive correlation with the proportion of merged quantities.

5. The intelligent ground stake monitoring method based on BeiDou positioning according to claim 1, characterized in that, The filtering process for vibration and attitude data also includes the following sub-steps: Vibration data is smoothed using a preset first window, and attitude data is smoothed using a preset second window; the lengths of both the first and second windows are less than the second period. The frequency at which the vibration component is triggered is calculated as the vibration trigger frequency. The vibration trigger value is calculated based on the vibration trigger frequency and the preset first frequency. The length of the first window is adjusted according to the negative correlation between the vibration trigger value and the vibration trigger value. The frequency at which attitude components are triggered is called the attitude trigger frequency. The attitude trigger value is calculated based on the attitude trigger frequency and a preset second frequency. The length of the second window is adjusted according to the positive correlation between the attitude trigger value and the attitude trigger value.

6. The intelligent ground stake monitoring method based on BeiDou positioning according to claim 1, characterized in that, The step of generating abnormal monitoring data by fusing vibration data, attitude data, and positioning data also includes the following sub-steps: The vibration data is converted into vibration frequency domain data using a frequency domain conversion algorithm, and the frequency domain fluctuation value is extracted from the vibration frequency domain data using a wave algorithm. The attitude data is converted into frequency domain data using a frequency domain conversion algorithm, and then the frequency domain attitude fluctuation value is extracted using a fluctuation algorithm based on the attitude frequency domain data. Calculate the fluctuation difference between the frequency domain fluctuation value and the frequency domain attitude fluctuation value. If the fluctuation difference is less than the preset fluctuation reference difference, the vibration data and attitude data are marked as normally correlated; otherwise, the vibration data and attitude data are marked as abnormally correlated. Vibration data, attitude data, correlation data, and positioning data are packaged to generate anomaly monitoring data.

7. The intelligent ground stake monitoring method based on BeiDou positioning according to claim 6, characterized in that, The fluctuation algorithm includes the following steps: Calculate the energy difference for the same frequency range based on the current frequency domain data and the previous frequency domain data; The frequency bands are sorted from largest to smallest based on the energy difference. A number of pre-set temporary frequency bands are selected, and the average frequency is calculated based on the selected frequency bands. The average frequency is then used as the fluctuation value.

8. The intelligent ground stake monitoring method based on BeiDou positioning according to claim 1, characterized in that, The method also includes the following steps: The vibration comparison value is calculated based on the distribution density value of the vibration timestamp and the first density value, and the second cycle is adjusted according to the negative correlation of the vibration comparison value; the attitude comparison value is calculated based on the distribution density value of the attitude timestamp and the second density value, and the recording time period is adjusted according to the positive correlation of the attitude comparison value.

9. The intelligent ground stake monitoring method based on BeiDou positioning according to claim 1, characterized in that, The method also includes the following steps: Obtain the percentage of overlap of all smart pegs within a preset area; The average of all overlapping percentages is calculated as the percentage mean. If the percentage mean is less than the preset standard percentage, the first cycle is adjusted according to the negative correlation between the percentage mean and the first percentage; where the first percentage is less than the reference percentage.

10. A smart ground stake monitoring device based on BeiDou positioning, characterized in that, The system includes a processor that performs the steps of the intelligent ground stake monitoring method based on BeiDou positioning as described in any one of claims 1-9.

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