Booster station slope automatic monitoring and early warning system based on Beidou technology

Abstract

The invention discloses a booster station slope automatic monitoring and early warning system based on the Beidou technology, and relates to the technical field of booster station slope monitoring, and the system comprises a monitoring terminal module, a preprocessing module, a positioning calculation module, a trend prediction module, an abrupt change detection module, an early warning decision module, and a sampling regulation and control module. The monitoring terminal module is used for arranging a plurality of Beidou receiving terminals on a booster station slope and collecting original observation data; the preprocessing module is used for performing cycle slip repair and quality evaluation on original observation data to generate an observation data sequence; the positioning resolving module is used for acquiring high-precision three-dimensional coordinates of each monitoring point of the booster station slope by adopting a precise single-point positioning and real-time difference mixed resolving method fused with multi-system information according to the observation data sequence output by the preprocessing module, and converting the high-precision three-dimensional coordinates into a real-time displacement sequence under a local coordinate system; and the trend prediction module is based on a displacement sequence.

Description

Technical Field

[0001] This invention relates to the field of substation slope monitoring technology, and in particular to an automatic monitoring and early warning system for substation slopes based on BeiDou technology. Background Technology

[0002] Substation slope monitoring technology refers to a technical system for continuous observation and safety assessment of the stability of surrounding mountain or embankment slopes at power substations, converter stations, and other power substations. Since substations are critical nodes in the power system, any instability or damage to their surrounding slopes, such as slippage or collapse, will directly threaten the safety of equipment within the station, cause power outages, or even major safety accidents. Therefore, it is necessary to deploy sensors such as GNSS receivers, inclinometers, crack gauges, and rain gauges to collect real-time multi-source data on slope displacement, tilt, vibration, and rainfall. Combined with data processing and early warning algorithms, the current state of the slope can be determined and its development trend predicted, enabling early identification and proactive prevention of potential geological hazards. Therefore, how to utilize advanced technologies to improve the intelligence and safety of substation slope monitoring technology has become one of the urgent problems to be solved.

[0003] In the field of substation slope monitoring technology, traditional GNSS monitoring systems mostly use dual-frequency receivers and standard difference or single-frequency calculation methods. In complex environments such as active ionosphere and severe multipath interference, they are easily affected by high-order ionospheric delay, resulting in decreased positioning accuracy. This makes it difficult to meet the millimeter-level deformation monitoring requirements of substation slopes. Moreover, before slope instability, there are often micro-fracture behaviors such as the expansion of internal cracks and local loosening of the soil and rock mass, which generate high-frequency vibration signals. However, traditional monitoring methods only focus on low-frequency macroscopic displacement and lack the ability to effectively extract and analyze high-frequency dynamic characteristics, resulting in a lack of perception of sudden structural disturbances. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an automatic monitoring and early warning system for substation slopes based on BeiDou technology. This solves the problem that traditional GNSS monitoring systems, which mostly use dual-frequency receivers and standard differential or single-frequency calculation methods, are easily affected by high-order ionospheric delays in complex environments such as active ionosphere and severe multipath interference, leading to decreased positioning accuracy and making it difficult to meet the millimeter-level deformation monitoring requirements of substation slopes. Furthermore, before slope instability, there are often micro-fracture behaviors such as the expansion of internal cracks and local loosening of the soil and rock mass, which generate high-frequency vibration signals. However, traditional monitoring methods only focus on low-frequency macroscopic displacement and lack the ability to effectively extract and analyze high-frequency dynamic characteristics, resulting in a lack of perception of sudden structural disturbances.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides an automatic monitoring and early warning system for slopes of booster stations based on BeiDou technology, comprising: The system includes a monitoring terminal module, a preprocessing module, a location calculation module, a trend prediction module, a mutation detection module, an early warning decision-making module, and a sampling and control module. The monitoring terminal module is used to deploy multiple BeiDou receiving terminals on the slope of the booster station to collect raw observation data; The preprocessing module is used to perform cycle slip repair and quality assessment on the raw observation data and generate an observation data sequence; The positioning and calculation module is used to obtain the high-precision three-dimensional coordinates of each monitoring point on the slope of the booster station based on the observation data sequence output by the preprocessing module, using a precise single-point positioning and real-time differential hybrid calculation method that integrates information from multiple systems, and convert it into a real-time displacement sequence in the local coordinate system. The trend prediction module is used to extract deformation evolution laws based on displacement sequences through a time-series deep learning model, and output future displacement prediction values ​​and the prediction residuals at the current moment. The mutation detection module is used to perform rate of change analysis on the displacement sequence output by the positioning solution module, decompose its dynamic spectrum characteristics, identify high-frequency energy surge behavior, and determine whether there is a sudden structural disturbance. The early warning decision module is used to combine the prediction residual output by the trend prediction module and the high-frequency energy change state output by the mutation detection module. When both exceed the set response conditions at the same time, a red early warning signal is generated, and the corresponding level of early warning is output according to the single indicator exceeding the limit. The sampling and control module is used to dynamically adjust the data acquisition frequency of the monitoring terminal module based on the displacement change rate output by the positioning calculation module, thereby achieving adaptive optimization of the system's operating power consumption. As a preferred embodiment of the automatic monitoring and early warning system for substation slopes based on BeiDou technology described in this invention, the specific steps for deploying multiple BeiDou receiving terminals on the substation slope to collect raw observation data are as follows: Multi-mode GNSS receivers supporting BeiDou-3 B1I, B2a, B3I frequencies and GPS L1, L2, L5 frequencies will be deployed in key locations in the potential landslide area of ​​the substation slope. Each terminal is equipped with a choke coil antenna and is fixed to a stable rock and soil layer through a deep-buried stainless steel base. Synchronous acquisition is initiated to continuously acquire pseudorange, carrier phase, signal-to-noise ratio, and elevation angle information of each visible satellite using a unified time reference, and to form a raw observation data stream.

[0007] As a preferred embodiment of the automatic monitoring and early warning system for substation slopes based on BeiDou technology described in this invention, the specific steps for performing cycle slip repair and quality assessment on the original observation data to generate an observation data sequence are as follows: The TurboEdit detection algorithm is applied to the carrier phase observations of the receiving self-monitoring terminal module to identify the timing and magnitude of cycle slips by the rate of change of ionospheric residuals and polynomial fitting residuals. The detected cycle slip positions are repaired using low-order polynomial interpolation or adjacent epoch difference method. Calculate the multipath error estimate for each satellite and combine it with the signal-to-noise ratio and elevation angle to construct a comprehensive quality score; Based on the scoring results, weight coefficients are assigned to each satellite observation value, observation data below the set quality threshold are removed, and the observation data sequence after reliability screening and weighting is output.

[0008] As a preferred embodiment of the automatic monitoring and early warning system for substation slopes based on BeiDou technology described in this invention, the following steps are taken: Based on the observation data sequence output by the preprocessing module, a precise single-point positioning method integrating multi-system information and a real-time differential hybrid solution is used to obtain the high-precision three-dimensional coordinates of each monitoring point on the substation slope, and convert them into a real-time displacement sequence in the local coordinate system. Establish a non-differential, non-combined PPP-RTK observation equation, which includes position parameters, receiver clock error, tropospheric delay, and ambiguity parameters; The geometric configuration is enhanced by incorporating observation data from multiple global navigation satellite systems, and combining observation information from BeiDou, GPS, GLONASS, and Galileo systems. The first-order effects of the ionospheric delay term are eliminated by combining external grid correction with internal parameter estimation. To eliminate second-order and higher ionospheric delay, a three-frequency geometrically uncombined observation is constructed. By linearly combining pseudorange and carrier phase observations at three different frequencies, the geometric distance term is completely canceled out, leaving only the ionospheric delay-related term. Let the pseudorange observations at the first, second, and third frequency points be respectively , , The corresponding carrier phase observation value is , , Then the expression for the three frequencies without geometric combination is: ; in, This indicates the result of three-frequency geometric combination without geometry, used to extract higher-order ionospheric delay residue. , , These represent the pseudorange observations at the first, second, and third frequency points, respectively. , , These represent the carrier phase observations at the first, second, and third frequency points, respectively. , , These are combination coefficients, determined by the frequency of the signal at each frequency point; The combination coefficients satisfy the following constraints: ; ; in, , , These represent the signal frequencies at the first, second, and third frequency points, respectively. The dynamic three-dimensional coordinates of the monitoring point in the WGS-84 coordinate system are obtained by model calculation, and then converted into a relative displacement sequence in the northeast-sky direction with the initial stable period coordinates as a reference.

[0009] As a preferred embodiment of the automatic monitoring and early warning system for substation slopes based on BeiDou technology described in this invention, the specific steps of extracting deformation evolution patterns based on displacement sequences using a time-series deep learning model, and outputting future displacement prediction values ​​and the prediction residuals at the current moment are as follows: The displacement sequence output by the localization solution module is divided into a sliding time window input bidirectional LSTM neural network. The forward LSTM captures the information flow from the past to the present, and the backward LSTM captures the contextual dependency from the future to the present. The hidden states in both directions are concatenated at each time step and fed into the attention layer to calculate attention weights to highlight the impact of key historical moments. Let the first The hidden state of splicing at each time step is The attention kernel vector is The weight matrix is The bias vector is Attention weights are calculated as follows: ; in, Indicates the first Attention weights at each time step Indicates the first Hidden states of bidirectional LSTM splicing at each time step This indicates that the attention layer can be trained using a weight matrix. This indicates that the attention layer can be trained with bias vectors. This represents the kernel vector in the attention mechanism. The hyperbolic tangent activation function is used. It is an exponential function; The context vector is obtained after weighted summation and input into the fully connected layer to output the displacement prediction value for future time periods; The difference between the current actual displacement and the model's backtracking prediction is used to obtain the prediction residual.

[0010] As a preferred embodiment of the automatic monitoring and early warning system for substation slopes based on BeiDou technology described in this invention, the specific steps for performing rate-of-change analysis on the displacement sequence output by the positioning calculation module, decomposing its dynamic spectrum characteristics, identifying high-frequency energy surge behavior, and determining whether there is a sudden structural disturbance are as follows: Perform a first-order difference operation on the displacement sequence to obtain the velocity sequence; The velocity sequence was subjected to three-level wavelet packet decomposition, and the db4 wavelet basis function was selected to decompose the signal into eight equal-width frequency bands. Calculate the energy of each frequency band, focusing on monitoring the total energy of the high-frequency components corresponding to the fifth to eighth frequency bands. This part of the energy reflects the transient vibration signal generated by micro-fractures or structural loosening inside the rock and soil.

[0011] Let the first Layer The wavelet packet coefficients of each node are Then the energy of this node is: ; in, Indicates the first Layer Wavelet packet energy of each node Indicates the th wavelet packet after decomposition Layer The coefficient values ​​of each node; The total energy of a layer is obtained by summing the energies of all nodes in the same layer, expressed as: ; in, Indicates the first Total energy of the layer This represents the index variable for all wavelet packet decomposition nodes within this layer; The high-frequency energy percentage is defined by the following expression: ; in, This indicates the proportion of high-frequency energy in the total energy. This represents the total energy of frequency bands 5 through 8. This represents the total energy across all eight frequency bands; when When the energy level rises rapidly over multiple consecutive epochs and the rate of change exceeds the set criteria, it is determined to be a sudden increase in high-frequency energy, indicating a risk of sudden structural disturbance.

[0012] As a preferred embodiment of the automatic monitoring and early warning system for substation slopes based on BeiDou technology described in this invention, the prediction residual output by the comprehensive trend prediction module and the high-frequency energy change status output by the abrupt change detection module generate a red early warning signal when both exceed the set response conditions. Furthermore, based on the single indicator exceeding the limit, a corresponding level of early warning is output. The specific steps are as follows: Receiver prediction residual and the proportion of high-frequency energy ; Set residual threshold and energy percentage change rate threshold ; like > and The rate of change is greater than If so, a red alert will be triggered; If only > If established, a yellow alert will be triggered; If only If the rapid increase is confirmed, an orange alert will be triggered; All early warning signals are uploaded to the monitoring center via the communication module, activating the local audible and visual alarm devices.

[0013] As a preferred embodiment of the automatic monitoring and early warning system for substation slopes based on BeiDou technology described in this invention, the specific steps for dynamically adjusting the data acquisition frequency of the monitoring terminal module based on the displacement change rate output by the positioning calculation module to achieve adaptive optimization of system operating power consumption are as follows: Obtain the current displacement rate of change output by the positioning solution module. ; Setting a low-speed reference With high-speed response threshold And set a hysteresis interval ; when < At that time, the control and monitoring terminal module operates at the lowest frequency. run; when > + At that time, increase to the highest frequency. ; when In The current frequency remains unchanged during the interval. In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the automatic monitoring and early warning system for substation slopes based on Beidou technology as described in the first aspect of the present invention.

[0014] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the automatic monitoring and early warning system for substation slopes based on Beidou technology as described in the first aspect of the present invention.

[0015] The beneficial effects of this invention are as follows: By constructing a system consisting of a monitoring terminal module, a preprocessing module, a positioning and calculation module, a trend prediction module, a mutation detection module, an early warning decision module, and a sampling and control module, high-precision, automated, and real-time monitoring and intelligent early warning of the substation slope are achieved. Multi-system, multi-frequency BeiDou observation and a three-frequency geometric-free combination algorithm are used to suppress high-order ionospheric errors, improving dynamic positioning accuracy to millimeter level. A time-series model combining bidirectional LSTM and an attention mechanism is used to accurately predict deformation trends. Furthermore, wavelet packet high-frequency energy surge detection is introduced to identify internal micro-fracture signals, forming a dual-criteria fusion early warning mechanism of macroscopic displacement anomalies and microscopic structural disturbances, significantly reducing false alarm and missed alarm rates. Simultaneously, the acquisition frequency is adaptively adjusted based on the displacement change rate, balancing response sensitivity and long-term operational energy efficiency. This solves the problems of insufficient accuracy, delayed early warning, and high power consumption in traditional slope monitoring systems, improving the reliability and intelligence level of substation slope safety monitoring. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of the automatic monitoring and early warning system for the substation slope based on BeiDou technology in Example 1. Figure 2 This is a schematic diagram of the deployment of the monitoring terminal module on the slope of the booster station in Example 1. Detailed Implementation

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0019] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0020] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0021] Example, refer to Figure 1 As an embodiment of the present invention, this embodiment provides an automatic monitoring and early warning system for substation slopes based on BeiDou technology, comprising: The system includes a monitoring terminal module, a preprocessing module, a location calculation module, a trend prediction module, a mutation detection module, an early warning decision-making module, and a sampling and control module. The monitoring terminal module is used to deploy multiple BeiDou receiving terminals on the slope of the booster station to collect raw observation data. Furthermore, multi-mode GNSS receivers supporting BeiDou-3 B1I, B2a, B3I frequencies and GPS L1, L2, L5 frequencies will be deployed in key locations in the potential landslide area of ​​the substation slope. Each terminal is equipped with a choke coil antenna and is fixed to a stable rock and soil layer through a deep-buried stainless steel base. Synchronous acquisition is initiated to continuously acquire pseudorange, carrier phase, signal-to-noise ratio, and elevation angle information of each visible satellite using a unified time reference, and to form a raw observation data stream; It should be noted that by deploying multiple GNSS receiving terminals with multi-system and multi-frequency observation capabilities in the potential slip zone of the substation slope, and using a combination of deeply buried stainless steel bases and choke antennas, the antenna phase center offset and multipath effect can be effectively suppressed, ensuring the long-term stability and spatial representativeness of the original observation data, and providing a reliable hardware foundation for subsequent high-precision calculations.

[0022] The preprocessing module is used to perform cycle slip repair and quality assessment on the raw observation data and generate observation data sequences; Furthermore, the TurboEdit detection algorithm is applied to the carrier phase observations of the receiving self-monitoring terminal module to identify the timing and magnitude of cycle slips by using the ionospheric residual change rate and polynomial fitting residuals. The detected cycle slip positions are repaired using low-order polynomial interpolation or adjacent epoch difference method. Calculate the multipath error estimate for each satellite and combine it with the signal-to-noise ratio and elevation angle to construct a comprehensive quality score; Based on the scoring results, weight coefficients are assigned to each satellite observation value, observation data below the set quality threshold are removed, and the observation data sequence after reliability screening and weighting is output. It should be noted that by implementing TurboEdit cycle slip detection and repair on carrier phase observations, and constructing a comprehensive quality scoring system in conjunction with signal-to-noise ratio, elevation angle, and multipath error, the system achieves automatic identification and weighted elimination of low-quality observation data, improves the reliability of the observation data sequence, avoids the contamination of positioning solution results by abnormal observations, and ensures the robustness of the solution process.

[0023] The positioning and calculation module is used to obtain the high-precision three-dimensional coordinates of each monitoring point on the slope of the booster station based on the observation data sequence output by the preprocessing module, using a precise single-point positioning method that integrates information from multiple systems and a real-time differential hybrid calculation method, and convert it into a real-time displacement sequence in the local coordinate system. Furthermore, an undifferentiated and uncombined PPP-RTK observation equation is established, which includes position parameters, receiver clock error, tropospheric delay, and ambiguity parameters. The geometric configuration is enhanced by incorporating observation data from multiple global navigation satellite systems, and combining observation information from BeiDou, GPS, GLONASS, and Galileo systems. The first-order effects of the ionospheric delay term are eliminated by combining external grid correction with internal parameter estimation. To eliminate second-order and higher ionospheric delay, a three-frequency geometrically uncombined observation is constructed. By linearly combining pseudorange and carrier phase observations at three different frequencies, the geometric distance term is completely canceled out, leaving only the ionospheric delay-related term. Let the pseudorange observations at the first, second, and third frequency points be respectively , , The corresponding carrier phase observation value is , , Then the expression for the three frequencies without geometric combination is: ; in, This indicates the result of three-frequency geometric combination without geometry, used to extract higher-order ionospheric delay residue. , , These represent the pseudorange observations at the first, second, and third frequency points, respectively. , , These represent the carrier phase observations at the first, second, and third frequency points, respectively. , , These are combination coefficients, determined by the frequency of the signal at each frequency point; The combination coefficients must satisfy the following constraints: ; ; in, , , These represent the signal frequencies at the first, second, and third frequency points, respectively. The dynamic three-dimensional coordinates of the monitoring points in the WGS-84 coordinate system are obtained by model calculation, and then the initial stable period coordinates are used as a reference to convert them into a relative displacement sequence in the northeast direction. It should be noted that the non-difference, non-combination PPP-RTK fusion solution strategy is adopted, which combines external grid correction information with internal parameter estimation to eliminate the first-order ionospheric term. Furthermore, the second-order and higher-order ionospheric delays are suppressed through a three-frequency geometric combination model. This breaks through the technical bottleneck of the traditional dual-frequency solution's accuracy decline during periods of ionospheric activity and achieves continuous and stable output of millimeter-level dynamic positioning under complex meteorological conditions.

[0024] The trend prediction module is used to extract deformation evolution patterns based on displacement sequences through a time-series deep learning model, and output future displacement prediction values ​​and the prediction residuals at the current moment. Furthermore, the displacement sequence output by the localization solution module is divided into a sliding time window input bidirectional LSTM neural network. The forward LSTM captures the information flow from the past to the present, and the backward LSTM captures the contextual dependency from the future to the present. The hidden states in both directions are concatenated at each time step and fed into the attention layer to calculate attention weights to highlight the impact of key historical moments. Let the first The hidden state of splicing at each time step is The attention kernel vector is The weight matrix is The bias vector is Attention weights are calculated as follows: ; in, Indicates the first Attention weights at each time step Indicates the first Hidden states of bidirectional LSTM splicing at each time step This indicates that the attention layer can be trained using a weight matrix. This indicates that the attention layer can be trained with bias vectors. This represents the kernel vector in the attention mechanism. The hyperbolic tangent activation function is used. It is an exponential function; The context vector is obtained after weighted summation and input into the fully connected layer to output the displacement prediction value for future time periods; The difference between the current actual displacement and the model's backtracking prediction value is used to obtain the prediction residual. It should be noted that by introducing a bidirectional LSTM network combined with an attention mechanism to perform temporal modeling of the displacement sequence, it is possible to simultaneously capture the historical evolution trend and future contextual dependencies of the deformation process. By adaptively focusing on key historical moments through attention weights, the model's prediction accuracy for nonlinear deformation stages is improved, and the generated prediction residuals provide a sensitive criterion for early identification of minor anomalies.

[0025] The mutation detection module is used to perform rate of change analysis on the displacement sequence output by the positioning solution module, decompose its dynamic spectrum characteristics, identify high-frequency energy surge behavior, and determine whether there is a sudden structural disturbance. Furthermore, a first-order difference operation is performed on the displacement sequence to obtain the velocity sequence; The velocity sequence was subjected to three-level wavelet packet decomposition, and the db4 wavelet basis function was selected to decompose the signal into eight equal-width frequency bands. Calculate the energy of each frequency band, focusing on monitoring the total energy of the high-frequency components corresponding to the fifth to eighth frequency bands. This part of the energy reflects the transient vibration signal generated by micro-fractures or structural loosening inside the rock and soil.

[0026] Let the first Layer The wavelet packet coefficients of each node are Then the energy of this node is: ; in, Indicates the first Layer Wavelet packet energy of each node Indicates the th wavelet packet after decomposition Layer The coefficient values ​​of each node; The total energy of a layer is obtained by summing the energies of all nodes in the same layer, expressed as: ; in, Indicates the first Total energy of the layer This represents the index variable for all wavelet packet decomposition nodes within this layer; The high-frequency energy percentage is defined by the following expression: ; in, This indicates the proportion of high-frequency energy in the total energy. This represents the total energy of frequency bands 5 through 8. This represents the total energy across all eight frequency bands; when When the energy level rises rapidly over multiple consecutive epochs and the rate of change exceeds the set criteria, it is determined to be a high-frequency energy surge, which poses a risk of sudden structural disturbance. It should be noted that using three-layer wavelet packet decomposition to perform frequency domain analysis on the velocity sequence can finely separate the energy distribution of different frequency components, especially effectively extracting the energy changes in the high-frequency band above 0.5Hz. This part of the energy is closely related to sudden structural behaviors such as crack propagation and local loosening inside the rock and soil mass. Compared with the traditional judgment method that only relies on displacement threshold, it improves the sensitivity of sensing early instability signs.

[0027] The early warning decision module is used to combine the prediction residual output by the trend prediction module and the high-frequency energy change status output by the mutation detection module. When both exceed the set response conditions at the same time, a red early warning signal is generated, and the corresponding level of early warning is output according to the single indicator exceeding the limit. Furthermore, receive the predicted residuals and the proportion of high-frequency energy ; Set residual threshold and energy percentage change rate threshold ; like > and The rate of change is greater than If so, a red alert will be triggered; If only > If established, a yellow alert will be triggered; If only If the rapid increase is confirmed, an orange alert will be triggered; All early warning signals are uploaded to the monitoring center via the communication module, activating the local audible and visual alarm devices; It should be noted that the early warning decision module adopts a dual-parameter coupling criterion, which performs logical linkage analysis between the residual abnormality output by the trend prediction module and the high-frequency energy surge output by the mutation detection module. The highest level red warning is only triggered when the macroscopic deformation deviates from the normal trend and is accompanied by microscopic high-frequency disturbances. This avoids the problem of false alarms caused by the mis-triggered single indicator and improves the credibility of the early warning results.

[0028] The sampling and control module is used to dynamically adjust the data acquisition frequency of the monitoring terminal module based on the displacement change rate output by the positioning calculation module, so as to achieve adaptive optimization of the system's operating power consumption. Furthermore, obtain the current displacement change rate output by the positioning calculation module. ; Setting a low-speed reference With high-speed response threshold And set a hysteresis interval ; when < At that time, the control and monitoring terminal module operates at the lowest frequency. run; when > + At that time, increase to the highest frequency. ; when In The current frequency remains unchanged during the interval. It should be noted that the sampling control module dynamically adjusts the data acquisition frequency according to the real-time displacement change rate and sets a hysteresis interval to prevent frequent switching of working modes near the critical value. This ensures the data acquisition density during the accelerated deformation stage of the slope and reduces system power consumption during the stable period, extending the battery life of the unattended field equipment and achieving an optimized balance between monitoring sensitivity and operational economy.

[0029] This embodiment also provides a computer device applicable to the automatic monitoring and early warning system for substation slopes based on BeiDou technology, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the automatic monitoring and early warning system for substation slopes based on BeiDou technology as proposed in the above embodiment.

[0030] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0031] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the automatic monitoring and early warning system for substation slopes based on BeiDou technology as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0032] In summary, this invention, by constructing a system consisting of a monitoring terminal module, a preprocessing module, a positioning and calculation module, a trend prediction module, a mutation detection module, an early warning decision-making module, and a sampling and control module, achieves high-precision, automated, and real-time monitoring and intelligent early warning of substation slopes. It employs multi-system, multi-frequency BeiDou observation and a three-frequency geometric-free combination algorithm to suppress high-order ionospheric errors, improving dynamic positioning accuracy to millimeter level. A time-series model combining bidirectional LSTM and an attention mechanism enables accurate prediction of deformation trends. Furthermore, it introduces wavelet packet high-frequency energy surge detection to identify internal micro-fracture signals, forming a dual-criteria fusion early warning mechanism based on macroscopic displacement anomalies and microscopic structural disturbances, significantly reducing false alarm and missed alarm rates. Simultaneously, it adaptively adjusts the acquisition frequency based on the displacement change rate, balancing response sensitivity and long-term operational efficiency. This invention solves the problems of insufficient accuracy, delayed early warning, and high power consumption in traditional slope monitoring systems, improving the reliability and intelligence level of substation slope safety monitoring.

[0033] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An automatic monitoring and early warning system for substation slopes based on BeiDou technology, characterized in that: include: The system includes a monitoring terminal module, a preprocessing module, a location calculation module, a trend prediction module, a mutation detection module, an early warning decision-making module, and a sampling and control module. The monitoring terminal module is used to deploy multiple BeiDou receiving terminals on the slope of the booster station to collect raw observation data; The preprocessing module is used to perform cycle slip repair and quality assessment on the raw observation data and generate an observation data sequence; The positioning and calculation module is used to obtain the high-precision three-dimensional coordinates of each monitoring point on the slope of the booster station based on the observation data sequence output by the preprocessing module, using a precise single-point positioning and real-time differential hybrid calculation method that integrates information from multiple systems, and convert it into a real-time displacement sequence in the local coordinate system. The trend prediction module is used to extract deformation evolution laws based on displacement sequences through a time-series deep learning model, and output future displacement prediction values ​​and the prediction residuals at the current moment. The mutation detection module is used to perform rate of change analysis on the displacement sequence output by the positioning solution module, decompose its dynamic spectrum characteristics, identify high-frequency energy surge behavior, and determine whether there is a sudden structural disturbance. The early warning decision module is used to combine the prediction residual output by the trend prediction module and the high-frequency energy change state output by the mutation detection module. When both exceed the set response conditions at the same time, a red early warning signal is generated, and the corresponding level of early warning is output according to the single indicator exceeding the limit. The sampling control module is used to dynamically adjust the data acquisition frequency of the monitoring terminal module based on the displacement change rate output by the positioning calculation module, thereby achieving adaptive optimization of the system's operating power consumption.

2. The automatic monitoring and early warning system for substation slopes based on BeiDou technology as described in claim 1, characterized in that: The specific steps for deploying multiple BeiDou receiving terminals on the slope of the booster station to collect raw observation data are as follows: Multi-mode GNSS receivers supporting BeiDou-3 B1I, B2a, B3I frequencies and GPS L1, L2, L5 frequencies will be deployed in key locations in the potential landslide area of ​​the substation slope. Each terminal is equipped with a choke coil antenna and is fixed to a stable rock and soil layer through a deep-buried stainless steel base. Synchronous acquisition is initiated to continuously acquire pseudorange, carrier phase, signal-to-noise ratio, and elevation angle information of each visible satellite using a unified time reference, and to form a raw observation data stream.

3. The automatic monitoring and early warning system for substation slopes based on BeiDou technology as described in claim 2, characterized in that: The specific steps for performing cycle slip repair and quality assessment on the original observation data to generate an observation data sequence are as follows: The TurboEdit detection algorithm is applied to the carrier phase observations of the receiving self-monitoring terminal module to identify the timing and magnitude of cycle slips by the rate of change of ionospheric residuals and polynomial fitting residuals. The detected cycle slip positions are repaired using low-order polynomial interpolation or adjacent epoch difference method. Calculate the multipath error estimate for each satellite and combine it with the signal-to-noise ratio and elevation angle to construct a comprehensive quality score; Based on the scoring results, weight coefficients are assigned to each satellite observation value, observation data below the set quality threshold are removed, and the observation data sequence after reliability screening and weighting is output.

4. The automatic monitoring and early warning system for substation slopes based on BeiDou technology as described in claim 3, characterized in that: The method of using a precise single-point positioning and real-time differential hybrid solution method that integrates multi-system information to obtain high-precision three-dimensional coordinates of each monitoring point on the slope of the booster station, based on the observation data sequence output by the preprocessing module, is as follows: Establish a non-differential, non-combined PPP-RTK observation equation, which includes position parameters, receiver clock error, tropospheric delay, and ambiguity parameters; The geometric configuration is enhanced by incorporating observation data from multiple global navigation satellite systems, and combining observation information from BeiDou, GPS, GLONASS, and Galileo systems. The first-order effects of the ionospheric delay term are eliminated by combining external grid correction with internal parameter estimation. To eliminate second-order and higher ionospheric delay, a three-frequency geometrically uncombined observation is constructed. By linearly combining pseudorange and carrier phase observations at three different frequencies, the geometric distance term is completely canceled out, leaving only the ionospheric delay-related term. Let the pseudorange observations at the first, second, and third frequency points be respectively , , The corresponding carrier phase observation value is , , Then the expression for the three frequencies without geometric combination is: ； in, This indicates the result of three-frequency geometric combination without geometry, used to extract higher-order ionospheric delay residue. , , These represent the pseudorange observations at the first, second, and third frequency points, respectively. , , These represent the carrier phase observations at the first, second, and third frequency points, respectively. , , These are combination coefficients, determined by the frequency of the signal at each frequency point; The combination coefficients satisfy the following constraints: ； ； in, , , These represent the signal frequencies at the first, second, and third frequency points, respectively. The dynamic three-dimensional coordinates of the monitoring point in the WGS-84 coordinate system are obtained by model calculation, and then converted into a relative displacement sequence in the northeast-sky direction with the initial stable period coordinates as a reference.

5. The automatic monitoring and early warning system for substation slopes based on BeiDou technology as described in claim 4, characterized in that: The specific steps for extracting deformation evolution patterns based on displacement sequences using a time-series deep learning model, and outputting predicted future displacement values ​​and the prediction residuals at the current moment are as follows: The displacement sequence output by the localization solution module is divided into a sliding time window input bidirectional LSTM neural network. The forward LSTM captures the information flow from the past to the present, and the backward LSTM captures the contextual dependency from the future to the present. The hidden states in both directions are concatenated at each time step and fed into the attention layer to calculate attention weights to highlight the impact of key historical moments. Let the first The hidden state of splicing at each time step is The attention kernel vector is The weight matrix is The bias vector is Attention weights are calculated as follows: ； in, Indicates the first Attention weights at each time step Indicates the first Hidden states of bidirectional LSTM splicing at each time step This indicates that the attention layer can be trained using a weight matrix. This indicates that the attention layer can be trained with bias vectors. This represents the kernel vector in the attention mechanism. The hyperbolic tangent activation function is used. It is an exponential function; The context vector is obtained after weighted summation and input into the fully connected layer to output the displacement prediction value for future time periods; The difference between the current actual displacement and the model's backtracking prediction is used to obtain the prediction residual.

6. The automatic monitoring and early warning system for substation slopes based on BeiDou technology as described in claim 5, characterized in that: The steps for performing rate-of-change analysis on the displacement sequence output by the positioning calculation module, decomposing its dynamic spectrum characteristics, identifying high-frequency energy surge behavior, and determining whether there is a sudden structural disturbance are as follows: Perform a first-order difference operation on the displacement sequence to obtain the velocity sequence; The velocity sequence was subjected to three-level wavelet packet decomposition, and the db4 wavelet basis function was selected to decompose the signal into eight equal-width frequency bands. Calculate the energy of each frequency band, and focus on monitoring the total energy of the high-frequency components corresponding to the fifth to eighth frequency bands. This part of the energy reflects the transient vibration signal generated by micro-fractures or structural loosening inside the rock and soil mass. Let the first Layer The wavelet packet coefficients of each node are Then the energy of this node is: ； in, Indicates the first Layer Wavelet packet energy of each node Indicates the th wavelet packet after decomposition Layer The coefficient values ​​of each node; The total energy of a layer is obtained by summing the energies of all nodes in the same layer, expressed as: ； in, Indicates the first Total energy of the layer This represents the index variable for all wavelet packet decomposition nodes within this layer; The high-frequency energy percentage is defined by the following expression: ； in, This indicates the proportion of high-frequency energy in the total energy. This represents the total energy of frequency bands 5 through 8. This represents the total energy across all eight frequency bands; when When the energy level rises rapidly over multiple consecutive epochs and the rate of change exceeds the set criteria, it is determined to be a sudden increase in high-frequency energy, indicating a risk of sudden structural disturbance.

7. The automatic monitoring and early warning system for substation slopes based on BeiDou technology as described in claim 6, characterized in that: The prediction residual output by the integrated trend prediction module and the high-frequency energy change status output by the abrupt change detection module generate a red warning signal when both exceed the set response conditions. Furthermore, a corresponding level of warning is output based on the single indicator exceeding the limit. The specific steps are as follows: Receiver prediction residual and the proportion of high-frequency energy ; Set residual threshold and energy percentage change rate threshold ; like > and The rate of change is greater than If so, a red alert will be triggered; If only > If established, a yellow alert will be triggered; If only If the rapid increase is confirmed, an orange alert will be triggered; All early warning signals are uploaded to the monitoring center via the communication module, activating the local audible and visual alarm devices.

8. The automatic monitoring and early warning system for substation slopes based on BeiDou technology as described in claim 7, characterized in that: The specific steps are as follows: Based on the displacement change rate output by the positioning calculation module, the data acquisition frequency of the monitoring terminal module is dynamically adjusted to achieve adaptive optimization of the system's operating power consumption. Obtain the current displacement rate of change output by the positioning solution module. ; Setting a low-speed reference With high-speed response threshold And set a hysteresis interval ; when < At that time, the control and monitoring terminal module operates at the lowest frequency. run; when > + At that time, increase to the highest frequency. ; when In The frequency remains unchanged during the interval.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the automatic monitoring and early warning system for substation slopes based on Beidou technology as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the automatic monitoring and early warning system for the substation slope based on Beidou technology as described in any one of claims 1 to 8.

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