Beidou Continuous Positioning Method and System Applicable to Landslide Geological Disaster Monitoring

Abstract

The present invention provides a Beidou continuous positioning method and system applicable to landslide geological disaster monitoring, including the determination of landslide body state and the adaptive adjustment of monitoring frequency based on a sliding window and Gaussian fitting, which is used to adaptively adjust the monitoring frequency according to the state of the landslide body in the case of only Beidou pseudo-range phase observations without other auxiliary information; the real-time determination of the missing situation of Beidou observations based on the time of the cloud server; and the realization of maintaining positioning continuity in the case of missing Beidou observations based on the predictability of Beidou satellite orbits, clock errors, pseudo-range phase observations, and receiver antenna coordinates in the short term. The present invention is applicable to the field of landslide and other geological disaster monitoring, and solves the problems of discontinuous relative positioning results and re-convergence caused by the "interruption" of Beidou observations at monitoring points (data interruption, few observed satellites, poor data quality, etc.).

Description

Technical Field

[0001] The present invention belongs to the fields of satellite high-precision positioning and landslide geological disaster monitoring, and particularly relates to a Beidou continuous positioning method and system suitable for landslide geological disaster monitoring. Background Art

[0002] In the field of wild landslide geological disaster monitoring, generally, Beidou monitoring points are placed on the surface of a potential deformable body, and a reference station is placed in an open and stable area near the monitoring point. By jointly processing the Beidou observation values of the monitoring point and the reference station for relative positioning, the coordinates of the monitoring point are calculated to form a coordinate time series, which helps to determine whether the monitoring point has undergone displacement. A continuous and accurate coordinate time series of Beidou monitoring points is the scientific basis for important decisions such as safety early warning and personnel evacuation during geological disaster monitoring. To obtain a continuous and accurate coordinate time series of Beidou monitoring points, observation values with a fixed sampling rate are usually used for calculation during the real-time monitoring process, and Kalman filtering is used as the estimator for Beidou relative positioning.

[0003] For a conventional Beidou relative positioning method based on Kalman filtering, the state parameters such as the coordinates of the previous epoch and the corresponding variance-covariance matrix are incorporated into the current epoch as the input of the Kalman filtering prediction equation, that is: the coordinates and ambiguity parameters of the previous epoch are used as the predicted coordinates and ambiguity parameters of the current epoch, and a proper amount of noise is added to the coordinates and ambiguity variance-covariance of the previous epoch as the variance-covariance of the predicted coordinates and ambiguity of the current epoch. The problems existing in the conventional method are as follows:

[0004] (1) It cannot meet the requirements of the sampling rate of the positioning results for different geological movement stages such as the stable, slow-changing, and impending landslide stages of the landslide body, and cannot reflect the missing situation of the observation values in real time.

[0005] (2) Only the historical information of 1 epoch is stored in the Kalman filtering estimator. In the case where the Beidou monitoring device only supports pseudorange and phase observation values, the velocity and acceleration information of the monitoring point cannot be obtained.

[0006] (3) When cycle slips or even interruptions occur in the Beidou observation values, the estimator will not be able to utilize the historical information, resulting in the re-initialization of the filtering and discontinuous positioning results.

[0007] Therefore, the conventional Beidou relative positioning method is mainly applicable to scenarios with good power, communication, and observation environments such as cities. At this time, the Beidou observations are not likely to have cycle slips or interruptions, and the positioning results can usually be maintained continuously. In the field landslide geological disaster monitoring project, the power is provided by the solar system, the data communication depends on the mobile public network, and the observation environment is affected by the shielding of trees and vegetation. Under the combined influence of these factors, the Beidou observations are prone to cycle slips and interruptions, which in turn cause abnormal jumps and omissions in the positioning results. In the stable or slow deformation stage of the landslide body, this problem may lead to false alarms; in the stage approaching the landslide, this problem may cause missed alarms. Summary of the Invention

[0008] The present invention proposes a Beidou continuous positioning scheme applicable to landslide geological disaster monitoring to solve the problems existing in the above-mentioned background technology.

[0009] The technical solution of the present invention provides a Beidou continuous positioning method applicable to landslide geological disaster monitoring, including the following steps.

[0010] Step 1, landslide body state determination and monitoring frequency adaptive adjustment based on a sliding window and Gaussian fitting, which is used to adaptively adjust the monitoring frequency according to the state of the landslide body in the case of only Beidou pseudorange phase observations without other auxiliary information.

[0011] Step 2, real-time determination of the missing situation of Beidou observations based on the time of the cloud server.

[0012] Step 3, based on the short-term predictability of the Beidou satellite orbit, clock error, pseudorange phase observations, and receiver antenna coordinates, maintain the continuity of positioning in the case of missing Beidou observations.

[0013] Moreover, the implementation method of the landslide body state determination and monitoring frequency adaptive adjustment based on a sliding window and Gaussian fitting includes the following steps.

[0014] Step 1.1, set a time-domain sliding window, and record the window time span as w1.

[0015] If the time span of the currently obtained positioning results is less than w1, wait until enough positioning results are accumulated; if the time span of the currently obtained positioning results is greater than or equal to w1, based on the least squares method, use the Gaussian fitting method to fit the coordinate time series curves of the positioning results in the window in the north, east, and up directions.

[0016] Step 1.2, take the derivative of the fitted curve to obtain the change rates V of the coordinates in the north, east, and up directions. i ;

[0017] Step 1.3, respectively calculate the root mean square values of the coordinate change rates in the north, east, and up directions at each moment in the window.

[0018] Step 1.4, determine whether is greater than the corresponding preset threshold V Thresh ;

[0019] If not, it is determined that the landslide body is in a stable or slow deformation stage. At this time, set the sampling rate of the Beidou receiver observation value to the first value;

[0020] If so, it is determined that the landslide body is in a pre-sliding state. At this time, set the sampling rate of the Beidou receiver observation value to the second value;

[0021] The first value is greater than the second value;

[0022] Step 1.5, make the sliding window step forward by 1 epoch, and repeat the above steps.

[0023] Moreover, the real-time determination of the missing situation of Beidou observation values based on the time of the cloud server is realized in the following steps.

[0024] Step 2.1, the cloud server receives the Beidou observation values sent by the field Beidou receiver in real time through the Internet. Taking the time t of the cloud server server as a reference, subtract the time stamp t obs of the real-time received Beidou observation value from it to obtain the network delay L of the Beidou observation value (t) ;

[0025] Step 2.2, use a sliding window to intercept historical observation values, and statistically calculate the average value and standard deviation

[0026] Step 2.3, according to the current time of the cloud server, calculate the time stamp range [t (min) , t (max) of the Beidou observation value that should be received currently. The calculation method is as follows.

[0027]

[0028]

[0029] Among them, t (min) represents the minimum value of the Beidou observation value time stamp range, and t (max) represents the maximum value of the Beidou observation value time stamp range;

[0030] Step 2.4, determine whether the time stamp of the Beidou observation value currently received by the current cloud server is within the above range;

[0031] If so, it is determined that the observation value is not missing;

[0032] If not, it is determined that the observation value is missing, and step 3 is entered to process the epoch with missing observation values.

[0033] Moreover, to achieve continuous positioning when Beidou observation values are missing, the implementation method includes the following steps:

[0034] Step 3.1: Let the time stamp of the missing Beidou observation value be t + Δt, where t is the time stamp of the most recently received observation value, and Δt is the Beidou receiver observation value sampling rate set according to step 1; calculate the orbit Orb of the Beidou satellite at the missing epoch time using the broadcast ephemeris (t+Δt) and the clock error Clk (t+Δt) ;

[0035] Step 3.2: Use a sliding window to intercept historical pseudorange and phase observation values, and subtract the integer ambiguity from the phase observation values to obtain processed phase observation values;

[0036] Perform Gaussian fitting on the pseudorange and the processed phase observation values to obtain a fitting model;

[0037] Step 3.3: Predict the pseudorange observation value P (t+Δt) and the processed phase observation value (L0) (t+Δt) at time t + Δt through the obtained fitting model, and add the integer ambiguity back to the phase observation value to obtain the predicted phase observation value L (t+Δt) ;

[0038] Step 3.4: Use the monitoring results of the historical n epochs and their variance-covariance matrix to predict the state quantity X t+Δt|t-1 ,

[0039] If the historical observation values are continuous, predict the state quantity of the current epoch through the first three epochs;

[0040] Ignore the correlation between coordinates between epochs, and according to the error propagation law, obtain the variance-covariance matrix of the state quantity through the variance-covariance matrix of the state quantity estimates of the historical 3 epochs

[0041] Step 3.5: Output the results, including substituting the Beidou satellite orbit Orb (t+Δt) and the clock error Clk (t+Δt) , the predicted pseudorange observation value P (t+Δt) and the phase observation value L (t+Δt) , the predicted monitoring point coordinates X t+Δt|t-1 and the variance-covariance matrix into the Kalman filter together to calculate the state quantity estimate of the current epoch, so as to obtain the positioning result of the epoch with missing Beidou observation values.

[0042] The present invention provides a Beidou continuous positioning system applicable to landslide geological disaster monitoring, which is used to implement a Beidou continuous positioning method applicable to landslide geological disaster monitoring as described above.

[0043] Moreover, it includes the following modules

[0044] The first module is used for the determination of the landslide body state and the adaptive adjustment of the monitoring frequency based on a sliding window and Gaussian fitting, and is used to adaptively adjust the monitoring frequency according to the state of the landslide body in the case where there are only Beidou pseudorange-phase observations and no other auxiliary information.

[0045] The second module is used for the real-time determination of the missing situation of Beidou observations based on the time of the cloud server.

[0046] The third module is used to maintain the positioning continuity in the case of missing Beidou observations based on the short-term predictability of the Beidou satellite orbit, clock error, pseudorange-phase observations, and receiver antenna coordinates in the short term.

[0047] Alternatively, it includes a processor and a memory. The memory is used to store program instructions, and the processor is used to call the stored instructions in the memory to execute a Beidou continuous positioning method applicable to landslide geological disaster monitoring as described above.

[0048] Alternatively, it includes a readable storage medium, on which a computer program is stored. When the computer program is executed, a Beidou continuous positioning method applicable to landslide geological disaster monitoring as described above is implemented.

[0049] A Beidou continuous positioning solution applicable to landslide geological disaster monitoring proposed by the present invention has the following beneficial effects: ① The determination of the landslide body state and the adaptive adjustment of the monitoring frequency based on a sliding window and Gaussian fitting solve the problem that in the conventional Beidou landslide geological disaster monitoring method, in the case where there are only Beidou pseudorange / phase observations and no other auxiliary information, the monitoring frequency cannot be adaptively adjusted according to the state of the landslide body; ② The real-time determination of the missing situation of Beidou observations based on the time of the cloud server solves the problem that in the conventional Beidou landslide geology, the missing situation of observations cannot be determined in real time; ③ Maintaining the positioning continuity in the case of missing Beidou observations, based on the short-term predictability of the Beidou satellite orbit, clock error, pseudorange / phase observations, and receiver antenna coordinates in the short term, solves the problem of missing and discontinuous positioning results in the conventional Beidou relative positioning method in the case of missing Beidou observations, and improves the usability of Beidou in landslide geological disaster monitoring projects. The technical solution of the present invention is applicable to various fields using Beidou for real-time high-precision positioning, and is particularly applicable to the monitoring of field landslide geological disasters. Description of the Drawings

[0050] Figure 1Schematic diagram of the overall process of the embodiment of the present invention.

[0051] Figure 2 Schematic diagram of the landslide body state determination and monitoring frequency adaptive adjustment process based on a sliding window and Gaussian fitting in the embodiment of the present invention.

[0052] Figure 3 Schematic diagram of the real-time determination process of the missing situation of Beidou observation values based on the time of the cloud server in the embodiment of the present invention.

[0053] Figure 4 Schematic diagram of the positioning continuity maintenance process in the case of missing Beidou observation values in the embodiment of the present invention. Detailed implementation manners

[0054] For the convenience of those of ordinary skill in the art to understand and implement the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0055] The present invention is applicable to the field of geological disaster monitoring such as landslides, and solves the problems of discontinuous relative positioning results and re-convergence caused by the "interruption" of Beidou observation values at monitoring points (data interruption, few observed satellites, poor data quality, etc.).

[0056] As Figure 1 shown, a Beidou continuous positioning method applicable to geological disaster monitoring provided by an embodiment of the present invention includes the following steps:

[0057] Step 1, determination of the landslide body state and adaptive adjustment of the monitoring frequency based on a sliding window and Gaussian fitting.

[0058] Referring to Figure 2 , step 1 in the embodiment preferably includes the following sub-steps:

[0059] Step 1.1, set a time-domain sliding window with a window time span of w1. Specifically, the value of w1 can be set according to specific circumstances.

[0060] If the time span of the currently obtained positioning results is less than w1, wait until enough positioning results are accumulated. If the time span of the currently obtained positioning results is greater than or equal to w1, then based on the least squares method, use the following Gaussian fitting formula to fit the coordinate time series curves of the positioning results in the window in the north, east, and height directions:

[0061]

[0062] In the above formula, x i represents the coordinates in the north, east, and height directions, and the subscript i represents the coordinate direction. a i 、bi , c i is the polynomial coefficient, e is the natural constant, and t represents the epoch time.

[0063] Step 1.2: Differentiate the curve obtained by fitting to obtain the change rates V of the coordinates in the north, east, and up directions i , that is:

[0064]

[0065] Figure 2 In, V 北(t) , V 东(t) , V 高(t) specifically refers to the change rates in the north, east, and up directions at time t.

[0066] Step 1.3: Statistically calculate the root mean square value of the change rates of the north / east / up coordinates at each moment within the window

[0067]

[0068] In the above formula, n is the total number of epochs of the positioning results within the window, and k is the epoch label of the positioning results within the window; Figure 2 In, RMS V北 , RMS V东 , RMS V高 specifically refers to the root mean square values of the change rates in the north, east, and up directions.

[0069] Step 1.4: Determine whether is greater than the corresponding preset threshold V Thresh .

[0070] If not, it is determined that the landslide body is in a stable or slowly deforming stage. At this time, set the sampling rate of the Beidou receiver observations to the first value, and the embodiment is 15 s;

[0071] If so, it is determined that the landslide body is in a pre-sliding state. At this time, set the sampling rate of the Beidou receiver observations to the second value, and the embodiment is 1 s.

[0072] In specific implementation, the values can be set according to the specific situation, and the first value is greater than the second value.

[0073] Step 1.5: Move the sliding window forward by 1 epoch and repeat the above steps, that is, steps 1.1 - 1.4.

[0074] Step 2: Make a real-time determination based on the missing situation of Beidou observations based on the time of the cloud server.

[0075] See Figure 3 , the steps of step 2 in the embodiment preferably include the following sub-steps:

[0076] Step 2.1, the cloud server receives the Beidou observation values sent by the field Beidou receiver in real time through the Internet. Taking the cloud server time t server as a reference, subtract the time stamp t obs of the real-time received Beidou observation values from it to obtain the network delay L (t) of the Beidou observation values, that is:

[0077] L (t) = t server - t obs (4)

[0078] Step 2.2, use a sliding window with a time span of w2 to intercept historical observation values, and statistically calculate the average value and standard deviation of the observation value delays within the window. In specific implementation, the value of w2 can be set according to specific situations.

[0079] Step 2.3, according to the current time of the cloud server, calculate the time stamp range [t (min) , t (max) of the Beidou observation values that should be received currently. The calculation method is as follows:

[0080]

[0081] where t (min) represents the minimum value of the Beidou observation value time stamp range, and t (max) represents the maximum value of the Beidou observation value time stamp range.

[0082] Step 2.4, determine whether the time stamp of the Beidou observation values currently received by the current cloud server is within the above range. If so, it is determined that the observation values are not missing; if not, it is determined that the observation values are missing, and go to Step 3 to process the epochs with missing observation values.

[0083] Step 3, maintain the positioning continuity in the case of missing Beidou observation values.

[0084] See Figure 4 , the steps in the embodiment of Step 3 preferably include the following sub-steps:

[0085] Step 3.1, let the time stamp of the missing Beidou observation value be t + Δt, where t is the time stamp of the most recently received observation value, and Δt is the sampling rate of the Beidou receiver observation values set according to Step 1. Use the broadcast ephemeris to calculate the Beidou satellite orbit Orb (t+Δt) and clock offset Clk (t+Δt) at the moment of the missing epoch.

[0086] Step 3.2, use a sliding window with a time span of w3 to intercept historical pseudorange {P1, P2,..., P k} and phase observation values {L1, L2,..., Lk}, and subtract the integer ambiguity N from the phase observations therein to obtain the processed phase observations {(L0)1, (L0)2, ..., (L0) k}. That is:

[0087] (L0) k = L k - N (6)

[0088] Where k represents the number of epochs within the window, P represents the pseudorange observation, L represents the phase observation, N represents the integer ambiguity of the phase observation, and L0 represents the phase observation after subtracting the integer ambiguity.

[0089] Perform Gaussian fitting on the pseudorange and the processed phase observations to obtain a fitting model. Specifically, the value of w3 can be set according to the specific situation during implementation.

[0090] Step 3.3, predict the pseudorange observation P (t+Δt) and the processed phase observation (L0) (t+Δt) at time t + Δt through the obtained fitting model, and re-add the integer ambiguity to the phase observation to obtain the predicted phase observation L (t+Δt) . That is:

[0091] L (t+Δt) = (L0) (t+Δt) + N (7)

[0092] Where Δt is the sampling rate of the Beidou receiver observations set according to Step 1.

[0093] Step 3.4, use the monitoring results X t-n|t-n (n = 1, 2, 3...) of the historical n epochs and its variance-covariance matrix Use historical information to predict the state quantity X t+Δt|t-1 at the current moment:

[0094]

[0095] In the formula, Δt is the sampling rate of the Beidou receiver observations set according to Step 1, V t-1 is the velocity of the second nearest epoch, a t-1 is the acceleration of the nearest (before interruption) epoch, and both of these physical quantities are calculated through a constant acceleration motion model. Their calculation methods are respectively:

[0096]

[0097] Where V t-2 is the velocity of the second nearest epoch.

[0098] If the historical observation values are continuous, the acceleration a t-1 can be expressed as:

[0099]

[0100] where Δt is the sampling rate of the Beidou receiver observation values set according to step 1.

[0101] Furthermore, the state quantity X of the current epoch can be predicted through the first three epochs t+Δt|t-1 as:

[0102]

[0103] where X t-1 、X t-2 、X t-3 are the estimated values of the state quantities of the previous 3 epochs.

[0104] Ignoring the correlation between coordinates between epochs, according to the error propagation law, the variance-covariance matrix of the state quantity can be obtained, and its calculation formula is:

[0105]

[0106] where is the variance-covariance matrix of the estimated values of the state quantities of the previous 3 epochs.

[0107] Step 3.5, output the results. Substitute the predicted Beidou satellite orbit Orb (t+Δt) and clock error Clk (t+Δt) , the predicted pseudorange observation value P (t+Δt) and phase observation value L (t+Δt) , the predicted coordinates X of the monitoring point t+Δt|t-1 and the variance-covariance matrix into the Kalman filter together to calculate the estimated value of the state quantity of the current epoch, so as to obtain the positioning result of the missing epoch of the Beidou observation value.

[0108] Specifically, when implemented, the method proposed by the technical solution of the present invention can be automatically run by those skilled in the art using computer software technology. The system device for implementing the method, such as a computer-readable storage medium storing the corresponding computer program of the technical solution of the present invention and a computer device including running the corresponding computer program, should also be within the protection scope of the present invention.

[0109] In some possible embodiments, a Beidou continuous positioning system applicable to landslide geological disaster monitoring is provided, including the following modules,

[0110] The first module is used for determining the state of the landslide body and adaptively adjusting the monitoring frequency based on a sliding window and Gaussian fitting, and is used for adaptively adjusting the monitoring frequency according to the state of the landslide body in the case of only BeiDou pseudo-range phase observations without other auxiliary information.

[0111] The second module is used for real-time determination of the missing situation of BeiDou observations based on the time of the cloud server.

[0112] The third module is used for maintaining the positioning continuity in the case of missing BeiDou observations based on the short-term predictability of the BeiDou satellite orbit, clock error, pseudo-range phase observations, and receiver antenna coordinates in the short term.

[0113] In some possible embodiments, a BeiDou continuous positioning system applicable to landslide geological disaster monitoring is provided, including a processor and a memory. The memory is used for storing program instructions, and the processor is used for calling the stored instructions in the memory to execute a BeiDou continuous positioning method applicable to landslide geological disaster monitoring as described above.

[0114] In some possible embodiments, a BeiDou continuous positioning system applicable to landslide geological disaster monitoring is provided, including a readable storage medium. A computer program is stored on the readable storage medium, and when the computer program is executed, a BeiDou continuous positioning method applicable to landslide geological disaster monitoring as described above is implemented.

[0115] It should be understood that the above description of the preferred embodiments is relatively detailed, and thus it should not be considered as a limitation on the protection scope of the present invention. Under the inspiration of the present invention, those of ordinary skill in the art can also make substitutions or deformations without departing from the protection scope defined by the claims of the present invention, and all fall within the protection scope of the present invention. The scope of the present invention claimed should be subject to the appended claims.

Claims

1. A Beidou continuous positioning method applicable to landslide geological disaster monitoring, characterized in that: including the following steps, Step 1, landslide state determination and monitoring frequency adaptive adjustment based on sliding window and Gaussian fitting, which is used to adaptively adjust the monitoring frequency according to the state of the landslide when there are only Beidou pseudorange-phase observations and no other auxiliary information; Step 2, real-time determination of the missing situation of Beidou observations based on the time of the cloud server; Step 3, based on the short-term predictability of Beidou satellite orbits, clock errors, pseudorange-phase observations, and receiver antenna coordinates, realize the positioning continuity maintenance in the case of missing Beidou observations; The landslide state determination and monitoring frequency adaptive adjustment based on sliding window and Gaussian fitting are implemented as follows: Step 1.1, set a time-domain sliding window, and record the window time span as w1; If the time span of the currently obtained positioning results is less than w1, wait until enough positioning results are accumulated; if the time span of the currently obtained positioning results is greater than or equal to w1, then based on the least squares method, use the Gaussian fitting method to fit the coordinate time series curves of the positioning results in the window in the north, east, and up directions; Step 1.2, take the derivative of the fitted curve to obtain the change rates V of the coordinates in the north, east, and up directions i ; Step 1.3, calculate the root mean square (RMS) value of the change rates of the north, east, and altitude coordinates at each moment within the window Vi ; Step 1.4, determine RMS Vi is greater than the corresponding preset threshold value V Thresh ; If not, it is determined that the landslide is in a stable or slowly deforming stage, and at this time, set the sampling rate of Beidou receiver observations to the first value; If so, it is determined that the landslide is in the pre-sliding state, and at this time, set the sampling rate of Beidou receiver observations to the second value; The first value is greater than the second value; Step 1.5, make the sliding window step forward by 1 epoch, and repeat the above steps.

2. The Beidou continuous positioning method applicable to landslide geological disaster monitoring according to claim 1, wherein: The real-time determination of the missing situation of Beidou observations based on the time of the cloud server is implemented as follows: Step 2.1, the cloud server receives the Beidou observation values sent by the field Beidou receiver in real time through the Internet. Taking the cloud server time t server as a reference, the time stamp t obs of the real-time received Beidou observation values is subtracted from it to obtain the network delay L( t ); Step 2.2, use a sliding window to intercept historical observations and calculate the average value of the delays of the observations within the window and the standard deviation Step 2.3, calculate the time scale range [t (min) , t (max) of the Beidou observation values that should be received currently according to the current time of the cloud server. The calculation method is as follows: where t (min) represents the minimum value of the time scale range of Beidou observations, and t (max) represents the maximum value of the time scale range of Beidou observations; Step 2.4, judge whether the time stamps of the Beidou observations currently received by the current cloud server are within the above range; If so, it is determined that the observations are not missing; If not, it is determined that the observations are missing, and go to Step 3 to process the missing epochs of the observations.

3. The Beidou continuous positioning method applicable to landslide geological disaster monitoring according to claim 1 or 2, characterized in that: The realization of positioning continuity maintenance in the case of missing Beidou observations is implemented as follows: Step 3.1, let the time stamp of the missing Beidou observation value be \(t + \Delta t\), where \(t\) is the time stamp of the most recently received observation value, and \(\Delta t\) is the Beidou receiver observation value sampling rate set according to Step 1; calculate the orbits Orb (t+Δt) and clock offsets Clk (t+Δt) ; Step 3.2, use a sliding window to intercept historical pseudorange and phase observations, and subtract the integer ambiguity from the phase observations to obtain the processed phase observations; Perform Gaussian fitting on the pseudorange and the processed phase observations to obtain a fitting model; Step 3.3, predict the pseudorange observation value P at time t + Δt through the obtained fitting model (t+Δt) and the processed phase observation value (L0) (t+Δt) , and re-add the integer ambiguity to the phase observation value to obtain the predicted phase observation value L (t+Δt) ; Step 3.4, using the monitoring results and their variance-covariance matrices of the historical n epochs, predict the state quantity X at the current moment using historical information t+Δt|t-1 , If the historical observations are continuous, predict the current epoch state quantity through the first three epochs; Ignoring the correlation between coordinates between epochs, according to the error propagation law, the variance-covariance matrix of the state quantity is obtained through the variance-covariance matrix of the state quantity estimates of the historical three epochs. Step 3.5, output the results, including substituting the Beidou satellite orbit Orb (t+Δt) and clock offset Clk (t+Δt) , the predicted pseudorange observation value P (t+Δt) and phase observation value L (t+Δt) , the predicted coordinates X of the monitoring point t+Δt|t-1 and the variance-covariance matrix into the Kalman filter together to calculate the estimated state quantity at the current epoch, so as to obtain the positioning result of the epoch with missing Beidou observations.

4. A Beidou continuous positioning system applicable to landslide geological disaster monitoring, characterized in that: It is used to implement a Beidou continuous positioning method applicable to landslide geological disaster monitoring as described in any one of claims 1-3.

5. The Beidou continuous positioning system applicable to landslide geological disaster monitoring according to claim 4, characterized in that: including the following modules, The first module is used for landslide state determination and monitoring frequency adaptive adjustment based on sliding window and Gaussian fitting, which is used to adaptively adjust the monitoring frequency according to the state of the landslide when there are only Beidou pseudorange-phase observations and no other auxiliary information; The second module is used for real-time determination of the missing situation of Beidou observations based on the time of the cloud server; The third module is used for realizing the positioning continuity maintenance in the case of missing Beidou observations based on the short-term predictability of Beidou satellite orbits, clock errors, pseudorange-phase observations, and receiver antenna coordinates in the short term.

6. The Beidou continuous positioning system applicable to landslide geological disaster monitoring according to claim 4, characterized in that: It includes a processor and a memory. The memory is used to store program instructions, and the processor is used to call the stored instructions in the memory to execute a Beidou continuous positioning method applicable to landslide geological disaster monitoring as described in any one of claims 1-3.

7. The Beidou continuous positioning system applicable to landslide geological disaster monitoring according to claim 4, wherein: It includes a readable storage medium, on which a computer program is stored. When the computer program is executed, it implements a Beidou continuous positioning method applicable to landslide geological disaster monitoring as described in any one of claims 1-3.

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