GNSS / MEMS (Global Navigation Satellite System / Micro Electro Mechanical System) combined collapse geological disaster monitoring and early warning method and system

Abstract

The invention provides a collapse geological disaster monitoring and early warning method and system combined with GNSS / MEMS, and belongs to the technical field of geological disaster early warning, and the method comprises the steps: S1, deploying a GNSS receiver in a collapse geological region to be monitored, and obtaining the geological disaster data of a monitoring point through the GNSS receiver; s2, performing robust processing, ambiguity fixing processing and differential calculation on the GNSS data to obtain GNSS displacement information, and determining a GNSS speed and a GNSS acceleration according to the GNSS displacement information and the sampling time interval; determining MEMS acceleration based on the MEMS data; and S3, judging whether the GNSS displacement information, the GNSS speed, the GNSS acceleration and the MEMS acceleration exceed corresponding early warning thresholds or not, and performing geological collapse early warning according to a judgment result, so that a complex geological environment can be effectively dealt with, and the accuracy of geological collapse prediction in the complex environment is improved.

Description

Technical Field

[0001] The present invention belongs to the field of geological disaster early warning technology, and relates to a technology for geological disaster early warning combined with GNSS / MEMS, and specifically to a collapse geological disaster monitoring and early warning method and system combined with GNSS / MEMS. Background Art

[0002] GNSS global satellite navigation system is an air-based radio navigation and positioning system that can provide users with all-weather 3D coordinates, speed and time information at any location on the earth's surface or near-earth space. It is widely used in military, resource environment, disaster prevention and mitigation, surveying and mapping, power telecommunications, urban management, engineering construction, mechanical control, transportation, forestry, fishery and animal husbandry, archaeology, life, Internet of Things and location services.

[0003] When the GNSS global satellite navigation system is used for monitoring collapse geological disasters, the positioning results of each epoch are obtained through GNSS real-time positioning, and surface deformation monitoring is carried out to achieve the purpose of early warning of collapse geological disasters. For example, the patent application document with application number CN202211169517.4 discloses a geological disaster monitoring and early warning method based on deformation monitoring and deep learning, which uses GNSS data to assist high-resolution satellite images to monitor surface deformation in the monitoring area, effectively saving labor costs while ensuring monitoring accuracy and real-time performance. However, in actual applications, collapses often occur in complex environments, and pseudo-range observations are prone to large errors, which reduces the accuracy of GNSS solution results and leads to frequent false warnings during collapse monitoring. Summary of the invention

[0004] As described in the above background technology, collapses often occur in complex environments, and pseudo-range observations are prone to large errors, resulting in a technical problem of low accuracy of GNSS solution results. To address this technical problem, the present invention proposes a combined GNSS / MEMS collapse geological disaster monitoring and early warning method and system.

[0005] The present invention performs anti-error processing, ambiguity fixing processing and differential analysis on GNSS data respectively, thereby improving the accuracy of GNSS data solution and reducing the error of pseudo-range observation values ​​(GNSS data). At the same time, GNSS data and MEMS data are combined to jointly perform geological early warning judgment, thereby improving the accuracy of geological collapse prediction in complex environments.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] The present invention combines GNSS / MEMS to monitor and warn of geological collapse disasters, including:

[0008] S1: deploying a GNSS receiver in a geological collapse area to be monitored, setting up monitoring points, and obtaining geological disaster data of the monitoring points through the GNSS receiver, wherein the geological disaster data includes GNSS data and MEMS data;

[0009] S2: Perform robust processing, ambiguity fixation processing and differential analysis on the GNSS data to obtain GNSS displacement information, and determine the GNSS velocity and GNSS acceleration based on the GNSS displacement information and the sampling time interval;

[0010] Determine MEMS acceleration based on MEMS data;

[0011] S3: Determine whether the GNSS displacement information, GNSS velocity, GNSS acceleration and MEMS acceleration exceed corresponding warning thresholds;

[0012] If the GNSS displacement information, GNSS velocity, GNSS acceleration and MEMS acceleration all exceed the corresponding warning thresholds, a collapse warning is issued.

[0013] It is further defined that in step S2, the anti-error processing specifically includes:

[0014] The GNSS data is processed by pseudo-range single point positioning using the robust least squares method to obtain the initial coordinates of the mobile station;

[0015] The robust Kalman filter algorithm is used to denoise the initial coordinates of the mobile station and obtain the floating-point solution after robust Kalman filtering.

[0016] It is further defined that in step S2, the ambiguity fixing process specifically includes: performing ambiguity fixing process on the floating point solution after the difference Kalman algorithm to obtain an ambiguity fixed value.

[0017] It is further defined that the ambiguity fixing process is a full ambiguity fixing solution process and / or a partial ambiguity fixing solution process.

[0018] It is further defined that in step S2, the differential analysis specifically includes: performing differential analysis on the ambiguity fixed value using a differential method.

[0019] It is further defined that the GNSS data includes GPS data and BDS data.

[0020] It is further defined that the MEMS data is acquired based on a MEMS acceleration sensor integrated in a GNSS receiver.

[0021] The present invention provides a GNSS / MEMS-based collapse geological disaster monitoring and early warning system, which is applied to the above-mentioned GNSS / MEMS-based collapse geological disaster monitoring and early warning method, and includes:

[0022] GNSS receiver: deployed in the geological collapse area to be monitored, used to obtain geological disaster data at the monitoring point, wherein the geological disaster data includes GNSS data and MEMS data;

[0023] Data processing module: used to perform anti-error processing, ambiguity fixation processing and differential analysis on GNSS data to obtain GNSS displacement information, determine GNSS velocity and GNSS acceleration based on GNSS displacement information and sampling time interval; determine MEMS acceleration based on MEMS data;

[0024] And the early warning judgment module: used to judge whether the GNSS displacement information, GNSS speed, GNSS acceleration and MEMS acceleration exceed the corresponding early warning threshold; if the GNSS displacement information, GNSS speed, GNSS acceleration and MEMS acceleration all exceed the corresponding early warning threshold, a collapse warning is issued.

[0025] A device of the present invention includes a processor and a memory coupled to each other;

[0026] The memory is used to store program instructions formed by the above-mentioned combined GNSS / MEMS collapse geological disaster monitoring and early warning method;

[0027] The processor is used to execute program instructions stored in the memory.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. The present invention combines GNSS / MEMS to monitor and warn of geological collapse disasters. It can simultaneously acquire GNSS data and MEMS data based on a GNSS receiver, and perform anti-error processing, ambiguity fixing processing and differential analysis on the GNSS data, thereby improving the accuracy of GNSS data solution and reducing the error of pseudo-range observation values ​​(GNSS data). At the same time, the present invention combines GNSS data and MEMS data to jointly perform geological early warning judgments, which can effectively cope with complex geological environments and improve the accuracy of geological collapse predictions in complex environments.

[0030] 2. The present invention combines multiple parameters such as GNSS displacement information, GNSS velocity, GNSS acceleration and MEMS acceleration to jointly warn of geological collapse, which can effectively improve the reliability of geological collapse warning information. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic diagram of the collapse geological disaster monitoring and early warning method combined with GNSS / MEMS of the present invention;

[0032] Figure 2It is a schematic diagram of the collapse geological disaster monitoring and early warning system combined with GNSS / MEMS of the present invention. DETAILED DESCRIPTION

[0033] The technical solution of the present invention is further explained below in conjunction with the accompanying drawings and embodiments, but the present invention is not limited to the implementation modes described below.

[0034] See also Figure 1 The present invention combines GNSS / MEMS to monitor and warn of geological collapse disasters, including:

[0035] S1: Deploy GNSS receivers in the geological collapse area to be monitored, set up monitoring points, and obtain geological disaster data of the monitoring points through the GNSS receivers, wherein the geological disaster data includes GNSS data and MEMS data; specifically, the GNSS data includes GPS data and BDS data; a MEMS acceleration sensor is integrated in the GNSS receiver, and the MEMS data is obtained based on the MEMS acceleration sensor.

[0036] S2: Perform robust processing, ambiguity fixation processing and differential analysis on the GNSS data to obtain GNSS displacement information, determine the GNSS velocity and GNSS acceleration based on the GNSS displacement information and the sampling time interval; determine the MEMS acceleration based on the MEMS data;

[0037] S3: Determine whether the GNSS displacement information, GNSS velocity, GNSS acceleration and MEMS acceleration exceed the corresponding warning thresholds; if the GNSS displacement information, GNSS velocity, GNSS acceleration and MEMS acceleration all exceed the corresponding warning thresholds, a collapse warning is issued; if at least one of the GNSS displacement information, GNSS velocity, GNSS acceleration and MEMS acceleration does not exceed the corresponding warning threshold, no processing is performed, that is, no collapse warning is issued.

[0038] Before the monitoring process, the warning threshold is set by the user according to actual needs. Specifically, the warning threshold includes the GNSS displacement information warning threshold, the GNSS velocity warning threshold, the GNSS acceleration warning threshold and the MEMS acceleration warning threshold; the GNSS displacement information warning threshold is used as the standard value and compared with the GNSS displacement information; the GNSS velocity warning threshold is used as the standard value and compared with the GNSS velocity; the GNSS acceleration warning threshold is used as the standard value and compared with the GNSS acceleration; the MEMS acceleration warning threshold is used as the standard value and compared with the MEMS acceleration.

[0039] In the present invention, in step S2, the robust processing specifically includes: using the IGGIII model to perform robust processing on the least squares method and the Kalman filter algorithm to obtain the robust least squares method and the robust Kalman filter algorithm; using the robust least squares method to perform pseudo-range single-point positioning processing on the GNSS data to obtain the initial coordinates of the mobile station; using the robust Kalman filter algorithm to perform denoising processing on the initial coordinates of the mobile station to obtain a floating-point solution after robust Kalman.

[0040] The IGGIII model is:

[0041]

[0042] In the formula, ω(v i ) is the IGGIII weight factor, dimensionless; v i is the standardized observation value, dimensionless; k0 and k1 are constants, dimensionless.

[0043] The Kalman filter algorithm is:

[0044]

[0045] P k|k =(IK k H k ) k|k-1

[0046] In the formula, k represents the time value; represents the state prediction value at time k; represents the estimated value of the state at time k-1; F k represents the state transfer matrix; B k represents the control input matrix; u k Represents control input; P k|k-1 is the predicted value of the state covariance at time k; P k-1|k-1 is the predicted value of the state covariance at time k-1; Q k is the covariance matrix of process noise; K k is the Kalman gain; H k is the observation matrix; R k is the covariance matrix of the observation noise; z k is the observed value at time k; T is the transposed symbol; is the estimated value of the state vector at time k; P k|k is the state covariance matrix at time k; I is the identity matrix.

[0047] In the present invention, in step S2, the ambiguity fixing process specifically includes: performing ambiguity fixing process on the floating point solution after the differential Kalman algorithm to obtain an ambiguity fixed value.

[0048] The ambiguity fixing process is a full ambiguity fixing solution process and / or a partial ambiguity fixing solution process. Preferably, in the present invention, the full ambiguity fixing solution process is first performed, and the MLAMBDA algorithm is used to achieve ambiguity reduction correlation performance and search for ambiguity. If the fixation fails, the partial ambiguity fixing solution process is then performed.

[0049] The process of partial ambiguity fixed solution is as follows: add an identifier to each frequency of a single satellite and select an ambiguity subset; based on the satellite altitude angle weighting model, give priority to deleting the ambiguity information of low altitude angle satellite frequencies; re-enter the ambiguity fixed solution and calculate the Ratio value. If the Ratio is greater than the threshold, enter the next epoch; otherwise, remove the ambiguity of the satellite with the smallest weight and re-enter the loop until the number of satellites is less than 5, and output a floating-point solution; if the ambiguity is fixed successfully and the solution of this epoch is completed, enter the ambiguity fixed solution of the next epoch and complete the partial ambiguity fixed solution.

[0050] In the present invention, in step S2, the differential analysis specifically includes: performing differential analysis on the ambiguity fixed value using a differential method.

[0051] The present invention combines GNSS data and MEMS data to jointly make geological early warning judgments, and uses multiple parameters such as GNSS displacement information, GNSS velocity, GNSS acceleration, and MEMS acceleration to jointly warn of geological collapse, which can effectively cope with complex geological environments and improve the accuracy of geological collapse prediction in complex environments.

[0052] See also Figure 2 The present invention combines the GNSS / MEMS collapse geological disaster monitoring and early warning system, which is applied to the above-mentioned combined GNSS / MEMS collapse geological disaster monitoring and early warning method, and includes:

[0053] GNSS receiver: deployed in the geological collapse area to be monitored to obtain geological disaster data at the monitoring point, where the geological disaster data includes GNSS data and MEMS data;

[0054] Data processing module: used to perform anti-error processing, ambiguity fixation processing and differential analysis on GNSS data to obtain GNSS displacement information, determine GNSS velocity and GNSS acceleration based on GNSS displacement information and sampling time interval; determine MEMS acceleration based on MEMS data;

[0055] And the early warning judgment module: used to judge whether the GNSS displacement information, GNSS speed, GNSS acceleration and MEMS acceleration exceed the corresponding early warning threshold; if the GNSS displacement information, GNSS speed, GNSS acceleration and MEMS acceleration all exceed the corresponding early warning threshold, a collapse warning is issued.

[0056] The combined GNSS / MEMS collapse geological disaster monitoring and early warning system of the present invention corresponds to the combined GNSS / MEMS collapse geological disaster monitoring and early warning method of the present invention. Specifically, the contents of the GNSS receiver, data processing module and early warning judgment module refer to the description of the above-mentioned combined GNSS / MEMS collapse geological disaster monitoring and early warning method.

[0057] A device of the present invention includes a processor and a memory coupled to each other;

[0058] Memory: used to store program instructions formed by the above-mentioned combined GNSS / MEMS collapse geological disaster monitoring and early warning method; Processor: used to execute the program instructions stored in the memory.

[0059] The electronic device in this embodiment includes any electronic device that can execute program instructions, such as a computer, a mobile terminal or a wearable device.

[0060] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some or all of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the present application.

Claims

1. A GNSS / MEMS-based collapse geological disaster monitoring and early warning method, characterized in that: include: S1: deploying a GNSS receiver in a geological collapse area to be monitored, setting up monitoring points, and obtaining geological disaster data of the monitoring points through the GNSS receiver, wherein the geological disaster data includes GNSS data and MEMS data; S2: Perform robust processing, ambiguity fixation processing and differential analysis on the GNSS data to obtain GNSS displacement information, and determine the GNSS velocity and GNSS acceleration based on the GNSS displacement information and the sampling time interval; Determine MEMS acceleration based on MEMS data; S3: Determine whether the GNSS displacement information, GNSS velocity, GNSS acceleration and MEMS acceleration exceed corresponding warning thresholds; If the GNSS displacement information, GNSS velocity, GNSS acceleration and MEMS acceleration all exceed the corresponding warning thresholds, a collapse warning is issued.

2. The combined GNSS / MEMS collapse geological disaster monitoring and early warning method according to claim 1 is characterized in that: In step S2, the robustness processing specifically includes: The GNSS data is processed by pseudo-range single point positioning using the robust least squares method to obtain the initial coordinates of the mobile station; The robust Kalman filter algorithm is used to denoise the initial coordinates of the mobile station and obtain the floating-point solution after robust Kalman filtering.

3. The combined GNSS / MEMS collapse geological disaster monitoring and early warning method according to claim 2 is characterized in that: In step S2, the ambiguity fixing process specifically includes: The floating point solution after the adversarial Kalman filter is subjected to ambiguity fixation processing to obtain an ambiguity fixed value.

4. The combined GNSS / MEMS collapse geological disaster monitoring and early warning method according to claim 3 is characterized in that: The ambiguity fixing process is a full ambiguity fixing solution process and / or a partial ambiguity fixing solution process.

5. The combined GNSS / MEMS collapse geological disaster monitoring and early warning method according to claim 3 or 4 is characterized in that: In step S2, the differential decomposition specifically includes: The ambiguity fixed value is differentially resolved using the differential method.

6. The combined GNSS / MEMS collapse geological disaster monitoring and early warning method according to claim 1 is characterized in that: The GNSS data includes GPS data and BDS data.

7. The combined GNSS / MEMS collapse geological disaster monitoring and early warning method according to claim 1 is characterized in that: The MEMS data is acquired based on a MEMS acceleration sensor integrated in a GNSS receiver.

8. A combined GNSS / MEMS collapse geological disaster monitoring and early warning system, applied to the combined GNSS / MEMS collapse geological disaster monitoring and early warning method according to claim 1, characterized in that: include: GNSS receiver: deployed in the geological collapse area to be monitored, used to obtain geological disaster data at the monitoring point, wherein the geological disaster data includes GNSS data and MEMS data; Data processing module: used to perform anti-error processing, ambiguity fixation processing and differential analysis on GNSS data to obtain GNSS displacement information, determine GNSS velocity and GNSS acceleration based on GNSS displacement information and sampling time interval; determine MEMS acceleration based on MEMS data; And the early warning judgment module: used to judge whether the GNSS displacement information, GNSS speed, GNSS acceleration and MEMS acceleration exceed the corresponding early warning threshold; if the GNSS displacement information, GNSS speed, GNSS acceleration and MEMS acceleration all exceed the corresponding early warning threshold, a collapse warning is issued.

9. A device, characterized in that: comprising a processor and a memory coupled to each other; The memory is used to store program instructions formed by the combined GNSS / MEMS collapse geological disaster monitoring and early warning method according to any one of claims 1 to 7; The processor is used to execute program instructions stored in the memory.

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