Displacement determination method and device, electronic equipment, storage medium and program product

Abstract

The invention provides a displacement determination method and device, electronic equipment, a storage medium and a program product, relates to the technical field of communication, and is used for improving the accuracy of displacement determination. The method comprises the following steps: acquiring GNSS observation data acquired by a base station and at least two monitoring stations; based on the GNSS observation data, real-time dynamic difference RTK calculation is carried out to obtain a plurality of RTK baseline vectors, and the plurality of RTK baseline vectors comprise at least two first RTK baseline vectors and at least one second RTK baseline vector; performing anomaly detection processing on the at least two first RTK baseline vectors, and determining at least one first RTK baseline vector without anomaly as a target RTK baseline vector; determining a PPP displacement estimated value of the base station based on the GNSS observation data; and based on the plurality of RTK baseline vectors and the PPP displacement estimation value, displacement compensation processing is carried out on the target RTK baseline vector to obtain the absolute displacement of the monitoring station after reference station displacement compensation, and the absolute displacement is used for state monitoring. The method is applied to a displacement monitoring scene.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, and particularly relates to a displacement determination method and device, electronic equipment, storage medium and program product. BACKGROUND

[0002] In the field of Beidou displacement monitoring, static monitoring usually adopts multiple receivers to arrange reference stations and monitoring stations, and obtains the displacement value of the monitoring station relative to the reference station through relative positioning solution, so as to realize the state monitoring of the ground surface on which the monitoring station is located (such as whether the ground surface is deformed). However, in actual application, the reference station may be displaced due to geological activities, human factors and the like, resulting in deviation of the monitored displacement value.

[0003] In the related art, in order to solve the above problem, multiple sensors (such as accelerometers and gyroscopes) are arranged around the reference station to detect the displacement of the reference station through the sensors, so as to assist in judging whether the reference station is abnormally displaced. However, the accuracy of the sensors is limited, so the displacement of the reference station cannot be effectively distinguished and quantified, resulting in poor accuracy of displacement detection. SUMMARY

[0004] The present application provides a displacement determination method, device, electronic equipment, storage medium and program product, which are used to improve the accuracy of displacement detection.

[0005] In a first aspect, the present application provides a displacement determination method, comprising: acquiring global navigation satellite system (GNSS) observation data collected by a reference station and at least two monitoring stations; performing real-time kinematic (RTK) solution based on the GNSS observation data to obtain a plurality of RTK baseline vectors, wherein the plurality of RTK baseline vectors include at least two first RTK baseline vectors and at least one second RTK baseline vector, each first RTK baseline vector representing the relative displacement of one monitoring station relative to the reference station, and each second RTK baseline vector representing the relative displacement between two monitoring stations; performing abnormality detection processing on the at least two first RTK baseline vectors, and determining at least one first RTK baseline vector without abnormality as a target RTK baseline vector; determining a precise point positioning (PPP) displacement estimation value of the reference station based on the GNSS observation data; and performing displacement compensation processing on the target RTK baseline vector based on the plurality of RTK baseline vectors and the PPP displacement estimation value to obtain the absolute displacement of the monitoring station after displacement compensation of the reference station, wherein the absolute displacement is used for state monitoring.

[0006] The technical solution provided in this application offers at least the following advantages: By acquiring GNSS observation data from a base station and at least two monitoring stations, multiple types of RTK baseline vectors, including those between monitoring stations and base stations, and between monitoring stations, are calculated. Anomaly detection processing is performed on these RTK baseline vectors to identify target RTK baseline vectors without anomalies. Based on these multiple RTK baseline vectors and the PPP displacement estimate determined from the GNSS data, displacement compensation processing is applied to the target RTK baseline vector. Through this solution, by constructing baseline vectors that include inter-station baselines, the displacement of the base station and the actual deformation of the monitoring stations can be effectively identified and separated, solving the inherent problem of unobservable base station displacement in traditional RTK monitoring. Secondly, by fusing high-precision RTK relative observations with PPP observations possessing absolute reference characteristics, the absolute position constraints of PPP are introduced while retaining the high precision of RTK. This allows for accurate calculation of the absolute displacement of all stations, including the base station, ensuring accurate capture of actual deformation and thus improving the reliability and accuracy of displacement detection.

[0007] One possible implementation, the process of performing displacement compensation processing on the target RTK baseline vector based on the plurality of RTK baseline vectors and the PPP displacement estimate, may include: constructing at least two state vectors based on the station status information of the base station and the at least two monitoring stations, each state vector being used to estimate the absolute displacement of the base station and one monitoring station at the current time; recursively estimating the state vectors using a filtering model based on the plurality of RTK baseline vectors and the PPP displacement estimate of the base station, jointly calculating the absolute displacement estimate of the base station and each monitoring station; and performing displacement compensation on the target RTK baseline vector based on the absolute displacement estimate.

[0008] Another possible implementation method is that the above anomaly detection processing includes at least one of the following: cycle slip detection, multipath error detection, solution state screening, and threshold check based on signal-to-noise ratio (SNR) and position precision factor (PDOP).

[0009] Another possible implementation, the process of determining the precise point positioning (PPP) displacement estimate of the reference station based on the GNSS observation data, includes: performing precise point positioning (PPP) calculation based on the GNSS observation data and ephemeris information of the reference station, and acquiring the convergence status and residual statistics of the PPP calculation in real time; determining the covariance information of the PPP displacement estimate based on the convergence status and residual statistics; obtaining the PPP displacement estimate of the reference station based on the covariance information; wherein, when the PPP calculation is determined to be non-converged or the residual statistics exceed a preset threshold, the determined covariance information is increased.

[0010] Another possible implementation, after recursively estimating the state vector using the filtering model, further includes: determining motion source information based on the observation residuals generated during the recursive estimation process; triggering a displacement anomaly warning based on the motion source information; wherein, if the observation residuals exhibit common-mode characteristics on any first RTK baseline vector and any second RTK baseline vector, the motion source is determined to be the base station displacement; if the observation residuals exceed the corresponding noise level threshold on any second RTK baseline vector with a single monitoring station as the endpoint, the motion source is determined to be the monitoring station displacement.

[0011] Another possible implementation is that the filtering model described above is a state-space filtering model, which includes an observation noise covariance matrix. The recursive estimation of the state vector based on the multiple RTK baseline vectors and the PPP displacement estimate of the base station through the filtering model includes: recursively estimating the state vector based on the multiple RTK baseline vectors, the PPP displacement estimate of the base station, and the observation noise covariance matrix to obtain the corresponding solution residual statistics; and updating the parameters of the noise covariance matrix based on the solution residual statistics.

[0012] Another possible implementation, after performing displacement compensation processing on the target RTK baseline vector to obtain the absolute displacement of the monitoring station after displacement compensation at the base station, further includes: generating and outputting comprehensive monitoring results based on the absolute displacement of the monitoring station after displacement compensation at the base station; wherein, the comprehensive monitoring results include: the absolute displacement values ​​of each monitoring station after displacement compensation, the estimated absolute displacement value of the base station, motion source information, and the displacement trends of the base station and / or the at least two monitoring stations.

[0013] Secondly, this application provides a displacement determination device, comprising: an acquisition module and a processing module, wherein: the acquisition module is used to acquire GNSS observation data collected by a base station and at least two monitoring stations; the processing module is used to perform real-time dynamic differential RTK calculation based on the GNSS observation data to obtain multiple RTK baseline vectors, wherein the multiple RTK baseline vectors include: at least two first RTK baseline vectors and at least one second RTK baseline vector, each first RTK baseline vector representing the relative displacement of a monitoring station relative to the base station, and each second RTK baseline vector representing the relative displacement between two monitoring stations; the processing module is further used to perform anomaly detection processing on the at least two first RTK baseline vectors, and determine at least one first RTK baseline vector without anomalies as the target RTK baseline vector; the processing module is further used to determine the precise point positioning (PPP) displacement estimate of the base station based on the GNSS observation data; the processing module is further used to perform displacement compensation processing on the target RTK baseline vector based on the multiple RTK baseline vectors and the PPP displacement estimate, to obtain the absolute displacement of the monitoring station after displacement compensation by the base station, the absolute displacement being used for status monitoring.

[0014] One possible implementation is that the aforementioned processing module is specifically used to: construct at least two state vectors based on the site status information of the base station and at least two monitoring stations, each state vector being used to estimate the absolute displacement of the base station and one monitoring station at the current time; based on multiple RTK baseline vectors and the PPP displacement estimate of the base station, recursively estimate the state vectors through a filtering model, and jointly calculate the absolute displacement estimate of the base station and each monitoring station; and perform displacement compensation on the target RTK baseline vector based on the absolute displacement estimate.

[0015] Another possible implementation method is that the above anomaly detection processing includes at least one of the following: cycle slip detection, multipath error detection, solution state screening, and threshold check based on signal-to-noise ratio (SNR) and position precision factor (PDOP).

[0016] Another possible implementation is that the aforementioned processing module is specifically used to perform precise single-point positioning (PPP) calculation based on GNSS observation data and ephemeris information from the reference station, and to acquire the convergence status and residual statistics of the PPP calculation in real time; based on the convergence status and residual statistics, to determine the covariance information of the PPP displacement estimate; based on the covariance information, to obtain the PPP displacement estimate of the reference station; wherein, when the PPP calculation is determined to be non-converged or the residual statistics exceed a preset threshold, the determined covariance information is increased.

[0017] In another possible implementation, the aforementioned processing module is further configured to determine the motion source information based on the observation residuals generated during the recursive estimation process after recursively estimating the state vector using a filtering model; and to trigger a displacement anomaly warning based on the motion source information. Specifically, if the observation residuals exhibit common-mode characteristics on any first RTK baseline vector and any second RTK baseline vector, the motion source is determined to be the displacement of the base station; if the observation residuals exceed the corresponding noise level threshold on any second RTK baseline vector with a single monitoring station as the endpoint, the motion source is determined to be the displacement of the monitoring station.

[0018] Another possible implementation is that the above filtering model is a state-space filtering model, which includes the observation noise covariance matrix. The above processing module is specifically used to: recursively estimate the state vector based on multiple RTK baseline vectors, the PPP displacement estimate of the base station, and the observation noise covariance matrix to obtain the corresponding solution residual statistics; and update the parameters of the noise covariance matrix based on the solution residual statistics.

[0019] In another possible implementation, the aforementioned processing module is further configured to, after performing displacement compensation processing on the target RTK baseline vector to obtain the absolute displacement of the monitoring station after displacement compensation by the base station, generate and output comprehensive monitoring results based on the absolute displacement of the monitoring station after displacement compensation by the base station; wherein, the comprehensive monitoring results include: the absolute displacement values ​​of each monitoring station after displacement compensation, the estimated absolute displacement value of the base station, motion source information, and the displacement trends of the base station and / or at least two monitoring stations.

[0020] Thirdly, this application provides an electronic device comprising: a processor and a memory; the memory storing processor-executable instructions; when the processor is configured to execute the instructions, causing the electronic device to implement the method of the first aspect described above.

[0021] Fourthly, this application provides a computer-readable storage medium comprising: computer software instructions; which, when executed in an electronic device, cause the electronic device to implement the method described in the first aspect.

[0022] Fifthly, this application provides a computer program product comprising a computer program; when the computer program is run in an electronic device, the electronic device performs the method described in the first aspect.

[0023] The beneficial effects of the second to fifth aspects mentioned above are described in the corresponding description of the first aspect and will not be repeated here. Attached Figure Description

[0024] Figure 1A schematic diagram illustrating the application environment of the displacement determination method provided in this application; Figure 2 A flowchart illustrating a displacement determination method provided in this application; Figure 3 This is a schematic diagram of the equipment distribution of the base station displacement compensation monitoring system provided in the embodiments of this application; Figure 4 A schematic diagram illustrating the baseline station displacement anomaly monitoring and displacement compensation process provided in this application embodiment; Figure 5 A flowchart illustrating another displacement determination method provided in this application; Figure 6 A flowchart illustrating another displacement determination method provided in this application; Figure 7 A schematic diagram of a system architecture is provided for this application; Figure 8 A schematic diagram of the composition of a displacement determining device provided in this application; Figure 9 This is a schematic diagram of the composition of an electronic device provided in this application. Detailed Implementation

[0025] The displacement determination method, apparatus, electronic device, storage medium, and program product provided in this application will now be described in detail with reference to the accompanying drawings.

[0026] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0027] The terms "first" and "second," etc., used in the specification and drawings of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0028] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0029] It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0030] To facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish the same or similar items with essentially the same function and effect. Those skilled in the art can understand that the terms "first" and "second" are not intended to limit the quantity or execution order.

[0031] In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0032] The displacement determination method provided in this application can be applied to high-precision, automated safety monitoring scenarios for the ground surface or man-made structures.

[0033] For example, the embodiments of this application can be applied to fields such as geological disaster early warning (e.g., landslides, ground subsidence, slope instability) and health monitoring of large infrastructure (e.g., dams, bridges, high-rise buildings, highways). In these scenarios, GNSS monitoring stations are typically deployed to continuously track deformation and displacement at the millimeter to centimeter level, thereby determining the stability of the structure and issuing timely warnings.

[0034] In existing technologies, static monitoring schemes based on real-time dynamic differential (RTK) technology are commonly used. This scheme assumes that the base station deployed in a stable area is stationary, and high-precision measurements are achieved by calculating the displacement of the monitoring station relative to the base station. However, in real-world engineering environments, the base station itself may shift due to unstable foundations, the influence of surrounding construction, or slow geological activity. Once the base station becomes unstable, its displacement acts as a common-mode error, systematically contaminating the calculation results of all monitoring stations. This makes it impossible for the monitoring system to distinguish between large-scale uniform deformation and the base station's own movement, potentially leading to false alarms (misjudging base station displacement as general deformation) or missed alarms (ignoring actual local deformation), seriously threatening the reliability and safety of the monitoring project.

[0035] Thus, existing technologies suffer from data distortion and reduced reliability of the entire monitoring system due to unexpected displacement of the base station.

[0036] To address the aforementioned technical problems, this application provides a displacement determination method, apparatus, electronic device, storage medium, and program product.

[0037] The embodiments provided in this application will now be described in detail with reference to the accompanying drawings.

[0038] The displacement determination method provided in this application can be applied to, for example... Figure 1 The application environment shown. For example... Figure 1 As shown, the application environment includes a displacement determining device 101 and a front-end device 102. The displacement determining device 101 and the front-end device 102 are interconnected.

[0039] In some embodiments, the displacement determining device 101 may be a server cluster consisting of multiple servers, a single server, a computer, or a processor or processing chip in a server or computer, etc. This application does not limit the specific device form of the displacement determining device 101. Figure 1 The displacement determination device 101 is shown as a single server as an example.

[0040] In some embodiments, the front-end device 102 can be a device with wireless transceiver capabilities, such as a mobile phone, tablet computer, wearable device, in-vehicle device, augmented reality (AR) / virtual reality (VR) device, laptop computer, ultra-mobile personal computer (UMPC), netbook, personal digital assistant (PDA), etc. This application embodiment does not limit the specific device form of the front-end device 102. Figure 1 The example shown is a mobile phone, with the front-end device 102 as the illustration.

[0041] In some embodiments, the displacement determination device 101 acquires GNSS observation data from a base station and at least two monitoring stations, calculates multiple types of RTK baseline vectors, including those between monitoring stations and base stations and between monitoring stations, performs anomaly detection processing on the RTK baseline vectors, determines target RTK baseline vectors without anomalies, and performs displacement compensation processing on the target RTK baseline vectors based on the multiple RTK baseline vectors and the PPP displacement estimate determined based on GNSS data, and sends the absolute displacement of the monitoring station after displacement compensation to the front-end device 102. This absolute displacement is used for surface condition monitoring.

[0042] It should be noted that the system architecture described in the embodiments of this application is for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and does not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of system architecture, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0043] See Figure 2 This is a flowchart of a displacement determination method provided in an embodiment of this application. Figure 2 As shown, the displacement determination method provided in this application can be implemented by the above-mentioned displacement determination device, specifically including the following steps 201 to 205: Step 201: The displacement determination device acquires GNSS observation data from the reference station and at least two monitoring stations.

[0044] In some embodiments of this application, the displacement determination device described above can receive raw GNSS observation data from a base station and at least two monitoring stations via a communication interface.

[0045] In some embodiments of this application, the aforementioned GNSS observation data includes, but is not limited to: pseudorange observations, carrier phase observations, Doppler shift observations, and satellite navigation messages.

[0046] It should be noted that the base station is set up at a reference point that is considered stable, while the monitoring station is set up in the target area to be monitored.

[0047] For example, in a landslide monitoring scenario, base station A is deployed on stable bedrock far from the landslide body, while monitoring stations B and C are deployed at key monitoring points within the landslide body. The three stations simultaneously collect GNSS observation data from one or more systems, such as GPS, BDS, and GLONASS, and transmit this data in real-time to the displacement determination device via a wireless network.

[0048] The following illustration, in conjunction with the accompanying drawings, provides an example of the positional relationship between the base station and at least two monitoring stations.

[0049] Figure 3 This is a schematic diagram of the equipment distribution of the base station displacement compensation monitoring system provided in the embodiments of this application, such as... Figure 3As shown, the monitoring system comprises a base station A, monitoring station B, and monitoring station C. Base station A is deployed in a stable geological area far from the landslide body, serving as the reference benchmark for the entire monitoring network. Monitoring stations B and C are directly deployed at key locations on the surface of the landslide to be monitored, used to capture the actual deformation and displacement of the landslide. Base station A and monitoring stations B and C can all receive navigation signals from GNSS satellites. Through this deployment, the system can acquire the first RTK baseline vector of monitoring station B relative to base station A, the first RTK baseline vector of monitoring station C relative to base station A, and the second RTK baseline vector between monitoring stations B and C. These baseline vectors together constitute a complete observation network. Specifically, since monitoring stations B and C are both located on the same landslide body, when the landslide undergoes overall displacement, the second RTK baseline vector between the monitoring stations remains relatively stable; however, when base station A experiences unexpected displacement, all first RTK baseline vectors referenced to the base station will exhibit consistent changes. This difference provides a crucial geometric observation basis for accurately identifying the source of motion and implementing precise base station displacement compensation. Figure 3 The image also shows a residential area located below the landslide.

[0050] Step 202: Based on the above GNSS observation data, the displacement determination device performs real-time dynamic differential RTK calculation to obtain multiple RTK baseline vectors.

[0051] The aforementioned plurality of RTK baseline vectors include: at least two first RTK baseline vectors and at least one second RTK baseline vector, each first RTK baseline vector representing the relative displacement of a monitoring station relative to a base station, and each second RTK baseline vector representing the relative displacement between two monitoring stations.

[0052] In some embodiments of this application, the displacement determination device uses real-time dynamic differential positioning technology to process GNSS observation data. Specifically, this includes: using carrier phase double-difference observations to eliminate common errors such as satellite clock bias and receiver clock bias, and obtaining integer solutions through an ambiguity fixing algorithm, thereby obtaining high-precision relative position information.

[0053] For example, the displacement determination device simultaneously calculates the baseline vectors AB (monitoring station B relative to reference station A), AC (monitoring station C relative to reference station A) and BC (between monitoring station B and monitoring station C), forming a complete baseline vector network.

[0054] Step 203: The displacement determination device performs anomaly detection processing on at least two first RTK baseline vectors and determines at least one first RTK baseline vector without anomalies as the target RTK baseline vector.

[0055] In some embodiments of this application, the displacement determination device performs quality control on the first RTK baseline vector, identifying and excluding unreliable observation data.

[0056] In some embodiments of this application, the above-mentioned anomaly detection processing includes at least one of the following: cycle slip detection, multipath error detection, solution state filtering, and threshold checking based on signal-to-noise ratio (SNR) and position dilution of precision (PDOP).

[0057] For example, cycle slip detection refers to detecting carrier phase jumps through MW combination and GF combination; path error detection refers to identifying multipath effects based on signal-to-noise ratio (SNR) changes; solution state screening refers to checking the ambiguity fixation success rate; and threshold checks based on SNR and position accuracy factor (PDOP) are used to ensure good observation geometry.

[0058] For example, when the PDOP value of the RTK baseline vector AB of monitoring station B exceeds 6.0, or the SNR is lower than 35dB-Hz, the baseline vector is marked as abnormal and excluded.

[0059] Step 204: The displacement determination device determines the precise single-point positioning PPP displacement estimate of the base station based on GNSS observation data.

[0060] In some embodiments of this application, the displacement determination device employs precise point positioning (PPP) technology to process GNSS observation data from a reference station. Specifically, this includes: using precise ephemeris and clock error products, and employing a precise point positioning algorithm to calculate the absolute position of the reference station. By real-time monitoring of the convergence status and residual statistics of the PPP calculation, the quality of the calculation is dynamically evaluated, and a PPP displacement estimate with covariance information is output.

[0061] For example, the PPP solution of reference station A uses WHU ultrafast precise ephemeris to obtain a three-dimensional coordinate sequence through ionospheric-free combined observations, and the solution accuracy is evaluated by residual RMS.

[0062] Step 205: The displacement determination device performs displacement compensation processing on the target RTK baseline vector based on multiple RTK baseline vectors and PPP displacement estimates to obtain the absolute displacement of the monitoring station after displacement compensation by the reference station.

[0063] The aforementioned absolute displacement is used for condition monitoring.

[0064] In some embodiments of this application, RTK relative observations and PPP absolute observations are fused. By establishing a state-space model, the absolute displacements of the base station and each monitoring station are jointly estimated. Specifically, this includes: constructing a state vector containing the displacements of all stations, establishing observation equations and state equations, using Kalman filtering for optimal estimation, and finally outputting the absolute displacements of the monitoring stations after base station displacement compensation.

[0065] For example, when the reference station A experiences an eastward displacement of 20 mm, the displacement compensation process in this step allows the displacement estimates of monitoring stations B and C to eliminate the influence of the reference station's displacement and accurately reflect the true deformation of the landslide body.

[0066] The following example illustrates the solution proposed in this claim using a reservoir dam safety monitoring scenario. In this scenario, a base station is deployed on stable bedrock downstream of the dam, while monitoring stations B and C are deployed at opposite ends of the dam's crest. GNSS observation data is acquired in real time, and baseline vectors AB, AC, and BC are calculated using RTK. When the base station shifts due to local geological activity, the displacement is accurately estimated using PPP technology. This displacement is then fused with RTK baseline vector network observation information, and displacement compensation processing is used to obtain the true absolute displacements of monitoring stations B and C. This effectively distinguishes between the actual dam deformation and the base station displacement, providing reliable data support for dam safety early warning.

[0067] The displacement determination method provided in this application acquires GNSS observation data from a base station and at least two monitoring stations, calculates multiple types of RTK baseline vectors, including those between monitoring stations and the base station, and between monitoring stations. Anomaly detection processing is performed on the RTK baseline vectors to identify target RTK baseline vectors without anomalies. Based on these multiple RTK baseline vectors and PPP displacement estimates determined from GNSS data, displacement compensation processing is applied to the target RTK baseline vectors. This scheme effectively identifies and separates the base station displacement from the actual deformation of the monitoring stations by constructing baseline vectors that include inter-station baselines, solving the inherent problem of unobservable base station displacement in traditional RTK monitoring. Furthermore, by fusing high-precision RTK relative observations with PPP observations possessing absolute benchmark characteristics, the absolute position constraints of PPP are introduced while retaining the high precision of RTK. This allows for accurate calculation of the absolute displacement of all stations, including the base station, ensuring accurate capture of actual deformation and improving the reliability and accuracy of displacement detection.

[0068] In some embodiments of this application, the process of performing displacement compensation processing on the target RTK baseline vector based on multiple RTK baseline vectors and the PPP displacement estimate in step 205 above may include steps 205a to 205c: Step 205a: The displacement determination device constructs at least two state vectors based on the site status information of the reference station and at least two monitoring stations.

[0069] Each of the above state vectors is used to estimate the absolute displacement of the base station and a monitoring station at the current moment.

[0070] Step 205b: The displacement determination device recursively estimates the state vector based on multiple RTK baseline vectors and the PPP displacement estimate of the reference station through a filtering model, and jointly calculates the absolute displacement estimate of the reference station and each monitoring station.

[0071] Step 205c: The displacement determination device performs displacement compensation on the target RTK baseline vector based on the absolute displacement estimate.

[0072] In some embodiments of this application, the displacement determination device constructs a state vector for state estimation based on the deployed station information.

[0073] In some embodiments of this application, the aforementioned state vector includes the absolute displacement components of the base station and all monitoring stations at the current moment, and can be in the northeast-sky coordinate system or the geocentric-earth-fixed coordinate system in a three-dimensional coordinate system.

[0074] In some embodiments of this application, the aforementioned site status information includes prior information such as the site's initial coordinates and motion characteristics.

[0075] For example, for a system containing one base station and two monitoring stations, the state vector can be represented as: x=[dA_x, dA_y, dA_z, dB_x, dB_y, dB_z, dC_x, dC_y, dC_z]^T, where d represents the displacement of each station in the three coordinate axes.

[0076] In some embodiments of this application, the displacement determination device can establish a process model describing the change of the state vector over time, and an observation model describing the relationship between the observed values ​​and the state vector. Using Kalman filtering or a variant thereof, RTK baseline vector observations and PPP displacement estimates are fused to jointly calculate the absolute displacement estimates for all stations.

[0077] For example, an extended Kalman filter is used to process the nonlinear observation model. The process noise covariance matrix is ​​set according to the station motion characteristics, and the observation noise covariance is dynamically adjusted according to the RTK solution accuracy and PPP covariance information.

[0078] In some embodiments of this application, the displacement determination device can use the absolute displacement of the base station obtained by filtering estimation to correct the target RTK baseline vector. Specifically, this includes: calculating the impact of the estimated displacement value of the base station on the relative displacement of each monitoring station, and subtracting the impact from the original RTK baseline vector to obtain the absolute displacement of the monitoring station after base station displacement compensation.

[0079] For example, when the filtered estimation shows that the base station A has a displacement of [dx,dy,dz], the compensated absolute displacement of the monitoring station B is: dB_corrected = dB_original + [dx,dy,dz], where dB_original is the relative displacement obtained based on the RTK baseline vector AB.

[0080] The flow of the above displacement determination method is illustrated below with reference to the accompanying drawings.

[0081] Figure 4 This is a schematic diagram of the base station displacement anomaly monitoring and displacement compensation process provided in the embodiments of this application, as shown below. Figure 4 As shown, data acquisition and preprocessing are performed at the monitoring station edge and the base station edge, respectively. The monitoring station edge receives and preliminarily processes its own GNSS observation data, while the base station edge performs a similar operation synchronously. The displacement determination device acquires GNSS observation data from both the monitoring station edge and the base station edge and performs anomaly detection on the collected GNSS observation data. If an anomaly is detected, a judgment is made on whether the base station status is abnormal to distinguish whether the source of the anomaly is the base station displacement or other factors. If the base station status is determined to be abnormal, the device further checks whether there are multiple monitoring stations. If multiple monitoring stations exist (i.e., the condition of at least two monitoring stations is met), joint calculation is initiated, fusing the PPP displacement estimate from the base station with the RTK baseline vectors between multiple monitoring stations (including monitoring station-base station vectors and monitoring station-monitoring station vectors), constructing a state space model for filtering and updating to achieve accurate estimation of the base station displacement; otherwise, the process ends. Subsequently, the base station displacement compensation module uses the calculated base station displacement to compensate for the original observation results of the monitoring station. If the base station is determined to be in normal condition during the base station status determination process, the anomaly is attributed to the movement of the monitoring station, and the displacement data of the monitoring station is interpolated and corrected before output. For data that fails the anomaly detection, filtering and updating are performed directly, and the current state is recursively derived using historical states and normal observations to output a stable result. Ultimately, all processing paths converge on the output of a stable result, which includes the absolute displacement information of the monitoring station after reliability assessment and necessary compensation.

[0082] In this way, through preliminary calculations at the edge and intelligent discrimination at the center, rapid identification of observed anomalies and accurate location of motion sources are achieved. High-precision joint calculation and displacement compensation are initiated only under the optimization conditions that meet multi-station constraints, thereby ensuring the overall reliability and accuracy of the system while achieving reasonable and efficient allocation of computing resources.

[0083] In this embodiment, the displacements of the base station and the monitoring station are estimated as a whole using a state-space model. This fully utilizes the geometric constraints between the observation data, significantly improving the accuracy and reliability of displacement estimation. The filtering recursive algorithm can adapt to the time-varying characteristics of station displacements and update the state estimation in real time. Furthermore, by fusing RTK and PPP observation data within a unified state-space framework, the accuracy of displacement determination is greatly improved.

[0084] In some embodiments of this application, in conjunction with the above... Figure 2 ,like Figure 5 As shown, step 204 above may include steps 204a to 204c: Step 204a: The displacement determination device performs precise single-point positioning PPP calculation based on the GNSS observation data and ephemeris information of the reference station, and obtains the convergence status and residual statistics of the PPP calculation in real time.

[0085] Step 204b: The displacement determination device determines the covariance information of the PPP displacement estimate based on the convergence state and residual statistics.

[0086] Step 204c: The displacement determination device obtains the estimated PPP displacement of the base station based on the covariance information.

[0087] Specifically, when the PPP solution is determined to be non-converged or the residual statistics exceed a preset threshold, the determined covariance information is increased.

[0088] In some embodiments of this application, the displacement determination device dynamically evaluates the quality of the PPP solution by real-time monitoring the convergence status of the PPP solution (including the stability of the position estimate, the rate of change of variance, etc.) and residual statistics (such as the RMS of the observation residuals). For example, when PPP non-convergence or residual exceeding the limit is detected, the weight of the PPP observation in subsequent fusion processing is reduced by increasing the covariance information, so as to avoid low-quality PPP data from polluting the overall solution result.

[0089] For example, the convergence criteria for PPP solution are set as follows: the positional change over 10 consecutive epochs is less than 2 cm, and the residual RMS is less than 3 cm. When the convergence criteria are not met or the residual RMS exceeds 5 cm, the diagonal element of the covariance of the PPP displacement estimate is increased to four times its original value.

[0090] In this embodiment of the application, by introducing an adaptive PPP quality control mechanism, various abnormal situations in the PPP solution process can be effectively dealt with, ensuring that the impact of poor PPP solution quality is automatically reduced, thereby improving the robustness and reliability of the processing.

[0091] In some embodiments of this application, after recursively estimating the state vector using a filtering model in step 205b, the displacement determination method provided in this application may further include steps 206 and 207: Step 206: The displacement determination device determines the motion source information based on the observation residuals generated during the recursive estimation process.

[0092] Step 207: The displacement determination device triggers a displacement anomaly warning based on the motion source information; Specifically, if the observation residuals exhibit common-mode characteristics on any first RTK baseline vector and any second RTK baseline vector, the motion source is determined to be the displacement of the base station; if the observation residuals exceed the corresponding noise level threshold on any second RTK baseline vector with a single monitoring station as the endpoint, the motion source is determined to be the displacement of the monitoring station.

[0093] In some embodiments of this application, motion source analysis and early warning functions are added to analyze the observation residuals generated during the filtering process and identify the sources of abnormal displacements. Specifically, this includes: when multiple first RTK baseline vectors and second RTK baseline vectors simultaneously show significant residuals and exhibit common-mode characteristics, it is determined to be a base station displacement; when only a single monitoring station's baseline vector shows a significant residual, it is determined to be a displacement of that monitoring station. Corresponding early warning information is triggered based on the judgment results.

[0094] For example, the threshold for judging the significance of residuals is set to 3 times the noise level. When the residuals of baseline vectors AB, AC and BC exceed the threshold at the same time and the trend of change is consistent, the displacement warning of the base station is triggered; when only the residual of baseline vector BC is significant, the displacement warning of monitoring station B or monitoring station C is triggered.

[0095] In this embodiment of the application, the motion source discrimination mechanism can intelligently identify the root cause of displacement anomalies, providing maintenance personnel with accurate fault location information and greatly improving maintainability and practicality.

[0096] In some embodiments of this application, the filtering model described above is a state-space filtering model, which includes an observation noise covariance matrix. For example, the process in step 205b above, which recursively estimates the state vector based on multiple RTK baseline vectors and the PPP displacement estimates of the base station using the filtering model, may include steps 205b1 and 205b2: Step 205b1: The displacement determination device recursively estimates the state vector based on multiple RTK baseline vectors, the PPP displacement estimate of the reference station, and the observation noise covariance matrix, and obtains the corresponding solution residual statistics.

[0097] Step 205b2: The displacement determination device updates the parameters of the noise covariance matrix based on the solved residual statistics.

[0098] In some embodiments of this application, the displacement determination device calculates the statistical characteristics of the solution residual in real time during the filtering recursion process, and dynamically updates the parameters of the observation noise covariance matrix based on the residual statistics, thereby realizing the adaptive adjustment of the observation noise covariance matrix. In this way, the weight of each observation value can be adjusted according to the actual observation quality through the adaptive mechanism, thereby improving the accuracy of displacement estimation.

[0099] For example, a moving window is used to calculate the sample covariance of the residuals, and the scaling factor of the observation noise covariance matrix is ​​adjusted according to the proportion of the difference between the residual covariance and the theoretical covariance.

[0100] In this embodiment, by introducing an adaptive noise adjustment mechanism, the filtering model can automatically adapt to changes in the observation environment, maintain estimation performance when the observation quality changes, and improve stability under different environments.

[0101] In some embodiments of this application, in conjunction with the above... Figure 2 ,like Figure 6 As shown, after step 205 above, the displacement determination method provided in this application embodiment may further include the following step 208: Step 208: The displacement determination device generates and outputs comprehensive monitoring results based on the absolute displacement of the monitoring station after displacement compensation by the reference station.

[0102] The comprehensive monitoring results include: the absolute displacement values ​​of each monitoring station after displacement compensation, the estimated absolute displacement values ​​of the aforementioned benchmark station, motion source information, and the displacement trends of the benchmark station and / or at least two monitoring stations.

[0103] In some embodiments of this application, a function for generating comprehensive monitoring results is added on top of displacement compensation. For example, this comprehensive monitoring result includes not only the absolute displacement values ​​of each monitoring station after displacement compensation, but also the estimated absolute displacement value of the base station, motion source information, and displacement trend analysis. Thus, through multi-dimensional monitoring information, a comprehensive deformation monitoring and analysis is provided to the user.

[0104] For example, the comprehensive monitoring results are output in a structured data format, which may include derived information such as displacement time series, displacement velocity, and acceleration of each station, as well as a safety level assessment based on displacement trends.

[0105] In this embodiment of the application, by providing comprehensive monitoring results, not only basic displacement data can be provided, but also in-depth safety analysis information can be provided, which greatly enhances the practical value of the processing operation and the decision support capability.

[0106] The displacement determination method of this application embodiment is described below with reference to a specific example. The specific implementation process of the displacement determination method includes the following S1 to S6: Step S1: The displacement determination device acquires data and performs edge preprocessing.

[0107] For example, the monitoring station continuously receives raw observation data and broadcast products from base station A; RTK baseline solutions are executed at the edge of monitoring station B and base station C, and a sliding time window is used for quality control and anomaly detection; and base station A collects its own raw observation data at the edge and acquires auxiliary data such as precise ephemeris and precise clock bias in real time, performing real-time PPP solution calculation. The PPP result is used as a "covariance-based prior" for subsequent fusion.

[0108] For example, the above edge preprocessing includes: cycle slip and multipath detection and repair, solution state filtering (fixed / floating ambiguity), SNR / PDOP threshold checking, residual statistics, and sliding window robust scaling (MAD).

[0109] For example, the noise scale of the MAD estimate is denoted as... (The x, y, and z axes are calculated independently in the ENU coordinate system); the rules for triggering candidate anomalies are shown in formula (1): (1) in, The x, y, and z axes are defined as the standard errors, with the x and y axes using a factor of 3 and the z axis using a factor of 5.

[0110] For example, if the RTK baseline solution is not fixed or the PDOP exceeds the threshold, the weighting is reduced or the discrimination is temporarily suspended.

[0111] Step S1: The displacement determination device determines the PPP observation value of the base station.

[0112] For example, assume that the actual displacements of the three axes of base station A, monitoring stations B and C are as follows: ; For example, the RTK relative displacement baseline solution , , As shown in formulas (2), (3), and (4): (2) (3) (4) in, The baseline vector of monitoring station B relative to reference station A. The baseline vector of monitoring station C relative to reference station A. The baseline vector of monitoring station B with monitoring station C as the reference. , , This represents the baseline measurement error.

[0113] For example, the PPP observation values ​​of the base station are shown in formula (5): (5) in For the three-dimensional displacement information of base station A, Real-time PPP solution results from base station A Determined by PPP residual RMS, convergence state, and product quality; when PPP quality is poor Increase the weight, thus automatically reducing the weight.

[0114] Step S3: The displacement determination device performs fusion estimation and filtering modeling.

[0115] For example, the fused estimated state vector is:

[0116] For example, the process model is as follows:

[0117] For example, the observation model is:

[0118] Where x is the fusion estimated state vector, For the current observation value, The value observed at the previous moment. To observe the noise, For measured values, It is composed of RTK residual statistics and PPP covariance.

[0119] Step S4: The displacement determination device determines the criteria for abnormal detection and large fluctuations.

[0120] For example, the displacement determination device uses a sliding window to statistically analyze the RTK baseline solution data of the monitoring station for each axis: defining the standardized deviation centered on the median and measured by MAD. As shown in formula (6): (6) For example, if >k (k is 3 for x and y axes, and k is 5 for z axis) is judged as a candidate anomaly.

[0121] For example, a sequential test (CUSUM / GLRT) can be introduced to determine whether the data is abruptly generated data or random noise drift; only when the candidate anomaly and the sequential test are satisfied at the same time, and the quality indicators meet the standards (fixed solution, reasonable PDOP, small residuals), can the motion source be determined.

[0122] For example, the displacement determination device performs a PPP quality threshold check. Specifically, if the PPP solution result does not converge, the residual RMS is too large, the tropospheric estimation is unstable, or the product delay is abnormal, the threshold is increased. Alternatively, PPP may not be used for the time being, but its "weak prior" function may be retained.

[0123] Step S5: The displacement determining device identifies the source of motion.

[0124] For example, the displacement determination device uses the following rules to verify and fuse data based on three relative observations and the fused estimated residuals at the current time: For example, the displacement determining device performs a common-mode error check, specifically, if and Meanwhile, PPP priors do not indicate significant If the change is small but the weight is low, it is judged as "possibly due to A-mode or base station movement" and the result is given by the fusion estimation. And compensation will be made accordingly.

[0125] For example, if Significant and Insignificant and If the motion is determined to be primarily at monitoring station B, then the motion criterion for single-point monitoring station C is similarly symmetrical to that for point B.

[0126] For example, if none of the above conditions are met and the residuals are significant, the system is marked as "simultaneous movement of multiple stations or abnormal observations," enters the low-confidence channel, and requests manual / more sensor verification. Threshold Adaptively set by noise covariance, for example .

[0127] Step S6: The displacement determination device performs displacement compensation and outputs the results.

[0128] For example, after the fusion estimation is obtained, base station motion compensation is performed on the monitoring station displacement, and the compensation process is shown in formulas (7) and (8): (7) (8) in, The RTK monitoring value at point B after displacement compensation of the base station; The RTK monitoring value at point C after displacement compensation from the base station; The motion amount is for base station A.

[0129] For example, the displacement determining device outputs , , Three-dimensional displacement and covariance; confidence intervals for three-axis displacement; discrimination labels: A motion, B motion, C motion, unknown common mode, suspected multi-point, and other possible anomalies; quality indicators: solution status (fixed / floating point), PDOP, residual RMS, PPP convergence and quality level, etc.

[0130] For example, if there is only a single monitoring station and there is a lack of constraints from other stations, then cross-day interpolation "correction" is prohibited; instead, filtered prediction is used and low confidence is explicitly labeled, to be supplemented by post-processing or external sensing.

[0131] The displacement determination method provided in this application can compensate for the displacement of the base station in real time, significantly improve the monitoring accuracy, meet the industry's urgent need for high-precision monitoring data, and effectively avoid false alarms or missed alarms caused by the displacement of the base station.

[0132] Figure 7 The system architecture diagram of this application embodiment is shown. The system can be a displacement determination system. The displacement determination system 800 may include: an observation data acquisition module 801, an RTK calculation module 802, an anomaly detection and processing module 803, a PPP displacement estimation module 804, and a displacement compensation processing module 805.

[0133] The aforementioned observation data acquisition module 801 is used to acquire GNSS observation data of the Global Navigation Satellite System collected by the base station and at least two monitoring stations, and is applied to step 201 and the related scheme of step 201. The aforementioned RTK calculation module 802 is used to perform real-time dynamic differential RTK calculation based on GNSS observation data to obtain multiple RTK baseline vectors. These multiple RTK baseline vectors include at least two first RTK baseline vectors and at least one second RTK baseline vector. Each first RTK baseline vector represents the relative displacement of a monitoring station relative to a base station, and each second RTK baseline vector represents the relative displacement between two monitoring stations. The aforementioned anomaly detection and processing module 803 is used to perform anomaly detection processing on at least two first RTK baseline vectors and determine at least one first RTK baseline vector without anomalies as the target RTK baseline vector. The aforementioned PPP displacement estimation module 804 is used to determine the precise single-point positioning PPP displacement estimate of the base station based on GNSS observation data. The aforementioned displacement compensation processing module 805 is used to perform displacement compensation processing on the target RTK baseline vector based on the multiple RTK baseline vectors and the PPP displacement estimate, obtaining the absolute displacement of the monitoring station after base station displacement compensation. This absolute displacement is used for status monitoring.

[0134] It should be noted that for a detailed explanation of the steps performed by each module and their beneficial effects, please refer to the description in the above embodiments, which will not be repeated here.

[0135] As can be seen, the above mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, the embodiments of this application provide corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the modules and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0136] This application embodiment can divide the displacement determining device into functional modules according to the above method example. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. Optionally, the module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0137] In some embodiments, this application also provides a displacement determination device. The displacement determination device may include one or more functional modules for implementing the displacement determination method of the above method embodiments.

[0138] For example, Figure 8 This is a schematic diagram illustrating the composition of a displacement determining device provided in an embodiment of this application. Figure 8 As shown, the displacement determination device 900 includes an acquisition module 901 and a processing module 902.

[0139] The system comprises the following components: an acquisition module 901, used to acquire GNSS observation data collected by a base station and at least two monitoring stations; a processing module 902, used to perform real-time dynamic differential RTK calculation based on the GNSS observation data to obtain multiple RTK baseline vectors, wherein the multiple RTK baseline vectors include at least two first RTK baseline vectors and at least one second RTK baseline vector, each first RTK baseline vector representing the relative displacement of a monitoring station relative to the base station, and each second RTK baseline vector representing the relative displacement between two monitoring stations; the processing module 902 is also used to perform anomaly detection processing on the at least two first RTK baseline vectors and determine at least one first RTK baseline vector without anomalies as the target RTK baseline vector; the processing module 902 is also used to determine the precise point positioning (PPP) displacement estimate of the base station based on the GNSS observation data; and the processing module 902 is also used to perform displacement compensation processing on the target RTK baseline vector based on the multiple RTK baseline vectors and the PPP displacement estimate to obtain the absolute displacement of the monitoring station after base station displacement compensation, the absolute displacement being used for status monitoring.

[0140] The displacement determination device provided in this application acquires GNSS observation data from a base station and at least two monitoring stations, calculates multiple types of RTK baseline vectors, including those between monitoring stations and the base station, and between monitoring stations. It then performs anomaly detection processing on these RTK baseline vectors to identify target RTK baseline vectors without anomalies. Based on these multiple RTK baseline vectors and PPP displacement estimates determined from GNSS data, it performs displacement compensation processing on the target RTK baseline vectors. This scheme effectively identifies and separates the base station displacement from the actual deformation of the monitoring stations by constructing baseline vectors that include inter-station baselines, solving the inherent problem of unobservable base station displacement in traditional RTK monitoring. Furthermore, by fusing high-precision RTK relative observations with PPP observations possessing absolute reference characteristics, it introduces the absolute position constraints of PPP while retaining the high precision of RTK, thereby accurately calculating the absolute displacement of all stations, including the base station, ensuring accurate capture of actual deformation and improving the reliability and accuracy of displacement detection.

[0141] One possible implementation is that the aforementioned processing module is specifically used to: construct at least two state vectors based on the site status information of the base station and at least two monitoring stations, each state vector being used to estimate the absolute displacement of the base station and one monitoring station at the current time; based on multiple RTK baseline vectors and the PPP displacement estimate of the base station, recursively estimate the state vectors through a filtering model, and jointly calculate the absolute displacement estimate of the base station and each monitoring station; and perform displacement compensation on the target RTK baseline vector based on the absolute displacement estimate.

[0142] Another possible implementation method is that the above anomaly detection processing includes at least one of the following: cycle slip detection, multipath error detection, solution state screening, and threshold check based on signal-to-noise ratio (SNR) and position precision factor (PDOP).

[0143] Another possible implementation is that the aforementioned processing module is specifically used to perform precise single-point positioning (PPP) calculation based on GNSS observation data and ephemeris information from the reference station, and to acquire the convergence status and residual statistics of the PPP calculation in real time; based on the convergence status and residual statistics, to determine the covariance information of the PPP displacement estimate; based on the covariance information, to obtain the PPP displacement estimate of the reference station; wherein, when the PPP calculation is determined to be non-converged or the residual statistics exceed a preset threshold, the determined covariance information is increased.

[0144] In another possible implementation, the aforementioned processing module is further configured to determine the motion source information based on the observation residuals generated during the recursive estimation process after recursively estimating the state vector using a filtering model; and to trigger a displacement anomaly warning based on the motion source information. Specifically, if the observation residuals exhibit common-mode characteristics on any first RTK baseline vector and any second RTK baseline vector, the motion source is determined to be the displacement of the base station; if the observation residuals exceed the corresponding noise level threshold on any second RTK baseline vector with a single monitoring station as the endpoint, the motion source is determined to be the displacement of the monitoring station.

[0145] Another possible implementation is that the above filtering model is a state-space filtering model, which includes the observation noise covariance matrix. The above processing module is specifically used to: recursively estimate the state vector based on multiple RTK baseline vectors, the PPP displacement estimate of the base station, and the observation noise covariance matrix to obtain the corresponding solution residual statistics; and update the parameters of the noise covariance matrix based on the solution residual statistics.

[0146] In another possible implementation, the aforementioned processing module is further configured to, after performing displacement compensation processing on the target RTK baseline vector to obtain the absolute displacement of the monitoring station after displacement compensation by the base station, generate and output comprehensive monitoring results based on the absolute displacement of the monitoring station after displacement compensation by the base station; wherein, the comprehensive monitoring results include: the absolute displacement values ​​of each monitoring station after displacement compensation, the estimated absolute displacement value of the base station, motion source information, and the displacement trends of the base station and / or at least two monitoring stations.

[0147] It should be noted that the above-mentioned displacement determining device can realize all the processes implemented in the above-mentioned displacement determining method embodiment and can achieve the same beneficial effects. To avoid repetition, it will not be described again here.

[0148] In the case where the functions of the integrated modules described above are implemented in hardware, this application provides a possible structural schematic diagram of the electronic device involved in the above embodiments. For example... Figure 9 As shown, the electronic device 90 includes: a processor 92, a communication interface 93, and a bus 94. Optionally, the electronic device 90 may also include a memory 91.

[0149] Processor 92 may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 92 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 92 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0150] Communication interface 93 is used to connect with other devices via a communication network. This communication network can be Ethernet, wireless access network, wireless local area network (WLAN), etc.

[0151] The memory 91 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0152] As one possible implementation, the memory 91 can exist independently of the processor 92. The memory 91 can be connected to the processor 92 via a bus 94 and is used to store instructions or program code. When the processor 92 calls and executes the instructions or program code stored in the memory 91, it can implement the displacement determination method provided in the embodiments of this application.

[0153] In another possible implementation, memory 91 can also be integrated with processor 92.

[0154] Bus 94 can be an Extended Industry Standard Architecture (EISA) bus, etc. Bus 94 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 9 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0155] Through the above description of the implementation methods, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the service calling device can be divided into different functional modules to complete all or part of the functions described above.

[0156] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be executed by computer instructions instructing related hardware. The program can be stored in the aforementioned computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be any of the foregoing embodiments or memory. The aforementioned computer-readable storage medium can also be an external storage device of the aforementioned service invocation device, such as a plug-in hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the aforementioned service invocation device. Further, the aforementioned computer-readable storage medium can include both internal storage units of the aforementioned service invocation device and external storage devices. The aforementioned computer-readable storage medium is used to store the aforementioned computer program and other programs and data required by the aforementioned service invocation device. The aforementioned computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0157] This application also provides a computer program product comprising a computer program that, when run on a computer, causes the computer to execute any of the displacement determination methods provided in the above embodiments.

[0158] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for determining displacement, characterized in that, include: Acquire GNSS observation data of the Global Navigation Satellite System collected by a base station and at least two monitoring stations; Based on the GNSS observation data, real-time dynamic differential RTK solution is performed to obtain multiple RTK baseline vectors. The multiple RTK baseline vectors include at least two first RTK baseline vectors and at least one second RTK baseline vector. Each first RTK baseline vector represents the relative displacement of a monitoring station relative to the base station, and each second RTK baseline vector represents the relative displacement between two monitoring stations. Anomaly detection processing is performed on the at least two first RTK baseline vectors, and at least one first RTK baseline vector without anomalies is determined as the target RTK baseline vector; Based on the GNSS observation data, the precise single-point positioning PPP displacement estimate of the reference station is determined; Based on the multiple RTK baseline vectors and the PPP displacement estimate, the target RTK baseline vector is subjected to displacement compensation processing to obtain the absolute displacement of the monitoring station after base station displacement compensation. The absolute displacement is used for condition monitoring.

2. The method according to claim 1, characterized in that, The displacement compensation process for the target RTK baseline vector based on the plurality of RTK baseline vectors and the PPP displacement estimate includes: Based on the station status information of the base station and the at least two monitoring stations, at least two state vectors are constructed, each state vector being used to estimate the absolute displacement of the base station and one monitoring station at the current moment; Based on the multiple RTK baseline vectors and the PPP displacement estimates of the base station, the state vector is recursively estimated through a filtering model, and the absolute displacement estimates of the base station and each monitoring station are jointly calculated. Based on the absolute displacement estimate, displacement compensation is performed on the target RTK baseline vector.

3. The method according to claim 1 or 2, characterized in that, The anomaly detection process includes at least one of the following: cycle slip detection, multipath error detection, solution state screening, and threshold checking based on signal-to-noise ratio (SNR) and position precision factor (PDOP).

4. The method according to claim 1, characterized in that, The determination of the precise single-point positioning PPP displacement estimate of the reference station based on the GNSS observation data includes: Based on the GNSS observation data and ephemeris information of the reference station, precise single-point positioning PPP solution is performed, and the convergence status and residual statistics of the PPP solution are obtained in real time. Based on the convergence state and residual statistics, the covariance information of the PPP displacement estimate is determined; Based on the covariance information, the estimated PPP displacement of the base station is obtained; Specifically, when the PPP solution is determined to be non-converged or the residual statistics exceed a preset threshold, the determined covariance information is increased.

5. The method according to claim 2, characterized in that, After recursively estimating the state vector using a filtering model, the method further includes: Based on the observation residuals generated during the recursive estimation process, the motion source information is determined; Based on the motion source information, a displacement anomaly warning is triggered; Specifically, if the observation residual exhibits common mode characteristics on any first RTK baseline vector and any second RTK baseline vector, the motion source is determined to be the displacement of the base station; if the observation residual exceeds the corresponding noise level threshold on any second RTK baseline vector with a single monitoring station as the endpoint, the motion source is determined to be the displacement of the monitoring station.

6. The method according to claim 2, characterized in that, The filtering model is a state-space filtering model, which includes the observation noise covariance matrix; The recursive estimation of the state vector based on the multiple RTK baseline vectors and the PPP displacement estimate of the reference station, using a filtering model, includes: Based on the multiple RTK baseline vectors, the PPP displacement estimate of the base station, and the observation noise covariance matrix, the state vector is recursively estimated to obtain the corresponding solution residual statistics. Based on the calculated residual statistics, the parameters of the noise covariance matrix are updated.

7. The method according to claim 1, characterized in that, After performing displacement compensation processing on the target RTK baseline vector to obtain the absolute displacement of the monitoring station after base station displacement compensation, the process further includes: Based on the absolute displacement of the monitoring station after displacement compensation by the reference station, a comprehensive monitoring result is generated and output. The comprehensive monitoring results include: the absolute displacement values ​​of each monitoring station after displacement compensation, the estimated absolute displacement value of the reference station, motion source information, and the displacement trends of the reference station and / or the at least two monitoring stations.

8. A displacement determining device, characterized in that, include: The module for acquisition and the module for processing, wherein: The acquisition module is used to acquire GNSS observation data of the Global Navigation Satellite System collected by the base station and at least two monitoring stations; The processing module is used to perform real-time dynamic differential RTK calculation based on the GNSS observation data to obtain multiple RTK baseline vectors, wherein the multiple RTK baseline vectors include: at least two first RTK baseline vectors and at least one second RTK baseline vector, each first RTK baseline vector representing the relative displacement of a monitoring station relative to the base station, and each second RTK baseline vector representing the relative displacement between two monitoring stations. The processing module is further configured to perform anomaly detection processing on the at least two first RTK baseline vectors, and determine at least one first RTK baseline vector that does not have anomalies as the target RTK baseline vector. The processing module is also used to determine the precise single-point positioning PPP displacement estimate of the reference station based on the GNSS observation data; The processing module is further configured to perform displacement compensation processing on the target RTK baseline vector based on the plurality of RTK baseline vectors and the PPP displacement estimate, to obtain the absolute displacement of the monitoring station after the base station displacement compensation, and the absolute displacement is used for condition monitoring.

9. An electronic device, characterized in that, The device includes a processor and a memory, the processor being coupled to the memory; the memory is used to store computer instructions, which are loaded and executed by the processor to enable the computer device to implement the displacement determination method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes computer-executable instructions that, when executed on a computer, cause the computer to perform the displacement determination method according to any one of claims 1 to 7.

11. A computer program product, characterized in that, The computer program product includes a computer program that, when run on an electronic device, causes the electronic device to perform the displacement determination method as described in any one of claims 1 to 7.

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