Deformation monitoring method, device and system, storage medium, program product and computer equipment

Abstract

The invention discloses a deformation monitoring method, device and system, a storage medium, a program product and computer equipment. The method comprises the following steps: determining a reported monitoring state of a monitoring station according to sensor data of the monitoring station; performing data acquisition according to the reported monitoring state to obtain first observation data; a report message is sent to the cloud, the report message carries the first observation data and state information used for indicating the reported monitoring state, and the report message is suitable for indicating the cloud to use a resolving algorithm matched with the reported monitoring state to carry out deformation analysis according to the first observation data, so that only the monitoring station is required to serve as a data sampling end; and meanwhile, the cloud can use an adaptive resolving algorithm according to the reported monitoring state indicated in the reported message so as to timely and accurately find whether the area corresponding to the monitoring station is deformed or not, so that the real-time performance and the accuracy of deformation analysis can be considered while the requirement on the performance of the terminal can be reduced.

Description

Technical Field

[0001] This application relates to the field of monitoring technologies, and in particular, to a deformation monitoring method, device, system, storage medium, program product, and computer device. Background Art

[0002] In existing GNSS (Global Navigation Satellite System) deformation monitoring technical solutions, it is usually necessary to install GNSS receiver terminals in the monitoring area. The terminals collect satellite observation data and directly calculate the position deformation information of the area where the terminals are located based on this.

[0003] However, this places high performance requirements on the terminals. Even if deformation analysis can be completed on the terminal side, there are easily problems with accuracy and stability in actual applications. Summary of the Invention

[0004] To solve the above technical problems, embodiments of this application propose a deformation monitoring method, device, system, storage medium, program product, and computer device.

[0005] In a first aspect, an embodiment of this application provides a deformation monitoring method, which is applicable to a monitoring station. The method includes:

[0006] Determine the reported monitoring status of the monitoring station according to the sensor data of the monitoring station;

[0007] Collect data according to the reported monitoring status to obtain first observation data;

[0008] Send a reporting message to the cloud. The reporting message carries the first observation data and status information for indicating the reported monitoring status. The reporting message is suitable for indicating that the cloud uses a solution algorithm adapted to the reported monitoring status to perform deformation analysis based on the first observation data.

[0009] Optionally, the reported monitoring status includes a non-repeated reporting status or a repeated reporting status. Sending the reporting message to the cloud includes:

[0010] In the case where the reported monitoring status is the repeated reporting status, send the reporting message to the cloud at a first interval time;

[0011] In the case where the reported monitoring status is the non-repeated reporting status, send the reporting message to the cloud at a second interval time, where the first interval time is less than the second interval time.

[0012] Optionally, when the reported monitoring status is the additional reporting status, the target filtering processing method adopted in the solution algorithm is determined by the first interval time.

[0013] Optionally, the deformation analysis based on the first observation data using the solution algorithm adapted to the reported monitoring status includes:

[0014] When the reported monitoring status is the additional reporting status, a fixed solution is determined based on the first observation data and the second observation data from the reference station;

[0015] According to the first interval time, determine the target filtering weight of the target filtering processing method;

[0016] Using the target filtering weight as the parameter of the state transition equation, perform deformation analysis based on the fixed solution using the Kalman filtering algorithm.

[0017] Optionally, the determining the target filtering weight of the target filtering processing method according to the first interval time includes:

[0018] Based on the first interval time, the attenuation coefficient, and the initial weight, determine the weight decay function;

[0019] Based on the weight decay function and the current time, determine the target filtering weight.

[0020] Optionally, the reported monitoring status includes a non-additional reporting status or an additional reporting status, and the data collection according to the reported monitoring status includes:

[0021] When the reported monitoring status is the additional reporting status, data collection is performed at the first sampling frequency;

[0022] When the reported monitoring status is the non-additional reporting status, data collection is performed at the second sampling frequency, where the first sampling frequency is higher than the second sampling frequency.

[0023] Optionally, the determining the reported monitoring status of the monitoring station according to the sensor data of the monitoring station includes:

[0024] Based on the first comparison result between the acceleration threshold and the current acceleration of the monitoring station, and / or the second comparison result between the inclination threshold and the current inclination of the monitoring station, determine the reported monitoring status of the monitoring station, where the current acceleration is determined by the sensor data, and the current inclination is determined by the sensor data.

[0025] In a second aspect, an embodiment of the present application provides a deformation monitoring method applicable to the cloud, and the method includes:

[0026] Receive the reporting message from the monitoring station. Among them, the reporting message carries the first observation data and the status information used to indicate the reporting monitoring status of the monitoring station. The reporting monitoring status is determined by the sensor data of the monitoring station, and the first observation data is collected by the monitoring station according to the reporting monitoring status;

[0027] Perform deformation analysis on the first observation data using a solution algorithm adapted to the reporting monitoring status.

[0028] In a third aspect, an embodiment of the present application provides a deformation monitoring device applicable to a monitoring station. The device includes:

[0029] A status determination module for determining the reporting monitoring status of the monitoring station according to the sensor data of the monitoring station;

[0030] A data acquisition module for acquiring data according to the reporting monitoring status to obtain the first observation data;

[0031] A sending module for sending a reporting message to the cloud. Among them, the reporting message carries the first observation data and the status information used to indicate the reporting monitoring status, and the reporting message is suitable for instructing the cloud to perform deformation analysis on the first observation data using a solution algorithm adapted to the reporting monitoring status.

[0032] In a fourth aspect, an embodiment of the present application provides a deformation monitoring device applicable to the cloud. The device includes:

[0033] A receiving module for receiving the reporting message from the monitoring station. Among them, the reporting message carries the first observation data and the status information used to indicate the reporting monitoring status of the monitoring station. The reporting monitoring status is determined by the sensor data of the monitoring station, and the first observation data is collected by the monitoring station according to the reporting monitoring status;

[0034] An analysis module for performing deformation analysis on the first observation data using a solution algorithm adapted to the reporting monitoring status.

[0035] In a fifth aspect, an embodiment of the present application provides a deformation monitoring system, including:

[0036] A monitoring station configured to execute the method described in any one of the first aspects above, or the monitoring station includes the device described in the third aspect above; and,

[0037] The cloud is configured to execute the method described in the second aspect above, or the cloud includes the device described in the fourth aspect above.

[0038] In a sixth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0039] In a seventh aspect, an embodiment of the present application provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, the steps of the method described in any one of the above are implemented.

[0040] In an eighth aspect, an embodiment of the present application provides a computer device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0041] In summary, the embodiments of the present application at least have the following beneficial effects:

[0042] By using the embodiments of the present application, according to the sensor data of the monitoring station, the reported monitoring state where the monitoring station is located is determined; data collection is performed according to the reported monitoring state to obtain first observation data; a reporting message is sent to the cloud, where the reporting message carries the first observation data and status information for indicating the reported monitoring state, and the reporting message is adapted to instruct the cloud to use a solution algorithm adapted to the reported monitoring state to perform deformation analysis according to the first observation data. Thus, only the monitoring station is required to be a data sampling end, and at the same time, the cloud can use an adapted solution algorithm according to the reported monitoring state indicated in the reporting message to timely and accurately detect whether deformation has occurred in the area corresponding to the monitoring station, thereby being able to reduce the requirements for the performance of the terminal while taking into account the real-time performance and accuracy of deformation analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a schematic flowchart of the deformation monitoring method provided by an embodiment of the present application;

[0044] Figure 2 is a schematic diagram of the deformation monitoring system provided by an embodiment of the present application;

[0045] Figure 3 is a schematic diagram of the deformation monitoring provided by an embodiment of the present application;

[0046] Figure 4 is a schematic flowchart of the deformation monitoring method provided by an embodiment of the present application;

[0047] Figure 5 is a schematic structural diagram of the deformation monitoring device provided by an embodiment of the present application;

[0048] Figure 6 It is a schematic structural diagram of the deformation monitoring device provided by the embodiment of the present application;

[0049] Figure 7 It is a schematic diagram of the computer device provided by the embodiment of the present application. Specific Embodiments

[0050] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present application.

[0051] In the description of the present application, the terms "first", "second", "third", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise stated, the meaning of "a plurality" is two or more. In the description of the present application, the term "including" and its variants are open-ended, that is, "including but not limited to". The term "based on" is "at least partially based on". The term "according to" is "at least partially according to". The term "an embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments".

[0052] In the description of the present application, it should be noted that unless otherwise clearly defined and limited, the terms "installed", "connected", "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific situations.

[0053] In the description of the present application, it should be noted that unless otherwise defined, all the technical and scientific terms used in the present application have the same meaning as those commonly understood by those skilled in the technical field to which this application belongs. The terms used in the specification of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific situations.

[0054] The following explains some term concepts related to the embodiments of the present application:

[0055] GNSS (Global Navigation Satellite System), usually includes: GPS (Global Positioning System), GLONASS, Galileo satellite navigation system and Beidou satellite navigation and positioning system. The observation data described in the embodiments of the present application refers to GNSS satellite observation data.

[0056] The monitoring station is a key component in GNSS deformation monitoring. Usually, a GNSS receiver is installed in the monitoring station to continuously or regularly receive signals from multiple GNSS satellites and record the arrival time of these signals, so as to collect the corresponding observation data. When the monitoring station is set in the area to be monitored, the change of the position of the monitoring station itself can be captured through the observation data, such as monitoring the small displacements of structures such as the ground surface, buildings, and bridges.

[0057] The reference station is a key component in GNSS deformation monitoring. The location of the reference station is often a fixed point with known accurate coordinates, usually located in a relatively stable area that is not prone to deformation. The reference station works synchronously with the monitoring station by receiving the same GNSS satellite signals, so as to provide a stable reference information for the data of the monitoring station.

[0058] The solution algorithm. The solution algorithm described in the embodiments of the present application refers to the solution algorithm in deformation monitoring, which is used to process and analyze the observation data to identify the small changes of the monitored ground surface and / or structures, so as to realize deformation analysis. The solution algorithm aims to improve the data accuracy, reduce errors, and effectively extract useful deformation information. Exemplarily, the solution algorithm may include, but is not limited to, at least one of the following: the least squares method (estimating the optimal value of parameters by minimizing the sum of the squares of the residuals between the observed values and the model predicted values), Kalman filtering, wavelet transform, differential GNSS (eliminating common error sources such as ionospheric delay and satellite clock error by synchronously receiving the same GNSS satellite signals at the reference station and the monitoring station, so as to improve the accuracy of relative position solution). The solution algorithm may also include a static solution with baseline constraint.

[0059] In a first aspect, referring to Figure 1 , it shows a schematic flow chart of a deformation monitoring method provided by an embodiment of the present application. The method includes steps S101 - S103. This deformation monitoring method is applicable to the monitoring station, specifically as follows:

[0060] S101, determining the reported monitoring state of the monitoring station according to the sensor data of the monitoring station;

[0061] In one example, the monitoring station may include a MEMS (Micro Electromechanical System) module for collecting sensor data.

[0062] In one example, the sensors of the monitoring station may include at least one of the following: an acceleration sensor (such as a capacitive / piezoelectric acceleration sensor), an inclination sensor, etc.

[0063] S102, perform data collection according to the reported monitoring status to obtain first observation data;

[0064] In one example, the monitoring station may include a GNSS receiver. Since this embodiment only requires the monitoring station to receive observation data, this GNSS receiver may be an ordinary receiver with the function of receiving observation data but without the function of solving, thereby reducing the deployment cost.

[0065] S103, send a reporting message to the cloud, where the reporting message carries the first observation data and status information for indicating the reported monitoring status, and the reporting message is adapted to instruct the cloud to perform deformation analysis according to the first observation data using a solution algorithm adapted to the reported monitoring status.

[0066] It can be understood that the sensor data described in this embodiment is suitable for characterizing the deformation trend of the location where the monitoring station is located, so as to determine the deformation trend of the location where the monitoring station is located represented by the reported monitoring status. When the deformation trend becomes larger, an anomaly usually appears in the sensor data to capture the increase in the trend. At this time, the reported monitoring status is suitable for indicating that the monitoring station is in a high-power consumption state (such as increasing the data collection frequency and / or shortening the interval time for sending the reporting message), and when no anomaly appears in the sensor data, the reported monitoring status is suitable for indicating that the monitoring station is in a low-power consumption state (such as reducing the data collection frequency and / or increasing the interval time for sending the reporting message). Exemplarily, the reported monitoring status may include multiple levels of status, and the multiple levels of status may correspond one-to-one to multiple sampling frequencies and / or multiple reporting interval times. The sampling frequency may become higher as the level of the status increases, and the reporting interval time may become shorter as the level of the status increases. Among them, the sampling frequency is the frequency at which the monitoring station performs data collection, and the reporting interval time is the interval time for the monitoring station to send the reporting message. In this way, this embodiment can enable the monitoring station to dynamically adjust the frequency of its data collection and / or the interval time for sending the reporting message, thereby dynamically adjusting the power consumption of the monitoring station during long-term operation. In addition, the solution algorithm used by the cloud can also be dynamically adjusted to make the solution algorithm adapted to the reported monitoring status, so that the cloud can timely and accurately detect whether deformation has occurred in the area corresponding to the monitoring station.

[0067] It should be noted that there can be multiple such resolution algorithms in this embodiment. At this time, multiple reported monitoring statuses can correspond to several resolution algorithms, and / or multiple reported monitoring statuses can correspond to several algorithm parameters configured for the same resolution algorithm, so as to adapt the resolution algorithm to the reported monitoring status, facilitating the cloud to use the most suitable resolution algorithm for the reported monitoring status to calculate in a timely and accurate manner whether deformation has occurred in the area corresponding to the monitoring station.

[0068] In one example, the monitoring station can send a reporting message to the cloud according to the reported monitoring status or the corresponding interval time.

[0069] In one example, a relationship table suitable for indicating the mapping relationship between the reported monitoring status and the resolution algorithm can be preset in the cloud and / or the monitoring station, so that the monitoring station can determine the resolution algorithm adapted to the reported monitoring status and notify the cloud to use the determined resolution algorithm, and / or the cloud can determine and directly use the resolution algorithm adapted to the reported monitoring status. Among them, the mapping relationship is suitable for indicating the relationship between different reported monitoring statuses and different resolution algorithms, and / or the relationship between different reported monitoring statuses and different algorithm parameters configured for the same resolution algorithm.

[0070] In one example, the cloud can be a millimeter-level resolution cloud platform.

[0071] In one example, communication can be carried out between the cloud and the monitoring station via the HTTP (Hypertext Transfer Protocol) protocol.

[0072] In one example, the cloud can also be configured to: send the deformation analysis result to the user terminal, so that the user terminal displays the deformation analysis result.

[0073] In an alternative implementation manner, the reported monitoring status includes a non-reinforced reporting status or a reinforced reporting status, and the sending of the reporting message to the cloud includes:

[0074] When the reported monitoring status is the reinforced reporting status, send the reporting message to the cloud at a first interval time;

[0075] When the reported monitoring status is the non-reinforced reporting status, send the reporting message to the cloud at a second interval time, where the first interval time is less than the second interval time.

[0076] In this embodiment, the first interval time corresponding to the reporting state is smaller than the second interval time corresponding to the non-reporting state, which means that after entering the reporting state, the monitoring station can periodically send reporting messages to the cloud at a shorter interval, thereby dynamically adjusting the communication power consumption of the monitoring station. In addition, the monitoring station can also be configured to the non-reporting state by default to reduce the communication power consumption and network transmission requirements under normal circumstances.

[0077] In an example, the second interval time may include 1 hour, and the time point for sending the report message may be every hour.

[0078] In an example, the first interval time may include 1 minute or several minutes.

[0079] In one example, the solution algorithm may be determined by the first interval time, for example, multiple first interval times may correspond to several solution algorithms, and / or multiple first interval times may correspond to several algorithm parameters configured for the same solution algorithm.

[0080] In an optional implementation, when the reported monitoring state is the additional reporting state, the target filtering processing method used in the solution algorithm is determined by the first interval time.

[0081] In one example, the solution algorithm includes at least filtering processing. At this time, the method for determining the target filtering processing may include: determining the filtering processing corresponding to the first interval time among multiple filtering processings, and / or, determining the processing parameters corresponding to the first interval time among several processing parameters configured for the same filtering processing to determine the target filtering processing.

[0082] In this embodiment, different first interval times often represent different deformation trends. For example, a smaller first interval time usually indicates that the trend is larger, thereby instructing the cloud to use a more accurate and / or more responsive solution algorithm to perform deformation analysis. Conversely, when the trend is smaller (for example, when the reported monitoring status is a non-reporting status), the cloud can be instructed to use a less accurate and / or slower responsive solution algorithm to perform deformation analysis, thereby reducing the daily power consumption of the cloud.

[0083] In an optional implementation, the using a solution algorithm adapted to the reported monitoring state to perform deformation analysis according to the first observation data includes:

[0084] When the reported monitoring state is the additional reporting state, a fixed solution is determined based on the first observation data and the second observation data from the reference station; illustratively, the fixed solution may be a single-period fixed solution.

[0085] Determine the target filtering weight of the target filtering processing method according to the first interval time; exemplarily, multiple first interval times can correspond to several filtering weights, so that the target filtering weight corresponding to the first interval time can be determined from several filtering weights.

[0086] Use the Kalman filtering algorithm to perform deformation analysis according to the fixed solution with the target filtering weight as the parameter of the state transition equation. It should be understood that using the target filtering weight as the parameter of the state transition equation can adjust the weight of the historical data considered by the Kalman filtering algorithm according to the magnitude of the target filtering weight.

[0087] In one example, the reference station and the cloud can communicate via a 4G / 5G IoT card using the NTRIP (Networked Transport of RTCM via Internet Protocol) protocol. It should be understood that the reference station usually has a GNSS receiver and is usually installed at a location in the disaster monitoring area where deformation is not likely to occur and the observation conditions are good.

[0088] In one example, refer to Figure 2 , the cloud can be communicatively connected to the reference station and the monitoring station respectively. The cloud can be a solution calculation cloud platform, and the cloud can include a device gateway, a data storage unit, a baseline solution unit, and a deformation monitoring platform, where the cloud is communicatively connected to the reference station and the monitoring station respectively through the device gateway.

[0089] In one example, refer to Figure 3 , determining the fixed solution based on the first observation data and the second observation data from the reference station may include: the cloud reads the baseline solution configuration information, preprocesses the first observation data and the second observation data, constructs a double-difference observation equation according to the preprocessed data, and then uses the least squares method / Kalman filtering to calculate the floating solution, and then performs double-difference residual cycle slip detection processing and ambiguity fixing according to the floating solution to obtain the fixed solution.

[0090] In one example, refer to Figure 3 , when the reported monitoring status is the non-additional reporting status, the cloud can determine the fixed solution using the above embodiment, and according to the fixed solution, use the static Kalman filtering to calculate the position and deformation information of the monitoring station. When the reported monitoring status is the additional reporting status, the deformation filtering (i.e., the target filtering processing method) is used to process the fixed solution.

[0091] In this embodiment, it is equivalent to, after obtaining the single - period fixed solution, screening out more accurate historical reference data based on a dynamic weight decay function model, and performing secondary filtering on the results using a suitable dynamic - static Kalman filter model. Compared with the general static post - processing algorithm that directly outputs the solution result after obtaining the fixed solution or smooths based on a fixed number of historical solution results, the accuracy of displacement monitoring and the response efficiency of deformation are improved.

[0092] In addition, in some cases, the general platform - side solution technology scheme does not consider fusing sensor signals, and it is difficult to quickly reflect the deformation result when actual deformation occurs. In result processing, it mostly filters based on a single static model and a fixed number of reference data, resulting in poor stability in deformation monitoring accuracy. In this embodiment, the cloud - side solution platform adopts a static post - processing algorithm that fuses sensor reporting information (i.e., status information), and adopts an adaptive algorithm processing strategy in the case of whether the monitoring station reports or not. After obtaining the single - period fixed solution, it screens out more accurate historical reference data based on a dynamic weight decay function model, and performs secondary filtering on the results using a suitable dynamic - static Kalman filter model. The entire solution scheme has more stable accuracy and can more accurately reflect deformation information.

[0093] In an alternative embodiment, determining the target filtering weight of the target filtering processing method according to the first interval time includes:

[0094] Determining a weight decay function based on the first interval time, the decay coefficient, and the initial weight;

[0095] Determining the target filtering weight based on the weight decay function and the current time.

[0096] In an example, the weight decay function may include the following formula:

[0097]

[0098] Where W0 is the initial weight, W(t) is the weight decay function, t is the current time, t0 is the first interval time, and α is the decay coefficient.

[0099] In this embodiment, the decay coefficient may increase as the first interval time decreases, that is, the decay coefficient is larger in the reporting state, so that the target filtering weight is smaller. Thus, the proportion of historical reference result data within the effective weight range screened out by the weight decay function in this embodiment will decrease, and then the weight is used as a parameter of the state transition equation to perform dynamic Kalman filtering on the single - period fixed solution before reporting, so that the calculated position can more accurately reflect deformation information.

[0100] Accordingly, when in the non-reporting state, the attenuation coefficient is small, so that the target filtering weight is large. Therefore, in this embodiment, the proportion of historical reference result data within the effective weight range selected by the weight attenuation function is relatively large, and the historical calculation results before reporting can be utilized more effectively. Moreover, by using the static Kalman filter model to smooth the noise, stable calculation accuracy can be maintained.

[0101] It should be understood that the expression of the weight attenuation function is not unique and can also be in other forms. Taking the principle reflected by the above expression as an example, generally, it only needs to reflect that the attenuation coefficient increases as the first interval time decreases and the target filtering weight decreases. Of course, relevant functional expressions that can reflect that the attenuation coefficient decreases as the first interval time decreases but also cause the target filtering weight to decrease can also be constructed by adjusting the attenuation coefficient or changing e to a value less than 1, etc. Specific limitations are not provided here.

[0102] In an optional implementation manner, the reporting monitoring state includes a non-reporting state or a reporting state. The data collection according to the reporting monitoring state includes:

[0103] When the reporting monitoring state is the reporting state, data is collected at the first sampling frequency. In one example, the data collection according to the reporting monitoring state to obtain the first observation data may include: when the reporting monitoring state is the reporting state, data is collected at the first sampling frequency, and the collected data is compressed according to the first set period to form the first observation data.

[0104] When the reporting monitoring state is the non-reporting state, data is collected at the second sampling frequency, where the first sampling frequency is higher than the second sampling frequency. In one example, the data collection according to the reporting monitoring state to obtain the first observation data may include: when the reporting monitoring state is the non-reporting state, data is collected at the second sampling frequency, and the collected data is compressed according to the second set period to form the second observation data, where the first set period is less than the second set period. Preferably, the first set period is determined by the first interval time, and the second set period is determined by the second interval time.

[0105] In this embodiment, the first sampling frequency corresponding to the reporting state is higher than the second sampling frequency corresponding to the non-reporting state, which means that after the monitoring station enters the reporting state, data can be collected at a higher sampling frequency, thereby dynamically adjusting the power consumption of the acquisition equipment of the monitoring station. In addition, the monitoring station can also be default-configured to the non-reporting state to reduce the power consumption of the acquisition equipment under normal circumstances.

[0106] In one example, the first sampling frequency may include 1 Hz. At this time, the monitoring station is in the additional reporting state. It can also merge and compress the observation data collected every 15 minutes into an RTCM (differential correction data following the standard format of the Radio Technical Commission for Maritime Services) file (this file can be named with the time corresponding to the first epoch of the collected data), so as to form the first observation data, and upload the first observation data and status information to the cloud for static millimeter-level solution using the HTTP protocol; after continuously reporting 5 times, if the sensor data of the monitoring station is normal, the monitoring station can return to the non-additional reporting state.

[0107] In one example, the second sampling frequency may be once every 15 s, that is, (1 / 15) Hz, but it should be understood that this second sampling frequency can be configured by the user. At this time, the monitoring station is in the non-additional reporting state. It can collect and cache satellite observation data at a lower sampling frequency of once every 15 s, and merge and compress the observation data collected at the whole hour into an RTCM file (this file can be named with the time corresponding to the first epoch of the collected data), so as to form the first observation data, and upload the first observation data and status information to the solution cloud platform for static millimeter-level solution using the HTTP protocol.

[0108] In this embodiment, the monitoring station utilizes the sensor data. According to the sensor data (such as acceleration and inclination data), the working mode of the monitoring station can be dynamically adjusted. In the non-additional reporting working mode, the device can work at a lower sampling and data reporting frequency. In the actual deformation monitoring application scenario when multiple monitoring stations are deployed, the system power consumption of the monitoring stations can be effectively reduced. In addition, the monitoring station does not need to use the NTRIP protocol to report observation data in real time like the reference station. It only needs to upload the compressed data once through HTTP at the whole hour (determined by the second interval time) or according to the additional reporting time period (determined by the first interval time), which can greatly save network resources, and also reduces the pressure on the observation data processing of the solution platform through this mechanism.

[0109] In an alternative implementation manner, determining the reporting monitoring state of the monitoring station according to the sensor data of the monitoring station includes:

[0110] Based on the first comparison result between the acceleration threshold and the current acceleration of the monitoring station, and / or the second comparison result between the inclination threshold and the current inclination of the monitoring station, determine the reporting monitoring state of the monitoring station, where the current acceleration is determined by the sensor data, and the current inclination is determined by the sensor data.

[0111] In one example, the sensors of the monitoring station may include MEMS sensors. The MEMS sensors are adapted to monitor acceleration in real time to generate acceleration data for characterizing the current acceleration, and / or the MEMS sensors are adapted to monitor the change in inclination in real time to generate inclination data for characterizing the current inclination.

[0112] In one example, the monitoring station may communicate with the device gateway in the cloud via the HTTP protocol.

[0113] In one example, determining the reporting monitoring status of the monitoring station based on the first comparison result between the acceleration threshold and the current acceleration of the monitoring station, and / or the second comparison result between the inclination threshold and the current inclination of the monitoring station may include: when the first comparison result indicates that the current acceleration a t is greater than the acceleration threshold a threshold , that is, a t >a threshold , determining that the reporting monitoring status is the additional reporting status; and / or when the second comparison result indicates that the current inclination is greater than the inclination threshold, determining that the reporting monitoring status is the additional reporting status.

[0114] In one example, the current acceleration a t may be calculated based on the triaxial accelerations a x , a y , a z through the following formula: wherein, the triaxial accelerations are determined from the sensor data.

[0115] In one example, the current inclination may include the X-axis inclination θ x , the Y-axis inclination θ y and / or the Z-axis inclination θ z , at this time:

[0116] When the second comparison result indicates that the absolute value of the X-axis inclination is greater than the corresponding X-axis inclination threshold θ threshold , determining that the reporting monitoring status is the additional reporting status; that is, |θ x |>θ threshold ;

[0117] and / or,

[0118] When the second comparison result indicates that the absolute value of the Y-axis inclination is greater than the corresponding Y-axis inclination threshold θ threshold , determining that the reporting monitoring status is the additional reporting status; that is, |θ y |>θ threshold ;

[0119] and / or,

[0120] When the absolute value of the Z-axis inclination angle indicated by the second comparison result is greater than the corresponding Z-axis inclination angle threshold θ threshold , determine that the reported monitoring status is the additional reporting status, that is, |θ z | > θ threshold .

[0121] The following gives a specific embodiment, denoted as Embodiment 1: Geological disaster deformation monitoring, including the following 1-5.

[0122] 1. Installation: Install GNSS reference stations and GNSS monitoring stations in geological disaster-prone areas. The reference stations are placed in stable geological environments, and the monitoring stations are placed in areas where geological disasters may occur.

[0123] 2. Data acquisition: The reference stations and the monitoring stations start data acquisition simultaneously. The monitoring stations are equipped with MEMS modules to monitor acceleration and inclination data in real time. The monitoring stations default to collect observation data at a lower second sampling frequency. When the MEMS detects that the acceleration and / or inclination changes exceed their respective corresponding thresholds, it automatically switches to the additional reporting status and samples the observation data at a higher second sampling frequency.

[0124] 3. Data transmission: The data of the reference stations is transmitted to the cloud platform through the NTRIP protocol. The monitoring stations upload the observation data collected in the previous cycle once every hour through HTTP when in the non-additional reporting status, and upload the reporting messages periodically according to the preset first reporting interval time when in the additional reporting status.

[0125] 4. Data processing: The cloud receives the data and performs static post-processing millimeter-level algorithm solution. The position deformation information of the monitoring stations is output once an hour when in the non-additional reporting status; when the monitoring stations enter the additional reporting status, the cloud platform adopts a deformation filtering model when performing static post-processing millimeter-level solution, and can output the position deformation information of the monitoring stations once based on the data of each additional report.

[0126] 5. Result output: Output and display the solution results on the user terminal to provide real-time geological disaster monitoring information. Through this embodiment, more accurate millimeter-level deformation can be reflected in a shorter time during actual monitoring.

[0127] In this application, the actual application cost of deformation monitoring is lower and it can be dynamically expanded. In existing deformation monitoring applications, GNSS devices with terminal solution capabilities are generally used as monitoring stations. Whether the device itself integrates MEMS sensors or not, compared with the general-purpose GNSS devices that only need to be able to receive satellite observation data in the present invention, the cost is higher. In addition, the device gateway, data storage, baseline solution, and deformation monitoring platform in the cloud in the present invention can all be elastically deployed based on cloud services, and can be dynamically expanded to support large-scale geological disaster deformation monitoring.

[0128] Second aspect, refer to Figure 4 , which shows a schematic flowchart of a deformation monitoring method provided by an embodiment of the present application. This deformation monitoring method is applicable to the cloud, and this method includes steps S401 - S402, specifically as follows:

[0129] S401, receive a reported message from a monitoring station. Among them, the reported message carries first observation data and status information for indicating the reported monitoring status of the monitoring station. The reported monitoring status is determined by the sensor data of the monitoring station, and the first observation data is collected by the monitoring station according to the reported monitoring status;

[0130] S402, perform deformation analysis on the first observation data using a solution algorithm adapted to the reported monitoring status.

[0131] In an optional implementation manner, the reported monitoring status includes a non - additional reporting status or an additional reporting status;

[0132] When the reported monitoring status is the additional reporting status, the reported message is sent at a first interval time;

[0133] When the reported monitoring status is the non - additional reporting status, the reported message is sent at a second interval time, where the first interval time is less than the second interval time.

[0134] In an optional implementation manner, when the reported monitoring status is the additional reporting status, the target filtering processing method adopted in the solution algorithm is determined by the first interval time.

[0135] In an optional implementation manner, the step of performing deformation analysis on the first observation data using a solution algorithm adapted to the reported monitoring status includes:

[0136] When the reported monitoring status is the additional reporting status, determine a fixed solution based on the first observation data and second observation data from a reference station;

[0137] Determine the target filtering weight of the target filtering processing method according to the first interval time;

[0138] Use the Kalman filtering algorithm to perform deformation analysis on the fixed solution with the target filtering weight as the state transition equation parameter.

[0139] In an optional implementation manner, the step of determining the target filtering weight of the target filtering processing method according to the first interval time includes:

[0140] Determine a weight decay function based on the first interval time, the decay coefficient, and the initial weight;

[0141] Determine the target filtering weight based on the weight decay function and the current time.

[0142] In an alternative embodiment, the reported monitoring status includes a non - additional reporting status or an additional reporting status;

[0143] When the reported monitoring status is the additional reporting status, the first observed data is collected at a first sampling frequency;

[0144] When the reported monitoring status is the non - additional reporting status, the first observed data is collected at a second sampling frequency, where the first sampling frequency is higher than the second sampling frequency.

[0145] In an alternative embodiment, the reported monitoring status is determined based on a first comparison result between an acceleration threshold and the current acceleration of the monitoring station, and / or a second comparison result between an inclination threshold and the current inclination of the monitoring station, where the current acceleration is determined from the sensor data and the current inclination is determined from the sensor data.

[0146] In a third aspect, correspondingly, an embodiment of the present application further provides a deformation monitoring device, which can implement all the processes of the deformation monitoring method provided in any of the embodiments in the first aspect above.

[0147] See Figure 5 , which shows a schematic structural diagram of the deformation monitoring device provided in an embodiment of the present application. The deformation monitoring device is applicable to a monitoring station, and the device includes:

[0148] A status determination module 501, configured to determine the reported monitoring status of the monitoring station according to the sensor data of the monitoring station;

[0149] A data acquisition module 502, configured to perform data acquisition according to the reported monitoring status to obtain first observed data;

[0150] A sending module 503, configured to send a reporting message to the cloud, where the reporting message carries the first observed data and status information for indicating the reported monitoring status, and the reporting message is adapted to instruct the cloud to perform deformation analysis according to the first observed data using a solution algorithm adapted to the reported monitoring status.

[0151] In an alternative embodiment, the reported monitoring status includes a non - additional reporting status or an additional reporting status, and sending the reporting message to the cloud includes:

[0152] When the reported monitoring status is the additional reporting status, send the reported message to the cloud at a first interval time;

[0153] When the reported monitoring status is the non - additional reporting status, send the reported message to the cloud at a second interval time, where the first interval time is less than the second interval time.

[0154] In an optional implementation manner, when the reported monitoring status is the additional reporting status, the target filtering processing method adopted in the solution algorithm is determined by the first interval time.

[0155] In an optional implementation manner, the use of a solution algorithm adapted to the reported monitoring status to perform deformation analysis based on the first observation data includes:

[0156] When the reported monitoring status is the additional reporting status, determine a fixed solution based on the first observation data and the second observation data from a reference station;

[0157] Determine the target filtering weight of the target filtering processing method according to the first interval time;

[0158] Use the target filtering weight as the parameter of the state transition equation, and use the Kalman filtering algorithm to perform deformation analysis based on the fixed solution.

[0159] In an optional implementation manner, the determining the target filtering weight of the target filtering processing method according to the first interval time includes:

[0160] Determine a weight decay function based on the first interval time, a decay coefficient, and an initial weight;

[0161] Determine the target filtering weight based on the weight decay function and the current time.

[0162] In an optional implementation manner, the reported monitoring status includes a non - additional reporting status or an additional reporting status, and the data collection according to the reported monitoring status includes:

[0163] When the reported monitoring status is the additional reporting status, perform data collection at a first sampling frequency;

[0164] When the reported monitoring status is the non - additional reporting status, perform data collection at a second sampling frequency, where the first sampling frequency is higher than the second sampling frequency.

[0165] In an optional implementation manner, the determining the reported monitoring status of the monitoring station according to the sensor data of the monitoring station includes:

[0166] Based on the first comparison result between the acceleration threshold and the current acceleration of the monitoring station, and / or the second comparison result between the inclination threshold and the current inclination of the monitoring station, determine the reporting monitoring state of the monitoring station, where the current acceleration is determined by the sensor data, and the current inclination is determined by the sensor data.

[0167] Fourthly, correspondingly, the embodiment of the present application further provides a deformation monitoring device, which can implement all processes of the deformation monitoring method provided in any embodiment of the second aspect.

[0168] See Figure 6 , which shows a schematic structural diagram of the deformation monitoring device provided by the embodiment of the present application. The deformation monitoring device is applicable to the cloud, and the device includes:

[0169] A receiving module 601, configured to receive a reporting message from a monitoring station, where the reporting message carries first observation data and status information for indicating the reporting monitoring state of the monitoring station. The reporting monitoring state is determined by the sensor data of the monitoring station, and the first observation data is collected by the monitoring station according to the reporting monitoring state;

[0170] An analysis module 602, configured to perform deformation analysis on the first observation data using a solution algorithm adapted to the reporting monitoring state.

[0171] In an optional implementation manner, the reporting monitoring state includes a non-reporting state or a reporting state;

[0172] When the reporting monitoring state is the reporting state, the reporting message is sent at a first interval;

[0173] When the reporting monitoring state is the non-reporting state, the reporting message is sent at a second interval, where the first interval is less than the second interval.

[0174] In an optional implementation manner, when the reporting monitoring state is the reporting state, the target filtering processing method adopted in the solution algorithm is determined by the first interval.

[0175] In an optional implementation manner, the performing deformation analysis on the first observation data using a solution algorithm adapted to the reporting monitoring state includes:

[0176] When the reporting monitoring state is the reporting state, determine a fixed solution based on the first observation data and second observation data from a reference station;

[0177] Determine the target filtering weight of the target filtering processing method according to the first interval time;

[0178] Use the target filtering weight as the parameter of the state transition equation, and perform deformation analysis according to the fixed solution using the Kalman filtering algorithm.

[0179] In an alternative embodiment, the determining the target filtering weight of the target filtering processing method according to the first interval time includes:

[0180] Determine a weight decay function based on the first interval time, the decay coefficient, and the initial weight;

[0181] Determine the target filtering weight based on the weight decay function and the current time.

[0182] In an alternative embodiment, the reported monitoring status includes a non-additional reporting status or an additional reporting status;

[0183] When the reported monitoring status is the additional reporting status, the first observation data is collected at the first sampling frequency;

[0184] When the reported monitoring status is the non-additional reporting status, the first observation data is collected at the second sampling frequency, where the first sampling frequency is higher than the second sampling frequency.

[0185] In an alternative embodiment, the reported monitoring status is determined based on a first comparison result between an acceleration threshold and the current acceleration of the monitoring station, and / or a second comparison result between an inclination threshold and the current inclination of the monitoring station, where the current acceleration is determined from the sensor data, and the current inclination is determined from the sensor data.

[0186] In a fifth aspect, an embodiment of the present application provides a deformation monitoring system, including:

[0187] A monitoring station configured to execute the method described in any one of the first aspects above, or the monitoring station includes the device described in the third aspect above; and,

[0188] A cloud configured to execute the method described in the second aspect above, or the cloud includes the device described in the fourth aspect above.

[0189] Exemplarily, the deformation monitoring system may further include a reference station and / or a user terminal.

[0190] In a sixth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the above methods are implemented.

[0191] In a seventh aspect, an embodiment of the present application provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, the steps of any one of the above methods are implemented.

[0192] In an eighth aspect, an embodiment of the present application provides a computer device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the steps of any one of the above methods are implemented.

[0193] See Figure 7 , the computer device of this embodiment includes: a processor 701, a memory 702, and a computer program stored in the memory 702 and operable on the processor 701, such as a deformation monitoring program. When the processor 701 executes the computer program, the steps in each of the above deformation monitoring method embodiments are implemented, such as Figure 1 the steps S101 - S103 shown.

[0194] Exemplarily, the computer program may be divided into one or more modules / units. The one or more modules / units are stored in the memory 702 and executed by the processor 701 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.

[0195] The computer device may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device may include, but is not limited to, a processor 701 and a memory 702. Those skilled in the art can understand that the schematic diagram is only an example of the computer device and does not constitute a limitation on the computer device. It may include more or fewer components than shown, or combine some components, or different components. For example, the computer device may further include input / output devices, network access devices, a bus, etc.

[0196] The processor 701 may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor 701 may also be any conventional processor, etc. The processor 701 is the control center of the computer device, and connects all parts of the computer device through various interfaces and lines.

[0197] The memory 702 can be used to store the computer programs and / or modules. The processor 701 realizes various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 702, and by invoking the data stored in the memory 702. The memory 702 may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory 702 may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0198] Among them, if the modules / units integrated in the computer device are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present application, it can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor 701, the steps of the above-described method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0199] In summary, the embodiments of the present application at least have the following beneficial effects:

[0200] By adopting the embodiments of the present application, according to the sensor data of the monitoring station, the reported monitoring state where the monitoring station is located is determined; data collection is performed according to the reported monitoring state to obtain first observation data; a reporting message is sent to the cloud, where the reporting message carries the first observation data and status information for indicating the reported monitoring state, and the reporting message is adapted to instruct the cloud to use a solution algorithm adapted to the reported monitoring state to perform deformation analysis based on the first observation data. Thus, only the monitoring station is required to be the data sampling end, and at the same time, the cloud can use the adapted solution algorithm according to the reported monitoring state indicated in the reporting message to timely and accurately detect whether deformation has occurred in the area corresponding to the monitoring station, and further, while reducing the requirements for the performance of the terminal, the real-time performance and accuracy of deformation analysis can be taken into account.

[0201] Through the description of the above embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus a necessary hardware platform. Of course, it can also be implemented entirely through hardware. Based on such an understanding, all or part of the technical solution of this application that contributes to the background art can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM (Read-Only Memory), RAM (Random Access Memory), magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0202] The above is the preferred embodiment of this application. It should be noted that for those of ordinary skill in the art, without departing from the principle of this application, several improvements and refinements can still be made, and these improvements and refinements are also regarded as the protection scope of this application.

Claims

1. A deformation monitoring method, characterized in that, Applicable to a monitoring station, the method includes: Determine the reporting monitoring status of the monitoring station according to the sensor data of the monitoring station; Perform data collection according to the reporting monitoring status to obtain first observation data; Send a reporting message to the cloud, where the reporting message carries the first observation data and status information for indicating the reporting monitoring status, and the reporting message is adapted to instruct the cloud to perform deformation analysis according to the first observation data using a solution algorithm adapted to the reporting monitoring status.

2. The method according to claim 1, characterized in that, The reporting monitoring status includes a non-repeated reporting status or a repeated reporting status. The sending of the reporting message to the cloud includes: In the case where the reporting monitoring status is the repeated reporting status, send the reporting message to the cloud at a first interval; In the case where the reporting monitoring status is the non-repeated reporting status, send the reporting message to the cloud at a second interval, where the first interval is less than the second interval.

3. The method according to claim 2, wherein In the case where the reporting monitoring status is the repeated reporting status, the target filtering processing method adopted in the solution algorithm is determined by the first interval.

4. The method according to claim 3, wherein The performing of deformation analysis according to the first observation data using a solution algorithm adapted to the reporting monitoring status includes: In the case where the reporting monitoring status is the repeated reporting status, determine a fixed solution based on the first observation data and second observation data from a reference station; Determine the target filtering weight of the target filtering processing method according to the first interval; Use the Kalman filtering algorithm to perform deformation analysis according to the fixed solution with the target filtering weight as the state transition equation parameter.

5. The method according to claim 4, characterized in that, The determining of the target filtering weight of the target filtering processing method according to the first interval includes: Determine a weight decay function based on the first interval, a decay coefficient, and an initial weight; Determine the target filtering weight based on the weight decay function and the current time.

6. The method according to claim 1, characterized in that, The reporting monitoring status includes a non-repeated reporting status or a repeated reporting status. The performing of data collection according to the reporting monitoring status includes: In the case where the reporting monitoring status is the repeated reporting status, perform data collection at a first sampling frequency; In the case where the reporting monitoring status is the non-repeated reporting status, perform data collection at a second sampling frequency, where the first sampling frequency is higher than the second sampling frequency.

7. The method according to any one of claims 1 to 6, characterized in that, The determining of the reporting monitoring status of the monitoring station according to the sensor data of the monitoring station includes: Determine the reporting monitoring status of the monitoring station based on a first comparison result between an acceleration threshold and the current acceleration of the monitoring station, and / or a second comparison result between an inclination threshold and the current inclination of the monitoring station, where the current acceleration is determined from the sensor data and the current inclination is determined from the sensor data.

8. A deformation monitoring method, characterized in that, Applicable to the cloud, the method includes: Receive the reported message from the monitoring station. The reported message carries first observation data and status information for indicating the reported monitoring status of the monitoring station. The reported monitoring status is determined by the sensor data of the monitoring station, and the first observation data is collected by the monitoring station according to the reported monitoring status. Perform deformation analysis on the first observation data using a solution algorithm adapted to the reported monitoring status.

9. A deformation monitoring device, characterized in that, Applicable to the monitoring station, the device includes: A status determination module, configured to determine the reported monitoring status of the monitoring station according to the sensor data of the monitoring station; A data collection module, configured to collect data according to the reported monitoring status to obtain first observation data; A sending module, configured to send a reported message to the cloud. The reported message carries the first observation data and status information for indicating the reported monitoring status. The reported message is suitable for instructing the cloud to perform deformation analysis on the first observation data using a solution algorithm adapted to the reported monitoring status.

10. A deformation monitoring device, characterized in that, Applicable to the cloud, the device includes: A receiving module, configured to receive the reported message from the monitoring station. The reported message carries first observation data and status information for indicating the reported monitoring status of the monitoring station. The reported monitoring status is determined by the sensor data of the monitoring station, and the first observation data is collected by the monitoring station according to the reported monitoring status; An analysis module, configured to perform deformation analysis on the first observation data using a solution algorithm adapted to the reported monitoring status.

11. A deformation monitoring system, characterized in that, Includes: A monitoring station, the monitoring station is configured to execute the method according to any one of claims 1-7, or the monitoring station includes the device according to claim 9; and, A cloud, the cloud is configured to execute the method according to claim 8, or the cloud includes the device according to claim 10.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the method according to any one of claims 1-8 is implemented.

13. A computer program product comprising computer instructions, characterized in that, When the computer instruction is executed by a processor, the method according to any one of claims 1-8 is implemented.

14. A computer device, characterized in that, Includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method according to any one of claims 1-8 is implemented.

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