Dam surface inclination monitoring method and system based on kalman filtering and medium

Abstract

The application discloses a dam surface inclination monitoring method and system based on Kalman filtering, a medium, and the like. According to original acceleration data collected by an inclination sensor installed on a dam surface, effective acceleration data is obtained by performing median value filtering on the original acceleration data. The effective acceleration data is analyzed and judged to obtain original data. The current state of the dam is analyzed according to the original data. The Kalman filter parameters are adaptively adjusted according to the current state of the dam. The original data is input into the Kalman filter with the adjusted parameters for filtering processing to obtain filtered data. The original data and the filtered data are uploaded to a management end and a server, respectively. The state of the dam is analyzed through the original data, and then the Kalman filter parameters are adjusted to avoid excessive smoothing of the original data caused by fixed Kalman filter parameters, thereby ensuring accurate monitoring of the safety of the reservoir dam.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydraulic engineering safety monitoring, and particularly relates to a dam surface inclination monitoring method and system based on Kalman filtering and a medium. BACKGROUND

[0002] Reservoir safety has always been a difficult and important point in flood prevention and control. The most important thing for reservoir safety is the safety monitoring of reservoir dams, especially the inclination monitoring of dams, which affects the safety of reservoirs. However, the slope inclination monitoring in the current dam safety monitoring is mainly centralized data processing, that is, a certain number of sensors are installed on the dam surface, and all sensor data are uploaded to the server for processing through the gateway. In this way, the cloud is strongly dependent, and the original inclination data is directly uploaded to the cloud for processing through the gateway. If the network is interrupted, the gateway cannot upload data, resulting in the loss of dam monitoring. In addition, the processing filter of the original inclination data has poor adaptability. In the sudden fluctuation scene of rainstorm / earthquake, the Kalman filter with fixed parameters will cause excessive smoothing when processing data, which will cover up the real danger. And the existing gateway is only used as a data transfer station, which causes waste of hardware resources. SUMMARY

[0003] The purpose of the present application is to provide a dam surface inclination monitoring method and system based on Kalman filtering and a medium. The original data is used to analyze the state of the dam body, and then the Kalman filter parameters are adjusted to avoid excessive smoothing of the original data caused by fixed Kalman filter parameters, and to ensure accurate monitoring of the safety of reservoir dams.

[0004] To achieve the above purpose, the present application provides the following solutions:

[0005] On the one hand, the present application provides a dam surface inclination monitoring method based on Kalman filtering, which executes the following steps through a gateway installed in a dam top machine room:

[0006] S1, obtaining effective acceleration data uploaded by an edge computing module, the effective acceleration data being obtained by screening original acceleration data after median value filtering of the original acceleration data collected by an inclination sensor installed on the surface of the dam body;

[0007] S2, analyzing and judging the received effective acceleration data to obtain original data;

[0008] S3, analyzing the current state of the dam body according to the original data, and self-adaptively adjusting the Kalman filter parameters according to the current state of the dam body, and inputting the original data into the Kalman filter with adjusted parameters for filtering processing to obtain filtered data;

[0009] S4, uploading the original data and the filtered data to a management end and a server respectively.

[0010] In some embodiments, the specific process of obtaining valid acceleration data in step S1 is as follows:

[0011] S11, real-time acceleration data collected by the tilt sensor is received in real time, and it is determined whether the real-time acceleration data is valid;

[0012] S12, if the real-time acceleration data is valid, the maximum and minimum values in the data buffer area are obtained from the data buffer area, and the real-time acceleration data is subjected to median filtering processing to obtain raw acceleration data which is stored in the data buffer area.

[0013] In some embodiments, the determination method of determining whether the real-time acceleration data is valid in step S11 is as follows:

[0014] The reading time of the real-time acceleration data and the data check are both correct;

[0015] The real-time acceleration data is within the set valid range.

[0016] In some embodiments, the raw acceleration data S is:

[0017]

[0018] Wherein, Xi is the real-time acceleration data, Xmax is the maximum value in the current data buffer area, Xmin is the minimum value in the current data buffer area, and n is the length of the data buffer area.

[0019] In some embodiments, the specific process of adaptively adjusting the Kalman filter parameters according to the current state of the dam is as follows:

[0020] S31, initializing the Q value, P value and G value of the Kalman filter, and loading the default Q value, P value and G value;

[0021] S32, obtaining the last Kalman output data of the Kalman filter, and calculating the absolute value of the change in the tilt angle of the raw data and the last Kalman output data;

[0022] S33, analyzing the instantaneous fluctuation intensity of the dam deformation according to the absolute value of the change in the tilt angle, and adjusting the Q value according to the instantaneous fluctuation intensity;

[0023] S34, inputting the raw data into the Kalman filter with the adjusted Q value, and filtering the raw data according to the Kalman filtering calculation rule to obtain filtered data.

[0024] In some embodiments, the raw data includes three-axis acceleration: X-axis acceleration Gx, Y-axis acceleration Gy and Z-axis acceleration Gz, and the process of obtaining the instantaneous fluctuation intensity is as follows:

[0025] According to the three-axis acceleration, the X-axis inclination angle θ is calculated:

[0026]

[0027] The absolute value |Δθ| of the difference between the X-axis inclination angle of the current raw data and the X-axis inclination angle of the last Kalman output data is calculated.

[0028] In some specific embodiments, when |Δθ| is less than a first threshold value, the Q value is adjusted to 0.001 to strengthen the filtering smoothing effect;

[0029] When the instantaneous fluctuation intensity is greater than or equal to the first threshold value and less than a second threshold value, the Q value is adjusted to 0.01*|Δθ|.

[0030] In some specific embodiments, the first threshold value is 0.1° and the second threshold value is 0.5°.

[0031] In a second aspect, the application provides a dam surface inclination monitoring system based on Kalman filtering, comprising: a sensor edge computing module, an edge gateway with a built-in microcontroller, and a server cloud, wherein:

[0032] The sensor edge computing module is used to filter and screen the effective acceleration data by performing median value filtering on the raw acceleration data collected by the inclination sensor installed on the dam surface.

[0033] The edge gateway with a built-in microcontroller comprises:

[0034] The data acquisition module acquires the effective acceleration data uploaded by the edge computing module.

[0035] The data screening module is used to analyze and judge the received effective acceleration data to obtain raw data.

[0036] The adaptive filtering processing module is used to analyze the current state of the dam according to the raw data, to adaptively adjust the Kalman filter parameters according to the current state of the dam, to input the raw data into the Kalman filter with adjusted parameters for filtering processing, and to obtain filtered data.

[0037] The dual-mode data uploading module is used to upload the raw data and the filtered data to the management end and the server, respectively.

[0038] In a third aspect, the application provides a computer readable storage medium, comprising:

[0039] One or more processors;

[0040] A storage unit is configured to store one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement the method for monitoring dam surface inclination based on Kalman filtering.

[0041] The present application has the following beneficial effects:

[0042] The present application utilizes the edge computing module and the adaptive Kalman filtering processing module to realize local data caching when the gateway is offline, direct transmission and uploading when the monitoring data exceeds the second threshold, fully utilizes the processor operation capacity through the edge computing module, improves the overall computing efficiency and enhances the dynamic analysis capability of the on-site environment anomaly.

[0043] The adaptive filtering processing module analyzes the dam body state through the original data, and then adjusts the Kalman filter parameters to avoid excessive smoothing of the original data caused by fixed Kalman filter parameters, and ensures accurate monitoring of the safety of the reservoir dam. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 The present application provides a method for monitoring dam surface inclination based on Kalman filtering.

[0045] Figure 2 The present application provides a system block diagram for monitoring dam surface inclination based on Kalman filtering.

[0046] Figure 3 The present application provides a working process diagram of the edge computing module.

[0047] Figure 4 The present application provides a working process diagram of the edge gateway. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. The description of the at least one exemplary embodiment is actually only illustrative, but not as any limitation on the present application and its application or use. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0049] Unless otherwise specified, the relative arrangement, numerical expression and values of the components and steps described in the embodiments do not limit the scope of the present application.

[0050] Meanwhile, it should be understood that the sizes of the various parts shown in the drawings are not drawn in accordance with the actual proportional relationship for the convenience of description.

[0051] In addition, descriptions of known structures, functions and configurations can be omitted for clarity and conciseness. Those of ordinary skill in the art will recognize that various changes and modifications can be made to the examples described herein without departing from the spirit and scope of the present disclosure.

[0052] Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail herein, but should be considered part of the enabling description where appropriate.

[0053] In all examples shown and discussed herein, any specific values should be interpreted as merely exemplary and not limiting. Thus, other examples of the example embodiments can have different values.

[0054] Embodiment 1

[0055] As shown in the Figure 1 The present embodiment provides a Kalman filter-based dam surface inclination monitoring method, which performs the following steps in the edge gateway:

[0056] S1, obtaining effective acceleration data uploaded by the edge computing module, the effective acceleration data being obtained by screening the original acceleration data collected by the inclination sensor installed on the dam surface after median value filtering the original acceleration data;

[0057] The specific process of obtaining the effective acceleration data in step S1 is as follows:

[0058] S11, real-time receiving real-time acceleration data collected by the inclination sensor, determining whether the real-time acceleration data is valid; if not, determining whether the collection time is overdue; if not, re-receiving;

[0059] The inclination sensor acceleration collection chip collects X-axis, Y-axis and Z-axis gravity acceleration, and the determination method of determining whether the real-time acceleration data is valid in step S11 is as follows:

[0060] 1) The acceleration chip reads data normally, and there is no data reading timeout, data verification error, etc.

[0061] 2) The acceleration data range is within the set range, and there is no data beyond the collection range.

[0062] Then output the collection result and fill the effective data into the fixed-length data buffer.

[0063] The X-axis inclination calculation formula is:

[0064] Gx is the X-axis acceleration, Gy is the Y-axis acceleration, and Gz is the Z-axis acceleration.

[0065] The Y-axis inclination angle calculation formula is:

[0066]

[0067] Gx is the X-axis direction acceleration, Gy is the Y-axis acceleration, and Gz is the Z-axis acceleration.

[0068] S12, if the real-time acceleration data is valid, the maximum value and the minimum value in the data buffer area are obtained from the data buffer area, the real-time acceleration data is subjected to median filtering processing, and the original acceleration data is stored in the data buffer area.

[0069] The original acceleration data S is:

[0070]

[0071] Xi is the real-time acceleration data, Xmax is the maximum value in the current data buffer area, Xmin is the minimum value in the current data buffer area, and n is the data buffer area length.

[0072] S2, the received valid acceleration data is analyzed and judged to obtain the original data;

[0073] S3, the current state of the dam body is analyzed according to the original data, the Kalman filter parameters are adaptively adjusted according to the current state of the dam body, the original data is input into the Kalman filter with the adjusted parameters for filtering processing, and the filtered data is obtained;

[0074] The specific process of adaptively adjusting the Kalman filter parameters according to the current state of the dam body is:

[0075] S31, the Q value, the P value and the G value of the Kalman filter are initialized, and the default Q value, the P value and the G value are loaded;

[0076] S32, the last Kalman output data of the Kalman filter is obtained, and the absolute value of the inclination angle change of the original data and the last Kalman output data is calculated; the last Kalman output data is the data output after the original data at the last moment is input into the Kalman filter; that is, the absolute value of the inclination angle change is the difference between the X-axis inclination angle of the original data at the current moment and the X-axis inclination angle output after the original data at the last moment is input into the Kalman filter;

[0077] The original data includes three-axis acceleration: X-axis acceleration Gx, Y-axis acceleration Gy and Z-axis acceleration Gz, and the process of obtaining the instantaneous fluctuation intensity is:

[0078] The X-axis inclination angle θ is calculated according to the three-axis acceleration:

[0079]

[0080] Calculate the absolute value of the difference between the X-axis inclination of the current original data and the X-axis inclination of the last Kalman output data |Δθ|.

[0081] S33, analyze the instantaneous fluctuation intensity of the dam deformation according to the inclination angle change absolute value, and adjust the Q value according to the instantaneous fluctuation intensity;

[0082] When |Δθ| is less than the first threshold value, adjust the Q value = 0.001, which means that the current dam deformation is slow, and the filtering smoothing effect can be enhanced;

[0083] When the instantaneous fluctuation intensity is greater than or equal to the first threshold value and less than the second threshold value (specifically, the first threshold value is 0.1°, and the second threshold value is 0.5°), adjust the Q value = 0.01*|Δθ|.

[0084] S34, input the original data into the Kalman filter with adjusted Q value, and perform filtering processing on the original data according to the Kalman filtering calculation rule to obtain filtered data.

[0085] The adaptive Kalman is based on the basic Kalman filter and dynamically adjusts the related parameters according to the filtered data. The basic Kalman filter calculation rule in this method is as follows:

[0086] Current estimated covariance P k '=last estimated covariance P k-1 +adaptive parameter Q

[0087] Kalman gain G=current estimated covariance P k ' / (current estimated covariance P k '+measurement difference R between current temperature and standard temperature)

[0088] Current Kalman output=last Kalman output+Kalman gain G*(current angle measurement value Z k -last Kalman output)

[0089] Updated current estimated covariance P k =(1-Kalman gain G)*current estimated covariance P k '

[0090] When the original data first enters the adaptive Kalman filter, load the default Q value, P value and G value.

[0091] The process of adaptive Kalman filtering is as follows:

[0092] If the absolute value of the current input angle minus the predicted angle of the Kalman output is greater than the second threshold value (second threshold value=historical fluctuation standard deviation x2), it means that the data fluctuation is too large, and the filtering is disabled and directly output, if it is less than the threshold value, the Kalman filtering processing is executed. The pseudo code is:

[0093] If(abs(current angle-predicted angle>threshold) / / threshold=history fluctuation standard deviation x 2

[0094] Directly output the original value; / / fluctuation is too large, disable filtering

[0095] Else

[0096] Perform Kalman filtering (Q, R parameters adaptive adjustment) / / Q (process noise) dynamically increases with the angle change rate.

[0097] S4, upload the original data and the filtered data to the management end and the server respectively.

[0098] Upload data to the server cloud and the management end at the same time, the cloud data is adaptive Kalman filtering data, the management end is the original data, and the filtered data is cached locally when uploading to the cloud fails; when the network is normal, the local historical data is reported to the cloud one by one.

[0099] Embodiment 2

[0100] As shown in Figure 2 , the embodiment provides a dam surface inclination monitoring system based on Kalman filtering, which applies the dam surface inclination monitoring method based on Kalman filtering of embodiment 1, and includes a sensor edge computing module, an edge gateway with a built-in microcontroller, and a server cloud, wherein:

[0101] 1. The sensor edge computing module is used to collect effective acceleration data by performing median value filtering on the original acceleration data collected by the inclination sensor installed on the dam surface.

[0102] The inclination sensor is installed on the dam surface and includes an acceleration collection chip (ADXL355 is used, which measures a tilt angle of ±30° and integrates vibration monitoring with a delay of ≤50 milliseconds), and the tilt angle resolution after acceleration conversion is ≤0.0001°.

[0103] As shown in Figure 3 , the workflow of the edge computing module is as follows: after starting, the acceleration data collected by the acceleration chip of the inclination sensor is read; when the collected acceleration data is invalid, it is determined whether the collection is overdue, if not, the acceleration collection continues, and if yes, the acceleration collection ends.

[0104] When the data is valid, the acceleration data is subjected to median value filtering, and after the filtering is completed, the data is packaged and sent to the edge gateway through Lora wireless.

[0105] The acceleration data is monitored whether to be sent to the wireless gateway, the sending result is judged, when the sending succeeds, the current collection is ended, when the sending fails, whether the sending is timed out is judged, if not, the sending is re-performed, if yes, the current collection is ended.

[0106] 2. The edge gateway with a built-in microcontroller comprises:

[0107] A data collection module acquires valid acceleration data uploaded by the edge computing module;

[0108] A data screening module is configured to analyze and judge the received valid acceleration data to obtain original data.

[0109] An adaptive filtering processing module is configured to analyze the current state of the dam body according to the original data, to adjust the Kalman filter parameters according to the current state of the dam body, to input the original data into the Kalman filter with the adjusted parameters for filtering processing to obtain filtered data.

[0110] A dual-mode data uploading module is configured to upload the original data and the filtered data to the management terminal and the server respectively.

[0111] The edge gateway is installed in the dam top machine room, and is built-in with an ARM Cortex-M4 processor (integrating an FPU unit), adopts a GD32F407 main controller for data management and communication, and adopts a W25Q128 cache chip to store offline data. The local can cache 10 days of historical data (1 node / 10 minutes frequency) when the server cloud is disconnected. The gateway microcontroller is built-in with a floating point calculation unit, which accelerates the floating point calculation in the Kalman filter (according to the test, the floating point calculation can be reduced from 32 milliseconds to 0.5 milliseconds), and the edge gateway has a dual-mode data output interface normal channel: reporting the filtered data to the cloud; and a backup channel: reporting the original data to the management background, and the availability of the tilt data is more than 99%.

[0112] As shown in Figure 4 the working process of the edge gateway is as follows:

[0113] After starting, the Lora waits for receiving data; the Lora receives the acceleration data uploaded by the sensor edge computing module and analyzes the original data; the original data is subjected to data validity judgment, and the valid original data is uploaded to the management terminal; the original data is subjected to adaptive Kalman filtering processing, and the obtained filtered data is uploaded to the cloud, while monitoring whether the filtered data is uploaded to the cloud, and when the cloud reporting fails, the data is cached locally; when the network is normal, the local historical data is reported to the cloud one by one.

[0114] Embodiment 3

[0115] The embodiment provides a computer readable storage medium, comprising:

[0116] one or more processors;

[0117] a storage unit, configured to store one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the Kalman filtering based dam surface inclination monitoring method of the first aspect.

[0118] The above is only the preferred embodiment of the present application, not any form of the present application for any restrictions, according to the technical essence of the present application, in the spirit and principles of the present application, any simple modification, equivalent replacement and improvement of the above embodiment, etc., still belong to the protection scope of the technical scheme of the present application.

Claims

1. A dam surface inclination monitoring method based on Kalman filtering, characterized in that, The following steps are performed through the gateway installed in the dam top machine room: S1, obtaining effective acceleration data uploaded by an edge computing module, the effective acceleration data being obtained by filtering raw acceleration data collected by an inclination sensor installed on the dam body surface after median value filtering of the raw acceleration data; S2, analyzing and judging the received effective acceleration data to obtain raw data; S3, analyzing the current state of the dam body according to the raw data, and adaptively adjusting the Kalman filter parameters according to the current state of the dam body, and inputting the raw data into the Kalman filter with adjusted parameters for filtering processing to obtain filtered data; The specific process of adaptively adjusting the Kalman filter parameters according to the current state of the dam body is as follows: S31, initializing the Q value, P value and G value of the Kalman filter, and loading the default Q value, P value and G value; S32, obtaining the last Kalman output data of the Kalman filter, and calculating the absolute value of the change in inclination angle of the raw data and the last Kalman output data; S33, analyzing the instantaneous fluctuation intensity of the dam body deformation according to the absolute value of the change in inclination angle, and adjusting the Q value according to the instantaneous fluctuation intensity; The raw data includes three-axis acceleration: X-axis acceleration Gx, Y-axis acceleration Gy and Z-axis acceleration Gz, and the process of obtaining the instantaneous fluctuation intensity is as follows: Calculate the X-axis inclination angle θ according to the three-axis acceleration: Calculate the absolute value |Δθ| of the difference between the X-axis inclination angle of the current raw data and the X-axis inclination angle of the last Kalman output data; When |Δθ| is less than the first threshold value, adjust Q value = 0.001 to strengthen the filtering smoothing effect; When |Δθ| is greater than or equal to the first threshold value and less than the second threshold value, adjust Q value = 0.01*|Δθ|; S34, inputting the raw data into the Kalman filter with adjusted Q value, and filtering the raw data according to the Kalman filtering calculation rule to obtain filtered data; S4, uploading the raw data and the filtered data to the management end and the server respectively.

2. The dam surface inclination monitoring method based on Kalman filtering according to claim 1, characterized in that, The specific process of obtaining effective acceleration data in step S1 is as follows: S11, real-time receiving real-time acceleration data collected by the inclination sensor, and determining whether the real-time acceleration data is valid; S12, if the real-time acceleration data is valid, obtaining the maximum value and the minimum value in the data buffer area from the data buffer area, and performing median filtering processing on the real-time acceleration data to obtain raw acceleration data and store it in the data buffer area.

3. The dam surface inclination monitoring method based on Kalman filtering according to claim 2, characterized in that, The determination method for determining whether the real-time acceleration data is valid in step S11 is as follows: The reading time of the real-time acceleration data and the data verification are correct; The real-time acceleration data is within the set valid range.

4. The dam surface inclination monitoring method based on Kalman filtering according to claim 2, characterized in that, Raw acceleration data S: Wherein, Xi is the real-time acceleration data, Xmax is the maximum value in the current data buffer area, Xmin is the minimum value in the current data buffer area, and n is the data buffer area length.

5. The dam surface inclination monitoring method based on Kalman filtering according to claim 1, characterized in that, The first threshold value is 0.1°, and the second threshold value is 0.5°.

6. A dam surface inclination monitoring system based on Kalman filtering, characterized by, It includes: A sensor edge computing module, an edge gateway with a built-in microcontroller, and a server cloud, wherein: The sensor edge computing module is configured to collect original acceleration data from the tilt sensor installed on the dam surface, filter the original acceleration data by median filtering, and obtain effective acceleration data; The edge gateway with a built-in microcontroller comprises: A data acquisition module configured to acquire the effective acceleration data uploaded by the edge computing module; A data screening module configured to analyze and judge the received effective acceleration data to obtain original data; An adaptive filtering processing module configured to analyze the current state of the dam according to the original data, adaptively adjust the Kalman filter parameters according to the current state of the dam, input the original data into the Kalman filter with the adjusted parameters for filtering processing, and obtain filtered data; The specific process of adaptively adjusting the Kalman filter parameters according to the current state of the dam comprises: Initializing the Q value, P value and G value of the Kalman filter, and loading default Q value, P value and G value; Obtaining the last Kalman output data of the Kalman filter, and calculating the absolute value of the change in the tilt angle of the original data and the last Kalman output data; Analyzing the instantaneous fluctuation intensity of the dam deformation according to the absolute value of the change in the tilt angle, and adjusting the Q value according to the instantaneous fluctuation intensity; The original data includes three-axis acceleration: X-axis acceleration Gx, Y-axis acceleration Gy and Z-axis acceleration Gz, and the process of obtaining the instantaneous fluctuation intensity comprises: Calculating the X-axis tilt angle θ according to the three-axis acceleration: Calculating the absolute value |Δθ| of the difference between the X-axis tilt angle of the current original data and the X-axis tilt angle of the last Kalman output data; When |Δθ| is less than a first threshold value, adjusting the Q value = 0.001 to strengthen the filtering smoothing effect; When |Δθ| is greater than or equal to the first threshold value and less than a second threshold value, adjusting the Q value = 0.01*|Δθ|; Inputting the original data into the Kalman filter with the adjusted Q value, and performing filtering processing on the original data according to the Kalman filtering calculation rule to obtain filtered data; A dual-mode data uploading module configured to upload the original data and the filtered data to a management terminal and a server, respectively.

7. A computer readable storage medium characterized in that, The system comprises: One or more processors; A storage unit configured to store one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement the dam surface tilt monitoring method based on Kalman filtering according to any one of claims 1-5.

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