Dam face inclination monitoring method and system based on Kalman filtering and medium

Abstract

The invention discloses a dam face inclination monitoring method and system based on Kalman filtering and a medium, and the method comprises the steps: carrying out the median filtering of the original acceleration data according to the original acceleration data collected by an inclination sensor disposed on the surface of a dam body, carrying out the screening of the obtained effective acceleration data, carrying out the analysis and judgment of the effective acceleration data, and obtaining an inclination monitoring result. Obtaining original data; analyzing the current state of the dam body according to the original data, adaptively adjusting parameters of a Kalman filter according to the current state of the dam body, and inputting the original data into the Kalman filter with the adjusted parameters for filtering processing to obtain filtered data; the original data and the filtering data are uploaded to the management end and the server respectively, the dam body state is analyzed through the original data, then Kalman filter parameters are adjusted, the situation that the original data are too smooth due to the fact that the Kalman filter parameters are fixed is avoided, and it is guaranteed that the safety of the reservoir dam is accurately monitored.

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 through the gateway for processing. 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: 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: 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; S2, analyzing and judging the received effective acceleration data to obtain original data; 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; S4, uploading the original data and the filtered data to a management end and a server respectively.

[0005] In some specific embodiments, the specific process of obtaining effective acceleration data in step S1 is as follows: S11, real-time acceleration data collected by the tilt sensor is received in real time to determine whether the real-time acceleration data is valid; 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, the real-time acceleration data is subjected to median filtering processing to obtain the original acceleration data and store it in the data buffer area.

[0006] In some embodiments, the determination method for determining whether the real-time acceleration data is valid in step S11 is: The reading time of the real-time acceleration data is correct, and the data verification is correct. The real-time acceleration data is within the set valid range.

[0007] In some embodiments, the original 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 length of the data buffer area.

[0008] In some embodiments, the specific process of adaptively adjusting the Kalman filter parameters according to the current state of the dam body is: S31, initialize the Q value, P value and G value of the Kalman filter, and load the default Q value, P value and G value; S32, obtain the last Kalman output data of the Kalman filter, and calculate the absolute value of the change in the tilt angle of the original data and the last Kalman output data; S33, analyze the instantaneous fluctuation intensity of the dam deformation according to the absolute value of the change in the tilt angle, and adjust the Q value according to the instantaneous fluctuation intensity; S34, input the original data into the Kalman filter after adjusting the Q value, and filter the original data according to the Kalman filtering calculation rule to obtain the filtered data.

[0009] In some embodiments, 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: According to the three-axis acceleration, the X-axis tilt angle θ is calculated: 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 is calculated.

[0010] In some embodiments, when |Δθ| is less than the first threshold value, the Q value is adjusted to 0.001 to enhance the filtering smoothing effect. When the instantaneous fluctuation intensity is greater than or equal to the first threshold value and less than the second threshold value, the adjustment Q value = 0.01 * |Delta theta|.

[0011] In some embodiments, the first threshold value is 0.1°, and the second threshold value is 0.5°.

[0012] 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: The sensor edge computing module is used to filter and screen the original acceleration data after median filtering of the original acceleration data collected by the inclination sensor installed on the dam surface to obtain effective acceleration data. The edge gateway with a built-in microcontroller comprises: The data acquisition module acquires the effective acceleration data uploaded by the edge computing module; The data screening module is used to analyze and judge the received effective acceleration data to obtain original data; The adaptive filtering processing module is used to analyze the current state of the dam according to the original data, and to adjust the Kalman filter parameters according to the current state of the dam, and to input the original data into the Kalman filter with adjusted parameters for filtering processing to obtain filtered data. The dual-mode data uploading module is used to upload the original data and the filtered data to the management end and the server respectively.

[0013] In a third aspect, the application provides a computer readable storage medium, comprising: One or more processors; A storage unit for storing one or more programs, which can enable the one or more processors to implement the dam surface inclination monitoring method based on Kalman filtering of the first aspect when the one or more programs are executed by the one or more processors.

[0014] The application has the following beneficial effects: The application utilizes the edge computing module and the adaptive Kalman filtering processing module to realize local data caching when the gateway is offline, direct uploading of monitoring data exceeding the second threshold value, and fully utilizes the processor computing power through the edge computing module to improve the overall computing efficiency and enhance the dynamic analysis capability of the on-site environment exception. The adaptive filtering processing module analyzes the dam 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

[0015] Figure 1A flow chart of a dam surface inclination monitoring method based on Kalman filtering is provided for the embodiment of the present application. Figure 2 A system block diagram of a dam surface inclination monitoring system based on Kalman filtering is provided for the embodiment of the present application. Figure 3 A work flow schematic diagram of an edge computing module is provided for the embodiment of the present application. Figure 4 A work flow schematic diagram of an edge gateway is provided for the embodiment of the present application. DETAILED DESCRIPTION

[0016] 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, rather than all the embodiments of the present application. 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 of ordinary skill in the art without creative labor fall within the scope of the present application.

[0017] Unless otherwise specifically stated, the relative arrangement of parts and steps, numerical expressions, and numerical values set forth in the various examples are not intended to limit the scope of the present application.

[0018] At the same time, it should be understood that, for the convenience of description, the sizes of the various parts shown in the drawings are not drawn in accordance with the actual proportional relationship.

[0019] In addition, for the sake of brevity and clarity, descriptions of well-known structures, functions and configurations are omitted. Those of ordinary skill in the art will recognize that various changes and modifications of the examples described herein can be made without departing from the spirit and scope of the disclosure.

[0020] Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail herein. However, where appropriate, such techniques, methods, and devices can be regarded as part of the licensed disclosure.

[0021] In all the examples shown and discussed herein, any specific values should be interpreted as merely illustrative and not as a limitation. Thus, other examples of the example embodiments can have different values.

[0022] Embodiment 1 As Figure 1 shown, the present embodiment provides a dam surface inclination monitoring method based on Kalman filtering, which performs the following steps in an edge gateway: S1, acquire 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 a dam surface; The specific process of obtaining the effective acceleration data in step S1 is as follows: S11, receive real-time acceleration data collected by the inclination sensor in real time, and determine whether the real-time acceleration data is valid; if not, determine whether the collection time is overdue; if not, receive again; The inclination sensor acceleration collection chip collects X-axis, Y-axis and Z-axis gravitational acceleration, and the determination method of determining whether the real-time acceleration data is valid in step S11 is as follows: 1) The acceleration chip reads data normally, and there is no data reading timeout, data verification error or the like.

[0023] 2) The acceleration data range is within the set range, and there is no data beyond the collection range.

[0024] Then output the collection result and fill the effective data into a fixed-length data buffer.

[0025] The X-axis inclination calculation formula is: Gx is the X-axis acceleration, Gy is the Y-axis acceleration, and Gz is the Z-axis acceleration.

[0026] The Y-axis inclination calculation formula is: Gx is the X-axis acceleration, Gy is the Y-axis acceleration, and Gz is the Z-axis acceleration.

[0027] S12, if the real-time acceleration data is valid, obtain the maximum value and the minimum value in the data buffer area, perform median filtering processing on the real-time acceleration data, and store the raw acceleration data in the data buffer area.

[0028] The raw acceleration data S is: 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.

[0029] S2, analyze the received effective acceleration data to obtain raw data; S3, analyze the current state of the dam according to the raw data, adaptively adjust the Kalman filter parameters according to the current state of the dam, input the raw data into the Kalman filter with 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 body is as follows: S31, initializing the Q value, P value and G value of the Kalman filter, 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 the tilt angle of the original data and the last Kalman output data; 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 change in the tilt angle is the difference between the X-axis tilt angle of the original data at the current moment and the X-axis tilt angle output after the original data at the last moment is input into the Kalman filter; 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 as follows: According to the three-axis acceleration, the X-axis tilt angle θ is calculated: 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 is calculated.

[0030] S33, analyzing the instantaneous fluctuation intensity of the dam body deformation according to the absolute value of the change in the tilt angle, and adjusting the Q value according to the instantaneous fluctuation intensity; When |Δθ| is less than the first threshold value, the Q value is adjusted to 0.001, indicating that the current dam body deformation is slow, and the filtering smoothing effect can be enhanced; 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°), the Q value is adjusted to 0.01*|Δθ|.

[0031] S34, 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.

[0032] The adaptive Kalman is based on the basic Kalman filter and dynamically adjusts the related parameters according to the filtering data. The basic Kalman filter calculation rule in this method is as follows: Current estimated covariance P k '=last estimated covariance P k-1 +adaptive parameter Q Kalman gain G=current estimated covariance P k ' / (current estimated covariance P k '+measurement difference R between current temperature and standard temperature) Current Kalman output=last Kalman output+Kalman gain G*(current angle measurement value Z k -last Kalman output) Updated current estimated covariance P k =(1-Kalman gain G)*Current estimated covariance P k ' When the raw data first enters the adaptive Kalman filter, the default Q, P, and G values ​​are loaded.

[0033] The process of adaptive Kalman filtering is as follows: If the absolute value of the difference between the current input angle and the predicted angle from the Kalman filter is greater than the second threshold (second threshold = historical fluctuation standard deviation x 2), it indicates that the data fluctuation is too large, and filtering is disabled for direct output. If it is less than the threshold, Kalman filtering is performed. The pseudocode is: If (abs(current angle - predicted angle > threshold)) / / Threshold = historical fluctuation standard deviation x 2 Output the original value directly; / / If the fluctuation is too large, disable filtering. Else Perform Kalman filtering (Q and R parameters adaptively adjusted) / / Q (process noise) increases dynamically with the rate of change of angle.

[0034] S4. Upload the raw data and filtered data to the management terminal and the server respectively.

[0035] Data is uploaded to both the cloud server and the management terminal simultaneously. The cloud data is adaptive Kalman filtered data, while the management terminal data is the raw data. If the cloud reporting fails when the filtered data is uploaded to the cloud, it is cached locally. When the network is normal, the local historical data is reported to the cloud one by one.

[0036] Example 2 like Figure 2 As shown, this embodiment provides a dam tilt monitoring system based on Kalman filtering, applying the dam tilt monitoring method based on Kalman filtering in Embodiment 1, including: a sensor edge computing module, an edge gateway with a built-in microcontroller, and a server cloud, wherein: 1. Sensor edge computing module, used to filter the raw acceleration data collected by the tilt sensor installed on the dam surface by performing median filtering on the raw acceleration data to obtain effective acceleration data; The tilt sensor is installed on the dam surface and includes an acceleration acquisition chip (ADXL355, measuring ±30° tilt angle, integrating vibration monitoring, delay ≤50 milliseconds). The converted acceleration has a tilt angle resolution ≤0.0001°. like Figure 3As shown, the workflow of the edge computing module is as follows: after startup, it reads the acceleration data collected from the acceleration chip of the tilt sensor; it performs a data validity determination on the collected acceleration data; when the data is invalid, it continues to determine whether the acquisition has timed out; if it has not timed out, it continues acceleration acquisition; if it has timed out, it ends acceleration acquisition. When the data is valid, the acceleration data is filtered by median value. After filtering, the data is packaged and sent to the edge gateway via LoRa wireless. Monitor whether acceleration data is sent to the wireless gateway, and determine the sending result. If the sending is successful, end the current collection. If the sending fails, determine whether the sending timeout has occurred. If the timeout has not occurred, resend the data. If the timeout has occurred, end the current collection.

[0037] 2. The edge gateway with a built-in microcontroller includes: The data acquisition module acquires valid acceleration data uploaded by the edge computing module; The data filtering module is used to analyze and judge the received valid acceleration data to obtain the raw data; The adaptive filtering module is used to analyze the current state of the dam body based on the raw data, adaptively adjust the parameters of the Kalman filter according to the current state of the dam body, and input the raw data into the Kalman filter with adjusted parameters for filtering to obtain filtered data. The dual-mode data upload module is used to upload raw data and filtered data to the management terminal and the server, respectively.

[0038] The edge gateway is installed in the dam-top data center, featuring a built-in ARM Cortex-M4 processor (integrated FPU unit), a GD32F407 main controller for data management and communication, and a W25Q128 cache chip for offline data storage. When the server in the cloud is offline, it can cache 10 days' worth of historical data locally (1 node / 10-minute frequency). The gateway microcontroller has a built-in floating-point computing unit, accelerating floating-point calculations in Kalman filtering (tests show a reduction from 32 milliseconds to 0.5 milliseconds). The edge gateway has dual-mode data output interfaces: a normal channel for reporting filtered data to the cloud; and a backup channel for reporting raw data to the management backend, ensuring over 99% availability for skewed data. like Figure 4 As shown, the workflow of the edge gateway is as follows: After startup, LoRa waits to receive data; LoRa receives acceleration data uploaded by the sensor edge computing module and parses out the raw data; it performs data validity determination on the raw data, uploads valid raw data to the management terminal, performs adaptive Kalman filtering on the raw data, uploads the filtered data to the cloud, and monitors whether the filtered data has been uploaded to the cloud. When the cloud fails to report data, it caches the data locally; when the network is normal, it reports the local historical data to the cloud one by one.

[0039] Embodiment 3 The embodiment provides a computer readable storage medium comprising: one or more processors; 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 dam surface inclination monitoring method based on Kalman filtering in the first aspect.

[0040] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application in any form. According to the technical essence of the present application, any simple modification, equivalent replacement and improvement of the above embodiments, and the like, all 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 through median value filtering; 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; S4, uploading the raw data and the filtered data to a management end and a 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 the 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, 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 of 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, The raw acceleration data S is as follows: 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 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 inclination angle change 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 inclination angle change, and adjusting the Q value according to the instantaneous fluctuation intensity; S34, inputting the raw data into the Kalman filter with adjusted Q value, and performing filtering processing on the raw data according to the Kalman filtering calculation rule to obtain filtered data.

6. The dam surface inclination monitoring method based on Kalman filtering according to claim 5, characterized in that, The process of obtaining the instantaneous fluctuation intensity is as follows: According to the three-axis acceleration, the X-axis inclination angle θ is calculated: 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.

7. The dam surface inclination monitoring method based on Kalman filtering according to claim 6, characterized in that, When |Δθ| is less than the first threshold value, the Q value is adjusted to 0.001 to strengthen the filtering smoothing effect; When the instantaneous fluctuation intensity is greater than or equal to the first threshold value and less than the second threshold value, the Q value is adjusted to 0.01*|Δθ|.

8. The dam surface inclination monitoring method based on Kalman filtering according to claim 7, characterized in that, The first threshold value is 0.1°, and the second threshold value is 0.5°.

9. A dam surface inclination monitoring system based on Kalman filtering, characterized by, It comprises: 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, and to obtain effective acceleration data by performing median filtering and screening on the original acceleration data. The edge gateway with the 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, to adaptively adjust the Kalman filter parameters according to the current state of the dam, to input the original data into the Kalman filter with the adjusted parameters for filtering processing, and to obtain filtered data; A dual-mode data uploading module configured to upload the original data and the filtered data to a management end and a server, respectively.

10. 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 a dam surface tilt monitoring method based on Kalman filtering as claimed in any one of claims 1-8.

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