Groundwater level abnormality detection method and system based on pressure sensing and image combination

Abstract

The present invention belongs to the field of groundwater monitoring technology, and relates to a groundwater level state abnormality detection method and system based on the combination of pressure sensing and images. The method firstly obtains water level pressure sequence data at different positions and depths in a to-be-detected area, and pre-processes it using an extension interpolation model to generate a pre-processed water level pressure sequence; at the same time, a virtual three-dimensional space is constructed in combination with geographic state information, and the pre-processed data is input into a three-dimensional heat map generation model to generate a three-dimensional water level distribution thermal space; secondly, by configuring water level classification abnormality intervals and water level color mapping functions, a three-dimensional water level abnormality distribution thermal space is obtained and updated and displayed in real time; in addition, the method uses historical rainfall and groundwater level change data to obtain predicted water level values, and generates a predicted three-dimensional water level abnormality distribution thermal space, which is displayed together with real-time data to realize secondary early warning; the method combines pressure sensing detection with visualization technology to improve the accuracy of groundwater level state monitoring.

Description

Technical Field

[0001] The present invention belongs to the technical field of groundwater monitoring, and in particular relates to a method and system for detecting abnormal groundwater level status based on the combination of pressure sensing and images. Background Art

[0002] In the process of foundation pit monitoring, groundwater level monitoring is a vital task. Traditional monitoring methods mainly include two categories: manual measurement and automated measurement. Manual measurement usually uses a steel tape measure or a water gauge to measure the water level. This method is not only inefficient, but also easily affected by human factors, resulting in large reading errors. On the other hand, with the advancement of technology, automated measurement has gradually become the mainstream, which monitors the changes in groundwater levels in real time by arranging sensors in the foundation pit. However, although the automated measurement method provides higher accuracy and real-time performance, it also has obvious shortcomings. First, the installation of the automated monitoring system requires the burial of a large number of sensors, data cables, and power cables on site, which not only has high initial investment costs, but also makes the installation and maintenance of sensors extremely difficult under complex geological conditions. Second, the sensors are easily disturbed by environmental factors, such as changes in soil moisture or mechanical vibrations, resulting in unstable data or even failure.

[0003] For example, a Chinese patent application with authorization announcement number CN117990187B discloses a method for dynamic monitoring of groundwater levels based on artificial intelligence, including: obtaining a real-time data curve for a current sampling period and several real-time data curves for historical sampling periods; obtaining a fluctuation evaluation of the real-time data curve for the current sampling period and the real-time data curve for each historical sampling period based on the difference in water level data on the real-time data curve for the current sampling period; obtaining the degree of abnormality of the adjusted water level data at each moment based on the difference between the real-time data curve for the current sampling period and the real-time data curve for each historical sampling period; obtaining the abnormal water level based on the degree of abnormality of the adjusted water level data at each moment, and completing dynamic monitoring of the groundwater level.

[0004] The above existing technologies have the following problems: the existing monitoring systems can often only provide simple water level data reports or two-dimensional charts, and cannot intuitively display the water level changes at different depths. Especially under complex geological conditions, it is difficult to accurately capture the details of water level anomalies. For this reason, this application provides a method and system for detecting groundwater level anomalies based on the combination of pressure sensing and images. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention proposes a method and system for detecting groundwater level anomalies based on the combination of pressure sensing and images. The method first obtains water level pressure sequence data at different locations and depths in the area to be detected, and preprocesses it using an extension interpolation model to generate a preprocessed water level pressure sequence; at the same time, a virtual three-dimensional space is constructed in combination with geographic status information, and the preprocessed data is input into a three-dimensional heat map generation model to generate a three-dimensional water level distribution thermal space; secondly, by configuring the water level classification abnormality interval and the water level color mapping function, the three-dimensional water level abnormal distribution thermal space is obtained and updated and displayed in real time; in addition, the method uses historical rainfall and groundwater level change data to obtain predicted water level values, and generates a predicted three-dimensional water level abnormal distribution thermal space that is displayed together with real-time data to achieve secondary early warning; the method combines pressure sensing detection with visualization technology to improve the accuracy of groundwater level status monitoring.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The method for detecting abnormal groundwater level status based on the combination of pressure sensing and image includes the following steps:

[0008] S1. Obtain water level pressure sequence data at different depths at different locations in the area to be detected, and preprocess the water level pressure sequence at different depths at each location through the configured continuation interpolation model to obtain the preprocessed water level pressure sequence;

[0009] S2, obtaining geographical status information of the area to be detected, generating a virtual three-dimensional space of the area to be detected, integrating a three-dimensional heat map generation model in the three-dimensional space of the virtual area to be detected, inputting the preprocessed water level pressure sequence into the three-dimensional heat map generation model, and generating corresponding three-dimensional water level distribution thermal space at different water level monitoring position points in the three-dimensional space of the virtual area to be detected;

[0010] S3, configure the water level classification abnormal interval, and obtain the color mapping interval through the water level classification abnormal interval and the YUV three-channel color space, obtain the water level color mapping function through the color mapping interval combined with the YUV three-channel color space, and configure the water level color mapping function to the three-dimensional space of the virtual area to be detected, render the two-dimensional water level distribution thermal map corresponding to each water level depth in the three-dimensional water level distribution thermal space, and obtain the rendered three-dimensional water level abnormal distribution thermal space;

[0011] S4. Input the rendered three-dimensional water level abnormal distribution thermal space into a display device for display, and update the three-dimensional water level abnormal distribution thermal space in real time according to the water level pressure sequence at different depths obtained in real time.

[0012] Specifically, the method for detecting abnormal groundwater level status based on the combination of pressure sensing and images further includes the following steps:

[0013] S5, obtaining historical well point precipitation and corresponding groundwater level change data in the area to be detected, inputting the obtained data into the water level prediction model constructed by the support vector machine for training, obtaining the trained water level prediction model, and integrating the trained water level prediction model into the virtual three-dimensional space of the area to be detected;

[0014] S6. Obtain the corresponding well point precipitation, enclosure structure leakage and groundwater level change data 7 days before the current detection time and input them into the water level prediction model to obtain the water level value for the next 12 hours;

[0015] S7. Based on the water level value in the next 12 hours and the configured water level classification abnormal range, the predicted three-dimensional water level abnormal distribution thermal space is obtained, and the predicted three-dimensional water level abnormal distribution thermal space is input into the display device for joint display and pre-alarm together with the real-time three-dimensional water level abnormal distribution thermal space of the corresponding position point.

[0016] Specifically, different water level monitoring locations generate corresponding three-dimensional water level distribution thermal spaces, and the generation steps include:

[0017] S201, input the geographical status information of the area to be detected into the Figure 3 In the 3D generation algorithm, the 3D space of the virtual area to be detected is obtained, and the position information of the water level monitoring points is automatically marked in real time in the 3D space of the virtual area to be detected;

[0018] S202, inputting the annotated three-dimensional space of the virtual area to be detected into a three-dimensional grid algorithm, performing equal-area division, obtaining a sequence of equal-area primary division sub-areas and the number of corresponding water level monitoring position points in each primary division sub-area;

[0019] S203, according to the proportion of the number of water level monitoring points in each initially divided sub-area and the soil moisture content at the same depth, the secondary division factor corresponding to each initially divided sub-area is calculated, specifically: ,in, is the secondary division factor corresponding to the i-th primary division sub-region, represents the proportion of water level monitoring points corresponding to the i-th initial division sub-area, It represents the average soil moisture content of all water level monitoring points at the same depth in the i-th initial sub-division area;

[0020] S204, setting a sub-region secondary division threshold. When the secondary division factor corresponding to the initial sub-region is greater than the sub-region secondary division threshold, the corresponding initial sub-region and the secondary division factor are input into the three-dimensional grid algorithm for secondary region division to obtain a secondary water level monitoring sub-region sequence.

[0021] Specifically, different water level monitoring positions generate corresponding three-dimensional water level distribution thermal spaces, and the generation step also includes:

[0022] S205: construct a heat map input sequence based on the pre-processed water level pressure sequence of each sub-area corresponding to the water level monitoring position point in the secondary water level monitoring sub-area sequence ([ x i ， y i ， z i ] ， ( h ik ， p ik )) ,in, represents the longitude, latitude and height from sea level of the i-th water level monitoring point. It represents the monitoring water level depth corresponding to the kth monitoring at the i-th water level monitoring location. Indicates the water pressure value corresponding to the kth monitoring at the i-th water level monitoring location;

[0023] S206, corresponding to different monitoring points ([ x i ， y i ， z i ] ， ( h ik ， p ik )) The three-dimensional heat map generation model is input into the generation model to generate a two-dimensional water level distribution heat map corresponding to each monitoring water level depth of the corresponding water level monitoring location point, and the two-dimensional water level distribution heat map corresponding to each monitoring water level depth is stacked to obtain the three-dimensional water level distribution thermal space of the corresponding water level monitoring location point.

[0024] Specifically, the steps of constructing the water level color mapping function include:

[0025] S301. Construct a water level classification abnormal interval based on the historical monitoring water level of the area to be detected and the industry safety standards, specifically:

[0026] Normal water level range is: , the warning water level range is: , the dangerous water level range is: [ μ 0 + k 3 σ 2 ， z i ] ,in, is the average water level corresponding to the historical normal water period of the area to be detected, It represents the standard deviation of low water fluctuation corresponding to the historical water level fluctuation data from normal water period to dry water period in the area to be detected. It represents the standard deviation of flood fluctuation corresponding to the historical water level fluctuation data from normal water period to flood period in the area to be detected. represents the low water fluctuation coefficient, and It represents the warning water level coefficient and dangerous water level coefficient corresponding to different water level ranges in the flood season in turn;

[0027] S302: Construct a color mapping interval according to the set water level classification abnormal interval and YUV three-channel color space, specifically: It is the green mapping interval when It is the yellow mapping interval, when When is the orange mapping interval, when h ik ∈ [ μ 0 + k 3 σ 2 ， z i ] It is the red mapping interval.

[0028] Specifically, the steps of constructing the water level color mapping function also include:

[0029] S303: construct a dynamic water level color mapping function within the color mapping interval according to the color mapping interval and the YUV three-channel color space, specifically:

[0030] ;

[0031] in, and Indicates that the water level depth at the i-th water level monitoring location is The chromaticity values ​​corresponding to U and V in the YUV three-channel color space; and Indicates the upper and lower limits of the color mapping interval corresponding to the current water depth, corresponding to the upper and lower limits of the U chromaticity. and Indicates the upper and lower limits of the V chromaticity corresponding to the upper and lower limits of the color mapping interval corresponding to the current water level depth. and In turn, they represent the upper and lower limits of the color mapping interval corresponding to the water level depth at the current moment;

[0032] S304. According to the constructed dynamic water level color mapping function, the two-dimensional water level distribution thermal map corresponding to each monitored water level depth in the three-dimensional water level distribution thermal space is rendered with corresponding colors and the corresponding abnormal level and water level depth are marked to obtain a rendered three-dimensional water level abnormality distribution thermal space.

[0033] Specifically, the specific steps of S7 include:

[0034] S701, aligning the real-time acquired three-dimensional water level abnormal distribution thermal space and the predicted three-dimensional water level abnormal distribution thermal space in the time dimension, and obtaining the real data information of the real-time three-dimensional water level abnormal distribution thermal space at time t+1;

[0035] S702, obtaining a prediction error value according to the real water level information of the real-time three-dimensional water level abnormal distribution thermal space at time t+1 and the predicted water level information corresponding to the predicted three-dimensional water level abnormal distribution thermal space at time t+1;

[0036] S703, feeding back the prediction error value to the water level prediction model, making real-time adjustments to the predicted three-dimensional water level abnormal distribution thermal space, and using the adjusted predicted water level information and water level classification abnormal range to issue water level advance warnings.

[0037] The groundwater level state anomaly detection system based on the combination of pressure sensing and images includes: a data preprocessing module, a three-dimensional heat map generation module and an anomaly mapping module;

[0038] The data preprocessing module includes a data acquisition unit and a preprocessing unit;

[0039] A data acquisition unit, used to acquire water level pressure sequence data at different depths at different locations in the area to be detected, geographical status information, historical rainfall and corresponding groundwater level change data;

[0040] The preprocessing unit preprocesses the water level pressure sequence at different depths of each location point through the configured continuation interpolation model to obtain the preprocessed water level pressure sequence;

[0041] The three-dimensional heat map generation module includes a three-dimensional construction unit and a heat map unit;

[0042] The three-dimensional construction unit is used to generate a three-dimensional space of the virtual area to be detected through a three-dimensional generation algorithm according to the geographical status information of the area to be detected; the thermal map unit is used to generate a corresponding three-dimensional water level distribution thermal space at different water level monitoring positions in the three-dimensional space of the virtual area to be detected according to the preprocessed water level pressure sequence through the configured three-dimensional thermal map generation model.

[0043] Specifically, the exception mapping module includes a mapping generation unit and a rendering unit;

[0044] A mapping generation unit is used to configure the water level classification anomaly interval, and to construct a water level color mapping function using the water level classification anomaly interval and the YUV three-channel color space; a rendering unit is used to configure the water level color mapping function into the three-dimensional space of the virtual area to be detected, and to render the two-dimensional water level distribution thermal map corresponding to each water level depth in the three-dimensional water level distribution thermal space to obtain a rendered three-dimensional water level anomaly distribution thermal space.

[0045] A computer-readable storage medium stores computer instructions, which, when executed, execute a method for detecting abnormal groundwater level status based on the combination of pressure sensing and images.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] In view of the deficiencies in the prior art, the present invention obtains water level pressure sequence data at different locations and depths in the area to be detected, and pre-processes it in combination with the extension interpolation model, thereby generating a three-dimensional space of a virtual area to be detected and a three-dimensional water level distribution thermal space; secondly, by configuring the water level classification abnormality interval and the YUV three-channel color space to construct a water level color mapping function, the fine rendering of the two-dimensional water level distribution thermal map corresponding to each water level depth in the three-dimensional water level distribution thermal space is realized, and finally a three-dimensional water level abnormal distribution thermal space is formed, which can be updated and displayed in real time on a display device. The present invention overcomes the limitation of the existing monitoring system that can only provide simple data reports or two-dimensional charts, realizes the precise capture and intuitive display of water level anomalies under complex geological conditions, and significantly improves the accuracy and real-time performance of the monitoring results. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of a method for detecting abnormal groundwater level status based on the combination of pressure sensing and images according to Embodiment 1 of the present invention;

[0049] Figure 2 This is a flow chart of dividing the secondary water level monitoring sub-areas according to Embodiment 1 of the present invention;

[0050] Figure 3 This is a module diagram of a groundwater level abnormality detection system based on the combination of pressure sensing and images according to Embodiment 2 of the present invention;

[0051] Figure 4 This is a diagram of an electronic device according to embodiment 3 of the present invention. DETAILED DESCRIPTION

[0052] Example 1

[0053] See also Figure 1 The present invention provides an embodiment: a method for detecting abnormal groundwater level status based on the combination of pressure sensing and images, the steps comprising:

[0054] S1. Obtain water level pressure sequence data at different depths at different locations in the area to be detected, and preprocess the water level pressure sequence at different depths at each location through the configured continuation interpolation model to obtain the preprocessed water level pressure sequence;

[0055] Furthermore, in this embodiment, the area to be detected is the current construction area, and the different location points are for different foundation pit location points in the construction area. Furthermore, in this embodiment, the water level pressure sequence data is collected through a wireless Bluetooth water level pressure monitoring device;

[0056] Furthermore, in this embodiment, a continuation interpolation model is constructed according to a generalized continuation approximation algorithm to interpolate and expand the water level pressure sequence data obtained at different depths to obtain more complete water level pressure sequence data;

[0057] S2. Obtain geographic status information of the area to be detected, generate a virtual three-dimensional space of the area to be detected through a three-dimensional generation algorithm, integrate a three-dimensional heat map generation model in the three-dimensional space of the virtual area to be detected, input the preprocessed water level pressure sequence into the three-dimensional heat map generation model, and generate corresponding three-dimensional water level distribution thermal space at different water level monitoring position points in the three-dimensional space of the virtual area to be detected;

[0058] Furthermore, the geographical status information of the area to be detected in this embodiment is obtained from the historical hydrological storage data of the corresponding area;

[0059] For further information, see Figure 2 In this embodiment, different water level monitoring positions generate corresponding three-dimensional water level distribution thermal spaces, and the generation steps include:

[0060] S201, input the geographical status information of the area to be detected into the Figure 3 In the 3D generation algorithm, the 3D space of the virtual area to be detected is obtained, and the position information of the water level monitoring points is automatically marked in real time in the 3D space of the virtual area to be detected;

[0061] S202, inputting the annotated three-dimensional space of the virtual area to be detected into a three-dimensional grid algorithm, performing equal-area division, obtaining a sequence of equal-area primary division sub-areas and the number of corresponding water level monitoring position points in each primary division sub-area;

[0062] S203, according to the proportion of the number of water level monitoring points in each initially divided sub-area and the soil moisture content at the same depth, the secondary division factor corresponding to each initially divided sub-area is calculated, specifically: ,in, is the secondary division factor corresponding to the i-th primary division sub-region, represents the proportion of water level monitoring points corresponding to the i-th initial division sub-area, It represents the average soil moisture content of all water level monitoring points at the same depth in the i-th initial sub-division area;

[0063] S204, setting a threshold for secondary division of sub-regions. When the secondary division factor corresponding to the initial division of sub-regions is greater than the threshold for secondary division of sub-regions, the corresponding initial division of sub-regions and the secondary division factor are input into the three-dimensional grid algorithm for secondary region division to obtain a sequence of secondary water level monitoring sub-regions.

[0064] S205: construct a heat map input sequence based on the pre-processed water level pressure sequence of each sub-area corresponding to the water level monitoring position point in the secondary water level monitoring sub-area sequence ([ x i ， y i ， z i ] ， ( h ik ， p ik )) ,in, represents the longitude, latitude and height from sea level of the i-th water level monitoring point. It represents the monitoring water level depth corresponding to the kth monitoring at the i-th water level monitoring location. Indicates the water pressure value corresponding to the kth monitoring at the i-th water level monitoring location;

[0065] S206: Input the heat maps corresponding to different monitoring points into the sequence ([ x i ， y i ， z i ] ， ( h ik ， p ik )) The three-dimensional heat map generation model is input into the generation model to generate a two-dimensional water level distribution heat map corresponding to each monitoring water level depth of the corresponding water level monitoring location point, and the two-dimensional water level distribution heat map corresponding to each monitoring water level depth is stacked to obtain the three-dimensional water level distribution thermal space of the corresponding water level monitoring location point.

[0066] This process preprocesses the water level pressure series data at different depths by using the continuation interpolation model constructed by the generalized continuation approximation algorithm, effectively filling the missing values ​​in the monitoring data and obtaining more complete water level pressure series data; in the process of generating the virtual three-dimensional space of the area to be detected, the different water level monitoring locations are automatically labeled in real time, and the equal area division is combined with the three-dimensional grid algorithm to achieve refined management of the area to be detected; in addition, by calculating the secondary division factor of each initially divided sub-area and performing secondary area division according to the size of the factor, it is ensured that important areas receive more detailed attention; finally, by constructing a thermal map input sequence and inputting the data into the three-dimensional thermal map generation model, a three-dimensional water level distribution thermal space is generated, which significantly improves the visualization effect and accuracy of the monitoring results.

[0067] S3, configure the water level classification abnormal interval, and obtain the color mapping interval through the water level classification abnormal interval and the YUV three-channel color space, obtain the water level color mapping function through the color mapping interval combined with the YUV three-channel color space, and configure the water level color mapping function to the three-dimensional space of the virtual area to be detected, render the two-dimensional water level distribution thermal map corresponding to each water level depth in the three-dimensional water level distribution thermal space, and obtain the rendered three-dimensional water level abnormal distribution thermal space;

[0068] Specifically, the steps of constructing the water level color mapping function in this embodiment include:

[0069] S301. Construct a water level classification abnormal interval based on the historical monitoring water level of the area to be detected and the industry safety standards, specifically:

[0070] Normal water level range is: , the warning water level range is: , the dangerous water level range is: [ μ 0 + k 3 σ 2 ， z i ] ,in, is the average water level corresponding to the historical normal water period of the area to be detected, It represents the standard deviation of low water fluctuation corresponding to the historical water level fluctuation data from normal water period to dry water period in the area to be detected. It represents the standard deviation of flood fluctuation corresponding to the historical water level fluctuation data from normal water period to flood period in the area to be detected. represents the low water fluctuation coefficient, and It represents the warning water level coefficient and dangerous water level coefficient corresponding to different water level ranges in the flood season in turn;

[0071] S302: Construct a color mapping interval according to the set water level classification abnormal interval and YUV three-channel color space, specifically: It is the green mapping interval when It is the yellow mapping interval, when When is the orange mapping interval, when h ik ∈ [ μ 0 + k 3 σ 2 ， z i ] It is the red mapping interval.

[0072] Specifically, the steps of constructing the water level color mapping function also include:

[0073] S303: construct a dynamic water level color mapping function within the color mapping interval according to the color mapping interval and the YUV three-channel color space, specifically:

[0074] ;

[0075] in, and Indicates that the water level depth at the i-th water level monitoring location is The chromaticity values ​​corresponding to U and V in the YUV three-channel color space; and Indicates the upper and lower limits of the color mapping interval corresponding to the current water depth, corresponding to the upper and lower limits of the U chromaticity. and Indicates the upper and lower limits of the V chromaticity corresponding to the upper and lower limits of the color mapping interval corresponding to the current water level depth. and In turn, they represent the upper and lower limits of the color mapping interval corresponding to the water level depth at the current moment;

[0076] Furthermore, in the YUV three-channel color space, the Y channel represents the brightness information of the image, that is, the grayscale information. It reflects the brightness and darkness of the image, the U channel represents the blue chromaticity information, that is, the difference between the blue component and the brightness in the image, and the V channel represents the red chromaticity information, that is, the difference between the red component and the brightness in the image;

[0077] Furthermore, in this embodiment, the brightness in the YUV three-channel color space is set to be fixed at 128, and the U and V chromaticity change values ​​corresponding to different water depths are obtained through a dynamic water level color mapping function to perform color mapping corresponding to the water level depth.

[0078] S304. According to the constructed dynamic water level color mapping function, the two-dimensional water level distribution thermal map corresponding to each monitored water level depth in the three-dimensional water level distribution thermal space is rendered with corresponding colors and the corresponding abnormal level and water level depth are marked to obtain a rendered three-dimensional water level abnormality distribution thermal space.

[0079] The process realizes the accurate identification and visual expression of water level anomalies by constructing water level classification anomaly intervals and YUV three-channel color space. Firstly, according to the historical monitoring water level and industry safety standards of the area to be detected, the water level classification anomaly intervals are set scientifically and reasonably, and the three levels of normal, warning and dangerous are distinguished, which lays a solid foundation for the subsequent color mapping. Secondly, through the YUV three-channel color space, different water level intervals are mapped to specific color intervals, that is, green represents normal water level, yellow, orange and red represent different degrees of warning and dangerous water levels respectively. This intuitive color coding method greatly enhances the readability and warning effect of the monitoring results. Finally, by constructing a dynamic water level color mapping function, the real-time rendering and abnormal level identification of each water level depth in the three-dimensional water level distribution thermal space are realized, so that users can understand the water level status and its changing trend of the entire area to be detected at a glance, take timely response measures, and effectively avoid safety hazards caused by water level anomalies.

[0080] S4. Input the rendered three-dimensional water level abnormal distribution thermal space into a display device for display, and perform real-time update and early warning on the three-dimensional water level abnormal distribution thermal space according to the real-time acquired water level pressure sequence at different depths.

[0081] S5, obtaining historical well point precipitation and corresponding groundwater level change data in the area to be detected, inputting the obtained data into the water level prediction model constructed by the support vector machine for training, obtaining the trained water level prediction model, and integrating the trained water level prediction model into the virtual three-dimensional space of the area to be detected;

[0082] Furthermore, the steps of training the water level prediction model in this embodiment include:

[0083] S51, inputting historical rainfall and corresponding groundwater level change data into a water level prediction model to obtain corresponding predicted water level sequence data;

[0084] S52, repeat S2-S3, obtain the first three-dimensional water level abnormal distribution thermal space and the second three-dimensional water level abnormal distribution thermal space corresponding to the historical groundwater level sequence data and the water level change data predicted in the corresponding time period;

[0085] S53, obtaining a thermal map pixel error according to the first three-dimensional water level abnormal distribution thermal space and the second three-dimensional water level abnormal distribution thermal space;

[0086] S54, inversely mapping the pixel error of the heat map through the water level color mapping function to obtain the corresponding water level prediction error;

[0087] S55, configuring a water level prediction error threshold, and inputting the obtained water level prediction error and the water level prediction error threshold into a water level prediction model for training, and when the water level prediction error is less than the water level prediction error threshold, obtaining a trained water level prediction model;

[0088] S6. Obtain the corresponding well point precipitation, enclosure structure leakage and groundwater level change data 7 days before the current detection time and input them into the water level prediction model to obtain the water level value for the next 12 hours;

[0089] S7. Based on the water level value in the next 12 hours and the configured water level classification abnormal range, the predicted three-dimensional water level abnormal distribution thermal space is obtained, and the predicted three-dimensional water level abnormal distribution thermal space is input into the display device and the real-time three-dimensional water level abnormal distribution thermal space of the corresponding position point for integrated display and pre-alarm.

[0090] The specific implementation process of this step further includes:

[0091] S701, aligning the real-time acquired three-dimensional water level abnormal distribution thermal space and the predicted three-dimensional water level abnormal distribution thermal space in the time dimension, and obtaining the real data information of the real-time three-dimensional water level abnormal distribution thermal space at time t+1;

[0092] S702, obtaining a prediction error value according to the real water level information of the real-time three-dimensional water level abnormal distribution thermal space at time t+1 and the predicted water level information corresponding to the predicted three-dimensional water level abnormal distribution thermal space at time t+1;

[0093] S703, feeding back the prediction error value to the water level prediction model, making real-time adjustments to the predicted three-dimensional water level abnormal distribution thermal space, and using the adjusted predicted water level information and water level classification abnormal range to issue water level advance warnings.

[0094] The process achieves accurate prediction and real-time monitoring of water level changes by updating and warning the three-dimensional water level abnormal distribution thermal space in real time, and training the water level prediction model in combination with historical rainfall and groundwater level change data. First, the rendered three-dimensional water level abnormal distribution thermal space is input into the display device for display, and is updated in real time according to the real-time acquired water level pressure series data at different depths, ensuring the timeliness and effectiveness of the monitoring results. Second, by obtaining the historical rainfall and groundwater level change data of the area to be detected, the trained water level prediction model can accurately predict the future water level change trend. The training process In the process, the errors between the historical groundwater level sequence data and the predicted data were compared, and the model parameters were continuously optimized to ensure the high accuracy of the prediction results. Finally, the rainfall and groundwater level change data 7 days before the current detection time were input into the trained water level prediction model to obtain the water level prediction values ​​for the next 12 hours. These prediction values ​​were integrated into the three-dimensional water level anomaly distribution thermal space for real-time display and pre-warning. This process not only improved the real-time response capability of the monitoring system, but also enhanced the prediction accuracy and reliability of the system by adjusting the prediction error value in real time, providing comprehensive and accurate support for foundation pit monitoring.

[0095] Example 2

[0096] See also Figure 3 ,Another embodiment provided by the present invention: A groundwater level state anomaly detection system based on the combination of pressure sensing and images, comprising: a data preprocessing module, a three-dimensional heat map generation module, an anomaly mapping module, a visualization module, a prediction module and an integration module;

[0097] A data preprocessing module is used to acquire different types of data and preprocess the acquired data; the data preprocessing module includes a data acquisition unit and a preprocessing unit;

[0098] A data acquisition unit, used to acquire water level pressure sequence data at different depths at different locations in the area to be detected, geographical status information, historical rainfall and corresponding groundwater level change data;

[0099] The preprocessing unit preprocesses the water level pressure sequence at different depths of each location point through the configured continuation interpolation model to obtain the preprocessed water level pressure sequence;

[0100] A three-dimensional heat map generation module is used to generate a three-dimensional water level distribution thermal space corresponding to different water level monitoring locations; the three-dimensional heat map generation module includes a three-dimensional construction unit and a heat map unit;

[0101] The three-dimensional construction unit is used to generate a three-dimensional space of a virtual area to be detected through a three-dimensional generation algorithm according to the geographical status information in the area to be detected; the thermal map unit is used to generate a corresponding three-dimensional water level distribution thermal space through a configured three-dimensional thermal map generation model at different water level monitoring positions in the three-dimensional space of the virtual area to be detected according to the pre-processed water level pressure sequence;

[0102] An anomaly mapping module is used to obtain the rendered three-dimensional water level anomaly distribution thermal space; the anomaly mapping module includes a mapping generation unit and a rendering unit;

[0103] A mapping generation unit is used to configure the water level classification anomaly interval, and to construct a water level color mapping function using the water level classification anomaly interval and the YUV three-channel color space; a rendering unit is used to configure the water level color mapping function into the three-dimensional space of the virtual area to be detected, and to render the two-dimensional water level distribution thermal map corresponding to each water level depth in the three-dimensional water level distribution thermal space, so as to obtain a rendered three-dimensional water level abnormality distribution thermal space;

[0104] The visualization module is used to input the rendered three-dimensional water level abnormal distribution thermal space into the display device for display, and to update and warn the three-dimensional water level abnormal distribution thermal space in real time according to the real-time acquired water level pressure sequence at different depths;

[0105] The prediction module is used to monitor the water level value in the next 12 hours and obtain the predicted three-dimensional water level abnormal distribution thermal space based on the historical rainfall in the area to be detected and the corresponding groundwater level change data through the trained water level prediction model;

[0106] The prediction module includes a prediction unit and a dynamic graph unit; the prediction unit is used to obtain the corresponding rainfall and groundwater level change data 7 days before the current detection time, and obtain the water level value in the next 12 hours through the trained water level prediction model;

[0107] The dynamic map unit is used to obtain a three-dimensional water level abnormal distribution thermal dynamic map corresponding to the next 12 hours at the monitoring time through a three-dimensional heat map generation model and a water level color mapping function according to the water level value in the next 12 hours;

[0108] The integrated module is used to feed back the three-dimensional water level abnormal distribution thermal dynamic diagram for the next 12 hours from the monitoring time to the display device for integrated display and secondary warning in the real-time three-dimensional water level abnormal distribution thermal space at the corresponding position point.

[0109] Example 3

[0110] See also Figure 4An electronic device includes a pressure sensor 1, a data transmission line 2, a collection and transmission subsystem 3, a memory and a processor 4, wherein the pressure sensor 1 is used to obtain the pressure exerted on the sensor, calculate the depth of water at the location of the sensor, and convert the depth data into an electrical signal; the data transmission line 2 is used to transmit the electrical signal to the collection and transmission subsystem 3; the collection and transmission subsystem 3 is used to transmit the electrical signal transmitted by the data transmission line 2 through Bluetooth; the memory stores a computer program and the received monitoring water depth electrical signal, and the processor 4 implements a groundwater level abnormality detection method based on the combination of pressure sensing and image when executing the computer program.

[0111] A computer-readable storage medium stores computer instructions, which, when executed, execute a method for detecting abnormal groundwater level status based on the combination of pressure sensing and images.

[0112] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in the field may also change, modify, replace and modify the above-mentioned embodiments without departing from the scope of protection of the purpose of the present invention and the claims, and all of these are within the protection of the present invention.

[0113] If the disclosed technical solution involves personal information, the product using the disclosed technical solution has clearly informed the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the disclosed technical solution involves sensitive personal information, the product using the disclosed technical solution has obtained the individual's separate consent before processing the sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, clear and prominent signs are set to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that he or she agrees to the collection of his or her personal information; or on the device that processes personal information, when the personal information processing rules are notified by obvious signs / information, the individual's authorization is obtained through pop-up information or by asking the individual to upload his or her personal information; among them, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

Claims

1. A method for detecting abnormal groundwater level based on pressure sensing and image, characterized in that the steps include: S1. Obtain water level pressure sequence data at different depths at different locations in the area to be detected, and preprocess the water level pressure sequence at different depths at each location through the configured continuation interpolation model to obtain the preprocessed water level pressure sequence; S2, obtaining geographical status information of the area to be detected, generating a virtual three-dimensional space of the area to be detected, integrating a three-dimensional heat map generation model in the three-dimensional space of the virtual area to be detected, inputting the preprocessed water level pressure sequence into the three-dimensional heat map generation model, and generating corresponding three-dimensional water level distribution thermal space at different water level monitoring position points in the three-dimensional space of the virtual area to be detected; S3, configure the water level classification abnormal interval, and obtain the color mapping interval through the water level classification abnormal interval and the YUV three-channel color space, obtain the water level color mapping function through the color mapping interval combined with the YUV three-channel color space, and configure the water level color mapping function to the three-dimensional space of the virtual area to be detected, render the two-dimensional water level distribution thermal map corresponding to each water level depth in the three-dimensional water level distribution thermal space, and obtain the rendered three-dimensional water level abnormal distribution thermal space; S4. Input the rendered three-dimensional water level abnormal distribution thermal space into a display device for display, and update the three-dimensional water level abnormal distribution thermal space in real time according to the water level pressure sequence at different depths obtained in real time.

2. The method for detecting abnormal groundwater level status based on the combination of pressure sensing and image according to claim 1, characterized in that: The method for detecting abnormal groundwater level status further comprises the following steps: S5, obtaining historical well point precipitation and corresponding groundwater level change data in the area to be detected, inputting the obtained data into the water level prediction model constructed by the support vector machine for training, obtaining the trained water level prediction model, and integrating the trained water level prediction model into the virtual three-dimensional space of the area to be detected; S6. Obtain the corresponding well point precipitation, enclosure structure leakage and groundwater level change data 7 days before the current detection time and input them into the water level prediction model to obtain the water level value for the next 12 hours; S7. Based on the water level value in the next 12 hours and the configured water level classification abnormal range, the predicted three-dimensional water level abnormal distribution thermal space is obtained, and the predicted three-dimensional water level abnormal distribution thermal space is input into the display device for joint display and pre-alarm together with the real-time three-dimensional water level abnormal distribution thermal space of the corresponding position point.

3. The method for detecting abnormal groundwater level status based on the combination of pressure sensing and image according to claim 2, characterized in that: The steps of generating corresponding three-dimensional water level distribution thermal space at different water level monitoring positions include: S201, inputting the geographical status information of the area to be detected into the three-dimensional map generation algorithm to obtain the three-dimensional space of the virtual area to be detected, and automatically marking the location information of the water level monitoring points in the three-dimensional space of the virtual area to be detected in real time; S202, inputting the annotated three-dimensional space of the virtual area to be detected into a three-dimensional grid algorithm, performing equal-area division, obtaining a sequence of equal-area primary division sub-areas and the number of corresponding water level monitoring position points in each primary division sub-area; S203, according to the proportion of the number of water level monitoring points in each initially divided sub-area and the soil moisture content at the same depth, the secondary division factor corresponding to each initially divided sub-area is calculated, specifically: ,in, is the secondary division factor corresponding to the i-th primary division sub-region, represents the proportion of water level monitoring points corresponding to the i-th initial division sub-area, It represents the average soil moisture content of all water level monitoring points at the same depth in the i-th initial sub-division area; S204, setting a sub-region secondary division threshold. When the secondary division factor corresponding to the initial sub-region is greater than the sub-region secondary division threshold, the corresponding initial sub-region and the secondary division factor are input into the three-dimensional grid algorithm for secondary region division to obtain a secondary water level monitoring sub-region sequence.

4. The method for detecting abnormal groundwater level status based on the combination of pressure sensing and image according to claim 3, characterized in that: The step of generating corresponding three-dimensional water level distribution thermal space at different water level monitoring positions also includes: S205: construct a heat map input sequence based on the pre-processed water level pressure sequence of each sub-area corresponding to the water level monitoring position point in the secondary water level monitoring sub-area sequence ,in, represents the longitude, latitude and height from sea level of the i-th water level monitoring point. It represents the monitoring water level depth corresponding to the kth monitoring at the i-th water level monitoring location. Indicates the water pressure value corresponding to the kth monitoring at the i-th water level monitoring location; S206, corresponding to different monitoring points The three-dimensional heat map generation model is input into the generation model to generate a two-dimensional water level distribution heat map corresponding to each monitoring water level depth of the corresponding water level monitoring location point, and the two-dimensional water level distribution heat map corresponding to each monitoring water level depth is stacked to obtain the three-dimensional water level distribution thermal space of the corresponding water level monitoring location point.

5. The method for detecting abnormal groundwater level status based on the combination of pressure sensing and image according to claim 4, characterized in that: The steps of constructing the water level color mapping function include: S301. Construct a water level classification abnormal interval based on the historical monitoring water level of the area to be detected and the industry safety standards, specifically: Normal water level range is: , the warning water level range is: , the dangerous water level range is: ,in, is the average water level corresponding to the historical normal water period of the area to be detected, It represents the standard deviation of low water fluctuation corresponding to the historical water level fluctuation data from normal water period to dry water period in the area to be detected. It represents the standard deviation of flood fluctuation corresponding to the historical water level fluctuation data from normal water period to flood period in the area to be detected. represents the low water fluctuation coefficient, and It represents the warning water level coefficient and dangerous water level coefficient corresponding to different water level ranges in the flood season in turn; S302: Construct a color mapping interval according to the set water level classification abnormal interval and YUV three-channel color space, specifically: It is the green mapping interval when It is the yellow mapping interval, when When is the orange mapping interval, when It is the red mapping interval.

6. The method for detecting abnormal groundwater level status based on the combination of pressure sensing and image according to claim 5, characterized in that: The step of constructing the water level color mapping function also includes: S303: construct a dynamic water level color mapping function within the color mapping interval according to the color mapping interval and the YUV three-channel color space, specifically: ， in, and Indicates that the water level depth at the i-th water level monitoring location is The chromaticity values ​​corresponding to U and V in the YUV three-channel color space; and Indicates the upper and lower limits of the color mapping interval corresponding to the current water depth, corresponding to the upper and lower limits of the U chromaticity. and Indicates the upper and lower limits of the V chromaticity corresponding to the upper and lower limits of the color mapping interval corresponding to the current water level depth. and In turn, they represent the upper and lower limits of the color mapping interval corresponding to the water level depth at the current moment; S304. According to the constructed dynamic water level color mapping function, the two-dimensional water level distribution thermal map corresponding to each monitored water level depth in the three-dimensional water level distribution thermal space is rendered with corresponding colors and the corresponding abnormal level and water level depth are marked to obtain a rendered three-dimensional water level abnormality distribution thermal space.

7. The method for detecting abnormal groundwater level status based on the combination of pressure sensing and image according to claim 6, characterized in that: The specific steps of S7 include: S701, aligning the real-time acquired three-dimensional water level abnormal distribution thermal space and the predicted three-dimensional water level abnormal distribution thermal space in the time dimension, and obtaining the real data information of the real-time three-dimensional water level abnormal distribution thermal space at time t+1; S702, obtaining a prediction error value according to the real water level information of the real-time three-dimensional water level abnormal distribution thermal space at time t+1 and the predicted water level information corresponding to the predicted three-dimensional water level abnormal distribution thermal space at time t+1; S703, feeding back the prediction error value to the water level prediction model, making real-time adjustments to the predicted three-dimensional water level abnormal distribution thermal space, and using the adjusted predicted water level information and water level classification abnormal range to issue water level advance warnings.

8. A groundwater level abnormality detection system based on pressure sensing and image combination, used to implement the groundwater level abnormality detection method based on pressure sensing and image combination as described in any one of claims 1 to 7, characterized in that: include: Data preprocessing module, 3D heat map generation module and anomaly mapping module; The data preprocessing module includes a data acquisition unit and a preprocessing unit; The data acquisition unit is used to acquire water level pressure sequence data at different depths at different locations in the area to be detected, geographical status information, historical rainfall and corresponding groundwater level change data; The preprocessing unit preprocesses the water level pressure sequence at different depths of each location point through the configured continuation interpolation model to obtain the preprocessed water level pressure sequence; The three-dimensional heat map generation module includes a three-dimensional construction unit and a heat map unit; The three-dimensional construction unit is used to generate a three-dimensional space of a virtual area to be detected through a three-dimensional generation algorithm according to the geographical status information in the area to be detected; the thermal map unit is used to generate a corresponding three-dimensional water level distribution thermal space at different water level monitoring positions in the three-dimensional space of the virtual area to be detected according to the preprocessed water level pressure sequence through the configured three-dimensional thermal map generation model.

9. The groundwater level abnormality detection system based on pressure sensing and image combination as claimed in claim 8, characterized in that: The abnormal mapping module includes a mapping generation unit and a rendering unit; The mapping generation unit is used to configure the water level classification anomaly interval, and use the water level classification anomaly interval and the YUV three-channel color space to construct a water level color mapping function; the rendering unit is used to configure the water level color mapping function to the three-dimensional space of the virtual area to be detected, and render the two-dimensional water level distribution thermal map corresponding to each water level depth in the three-dimensional water level distribution thermal space to obtain a rendered three-dimensional water level abnormal distribution thermal space.

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