Slope safety monitoring and acquisition system based on big data of Internet of Things

Abstract

The invention particularly relates to a slope safety monitoring and acquisition system based on big data of the Internet of Things, and the system comprises the following parts: a multi-modal sensing module which is used for arranging sensors on a slope to collect all parameter data of the slope; the edge calculation module is used for analyzing various parameter data of the side slope to obtain a side slope evaluation value, and carrying out risk grading on the safety of the side slope according to the side slope evaluation value; and a cloud service module. According to the invention, the edge calculation module carries out deep analysis on data collected by the multi-modal sensing module, obtains a slope evaluation value through a series of scientific calculation methods, and carries out risk grading; abnormal conditions of various parameters can be comprehensively considered, and the safety condition of the slope can be accurately evaluated; the early warning module adopts early warning measures with different intensities and modes according to different risk levels, daily monitoring reminding of first-level risks is changed into emergency linkage emergency response of third-level risks, and it is ensured that related personnel can be timely notified under different risk levels and effective response measures are adopted.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster monitoring, and in particular to a slope safety monitoring and collection system based on Internet of Things big data. Background Art

[0002] Traditional single-point monitoring equipment can only collect data at its local location.

[0003] For example, a single-point displacement meter can only reflect displacement at its location and cannot capture information about the surrounding area. This is like having only one observation point on a vast expanse of land; it's difficult to fully understand the entire area. In complex slope environments, geological conditions and stress levels can vary from location to location. Single-point monitoring cannot capture these variations, making it impossible to accurately assess the overall slope condition.

[0004] Data collection is infrequent. Some displacement monitoring equipment may only collect data once a day or even several days, and analysis lags, making it difficult to reflect real-time changes in the slope. Manual inspections are costly, requiring significant manpower and material resources, and are susceptible to environmental conditions such as severe weather. Furthermore, manual judgments are subjective and subject to error. Traditional early warning models are often based on simple empirical formulas or a small number of parameters. They are unable to process the complex relationships between multi-dimensional data and make it difficult to accurately predict slope stability. As monitoring time increases, the amount of data increases dramatically. Traditional data storage faces problems such as insufficient space and easy data loss. Analytical capabilities are also unable to quickly process massive amounts of data and extract valuable information, making it impossible to meet long-term monitoring needs. Summary of the Invention

[0005] The purpose of the present invention is to solve the above problems and to propose a slope safety monitoring and collection system based on Internet of Things big data.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A slope safety monitoring and collection system based on IoT big data includes the following parts:

[0008] Multimodal perception module: Sensors are deployed on the slope to collect various parameter data of the slope;

[0009] Edge computing module: Analyzes various slope parameter data to obtain slope assessment values, and classifies slope safety risks based on the slope assessment values;

[0010] Cloud service module: Build a spatiotemporal database to store monitoring data at different times and the corresponding spatial location information.

[0011] Preferably, the multimodal sensing module further comprises: a sensor power supply submodule and a sensor positioning submodule;

[0012] The sensor power supply module includes a solar panel that converts solar energy into electrical energy and stores it in a battery to continuously power the sensor;

[0013] The sensor positioning submodule includes a displacement monitoring terminal that achieves millimeter-level high-precision three-dimensional positioning by receiving signals from multiple satellites.

[0014] Preferably, the various parameter data of the slope include slope inclination change data, slope displacement data, and slope rainfall change data.

[0015] Preferably, the analysis of the slope inclination change data, the slope displacement data, and the slope rainfall change data specifically includes the following parts:

[0016] Preset the standard parameter value of any parameter in the slope status information, calculate the difference between the parameter value of the slope at the current time point and the standard parameter value corresponding to the parameter, and obtain the standard deviation value corresponding to the parameter; set the allowable floating range of the standard deviation value of the parameter, and if the standard deviation value is not within the corresponding allowable floating range, mark the standard deviation value as deviating from the standard deviation value;

[0017] Calculate the difference between the maximum and minimum standard deviation values ​​within the preset time period to obtain the deviation range value;

[0018] Calculate the mean of all standard deviation values ​​within the preset time period to obtain the standard deviation mean;

[0019] The standard deviation value within the preset time period is calculated by standard deviation to obtain the standard deviation wave value;

[0020] Record the generation time of any deviation from the standard deviation value within the preset time period, calculate the time difference between the adjacent generation times, and obtain the adjacent time difference value; calculate the standard deviation of the adjacent time difference values ​​within the preset time period to obtain the deviation time fluctuation value;

[0021] Perform weighted calculation on the deviation standard deviation, deviation range, standard deviation mean, standard deviation wave value, and deviation time fluctuation value to obtain the abnormal impact value corresponding to the parameter; the abnormal impact value includes the abnormal value of inclination change, the abnormal value of displacement, and the abnormal value of rainfall;

[0022] The abnormal impact values ​​of all parameters in the status information are comprehensively processed with the preset environmental impact coefficient to obtain the slope assessment value.

[0023] Preferably, the preset weight factors of the deviation standard deviation value, deviation extreme value, standard deviation mean, standard deviation wave value, and time fluctuation value are obtained by multiplying the deviation standard deviation value, deviation extreme value, standard deviation mean, standard deviation wave value, and time fluctuation value with their corresponding weight factors, and then summing them to obtain the abnormal impact value corresponding to the parameter.

[0024] Preferably, after normalizing the inclination change anomaly values, displacement anomaly values, and rainfall anomaly values, the inclination change anomaly values ​​and displacement anomaly values ​​are respectively used as two right-angled sides of a triangle, and the remaining side is connected to form a complete right-angled triangle, and the residual anomaly value is used as the height of the triangle to construct a triangular pyramid model, calculate the volume of the triangular pyramid, and use the volume as the slope assessment value.

[0025] Preferably, the risk classification of the slope safety based on the slope assessment value specifically includes the following parts:

[0026] Three groups of threshold value ranges are preset, and the value range of each group of threshold values ​​corresponds to a risk level. The slope assessment value is matched with the value range of the three groups of threshold values ​​to obtain the risk level corresponding to the slope assessment value; the risk levels include level one risk, level two risk and level three risk; the larger the risk level, the greater the corresponding risk.

[0027] Preferably, the system further comprises an early warning module:

[0028] When the risk level corresponding to the slope assessment value is level 1 risk: an early warning message and the corresponding abnormal impact value are pushed to the smart terminal of the staff responsible for daily monitoring;

[0029] When the risk level corresponding to the slope assessment value is Level 2 risk: Based on the Level 1 risk warning, a text message warning will be sent to the relevant project leaders; the text message content concisely summarizes the slope assessment value, main abnormal parameters and risk level;

[0030] When the risk level corresponding to the slope assessment value is level three risk: immediately send emergency warning information to all relevant personnel, including project leaders, emergency rescue teams, superior management departments, etc. through APP, SMS, and email; the warning information clearly states the risk level of the slope and the corresponding slope assessment value.

[0031] Preferably, the cloud service module specifically includes the following parts:

[0032] The PostgreSQL database is combined with the PostGIS extension to build a spatiotemporal database. Monitoring data at different times are accurately recorded in the form of timestamps, accurate to the second level.

[0033] In terms of data storage format, different types of monitoring data are stored in a structured manner;

[0034] In terms of query functions, the spatio-temporal database provides rich query interfaces; users can query through time ranges; and multi-condition combined queries are also supported, which facilitates users to accurately obtain required data.

[0035] The spatio-temporal database supports multi-element analysis of monitoring data at different times and different locations.

[0036] To sum up, due to the adoption of the above technical solutions, the beneficial effects of the present application are:

[0037] 1. The edge computing module of the present application performs in-depth analysis on the data collected by the multi-modal perception module, obtains the edge slope evaluation value through a series of scientific calculation methods, and performs risk classification; it can comprehensively consider the abnormal conditions of various parameters, accurately evaluate the safety condition of the slope, and take different intensity and mode of early warning measures according to different risk levels, from daily monitoring reminders of first-level risk to emergency response of three-level risk, to ensure that relevant personnel can be notified in time and effective measures can be taken under different dangerous levels.

[0038] 2. The multi-modal perception module of the present application cooperates with a variety of high-precision sensors to comprehensively and accurately collect various parameter data of the slope; the high-precision inclination sensor, displacement meter and rain gauge respectively monitor the real-time changes of the slope inclination, displacement and rainfall, and can timely capture the subtle changes of the slope; at the same time, the sensor power supply module adopts solar power supply to ensure the continuous and stable work of the sensor. BRIEF DESCRIPTION OF DRAWINGS

[0039] In the following description of exemplary embodiments in conjunction with the drawings, more details, features and advantages of the present application are disclosed, in which:

[0040] Figure 1 The flowchart of the present application; DETAILED DESCRIPTION

[0041] Several embodiments of the present application will be described in more detail below with reference to the accompanying drawings so as to enable those skilled in the art to implement the present application. The present application can be embodied in many different forms and purposes and should not be limited to the embodiments described herein. These embodiments are provided to make the present application comprehensive and complete, and to fully convey the scope of the present application to those skilled in the art. The embodiments do not limit the present application.

[0042] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the relevant art and / or the context of this specification, and will not be interpreted in an idealized or overly formal sense unless expressly defined as such herein.

[0043] See also Figure 1 As shown, the present invention provides a technical solution:

[0044] A slope safety monitoring and collection system based on IoT big data includes the following parts:

[0045] Multimodal perception module: Sensors are deployed on the slope to collect various parameter data of the slope;

[0046] The various parameter data of the slope include slope inclination change data, slope displacement data, and slope rainfall change data;

[0047] Tilt sensor: Use a high-precision MEMS-based tilt sensor, such as a certain brand model, with an accuracy of ±0.01°. Its operating principle is based on gravity sensing, detecting the tilt angle of the sensitive element in the gravity field to determine the slope's tilt changes. For slope monitoring, it is installed at key locations on the slope surface, such as the boundaries of areas where sliding may occur and the junctions of different geological structures, to monitor changes in the slope's tilt angle and promptly identify potential sliding trends.

[0048] Displacement meter: Fiber Bragg grating (FBG) displacement meters achieve submillimeter measurement accuracy. Utilizing the characteristic that the wavelength of FBG varies with strain, they convert slope displacement into wavelength variation for measurement. Fiber Bragg grating (FBG) displacement meters are embedded in a diamond-shaped grid on the slope surface, with grid spacing set at 5-10 meters based on the complexity of the slope and the required monitoring accuracy. For key areas of concern, such as those near known landslide hazards, the spacing can be reduced to 3-5 meters to accurately measure displacement on and within the slope surface.

[0049] Rain gauge: A tipping bucket rain gauge can accurately record rainfall. Each time the bucket flips, a certain amount of rainfall is recorded. The rainfall is calculated by counting the number of times the bucket flips. Installing the gauge in an open, unobstructed location around the slope ensures accurate rainfall information collection, as rainfall is one of the key external factors affecting slope stability.

[0050] The multimodal perception module also includes: a sensor power supply submodule and a sensor positioning submodule;

[0051] The sensor power supply module includes a solar panel that converts solar energy into electrical energy and stores it in a battery to continuously power the sensor;

[0052] The sensor positioning submodule includes a high-precision displacement monitoring terminal based on Beidou / GNSS, which receives signals from multiple satellites and uses carrier phase difference technology to achieve millimeter-level high-precision three-dimensional positioning;

[0053] Edge computing module: Analyzes various slope parameter data to obtain slope assessment values, and classifies slope safety risks based on the slope assessment values;

[0054] The slope inclination change data, slope displacement data, and slope rainfall change data are analyzed, specifically including the following parts:

[0055] Preset the standard parameter value of any parameter in the slope status information, calculate the difference between the parameter value of the slope at the current time point and the standard parameter value corresponding to the parameter, and obtain the standard deviation value corresponding to the parameter; set the allowable floating range of the standard deviation value of the parameter, and if the standard deviation value is not within the corresponding allowable floating range, mark the standard deviation value as deviating from the standard deviation value;

[0056] Calculate the difference between the maximum and minimum standard deviation values ​​within the preset time period to obtain the deviation range value;

[0057] Calculate the mean of all standard deviation values ​​within the preset time period to obtain the standard deviation mean;

[0058] The standard deviation value within the preset time period is calculated by standard deviation to obtain the standard deviation wave value;

[0059] Record the generation time of any deviation from the standard deviation value within the preset time period, calculate the time difference between the adjacent generation times, and obtain the adjacent time difference value; calculate the standard deviation of the adjacent time difference values ​​within the preset time period to obtain the deviation time fluctuation value;

[0060] Perform weighted calculation on the deviation standard deviation, deviation range, standard deviation mean, standard deviation wave value, and deviation time fluctuation value to obtain the abnormal impact value corresponding to the parameter; the abnormal impact value includes the abnormal value of inclination change, the abnormal value of displacement, and the abnormal value of rainfall;

[0061] The abnormal impact values ​​of all parameters in the status information are comprehensively processed with the preset environmental impact coefficient to obtain the slope assessment value;

[0062] Preset weight factors for the standard deviation of deviation, the extreme deviation of deviation, the mean standard deviation, the standard deviation wave value, and the time-dependent fluctuation value. Multiply the standard deviation of deviation, the extreme deviation of deviation, the mean standard deviation, the standard deviation wave value, and the time-dependent fluctuation value with their corresponding weight factors, and then sum them to obtain the abnormal impact value corresponding to the parameter.

[0063] After normalizing the inclination change anomaly values, displacement anomaly values, and rainfall anomaly values, the inclination change anomaly values ​​and displacement anomaly values ​​are used as the two right-angled sides of the triangle, and the remaining side is connected to form a complete right-angled triangle. The residual anomaly value is used as the height of the triangle to construct a triangular pyramid model, and the volume of the triangular pyramid is calculated, and the volume is used as the slope assessment value;

[0064] The safety risk of the slope is graded based on the slope assessment value, which specifically includes the following parts:

[0065] Three groups of threshold value ranges are preset, and the value range of each group of threshold values ​​corresponds to a risk level. The slope assessment value is matched with the value range of the three groups of threshold values ​​to obtain the risk level corresponding to the slope assessment value; the risk levels include level one risk, level two risk, and level three risk; the larger the risk level, the greater the corresponding risk;

[0066] Early warning module:

[0067] When the risk level corresponding to the slope assessment value is level 1 risk: an early warning message and the corresponding abnormal impact value are pushed to the smart terminal of the staff responsible for daily monitoring;

[0068] At the same time, on the monitoring data visualization interface of the cloud service module, the corresponding slope area is marked with flashing blue, reminding staff to pay close attention to subsequent data changes in the area and increase the data monitoring frequency to once every 15 minutes;

[0069] When the risk level corresponding to the slope assessment value is Level 2 risk: Based on the Level 1 risk warning, a text message warning will be sent to the relevant project leaders; the text message content concisely summarizes the slope assessment value, main abnormal parameters and risk level;

[0070] The cloud service module's spatiotemporal database backs up the relevant data for the slope separately and marks it as key data. Simultaneously, the edge computing module is activated to conduct a more detailed analysis of the slope area, such as using higher-precision algorithms to perform secondary processing on displacement, inclination, and other data. On-site, professional technicians are dispatched to conduct preliminary surveys near the slope, avoiding direct access to potentially dangerous areas. Remote monitoring equipment is used to further confirm the slope's actual condition.

[0071] When the risk level corresponding to the slope assessment value is Level 3 risk: immediately send an emergency warning message via APP, SMS, or email to all relevant personnel, including the project leader, emergency rescue team, and higher-level management departments. The warning message clearly states the slope risk level and the corresponding slope assessment value.

[0072] On the cloud service module's visual interface, the slope area is highlighted with a flashing red light and an alarm sounding. Simultaneously, it automatically triggers linkage with the emergency management system. For example, in an emergency management system integrated with a GIS geographic information system, evacuation routes for surrounding personnel are quickly planned, and nearby emergency rescue resources, such as emergency vehicles and supplies, are dispatched to the scene. A cordon is erected around the scene, prohibiting unauthorized personnel from approaching. Drones are used to conduct all-round, real-time monitoring of the slope, and the acquired images and data are transmitted to the cloud service platform in real time, providing a more accurate basis for subsequent emergency decision-making.

[0073] Cloud service module: Build a spatiotemporal database to store monitoring data at different times and the corresponding spatial location information; through the spatiotemporal database, it is easy to query and analyze the changes in monitoring data at different times and locations;

[0074] The cloud service module specifically includes the following parts:

[0075] The PostgreSQL database is combined with the PostGIS extension to build a spatiotemporal database. Monitoring data at different times are accurately recorded in the form of timestamps, accurate to the second level.

[0076] In terms of data storage format, different types of monitoring data are stored in a structured manner;

[0077] For example, the displacement data storage format is set as (time, x, y, z, displacement), where time represents the monitoring time, (x, y, z) is the coordinate of the monitoring point in three-dimensional space, and displacement represents the displacement value; the inclination data is stored as (time, x, y, z, inclination), where inclination is the inclination value; the rainfall data is stored as (time, rainfall_amount), where time is the recording time and rainfall_amount is the rainfall amount;

[0078] In terms of query functions, the spatiotemporal database provides a rich query interface. Users can query by time range, such as querying the displacement change data of a specific area of ​​the slope in the past week; they can also query based on spatial location, such as querying the inclination change of all monitoring points within a 50-meter radius centered on a certain coordinate point within a certain time period. It also supports multi-condition combination queries, allowing users to accurately obtain the required data.

[0079] The spatiotemporal database supports multivariate analysis of monitoring data at different times and different positions. Through built-in analysis tools, trend analysis can be performed on displacement data, a curve of displacement change over time can be drawn, and the development trend of the slope displacement can be intuitively displayed. For inclination data at different positions, spatial distribution analysis can be performed to present the inclination difference of different regions of the slope in the form of a heat map. Meanwhile, in combination with rainfall data and other parameters, correlation analysis can be performed to explore the influence law of rainfall on the change of slope displacement, inclination, and the like, thereby providing comprehensive data support for slope safety condition assessment.

[0080] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value, and the influence weight factor and specific coefficient value in the formula are set by a person skilled in the art according to the actual situation, which can be adjusted and modified subsequently.

[0081] The above description of the embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A slope safety monitoring and acquisition system based on Internet of Things big data, characterized by: Includes the following sections: Multimodal perception module: Sensors are deployed on the slope to collect various parameter data of the slope; Edge computing module: Analyzes various slope parameter data to obtain a slope assessment value, and confirms the risk level of the slope safety based on the slope assessment value; Cloud service module: build a spatiotemporal database to store monitoring data at different times and corresponding spatial location information; Query and analyze the changes in monitoring data at different times and locations through the spatiotemporal database.

2. The slope safety monitoring and acquisition system based on Internet of Things big data according to claim 1 is characterized in that: The multimodal sensing module further includes: a sensor power supply submodule and a sensor positioning submodule; The sensor power supply module includes a solar panel that converts solar energy into electrical energy and stores it in a battery to continuously power the sensor; The sensor positioning submodule receives satellite signals and uses carrier phase differential technology to achieve millimeter-level high-precision three-dimensional positioning.

3. The slope safety monitoring and acquisition system based on Internet of Things big data according to claim 1 is characterized in that: The various parameter data of the slope include slope inclination change data, slope displacement data, and slope rainfall change data; Inclination sensor: Use high-precision inclination sensor; Displacement meter: Fiber Bragg grating displacement meter is used, and the measurement accuracy can reach sub-millimeter level. It uses the characteristic that the wavelength of fiber Bragg grating changes with strain to convert the displacement of the slope into wavelength change for measurement; Rain gauge: Use a tipping bucket rain gauge to accurately record rainfall.

4. The slope safety monitoring and acquisition system based on Internet of Things big data according to claim 3 is characterized in that: The slope inclination change data, slope displacement data, and slope rainfall change data are analyzed, specifically including the following parts: Preset the standard parameter value of any parameter in the slope state information, perform difference calculation between the parameter value of the slope at the current time point and the standard parameter value corresponding to the parameter, and obtain the standard deviation value corresponding to the parameter; Set the allowable floating range of the standard deviation value of the parameter. If the standard deviation value is not within its corresponding allowable floating range, the standard deviation value will be marked as deviating from the standard deviation value; Calculate the difference between the maximum and minimum standard deviation values ​​within the preset time period to obtain the deviation range value; Calculate the mean of all standard deviation values ​​within the preset time period to obtain the standard deviation mean; The standard deviation value within the preset time period is calculated by standard deviation to obtain the standard deviation wave value; Record any generation time that deviates from the standard deviation value within the preset time period, calculate the time difference between the adjacent generation times, and obtain the adjacent time difference value; The standard deviation of the adjacent time difference values ​​within the preset time period is calculated to obtain the time deviation fluctuation value; Perform weighted calculation on the deviation standard deviation, deviation range, standard deviation mean, standard deviation wave value, and time fluctuation value to obtain the abnormal impact value corresponding to the parameter; The abnormal impact values ​​include abnormal values ​​of inclination change, abnormal values ​​of displacement, and abnormal values ​​of rainfall; The abnormal impact values ​​of all parameters in the status information are comprehensively processed with the preset environmental impact coefficient to obtain the slope assessment value.

5. The slope safety monitoring and acquisition system based on Internet of Things big data according to claim 4 is characterized in that: The weight factors of the standard deviation of deviation, the extreme value of deviation, the mean standard deviation, the wave value of standard deviation, and the time-dependent fluctuation value are preset. The standard deviation of deviation, the extreme value of deviation, the mean standard deviation, the wave value of standard deviation, and the time-dependent fluctuation value are multiplied by their corresponding weight factors, and the sum is calculated to obtain the abnormal impact value corresponding to the parameter.

6. The slope safety monitoring and acquisition system based on Internet of Things big data according to claim 4 is characterized in that: After normalizing the inclination change anomalies, displacement anomalies, and rainfall anomalies, the inclination change anomalies and displacement anomalies are used as the two right-angled sides of the triangle respectively, and the remaining side is connected to form a complete right-angled triangle. The residual anomaly is used as the height of the triangle to construct a triangular pyramid model, and the volume of the triangular pyramid is calculated, and the volume is used as the slope assessment value.

7. The slope safety monitoring and acquisition system based on Internet of Things big data according to claim 6 is characterized in that: The risk classification of slope safety based on slope assessment values ​​specifically includes the following parts: Three groups of threshold value ranges are preset, and the value range of each group of threshold values ​​corresponds to a risk level. The slope assessment value is matched with the value ranges of the three groups of threshold values ​​to obtain the risk level corresponding to the slope assessment value; The risk levels include level one risk, level two risk and level three risk; the higher the risk level, the greater the corresponding risk.

8. The slope safety monitoring and acquisition system based on Internet of Things big data according to claim 7 is characterized in that: The system also includes an early warning module: When the risk level corresponding to the slope assessment value is level 1 risk: an early warning message and the corresponding abnormal impact value are pushed to the smart terminal of the staff responsible for daily monitoring; When the risk level corresponding to the slope assessment value is Level 2 risk: Based on the warning of Level 1 risk, a text message warning will be sent to the relevant project leaders; The text message clearly and concisely summarizes the slope assessment value, main abnormal parameters and risk level; When the risk level corresponding to the slope assessment value is level three risk: immediately send emergency warning information to all relevant personnel, including project leaders, emergency rescue teams, and superior management departments through APP, SMS, and email; the warning information clearly states the risk level of the slope and the corresponding slope assessment value.

9. The slope safety monitoring and acquisition system based on Internet of Things big data according to claim 1 is characterized in that: The cloud service module specifically includes the following parts: The preset database is selected and extended to build a spatiotemporal database, and the monitoring data at different times are accurately recorded in the form of timestamps; In terms of data storage format, different types of monitoring data are stored in a structured manner; In terms of query functions, the spatiotemporal database provides rich query interfaces.

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