Building surface stability prediction method and device, equipment and storage medium

Abstract

The invention discloses a building surface stability prediction method, device and equipment and a storage medium, and the method comprises the steps: obtaining multivariate data corresponding to a surface area where a power transmission line building is located, and the multivariate data comprises the historical position and the current position of monitoring equipment; the rainfall, the soil humidity, the soil temperature, the movement displacement and the inclination angle corresponding to the movement process of the equipment from the historical position to the current position are monitored; performing data calibration and fusion processing on the multivariate data to obtain multi-parameter comprehensive monitoring data; and inputting the multi-parameter comprehensive monitoring data into a pre-trained prediction model to obtain a stability prediction result of the earth surface area where the power transmission line building is located, the stability prediction result being used for representing the safety degree of the power transmission line building. By adopting the method, the accuracy of power transmission line building surface stability prediction can be improved.

Description

Technical Field

[0001] The present application relates to the field of monitoring technology, and in particular to a method, device, equipment, storage medium and computer program product for predicting building surface stability. Background Art

[0002] At present, in the process of monitoring the surface stability of transmission line buildings, monitoring equipment, such as geological disaster monitoring equipment and meteorological monitoring stations, are usually used to collect relevant parameters, and then the surface stability of transmission line buildings is predicted based on the relevant parameters. However, the results of the surface stability of transmission line buildings predicted by the above method are not accurate enough.

[0003] Therefore, how to improve the accuracy of surface stability prediction of transmission line buildings has become an urgent problem to be solved. Summary of the invention

[0004] The embodiments of the present application provide a method, device, equipment, storage medium and computer program product for predicting the surface stability of a building, which can improve the accuracy of predicting the surface stability of a transmission line building.

[0005] In a first aspect, an embodiment of the present application provides a method for predicting the surface stability of a building, which is applied to a monitoring device deployed in a surface area of ​​a power transmission line building, and the method includes:

[0006] Acquire multivariate data corresponding to the surface area where the transmission line building is located, the multivariate data including the historical position and current position of the monitoring device, and the corresponding rainfall, soil moisture and soil temperature, movement displacement and tilt angle during the movement of the monitoring device from the historical position to the current position;

[0007] Perform data calibration and fusion processing on multivariate data to obtain multi-parameter comprehensive monitoring data;

[0008] The multi-parameter comprehensive monitoring data is input into the pre-trained prediction model to obtain the stability prediction results of the surface area where the transmission line buildings are located. The stability prediction results are used to characterize the safety level of the transmission line buildings.

[0009] In one of the embodiments, the method further includes: obtaining the impact force, vertical rainfall velocity and vertical wind speed generated by raindrops hitting the piezoelectric ceramic kinetic rainfall sensor through the piezoelectric ceramic kinetic rainfall sensor; and determining the rainfall based on the impact force, vertical rainfall velocity and vertical wind speed.

[0010] In one of the embodiments, the method further includes: obtaining the conductivity and soil temperature of the humidity sensitive resistor through a soil moisture and temperature sensor; determining the resistance value of the humidity sensitive resistor based on the conductivity; and determining the soil moisture based on the corresponding relationship between the resistance value, soil temperature and soil moisture.

[0011] In one embodiment, the method further includes: obtaining, by means of a wire displacement sensor, the stretch length of the corresponding wire during the movement of the monitoring device from a historical position to a current position; and determining the movement displacement based on the stretch length of the wire.

[0012] In one of the embodiments, the method further includes: obtaining the acceleration of the monitoring device during the movement from the historical position to the current position through a tilt sensor; determining the sine function corresponding to the tilt angle based on the acceleration and the moving direction of the monitoring device; and determining the tilt angle of the monitoring device based on the sine function.

[0013] In one embodiment, the method also includes: using Beidou positioning technology to determine the first distances between the monitoring device and multiple satellites before moving, and based on the relationship between the multiple first distances and the earth's surface, using the spatial distance intersection method to determine the historical position of the monitoring device; and using Beidou positioning technology to determine the second distances between the monitoring device and multiple satellites after moving, and based on the relationship between the multiple second distances and the earth's surface, using the spatial distance intersection method to determine the current position of the monitoring device.

[0014] In one of the embodiments, data calibration and fusion processing are performed on multivariate data to obtain multi-parameter comprehensive monitoring data, including: data filtering and abnormal data elimination processing on the multivariate data to obtain processed multivariate data; calibration processing is performed on the processed multivariate data to obtain calibrated multivariate data; the weight of each data in the calibrated multivariate data is determined, and based on the weight of each data, data fusion processing is performed on the calibrated multivariate data to obtain multi-parameter comprehensive monitoring data.

[0015] In a second aspect, the present application provides a building surface stability prediction device, which is applied to a monitoring device deployed in a surface area of ​​a power transmission line building, and the device comprises:

[0016] An acquisition module is used to acquire multivariate data corresponding to the surface area where the transmission line building is located, the multivariate data including the historical position and current position of the monitoring device, and the corresponding rainfall, soil moisture and soil temperature, movement displacement and tilt angle during the movement of the monitoring device from the historical position to the current position;

[0017] The data processing module is used to perform data calibration and fusion processing on multivariate data to obtain multi-parameter comprehensive monitoring data;

[0018] The prediction module is used to input the multi-parameter comprehensive monitoring data into the pre-trained prediction model to obtain the stability prediction results of the surface area where the transmission line buildings are located. The stability prediction results are used to characterize the safety level of the transmission line buildings.

[0019] In a third aspect, the present application provides a monitoring device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0020] Acquire multivariate data corresponding to the surface area where the transmission line building is located, the multivariate data including the historical position and current position of the monitoring device, and the corresponding rainfall, soil moisture and soil temperature, movement displacement and tilt angle during the movement of the monitoring device from the historical position to the current position;

[0021] Perform data calibration and fusion processing on multivariate data to obtain multi-parameter comprehensive monitoring data;

[0022] The multi-parameter comprehensive monitoring data is input into the pre-trained prediction model to obtain the stability prediction results of the surface area where the transmission line buildings are located. The stability prediction results are used to characterize the safety level of the transmission line buildings.

[0023] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0024] Acquire multivariate data corresponding to the surface area where the transmission line building is located, the multivariate data including the historical position and current position of the monitoring device, and the corresponding rainfall, soil moisture and soil temperature, movement displacement and tilt angle during the movement of the monitoring device from the historical position to the current position;

[0025] Perform data calibration and fusion processing on multivariate data to obtain multi-parameter comprehensive monitoring data;

[0026] The multi-parameter comprehensive monitoring data is input into the pre-trained prediction model to obtain the stability prediction results of the surface area where the transmission line buildings are located. The stability prediction results are used to characterize the safety level of the transmission line buildings.

[0027] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0028] Acquire multivariate data corresponding to the surface area where the transmission line building is located, the multivariate data including the historical position and current position of the monitoring device, and the corresponding rainfall, soil moisture and soil temperature, movement displacement and tilt angle during the movement of the monitoring device from the historical position to the current position;

[0029] Perform data calibration and fusion processing on multivariate data to obtain multi-parameter comprehensive monitoring data;

[0030] The multi-parameter comprehensive monitoring data is input into the pre-trained prediction model to obtain the stability prediction results of the surface area where the transmission line buildings are located. The stability prediction results are used to characterize the safety level of the transmission line buildings.

[0031] The above-mentioned building surface stability prediction method, device, equipment, storage medium and computer program product, the monitoring equipment can obtain multivariate data corresponding to the surface area where the transmission line building is located, the multivariate data includes the historical position and current position of the monitoring equipment, and the corresponding rainfall, soil moisture and soil temperature, movement displacement and tilt angle during the movement of the monitoring equipment from the historical position to the current position; the multivariate data is calibrated and fused to obtain multi-parameter comprehensive monitoring data; the multi-parameter comprehensive monitoring data is input into the pre-trained prediction model to obtain the stability prediction result of the surface area where the transmission line building is located, and the stability prediction result is used to characterize the safety level of the transmission line building. Using this method, the monitoring equipment can determine the stability prediction result of the surface area where the transmission line building is located based on the multivariate data corresponding to the surface area where the transmission line building is located, that is, the historical position and current position of the monitoring equipment, and the corresponding rainfall, soil moisture and soil temperature, movement displacement and tilt angle during the movement of the monitoring equipment from the historical position to the current position and the pre-trained prediction model, thereby improving the accuracy of the surface stability prediction of the transmission line building. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 It is a schematic diagram of an application scenario of a method for predicting building surface stability provided in an embodiment of the present application;

[0034] Figure 2 It is a flow chart of a method for predicting building surface stability provided in an embodiment of the present application;

[0035] Figure 3 This is a schematic diagram of the corresponding relationship between soil temperature, soil humidity and the resistance value of a humidity-sensitive resistor provided in an embodiment of the present application;

[0036] Figure 4 It is a structural schematic diagram of a wire sensor provided in an embodiment of the present application;

[0037] Figure 5 is a schematic diagram of a local northeast celestial coordinate system provided in an embodiment of the present application;

[0038] Figure 6 It is a schematic diagram of a three-dimensional displacement time series of a monitoring point provided in an embodiment of the present application;

[0039] Figure 7 It is a flow chart of another method for predicting building surface stability provided in an embodiment of the present application;

[0040] Figure 8 It is a structural schematic diagram of a building surface stability prediction system provided in an embodiment of the present application;

[0041] Fig. 9 It is a structural schematic diagram of a building surface stability prediction device provided in an embodiment of the present application;

[0042] Fig.10 It is a structural schematic diagram of a monitoring device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0044] The following introduces the application scenarios of the building surface stability prediction method provided in the embodiments of the present application.

[0045] See also Figure 1 , Figure 1 Schematic diagram of an application scenario of a building surface stability prediction method provided in an embodiment of the present application. Figure 1 As shown, it includes monitoring equipment 101 and a transmission line building 102 .

[0046] Among them, the monitoring device 101 can obtain multivariate data corresponding to the surface area where the transmission line building 102 is located, and the multivariate data includes the historical position and current position of the monitoring device, as well as the corresponding rainfall, soil moisture and soil temperature, mobile displacement and tilt angle during the movement of the monitoring device from the historical position to the current position; the multivariate data is calibrated and fused to obtain multi-parameter comprehensive monitoring data; the multi-parameter comprehensive monitoring data is input into the pre-trained prediction model to obtain the stability prediction result of the surface area where the transmission line building is located, and the stability prediction result is used to characterize the safety level of the transmission line building. Using this method, the monitoring device can determine the stability prediction result of the surface area where the transmission line building is located based on the multivariate data corresponding to the surface area where the transmission line building is located, that is, the historical position and current position of the monitoring device, as well as the corresponding rainfall, soil moisture and soil temperature, mobile displacement and tilt angle during the movement of the monitoring device from the historical position to the current position and the pre-trained prediction model, thereby improving the accuracy of the surface stability prediction of the transmission line building.

[0047] The monitoring device 101 has multiple sensors and a Beidou positioning module built in. Optionally, the multiple sensors may include but are not limited to a piezoelectric ceramic kinetic energy rainfall sensor, a soil moisture and temperature sensor, a wire displacement sensor, an inclination sensor, and the like.

[0048] See also Figure 2 , Figure 2 1 is a flow chart of a method for predicting building surface stability provided in an embodiment of the present application. The method can be executed by a monitoring device (for example, the monitoring device 101 described above). Figure 2 As shown, the building surface stability prediction method may include but is not limited to the following steps:

[0049] S201. Acquire multivariate data corresponding to the surface area where the transmission line building is located, the multivariate data including the historical position and current position of the monitoring equipment, and the corresponding rainfall, soil moisture and soil temperature, movement displacement and tilt angle during the movement of the monitoring equipment from the historical position to the current position.

[0050] Among them, the monitoring equipment is equipped with multiple sensors and Beidou positioning modules. The multiple sensors may include but are not limited to piezoelectric ceramic kinetic rain sensors, soil moisture and temperature sensors, wire displacement sensors, inclination sensors, etc.

[0051] Among them, piezoelectric ceramics are materials that can convert mechanical energy (such as pressure and vibration) into electrical energy (or vice versa). Using a piezoelectric ceramic kinetic rain sensor, the impact force generated by raindrops hitting piezoelectric ceramics can be converted into an electrical signal to measure rainfall.

[0052] Optionally, the monitoring device can determine the historical position and current position of the monitoring device through the Beidou positioning module; determine the corresponding rainfall during the movement of the monitoring device from the historical position to the current position through a piezoelectric ceramic kinetic rain sensor; determine the corresponding soil moisture and soil temperature during the movement from the historical position to the current position through a soil moisture and temperature sensor; determine the corresponding movement displacement during the movement from the historical position to the current position through a pull-wire displacement sensor; and determine the corresponding inclination angle during the movement from the historical position to the current position through an inclination sensor.

[0053] Optionally, the monitoring device may acquire multivariate data corresponding to the surface area where the transmission line buildings are located in real time, or may periodically acquire multivariate data corresponding to the surface area where the transmission line buildings are located, which is not limited here.

[0054] S202, perform data calibration and fusion processing on the multivariate data to obtain multi-parameter comprehensive monitoring data.

[0055] Among them, the monitoring equipment performs data calibration and fusion processing on the multivariate data to obtain multi-parameter comprehensive monitoring data. The multivariate data can be pre-processed first, such as data filtering and abnormal data elimination, to obtain processed multivariate data; then, the processed multivariate data is calibrated and fused to obtain multi-parameter comprehensive monitoring data.

[0056] Exemplarily, the monitoring equipment performs data calibration and fusion processing on multivariate data to obtain multi-parameter comprehensive monitoring data. It may be that data preprocessing is performed on the multivariate data to obtain preprocessed data, and then the preprocessed multivariate data is calibrated. After eliminating data with obvious outliers, a weight is assigned to each data source or parameter through weighted averaging, and then the values ​​of each data source or parameter are weighted averaged to obtain a comprehensive value, thereby ensuring the accuracy and consistency of each sensor data and forming multi-parameter comprehensive monitoring data.

[0057] S203, inputting the multi-parameter comprehensive monitoring data into a pre-trained prediction model to obtain a stability prediction result of the surface area where the transmission line building is located, and the stability prediction result is used to characterize the safety level of the transmission line building.

[0058] In an optional implementation, the prediction model may be a machine learning model or a deep learning model, which is not limited here.

[0059] Among them, the monitoring equipment can monitor the deformation of transmission line buildings in real time, capture tiny displacements or vibration anomalies, and thus promptly discover potential safety hazards.

[0060] In the embodiment of the present application, the monitoring device can obtain multivariate data corresponding to the surface area where the transmission line building is located, and the multivariate data includes the historical position and current position of the monitoring device, and the corresponding rainfall, soil moisture and soil temperature, mobile displacement and tilt angle during the movement of the monitoring device from the historical position to the current position; the multivariate data is calibrated and fused to obtain multi-parameter comprehensive monitoring data; the multi-parameter comprehensive monitoring data is input into a pre-trained prediction model to obtain the stability prediction result of the surface area where the transmission line building is located, and the stability prediction result is used to characterize the safety level of the transmission line building. Using this method, the monitoring device can determine the stability prediction result of the surface area where the transmission line building is located based on the multivariate data corresponding to the surface area where the transmission line building is located, that is, the historical position and current position of the monitoring device, and the corresponding rainfall, soil moisture and soil temperature, mobile displacement and tilt angle during the movement of the monitoring device from the historical position to the current position and the pre-trained prediction model, thereby improving the accuracy of the surface stability prediction of the transmission line building.

[0061] In an optional embodiment, Figure 2 In the building surface stability prediction method shown, the monitoring equipment can also obtain the impact force, vertical rainfall velocity and vertical wind speed generated by raindrops hitting the piezoelectric ceramic kinetic rain sensor through the piezoelectric ceramic kinetic rain sensor; and determine the rainfall based on the impact force, vertical rainfall velocity and vertical wind speed.

[0062] Exemplarily, the monitoring equipment can use a piezoelectric ceramic kinetic energy rain sensor. By measuring the impact force generated by raindrops hitting the sensor, the weight of the raindrops is calculated according to the principle of conservation of momentum (P=mv), and then the rainfall is accumulated. The principle is: the raindrops are affected by the weight of the raindrops and the air resistance during the falling process. When they fall to the ground, the speed is a constant speed V, and the momentum of the raindrops is P=MV=M(Vr+Vw). Among them, M represents the mass of the raindrops, Vr represents the vertical raindrop terminal velocity, and Vw represents the vertical wind speed; among them, Vr, Vw and P can be obtained through the piezoelectric ceramic kinetic energy rain sensor. According to the impulse theorem, the momentum of the raindrops will be replaced by the impact force of the sensor element.

[0063] In an optional embodiment, Figure 2 In the building surface stability prediction method, the monitoring equipment can also obtain the conductivity and soil temperature of the hygrometer through soil moisture and temperature sensors; determine the resistance value of the hygrometer based on the conductivity; and determine the soil moisture based on the correspondence between the resistance, soil temperature and soil moisture.

[0064] Among them, the monitoring equipment can determine the soil moisture content (i.e. soil humidity) through the change of the conductivity of the hygroscopic resistor in the soil moisture and temperature sensor; at the same time, determine the soil temperature through the temperature sensor in the soil moisture and temperature sensor.

[0065] The monitoring device determines the resistance value of the humidity sensor based on the conductivity, and the reciprocal of the conductivity may be used as the resistance value of the humidity sensor.

[0066] Among them, soil temperature, soil humidity and soil moisture have a certain functional relationship with the resistance value of the humidity sensitive resistor in the temperature sensor. For example, see Figure 3 , Figure 3 Schematic diagram of the corresponding relationship between soil temperature, soil humidity and the resistance value of a humidity sensitive resistor provided in an embodiment of the present application. Figure 3 As shown in the figure, the horizontal axis represents the soil moisture and the vertical axis represents the resistance value of the humidity sensor. Figure 3 It can be seen that the resistance value of the hygroscopic resistor decreases with the increase of the relative humidity of the environment, and is also affected by the ambient temperature.

[0067] Optionally, the soil temperature, soil moisture and conductivity of the hygroscopic resistor can be hexadecimal data directly obtained by the sensor in the monitoring device. For example, the soil moisture is, for example, 0292H (hexadecimal) = 658 = 65.8% RH; the soil temperature is, for example, FF9BH (hexadecimal) = -101, that is, the soil temperature = -10.1°C, where the temperature data is uploaded in the form of a complement when the soil temperature is below 0°C; the conductivity of the hygroscopic resistor is, for example, 03E8H (hexadecimal) = 1000, that is, the conductivity = 1000μS / cm.

[0068] In an optional embodiment, Figure 2 In the building surface stability prediction method, the monitoring device can also obtain the extension length of the corresponding guy wire during the movement of the monitoring device from the historical position to the current position through a guy wire displacement sensor; and determine the movement displacement based on the extension length of the guy wire.

[0069] The cable of the cable displacement sensor is wound on a drum, which is connected to an encoder. The incremental encoder outputs a pulse sequence signal. The working principle of the cable displacement sensor is that when the cable is pulled out or retracted, the drum rotates accordingly, and the encoder calculates the extension length of the cable according to the rotation angle of the drum, thereby providing position information. For example, the structural diagram of the cable displacement sensor can be shown as follows: Figure 4 shown.

[0070] In an optional embodiment, Figure 2In the building surface stability prediction method, the monitoring device can also obtain the acceleration of the monitoring device during the movement from the historical position to the current position through the inclination sensor; determine the sine function corresponding to the inclination angle based on the acceleration and the moving direction of the monitoring device; and determine the inclination angle of the monitoring device based on the sine function.

[0071] For example, the monitoring device can use a 3D Micro Electro Mechanical System (3D-MEMS) inclination sensor to measure the acceleration of the monitoring device during its movement from the historical position to the current position, calculate the earth's gravity component of the measurement direction (or the movement direction of the monitoring device) through acceleration, output the sine function of the inclination angle, and then calculate the inclination angle. Among them, 3D micro electro mechanical system technology is a technology that integrates mechanical, electronic and optical components on a microscopic scale, and is used to manufacture miniaturized, high-performance sensors and actuators. In this technology, it is used to manufacture inclination sensors.

[0072] In an optional embodiment, Figure 2 In the method for predicting the surface stability of a building, the monitoring equipment can also use Beidou positioning technology to determine the first distances between the monitoring equipment and multiple satellites before moving, and based on the relationship between the multiple first distances and the earth's surface, use the spatial distance intersection method to determine the historical position of the monitoring equipment; and use Beidou positioning technology to determine the second distances between the monitoring equipment and multiple satellites after moving, and based on the relationship between the multiple second distances and the earth's surface, use the spatial distance intersection method to determine the current position of the monitoring equipment.

[0073] Among them, Beidou positioning technology is to regularly upload information such as clock parameters, navigation messages, ephemeris parameters, etc. on the satellite through the Beidou satellite system. This information is received, processed and calculated by the ground control center, and then the processed navigation information is transmitted back to the user terminal (the monitoring equipment in this application), which will receive and process the navigation information.

[0074] For example, in the process of receiving and processing navigation information, the monitoring device can measure the distance between the known satellite and itself (i.e., the location of the monitoring device) through the Beidou positioning module. This distance is calculated by multiplying the signal propagation time by the speed of light. Since the Beidou system contains multiple satellites, the precise latitude and longitude information of the disaster monitoring device can be determined by the relationship between the distances of three or more station satellites and the surface of the earth, using the three-sphere intersection positioning method (or called the spatial distance intersection method), that is, to achieve spatial positioning.

[0075] Beidou positioning technology can monitor the location changes of geological disaster points (or the location changes of monitoring equipment) in real time. Combined with the geological environment change data monitored by the sensor network (such as rainfall, surface displacement, etc.), by analyzing the high-precision landslide displacement monitoring sequence, the movement state of the landslide can be obtained, and then the deformation stage of the landslide can be analyzed, so as to issue an accurate warning. The rectangular coordinate system with the station center (such as the center of the Beidou receiving antenna) as the origin O of the coordinate system, the Z axis coincides with the ellipsoid normal, the upward direction is positive (celestial direction), the y axis coincides with the minor semi-axis of the ellipsoid (north), and the x axis coincides with the major semi-axis of the earth's ellipsoid (east) is called the local northeast celestial coordinate system (ENU). Figure 5 As shown, Figure 5 is a schematic diagram of a local northeast celestial coordinate system provided in an embodiment of the present application. The three-dimensional displacement time series diagram of the monitoring point can be as follows Figure 6 As shown, Figure 6 Schematic diagram of a three-dimensional displacement time series of a monitoring point provided in an embodiment of the present application. Figure 6 As shown, E, N, and U represent the displacement changes of three different monitoring points.

[0076] In an optional embodiment, Figure 2 In the building surface stability prediction method shown in the figure, the monitoring equipment performs data calibration and fusion processing on the multivariate data to obtain multi-parameter comprehensive monitoring data, which may include but is not limited to the following steps:

[0077] Step 1: Perform data filtering and abnormal data elimination on the multivariate data to obtain processed multivariate data.

[0078] Among them, the monitoring equipment can perform data filtering and abnormal data elimination on each data in the multivariate data to obtain processed multivariate data.

[0079] Step 2: calibrate the processed multivariate data to obtain calibrated multivariate data.

[0080] Step three: determine the weight of each data in the calibrated multivariate data, and based on the weight of each data, perform data fusion processing on the calibrated multivariate data to obtain multi-parameter comprehensive monitoring data.

[0081] By adopting this implementation mode, the monitoring equipment can improve the accuracy of the data by filtering the multivariate data and eliminating abnormal data, and calibrating and fusing the processed multivariate data, which is beneficial to subsequently improve the accuracy of the prediction of the surface stability of the building.

[0082] See also Figure 7 , Figure 7 FIG. 2 is a flow chart of another method for predicting building surface stability provided in an embodiment of the present application. Figure 7As shown, the building surface stability prediction method may include but is not limited to the following steps:

[0083] S701. Using Beidou positioning technology, determine the first distances between the monitoring device and multiple satellites before moving, and based on the relationship between the multiple first distances and the earth's surface, use the spatial distance intersection method to determine the historical position of the monitoring device; and, using Beidou positioning technology, determine the second distances between the monitoring device and multiple satellites after moving, and based on the relationship between the multiple second distances and the earth's surface, use the spatial distance intersection method to determine the current position of the monitoring device.

[0084] S702. Obtain, through the piezoelectric ceramic kinetic rain sensor, the impact force, vertical rainfall velocity, and vertical wind velocity generated by raindrops hitting the piezoelectric ceramic kinetic rain sensor.

[0085] S703: Determine the rainfall amount based on the impact force, the vertical rainfall velocity and the vertical wind velocity.

[0086] S704. Obtain the conductivity of the humidity sensitive resistor and the soil temperature through the soil moisture and temperature sensor.

[0087] S705: Determine soil moisture based on the corresponding relationship among electrical conductivity, soil temperature and soil moisture.

[0088] S706. Obtain the stretch length of the corresponding pull wire during the movement of the monitoring device from the historical position to the current position through the pull wire displacement sensor.

[0089] S707: Determine the displacement based on the stretching length of the pull line.

[0090] S708. Obtain the acceleration of the monitoring device during the movement from the historical position to the current position through the inclination sensor.

[0091] S709: Determine a sine function corresponding to the tilt angle based on the acceleration and the moving direction of the monitoring device.

[0092] S710. Determine the tilt angle of the monitoring device based on a sine function.

[0093] S711, perform data filtering and abnormal data elimination processing on rainfall, soil temperature, soil moisture, corresponding movement displacement and tilt angle of the monitoring equipment during the movement from the historical position to the current position to obtain processed data.

[0094] S712, calibrate the processed data to obtain calibrated data.

[0095] S713, determining the weight of each data in the calibrated data, and based on the weight of each data, performing data fusion processing on the calibrated metadata to obtain multi-parameter comprehensive monitoring data.

[0096] In an optional implementation, after obtaining the multi-parameter comprehensive monitoring data, the monitoring device may also display the multi-parameter comprehensive monitoring data in real time on a display interface.

[0097] Optionally, the monitoring device may also store multi-parameter comprehensive monitoring data for subsequent analysis of the changing trend of the building surface stability. For example, the pre-built prediction model is subsequently trained based on the stored multi-parameter comprehensive monitoring data to obtain a trained prediction model.

[0098] In an optional implementation, the monitoring device can also transmit multi-parameter comprehensive monitoring data in real time to a remote server or Internet of Things data platform through a built-in 4G network module or Beidou short message communication module, so that users can view the data in real time through computers, mobile phones and other terminals for remote monitoring and management.

[0099] S714. Input the multi-parameter comprehensive monitoring data into a pre-trained prediction model to obtain a stability prediction result of the surface area where the transmission line building is located. The stability prediction result is used to characterize the safety level of the transmission line building.

[0100] Among them, the prediction model is obtained by training the monitoring equipment based on the historical multivariate data of the surface area where the transmission line buildings are located.

[0101] In an embodiment of the present application, the monitoring device can determine the corresponding rainfall, soil moisture, soil temperature, movement displacement and tilt angle during the movement of the monitoring device from a historical position to a current position through multiple built-in sensors, and perform data filtering and abnormal data elimination on the above data to obtain processed data. Thereafter, the processed data are calibrated and fused to obtain multi-parameter comprehensive monitoring data. Finally, the multi-parameter comprehensive monitoring data is input into a pre-trained prediction model to determine the stability prediction result of the surface area where the transmission line building is located. In this way, since the multi-parameter comprehensive monitoring data is obtained by processing the multivariate data corresponding to the surface area where the transmission line building is located, therefore, by inputting the multi-parameter comprehensive monitoring data into the pre-trained prediction model, a more accurate transmission line building surface stability prediction result can be obtained, that is, the accuracy of the transmission line building surface stability prediction is improved.

[0102] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0103] Based on the same inventive concept, the embodiment of the present application also provides a building surface stability prediction device for implementing the above-mentioned building surface stability prediction method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the building surface stability prediction device provided below can refer to the limitations of the building surface stability prediction method above, and will not be repeated here.

[0104] See also Figure 8 , Figure 8 Schematic diagram of a building surface stability prediction system provided in an embodiment of the present application. Figure 8 As shown, the device includes a monitoring device 801 and a remote device 802. The monitoring device 801 includes a sensor module 8011, a data processing module 8012, a display and storage module 8013 and a communication module 8014; the remote device 802 includes a remote monitoring and management module 8021.

[0105] The sensor module 801 includes a piezoelectric ceramic kinetic rain sensor, a soil moisture and temperature sensor, a displacement sensor, and a dual-axis tilt sensor, and is responsible for data collection. After the sensor module 801 collects data, the data can be sent to the data processing module 802 for processing.

[0106] The data processing module 802 is composed of a microprocessor and is responsible for data filtering, abnormal data elimination, and calibration and fusion processing. The data processing module 802 can also send the processed data to the display and storage module 803 for local display and storage, and send the processed data to the communication module 804 for remote transmission.

[0107] The display and storage module 803 includes a display screen and a memory, and is responsible for local display and storage of data.

[0108] The communication module 804 includes a 4G network module and a Beidou short message communication module, which is responsible for the remote transmission of the processed data. The communication module 804 can send the processed data to the remote monitoring and management module 8021 in the remote device 802, which is received, stored and analyzed by the server or the Internet of Things data platform.

[0109] The remote monitoring and management module 8021 is composed of a server or an IoT data platform and is responsible for receiving, storing and analyzing data, as well as remote monitoring and management of users.

[0110] See also Fig. 9 , Fig. 9 Schematic diagram of a structure of a building surface stability prediction device provided in an embodiment of the present application. Fig. 9 As shown, the building surface stability prediction device may include but is not limited to:

[0111] An acquisition module 901 is used to acquire multivariate data corresponding to the surface area where the transmission line building is located, the multivariate data including the historical position and current position of the monitoring device, and the corresponding rainfall, soil moisture and soil temperature, movement displacement and tilt angle during the movement of the monitoring device from the historical position to the current position;

[0112] The data processing module 902 is used to perform data calibration and fusion processing on the multivariate data to obtain multi-parameter comprehensive monitoring data;

[0113] The prediction module 903 is used to input the multi-parameter comprehensive monitoring data into the pre-trained prediction model to obtain the stability prediction result of the surface area where the transmission line building is located. The stability prediction result is used to characterize the safety level of the transmission line building.

[0114] In one embodiment, the acquisition module 901 is also used to: obtain the impact force, vertical rainfall velocity and vertical wind speed generated by raindrops hitting the piezoelectric ceramic kinetic rainfall sensor through the piezoelectric ceramic kinetic rainfall sensor; and determine the rainfall based on the impact force, vertical rainfall velocity and vertical wind speed.

[0115] In one embodiment, the acquisition module 901 is also used to: obtain the conductivity and soil temperature of the hygroscopic resistor through the soil moisture and temperature sensor; determine the resistance value of the hygroscopic resistor based on the conductivity; and determine the soil moisture based on the corresponding relationship between the resistance value, soil temperature and soil moisture.

[0116] In one embodiment, the acquisition module 901 is also used to: obtain the extension length of the corresponding pull wire during the movement of the monitoring device from the historical position to the current position through a pull wire displacement sensor; and determine the movement displacement based on the extension length of the pull wire.

[0117] In one embodiment, the acquisition module 901 is also used to: obtain the acceleration of the monitoring device during the movement from the historical position to the current position through the incline sensor; determine the sine function corresponding to the inclination angle based on the acceleration and the moving direction of the monitoring device; and determine the inclination angle of the monitoring device based on the sine function.

[0118] In one embodiment, the acquisition module 901 is also used to: use Beidou positioning technology to determine the first distances between the monitoring device and multiple satellites before moving, and based on the relationship between the multiple first distances and the earth's surface, use the spatial distance intersection method to determine the historical position of the monitoring device; and use Beidou positioning technology to determine the second distances between the monitoring device and multiple satellites after moving, and based on the relationship between the multiple second distances and the earth's surface, use the spatial distance intersection method to determine the current position of the monitoring device.

[0119] In one embodiment, when the data processing module 902 is used to perform data calibration and fusion processing on multivariate data to obtain multi-parameter comprehensive monitoring data, it is specifically used to: perform data filtering and abnormal data elimination processing on the multivariate data to obtain processed multivariate data; perform calibration processing on the processed multivariate data to obtain calibrated multivariate data; determine the weight of each data in the calibrated multivariate data, and based on the weight of each data, perform data fusion processing on the calibrated multivariate data to obtain multi-parameter comprehensive monitoring data.

[0120] Each module in the above-mentioned building surface stability prediction device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the terminal device in the form of hardware, or can be stored in the memory in the terminal device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0121] In an exemplary embodiment, the present application provides a monitoring device, which may be a terminal device, and its internal structure diagram may be as follows: Fig.10As shown. The monitoring device includes a processor, a memory, an input / output interface, a communication interface, a display unit and a sensor group. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the sensor group are connected to the system bus through the input / output interface. The sensor group of the monitoring device is used to obtain multivariate data corresponding to the surface area where the transmission line building is located. The processor of the monitoring device is used to provide computing and control capabilities. The memory of the monitoring device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the monitoring device is used to exchange information between the processor and an external device. The communication interface of the monitoring device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (Near Field Communication, NFC) or other technologies. When the computer program is executed by the processor, a method for predicting the surface stability of a building is implemented. The display unit of the monitoring device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the monitoring device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the monitoring device housing, or an external keyboard, touchpad or mouse.

[0122] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the monitoring equipment to which the scheme of the present application is applied. The specific monitoring equipment may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0123] In an exemplary embodiment, the present application provides a monitoring device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned building surface stability prediction method when executing the computer program.

[0124] In an exemplary embodiment, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the above-mentioned method for predicting building surface stability are implemented.

[0125] In an exemplary embodiment, the present application provides a computer program product, including a computer program, which implements the steps in the above-mentioned building surface stability prediction method when executed by a processor.

[0126] Optionally, the method provided in the embodiments of the present application can also be applied to geological disaster early warning scenarios. For example, in high-risk areas for geological disasters such as mountainous areas, river banks, and swamps, high-precision displacement monitoring technology can be used to track surface displacement changes in real time, effectively warn of natural disasters such as landslides and mudslides, and provide strong protection for the safety of buildings and infrastructure.

[0127] It should be noted that the data involved in this application (including but not limited to multivariate data corresponding to the surface area where the transmission line buildings are located, multi-parameter comprehensive monitoring data, prediction models, etc.) are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0128] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0129] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0130] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for predicting building surface stability, characterized in that: Applied to monitoring equipment deployed in the surface area of ​​a power transmission line building, the method comprises: Acquire multivariate data corresponding to the surface area where the transmission line building is located, the multivariate data including the historical position and current position of the monitoring device, and the corresponding rainfall, soil moisture and soil temperature, movement displacement and tilt angle during the movement of the monitoring device from the historical position to the current position; Performing data calibration and fusion processing on the multivariate data to obtain multi-parameter comprehensive monitoring data; The multi-parameter comprehensive monitoring data is input into a pre-trained prediction model to obtain a stability prediction result of the surface area where the transmission line building is located. The stability prediction result is used to characterize the safety level of the transmission line building.

2. The method according to claim 1, characterized in that The method further comprises: The impact force, vertical rainfall velocity and vertical wind velocity generated by raindrops hitting the piezoelectric ceramic kinetic rain sensor are obtained by using the piezoelectric ceramic kinetic rain sensor; The rainfall amount is determined based on the impact force, the vertical rainfall velocity and the vertical wind velocity.

3. The method according to claim 1, characterized in that The method further comprises: The conductivity of the humidity sensitive resistor and the soil temperature are obtained through the soil moisture and temperature sensor; Based on the conductivity, determining the resistance value of the humidity sensor; The soil moisture is determined based on the corresponding relationship among the resistance value, the soil temperature and the soil moisture.

4. The method according to claim 1, characterized in that The method further comprises: By means of a wire displacement sensor, the extension length of the corresponding wire is obtained during the movement of the monitoring device from the historical position to the current position; The movement displacement is determined based on the stretched length of the pull wire.

5. The method according to claim 1, characterized in that The method further comprises: Acquiring, by means of a tilt sensor, the acceleration of the monitoring device during the movement from the historical position to the current position; Determining a sine function corresponding to the tilt angle based on the acceleration and the moving direction of the monitoring device; Based on the sine function, the tilt angle of the monitoring device is determined.

6. The method according to claim 1, characterized in that The method further comprises: Using Beidou positioning technology, determining first distances between the monitoring device and multiple satellites before the movement, and based on the relationship between the multiple first distances and the earth's surface, determining the historical position of the monitoring device using a spatial distance intersection method; and, Beidou positioning technology is used to determine the second distances between the moved monitoring device and multiple satellites respectively, and based on the relationship between multiple second distances and the earth's surface, the current position of the monitoring device is determined using the spatial distance intersection method.

7. The method according to any one of claims 1 to 6, characterized in that: The data calibration and fusion processing of the multivariate data to obtain multi-parameter comprehensive monitoring data includes: Performing data filtering and abnormal data elimination processing on the multivariate data to obtain processed multivariate data; Performing calibration processing on the processed multivariate data to obtain calibrated multivariate data; The weight of each data in the calibrated multivariate data is determined, and based on the weight of each data, data fusion processing is performed on the calibrated multivariate data to obtain multi-parameter comprehensive monitoring data.

8. A device for predicting building surface stability, characterized in that: A monitoring device for deployment in the surface area of ​​a power transmission line building, the device comprising: An acquisition module, used to acquire multivariate data corresponding to the surface area where the transmission line building is located, the multivariate data including the historical position and current position of the monitoring device, and the corresponding rainfall, soil moisture and soil temperature, movement displacement and tilt angle during the movement of the monitoring device from the historical position to the current position; A data processing module is used to perform data calibration and fusion processing on the multivariate data to obtain multi-parameter comprehensive monitoring data; The prediction module is used to input the multi-parameter comprehensive monitoring data into a pre-trained prediction model to obtain a stability prediction result of the surface area where the transmission line building is located, and the stability prediction result is used to characterize the safety level of the transmission line building.

9. A monitoring device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images