CN110929565A - Risk monitoring method and device based on machine learning, storage medium and electronic equipment - Google Patents
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Abstract
The application relates to a risk monitoring method, a risk monitoring device, a storage medium and electronic equipment based on machine learning, which belong to the technical field of building monitoring, and the method comprises the following steps: acquiring an inclination monitoring image of the building, and periodically acquiring a crack monitoring image of the building; acquiring a regular inclination according to the inclination monitoring image, and acquiring regular fracture element data according to the fracture monitoring image; generating a plurality of input data streams from the periodic fracture element data associated with each periodic inclination; generating a base data stream according to all regular gradients and all regular fracture element data; and acquiring a preset number of input data streams in the plurality of input data streams to form an attack stream, and inputting the attack stream into a machine learning model to obtain the risk value of the building. According to the method and the device, the risk value of the building is efficiently and accurately monitored through the machine learning model based on the building gradient of the building monitoring image and the attacked flow generated by the crack data.
Description
Technical Field
The application relates to the technical field of building monitoring, in particular to a risk monitoring method and device based on machine learning, a storage medium and electronic equipment.
Background
The cracking and deformation of the building structure are common technical problems at home and abroad, the building structure can be unstable and collapse due to the inclined deformation of the building structure, and particularly, the buildings with poor integrity or damaged buildings can cause great loss once the deformation limit is reached.
At present, the monitoring of the building is mostly detected by adopting a manual method or arrangement of sensors, the detection and observation are carried out manually, the period is long, the error is large, and the analysis of collected dynamic data cannot be realized, so that the house safety is difficult to be effectively ensured. When the building is monitored by adopting methods such as an inclination angle sensor, the problem of difficult arrangement of the sensor exists, and the deformation of the crack building is not detected in place. In the prior art, the problems that the building safety monitoring accuracy is not high and more human resources are consumed exist.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present application and therefore may include information that does not constitute prior art known to a person of ordinary skill in the art.
Disclosure of Invention
The utility model aims at providing a real-time monitoring scheme of building, and then at least to a certain extent based on the attacked flow that is produced according to building slope and crack data of building monitoring image, through the high-efficient, accurate control of machine learning model to the risk value of building.
According to one aspect of the application, a risk monitoring method based on machine learning is provided, which comprises the following steps:
the method comprises the steps that an inclination monitoring image of a building is obtained regularly through inclination image collecting equipment preset at a plurality of inclination monitoring positions of the building, and a crack monitoring image of the building is obtained regularly through crack image collecting equipment preset at a plurality of crack monitoring positions of the building;
acquiring regular gradients of the plurality of inclination monitoring positions according to the inclination monitoring images, and acquiring regular fracture element data of the plurality of fracture monitoring positions according to the fracture monitoring images;
generating a plurality of input data streams from periodic fracture factor data for fracture monitoring locations for which each of the periodic inclinations is associated with an inclination monitoring location from which each of the periodic inclinations originates;
generating a base data stream from all of the periodic inclinations and all of the periodic fracture element data;
and acquiring a preset number of input data streams in the plurality of input data streams to form attack streams, combining the attack streams with the base data stream to obtain attacked data streams, and inputting the attacked data streams into a pre-trained machine learning model to obtain the risk value of the building.
In an exemplary embodiment of the present application, the training method of the machine learning model is:
acquiring an attacked data stream sample set, wherein each sample calibrates a corresponding risk value of a building in advance;
respectively inputting the data of each sample into a machine learning model to obtain a risk value of the building output by the machine learning model;
if the risk value of the building obtained after the data of the sample is input into the machine learning model is inconsistent with the risk value of the building calibrated in advance for the sample, adjusting the coefficient of the machine learning model until the risk value is consistent;
and when the data of all the samples are input into the machine learning model, the obtained risk value of the building is consistent with the risk value of the building calibrated in advance for the data samples, and the training is finished.
In an exemplary embodiment of the present application, after said generating a base data stream from all of said periodic inclinations and all of said periodic fracture element data, said method further comprises:
acquiring a plurality of base data streams of a preset time period;
and inputting a plurality of base data streams into a pre-trained second machine learning model together to obtain the life prediction value of the building.
In an exemplary embodiment of the present application, after the acquiring periodic inclinations of the plurality of inclination monitoring positions from the inclination monitoring image and acquiring periodic fracture element data of the plurality of fracture monitoring positions from the fracture monitoring image, the method further comprises:
acquiring periodic fracture factor data of a target fracture monitoring position associated with the target inclination monitoring position when the periodic inclination of the target inclination monitoring position exceeds a preset threshold value in the periodic inclinations of the plurality of inclination monitoring positions;
calculating to obtain a first inclination risk value of the target inclination monitoring position on the building and a second inclination risk value of the target crack monitoring position on the building based on a preset building information registration table;
and acquiring a risk value of the building according to the regular inclination of the target inclination monitoring position, the regular fracture element data of the target fracture monitoring position, the first inclination risk value and the second inclination risk value.
In an exemplary embodiment of the present application, the obtaining a risk value of the building according to the periodic inclination of the target inclination monitoring position, the periodic fracture element data of the target fracture monitoring position, the first inclination risk value, and the second inclination risk value includes:
according to the formula W ═ (X S + Y T)o+pAnd acquiring a risk value of the building, wherein W is the risk value of the building, X is the weight corresponding to the regular inclination, S is the regular inclination, T is the data of one attribute in the regular fracture element data, Y is the weight corresponding to the data of one attribute in the regular fracture element data, O is a first inclination risk value, and P is a second inclination risk value.
In an exemplary embodiment of the application, the calculating a first inclination risk value of the target crack monitoring position on the building and a second inclination risk value of the target inclination monitoring position on the building based on a preset building information registry includes:
acquiring weather data of the place of the building by positioning the place of the building;
acquiring first inclination risk data of the target inclination monitoring position from a preset building information registration table;
acquiring the first inclination risk value according to the weather data and the first inclination risk data;
acquiring second inclination risk data of the target crack monitoring position from a preset building information registration table;
and acquiring the second inclination risk value through the weather data and the second inclination risk data.
In an exemplary embodiment of the present application, the acquiring periodic fracture element data of the target fracture monitoring location associated with the target inclination monitoring location when the periodic inclination of the target inclination monitoring location exceeds a predetermined threshold value among the periodic inclinations of the plurality of inclination monitoring locations is monitored, includes:
when the regular inclination of the target inclination monitoring position in the regular inclinations of the plurality of inclination monitoring positions exceeds a preset threshold value, acquiring all target crack monitoring positions related to the target inclination monitoring position from a preset monitoring position association relation table;
and acquiring regular fracture element data of the target fracture monitoring positions.
According to one aspect of the application, a risk monitoring device based on machine learning is provided, which is characterized by comprising:
the system comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for periodically acquiring the inclination monitoring images of the building through inclination image acquisition equipment preset at a plurality of inclination monitoring positions of the building and periodically acquiring the crack monitoring images of the building through crack image acquisition equipment preset at a plurality of crack monitoring positions of the building;
the acquisition module is used for acquiring the regular gradients of the plurality of inclination monitoring positions according to the inclination monitoring images and acquiring regular crack element data of the plurality of crack monitoring positions according to the crack monitoring images;
a first generating module for generating a plurality of input data streams from periodic fracture factor data for fracture monitoring locations for which each of said periodic inclinations is associated with an inclination monitoring location from which each of said periodic inclinations originates;
a second generation module for generating a base data stream according to all the regular gradients and all the regular fracture element data;
and the prediction module is used for acquiring a preset number of input data streams in the plurality of input data streams to form attack streams, combining the attack streams with the base data stream to obtain attacked data streams, and inputting the attacked data streams into a pre-trained machine learning model to obtain the risk value of the building.
According to an aspect of the present application, there is provided a computer-readable storage medium having a real-time monitoring program of a building stored thereon, wherein the real-time monitoring program of the building is executed by a processor to implement the method of any one of the above.
According to an aspect of the present application, there is provided an electronic device, comprising:
a processor; and
a memory for storing a real-time monitoring program of a building of the processor; wherein the processor is configured to perform any of the above methods via execution of a real-time monitoring program of the building.
The risk monitoring method and device based on machine learning are characterized by comprising the steps that firstly, slope monitoring images of a building are obtained regularly through slope image acquisition equipment preset at a plurality of slope monitoring positions of the building, and crack monitoring images of the building are obtained regularly through crack image acquisition equipment preset at a plurality of crack monitoring positions of the building; therefore, the inclination monitoring images and the crack monitoring images of all positions of the building can be quickly and accurately acquired according to the requirements, and the construction risk value and the risk source can be accurately acquired in the follow-up process. Further, obtaining periodic inclinations of the plurality of inclination monitoring positions according to the inclination monitoring image, obtaining periodic fracture element data of the plurality of fracture monitoring positions according to the fracture monitoring image, and generating a plurality of input data streams according to the periodic fracture element data of the fracture monitoring positions of which each periodic inclination is associated with the inclination monitoring position from which each periodic inclination originates; the input data stream which accurately represents each regular inclination and the correlation of the regular fracture element data is obtained in the way, and the input data stream can be used for efficiently and accurately calculating and analyzing the construction risk in the subsequent steps.
Then, generating a base data stream according to all the regular gradients and all the regular fracture element data; this allows a base data stream to be obtained that clearly characterizes the relationship between the monitored data at various locations of the building and the corresponding relationship to the building, in clear contrast to each input data stream. And finally, acquiring a preset number of input data streams in the plurality of input data streams to form attack streams, combining the attack streams with the base data stream to obtain attacked data streams, and inputting the attacked data streams into a pre-trained machine learning model to obtain the risk value of the building. Therefore, based on the attacked flow which is generated according to the building inclination and crack data of the building monitoring image and can clearly represent the hedging relation between the risk element data of each relevant position of the building and the whole data of the building, the risk value of the building can be efficiently and accurately monitored through the machine learning model.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 schematically shows a flow chart of a method of risk monitoring based on machine learning.
Fig. 2 schematically shows an application scenario example diagram of a risk monitoring method based on machine learning.
Fig. 3 schematically illustrates a flow chart of a method of obtaining a risk value for a building.
Fig. 4 schematically shows a block diagram of a machine learning based risk monitoring apparatus.
Fig. 5 schematically illustrates an example block diagram of an electronic device for implementing the above-described machine learning-based risk monitoring method.
Fig. 6 schematically illustrates a computer-readable storage medium for implementing the above-described machine learning-based risk monitoring method.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the subject matter of the present application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present application.
Furthermore, the drawings are merely schematic illustrations of the present application and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
In the present exemplary embodiment, a risk monitoring method based on machine learning is provided first, and the risk monitoring method based on machine learning may be run on a server, or may be run on a server cluster or a cloud server, and the like. Referring to fig. 1, the risk monitoring method based on machine learning may include the following steps:
step S110, regularly acquiring an inclination monitoring image of a building through inclination image acquisition equipment preset at a plurality of inclination monitoring positions of the building, and regularly acquiring a crack monitoring image of the building through crack image acquisition equipment preset at a plurality of crack monitoring positions of the building;
step S120, obtaining regular gradients of the plurality of inclination monitoring positions according to the inclination monitoring images, and obtaining regular fracture element data of the plurality of fracture monitoring positions according to the fracture monitoring images;
step S130, generating a plurality of input data streams according to the regular fracture element data of the fracture monitoring position associated with each regular inclination and the inclination monitoring position from which each regular inclination comes;
step S140, generating a base data stream according to all the regular gradients and all the regular fracture element data;
step S150, obtaining a preset number of input data streams in the plurality of input data streams to form attack streams, combining the attack streams with the base data streams to obtain attacked data streams, and inputting a pre-trained machine learning model to obtain the risk value of the building.
In the risk monitoring method based on machine learning, firstly, inclination monitoring images of a building are periodically acquired through inclination image acquisition equipment preset at a plurality of inclination monitoring positions of the building, and crack monitoring images of the building are periodically acquired through crack image acquisition equipment preset at a plurality of crack monitoring positions of the building; therefore, the inclination monitoring images and the crack monitoring images of all positions of the building can be quickly and accurately acquired according to the requirements, and the construction risk value and the risk source can be accurately acquired in the follow-up process. Further, obtaining periodic inclinations of the plurality of inclination monitoring positions according to the inclination monitoring image, obtaining periodic fracture element data of the plurality of fracture monitoring positions according to the fracture monitoring image, and generating a plurality of input data streams according to the periodic fracture element data of the fracture monitoring positions of which each periodic inclination is associated with the inclination monitoring position from which each periodic inclination originates; the input data stream which accurately represents each regular inclination and the correlation of the regular fracture element data is obtained in the way, and the input data stream can be used for efficiently and accurately calculating and analyzing the construction risk in the subsequent steps. Then, generating a base data stream according to all the regular gradients and all the regular fracture element data; this allows a base data stream to be obtained that clearly characterizes the relationship between the monitored data at various locations of the building and the corresponding relationship to the building, in clear contrast to each input data stream. And finally, acquiring a preset number of input data streams in the plurality of input data streams to form attack streams, combining the attack streams with the base data stream to obtain attacked data streams, and inputting the attacked data streams into a pre-trained machine learning model to obtain the risk value of the building. Therefore, based on the attacked flow which is generated according to the building inclination and crack data of the building monitoring image and can clearly represent the hedging relation between the risk element data of each relevant position of the building and the whole data of the building, the risk value of the building can be efficiently and accurately monitored through the machine learning model.
Hereinafter, each step in the above-described risk monitoring method based on machine learning in the present exemplary embodiment will be explained and explained in detail with reference to the drawings.
In step S110, an inclination monitoring image of a building is periodically acquired by an inclination image capturing device preset at a plurality of inclination monitoring positions of the building, and a crack monitoring image of the building is periodically acquired by a crack image capturing device preset at a plurality of crack monitoring positions of the building.
In the embodiment of the present example, referring to fig. 2, the server 201 periodically acquires the inclination monitoring image of the building through the inclination image capturing device 202 preset at a plurality of inclination monitoring positions of the building, and periodically acquires the crack monitoring image of the building through the crack image capturing device 203 preset at a plurality of crack monitoring positions of the building. This allows real-time monitoring of the building by the server 201 in subsequent steps based on the tilt monitoring image and the crack monitoring image. The server 201 may be any device with processing capability, such as a computer, a microprocessor, and the like, and the oblique image capturing device 202 and the crack image capturing device 203 may be any device with image capturing function, such as a camera, a mobile phone, and the like, which is not limited herein.
The plurality of tilt monitoring positions of the building are positions which are easy to tilt and are determined by experts on the periphery of the target building, such as the side or the back of a certain building. The inclined image acquisition equipment such as a camera at a plurality of inclined monitoring positions of the set building can acquire inclined monitoring images at preset positions of the building by shooting regularly.
The crack monitoring positions of the building are positions where building cracks are prone to occur, such as the top of the center of a longitudinal wall and the bottom windowsill, and crack monitoring images of the preset positions of the building can be acquired regularly through crack image acquisition equipment, such as cameras, preset at the crack monitoring positions of the building. The plurality of inclination monitoring positions and the plurality of crack monitoring positions of the building have preset incidence relations and are stored in the incidence relation table of the monitoring positions. For example, the correlation is that the inclination of a certain inclination monitoring position easily causes cracks to occur at certain crack monitoring positions, and the inclination monitoring position and the crack monitoring position have a correlation.
In step S120, the regular inclinations of the plurality of inclination monitoring positions are obtained from the inclination monitoring image, and regular fracture element data of the plurality of fracture monitoring positions is obtained from the fracture monitoring image.
In the embodiment of the present example, from the inclination monitoring image that is periodically acquired, for example, by locating the contour line of the wall and the floor, and then acquiring the inclination data such as the inclination angle and the inclination direction of the wall with respect to the floor accurately based on the angle between the contour line of the wall and the floor.
The method comprises the steps of detecting an edge contour of a crack through the conventional image detection from a crack monitoring image acquired periodically, then positioning points on the crack contour for fitting to obtain an inclined direction line of the crack, for example, a connecting line between two points with the farthest distance in the points on the crack contour, further calculating a periodic inclined direction of the crack according to the inclined direction line, and calculating periodic crack element data such as the area of the crack and the length of the crack according to an area surrounded by the contour line of the crack. Thus, the risk of building can be accurately analyzed through the inclination and crack element data in the subsequent step. In one embodiment, obtaining the inclination of the plurality of inclination monitoring positions according to the inclination monitoring image and obtaining the fracture element data of the plurality of fracture monitoring positions according to the fracture monitoring image includes: extracting the contour line of the building and the contour line of a preset perpendicularity reference element from the inclination monitoring image, and acquiring the inclination of the building based on an included angle between the contour line of the building and the contour line of the preset perpendicularity reference element; and extracting the contour line of the crack and the contour line of the preset perpendicularity reference element from the crack monitoring image, and fitting by positioning points on the crack contour to obtain the included angle between the inclined direction line of the crack and the contour line of the perpendicularity reference element to obtain the crack inclined directions of the plurality of crack monitoring positions. Wherein the perpendicularity reference element is, for example, a reference element obtained by suspending a weight ball from a perpendicular.
In step S130, a plurality of input data streams are generated based on the periodic fracture element data of the fracture monitoring position associated with each of the periodic gradients and the gradient monitoring position from which each of the periodic gradients originates.
In the embodiment of the example, each inclination monitoring position is associated with a plurality of crack monitoring positions in advance, the building is inclined towards a certain direction due to the collapse of the ground and the like, then the building can drive a plurality of positions on the building associated with the inclination direction to crack when the building is inclined, and the crack of the building is increased along with the increase of the inclination, and finally the building is damaged.
Generating a plurality of input data streams by the regular fracture element data of the fracture monitoring position associated with each regular inclination and the inclination monitoring position of each regular inclination source, namely processing the data of each regular inclination and the regular fracture element data of the corresponding associated fracture monitoring position into the data streams; in another embodiment, after adding a tag of the position and type of the data source to each data, the data are sequentially connected in series to form a data string, and an input data stream is obtained. The regular crack element data of the target crack monitoring position related to the target inclination monitoring position are obtained, the positions of the building with risks can be accurately analyzed in the subsequent steps by combining the inclination of the target inclination monitoring position and the related crack element data, and the input data stream which accurately represents the relevance of each regular inclination and the related regular crack element data is obtained and can be used for efficiently and accurately calculating and analyzing the building risks in the subsequent steps.
In step S140, a base data stream is generated based on all of the periodic inclinations and all of the periodic fracture element data.
In the exemplary embodiment, the base data stream is the data stream containing the monitoring data for all buildings. In one embodiment, a base data stream is generated according to all regular inclinations and all regular fracture element data, that is, each regular inclination data and each regular fracture element data are added to coordinates of corresponding positions of a plurality of two-dimensional structure charts of the building to obtain a two-dimensional building monitoring data dot matrix as the base data stream; in one embodiment, a base data stream is generated according to all the regular inclinations and all the regular fracture element data, that is, each regular inclination data and each regular fracture element data are added to coordinates of a corresponding position of the three-dimensional structure diagram of the building to obtain a three-dimensional building monitoring data dot matrix as the base data stream. Therefore, the relationship among the monitoring data of each position of the building and the corresponding relationship with the building can be clearly represented through the base data stream, and then the positions of the building with risks can be effectively analyzed through comparison after the input data stream is combined, so that the risks of the building can be efficiently and accurately analyzed based on the base data stream in the subsequent steps.
In step S150, an attack flow is formed by obtaining a predetermined number of input data flows from the plurality of input data flows, and an attacked data flow obtained by combining the attack flow and the base data flow is input to a machine learning model trained in advance, so as to obtain a risk value of the building.
In the present exemplary embodiment, a predetermined number of input data streams of the plurality of input data streams are obtained, that is, for example, 3 data streams are obtained from 5 data streams. The obtaining mode may be to obtain the input data streams of each combination in sequence after the plurality of input data streams are arranged and combined, so that the input data streams of various combinations can be obtained, that is, building risk monitoring data of various combinations can be obtained, and further, analysis of various conditions can be performed. And then, forming an attack flow, namely acquiring dynamic data such as weather, earthquake and the like in a preset time period and combining the dynamic data with a preset number of input data flows to obtain the attack flow capable of reflecting the dynamic risk of the building, wherein the instability of all the current building inclination data and the current building crack data of the building can be analyzed through the attack flow, namely the instability of all the current building inclination data and the current building crack data of the building in the current environment, namely the risk of the building. Therefore, the attack flow can attack the base data flow and is embodied as an attacked data flow obtained by combining the attack flow and the base data flow, wherein the combination mode can be that the two data flows are directly connected in series; or the attack stream may be inserted into a predetermined location of the base data stream. And finally, inputting the attacked data stream into a pre-trained machine learning model, so that the risk value of the building can be efficiently and accurately obtained.
In particular, in this way, the data input into the machine learning model is organized, so that the machine learning model can accurately predict risk monitoring sources, namely risk sources such as the inclination or cracks of a certain position causing risk to the current building, the geographic environment and the like.
In an embodiment of this example, the training method of the machine learning model is:
acquiring an attacked data stream sample set, wherein each sample calibrates a corresponding risk value of a building in advance;
respectively inputting the data of each sample into a machine learning model to obtain a risk value of the building output by the machine learning model;
if the risk value of the building obtained after the data of the sample is input into the machine learning model is inconsistent with the risk value of the building calibrated in advance for the sample, adjusting the coefficient of the machine learning model until the risk value is consistent;
and when the data of all the samples are input into the machine learning model, the obtained risk value of the building is consistent with the risk value of the building calibrated in advance for the data samples, and the training is finished.
The attacked data stream samples are historically collected separately for a large number of buildings. By collecting a sample set of attacked data streams as input to the first learning model, each sample is used by an expert to calibrate a risk value of a corresponding building in advance as output of the machine learning model. Then, after the data of all samples are input into the machine learning model through the adjustment coefficient, the obtained risk value of the building is consistent with the risk value of the building calibrated in advance for the samples, the training is finished, the trained machine learning model is obtained, and the training accuracy can be effectively guaranteed.
In one embodiment of this example, after said generating a base data stream from all of said periodic inclinations and all of said periodic fracture element data, said method further comprises:
acquiring a plurality of base data streams of a preset time period;
and inputting a plurality of base data streams into a pre-trained second machine learning model together to obtain the life prediction value of the building.
Therefore, the service life of the building, namely the service life predicted value of the building, can be predicted efficiently and accurately according to a plurality of base data streams of the building in a preset time period through the pre-trained second machine learning model.
In an embodiment of this example, the training method of the second machine learning model is:
acquiring a base data stream group sample set, wherein each sample calibrates a life prediction value of a corresponding building in advance;
respectively inputting the data of each sample into a machine learning model to obtain the service life of the building output by the machine learning model;
if the life prediction value of the building obtained after the data of the sample is input into the machine learning model is inconsistent with the life of the building calibrated in advance for the sample, adjusting the coefficient of the machine learning model until the life prediction value is consistent with the life of the building calibrated in advance for the sample;
and when the data of all the samples are input into the machine learning model, the obtained life prediction value of the building is consistent with the life of the building calibrated in advance for the data samples, and the training is finished.
The base data stream set samples are samples of a plurality of base data streams that historically correspond to predetermined time periods taken separately for a large number of buildings. By collecting a set of samples of the set of base data streams as input to the first learning model, each sample is used by an expert to calibrate the life of the corresponding building in advance as output from the machine learning model. Then, after the data of all samples are input into the machine learning model through the adjustment coefficient, the obtained life prediction value of the building is consistent with the life of the building calibrated in advance for the samples, the training is finished, the trained machine learning model is obtained, and the training accuracy can be effectively guaranteed.
In an embodiment of this example, referring to fig. 3, after the obtaining periodic inclinations of the plurality of inclination monitoring positions from the inclination monitoring image and obtaining periodic fracture element data of the plurality of fracture monitoring positions from the fracture monitoring image, the method further comprises:
step S310, when the fact that the regular inclination of the target inclination monitoring position in the regular inclinations of the plurality of inclination monitoring positions exceeds a preset threshold value is monitored, regular fracture element data of the target inclination monitoring position related to the target inclination monitoring position are obtained;
step S320, calculating to obtain a first inclination risk value of the target inclination monitoring position on the building and a second inclination risk value of the target crack monitoring position on the building based on a preset building information registration table;
and step S330, acquiring a risk value of the building according to the regular inclination of the target inclination monitoring position, the regular fracture element data of the target fracture monitoring position, the first inclination risk value and the second inclination risk value.
By calculating a predetermined threshold value for obtaining an early warning of the inclination at each inclination monitoring position of the target building, the expert can monitor whether the inclination of the target inclination monitoring position exceeds the predetermined threshold value among the inclinations of the plurality of inclination monitoring positions in real time. When the inclination of the target inclination monitoring position exceeds a preset threshold value, the inclination condition of the target inclination position reaches the early warning limit. The building is usually caused to incline in a certain direction due to the collapse of the ground, then the building can bring cracks to a plurality of positions on the building related to the inclined direction when the building inclines, and the cracks of the building are increased along with the larger the inclination, and finally the building is damaged. And then, by acquiring the crack element data of the target crack monitoring position associated with the target inclination monitoring position, the risk value of the building can be accurately calculated in the subsequent step by combining the inclination of the target inclination monitoring position and the associated crack element data.
The inclination risk value of the target inclination monitoring position on the building is the inclination risk value caused by the geology of the target inclination monitoring position or the target crack monitoring position on the building location, the load bearing condition, the current weather condition and the like. The geological condition of the place where the building is located and the bearing data of the target inclination monitoring position on the building can be accurately obtained from a preset building information registration table, meanwhile, the weather condition of the place where the building is located can be obtained in real time through building positioning, and then the first inclination risk value can be accurately calculated through calculating the weighted sum according to the data and the weight of each risk element. Meanwhile, stress data and weight of the target crack monitoring position on the building can be accurately acquired from a preset building information registration table, and then a second inclination risk value can be accurately calculated by calculating the weighted sum. Therefore, in the subsequent steps, the real-time risk value of the building can be accurately acquired by combining the inclination of the target inclination monitoring position, the crack element data of the target crack monitoring position, the first inclination risk value and the second inclination risk value.
The risk value of the building is the risk value of the building which collapses or topples under the current condition, and measures such as personnel evacuation and the like can be accurately and timely carried out on the building according to the risk value by monitoring the risk value of the building in real time.
The method for acquiring the risk value of the building according to the inclination of the target inclination monitoring position, the crack element data of the target crack monitoring position, the first inclination risk value and the second inclination risk value is that each data is used as a variable of a preset algorithm, and then the risk value of the building can be automatically and accurately acquired.
In one embodiment of this example, the obtaining the risk value of the building according to the periodic inclination of the target crack monitoring location, the periodic fracture element data of the target crack monitoring location, the first inclination risk value, and the second inclination risk value includes:
according to the formula W ═ (X × S + Y1 × T1+. + Yn × Tn)o+pAnd acquiring a risk value of the building, wherein W is the risk value of the building, X is the weight corresponding to the periodic gradient, S is the periodic gradient, Tn is the data of one attribute in the periodic fracture element data, Yn is the weight corresponding to the data of one attribute in the periodic fracture element data, O is a first gradient risk value, and P is a second gradient risk value.
In the above empirical formula, X × S + Y1 × T1+ ·+ Yn × Tn may obtain a weighted sum of the building risk element data, and then the sum o + p may be used as an index to effectively amplify the influence of the weighted sum to calculate the building risk value W.
In an embodiment of this example, the calculating a first inclination risk value of the target crack monitoring location on the building and a second inclination risk value of the target inclination monitoring location on the building based on a preset building information registry includes:
acquiring weather data of the place of the building by positioning the place of the building;
acquiring first inclination risk data of the target inclination monitoring position from a preset building information registration table;
acquiring the first inclination risk value according to the weather data and the first inclination risk data;
acquiring second inclination risk data of the target crack monitoring position from a preset building information registration table;
and acquiring the second inclination risk value through the weather data and the second inclination risk data.
Weather data of the building location is data such as rainfall, wind power and the like; first inclination risk data such as geology of the place where the building is located and bearing data of the target inclination monitoring position on the building can be accurately obtained from a preset building information registration table, meanwhile, weather conditions of the place where the building is located can be obtained in real time through building positioning, and then a first inclination risk value can be accurately calculated through calculating the weighted sum according to the data and the weight of each risk element. Meanwhile, second inclination risk data such as stress data and weight of the target crack monitoring position on the building can be accurately acquired from a preset building information registration table, and then a second inclination risk value can be accurately calculated through weighting sum of weather data and the second inclination risk data.
In one embodiment of this example, the obtaining periodic fracture factor data for the target fracture monitoring location associated with the target inclination monitoring location when it is monitored that the periodic inclination of the target inclination monitoring location exceeds a predetermined threshold from among the periodic inclinations of the plurality of inclination monitoring locations includes:
when the regular inclination of the target inclination monitoring position in the regular inclinations of the plurality of inclination monitoring positions exceeds a preset threshold value, acquiring all target crack monitoring positions related to the target inclination monitoring position from a preset monitoring position association relation table;
and acquiring regular fracture element data of the target fracture monitoring positions.
The positions of a plurality of inclined risks can be generated on the building at the same time, and the calculation accuracy can be effectively ensured by acquiring the regular crack element data of a plurality of target crack monitoring positions, so that the building monitoring accuracy is ensured.
The application also provides a risk monitoring device based on machine learning. Referring to fig. 4, the risk monitoring apparatus based on machine learning may include an acquisition module 410, an acquisition module 420, a first generation module 430, a second generation module 440, and a prediction module 450. Wherein:
the acquisition module 410 may be configured to periodically acquire an inclination monitoring image of a building through an inclination image acquisition device preset at a plurality of inclination monitoring positions of the building, and periodically acquire a crack monitoring image of the building through a crack image acquisition device preset at a plurality of crack monitoring positions of the building;
the obtaining module 420 may be configured to obtain periodic inclinations of the plurality of inclination monitoring positions according to the inclination monitoring image, and obtain periodic fracture element data of the plurality of fracture monitoring positions according to the fracture monitoring image;
the first generation module 430 may be configured to generate a plurality of input data streams based on periodic fracture factor data for fracture monitoring locations for which each of the periodic inclinations is associated with an inclination monitoring location from which each of the periodic inclinations originates;
the second generation module 440 may generate a base data stream based on all of the periodic inclinations and all of the periodic fracture element data;
the prediction module 450 may be configured to obtain an attack flow composed of a predetermined number of input data flows from the plurality of input data flows, combine the attack flow with the base data flow to obtain an attacked data flow, and input a pre-trained machine learning model to obtain the risk value of the building.
The specific details of each module in the risk monitoring device based on machine learning have been described in detail in the corresponding risk monitoring method based on machine learning, and therefore are not described herein again.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Moreover, although the steps of the methods herein are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.
In an exemplary embodiment of the present application, there is also provided an electronic device capable of implementing the above method.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 500 according to this embodiment of the invention is described below with reference to fig. 5. The electronic device 500 shown in fig. 5 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 5, the electronic device 500 is embodied in the form of a general purpose computing device. The components of the electronic device 500 may include, but are not limited to: the at least one processing unit 510, the at least one memory unit 520, and a bus 530 that couples various system components including the memory unit 520 and the processing unit 510.
Wherein the storage unit stores program code that is executable by the processing unit 510 to cause the processing unit 510 to perform steps according to various exemplary embodiments of the present invention as described in the above section "exemplary methods" of the present specification. For example, the processing unit 510 may execute step S110 as shown in fig. 1: the method comprises the steps that an inclination monitoring image of a building is obtained regularly through inclination image collecting equipment preset at a plurality of inclination monitoring positions of the building, and a crack monitoring image of the building is obtained regularly through crack image collecting equipment preset at a plurality of crack monitoring positions of the building; s120: acquiring regular gradients of the plurality of inclination monitoring positions according to the inclination monitoring images, and acquiring regular fracture element data of the plurality of fracture monitoring positions according to the fracture monitoring images; step S130: generating a plurality of input data streams from periodic fracture factor data for fracture monitoring locations for which each of the periodic inclinations is associated with an inclination monitoring location from which each of the periodic inclinations originates; step S140: generating a base data stream from all of the periodic inclinations and all of the periodic fracture element data; step S150: and acquiring a preset number of input data streams in the plurality of input data streams to form attack streams, combining the attack streams with the base data stream to obtain attacked data streams, and inputting the attacked data streams into a pre-trained machine learning model to obtain the risk value of the building.
The memory unit 520 may include a readable medium in the form of a volatile memory unit, such as a random access memory unit (RAM)5201 and/or a cache memory unit 5202, and may further include a read only memory unit (ROM) 5203.
The electronic device 500 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a client to interact with the electronic device 500, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 500 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 550. Also, the electronic device 500 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 560. As shown, the network adapter 560 communicates with the other modules of the electronic device 500 over the bus 530. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) execute the method according to the embodiments of the present application.
In an exemplary embodiment of the present application, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above section "exemplary methods" of the present description, when said program product is run on the terminal device.
Referring to fig. 6, a program product 600 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the client computing device, partly on the client device, as a stand-alone software package, partly on the client computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the client computing device over any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., over the internet using an internet service provider).
Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
Claims (10)
1. A risk monitoring method based on machine learning is characterized by comprising the following steps:
the method comprises the steps that an inclination monitoring image of a building is obtained regularly through inclination image collecting equipment preset at a plurality of inclination monitoring positions of the building, and a crack monitoring image of the building is obtained regularly through crack image collecting equipment preset at a plurality of crack monitoring positions of the building;
acquiring regular gradients of the plurality of inclination monitoring positions according to the inclination monitoring images, and acquiring regular fracture element data of the plurality of fracture monitoring positions according to the fracture monitoring images;
generating a plurality of input data streams from periodic fracture factor data for fracture monitoring locations for which each of the periodic inclinations is associated with an inclination monitoring location from which each of the periodic inclinations originates;
generating a base data stream from all of the periodic inclinations and all of the periodic fracture element data;
and acquiring a preset number of input data streams in the plurality of input data streams to form attack streams, combining the attack streams with the base data stream to obtain attacked data streams, and inputting the attacked data streams into a pre-trained machine learning model to obtain the risk value of the building.
2. The method of claim 1, wherein the training method of the machine learning model is:
acquiring an attacked data stream sample set, wherein each sample calibrates a corresponding risk value of a building in advance;
respectively inputting the data of each sample into a machine learning model to obtain a risk value of the building output by the machine learning model;
if the risk value of the building obtained after the data of the sample is input into the machine learning model is inconsistent with the risk value of the building calibrated in advance for the sample, adjusting the coefficient of the machine learning model until the risk value is consistent;
and when the data of all the samples are input into the machine learning model, the obtained risk value of the building is consistent with the risk value of the building calibrated in advance for the data samples, and the training is finished.
3. The method of claim 1, wherein after said generating a base data stream from all of said periodic inclinations and all of said periodic fracture element data, said method further comprises:
acquiring a plurality of base data streams of a preset time period;
and inputting a plurality of base data streams into a pre-trained second machine learning model together to obtain the life prediction value of the building.
4. The method of claim 1, wherein after the obtaining periodic inclinations of the plurality of inclination-monitoring locations from the inclination-monitoring image and periodic fracture factor data of the plurality of fracture-monitoring locations from the fracture-monitoring image, the method further comprises:
acquiring periodic fracture factor data of a target fracture monitoring position associated with the target inclination monitoring position when the periodic inclination of the target inclination monitoring position exceeds a preset threshold value in the periodic inclinations of the plurality of inclination monitoring positions;
calculating to obtain a first inclination risk value of the target inclination monitoring position on the building and a second inclination risk value of the target crack monitoring position on the building based on a preset building information registration table;
and acquiring a risk value of the building according to the regular inclination of the target inclination monitoring position, the regular fracture element data of the target fracture monitoring position, the first inclination risk value and the second inclination risk value.
5. The method of claim 4, wherein the obtaining the risk value for the building based on the periodic inclination of the target inclination monitoring location, the periodic fracture factor data for the target fracture monitoring location, the first inclination risk value, and the second inclination risk value comprises:
according to the formula W ═ (X S + Y T)o+pAnd acquiring a risk value of the building, wherein W is the risk value of the building, X is the weight corresponding to the regular inclination, S is the regular inclination, T is the data of one attribute in the regular fracture element data, Y is the weight corresponding to the data of one attribute in the regular fracture element data, O is a first inclination risk value, and P is a second inclination risk value.
6. The method of claim 4, wherein calculating a first inclination risk value of the target crack monitoring location on the structure and a second inclination risk value of the target crack monitoring location on the structure based on a preset building information registry comprises:
acquiring weather data of the place of the building by positioning the place of the building;
acquiring first inclination risk data of the target inclination monitoring position from a preset building information registration table;
acquiring the first inclination risk value according to the weather data and the first inclination risk data;
acquiring second inclination risk data of the target crack monitoring position from a preset building information registration table;
and acquiring the second inclination risk value through the weather data and the second inclination risk data.
7. The method of claim 4, wherein the obtaining periodic fracture factor data for the target fracture monitoring location associated with the target inclination monitoring location when the periodic inclination of the target inclination monitoring location of the periodic inclinations of the plurality of inclination monitoring locations is monitored to exceed a predetermined threshold comprises:
when the regular inclination of the target inclination monitoring position in the regular inclinations of the plurality of inclination monitoring positions exceeds a preset threshold value, acquiring all target crack monitoring positions related to the target inclination monitoring position from a preset monitoring position association relation table;
and acquiring regular fracture element data of the target fracture monitoring positions.
8. A risk monitoring device based on machine learning, comprising:
the system comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for periodically acquiring the inclination monitoring images of the building through inclination image acquisition equipment preset at a plurality of inclination monitoring positions of the building and periodically acquiring the crack monitoring images of the building through crack image acquisition equipment preset at a plurality of crack monitoring positions of the building;
the acquisition module is used for acquiring the regular gradients of the plurality of inclination monitoring positions according to the inclination monitoring images and acquiring regular crack element data of the plurality of crack monitoring positions according to the crack monitoring images;
a first generating module for generating a plurality of input data streams from periodic fracture factor data for fracture monitoring locations for which each of said periodic inclinations is associated with an inclination monitoring location from which each of said periodic inclinations originates;
a second generation module for generating a base data stream according to all the regular gradients and all the regular fracture element data;
and the prediction module is used for acquiring a preset number of input data streams in the plurality of input data streams to form attack streams, combining the attack streams with the base data stream to obtain attacked data streams, and inputting the attacked data streams into a pre-trained machine learning model to obtain the risk value of the building.
9. A computer-readable storage medium on which a real-time monitoring program of a building is stored, wherein the real-time monitoring program of the building, when executed by a processor, implements the method of any one of claims 1-7.
10. An electronic device, comprising:
a processor; and
a memory for storing a real-time monitoring program of a building of the processor; wherein the processor is configured to perform the method of any of claims 1-7 via execution of a real-time monitoring program of the building.
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