CN116720728A - Risk assessment method, electronic device and storage medium - Google Patents
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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Abstract
The application provides a risk assessment method, electronic equipment and a storage medium, wherein the method comprises the following steps: collecting historical accident data of a road in a target area, determining historical risk index data corresponding to the historical accident data, and constructing a sample data set based on the historical accident data and the historical index data; training a preset neural network by using the sample data set to obtain the risk assessment model; and inputting real-time data of the target road into the risk assessment model, and outputting the risk assessment grade of the target road by using the risk assessment model. The risk assessment method and the risk assessment system can assist in risk assessment, and accuracy of risk assessment is improved.
Description
Technical Field
The present application relates to the field of security technologies, and in particular, to a risk assessment method, an electronic device, and a storage medium.
Background
In order to avoid disaster loss caused by road collapse, the method has important significance in researching the risk of road collapse. In the related road collapse risk prediction method, expert experience is too depended, and the road collapse case data cannot be deeply analyzed, so that the prediction accuracy of the road collapse risk is lower.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a risk assessment method, an electronic device, and a storage medium, which can solve the problem of low accuracy in predicting the risk of road collapse.
The risk assessment method comprises the following steps: collecting historical accident data of a road in a target area, determining historical risk index data corresponding to the historical accident data, and constructing a sample data set based on the historical accident data and the historical index data; training a preset neural network by using the sample data set to obtain the risk assessment model; and inputting real-time data of the target road into the risk assessment model, and outputting the risk assessment grade of the target road by using the risk assessment model.
In one embodiment, the constructing a sample data set based on the historical incident data and the historical index data includes: constructing a first data set by utilizing the historical accident data and the historical index data, and preprocessing the first data set to obtain a second data set; and carrying out data enhancement on the second data set, and taking the second data set after data enhancement as the sample data set.
In one embodiment, the historical accident data comprises the position and time of occurrence of the road collapse accident, the accident result and the occurrence reason; the historical risk index data represents index data affecting road collapse accidents, and comprises geological data of a road and artificial influence data of the position of the road.
In one embodiment, the preprocessing includes: data cleaning, normalization and correlation test.
In one embodiment, the algorithm used for data enhancement includes: a minority class of oversampling algorithms is synthesized.
In one embodiment, the predetermined neural network comprises a convolutional neural network, and training the predetermined neural network using the sample data set comprises: dividing the sample data set into a training data set and a test data set; training the convolutional neural network by using the training data set to obtain an initial model; testing the initial model by using the test data set to obtain a test result; and optimizing the initial model according to the test result until a preset loss function converges to obtain the risk assessment model.
In one embodiment, the model structure of the convolutional neural network may include an encoder and a decoder, the method further comprising: inputting historical risk index data in a training data set into the encoder, and extracting common characteristics of the historical risk index data by utilizing nonlinear fitting capacity and data mining capacity of the encoder to obtain high-order characteristics of the historical risk index data; and inputting the high-order features into a decoder for decoding to obtain corresponding risk assessment grades.
In one embodiment, the method further comprises: and sending out early warning according to the risk assessment grade.
An embodiment of the present application provides a risk assessment apparatus, including: the construction module is used for collecting historical accident data of roads in a target area, determining historical risk index data corresponding to the historical accident data and constructing a sample data set based on the historical accident data and the historical index data; the training module is used for training a preset neural network by using the sample data set to obtain the risk assessment model; and the evaluation module is used for inputting real-time data of the target road into the risk evaluation model and outputting the risk evaluation grade of the target road by using the risk evaluation model.
Embodiments of the present application provide a computer readable storage medium storing at least one instruction that when executed by a processor implements the risk assessment method or the risk assessment method.
An embodiment of the application provides an electronic device comprising a memory and at least one processor, the memory storing at least one instruction that when executed by the at least one processor implements the risk assessment method.
Compared with the related art, the risk assessment method provided by the embodiment of the application has the advantages that the historical risk index data is determined based on the historical accident data of the road, the sample data set is established, the risk assessment model is trained by using the sample data set, the real-time road collapse is predicted by using the risk assessment model, the correlation analysis can be carried out on the sample data by using the deep neural network, and the accuracy and the reliability of risk identification are improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of an electronic device according to an embodiment of the present application.
Fig. 2 is a flowchart of a risk assessment method according to an embodiment of the present application.
FIG. 3 is a flow chart of constructing a sample dataset provided by an embodiment of the present application.
Fig. 4 is a flowchart of a risk assessment method according to another embodiment of the present application.
Fig. 5 is a block diagram of a risk assessment apparatus according to an embodiment of the present application.
The application will be further described in the following detailed description in conjunction with the above-described figures.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, and the described embodiments are merely some, rather than all, embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
In one embodiment, to avoid disaster damage from road collapse, it is important to study the risk of road collapse. In the related road collapse risk prediction method, expert experience is too depended, and the road collapse case data cannot be deeply analyzed, so that the prediction accuracy of the road collapse risk is lower.
In order to solve the problems, according to the risk assessment method provided by the embodiment of the application, historical risk index data is determined based on historical accident data of a road, a sample data set is established, a risk assessment model is trained by using the sample data set, real-time road collapse is predicted by using the risk assessment model, the sample data can be subjected to correlation analysis by using a deep neural network, and the accuracy and reliability of risk identification are improved.
For example, fig. 1 is a block diagram of an electronic device according to an embodiment of the present application. The risk assessment method provided by the embodiment of the application is executed by electronic equipment, and the electronic equipment can be a computer, a server, a notebook computer, a mobile phone and other equipment. The electronic device 1 comprises a memory 11, at least one processor 12, at least one communication bus 13 and a transceiver 14.
The configuration of the electronic device shown in fig. 1 is not limiting of the embodiments of the application, but may be a bus-type configuration or a star-type configuration, and the electronic device 1 may also include more or less other hardware or software than shown, or a different arrangement of components.
In some embodiments, the electronic device 1 is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and its hardware includes, but is not limited to, a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like. The electronic device 1 may also comprise other external devices, such as an input/output device like a keyboard, a mouse, a remote control, a display, a touch pad or a voice control device.
It should be noted that the electronic device 1 is only used as an example, and other electronic products that may be present in the present application or may be present in the future are also included in the scope of the present application by way of reference.
Fig. 2 is a flowchart of a risk assessment method according to an embodiment of the present application. The risk assessment method is applied to an electronic device, such as the electronic device 1 in fig. 1, and specifically includes the following steps, the order of the steps in the flowchart may be changed according to different requirements, and some may be omitted.
And S21, collecting historical accident data of the road in the target area, determining historical risk index data corresponding to the historical accident data, and constructing a sample data set based on the historical accident data and the historical index data.
In one embodiment, the target area represents an area where a prediction of road collapse risk is required, such as section a. The historical incident data includes, but is not limited to: the position and time of the road collapse accident, the accident result, the occurrence reason and the risk assessment grade.
For example, historical incident data one: road collapse occurs in C area of B city of A province in 2 months of 2018, 11 persons are in distress, 8 persons are injured, 1 person is out of connection, direct economic loss is about 5323.8 ten thousand yuan, and risk assessment grade is four; historical accident data two: road collapse occurs in the E region of D city in 12 months 1 of 2019, 3 people are in distress, the direct economic loss is about 2004.7 ten thousand yuan, and the risk assessment grade is two-level.
In one embodiment, the historical risk indicator data represents indicator data that affects road collapse incidents, including, but not limited to: geological data of the road and artificial influence data of the position of the road. In particular, historical risk indicator data may also be divided into a variety of categories of data, including, but not limited to: the type and material of the pipeline laid on the road, the lithology of the road area, the type of road soil, the size of the road breaking zone and the scale of the construction site where the road is located. Wherein each category of historical risk indicator data comprises a plurality of indicator data, for example, a pipeline type of road pavement comprises water pipes, cables and the like.
In one embodiment, the historical incident data and the historical risk indicator data may be obtained by searching for historical cases, downloading from open source data, and the like. In addition, the process of acquiring the historical risk index data is the process of constructing a risk assessment index system, and important index factors affecting road collapse can be quantified.
In one embodiment, because the actual number of urban road collapse cases is small, the historical accident data and the historical index data obtained by the method are directly used for constructing a sample data set training set to train the deep learning neural network, and the obtained risk assessment model has the over-fitting phenomenon, so that the prediction accuracy of the model cannot reach expectations.
In order to solve the above problems, when a sample data set is constructed based on the historical accident data and the historical index data, the historical accident data and the historical index data can be preprocessed to realize data noise reduction, and then data enhancement processing is performed to expand the data set, so that a deep learning model is effectively trained, the prediction accuracy of the model is improved, and a better risk assessment effect is achieved.
In one embodiment, the data in the historical incident data and the historical risk indicator data may be encoded (e.g., as alphanumeric) data, thereby enabling the neural network to identify the data in the sample dataset.
And S22, training a preset neural network by using the sample data set to obtain the risk assessment model.
In one embodiment, the predetermined neural network comprises a convolutional neural network, and training the predetermined neural network using the sample data set comprises: dividing the sample data set into a training data set and a test data set; training the convolutional neural network by using the training data set to obtain an initial model; testing the initial model by using the test data set to obtain a test result; and optimizing the initial model according to the test result until a preset loss function converges to obtain the risk assessment model.
In one embodiment, the model structure of the convolutional neural network may include an encoder and a decoder, the method further comprising: inputting historical risk index data in a training data set into the encoder, and extracting common characteristics of the historical risk index data by utilizing nonlinear fitting capacity and data mining capacity of the encoder to obtain high-order characteristics of the historical risk index data; and inputting the high-order features into a decoder for decoding to obtain corresponding risk assessment grades.
Specifically, the calculation process of the convolutional neural network is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,is the element of the ith row and the jth column in the feature matrix output by the first layer (l represents an integer greater than or equal to 1), and +.>An activation function representing the use of layer i, < ->Representing the connection weight between the mth neuron of the first layer and the nth neuron of the upper layer, b l Represents the bias parameters of the first layer, +.>And->The length and width of the convolution kernel used in the first layer are represented, respectively, and H and W represent the tensors (the largest dimension of the feature matrix) of the output of the previous layer of convolution.
In one embodiment, the loss function used by the convolutional neural network may be expressed as:where N represents the size of the lot, a represents the test result (predicted risk assessment level) output by the model, D is the actual risk assessment level, and t represents an integer from 1 to N.
In one embodiment, when deriving the loss value for the first layer of convolution layers based on the loss function, the weight gradient for the first layer of convolution layers may be derived by the chain law, which is expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,representation ofDeviation of the loss function from the output of the first layer,/->Representing the bias of the layer i output against the weights.
In one embodiment, the initial model is optimized according to the test result until the loss function converges, the risk assessment model is obtained, and network Net parameters θ of the trained risk assessment model are saved: θ=argminl ((Geo, HF)), where Geo represents a geological data class risk indicator, HF represents a human influence data class risk indicator, argmin represents a minimum function.
In one embodiment, in addition to the method of directly outputting the predicted risk assessment level, a risk assessment model may be used to output a risk assessment score, and a corresponding risk assessment level may be determined according to the risk assessment score. For example, the risk assessment model outputs risk assessment scores ranging in [0,100], the risk assessment scores are divided into five levels according to the risk assessment scores, and the higher the risk assessment score is, the higher the corresponding risk assessment level is.
Step S23, inputting real-time data of the target road into the risk assessment model, and outputting the risk assessment grade of the target road by using the risk assessment model.
In one embodiment, the real-time data of the target link includes real-time risk index data of the target link, and the real-time data has been subjected to the preprocessing (for a specific preprocessing process, reference may be made to step S31 in the flow shown in fig. 3 below). After the real-time data is input into the risk assessment model, the risk assessment model performs feature extraction on the real-time data, decodes the extracted features, and outputs a risk assessment grade corresponding to the real-time data.
In one embodiment, since the real-time data may change with time, the updated real-time data may be obtained at intervals of a preset time (for example, at intervals of seven days), and a corresponding updated risk assessment level is obtained according to the updated real-time data, so as to implement risk early warning. The method further comprises the steps of: and sending out early warning according to the risk assessment grade. For example, the prompt may be output by voice or other means, and different colors may be used to present different risk assessment levels.
In one embodiment, as shown in fig. 3, a flowchart for constructing a sample data set according to an embodiment of the present application specifically includes the following steps:
and S31, constructing a first data set by utilizing the historical accident data and the historical index data, and preprocessing the first data set to obtain a second data set.
In one embodiment, preprocessing the first data set includes preprocessing historical index data, the preprocessing including: data cleaning, normalization and correlation test.
In one embodiment, the data cleansing process includes, but is not limited to: deleting the repeated data, complementing the missing data, conforming the naming rules of the data, sequencing the data and deleting the abnormal data. The data cleaning process may clean the repeated data, the abnormal data, etc., thereby eliminating noise in the first data set.
In one embodiment, the normalization process uses methods including, but not limited to, zero-mean normalization (Z-score standardization). The normalization process can eliminate the dimension difference between different types of data, so that the different types of data are in the same order of magnitude, and the different types of data are comparable.
In one embodiment, the correlation test comprises: and calculating pearson correlation coefficients among different kinds of historical index data, and determining the correlation among the different kinds of historical index data according to the pearson correlation coefficients. Specifically, pearson correlation coefficient between the first type of history index data A1 and the second type of history index data a 2=a 1 and A2 covariance/(product of A1 variance and A2 variance), the pearson correlation coefficient has a value range of [ -1,1], and closer to 1, the stronger the correlation is expressed. By determining the relativity of different kinds of historical index data, the neural network can conveniently carry out comprehensive analysis on the related data, and the data analysis capability of the neural network is improved.
And step S32, carrying out data enhancement on the second data set, and taking the second data set with the enhanced data as the sample data set.
In one embodiment, the algorithm used for data enhancement includes: a minority class oversampling algorithm is synthesized (Synthetic Minority Oversampling Technique). Specifically, the synthetic minority class oversampling algorithm includes: randomly selecting any accident data in the preprocessed historical accident data as central data, and determining nearest neighbor data of the central data as similar data based on a nearest neighbor algorithm; generating new data by using a preset distance formula based on the center data and the similar data, wherein the distance formula comprises the following steps: z=x+t (y-x), where z represents the new data, x represents the center data, t represents a random number between 0 and 1, and y represents the same kind of data.
In one embodiment, the synthetic minority oversampling algorithm can manually synthesize new samples according to minority samples and add the new samples into the data set, so that data enhancement is realized, a foundation is provided for using a deep learning model for risk assessment, and the constraint of the lack of road collapse data sample size on training the deep learning model is solved.
In one embodiment, the risk assessment method provided by the application can be applied to assessing the risk of road collapse, and can also be applied to assessing the risk of other disasters, such as flood disasters and the like.
In one embodiment, as shown in fig. 4, a flowchart of a risk assessment method according to another embodiment of the present application is mainly divided into three steps: data collection, data enhancement and model training, model verification and evaluation result output. Wherein the data collection comprises: collecting collapse case data and evaluation index data, performing data cleaning on the collected data, and taking the data after data cleaning as original data; data enhancement and model training includes: performing data enhancement on the original data to obtain enhanced data, dividing the enhanced data into an enhanced data training set and an enhanced data testing set, and training a convolutional neural network by using the enhanced data training set so as to obtain an initial model; the model verification and evaluation result output comprises: and carrying out model verification on the initial model by using the enhanced data test set, carrying out optimization updating on the initial model according to a model verification result until a risk assessment model is obtained, and outputting a risk assessment result of the target area by using the risk assessment model.
In one embodiment, in the disaster assessment field, deep learning is widely cited because of its stronger nonlinear fitting capability, but for urban road collapse risk assessment, existing deep learning risk assessment techniques are less applicable. This is mainly because the assessment using the deep learning model requires a large amount of data for model training first, and the number of urban road collapse cases often lacks sufficient collapse accident case data to effectively train the deep learning model.
The application provides a road collapse risk assessment method based on a data enhancement strategy, which adopts a synthetic minority oversampling technology to complete data enhancement, so as to fully train a deep learning model, effectively make up for the deficiency of the ground collapse risk assessment capability of a risk assessment model in the current comprehensive urban scene, and further improve the accuracy and reliability of road collapse risk identification.
Fig. 5 is a block diagram of a risk assessment apparatus according to an embodiment of the present application.
In some embodiments, the risk assessment apparatus 40 may include a plurality of functional modules that are comprised of computer program segments. The computer program of the individual program segments in the risk assessment apparatus 40 may be stored in a memory of the electronic device and executed by at least one processor to perform the functions of risk assessment (see fig. 2 for details).
In this embodiment, the risk assessment apparatus 40 may be divided into a plurality of functional modules according to the functions performed by the risk assessment apparatus. The functional module may include: a construction module 401, a training module 402, an evaluation module 403. The module referred to in the present application refers to a series of computer program segments capable of being executed by at least one processor and of performing a fixed function, stored in a memory. In the present embodiment, reference is made to the above definition of the risk assessment method regarding the functional implementation of each module in the risk assessment apparatus 40, and the description thereof will not be repeated here.
The construction module 401 is configured to collect historical accident data of a road in a target area, determine historical risk indicator data corresponding to the historical accident data, and construct a sample dataset based on the historical accident data and the historical indicator data.
The training module 402 is configured to train a preset neural network by using the sample data set, and obtain the risk assessment model.
The evaluation module 403 is configured to input real-time data of a target road into the risk evaluation model, and output a risk evaluation level of the target road using the risk evaluation model.
Continuing from the description of fig. 1 above, the memory 11 stores a computer program which, when executed by the at least one processor 12, implements all or part of the steps of the risk assessment method as described. The Memory 11 includes Read-Only Memory (ROM), programmable Read-Only Memory (PROM), erasable programmable Read-Only Memory (EPROM), one-time programmable Read-Only Memory (One-time Programmable Read-Only Memory, OTPROM), electrically erasable rewritable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic tape Memory, or any other medium that can be used for computer-readable carrying or storing data.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
In one embodiment of the present application, the computer readable storage medium has stored thereon a computer program which, when executed by the processor 12, implements a flow as shown in fig. 2.
In some embodiments, the at least one processor 12 is a Control Unit (Control Unit) of the electronic device 1, connects the various components of the entire electronic device 1 using various interfaces and lines, and performs various functions of the electronic device 1 and processes data by running or executing programs or modules stored in the memory 11, and invoking data stored in the memory 11. For example, the at least one processor 12, when executing the computer program stored in the memory, implements all or part of the steps of the risk assessment method described in embodiments of the present application; or to implement all or part of the functionality of the risk assessment device. The at least one processor 12 may be comprised of integrated circuits, such as a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functionality, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like.
In some embodiments, the at least one communication bus 13 is arranged to enable a connection communication between the memory 11 and the at least one processor 12 or the like.
Although not shown, the electronic device 1 may further comprise a power source (such as a battery) for powering the various components, which may preferably be logically connected to the at least one processor 12 via a power management means, such that the functions of managing charging, discharging, and power consumption are performed by the power management means. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, camera devices, etc., which are not described herein.
The integrated units implemented in the form of software functional modules described above may be stored in a computer readable storage medium. The software functional modules described above are stored in a storage medium and include instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device, etc.) or a processor (processor) to perform portions of the methods described in the various embodiments of the application.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it will be obvious that the term "comprising" does not exclude other elements or that the singular does not exclude a plurality. Several of the elements or devices recited in the specification may be embodied by one and the same item of software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present application without departing from the spirit and scope of the technical solution of the present application.
Claims (10)
1. A risk assessment method, the method comprising:
collecting historical accident data of a road in a target area, determining historical risk index data corresponding to the historical accident data, and constructing a sample data set based on the historical accident data and the historical index data;
training a preset neural network by using the sample data set to obtain the risk assessment model;
and inputting real-time data of the target road into the risk assessment model, and outputting the risk assessment grade of the target road by using the risk assessment model.
2. The risk assessment method of claim 1, wherein the constructing a sample dataset based on the historical incident data and the historical index data comprises:
constructing a first data set by utilizing the historical accident data and the historical index data, and preprocessing the first data set to obtain a second data set;
and carrying out data enhancement on the second data set, and taking the second data set after data enhancement as the sample data set.
3. The risk assessment method according to claim 1 or 2, wherein the historical accident data comprises the position and time of occurrence of a road collapse accident, and the consequences and reasons of occurrence of the accident;
the historical risk index data represents index data affecting road collapse accidents, and comprises geological data of a road and artificial influence data of the position of the road.
4. The risk assessment method according to claim 2, wherein the preprocessing comprises: data cleaning, normalization and correlation test.
5. The risk assessment method according to claim 2, wherein the algorithm used for data enhancement comprises: a minority class of oversampling algorithms is synthesized.
6. The risk assessment method of claim 1, wherein the pre-set neural network comprises a convolutional neural network, and wherein training the pre-set neural network using the sample data set comprises:
dividing the sample data set into a training data set and a test data set;
training the convolutional neural network by using the training data set to obtain an initial model;
testing the initial model by using the test data set to obtain a test result;
and optimizing the initial model according to the test result until a preset loss function converges to obtain the risk assessment model.
7. The risk assessment method of claim 6, wherein the model structure of the convolutional neural network may include an encoder and a decoder, the method further comprising:
inputting historical risk index data in a training data set into the encoder, and extracting common characteristics of the historical risk index data by utilizing nonlinear fitting capacity and data mining capacity of the encoder to obtain high-order characteristics of the historical risk index data;
and inputting the high-order features into a decoder for decoding to obtain corresponding risk assessment grades.
8. The risk assessment method according to claim 1, wherein the method further comprises:
and sending out early warning according to the risk assessment grade.
9. An electronic device comprising a processor and a memory, wherein the processor is configured to implement the risk assessment method according to any one of claims 1 to 8 when executing a computer program stored in the memory.
10. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the risk assessment method according to any of claims 1 to 8.
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