Tunnel environment safety risk assessment method and system based on deep learning

Abstract

The invention discloses a tunnel environment safety risk assessment method and system based on deep learning, and the method comprises the steps: obtaining multi-source data of a tunnel, and obtaining environment data if a preset condition is satisfied; performing feature extraction on the image data to obtain a damage feature vector; inputting the environmental data and the damage feature vector into a prediction model to output a damage propagation risk index; calculating a structural stability coefficient of the tunnel based on the stress distribution data, and inputting the damage propagation risk index, the structural stability coefficient and the environmental data into a risk assessment model so as to generate a first influence weight of the damage propagation risk index and a second influence weight of the structural stability coefficient according to the environmental data, and the damage propagation risk index and the structure stability coefficient are fused based on the first influence weight and the second influence weight to obtain an environmental safety risk assessment result of the tunnel, so that accurate assessment of the tunnel collapse risk can be improved through implementation of the tunnel collapse risk assessment method and device.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of risk assessment, in particular to a tunnel environment safety risk assessment method and system based on deep learning. BACKGROUND

[0002] As a key node of the traffic network and an important carrier of underground space, the structural safety of the tunnel is directly related to the safety of people's lives and property and the stability of economic and social operation. Collapse is the most serious failure form of the tunnel structure, which has the characteristics of strong burst, great destructive and difficult rescue. Once it happens, it may not only cause significant casualties and facility damage, but also cause long-term interruption of the traffic aorta, resulting in huge direct and indirect economic losses, and even adverse social impact. Therefore, it is necessary to conduct real-time, quantitative and forward-looking assessment on the health condition of the tunnel structure, especially when stress anomaly or crack appears. If the risk of evolution into overall instability and collapse cannot be scientifically assessed, it may delay the warning due to misjudgment, or cause unnecessary operation and maintenance cost due to overreaction.

[0003] The existing tunnel safety risk assessment method often faces a bottleneck in accuracy when dealing with complex disaster-causing mechanisms. One method focuses on single-dimensional analysis, for example, relying only on structural mechanics model and stress data of limited measuring points to calculate safety coefficient, which is difficult to effectively capture and quantify the dynamic expansion threat of apparent crack damage. Another computer vision method based on image recognition mainly focuses on the static geometric features of cracks, lacks correlation analysis with internal stress state and inducing environmental factors, and ignores the "chain reaction" mechanism that local damage may be dramatically amplified and trigger overall instability under certain adverse environment, resulting in deviation between risk assessment results and real risk condition, and difficulty in accurately distinguishing high-risk and low-risk states, thereby affecting the timeliness and scientificity of early warning decision. SUMMARY

[0004] The present application provides a tunnel environment safety risk assessment method and system based on deep learning, which can improve the accurate assessment of tunnel collapse risk.

[0005] An embodiment of the present application provides a tunnel environment safety risk assessment method based on deep learning, comprising: Obtaining multi-source data of the tunnel, and obtaining environmental data in which the tunnel is located if the multi-source data meets a preset condition, wherein the multi-source data comprises stress distribution data and image data of cracks; Performing feature extraction on the image data to obtain a damage feature vector reflecting the change of crack morphology; Inputting the environmental data and the damage feature vector into a prediction model to output a damage propagation risk index representing the deterioration of damage in a future period; The structural stability coefficient of the tunnel is calculated based on the stress distribution data, and the damage propagation risk index, the structural stability coefficient and the environmental data are input into a risk assessment model to generate a first influence weight of the damage propagation risk index and a second influence weight of the structural stability coefficient according to an attention weight mechanism in the risk assessment model according to the environmental data, and the damage propagation risk index and the structural stability coefficient are fused based on the first influence weight and the second influence weight respectively to obtain an environmental safety risk assessment result of the tunnel.

[0006] The stress distribution data and crack image data are acquired, risk signals are preliminarily captured from two core dimensions of macroscopic mechanical state and microscopic defect appearance, detailed environmental data are acquired by triggering a preset condition, so that the system can focus on external environmental factors that induce risk deterioration when potential risks are found, redundancy and inefficiency caused by continuous collection of all environmental data are avoided, and timeliness and data pertinence of the assessment are ensured; feature extraction is performed on the crack image to capture the essence of the morphological change; the damage feature vector is input into the prediction model to quantify the possibility that the existing damage develops, expands and leads to accelerated degradation of the structure performance in a future period under a specific environmental load, and the time dimension and evolution path of the collapse risk are directly revealed, which is a key to improve the foresight and accuracy of the early warning; the structural stability coefficient is calculated based on the stress distribution data, which is an evaluation of the distance between the current overall bearing capacity of the tunnel and the failure threshold, and represents the possibility of instability of the structure under instantaneous load even without considering damage evolution, which is a basic statics dimension of the collapse risk assessment and is complementary to the dynamic damage propagation prediction; the damage propagation risk index, the structural stability coefficient and the environmental data are input into the risk assessment model at the same time, so that it can be identified which risk factor is dominant under different environments; the fusion based on the weights enables the final risk assessment result to be adaptive to the environmental context, dynamically highlight the current most urgent and dominant risk source, and then output a precise result that is closer to the actual engineering judgment and has high context relevance, thereby greatly improving the accuracy and reliability of the assessment of complex collapse risk situation.

[0007] Further, the feature extraction on the image data to obtain the damage feature vector reflecting the morphological change of the crack comprises: identifying a crack contour of the image data and calculating a geometric feature parameter of the crack contour; based on the geometric feature parameter and a preset reference feature parameter, wherein the reference feature parameter is calculated according to reference image data; calculating a morphological change amount of the crack contour relative to the reference feature parameter, and determining a damage feature vector reflecting the morphological change of the crack based on the morphological change amount.

[0008] In this way, by feature extraction on the crack image, the essence of the "morphological change" is captured.

[0009] Further, the environment data and the damage feature vector are input into a prediction model to output a damage propagation risk index representing deterioration of the damage in a future period, including: The environment data and the damage feature vector are input into a prediction model to perform time series encoding on the environment data through a first channel to obtain environment pattern features, wherein the prediction model includes the first channel, a second channel and a regression output layer; The environment pattern features are fused with the damage feature vector received by the second channel to obtain comprehensive state features; The comprehensive state features are input into the regression output layer to calculate a damage propagation risk index for quantifying the risk in a future set period.

[0010] In this way, by inputting the damage feature vector into the prediction model, the possibility of existing damage developing, expanding and leading to accelerated deterioration of the structure performance in a future period under a specific environmental load is quantified, directly revealing the time dimension and evolution path of the collapse risk, which is a key to improving the foresight and accuracy of the early warning.

[0011] Further, the structure stability coefficient of the tunnel is calculated based on the stress distribution data, including: According to the stress distribution data, a plurality of stress concentration regions in the tunnel structure and corresponding maximum stress values are determined; Based on each of the maximum stress values and a limit strength parameter of the tunnel, local stability safety coefficients of each of the stress concentration regions are calculated; Each of the local stability safety coefficients is normalized to obtain a plurality of processing results, and each of the processing results is weighted and fused to generate an overall structure stability coefficient of the tunnel.

[0012] In this way, the structure stability coefficient is calculated based on the stress distribution data, which is an evaluation of the distance between the current overall bearing capacity of the tunnel and the failure threshold, representing the possibility of instability of the structure under instantaneous load even without considering damage evolution, which is a basic statics dimension for evaluating the collapse risk, and is complementary to the dynamic damage propagation prediction.

[0013] Further, the damage propagation risk index, the structure stability coefficient and the environment data are input into a risk assessment model to generate a first influence weight of the damage propagation risk index and a second influence weight of the structure stability coefficient according to the environment data through an attention weight mechanism in the risk assessment model, including: inputting the damage propagation risk index, the structure stability coefficient and the environment data into a risk assessment model, wherein the risk assessment model comprises an environment encoder, a first fully connected network and a second fully connected network; encoding the environment data through the environment encoder to obtain an environment feature vector; concatenating the environment feature vector with the damage propagation risk index and the structure stability coefficient respectively to form a first concatenated vector and a second concatenated vector; inputting the first concatenated vector and the second concatenated vector into the first fully connected network and the second fully connected network respectively for calculation and normalization processing to obtain a first influence weight of the damage propagation risk index and a second influence weight of the structure stability coefficient.

[0014] In this way, by simultaneously inputting the damage propagation risk index, the structure stability coefficient and the environment data into the risk assessment model, it can be identified which risk factor is dominant under different environments; Further, based on the first influence weight and the second influence weight, the damage propagation risk index and the structure stability coefficient are fused respectively to obtain an environmental safety risk assessment result of the tunnel, comprising: multiplying the first influence weight and the damage propagation risk index to obtain a weighted risk value; multiplying the second influence weight and the structure stability coefficient to obtain a weighted stability value; summing the weighted risk value and the weighted stability value to obtain the environmental safety risk assessment result of the tunnel.

[0015] In this way, based on the weight fusion, the final risk assessment result can adapt to the environmental context, dynamically highlight the current most urgent and dominant risk source, and further output a precise result with high context relevance that is closer to the actual engineering judgment, thereby greatly improving the accuracy and reliability of the complex collapse risk assessment.

[0016] Further, if the multi-source data meets a preset condition, environment data of the tunnel is obtained, comprising: determining whether the stress distribution data and the image data meet a preset condition; if the uniformity of the stress distribution data is lower than a preset threshold or the image data shows a crack, it is determined that the multi-source data meets the preset condition, and environment data of the tunnel is triggered to be obtained.

[0017] In this way, the detailed environment data is triggered by the preset condition, so that the system can focus on the external environmental factors that induce the risk deterioration when the potential risk is found, avoid the redundancy and inefficiency caused by continuous collection of all environment data, and ensure the timeliness and data pertinence of the evaluation.

[0018] Further, after obtaining the environment safety risk evaluation result of the tunnel, the method further comprises: comparing the environment safety risk evaluation result with a preset risk threshold interval to determine a risk level of the tunnel collapse; if the risk level reaches a warning level, generating comprehensive warning information including the risk level, main risk source and recommended disposal measures.

[0019] In this way, the environment safety risk evaluation result is calibrated and quantified by the preset threshold interval, and structured warning information is generated, which ensures that the evaluation result is not only accurate but also usable, directly improving the accuracy and timeliness of risk decision-making.

[0020] Another embodiment of the application also provides a tunnel environment safety risk assessment system based on deep learning, comprising: The acquisition module is configured to acquire multi-source data of the tunnel, and if the multi-source data meets a preset condition, acquire environment data of the tunnel, wherein the multi-source data includes stress distribution data and image data of cracks. The extraction module is configured to perform feature extraction on the image data to obtain a damage feature vector reflecting the change in crack morphology. The prediction module is configured to input the environment data and the damage feature vector into a prediction model to output a damage propagation risk index representing the deterioration of damage in a future period. The evaluation module is configured to calculate a structure stability coefficient of the tunnel based on the stress distribution data, and input the damage propagation risk index, the structure stability coefficient and the environment data into a risk evaluation model to generate a first influence weight of the damage propagation risk index and a second influence weight of the structure stability coefficient according to the environment data by an attention weight mechanism in the risk evaluation model, and fuse the damage propagation risk index and the structure stability coefficient based on the first influence weight and the second influence weight respectively to obtain an environment safety risk evaluation result of the tunnel.

[0021] Further, the evaluation module comprises: The input unit is configured to input the damage propagation risk index, the structure stability coefficient and the environment data into a risk evaluation model, wherein the risk evaluation model comprises an environment encoder, a first fully connected network and a second fully connected network. an encoding unit, configured to encode the environment data by the environment encoder to obtain an environment feature vector; a splicing unit, configured to splice the environment feature vector with the damage propagation risk index and the structure stability coefficient respectively to form a first splicing vector and a second splicing vector; a processing unit, configured to input the first splicing vector and the second splicing vector into the first fully connected network and the second fully connected network respectively to perform calculation and normalization processing to obtain a first influence weight of the damage propagation risk index and a second influence weight of the structure stability coefficient. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0023] Figure 1 is a flowchart of an embodiment of the tunnel environment safety risk assessment method based on deep learning provided by the present application; Figure 2 is a flowchart of an embodiment of steps S201 to S203 provided by the present application; Figure 3 is a flowchart of an embodiment of steps S301 to S304 provided by the present application; Figure 4 is a structural schematic diagram of an embodiment of the tunnel environment safety risk assessment system based on deep learning provided by the present application. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "include" and "have" and any variations thereof in the specification and claims of the present application and the above description of drawings are intended to cover non-exclusive inclusion.

[0026] In the description of the embodiments of the present application, the technical terms "first", "second" and the like are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise explicitly and specifically limited.

[0027] Reference herein to "embodiments" means that the particular features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily mutually exclusive of other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " herein generally represents an "or" relationship between the associated objects before and after it.

[0029] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two), and similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0030] In the description of the embodiments of the present application, unless otherwise explicitly specified and limited, the technical terms "mounting", "connection", "connection", "fixing" and the like should be understood in a broad sense, for example, it can be fixedly connected, or it can be detachably connected, or it can be integrated; it can be mechanical connection, or it can be electrical connection; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0031] As a key node of the traffic network and an important carrier of underground space, the structure safety of the tunnel is directly related to the safety of people's life and property and the stability of economic and social operation. Collapse is the most serious failure form of tunnel structure, and once it occurs, it will cause serious consequences. The existing tunnel safety risk assessment methods focus on single-dimensional analysis or computer vision methods based on image recognition, but both of these two methods cannot accurately assess the collapse risk.

[0032] Reference is made to Figure 1To improve the accurate assessment of the tunnel collapse risk, an embodiment of the present application provides a tunnel environment safety risk assessment method based on deep learning, which comprises steps S101 to S104. In step S101, multi-source data of the tunnel is acquired, and if the multi-source data meets a preset condition, environment data of the tunnel is acquired, wherein the multi-source data comprises stress distribution data and image data of cracks. In some embodiments, the multi-source data of the tunnel is acquired, specifically, fiber bragg grating sensor arrays are pre-buried or installed at key structural sections (such as vaults, side walls, inverted arches) of the tunnel to continuously monitor the strain and stress inside the concrete, forming the stress distribution data. At the same time, high-definition industrial cameras or visual sensors carried by inspection robots are deployed at the corresponding positions to collect high-definition images of the tunnel surface according to a preset period or a triggering condition (such as stress anomaly), constituting the original crack image data.

[0033] It should be noted that after obtaining the initial multi-source data, the initial multi-source data needs to be preprocessed (such as cleaning, filtering and denoising, coordinate conversion) to form stress distribution data reflecting the stress size and spatial distribution of the tunnel structure, to facilitate subsequent judgment.

[0034] In some embodiments, if the multi-source data meets a preset condition, environment data of the tunnel is acquired, comprising: judging whether the stress distribution data and the image data meet a preset condition; if the uniformity of the stress distribution data is lower than a preset threshold or cracks appear in the image data, it is determined that the multi-source data meets the preset condition, and environment data of the tunnel is triggered to be acquired. Specifically, since uneven stress distribution or over-limit is the fundamental mechanical cause of crack generation in the lining structure, and cracks are the most direct and apparent manifestation of the above mechanical deterioration, after obtaining the stress distribution data and the image data, the stress distribution data needs to be analyzed to calculate its uniformity index (for example, calculating the standard deviation or coefficient of variation of the stress values of all measuring points, or evaluating the stress field gradient), and then comparing the uniformity index with a preset empirical threshold to determine whether the stress distribution is abnormal. At the same time, a crack recognition algorithm (such as a deep learning-based image segmentation model) is called to process the image data to detect whether the image contains a crack contour. If cracks are identified in the image, then if the uniformity of the stress distribution data is lower than the preset threshold or cracks appear in the image data, as long as any of the conditions is met, the system determines that the current multi-source data has met the preset condition for starting deep risk assessment. Once it is determined that the preset condition is met, it means that there are cracks or other reasons that may cause collapse risk, i.e. risk signs, so environment data of the tunnel needs to be immediately triggered and acquired by sensors, wherein the environment data includes but is not limited to temperature, humidity, vibration, groundwater pressure, etc.

[0035] In this way, the detailed environment data is triggered by the preset condition, so that the system can focus on the external environmental factors that induce the risk deterioration when the potential risk is found, avoiding the redundancy and inefficiency caused by continuous collection of all environment data, and ensuring the timeliness and data pertinence of the evaluation.

[0036] It should be noted that the tunnel collapse is the result of the combined action of the overall instability of the structure (reflected by stress data) and the local damage expansion (reflected by crack image) under adverse environment (reflected by environment data). However, if the tunnel stress data is found to be abnormal or cracks appear during the inspection, this situation does not necessarily lead to collapse, so the collapse risk needs to be evaluated in combination with the environment data.

[0037] In step S102, feature extraction is performed on the image data to obtain a damage feature vector reflecting the change in crack morphology. In some embodiments, the feature extraction on the image data to obtain the damage feature vector reflecting the change in crack morphology includes: identifying the crack contour of the image data, and calculating the geometric feature parameters of the crack contour; based on the geometric feature parameters and the preset reference feature parameters, wherein the reference feature parameters are calculated according to reference image data; calculating the morphological change amount of the crack contour relative to the reference feature parameters, and determining the damage feature vector reflecting the change in crack morphology based on the morphological change amount. Specifically, first, when it is determined that there is a crack in the tunnel, the contour of the crack is accurately identified from the acquired tunnel surface image, and the geometric feature parameters of the contour are calculated, wherein the geometric feature parameters generally include but are not limited to: crack length, maximum width, average width, strike (angle with the principal stress direction), number of branches, tortuosity (ratio of actual length to straight line distance between the beginning and end), and area. These parameters digitally describe the current spatial morphology of the crack from different dimensions, and the specific calculation method can be realized by existing technology, which is not limited in the present application. Then, the morphological change amount of the geometric feature parameters of the current crack contour relative to the reference feature parameters is calculated, for example, the growth rate of the length, the expansion amount of the width, the increase amount of the tortuosity, or the number of new branches, etc., wherein the change amount can be an absolute difference or a relative proportion. Finally, these calculated morphological change amounts are taken as an ordered set to form a damage feature vector, which dynamically reflects the evolution process and severity of the crack from the reference state to the current state.

[0038] It should be noted that the reference feature parameters are calculated according to reference image data, wherein the reference image data is typically selected from one of the following two cases: one is the baseline image collected at the initial stage of the tunnel construction or after the last repair when the tunnel is in a stable and healthy state; the other is an image at a selected historical time (such as when there is no obvious deterioration sign) as a dynamic reference in continuous monitoring. By performing the same contour identification and parameter calculation process on the reference image, a corresponding set of reference feature parameters can be obtained.

[0039] In this way, feature extraction is performed on the crack image, aiming to capture the essence of the "morphological change".

[0040] Step S103: inputting the environmental data and the damage feature vector into a prediction model to output a damage propagation risk index representing deterioration of the damage in a future period; Please refer to Figure 2 In some embodiments, the inputting the environmental data and the damage feature vector into a prediction model to output a damage propagation risk index representing deterioration of the damage in a future period includes steps S201 to S203: Step S201: inputting the environmental data and the damage feature vector into a prediction model to perform time series encoding on the environmental data through a first channel to obtain environmental pattern features, wherein the prediction model includes the first channel, a second channel, and a regression output layer. In some embodiments, the environmental data and the damage feature vector are input into two independent channels of the prediction model in parallel to perform feature encoding to obtain the environmental pattern features, wherein the first channel is dedicated to processing environmental data with time series characteristics (such as periodic temperature, humidity, vibration, or water pressure data).

[0041] It should be noted that the first channel is usually composed of a time series encoding network, such as a recurrent neural network (RNN) or a long short-term memory network (LSTM). The network reads the environmental data sequence in time step order, captures the dynamic pattern, trend, and periodicity of the data in the time dimension through the internal memory unit, and finally outputs a fixed-dimensional environmental pattern feature vector after processing at the last time step, to reflect the historical influence and potential driving pattern of the environmental conditions on the structural damage evolution.

[0042] Step S202: performing nonlinear transformation on the damage feature vector through the second channel to obtain a transformation result, and performing feature fusion on the environmental pattern features and the transformation result to obtain comprehensive state features. In some embodiments, the second channel includes a plurality of cascaded fully connected layers which, after receiving the damage feature vector reflecting the current static morphology of the crack obtained by image analysis, perform nonlinear transformation on the damage feature vector to obtain a transformed result suitable for fusion with the environmental features; then, the environmental pattern features output by the first channel are spliced with the damage feature vector output by the second channel through the feature fusion layer of the prediction model to form a more comprehensive integrated state feature which contains both the current state (geometric morphology) of the damage and the dynamic environmental background in which the damage is located.

[0043] Step S203: inputting the integrated state feature into the regression output layer to calculate a damage propagation risk index for quantitatively representing the damage propagation risk in a future set period.

[0044] In some embodiments, the integrated state feature after fusion is input into the regression output layer of the prediction model to perform a series of weighted calculations and dimension reduction on the integrated state feature, and finally output a continuous and quantitative scalar value, i.e., a damage propagation risk index, wherein the damage propagation risk index specifically represents an estimated value of the probability or severity of significant deterioration of the damage in a future preset period (such as the next 24 hours or the next week) under the combined action of the current damage state and the existing environmental pattern.

[0045] It should be noted that the regression output layer is usually composed of one or more fully connected layers, and the last layer uses a linear activation function. The role of this layer is to perform nonlinear mapping from a high-dimensional feature space to a one-dimensional risk score.

[0046] It should be noted that the training process of the prediction model includes, first, the training sample contains: 1) environmental data of a time series segment; 2) the crack damage feature vector observed and extracted at the end of the time period; 3) the actual observed damage deterioration amount (such as crack length growth rate, area expansion amount) in the subsequent fixed future period (such as the next week) as a supervision label. The training goal is to enable the model to learn to predict the future damage development degree according to the current environment sequence and the current damage state. In structure, the prediction model is an end-to-end neural network, and the core components and information flow are as follows: the first channel (time series encoding channel): usually composed of a long short-term memory network (LSTM) layer or a time series convolution network (TCN). The input of this channel is a fixed time window (such as the past 48 hours) of environmental data sequence. The LSTM layer learns the long-term dependence in the sequence through its gating mechanism, and the hidden state of the last time step is extracted as a fixed-dimensional environmental pattern feature vector, which encodes the dynamic trend and potential periodic pattern of environmental changes. The second channel (damage state channel): usually composed of two to three fully connected layers (FC), each followed by a nonlinear activation function such as ReLU. This channel receives the damage feature vector representing the current geometry of the crack (such as length, width, orientation, fractal dimension), and maps it to a hidden space with the same dimension as the output of the first channel through nonlinear transformation, forming the damage state feature for subsequent fusion. Feature fusion layer: the environmental pattern feature output by the first channel and the damage state feature output by the second channel are spliced to form a higher-dimensional comprehensive state feature. This operation ensures that the "static present situation" of the damage and the "dynamic background" information of the environment are preserved and jointly represented. Regression output layer: composed of one or more fully connected layers, with a linear activation function in the last layer. This layer receives the comprehensive state feature, and through a series of nonlinear transformations and final linear mapping, outputs a continuous scalar value, which is the damage propagation risk index. This index directly corresponds to the prediction of the future damage deterioration amount defined in the training label. During training, mean squared error is used as the loss function, and the Adam optimizer is used for backpropagation to minimize the error between the model's predicted risk index and the actual observed future damage deterioration amount. At the same time, techniques such as Dropout are usually used during training to prevent overfitting, and the model's generalization performance is monitored through a validation set. In this way, a trained prediction model can be obtained.

[0047] By inputting the damage feature vector into the prediction model, the possibility of existing damage developing, expanding and leading to accelerated degradation of structure performance in the future period under specific environmental load can be quantified, directly revealing the time dimension and evolution path of collapse risk, which is the key to improving the foresight and accuracy of early warning.

[0048] Step S104: Calculate the structural stability coefficient of the tunnel based on the stress distribution data, and input the damage propagation risk index, the structural stability coefficient, and the environmental data into the risk assessment model. Use the attention weighting mechanism in the risk assessment model to generate a first influence weight for the damage propagation risk index and a second influence weight for the structural stability coefficient based on the environmental data. Then, fuse the damage propagation risk index and the structural stability coefficient based on the first influence weight and the second influence weight respectively to obtain the environmental safety risk assessment result of the tunnel.

[0049] In some embodiments, calculating the structural stability coefficient of the tunnel based on the stress distribution data includes: determining several stress concentration regions and their corresponding maximum stress values ​​in the tunnel structure according to the stress distribution data; calculating the local stability safety factor of each stress concentration region based on each maximum stress value and the ultimate strength parameter of the tunnel; normalizing each local stability safety factor to obtain several processing results, and weighting and fusing the processing results to generate the overall structural stability coefficient of the tunnel. Specifically, firstly, using a spatial gradient algorithm or peak detection algorithm, points or regions in the stress distribution data whose stress values ​​are significantly higher than those of the surrounding areas and exceed a preset stress threshold are located, these regions are marked as stress concentration regions, and the maximum stress value in each region is recorded as the maximum stress value of that region. ,in Indicates the first There are several stress concentration regions. Next, based on the maximum stress value of each stress concentration region... Ultimate strength parameters of tunnel structures Calculate the local stability safety factor for each region. The calculation formula is: The ultimate strength parameter is determined based on the design strength of the tunnel lining material (such as concrete or steel) or the ultimate compressive / tensile strength measured in the laboratory. Then, since the dimensions and initial range of the safety factors in different areas may differ, to facilitate subsequent integration, the local stability safety factors are adjusted. Normalization is performed, where the Min-Max normalization method can be used to map it to the [0,1] interval, thus obtaining several normalization results. Finally, the normalization results were processed. With preset fusion weights Weighted fusion is performed to generate the overall structural stability coefficient S of the tunnel. The relevant calculation formula is as follows: , where n is the total number of stress concentration regions.

[0050] It should be noted that the fusion weight for each stress concentration region... According to the area size of the region, the importance of the structural position (such as the vault, side wall), and the degree of stress concentration, a preset weight distribution function or a weight configuration table can be used to obtain the dynamic determination, which is not limited by the present application.

[0051] It should be noted that the local stability safety factor reflects the safety margin of a single stress concentration region under the current load, and the local stability safety factor The larger the value, the more stable the region. The overall structure stability coefficient S obtained by calculation is used to represent the instantaneous stability state of the tunnel structure as a whole, and the higher the value, the better the overall stability of the structure.

[0052] Thus, based on the stress distribution data, the structure stability coefficient is calculated, which is an evaluation of the distance between the current overall bearing capacity of the tunnel and the failure threshold, representing the possibility of instability of the structure under the instantaneous load even without considering damage evolution, which is the basis of statics dimension for evaluating collapse risk, and is complementary to dynamic damage propagation prediction.

[0053] Please refer to Figure 3 In some embodiments, inputting the damage propagation risk index, the structure stability coefficient, and the environmental data into a risk assessment model to generate a first influence weight of the damage propagation risk index and a second influence weight of the structure stability coefficient according to the environmental data through an attention weight mechanism in the risk assessment model includes steps S301 to S304: Step S301, inputting the damage propagation risk index, the structure stability coefficient, and the environmental data into a risk assessment model, wherein the risk assessment model includes an environmental encoder, a first fully connected network, and a second fully connected network; In some embodiments, the damage propagation risk index, the structure stability coefficient S, and the environmental data (usually time series or instantaneous vectors containing temperature, humidity, vibration, groundwater level, etc. dimensions) calculated above are input into a risk assessment model. It should be noted that the risk assessment model is designed to include three core components: an environmental encoder, a first fully connected network, and a second fully connected network.

[0054] It should be noted that the training process of the risk assessment model is as follows: first, a historical data set is collected and constructed, which should contain multi-source data samples in the historical monitoring period of the tunnel project, each sample is composed of synchronous historical stress distribution data, historical crack image sequence, historical environmental monitoring data (temperature, humidity, vibration, etc.) and corresponding real risk level label marked by expert experience or subsequent accident record. After data preprocessing, the corresponding historical damage propagation risk index and historical structure stability coefficient are calculated based on the historical stress distribution data and historical image data offline, and the two are used as training features together with the historical environmental data. Then, a risk assessment model (including an environmental encoder, two fully connected networks, etc.) is initialized. During training, the historical risk index, historical stability coefficient and historical environmental data calculated as described above are used as model input, and the risk level marked by experts is used as supervision target. Through the back propagation algorithm, a composite loss function combining regression loss (such as mean square error MSE) and classification loss (such as cross entropy) is optimized, which aims to make the final output of the model as close as possible to the real risk label. In this process, the parameters of the environmental encoder and the two fully connected networks in the model are optimized simultaneously, so that the attention weight mechanism can automatically learn how to dynamically and reasonably allocate the weights of damage propagation risk and structure stability in the final judgment under different environmental feature patterns (for example, humid environment vs. strong vibration environment). Among them, the training adopts an iterative method, uses a validation set to monitor the performance to prevent overfitting, until the model converges, thereby obtaining a trained risk assessment model that can automatically evaluate the environmental safety risk of the tunnel according to real-time data.

[0055] In step S302, the environmental data is encoded by the environmental encoder to obtain an environmental feature vector. In some embodiments, to realize the dynamic modulation of environmental conditions on risk factors, high-dimensional feature extraction of environmental data is performed by the environmental encoder to obtain an environmental feature vector. The environmental encoder is usually a shallow neural network (such as a multi-layer perceptron MLP) or a time series encoding network (such as LSTM, GRU, suitable for time series environmental data), which receives the original environmental data vector as input. The encoder maps the original environmental data to a more representative and compact environmental feature vector through a series of nonlinear transformations (such as fully connected layers with ReLU activation function).

[0056] In step S303, the environmental feature vector is spliced with the damage propagation risk index and the structure stability coefficient respectively to form a first spliced vector and a second spliced vector. In some embodiments, after obtaining the environmental feature vector, in order to establish the association between the environmental state and each risk factor, first, the environmental feature vector is spliced with the damage propagation risk index to form a first spliced vector. Then, the same environmental feature vector is spliced with the structural stability coefficient to form a second spliced vector.

[0057] It should be noted that if the damage propagation risk index and the structural stability coefficient are scalars, they are directly connected with the environmental feature vector at the beginning and the end; if the damage propagation risk index and the structural stability coefficient are both vectors, the corresponding dimensions are connected.

[0058] In step S304, the first spliced vector and the second spliced vector are respectively input into the first fully connected network and the second fully connected network for calculation and normalization processing to obtain the first influence weight of the damage propagation risk index and the second influence weight of the structural stability coefficient.

[0059] In some embodiments, after obtaining the first spliced vector and the second spliced vector, the two vectors are respectively sent into independent first fully connected network and second fully connected network for processing, wherein each fully connected network is usually composed of one to more hidden layers, which receives the spliced vector, and then outputs a scalar value representing the initial importance of the risk factor through weight matrix calculation and activation function transformation. For example, the first fully connected network processes the first spliced vector to output the initial weight of the damage propagation risk index; the second fully connected network processes the second spliced vector to output the initial weight of the structural stability coefficient. In order to ensure that the sum of the two weights is 1 and form a reasonable distribution ratio, the two initial weights are sent into a Softmax normalization layer (which can be integrated at the end of the fully connected network) together to calculate the two initial weights and output the final normalized first influence weight (a) and second influence weight (b), and satisfy a + b = 1.

[0060] In this way, by simultaneously inputting the damage propagation risk index, the structural stability coefficient and the environmental data into the risk assessment model, it can be identified which risk factor is dominant under different environments.

[0061] In some embodiments, the fusing of the damage propagation risk index and the structure stability coefficient based on the first influence weight and the second influence weight respectively to obtain the environmental safety risk assessment result of the tunnel comprises: multiplying the first influence weight by the damage propagation risk index to obtain a weighted risk value; multiplying the second influence weight by the structure stability coefficient to obtain a weighted stability value; and summing the weighted risk value and the weighted stability value to obtain the environmental safety risk assessment result of the tunnel. Specifically, first, after obtaining the first influence weight and the second influence weight, the damage propagation risk index and the corresponding first influence weight are subjected to scalar multiplication operation to obtain a weighted risk value, so as to dynamically adjust the importance of the future deterioration risk of the crack according to the current environmental condition (determined by the attention mechanism), for example, in an environment with high humidity and easy to accelerate damage, the weight is adjusted to be high, so that the risk index occupies a larger proportion in the final evaluation. Then, the structure stability coefficient and the corresponding second influence weight are subjected to scalar multiplication operation to obtain a weighted stability value, so as to adjust the sensitivity of the structure stability to the environmental condition, for example, in the environment of seismic motion or large temperature difference, the influence of the structure stability on the overall safety may be more prominent, and the weight is increased accordingly. Finally, the weighted risk value and the weighted stability value obtained by calculation are subjected to algebraic summation to obtain the final environmental safety risk assessment result of the tunnel.

[0062] It should be noted that the environmental safety risk assessment result is not a simple average of the two factors, but a dynamic trade-off and integration of the "future potential threat" and the "current static defense capability" under the perception of environmental context. The value can be directly used for subsequent risk level division and early warning decision. The higher the value, the greater the comprehensive safety risk.

[0063] Such weight-based fusion enables the final risk assessment result to be self-adaptive to the environmental context, dynamically highlighting the current most urgent and dominant risk source, and further outputting a precise result with high context relevance that is closer to the actual engineering judgment, thereby greatly improving the accuracy and reliability of the complex collapse risk situation assessment.

[0064] In some embodiments, after obtaining the environmental safety risk assessment result of the tunnel, the method further comprises: comparing the environmental safety risk assessment result with a preset risk threshold interval to determine a risk level of tunnel collapse; and if the risk level reaches a warning level, generating comprehensive warning information including the risk level, a main risk source, and a recommended treatment measure. Specifically, first, a plurality of "risk threshold intervals" are pre-set based on historical accident data, engineering specifications, and expert experience, wherein each interval corresponds to a specific "risk level of tunnel collapse" (e.g., low risk, medium risk, high risk, and emergency risk). Then, the calculated environmental safety risk assessment result is automatically compared with these preset intervals to determine its specific risk level. When it is determined that the risk level reaches or exceeds a preset warning level (e.g., high risk or emergency risk), the system triggers a warning generation module to automatically generate a comprehensive warning information based on the intermediate data and weight analysis in the risk assessment process.

[0065] It should be noted that the comprehensive warning information includes a risk level indicating the severity of the current risk, a main risk source, and a recommended treatment measure, wherein the main risk source is determined by backtracking the first influence weight and the second influence weight to identify the factor that contributes most to the current high risk. For example, if the first influence weight is significantly higher than the second influence weight, the warning information can explicitly indicate that damage dynamic deterioration is the main risk source; otherwise, it may indicate that structural stress exceeding the limit is the main cause. The recommended treatment measure is obtained by matching the risk level and the identified main risk source with a preset treatment measure knowledge base, wherein the treatment measure knowledge base includes different risk levels and different risk source combinations. For example, for the case of high risk and damage dynamic deterioration as the main source, it may be recommended to immediately conduct a close-range detailed detection of the section and consider temporary reinforcement and increased monitoring frequency.

[0066] In this way, the environmental safety risk assessment result is calibrated and quantified by the preset threshold interval, and structured warning information is generated, ensuring that the assessment result is not only accurate but also useful, directly improving the accuracy and timeliness of risk decision-making.

[0067] The stress distribution data and the crack image data are acquired, risk signals can be preliminarily captured from two core dimensions of a macroscopic mechanical state and a microscopic defect appearance, detailed environment data is acquired by triggering a preset condition, so that the system can focus on external environment factors inducing risk deterioration when discovering potential risks, redundancy and inefficiency caused by continuous acquisition of all environment data are avoided, and timeliness and data pertinence of evaluation are ensured; the essence of the morphological change of the crack image is captured by feature extraction; the possibility that existing damage develops, expands and leads to accelerated degradation of structure performance in a future period under a specific environmental load is quantified by inputting the damage feature vector into a prediction model, and the time dimension and evolution path of the collapse risk are directly revealed, which is a key to improve the foresight and accuracy of early warning; the structure stability coefficient is calculated based on the stress distribution data, which is an evaluation of the distance between the current overall bearing capacity of the tunnel and the failure threshold, and represents the possibility of instability of the structure under instantaneous load even without considering damage evolution, which is a basic statics dimension of the evaluation of the collapse risk, and is complementary to the dynamic damage propagation prediction; the risk assessment model is inputted by the damage propagation risk index, the structure stability coefficient and the environment data, so that it can be identified that which risk factor is dominant under different environments; the final risk assessment result can be adapted to the environmental context based on the weight fusion, and the current most urgent and dominant risk source is dynamically highlighted, and then a precise result with high context relevance close to the actual engineering judgment is outputted, so that the accuracy and reliability of the assessment of the complex collapse risk situation are greatly improved.

[0068] As Figure 4 shown, on the basis of the above-mentioned method embodiment, a corresponding device embodiment is provided; An embodiment of the present application provides a tunnel environment safety risk assessment system based on deep learning, which comprises: An acquisition module 100 is used for acquiring multi-source data of a tunnel, and if the multi-source data meet a preset condition, environment data of the tunnel is acquired, wherein the multi-source data comprise stress distribution data and image data of cracks. An extraction module 200 is used for performing feature extraction on the image data to obtain a damage feature vector reflecting the morphological change of the cracks. A prediction module 300 is used for inputting the environment data and the damage feature vector into a prediction model to output a damage propagation risk index representing damage deterioration in a future period. The evaluation module 400 is configured to calculate a structure stability coefficient of the tunnel based on the stress distribution data, and input the damage propagation risk index, the structure stability coefficient and the environmental data into a risk evaluation model, so as to generate a first influence weight of the damage propagation risk index and a second influence weight of the structure stability coefficient according to an attention weight mechanism in the risk evaluation model based on the environmental data, and fuse the damage propagation risk index and the structure stability coefficient based on the first influence weight and the second influence weight respectively, to obtain an environmental safety risk evaluation result of the tunnel.

[0069] In some embodiments, the evaluation module 400 includes an input unit configured to input the damage propagation risk index, the structure stability coefficient and the environmental data into a risk evaluation model, wherein the risk evaluation model includes an environmental encoder, a first fully connected network and a second fully connected network; an encoding unit configured to encode the environmental data by using the environmental encoder to obtain an environmental feature vector; a splicing unit configured to splice the environmental feature vector with the damage propagation risk index and the structure stability coefficient respectively to form a first splicing vector and a second splicing vector; and a processing unit configured to input the first splicing vector and the second splicing vector into the first fully connected network and the second fully connected network respectively to perform calculation and normalization processing, so as to obtain the first influence weight of the damage propagation risk index and the second influence weight of the structure stability coefficient.

[0070] It can be understood that the above-mentioned device item embodiments correspond to the method item embodiments of the present application, and can realize the deep learning-based tunnel environmental safety risk evaluation method provided by any one of the above-mentioned method item embodiments.

[0071] It should be noted that the device embodiments described above are only schematic, and some or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the device embodiment provided by the present application, the connection relationship between the modules indicates that there is a communication connection between them, which can be realized as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0072] On the basis of the above-mentioned embodiment of the deep learning-based tunnel environmental safety risk evaluation method, another embodiment of the present application provides a terminal device, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the deep learning-based tunnel environmental safety risk evaluation method of any one of the embodiments of the present application is realized.

[0073] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present application. The one or more modules can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the terminal device.

[0074] The terminal device can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing devices. The terminal device can include, but is not limited to, a processor and a memory.

[0075] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and connects all parts of the terminal device through various interfaces and lines.

[0076] On the basis of the above-mentioned method embodiment, another embodiment of the present application provides a computer readable storage medium, including a stored computer program, wherein when the computer program runs, the device where the computer readable storage medium is located executes the deep learning-based tunnel environment safety risk assessment method described in any one of the above-mentioned method embodiments of the present application.

[0077] The modules / units integrated in the device / terminal equipment, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. When the computer program is executed by a processor, the steps of each method embodiment can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0078] The above is the preferred embodiment of the present application. It should be noted that, for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which are also considered within the scope of protection of the present application.

Claims

1.A method for tunnel environment safety risk assessment based on deep learning, characterized in that, The method comprises the following steps: Obtain multi-source data of the tunnel, and if the multi-source data meet preset conditions, obtain environmental data of the tunnel, wherein the multi-source data include stress distribution data and image data of cracks; Extract features from the image data to obtain a damage feature vector reflecting changes in crack morphology; Input the environmental data and the damage feature vector into a prediction model to output a damage propagation risk index representing deterioration of damage in a future period; Calculate a structural stability coefficient of the tunnel based on the stress distribution data, and input the damage propagation risk index, the structural stability coefficient, and the environmental data into a risk assessment model to generate a first influence weight of the damage propagation risk index and a second influence weight of the structural stability coefficient according to the environmental data through an attention weight mechanism in the risk assessment model, and fuse the damage propagation risk index and the structural stability coefficient based on the first influence weight and the second influence weight respectively to obtain an environmental safety risk assessment result of the tunnel. 2.The deep learning-based tunnel environment safety risk assessment method according to claim 1, characterized in that, The feature extraction from the image data to obtain a damage feature vector reflecting changes in crack morphology comprises the following steps: Identify the crack profile of the image data and calculate the geometric feature parameters of the crack profile; Based on the geometric feature parameters and preset reference feature parameters, wherein the reference feature parameters are calculated based on reference image data; Calculate the morphological change amount of the crack profile relative to the reference feature parameters, and determine the damage feature vector reflecting changes in crack morphology based on the morphological change amount. 3.The deep learning-based tunnel environment safety risk assessment method of claim 1, wherein, The input of the environmental data and the damage feature vector into the prediction model to output a damage propagation risk index representing deterioration of damage in a future period comprises the following steps: Input the environmental data and the damage feature vector into the prediction model to perform time series encoding on the environmental data through a first channel to obtain environmental pattern features, wherein the prediction model comprises the first channel, a second channel, and a regression output layer; Fuse the environmental pattern features with the damage feature vector received by the second channel to obtain comprehensive state features; Input the comprehensive state features into the regression output layer to calculate a damage propagation risk index for quantitatively representing the risk in a future set period. 4.The deep learning-based tunnel environment safety risk assessment method of claim 1, wherein, The calculation of the structural stability coefficient of the tunnel based on the stress distribution data comprises the following steps: Determine a plurality of stress concentration regions and corresponding maximum stress values in the tunnel structure according to the stress distribution data; Calculate local stability safety coefficients of the stress concentration regions based on the maximum stress values and the ultimate strength parameters of the tunnel; Normalize each local stability safety coefficient to obtain a plurality of processing results, and fuse the processing results to generate an overall structural stability coefficient of the tunnel. 5.The deep learning-based tunnel environment safety risk assessment method according to claim 1, wherein, The damage propagation risk index, the structure stability coefficient, and the environment data are input into a risk assessment model, and a first influence weight of the damage propagation risk index and a second influence weight of the structure stability coefficient are generated according to the environment data by an attention weight mechanism in the risk assessment model, including: The damage propagation risk index, the structure stability coefficient, and the environment data are input into a risk assessment model, and a first influence weight of the damage propagation risk index and a second influence weight of the structure stability coefficient are generated according to the environment data by an attention weight mechanism in the risk assessment model, including: The environment data is encoded by the environment encoder to obtain an environment feature vector; The environment feature vector is spliced with the damage propagation risk index and the structure stability coefficient respectively to form a first spliced vector and a second spliced vector; The first spliced vector and the second spliced vector are input into the first fully connected network and the second fully connected network respectively for calculation and normalization processing to obtain the first influence weight of the damage propagation risk index and the second influence weight of the structure stability coefficient. 6.The deep learning-based tunnel environment safety risk assessment method according to claim 1, wherein, The first influence weight and the damage propagation risk index are multiplied to obtain a weighted risk value; The second influence weight and the structure stability coefficient are multiplied to obtain a weighted stability value; The weighted risk value and the weighted stability value are summed to obtain the environmental safety risk assessment result of the tunnel. If the multi-source data meets the preset condition, the environment data of the tunnel is obtained, including: 7.The tunnel environment safety risk assessment method based on deep learning according to any one of claims 1-6, characterized in that, Determine whether the stress distribution data and the image data meet the preset condition; If the uniformity of the stress distribution data is lower than a preset threshold or the image data has a crack, it is determined that the multi-source data meets the preset condition, and the environment data of the tunnel is triggered to be obtained. After obtaining the environmental safety risk assessment result of the tunnel, further including: 8.The tunnel environment safety risk assessment method based on deep learning according to any one of claims 1-6, characterized in that, The environmental safety risk assessment result is compared with a preset risk threshold interval to determine the risk level of tunnel collapse; If the risk level reaches a warning level, comprehensive warning information including the risk level, main risk source, and recommended treatment measures is generated. including; 9.A deep learning-based tunnel environment safety risk assessment system, characterized in that, The acquisition module is configured to acquire multi-source data of a tunnel, and if the multi-source data meets a preset condition, environment data of the tunnel is obtained, wherein the multi-source data includes stress distribution data and image data of a crack; The extraction module is configured to perform feature extraction on the image data to obtain a damage feature vector reflecting crack morphology changes; The prediction module inputs the environment data and the damage feature vector into a prediction model to output a damage propagation risk index representing damage deterioration in a future period; ​ The evaluation module is configured to calculate a structure stability coefficient of the tunnel based on the stress distribution data, and input the damage propagation risk index, the structure stability coefficient, and the environmental data into a risk evaluation model, so as to generate a first influence weight of the damage propagation risk index and a second influence weight of the structure stability coefficient according to an attention weight mechanism in the risk evaluation model according to the environmental data, and fuse the damage propagation risk index and the structure stability coefficient based on the first influence weight and the second influence weight respectively, to obtain an environmental safety risk evaluation result of the tunnel. 10.The deep learning-based tunnel environment safety risk assessment system according to claim 9, wherein, The evaluation module comprises: An input unit configured to input the damage propagation risk index, the structure stability coefficient, and the environmental data into a risk evaluation model, wherein the risk evaluation model comprises an environmental encoder, a first fully connected network, and a second fully connected network; An encoding unit configured to encode the environmental data by using the environmental encoder to obtain an environmental feature vector; A splicing unit configured to splice the environmental feature vector with the damage propagation risk index and the structure stability coefficient respectively to form a first splicing vector and a second splicing vector; A processing unit configured to input the first splicing vector and the second splicing vector into the first fully connected network and the second fully connected network respectively to perform calculation and normalization processing, so as to obtain a first influence weight of the damage propagation risk index and a second influence weight of the structure stability coefficient.

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