A digital method for urban safety risk assessment standards

Abstract

This invention discloses a digital method for assessing urban safety risk standards, including: identifying risk factors from historical accident time series data, performing correlation analysis to obtain correlation functions, further constructing an urban network topology, determining the prior probability of nodes based on their similarity to pre-disaster data, generating simulated accident scenarios and adding disturbance terms to simulate sudden risk, combining these with the prior probabilities of the nodes to obtain risk propagation paths, constructing a risk assessment model, evaluating the spatiotemporal impact ranges of nodes along the propagation paths, and performing a weighted summation of the assessment results for each node to obtain an urban safety risk assessment result. This method utilizes the network topology constructed through multi-stage analysis of historical accident data, making risk propagation simulations more accurate and enabling precise quantification of urban risk assessments while maintaining good interpretability.

Description

Technical Field

[0001] The present invention relates to the field of public safety, and in particular to a digital method for evaluating urban safety risk standards. Background Art

[0002] With the rapid development of urbanization, cities continue to expand in size, with highly concentrated populations and diverse functions. The interweaving of key elements such as numerous buildings, transportation facilities, energy supply, and public service facilities exposes cities to numerous security risks, including natural disasters, accidents, disasters, public health incidents, and social security incidents.

[0003] Accurately assessing urban safety risks and formulating appropriate strategies is crucial for urban safety management. However, traditional urban safety risk assessment methods have numerous limitations. They often rely on manual field surveys, paper records, and simple empirical judgments. These data sources are limited and fail to fully capture the complex and diverse information elements within a city.

[0004] A digital approach to urban safety risk assessment standards integrates multi-source big data and leverages advanced algorithm models to address the shortcomings of traditional assessment methods, thereby providing a more accurate, real-time, comprehensive, and forward-looking risk assessment solution for urban safety management, effectively ensuring the safe, stable operation, and sustainable development of cities. Summary of the Invention

[0005] The purpose of the present invention is to provide a digital method for urban safety risk assessment standards.

[0006] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0007] A first aspect of the present invention provides a digital method for urban safety risk assessment standards, comprising:

[0008] S101 identifies risk factors in historical accident time series data to obtain a risk factor set; the historical accident time series data includes pre-disaster data, mid-disaster data, and post-disaster data;

[0009] S102 performs correlation analysis on the risk factor set to obtain a risk factor correlation function;

[0010] S103 constructs a city network topology based on the risk factor correlation function, and determines the prior probability of each node according to the similarity between each node and the pre-disaster data;

[0011] S104 generates a simulated accident scenario, adds a disturbance term to simulate an unexpected risk event, and obtains a risk propagation path in the urban network topology structure in combination with the prior probability of each node;

[0012] S105 obtains the spatiotemporal influence range of each node traversed by the propagation path, constructs a risk assessment model to assess the spatiotemporal influence range of each node, and performs weighted summation on the assessment results of each node to obtain a city safety risk assessment result.

[0013] As a further method, the method of identifying risk factors from historical accident time series data to obtain a risk factor set includes:

[0014] Extract environmental features from pre-disaster data, extract disaster characteristics from disaster data, and extract loss extent characteristics from post-disaster data, and combine them as a set of urban safety risk features;

[0015] Calculate the degree of correlation between features. The expression is:

[0016]

[0017] Among them, X i and Y j are the i-th item of feature sequence X and the j-th item of feature sequence Y, respectively. m and n are the number of feature sequences X and Y, respectively. ij and β ij X i and Y j The linear correlation weight and nonlinear correlation weight between ij For X i The positive covariance power adjustment coefficient, μ ij Y j The positive covariance power adjustment coefficient, δ ij Y j The absolute value covariance power adjustment coefficient, Cov(X i ,Y j ) is X i and Y j The covariance between and X i and Y j The norm of

[0018] If the correlation between environmental characteristics and disaster characteristics, and between environmental characteristics and loss degree characteristics is greater than the preset threshold, the corresponding environmental characteristics will be used as risk factors to obtain a risk factor set.

[0019] As a further method, the method of performing correlation analysis on the risk factor set to obtain a risk factor correlation function includes:

[0020] A support vector machine model was constructed, and the risk factors for correlation analysis were used as input and output to train the model. The kernel function of the model was used to represent the correlation function, which is expressed as follows:

[0021]

[0022] Where N is the number of support vectors, α i is the positive weight of the i-th support vector on the prediction result, is the negative weight of the i-th support vector on the prediction result, γ is the width of the kernel function, x is the current risk factor input vector, x i is the risk factor input vector of the i-th support vector, and b is the bias term.

[0023] As a further method, the method of constructing a city network topology structure based on the risk factor correlation function includes:

[0024] Each functional area in the city is defined as a node. Node types include geographical area nodes, infrastructure nodes, and personnel gathering nodes. Each node is assigned an attribute value based on risk element data.

[0025] The function of determining the edge between nodes is based on a multilayer perceptron neural network. Specifically, the number of neurons in the input layer and the output layer are set to the number of risk factors in the two nodes respectively, and the correlation function between risk factors is used as the mapping function of the corresponding nodes in the hidden layer of the neural network to obtain the edge function;

[0026] Establish a data collection system to continuously collect risk element data, update node attribute values ​​and edge functions based on the determined risk element data and risk element association functions, and dynamically adjust the network topology.

[0027] As a further method, the method of determining the prior probability of each node based on the similarity between each node and the pre-disaster data includes:

[0028] Get the pre-disaster data corresponding to the node and calculate the similarity between the node and the pre-disaster data. The expression is:

[0029]

[0030] Among them, s i is the similarity between the i-th node and the pre-disaster data, k is the number of features, ω j is the weight of the jth feature, x j and ξ ij are the jth eigenvalue of the node and the jth eigenvalue of the i-th sample in the historical pre-disaster data, max(ξ j ) and min(ξ j ) are the maximum and minimum values ​​of the jth feature in the historical pre-disaster data samples, ∈ is a smoothing term, which is set to 0.01;

[0031] Calculate the prior probability of each node, the expression is:

[0032]

[0033] Among them, P(s i ) is the prior probability of the i-th node, e is a constant, μ is the mean of the similarity distribution, σ is the standard deviation of the similarity, α1 is the amplitude adjustment parameter of the sine function, β1 is the period adjustment parameter of the sine function, γ1 is the translation parameter of the sine function, n is the total number of nodes, s j is the similarity between the jth node and the pre-disaster data.

[0034] As a further method, the method of generating a simulated accident scenario and adding a disturbance item to simulate an unexpected risk event includes:

[0035] A deep convolutional generative adversarial network is selected to construct the generator network and the discriminator network. The network is trained based on historical accident case data. The latent space distribution of historical accident data is input into the generator network to obtain a set of simulated accident scenarios.

[0036] Add a disturbance term to simulate the accident scenario. The disturbance term expression is:

[0037]

[0038] Among them, t is the time variable, m is the total number of disturbance terms, P i is the weight of the i-th disturbance term, χ i is the fluctuation adjustment coefficient of the ith disturbance term, ψ i is the fluctuation frequency parameter of the ith disturbance term, t 0i is the starting time of the i-th disturbance term, ω i is the Gaussian attenuation coefficient of the ith disturbance term, H(·) is a step function, when t≥t 0i When H(tt 0i )=1, indicating that the disturbance term begins to take effect, otherwise the disturbance term does not take effect.

[0039] As a further method, the method of obtaining the risk propagation path in the urban network topology structure in combination with the prior probability of each node includes:

[0040] The nodes in the urban topology network are used as nodes of the Bayesian network. The conditional probability table is determined based on the prior probability of the nodes, and the simulated accident scenarios are converted into constraint conditions.

[0041] The probability of a node propagating to an adjacent node is determined based on the maximum a posteriori probability estimation method under constraints. The expression is:

[0042]

[0043] in, At node N i In new state Under the condition that node N j Transition to a new state The probability of For node N i In new state Under the condition that node N j Transition to a new state The conditional probability of For node N i In new state The prior probability of α is the weight parameter of the prior probability, C is the constraint influence factor, β is the weight factor of the constraint, D ij For node N i With node N j The distance, S j For node N j All possible state values ​​of At node N i In new state Under the condition that node N j The probability of being in various possible states;

[0044] For each simulated accident scenario, the paths with the highest probability of propagating to adjacent nodes are selected from the nodes and combined to obtain the risk propagation path in the urban network topology.

[0045] As a further method, the method of constructing a risk assessment model to assess the spatiotemporal impact range of each node and performing weighted summation of the assessment results of each node to obtain the urban safety risk assessment result includes:

[0046] A risk assessment model was constructed based on a three-dimensional convolutional neural network. The model was trained using a combination of historical recovery time, loss extent, and the spatiotemporal impact range of historical accidents as a training set. The predicted recovery time and loss extent were used as evaluation indicators.

[0047] Calculate the importance of each node, the expression is:

[0048]

[0049] Among them, I i is the importance of node i, K i is the degree of node i in the topological network structure, q i is the resource flow of node i, C i is the expected recovery time of node i, η is the risk resistance sensitivity coefficient, R iis the risk resistance assessment value of node i, μ R and σ R are the mean and standard deviation of the risk resistance assessment values ​​of all nodes, θ is the node association strength weight coefficient, S i is the mean of the association degree between node i and the rest of the nodes;

[0050] The evaluation indicators of each node are weighted and summed based on the importance of the node to obtain the risk assessment value of the entire urban topological network structure.

[0051] In a second aspect, an embodiment of the present application further provides an electronic device, comprising: a processor; and a memory arranged to store computer-executable instructions, wherein the executable instructions, when executed, cause the processor to perform the method steps described in the first aspect.

[0052] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device executes the method steps described in the first aspect.

[0053] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0054] (1) By analyzing the time series data of historical accidents before, during, and after disasters, the present invention can comprehensively capture risk factors, so that risk assessment is no longer limited to a single stage, but covers the entire life cycle of the risk, thereby more systematically identifying and understanding urban safety risks.

[0055] (2) By adding disturbance terms to simulate sudden risk events, the present invention can more realistically reflect the uncertainty and suddenness of risks in reality, make the acquisition of risk transmission paths more in line with actual conditions, and enhance the reliability of the assessment results.

[0056] (3) The present invention obtains the city safety risk assessment result by weighted summing the assessment results of each node based on the node importance, so that the final assessment result can more accurately reflect the overall safety risk of the city. At the same time, based on the assessment results of a single node, the safety of key areas and key links can be prioritized under limited resource conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a flowchart of the steps of a digital method for evaluating urban safety risk standards in an embodiment of the present invention.

[0058] Figure 2 This is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0060] Reference Figure 1 As shown, the present invention provides a digital method for urban safety risk assessment standards, including:

[0061] S101 identifies risk factors in historical accident time series data to obtain a risk factor set; the historical accident time series data includes pre-disaster data, mid-disaster data, and post-disaster data;

[0062] In the actual evaluation, traffic data of a large city for the past three years was collected:

[0063] Pre-disaster data included road infrastructure data, with a total road mileage of approximately 5,000 kilometers, including 1,200 kilometers of main roads, 1,700 kilometers of secondary roads, and 2,100 kilometers of branch roads; road width data, with an average width of 40 meters for main roads, 25 meters for secondary roads, and 15 meters for branch roads; 8,700 traffic lights, distributed across different road types; and vehicle ownership data, which was approximately 2 million vehicles and growing at an annual rate of 8%.

[0064] The disaster data covers traffic flow data for various road sections during different time periods (7-9 am morning peak and 5-7 pm evening peak). The average traffic flow on main roads during the morning peak was 820 vehicles per lane per hour, 410 vehicles per lane on secondary roads, and 200 vehicles per lane on branch roads. The average traffic flow on main roads during the evening peak was 1,100 vehicles per lane per hour, 450 vehicles per lane on secondary roads, and 300 vehicles per lane on branch roads.

[0065] Post-disaster data include economic losses, time delays, and casualties caused by traffic accidents;

[0066] In the actual assessment, environmental characteristics are extracted from pre-disaster data, disaster characteristics are extracted from mid-disaster data, and loss degree characteristics are extracted from post-disaster data. The degree of correlation between the characteristics is calculated, and the preset threshold of the correlation degree is set to 0.5. Factors that meet the threshold conditions are identified as a set of risk factors, including: road width, motor vehicle ownership and public transportation capacity ratio.

[0067] S102 performs correlation analysis on the risk factor set to obtain a risk factor correlation function;

[0068] It needs to be explained that through correlation analysis, the complex interactions between risk factors can be obtained, which helps to reasonably set the relationship between nodes and edges when constructing the topological structure of the urban network;

[0069] In the actual assessment, the risk factors of road width, motor vehicle ownership and public transportation capacity ratio were used as input and output training models respectively to obtain the risk factor correlation function.

[0070] S103 constructs a city network topology based on the risk factor correlation function, and determines the prior probability of each node according to the similarity between each node and the pre-disaster data;

[0071] It should be explained that constructing the urban network topology based on the risk factor correlation function is to regard the city as a complex network system and abstract each functional area as a node. The topological structure constructed in this way can intuitively present the potential path and logical structure of risk transmission; and the prior probability is determined according to the similarity between each node and the pre-disaster data. Because the pre-disaster data reflects many characteristics of the city under normal conditions, high similarity means that the node is closer to the relatively safe state in the past, and its prior probability of a risk event is relatively low; otherwise, it is high. The prior probability provides an initial possibility judgment basis for the subsequent calculation of the risk transmission path in the simulated accident scenario, so that the risk assessment process can comprehensively consider the historical normal and current node characteristics, improving the accuracy and scientificity of the assessment.

[0072] In the actual assessment, the city is divided into multiple traffic area nodes, including city center nodes, railway station perimeter nodes, residential area nodes, school area nodes, commercial area nodes, and industrial area nodes. The edge functions between nodes are determined based on the multi-layer perceptron neural network to obtain the urban network topology. Through traffic monitoring cameras, geomagnetic sensors and other data collection systems, risk element data are continuously collected to monitor changes in traffic volume and road construction conditions in real time. Based on these data and risk element association functions, the node attribute values ​​and edge functions are updated to dynamically adjust the network topology. The pre-disaster data corresponding to the node are obtained, and the similarity between the node and the pre-disaster data is calculated. For the school area node, there are four characteristics: road grade, road width, traffic light density, and vehicle ownership. The calculated similarity between the school area node and the pre-disaster data is 0.7, and the prior probability of the school area node is 0.42.

[0073] S104 generates a simulated accident scenario, adds a disturbance term to simulate an unexpected risk event, and obtains a risk propagation path in the urban network topology structure in combination with the prior probability of each node;

[0074] It should be explained that by constructing a variety of simulation scenarios, we can cover accident situations of different types and severity, such as traffic accidents and traffic paralysis caused by bad weather, so as to comprehensively study the manifestation of risks in urban systems; adding disturbance items to simulate sudden risk events takes into account that in actual situations, risks do not exist in isolation and are often intervened by various sudden factors, such as sudden changes in wind direction in fire scenarios and unexpected road collapses in traffic scenarios. These disturbance items can make the simulation closer to the real and complex and changeable real environment; combining the prior probability of each node to obtain the risk propagation path, because the prior probability reflects the initial risk tendency of the node. Under the influence of simulation scenarios and disturbance items, based on the prior probability, using methods such as Bayesian networks, we can infer the possibility of risk propagation from the accident node to adjacent nodes, and then determine the most likely propagation path, providing a key basis for the timely formulation of targeted risk prevention and control and response strategies;

[0075] In the actual evaluation, a deep convolutional generative adversarial network was used to construct the generator network and the discriminator network. Training was based on historical traffic congestion accident case data. The latent spatial distribution of historical traffic data was input into the generator network to obtain a set of simulated traffic congestion accident scenarios. One scenario involved a traffic accident on a main road in the downtown business district, resulting in a 60% drop in the road's traffic capacity. Disturbance terms were added to the simulated accident scenario. The first disturbance term was inclement weather causing the road to become slippery, reducing vehicle speed by 30%. The second disturbance term was temporary construction on a nearby road. The third disturbance term was the end of a large-scale event, which caused a momentary increase in surrounding traffic volume.

[0076] In the actual assessment, for the case of risk transmission from downtown nodes to adjacent residential nodes, the probability of node transmission to adjacent nodes is determined based on the maximum a posteriori probability estimation method under constraints. Under the influence of severe weather disturbance terms, the probability of transmission from downtown business district nodes to adjacent residential nodes is calculated to be 0.72. For each simulated accident scenario, the paths with the highest probability of transmission to adjacent nodes are selected from the nodes and combined to obtain the risk transmission path in the urban network topology structure.

[0077] S105 obtains the spatiotemporal influence range of each node traversed by the propagation path, constructs a risk assessment model to assess the spatiotemporal influence range of each node, and performs weighted summation on the assessment results of each node to obtain a city safety risk assessment result.

[0078] In the actual assessment, a risk assessment model was constructed based on a three-dimensional convolutional neural network. The model was trained using a combination of historical traffic recovery time (the time it took for traffic in various regions to return to normal in past congestion accidents, averaging 30 minutes to 2 hours), loss level (economic losses, vehicle delay time), and the spatiotemporal impact range of historical accidents (the length of roads and regional area where congestion spread) as the training set. The predicted recovery time and loss level were used as evaluation indicators. For the nodes in the city center business district, their degree in the topological network structure was 6, the resource flow was 100, the risk resistance capacity assessment value was 0.6, the risk resistance capacity sensitivity coefficient was set to 0.2, the mean risk resistance capacity assessment value of all nodes was 0.55, the standard deviation was 0.1, the node association strength weight coefficient was 0.3, and the mean degree of association between the node and other nodes was 0.45. The importance of the nodes in the city center business district was calculated to be 0.78. After weighted summation of the evaluation indicators of all nodes, the traffic congestion safety risk assessment value of the entire city was 0.65.

[0079] In this embodiment, the method of identifying risk factors from historical accident time series data to obtain a risk factor set includes:

[0080] Extract environmental features from pre-disaster data, extract disaster characteristics from disaster data, and extract loss extent characteristics from post-disaster data, and combine them as a set of urban safety risk features;

[0081] Calculate the degree of correlation between features. The expression is:

[0082]

[0083] Among them, X i and Y j are the i-th item of feature sequence X and the j-th item of feature sequence Y, respectively. m and n are the number of feature sequences X and Y, respectively. ij and β ij X i and Y j The linear correlation weight and nonlinear correlation weight between ij For X i The positive covariance power adjustment coefficient, μ ij Y j The positive covariance power adjustment coefficient, δ ij Y j The absolute value covariance power adjustment coefficient, Cov(X i ,Y j ) is X i and Y j The covariance between and X i and Yj The norm of

[0084] If the correlation between environmental characteristics and disaster characteristics, and between environmental characteristics and loss degree characteristics is greater than the preset threshold, the corresponding environmental characteristics will be used as risk factors to obtain a risk factor set.

[0085] In this embodiment, the method of performing correlation analysis on the risk factor set to obtain a risk factor correlation function includes:

[0086] A support vector machine model was constructed, and the risk factors for correlation analysis were used as input and output to train the model. The kernel function of the model was used to represent the correlation function, which is expressed as follows:

[0087]

[0088] Where N is the number of support vectors, α i is the positive weight of the i-th support vector on the prediction result, is the negative weight of the i-th support vector on the prediction result, γ is the width of the kernel function, x is the current risk factor input vector, x i is the risk factor input vector of the i-th support vector, and b is the bias term.

[0089] In this embodiment, the method for constructing a city network topology structure based on the risk factor correlation function includes:

[0090] Each functional area in the city is defined as a node. Node types include geographical area nodes, infrastructure nodes, and personnel gathering nodes. Each node is assigned an attribute value based on risk element data.

[0091] The function of determining the edge between nodes is based on a multilayer perceptron neural network. Specifically, the number of neurons in the input layer and the output layer are set to the number of risk factors in the two nodes respectively, and the correlation function between risk factors is used as the mapping function of the corresponding nodes in the hidden layer of the neural network to obtain the edge function;

[0092] Establish a data collection system to continuously collect risk element data, update node attribute values ​​and edge functions based on the determined risk element data and risk element association functions, and dynamically adjust the network topology.

[0093] In this embodiment, the method for determining the prior probability of each node based on the similarity between each node and the pre-disaster data includes:

[0094] Get the pre-disaster data corresponding to the node and calculate the similarity between the node and the pre-disaster data. The expression is:

[0095]

[0096] Among them, s i is the similarity between the i-th node and the pre-disaster data, k is the number of features, ω j is the weight of the jth feature, x j and ξ ij are the jth eigenvalue of the node and the jth eigenvalue of the i-th sample in the historical pre-disaster data, max(ξ j ) and min(ξ j ) are the maximum and minimum values ​​of the jth feature in the historical pre-disaster data samples, ∈ is a smoothing term, which is set to 0.01;

[0097] Calculate the prior probability of each node, the expression is:

[0098]

[0099] Among them, P(s i ) is the prior probability of the i-th node, e is a constant, μ is the mean of the similarity distribution, σ is the standard deviation of the similarity, α1 is the amplitude adjustment parameter of the sine function, β1 is the period adjustment parameter of the sine function, γ1 is the translation parameter of the sine function, n is the total number of nodes, s j is the similarity between the jth node and the pre-disaster data.

[0100] In this embodiment, the method of generating a simulated accident scenario and adding a disturbance item to simulate an unexpected risk event includes:

[0101] A deep convolutional generative adversarial network is selected to construct the generator network and the discriminator network. The network is trained based on historical accident case data. The latent space distribution of historical accident data is input into the generator network to obtain a set of simulated accident scenarios.

[0102] Add a disturbance term to simulate the accident scenario. The disturbance term expression is:

[0103]

[0104] Among them, t is the time variable, m is the total number of disturbance terms, P i is the weight of the i-th disturbance term, χ i is the fluctuation adjustment coefficient of the ith disturbance term, ψ i is the fluctuation frequency parameter of the ith disturbance term, t 0i is the starting time of the i-th disturbance term, ω i is the Gaussian attenuation coefficient of the ith disturbance term, H(·) is a step function, when t≥t 0i When H(tt 0i )=1, indicating that the disturbance term begins to take effect, otherwise the disturbance term does not take effect.

[0105] In this embodiment, the method of obtaining the risk propagation path in the urban network topology structure by combining the prior probability of each node includes:

[0106] The nodes in the urban topology network are used as nodes of the Bayesian network. The conditional probability table is determined based on the prior probability of the nodes, and the simulated accident scenarios are converted into constraint conditions.

[0107] The probability of a node propagating to an adjacent node is determined based on the maximum a posteriori probability estimation method under constraints. The expression is:

[0108]

[0109] in, At node N i In new state Under the condition that node N j Transition to a new state The probability of For node N i In new state Under the condition that node N j Transition to a new state The conditional probability of For node N i In new state The prior probability of α is the weight parameter of the prior probability, C is the constraint influence factor, β is the weight factor of the constraint, D ij For node N i With node N j The distance, S j For node N j All possible state values ​​of At node N i In new state Under the condition that node N j The probability of being in various possible states;

[0110] For each simulated accident scenario, the paths with the highest probability of propagating to adjacent nodes are selected from the nodes and combined to obtain the risk propagation path in the urban network topology.

[0111] In this embodiment, the method of constructing a risk assessment model to assess the spatiotemporal impact range of each node and performing weighted summation of the assessment results of each node to obtain the urban safety risk assessment result includes:

[0112] A risk assessment model was constructed based on a three-dimensional convolutional neural network. The model was trained using a combination of historical recovery time, loss extent, and the spatiotemporal impact range of historical accidents as a training set. The predicted recovery time and loss extent were used as evaluation indicators.

[0113] Calculate the importance of each node, the expression is:

[0114]

[0115] Among them, I i is the importance of node i, K i is the degree of node i in the topological network structure, q i is the resource flow of node i, C i is the expected recovery time of node i, η is the risk resistance sensitivity coefficient, R i is the risk resistance assessment value of node i, μ R and σ R are the mean and standard deviation of the risk resistance assessment values ​​of all nodes, θ is the node association strength weight coefficient, S i is the mean of the association degree between node i and the rest of the nodes;

[0116] The evaluation indicators of each node are weighted and summed based on the importance of the node to obtain the risk assessment value of the entire urban topological network structure.

[0117] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2 At the hardware level, the electronic device includes a processor and, optionally, an internal bus, a network interface, and memory. The memory may include internal memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for its services.

[0118] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0119] The memory is used to store programs. Specifically, the program may include program code, which includes computer operating instructions. The memory may include internal memory and non-volatile memory, and provides instructions and data to the processor.

[0120] The processor reads the corresponding computer program from the non-volatile memory into the internal memory and then runs it, forming a digital device for urban safety risk assessment standards at the logical level. The processor executes the program stored in the memory and is specifically used to implement any of the aforementioned digital methods for urban safety risk assessment standards.

[0121] The present invention can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, the steps of the above method can be completed by hardware integrated logic circuits in the processor or by software instructions. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The various methods, steps, and logic block diagrams disclosed in the embodiments of this application can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0122] An embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by an electronic device including multiple applications, they execute any one of the aforementioned digital methods of urban safety risk assessment standards.

[0123] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

Claims

1. A digital method for urban safety risk assessment standards, characterized by: The following steps are involved: Identify risk factors from historical accident time series data and obtain a risk factor set; The historical accident time series data includes pre-disaster data, mid-disaster data, and post-disaster data; extracting environmental features from the pre-disaster data, extracting disaster characteristics from the mid-disaster data, and extracting loss extent characteristics from the post-disaster data; if the correlation between the environmental features and the disaster characteristics, or between the environmental features and the loss extent characteristics, is greater than a preset threshold, then the corresponding environmental features are used as risk factors to obtain a risk factor set; Performing correlation analysis on the risk factor set to obtain a risk factor correlation function; Constructing a city network topology based on the risk factor correlation function, and determining a priori probability of each node according to the similarity between each node and the pre-disaster data; Generate a simulated accident scenario, add a disturbance term to simulate an unexpected risk event, and obtain a risk propagation path in the urban network topology structure in combination with the prior probability of each node; Obtaining the spatiotemporal influence range of each node traversed by the propagation path, constructing a risk assessment model to evaluate the spatiotemporal influence range of each node, and performing weighted summation of the assessment results of each node to obtain an urban safety risk assessment result; The method for determining the prior probability of each node based on the similarity between each node and the pre-disaster data includes: Get the pre-disaster data corresponding to the node and calculate the similarity between the node and the pre-disaster data. The expression is: ， in, is the similarity between the i-th node and the pre-disaster data, k is the number of features, is the weight of the jth feature, and are the j-th eigenvalue of the node and the j-th eigenvalue of the i-th sample in the historical pre-disaster data, respectively. and are the maximum and minimum values ​​of the jth feature in the historical pre-disaster data samples, is the smoothing term, set to 0.01; Calculate the prior probability of each node, the expression is: ， in, is the prior probability of the i-th node, e is a constant, is the mean of the similarity distribution, is the standard deviation of similarity, is the amplitude adjustment parameter of the sine function, is the period adjustment parameter of the sine function, is the translation parameter of the sine function, n is the total number of nodes, is the similarity between the jth node and the pre-disaster data.

2. A digital method for urban safety risk assessment standards according to claim 1, characterized in that: The correlation degree between the features of the risk factor set is calculated as follows: ， in, and are the i-th item of feature sequence X and the j-th item of feature sequence Y, respectively. m and n are the number of feature sequences X and Y, respectively. and They are and The linear correlation weights and nonlinear correlation weights between for The positive covariance power adjustment coefficient of for The positive covariance power adjustment coefficient of for The absolute value covariance power adjustment coefficient, for and The covariance between and They are and The norm of .

3. The digital method for urban safety risk assessment standards according to claim 1 is characterized in that: The method of performing correlation analysis on the risk factor set to obtain a risk factor correlation function includes: A support vector machine model was constructed, and the risk factors for correlation analysis were used as input and output to train the model. The kernel function of the model was used to represent the correlation function, which is expressed as follows: ， Where N is the number of support vectors, is the positive weight of the i-th support vector on the prediction result, is the negative weight of the i-th support vector on the prediction result, is the width of the kernel function, Input vector for current risk factors, is the risk factor input vector of the i-th support vector, and b is the bias term.

4. The digital method for urban safety risk assessment standards according to claim 1 is characterized in that: The method of generating a simulated accident scenario and adding a disturbance item to simulate an unexpected risk event includes: A deep convolutional generative adversarial network is selected to construct the generator network and the discriminator network. The network is trained based on historical accident case data. The latent space distribution of historical accident data is input into the generator network to obtain a set of simulated accident scenarios. Add a disturbance term to simulate the accident scenario. The disturbance term expression is: ， Where t is the time variable, m is the total number of disturbance terms, is the weight of the i-th disturbance term, is the fluctuation adjustment coefficient of the ith disturbance term, is the fluctuation frequency parameter of the i-th disturbance term, is the starting time of the i-th disturbance term, is the Gaussian attenuation coefficient of the i-th disturbance term, is a step function, when hour, , indicating that the disturbance term begins to take effect, otherwise the disturbance term has no effect.

5. The digital method for urban safety risk assessment standards according to claim 1 is characterized in that: The method for constructing a city network topology structure based on the risk factor correlation function includes: Each functional area in the city is defined as a node. Node types include geographical area nodes, infrastructure nodes, and personnel gathering nodes. Each node is assigned an attribute value based on risk element data. The function of determining the edge between nodes is based on a multilayer perceptron neural network. Specifically, the number of neurons in the input layer and the output layer are set to the number of risk factors in the two nodes respectively, and the correlation function between risk factors is used as the mapping function of the corresponding nodes in the hidden layer of the neural network to obtain the edge function; Establish a data collection system to continuously collect risk element data, update node attribute values ​​and edge functions based on the determined risk element data and risk element association functions, and dynamically adjust the network topology.

6. The digital method for urban safety risk assessment standards according to claim 1 is characterized in that: The method of obtaining the risk propagation path in the urban network topology structure by combining the prior probability of each node includes: The nodes in the urban topology network are used as nodes of the Bayesian network. The conditional probability table is determined based on the prior probability of the nodes, and the simulated accident scenarios are converted into constraint conditions. The probability of a node propagating to an adjacent node is determined based on the maximum a posteriori probability estimation method under constraints. The expression is: ， in, For the node In new state Under the condition that the node Transition to a new state The probability of For nodes In new state Under the condition that the node Transition to a new state The conditional probability of For nodes In new state The prior probability of is the weight parameter of the prior probability, C is the constraint factor, is the weight factor of the constraint condition, For nodes With node distance, For nodes All possible state values ​​of For the node In new state Under the condition that the node The probability of being in various possible states; For each simulated accident scenario, the paths with the highest probability of propagating to adjacent nodes are selected from the nodes and combined to obtain the risk propagation path in the urban network topology.

7. The digital method for urban safety risk assessment standards according to claim 1 is characterized in that: The method of constructing a risk assessment model to assess the spatiotemporal impact range of each node and performing weighted summation of the assessment results of each node to obtain the urban safety risk assessment result includes: A risk assessment model was constructed based on a three-dimensional convolutional neural network. The model was trained using a combination of historical recovery time, loss extent, and the spatiotemporal impact range of historical accidents as a training set. The predicted recovery time and loss extent were used as evaluation indicators. Calculate the importance of each node, the expression is: ， in, is the importance of node i, is the degree of node i in the topological network structure, is the resource flow of node i, is node i is node is the risk resistance sensitivity coefficient, is the risk resistance assessment value of node i, and are the mean and standard deviation of the risk resilience assessment values ​​of all nodes, is the node association strength weight coefficient, is the mean of the association degree between node i and the rest of the nodes; The evaluation indicators of each node are weighted and summed based on the importance of the node to obtain the risk assessment value of the entire urban topological network structure.

8. An electronic device comprising: processor; as well as A memory arranged to store computer executable instructions, which, when executed, cause the processor to perform the method according to any one of claims 1 to 7.

9. A computer-readable storage medium storing one or more programs, wherein when the one or more programs are executed by an electronic device including a plurality of application programs, the electronic device executes the method according to any one of claims 1 to 7.

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