Infectious disease risk space-time correlation perception method based on shipping big data

Abstract

The invention relates to the technical field of public health big data analysis, in particular to an infectious disease risk space-time correlation perception method based on shipping big data, which comprises the following steps of: extracting ship flow records, regional definite case numbers and population flow trends based on global shipping network data, analyzing correlation between ship flow changes and regional case growth rates, and calculating the risk of infectious diseases according to the correlation between the ship flow changes and the regional case growth rates; correlation parameters are calculated, and shipping flow and case correlation coefficients are generated. According to the method, the selection of the propagation path is optimized by utilizing risk path screening and geographical adjacency characteristics, the prediction accuracy of the propagation trend is improved through accurate risk weight distribution in the construction of the dynamic propagation network, and the efficiency and the accuracy of resource allocation are improved through optimized control path weight distribution; the scientificity and effectiveness of infectious disease monitoring and prevention and control are improved, and a new technical support is provided for infectious disease risk management in a global range.

Description

Technical Field

[0001] The present invention relates to the technical field of public health big data analysis, and in particular to a method for sensing the spatiotemporal correlation of infectious disease risks based on shipping big data. Background Art

[0002] The field of public health big data analysis technology includes research and application based on big data technology in the fields of disease monitoring and prediction, epidemiological research, health management and optimal allocation of medical resources in the public health field. The core content of this technical field is to use large-scale data collection, data storage, data analysis and processing technology, combined with the needs of the public health field, to build a technical system for disease risk warning, transmission path identification and health management decision-making assistance. Through the comprehensive processing of data from multiple fields such as shipping, transportation, and meteorology, the spatiotemporal dynamic monitoring and risk assessment of epidemic transmission can be achieved, providing technical support for the formulation of public health prevention and control strategies.

[0003] Among them, the spatial-temporal correlation perception method of infectious disease risk based on shipping big data refers to the perception and identification of potential infectious disease transmission risks through the collection and analysis of big data in the shipping field, combined with the epidemiological model of infectious disease transmission and spatial-temporal analysis methods. This technology covers the analysis of infectious disease transmission paths, spatial-temporal hot spot area identification and risk level assessment based on shipping data. Specifically, through technical means such as multi-source data collection, cleaning, normalization processing and correlation analysis, a correlation relationship model between infectious disease transmission and shipping dynamics is established to achieve the perception and early warning of infectious disease transmission risks.

[0004] Existing technologies rely too much on static data in the study of transmission laws, lack dynamic analysis of changes in transmission rate and cumulative transmission characteristics, resulting in an incomplete understanding of transmission laws within the region. In the assessment of inter-regional transmission paths, existing technologies lack accurate quantification of the dynamic connection strength between paths, making it difficult to effectively screen risk paths, resulting in insufficient coverage of cross-regional transmission risk monitoring. In spatiotemporal dynamic analysis, existing technologies are mostly limited to single-dimensional assessments of time series or spatial distribution, ignoring the synergy between the two, and unable to fully reflect the spatiotemporal evolution characteristics of transmission paths. In terms of regional priority control, the risk assessment of existing technologies mostly relies on coarse-grained risk level division, ignoring the accuracy of risk path resource allocation, which may lead to unreasonable distribution of control resources. These deficiencies limit the applicability of existing technologies in responding to complex infectious disease transmission scenarios, especially in the comprehensive analysis of dynamic changes and cross-regional transmission. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and propose a method for sensing the spatiotemporal correlation of infectious disease risks based on shipping big data.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a method for sensing the spatiotemporal correlation of infectious disease risks based on shipping big data, comprising the following steps: S1: Based on the global shipping network data, extract the ship flow records, the number of confirmed cases in the region and the population flow trend, analyze the correlation between the changes in ship flow and the regional case growth rate, calculate the correlation parameters, and generate the correlation coefficient between shipping flow and cases; S2: Based on the correlation coefficient between shipping flow and cases, using the case growth rate and population flow rate data, calculate the transmission rate change rate and cumulative transmission value, extract the transmission law within the region, and generate the regional transmission characteristic index value; S3: Combine the regional propagation characteristic index value, extract the flow and geographical adjacency characteristics of the inter-regional connection path, set the risk threshold, analyze the path propagation rate dynamics, screen the risk path, and generate the propagation path risk intensity distribution result; S4: Based on the risk intensity distribution results of the propagation path, analyze the path rate fluctuation and time change, extract the time sensitivity coefficient, calculate the time evolution trend value, integrate the dynamic changes to generate the path time-space relationship, and generate the time-related propagation path characteristic value; S5: calling the time-related propagation path characteristic value and the propagation path risk intensity distribution result, integrating the risk path weight and the dynamic propagation characteristics, constructing the regional dynamic propagation network structure, and generating the dynamic risk propagation prediction value; S6: Based on the dynamic risk propagation prediction value, analyze the global regional propagation risk level distribution, extract the priority control path in combination with the inter-regional risk level, calculate the priority path distribution, and generate the global risk path priority distribution coefficient.

[0007] As a further scheme of the present invention, the shipping flow and case correlation coefficient includes the correlation of case flow between regions, shipping flow influencing factors, and case growth rate dynamic parameters; the propagation characteristic index value includes regional propagation intensity parameter, time propagation rate factor, and cumulative propagation volume characteristics; the propagation path risk intensity distribution result includes path propagation rate parameter, node connection strength coefficient, and risk path distribution characteristics; the time-related propagation path characteristic value includes time sensitivity parameter, dynamic trend characteristics, and spatiotemporal correlation factor; the dynamic risk propagation prediction value includes risk path weight distribution, dynamic propagation trend characteristics, and risk path prediction strength; the risk path priority distribution coefficient includes priority path control parameter, regional risk level distribution, and path control weight value.

[0008] As a further solution of the present invention, based on the global shipping network data, the ship flow records, the number of confirmed cases in the region and the population flow trend are extracted, the correlation between the ship flow changes and the regional case growth rate is analyzed, the correlation parameters are calculated, and the specific steps of generating the correlation coefficient between shipping flow and cases are as follows: S101: Based on the global shipping network data, extract the ship flow records on each shipping route, count the frequency of ships docking at ports in the region, analyze the population flow trends in different regions, build a time series data matrix based on the regional confirmed case data, classify and organize the data according to the regional distribution characteristics, and generate a ship flow and population flow trend analysis table; S102: Based on the ship flow and population flow trend analysis table, filter the paths of ship flow frequencies between regions, analyze the distribution characteristics of regional flow patterns, calculate the correlation values ​​of the case number change rate and the intersection data points of the flow paths, extract the flow characteristic parameters between regions, and establish a regional case and flow characteristic parameter matrix after integrated analysis; S103: Based on the regional case and flow characteristic parameter matrix, the linear and nonlinear correlation coefficients between the regional flow characteristic parameters and the case growth rate are calculated, the differences and directions between the parameters are analyzed, and significant regional feature pairs are screened according to the correlation coefficient to generate the shipping flow and case correlation coefficient.

[0009] As a further solution of the present invention, based on the correlation coefficient between shipping flow and cases, using case growth rate and population flow rate data, calculating the transmission rate change rate and cumulative transmission value, extracting the transmission law within the region, and generating the regional transmission characteristic index value are as follows: S201: Based on the correlation coefficient between shipping flow and cases, call the case growth rate and population flow rate data, analyze the fluctuation value of the case growth rate in the time series, adjust the adaptation range of the population flow rate data by time period, calculate and analyze the change of the transmission rate by time period, extract the key point value of the change of the transmission rate, and generate the transmission rate and change trend index; S202: Based on the propagation rate and the change trend indicator, extract the time period data of the cumulative propagation value, analyze the nonlinear correlation between the propagation rate and the cumulative value, calibrate the cumulative value data in combination with the key point value of the propagation rate, calculate the distribution value of the cumulative propagation value over time, extract the change characteristics of the propagation law in each stage, and generate a dynamic propagation law value matrix; S203: Based on the dynamic propagation law value matrix, analyze the key parameters of the propagation law within the region, select the propagation characteristic values ​​of each region, adjust the stage division standard of the propagation characteristics within the region, summarize the stage characteristics of the propagation within the region, calculate the propagation impact index based on the key characteristic parameters within the region, and generate the regional propagation characteristic index value.

[0010] As a further solution of the present invention, the calculation formula of the distribution value of the cumulative propagation value changing over time is specifically: ; in, Representative The characteristic parameters of the cumulative propagation value of the class feature over time, Representative Class Features The propagation rate in a period of time, Representative The average value of the propagation rate in all time periods in the class feature, Representative Class Features The length of the time period, Representative Class Features The cumulative propagation flow value of the propagation path, Representative Class Features The accumulated time value of each propagation path, For the The total number of time periods in the class feature, For the The total number of propagation paths in the class signature.

[0011] As a further solution of the present invention, the specific steps of extracting the flow and geographical adjacency characteristics of the inter-regional connection paths in combination with the regional propagation characteristic index value, setting the risk threshold, analyzing the path propagation rate dynamics, screening the risk path, and generating the propagation path risk intensity distribution result are as follows: S301: Based on the regional propagation characteristic index value, extract the flow data of the connecting paths between the regions of the global shipping network, analyze the flow change trend of the paths in the time series, analyze the spatial relationship strength of the geographical adjacency characteristics, set the risk threshold to screen the key paths, extract the flow characteristics of the risk paths, and generate the risk path flow and adjacency characteristic table; S302: Based on the risk path flow and adjacency characteristic table, analyze the dynamic characteristics of the path propagation rate in different time periods, extract the propagation rate variation range in combination with the node flow value, calculate the dynamic characteristic parameters of the propagation rate and path flow intersection data, extract the key characteristics of the propagation path, and generate a path propagation dynamic characteristic matrix; S303: Based on the path propagation dynamic characteristic matrix, the propagation correlation parameters and traffic distribution characteristics between nodes are analyzed, the path risk characteristics are graded and calibrated according to the propagation intensity parameters, the propagation intensity distribution law of the risk path is extracted, the propagation impact value between paths is adjusted in combination with the node characteristics, and the propagation path risk intensity distribution result is generated.

[0012] As a further solution of the present invention, the dynamic characteristic parameter calculation formula is specifically: ; in, The dynamic characteristic parameter representing the intersection data of propagation rate and path flow, Representative The first The transmission flow rate of the time period, Representative The average value of the propagation flow in all time periods on the path, Representative The first the length of the time period, Representative The first The risk value of the node, Representative The first The node's propagation capability value, Representative The first The weight value of the node, For the The total number of time segments in the path, For the The total number of nodes in the path.

[0013] As a further solution of the present invention, based on the risk intensity distribution result of the propagation path, the specific steps of analyzing the path rate fluctuation and time change, extracting the time sensitivity coefficient, calculating the time evolution trend value, integrating the dynamic change to generate the path time-space relationship, and generating the time-related propagation path characteristic value are as follows: S401: Based on the risk intensity distribution result of the propagation path, analyze the fluctuation range of the path propagation rate in the time series, extract the key node values ​​of the path rate change, calculate the dynamic correlation value between the time distribution characteristics of the fluctuation value and the range, calibrate the rate fluctuation data to generate the timing adjustment characteristic value, and generate the path rate fluctuation and time change characteristic table; S402: based on the path rate fluctuation and time variation characteristic table, extract the time sensitivity parameter of the path propagation rate, analyze the interval characteristics of the sensitivity parameter in the time distribution, correct the dynamic distribution value of the time sensitivity coefficient, calculate the differentiated characteristic value of the time response, and form a sensitivity quantification result to generate a path time sensitivity coefficient matrix; S403: Based on the path time sensitivity coefficient matrix, the interactive characteristics of the path propagation rate and the time variation law are analyzed, the dynamic characteristic values ​​of the path time-space correlation parameters are calculated, the evolution trend characteristics of the path changes under the time series are summarized, and the time-correlated propagation path characteristic values ​​are generated.

[0014] As a further solution of the present invention, the specific steps of calling the time-related propagation path characteristic value and the propagation path risk intensity distribution result, integrating the risk path weight and the dynamic propagation characteristics, constructing the regional dynamic propagation network structure, and generating the dynamic risk propagation prediction value are as follows: S501: Based on the time-related propagation path characteristic value and the propagation path risk intensity distribution result, extract the weight value of the risk path, analyze the change law of the path weight in time distribution in combination with the dynamic propagation characteristics, calculate the distribution range of the weight value and the correlation strength of the time series, extract the dynamic characteristics of the weight node, and generate the propagation network risk weight distribution data; S502: Based on the risk weight distribution data of the propagation network, the spatiotemporal propagation characteristics of the weighted paths are analyzed, the connection distribution values ​​between the weighted nodes are calculated, and the interaction relationships are analyzed to extract the characteristic values ​​of the dynamic correlation strengths between the paths. The temporal characteristics of the network structure are adjusted in combination with the propagation path weights to construct the spatiotemporal dynamic structure of the regional propagation network and generate a regional dynamic propagation network structure diagram; S503: Based on the regional dynamic propagation network structure diagram, the propagation trend parameters are extracted in combination with the dynamic trend of the weight distribution, the path evolution characteristics in the spatiotemporal propagation network are analyzed, the dynamic propagation risk value is calculated, and the time change range and trend characteristics are analyzed to extract the time impact characteristics of the risk path and generate a dynamic risk propagation prediction value.

[0015] As a further solution of the present invention, based on the dynamic risk propagation prediction value, the global regional propagation risk level distribution is analyzed, the priority control path is extracted in combination with the inter-regional risk level, the priority path distribution is calculated, and the specific steps of generating the global risk path priority distribution coefficient are as follows: S601: Based on the dynamic risk propagation prediction value, analyze the global regional propagation risk level distribution, extract the risk area according to the fluctuation characteristics of the risk value between regions, calculate the time distribution characteristics of the risk level inside and around the region, extract the risk association path and key node values, and generate a global propagation risk level distribution map; S602: Based on the global transmission risk level distribution map, extract the inter-regional risk critical paths, analyze the time variation characteristics of the weight distribution in the paths, screen the key node values ​​of the priority control paths, calculate the priority weight distribution range of the risk paths, and generate a priority control path weight distribution table; S603: Based on the priority control path weight distribution table, analyze the weight distribution characteristics of the priority paths, calculate the priority distribution value in combination with the association weights between the paths, extract the dynamic association characteristics and key influencing parameters of the paths, and integrate the priority weights to generate a global risk path priority distribution coefficient.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, the selection of transmission paths is optimized by utilizing risk path screening and geographic adjacency characteristics. The construction of a dynamic transmission network improves the prediction accuracy of transmission trends through precise risk weight distribution. The optimized control path weight allocation improves the efficiency and accuracy of resource allocation, improves the scientificity and effectiveness of infectious disease monitoring and prevention and control, and provides new technical support for infectious disease risk management on a global scale. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 It is a schematic diagram of the steps of the present invention; Figure 2 is a flow chart of the steps of S1 of the present invention; Figure 3 is a flow chart of the steps of S2 of the present invention; Figure 4 is a flow chart of the steps of S3 of the present invention; Figure 5 is a flow chart of the steps of S4 of the present invention; Figure 6 is a flow chart of the steps of S5 of the present invention; Figure 7 This is a flow chart of the steps of S6 of the present invention. DETAILED DESCRIPTION

[0019] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0020] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0021] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0022] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0023] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0024] See also Figure 1 ,The method of temporal and spatial correlation perception of infectious disease risk based on shipping big data includes the following steps: S1: Based on the global shipping network data, extract the ship flow records, the number of confirmed cases in the region and the population flow trend, analyze the correlation between the changes in ship flow and the regional case growth rate, calculate the correlation parameters, and generate the correlation coefficient between shipping flow and cases; S2: Based on the correlation coefficient between shipping flow and cases, using the case growth rate and population flow rate data, calculate the transmission rate change rate and cumulative transmission value, extract the transmission law within the region, and generate the regional transmission characteristic index value; S3: Combine the regional propagation characteristic index values, extract the flow and geographical adjacency characteristics of the inter-regional connection paths, set the risk threshold, analyze the path propagation rate dynamics, screen the risk path, and generate the propagation path risk intensity distribution results; S4: Based on the results of the risk intensity distribution of the propagation path, analyze the path rate fluctuation and time change, extract the time sensitivity coefficient, calculate the time evolution trend value, integrate the dynamic changes to generate the path time-space relationship, and generate the time-related propagation path characteristic value; S5: Call the time-related propagation path characteristic values ​​and the propagation path risk intensity distribution results, integrate the risk path weights and dynamic propagation characteristics, build the regional dynamic propagation network structure, and generate dynamic risk propagation prediction values; S6: Based on the dynamic risk propagation prediction value, analyze the global regional propagation risk level distribution, extract the priority control path based on the inter-regional risk level, calculate the priority path distribution, and generate the global risk path priority distribution coefficient.

[0025] The correlation coefficients between shipping flow and cases include the correlation of case flow between regions, shipping flow influencing factors, and dynamic parameters of case growth rate. The transmission characteristic index values ​​include regional transmission intensity parameters, time transmission rate factors, and cumulative transmission volume characteristics. The transmission path risk intensity distribution results include path transmission rate parameters, node connection strength coefficients, and risk path distribution characteristics. The time-related transmission path characteristic values ​​include time sensitivity parameters, dynamic trend characteristics, and spatiotemporal correlation factors. The dynamic risk transmission prediction values ​​include risk path weight distribution, dynamic transmission trend characteristics, and risk path prediction intensity. The risk path priority distribution coefficients include priority path control parameters, regional risk level distribution, and path control weight values.

[0026] See also Figure 2 , the specific steps of S1 are: S101: Based on the global shipping network data, extract the ship flow records on each shipping route, count the frequency of ships docking at ports in the region, analyze the population flow trends in different regions, build a time series data matrix based on the regional confirmed case data, classify and organize the data according to the regional distribution characteristics, and generate a ship flow and population flow trend analysis table; By monitoring the entry and exit records of ships on various shipping routes, combined with the ship trajectory data obtained by AIS (Automatic Identification System), the frequency of ship docking at each port in the region is counted, and the spatiotemporal data matching method is used to quantitatively analyze the population flow trend between regions. The regional confirmed case data is organized into a time series data matrix, and the data is classified according to regional distribution characteristics. After classification, it is summarized and organized into a matrix form according to the time series trend to generate a ship flow and population flow trend analysis table.

[0027] S102: Based on the ship flow and population flow trend analysis table, filter the paths of ship flow frequency between regions, analyze the distribution characteristics of regional flow patterns, calculate the correlation value between the case number change rate and the intersection data points of the flow path, extract the flow characteristic parameters between regions, and establish the regional case and flow characteristic parameter matrix after integration and analysis; Analyze the distribution characteristics of regional flow patterns and use the formula ; Calculate the flow characteristic parameters between regions, In the formula, Representatives from the region To area The flow characteristic ratio, Representatives from the region To area of traffic, Representatives from the region Total outgoing traffic.

[0028] The calculation of flow ratio is to characterize the flow characteristics between regions through the distribution characteristics of ship flow. The flow data is obtained through the flow statistics of ships entering and leaving the port. Assuming that a certain area The total traffic to other areas is 200 ships, of which to areas The flow rate is 50 ships, so substitute into the formula ; The calculated ratio is 0.25, which means that from the area To area The flow volume accounts for 25%. This result shows the flow intensity between regions. The higher the value, the more concentrated the flow, which can be used to further analyze the correlation characteristics between cases and flow.

[0029] S103: Based on the regional case and flow characteristic parameter matrix, the linear and nonlinear correlation coefficients between the flow characteristic parameters and the case growth rate between regions are calculated, the differences and directions between the parameters are analyzed, and the significant regional feature pairs are screened according to the correlation coefficient to generate the correlation coefficient between shipping flow and cases; Firstly, by calculating the time series data of regional case growth rate, the correlation parameters of regional case growth rate were calculated by linear fitting and nonlinear fitting methods. The Pearson correlation coefficient and Spearman correlation coefficient were selected as linear and nonlinear correlation measures, and the changing trends of regional characteristic parameters in inter-regional flow were statistically analyzed. By screening significant regional feature pairs, these parameters were correlated and organized into shipping flow and case correlation coefficients.

[0030] See also Figure 3 , the specific steps of S2 are: S201: Based on the correlation coefficient between shipping flow and cases, call the case growth rate and population flow rate data, analyze the fluctuation value of the case growth rate in the time series, adjust the adaptation range of the population flow rate data by time period, calculate and analyze the change of the transmission rate by time period, extract the key point values ​​of the change of the transmission rate, and generate the transmission rate and change trend indicators; The fluctuation values ​​of the case growth rate in the time series are analyzed. The case growth rate data are converted into a growth rate change matrix for each time period. It is dynamically adjusted in combination with the population mobility rate. The rate adjustment interval for each time period is set. The change in the transmission rate is calculated using the piecewise linear fitting method. The key point values ​​of the fluctuations in the change are extracted to generate transmission rate and change trend indicators.

[0031] S202: Based on the propagation rate and the change trend index, extract the time period data of the cumulative propagation value, analyze the nonlinear correlation between the propagation rate and the cumulative value, calibrate the cumulative value data in combination with the key point value of the propagation rate, calculate the characteristic parameters of the cumulative propagation value over time, extract the change characteristics of the propagation law in each stage, and generate a dynamic propagation law value matrix; The calculation formula of the cumulative propagation value distribution value over time is as follows: ; in, Representative The characteristic parameters of the cumulative propagation value of the class feature over time, Representative Class Features The propagation rate in a period of time, Representative The average value of the propagation rate in all time periods in the class feature, Representative Class Features The length of the time period, Representative Class Features The cumulative propagation flow value of the propagation path, Representative Class Features The accumulated time value of each propagation path, For the The total number of time periods in the class feature, For the The total number of propagation paths in the class signature.

[0032] The calculation of the distribution value of the cumulative propagation value over time includes two parts: the standardized change in the propagation rate and the dynamic flow-time ratio of the propagation path. The following is a step-by-step explanation and calculation of each parameter in the formula: Parameter description and acquisition method: Representative Class Features The propagation rate in a time period is calculated based on the monitoring data by dividing the flow value in a time period by the length of the time period. The unit is the propagation volume per unit time. For example, .

[0033] Representative The average value of the propagation rate in all time periods in the class feature is calculated by averaging the propagation rate in each time period. For example, .

[0034] Representative Class Features The length of a time period is obtained based on the difference between the start and end time of the monitoring, for example: .

[0035] Representative Class Features The cumulative propagation flow value of a propagation path is obtained through the path cumulative flow monitoring data, for example: .

[0036] Representative Class Features The accumulated time value of each propagation path is obtained through path time statistics, for example: .

[0037] For the The total number of time periods in the class feature, for example .

[0038] For the The total number of propagation paths in the class feature, for example .

[0039] Operation steps: Calculation of the standardized change in propagation rate: Calculate the rate change term: ; ; Total calculation time: ; Rate Normalized Change: ; Calculation of propagation path flow time ratio: Cumulative flow and: ; Cumulative time and: ; Flow time ratio: ; Results synthesis: ; Meaning of numerical results: The results show that the characteristic parameter of the cumulative propagation value changing over time is 12.46, which reflects the comprehensive characteristics between the dynamic change of the propagation rate and the path flow distribution. This value provides a basis for generating the dynamic propagation law value matrix.

[0040] S203: Based on the dynamic propagation law value matrix, analyze the key parameters of the propagation law within the region, select the propagation characteristic value of each region, adjust the stage division standard of the propagation characteristics within the region, summarize the stage characteristics of the propagation within the region, calculate the propagation impact index in combination with the key characteristic parameters within the region, and generate the regional propagation characteristic index value; By selecting the propagation characteristic values ​​corresponding to each area from the matrix, the propagation law curve of each area is divided into stages, and the division criteria are adjusted to optimize the time period distribution of key parameters. By calculating the impact of the propagation law on different areas, the propagation characteristic index value matrix is ​​generated in combination with the distribution of propagation characteristic values ​​in the region.

[0041] See also Figure 4 , the specific steps of S3 are: S301: Based on the regional propagation characteristic index value, extract the flow data of the connecting paths between regions of the global shipping network, analyze the flow change trend of the paths in time series, analyze the spatial relationship strength of the geographical adjacency characteristics, set the risk threshold to screen the key paths, extract the flow characteristics of the risk paths, and generate the risk path flow and adjacency characteristic table; Conduct trend analysis on changes in shipping path traffic in time series, extract change patterns through the time distribution curve of each path, analyze the spatial correlation strength of each path in the geographic adjacency characteristics, set risk thresholds based on historical traffic data, identify key paths through the joint screening method of traffic extreme value method and adjacency characteristic strength, organize the traffic time distribution data of risk paths into a standard format, and generate risk path traffic and adjacency characteristic tables in combination with spatial adjacency characteristic data.

[0042] S302: Based on the risk path flow and adjacency characteristic table, analyze the dynamic characteristics of the path propagation rate in different time periods, extract the propagation rate variation range in combination with the node flow value, calculate the dynamic characteristic parameters of the cross data of the propagation rate and the path flow, extract the key characteristics of the propagation path, and generate the path propagation dynamic characteristic matrix; The dynamic characteristic parameter calculation formula is as follows: ; in, The dynamic characteristic parameter representing the intersection data of propagation rate and path flow, Representative The first The transmission flow rate of the time period, Representative The average value of the propagation flow in all time periods on the path, Representative The first the length of the time period, Representative The first The risk value of the node, Representative The first The node's propagation capability value, Representative The first The weight value of the node, For the The total number of time segments in the path, For the The total number of nodes in the path.

[0043] Dynamic characteristic parameters of propagation rate and path flow intersection data The calculation consists of two parts: the normalized fluctuation of the propagation flow in the time period and the weighted calculation of the node flow and risk value. The following is a step-by-step explanation and calculation of each parameter in the formula: Parameter description and acquisition method:

[0044] Representative The first The propagation flow in a time period is obtained through monitoring data, for example .

[0045] Representative The average value of the propagation flow in all time periods in the path is calculated by averaging the flow. For example, .

[0046] Representative The first The length of the time period is based on the geographical distance or time span between the propagation nodes in the time period, for example .

[0047] Representative The first The risk value of a node is calculated or evaluated through the adjacency characteristic table of the risk path. For example, .

[0048] Representative The first The node's propagation capacity value is obtained from the node's historical traffic data statistics. For example, .

[0049] Representative The first The weight value of a node is calculated by the importance index of the node in the communication network. For example, .

[0050] For the The total number of time periods in the path, for example .

[0051] For the The total number of nodes in the path, for example .

[0052] Operation steps: Standardized calculation of propagation flow fluctuations in time period: Calculate the flow fluctuation term: ; ; Calculate the total length of the time period: ; Normalized volatility: ; Weighted calculation of node propagation capability: Calculate the total weighted transmission capacity: ; Calculate the total weight: ; Propagation capability weight ratio: ; Results synthesis: ; Meaning of numerical results: The results show that the dynamic characteristic parameter of the intersection data of propagation rate and path flow is 105.91, which characterizes the impact of path propagation rate fluctuation and node propagation capacity on the overall characteristics, and provides a quantitative basis for extracting the key characteristics of the propagation path and generating the dynamic characteristic matrix.

[0053] S303: Based on the path propagation dynamic characteristic matrix, the propagation correlation parameters and traffic distribution characteristics between nodes are analyzed, the path risk characteristics are graded and calibrated according to the propagation intensity parameters, the propagation intensity distribution law of the risk path is extracted, and the propagation impact value between paths is adjusted in combination with the node characteristics to generate the propagation path risk intensity distribution result; The propagation intensity parameters are extracted from the node-to-flow relationship diagram in the path propagation matrix, and the numerical adjustment rules for the propagation impact between paths are constructed in combination with the node characteristic data. The path risk characteristics are graded and calibrated according to different propagation intensity parameters. The distribution law of the propagation intensity of the risk path is statistically analyzed, and the distribution data of the path risk intensity is adjusted in combination with the node characteristic parameters. Finally, the distribution result of the propagation path risk intensity is generated.

[0054] See also Figure 5 , the specific steps of S4 are: S401: Based on the results of the risk intensity distribution of the propagation path, the fluctuation range of the path propagation rate in the time series is analyzed, the key node values ​​of the path rate change are extracted, the dynamic correlation value between the time distribution characteristics and the range of the fluctuation value is calculated, the rate fluctuation data is calibrated to generate the time series adjustment characteristic value, and the path rate fluctuation and time change characteristic table is generated; The fluctuation range of the path propagation rate in the time series is analyzed according to the formula ; Calculate the dynamic correlation between the time distribution characteristics of the fluctuation value and the range, In the formula, Represents the dynamic correlation characteristics of the fluctuating values, is the number of sampling points in the time series, For time point The rate value, is the mean of the time series rate values, is the function of the fluctuation range, and The start and end time of the time period.

[0055] The dynamic correlation characteristics of the fluctuation value are calculated by the standard deviation of the time series rate and the integral value of the fluctuation change function. The sampling point rate value is obtained through the real-time monitoring data of the shipping route. Assuming that the number of sampling points in the time period is , the rate value is , mean , the fluctuation function is , the time period is , .

[0056] Calculate the standard deviation of the rate: ;

[0057] ; Calculate the integral part of the fluctuation change: ; Synthetic Dynamic Correlation Features: ; The result shows that the value of the dynamic correlation characteristic is 1.2587, which represents the time distribution characteristics of the path rate fluctuation and the intensity of range change, providing data support for the generation of timing adjustment feature values.

[0058] S402: based on the path rate fluctuation and time variation characteristic table, extract the time sensitivity parameter of the path propagation rate, analyze the interval characteristics of the sensitivity parameter in the time distribution, correct the dynamic distribution value of the time sensitivity coefficient, calculate the differentiated characteristic value of the time response, and form the sensitivity quantification result to generate the path time sensitivity coefficient matrix; The time sensitivity is divided into multiple distribution intervals for analysis. The sensitivity change value is calculated by the mean and interval range of the rate fluctuation in the time period. The distribution value of the sensitivity parameter is adjusted based on the statistical data of the rate in the historical time period. The dynamic distribution curve of the time sensitivity coefficient is corrected. The differentiated characteristic values ​​of the time response are extracted using the numerical changes of the mean difference. The sensitivity quantification results are generated based on the path sensitivity data matrix, and finally the path time sensitivity coefficient matrix is ​​formed.

[0059] S403: Based on the path time sensitivity coefficient matrix, the interactive characteristics of the path propagation rate and the time variation law are analyzed, the dynamic characteristic values ​​of the path time-space correlation parameters are calculated, the evolution trend characteristics of the path changes under the time series are summarized, and the time-correlated propagation path characteristic values ​​are generated;

[0060] By calculating the dynamic distribution values ​​of the path spatiotemporal correlation parameters in the time series, combining the path rate change law in each time period to extract the evolution trend characteristics, summarizing the time distribution characteristics of the dynamic path propagation rate change, and integrating the path spatiotemporal characteristic parameters into the time-correlated eigenvalues ​​of the dynamic propagation path according to the time series law, and generating the final time-correlated propagation path eigenvalue matrix.

[0061] See also Figure 6 , the specific steps of S5 are: S501: Based on the time-related propagation path characteristic value and the propagation path risk intensity distribution result, the weight value of the risk path is extracted, and the change law of the path weight in the time distribution is analyzed in combination with the dynamic propagation characteristics, the distribution range of the weight value and the correlation strength of the time series are calculated, the dynamic characteristics of the weight node are extracted, and the propagation network risk weight distribution data is generated; Combined with the dynamic propagation characteristics, the change law of path weight in time distribution is analyzed. According to the formula ; Calculate the distribution range of weight values ​​and the strength of association with the time series, In the formula, Represents the distribution range and correlation strength of weight values ​​in the time series, is the risk intensity of the path, is the propagation period within the time period, is the propagation capacity of the path, is the dynamic characteristic change function of the time period, and The start and end time of the time period.

[0062] The weight value is calculated by weighted summation of the risk intensity, propagation cycle and propagation capacity of the path, and combined with the dynamic change integral result of the time period. The risk intensity is obtained from the dynamic characteristic matrix of the propagation path, the propagation cycle is obtained by the time span of the monitoring path, and the propagation capacity is determined by analyzing the historical traffic statistics. Assuming the number of paths is The risk intensities are The propagation cycles are , the transmission capacity is , the dynamic characteristic change function is , the time period is , .

[0063] Calculate the weighted part: ; ; Calculate the dynamic change integral part: ; Composite weight value: ; The results show that the distribution range of weight values ​​and the correlation strength of the time series are 335.13, which indicates the importance of risk paths in the propagation network and their temporal dynamic characteristics, providing precise data support for further analysis.

[0064] S502: Based on the risk weight distribution data of the propagation network, the spatiotemporal propagation characteristics of the weighted path are analyzed, the connection distribution values ​​between the weighted nodes are calculated, and the interaction relationship is analyzed. The characteristic values ​​of the dynamic correlation strength between the paths are extracted, and the temporal characteristics of the network structure are adjusted in combination with the propagation path weights. The spatiotemporal dynamic structure of the regional propagation network is constructed, and a regional dynamic propagation network structure diagram is generated; The connection relationship of the risk path is extracted through the dynamic distribution value of the weight node, and the connection distribution diagram between the nodes is generated in combination with the weight change trend. The dynamic characteristics of the path propagation are mapped to the network time series structure. The node characteristic values ​​of the time series structure are adjusted according to the propagation path weight. The data characteristics of the dynamic correlation strength between the paths are analyzed, the spatiotemporal dynamic structure of the regional propagation network is constructed, and a regional dynamic propagation network structure diagram is generated.

[0065] S503: Based on the regional dynamic propagation network structure diagram, the propagation trend parameters are extracted in combination with the dynamic trend of weight distribution, the path evolution characteristics in the spatiotemporal propagation network are analyzed, the dynamic propagation risk value is calculated, and the time variation range and trend characteristics are analyzed to extract the time impact characteristics of the risk path and generate a dynamic risk propagation prediction value; The time series law and evolution characteristics of the network propagation path are combined to calculate the dynamic propagation risk value of the weighted path. The trend characteristics are analyzed through the dynamic changes of the risk intensity of the propagation path within the time period, the time impact characteristics of the risk path are statistically analyzed, and the time series characteristic data of the propagation path risk are integrated to finally generate a dynamic risk propagation prediction value.

[0066] See also Figure 7 , the specific steps of S6 are: S601: Based on the dynamic risk propagation prediction value, analyze the global regional transmission risk level distribution, extract the risk area according to the fluctuation characteristics of the risk value between regions, calculate the time distribution characteristics of the risk level inside and around the region, extract the risk association path and key node values, and generate a global transmission risk level distribution map; Risk areas are extracted through the time fluctuation characteristics of risk values. The fluctuation range of risk levels between regions and the key node values ​​of risk values ​​of transmission paths are combined to associate regional risk distribution with dynamic risk data of adjacent paths. The time characteristics of the risk level distribution within the region are calculated, and the data representation format of the risk map is dynamically adjusted according to the node values ​​and path distribution. Finally, a global transmission risk level distribution map is generated.

[0067] S602: Based on the global transmission risk level distribution map, extract the key risk paths between regions, analyze the time variation characteristics of the weight distribution in the paths, screen the key node values ​​of the priority control paths, calculate the priority weight distribution range of the risk paths, and generate the priority control path weight distribution table; The time variation characteristics of the weight distribution in the parsing path are as follows: ; Calculate the priority weight distribution range of the risk path, In the formula, Represents the path priority weight distribution range, For the The dynamic propagation value of the path, is the weight value of the path, is a dynamic adjustment function within a time period, and The start and end points of the time period.

[0068] The priority weight is calculated by the weighted average of the dynamic propagation value and weight value of the path, and combined with the dynamic adjustment integral result of the time period. The dynamic propagation value and weight value of the path are obtained from the path risk distribution data. Assume that the number of paths is , the dynamic propagation values ​​are The weight values ​​are , the dynamic adjustment function is , the time period is , .

[0069] Calculate the weighted average part: ; ; Calculate the dynamically adjusted integral part: ; The distribution range of synthesis priority weights is: ; The results show that the priority weight distribution range is 27.7, which reflects the priority of the risk path and its time-varying characteristics, and provides data support for screening priority control paths and optimizing the transmission network structure.

[0070] S603: Based on the priority control path weight distribution table, analyze the weight distribution characteristics of the priority path, calculate the priority distribution value in combination with the association weights between the paths, extract the dynamic association characteristics and key influencing parameters of the paths, and integrate the priority weights to generate the global risk path priority distribution coefficient; By analyzing the correlation weights between paths, combining time series data to calculate priority distribution values, and using the dynamic propagation characteristics between weighted paths to extract key influencing parameters, the priority weight data of the paths are integrated into the regional propagation network, and combined with the dynamic risk propagation laws, a global risk path priority distribution coefficient is generated.

[0071] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for sensing the spatiotemporal correlation of infectious disease risks based on shipping big data, characterized by: The following steps are involved: S1: Based on the global shipping network data, extract the ship flow records, the number of confirmed cases in the region and the population flow trend, analyze the correlation between the changes in ship flow and the regional case growth rate, calculate the correlation parameters, and generate the correlation coefficient between shipping flow and cases; S2: Based on the correlation coefficient between shipping flow and cases, using the case growth rate and population flow rate data, calculate the transmission rate change rate and cumulative transmission value, extract the transmission law within the region, and generate the regional transmission characteristic index value; S3: Combine the regional propagation characteristic index value, extract the flow and geographical adjacency characteristics of the inter-regional connection path, set the risk threshold, analyze the path propagation rate dynamics, screen the risk path, and generate the propagation path risk intensity distribution result; S4: Based on the risk intensity distribution results of the propagation path, analyze the path rate fluctuation and time change, extract the time sensitivity coefficient, calculate the time evolution trend value, integrate the dynamic changes to generate the path time-space relationship, and generate the time-related propagation path characteristic value; S5: calling the time-related propagation path characteristic value and the propagation path risk intensity distribution result, integrating the risk path weight and the dynamic propagation characteristics, constructing the regional dynamic propagation network structure, and generating the dynamic risk propagation prediction value; S6: Based on the dynamic risk propagation prediction value, analyze the global regional propagation risk level distribution, extract the priority control path in combination with the inter-regional risk level, calculate the priority path distribution, and generate the global risk path priority distribution coefficient.

2. The method for sensing the spatiotemporal correlation of infectious disease risk based on shipping big data according to claim 1 is characterized in that: The shipping flow and case correlation coefficient includes the correlation of case flow between regions, shipping flow influencing factors, and dynamic parameters of case growth rate. The transmission characteristic index value includes regional transmission intensity parameter, time transmission rate factor, and cumulative transmission volume characteristics. The transmission path risk intensity distribution result includes path transmission rate parameter, node connection strength coefficient, and risk path distribution characteristics. The time-related transmission path characteristic value includes time sensitivity parameter, dynamic trend characteristics, and spatiotemporal correlation factor. The dynamic risk transmission prediction value includes risk path weight distribution, dynamic transmission trend characteristics, and risk path prediction intensity. The risk path priority distribution coefficient includes priority path control parameter, regional risk level distribution, and path control weight value.

3. The method for sensing the spatiotemporal correlation of infectious disease risk based on shipping big data according to claim 1 is characterized in that: Based on the global shipping network data, the ship flow records, the number of confirmed cases in the region and the population flow trend are extracted, the correlation between the changes in ship flow and the regional case growth rate is analyzed, the correlation parameters are calculated, and the specific steps to generate the correlation coefficient between shipping flow and cases are as follows: S101: Based on the global shipping network data, extract the ship flow records on each shipping route, count the frequency of ships docking at ports in the region, analyze the population flow trends in different regions, build a time series data matrix based on the regional confirmed case data, classify and organize the data according to the regional distribution characteristics, and generate a ship flow and population flow trend analysis table; S102: Based on the ship flow and population flow trend analysis table, filter the paths of ship flow frequencies between regions, analyze the distribution characteristics of regional flow patterns, calculate the correlation values ​​of the case number change rate and the intersection data points of the flow paths, extract the flow characteristic parameters between regions, and establish a regional case and flow characteristic parameter matrix after integrated analysis; S103: Based on the regional case and flow characteristic parameter matrix, the linear and nonlinear correlation coefficients between the regional flow characteristic parameters and the case growth rate are calculated, the differences and directions between the parameters are analyzed, and significant regional feature pairs are screened according to the correlation coefficient to generate the shipping flow and case correlation coefficient.

4. The method for sensing the spatiotemporal correlation of infectious disease risk based on shipping big data according to claim 1 is characterized in that: Based on the correlation coefficient between shipping flow and cases, using case growth rate and population flow rate data, the transmission rate change rate and cumulative transmission value are calculated, the transmission law within the region is extracted, and the specific steps for generating regional transmission characteristic index values ​​are as follows: S201: Based on the correlation coefficient between shipping flow and cases, call the case growth rate and population flow rate data, analyze the fluctuation value of the case growth rate in the time series, adjust the adaptation range of the population flow rate data by time period, calculate and analyze the change of the transmission rate by time period, extract the key point value of the change of the transmission rate, and generate the transmission rate and change trend index; S202: Based on the propagation rate and the change trend indicator, extract the time period data of the cumulative propagation value, analyze the nonlinear correlation between the propagation rate and the cumulative value, calibrate the cumulative value data in combination with the key point value of the propagation rate, calculate the distribution value of the cumulative propagation value over time, extract the change characteristics of the propagation law in each stage, and generate a dynamic propagation law value matrix; S203: Based on the dynamic propagation law value matrix, analyze the key parameters of the propagation law within the region, select the propagation characteristic values ​​of each region, adjust the stage division standard of the propagation characteristics within the region, summarize the stage characteristics of the propagation within the region, calculate the propagation impact index based on the key characteristic parameters within the region, and generate the regional propagation characteristic index value.

5. The method for sensing the spatiotemporal correlation of infectious disease risk based on shipping big data according to claim 4 is characterized in that: The calculation formula of the distribution value of the cumulative propagation value changing over time is specifically as follows: ； in, Representative The characteristic parameters of the cumulative propagation value of the class feature over time, Representative Class Features The propagation rate in a period of time, Representative The average value of the propagation rate in all time periods in the class feature, Representative Class Features The length of the time period, Representative Class Features The cumulative propagation flow value of the propagation path, Representative Class Features The accumulated time value of each propagation path, For the The total number of time periods in the class feature, For the The total number of propagation paths in the class signature.

6. The method for sensing the spatiotemporal correlation of infectious disease risk based on shipping big data according to claim 1 is characterized in that: The specific steps of combining the regional propagation characteristic index value, extracting the flow and geographical adjacency characteristics of the inter-regional connection path, setting the risk threshold, analyzing the path propagation rate dynamics, screening the risk path, and generating the propagation path risk intensity distribution result are as follows: S301: Based on the regional propagation characteristic index value, extract the flow data of the connecting paths between the regions of the global shipping network, analyze the flow change trend of the paths in the time series, analyze the spatial relationship strength of the geographical adjacency characteristics, set the risk threshold to screen the key paths, extract the flow characteristics of the risk paths, and generate the risk path flow and adjacency characteristic table; S302: Based on the risk path flow and adjacency characteristic table, analyze the dynamic characteristics of the path propagation rate in different time periods, extract the propagation rate variation range in combination with the node flow value, calculate the dynamic characteristic parameters of the propagation rate and path flow intersection data, extract the key characteristics of the propagation path, and generate a path propagation dynamic characteristic matrix; S303: Based on the path propagation dynamic characteristic matrix, the propagation correlation parameters and traffic distribution characteristics between nodes are analyzed, the path risk characteristics are graded and calibrated according to the propagation intensity parameters, the propagation intensity distribution law of the risk path is extracted, the propagation impact value between paths is adjusted in combination with the node characteristics, and the propagation path risk intensity distribution result is generated.

7. The method for sensing the spatiotemporal correlation of infectious disease risk based on shipping big data according to claim 6 is characterized in that: The dynamic characteristic parameter calculation formula is specifically: ； in, The dynamic characteristic parameter representing the intersection data of propagation rate and path flow, Representative The first The transmission flow rate of the time period, Representative The average value of the propagation flow in all time periods on the path, Representative The first the length of the time period, Representative The first The risk value of the node, Representative The first The node's propagation capability value, Representative The first The weight value of the node, For the The total number of time segments in the path, For the The total number of nodes in the path.

8. The method for sensing the spatiotemporal correlation of infectious disease risk based on shipping big data according to claim 1 is characterized in that: Based on the risk intensity distribution results of the transmission path, the specific steps of analyzing the path rate fluctuation and time change, extracting the time sensitivity coefficient, calculating the time evolution trend value, integrating the dynamic changes to generate the path time-space relationship, and generating the time-related transmission path characteristic value are as follows: S401: Based on the risk intensity distribution result of the propagation path, analyze the fluctuation range of the path propagation rate in the time series, extract the key node values ​​of the path rate change, calculate the dynamic correlation value between the time distribution characteristics of the fluctuation value and the range, calibrate the rate fluctuation data to generate the timing adjustment characteristic value, and generate the path rate fluctuation and time change characteristic table; S402: based on the path rate fluctuation and time variation characteristic table, extract the time sensitivity parameter of the path propagation rate, analyze the interval characteristics of the sensitivity parameter in the time distribution, correct the dynamic distribution value of the time sensitivity coefficient, calculate the differentiated characteristic value of the time response, and form a sensitivity quantification result to generate a path time sensitivity coefficient matrix; S403: Based on the path time sensitivity coefficient matrix, the interactive characteristics of the path propagation rate and the time variation law are analyzed, the dynamic characteristic values ​​of the path time-space correlation parameters are calculated, the evolution trend characteristics of the path changes under the time series are summarized, and the time-correlated propagation path characteristic values ​​are generated.

9. The method for sensing the spatiotemporal correlation of infectious disease risks based on shipping big data according to claim 1 is characterized in that: The specific steps of calling the time-related propagation path characteristic value and the propagation path risk intensity distribution result, integrating the risk path weight and dynamic propagation characteristics, constructing the regional dynamic propagation network structure, and generating the dynamic risk propagation prediction value are as follows: S501: Based on the time-related propagation path characteristic value and the propagation path risk intensity distribution result, extract the weight value of the risk path, analyze the change law of the path weight in time distribution in combination with the dynamic propagation characteristics, calculate the distribution range of the weight value and the correlation strength of the time series, extract the dynamic characteristics of the weight node, and generate the propagation network risk weight distribution data; S502: Based on the risk weight distribution data of the propagation network, the spatiotemporal propagation characteristics of the weighted paths are analyzed, the connection distribution values ​​between the weighted nodes are calculated, and the interaction relationships are analyzed to extract the characteristic values ​​of the dynamic correlation strengths between the paths. The temporal characteristics of the network structure are adjusted in combination with the propagation path weights to construct the spatiotemporal dynamic structure of the regional propagation network and generate a regional dynamic propagation network structure diagram; S503: Based on the regional dynamic propagation network structure diagram, the propagation trend parameters are extracted in combination with the dynamic trend of the weight distribution, the path evolution characteristics in the spatiotemporal propagation network are analyzed, the dynamic propagation risk value is calculated, and the time change range and trend characteristics are analyzed to extract the time impact characteristics of the risk path and generate a dynamic risk propagation prediction value.

10. The method for sensing the spatiotemporal correlation of infectious disease risk based on shipping big data according to claim 1 is characterized in that: Based on the dynamic risk propagation prediction value, the specific steps of analyzing the global regional propagation risk level distribution, extracting the priority control path in combination with the inter-regional risk level, calculating the priority path distribution, and generating the global risk path priority distribution coefficient are as follows: S601: Based on the dynamic risk propagation prediction value, analyze the global regional propagation risk level distribution, extract the risk area according to the fluctuation characteristics of the risk value between regions, calculate the time distribution characteristics of the risk level inside and around the region, extract the risk association path and key node values, and generate a global propagation risk level distribution map; S602: Based on the global transmission risk level distribution map, extract the inter-regional risk critical paths, analyze the time variation characteristics of the weight distribution in the paths, screen the key node values ​​of the priority control paths, calculate the priority weight distribution range of the risk paths, and generate a priority control path weight distribution table; S603: Based on the priority control path weight distribution table, analyze the weight distribution characteristics of the priority paths, calculate the priority distribution value in combination with the association weights between the paths, extract the dynamic association characteristics and key influencing parameters of the paths, and integrate the priority weights to generate a global risk path priority distribution coefficient.

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