Wildlife quarantine interception point layout method and system for epidemic-free areas

Abstract

The application discloses a wild animal quarantine interception point layout method and system for epidemic-free areas, and relates to the technical field of big data processing. The method comprises the following steps: acquiring epidemic source distribution information of an epidemic to be detected based on big data retrieval, performing epidemic invasion risk assessment on passable areas in the epidemic-free area, and constructing an epidemic invasion risk heat map; generating an initial interception point distribution network by taking quarantine resource constraints as the limit; monitoring and acquiring quarantine information of the initial interception point distribution network in a preset control period to generate a quarantine information sequence topology, identifying a positive transmission path and a risk transfer trend; based on the current epidemic source distribution information, combining the quarantine information sequence topology, the positive transmission path and the risk transfer trend, optimizing the interception point for the next control period, generating an optimal interception point distribution network, and performing epidemic detection. The application realizes dynamic optimization and closed-loop adjustment of the quarantine interception point, and solves the problem of disconnection between traditional static layout and real-time risk.

Description

Technical Field

[0001] This invention relates to the field of big data processing technology, specifically to a method and system for deploying wildlife quarantine and interception points in disease-free areas. Background Technology

[0002] A disease-free zone is a geographical area that has been assessed and certified by a national or international organization and has not experienced any specific major animal disease within a specified period. To maintain this disease-free status, quarantine checkpoints must be established at the zone's boundaries to intercept and quarantine vehicles transporting wild animals and passing live wild animals, preventing the invasion of major diseases such as African swine fever and foot-and-mouth disease. Unlike traditional livestock quarantine, the movement paths of wild animals are uncontrollable, and they may become mobile carriers of pathogens, posing a continuous threat to disease-free zones.

[0003] The existing quarantine interception point deployment technology has the following shortcomings. First, the acquisition of epidemic source distribution information is incomplete, relying heavily on manual patrols or fixed-point monitoring, failing to fully utilize big data retrieval technology systems to collect diverse information such as historical outbreak locations, host distribution, and surrounding epidemic situations, resulting in insufficient accuracy in risk assessment. Second, the allocation of quarantine resources lacks quantitative basis; the number of available interception points, testing equipment, and personnel is limited. Existing methods typically deploy them evenly along administrative boundaries, failing to differentiate allocation based on the risk of disease invasion and animal circulation in each passable area, leading to resource misallocation. Third, the deployment of interception points lacks dynamic optimization. The activity range of wild animals and the distribution of epidemic sources change over time; existing static deployment schemes cannot be dynamically adjusted based on periodic quarantine information such as detection rates and positive transmission routes, resulting in a decline in quarantine interception effectiveness. Summary of the Invention

[0004] This invention addresses the technical problems in existing technologies, such as incomplete acquisition of epidemic source distribution information leading to insufficient accuracy of risk assessment, lack of quantitative basis for quarantine resource allocation leading to resource misallocation, and lack of dynamic optimization in the deployment of interception points leading to a decline in quarantine effectiveness. It provides a method and system for deploying quarantine interception points for wild animals in disease-free areas.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0006] In a first aspect, the present invention provides a method for setting up wildlife quarantine interception points in disease-free areas, including:

[0007] Based on big data retrieval, information on the distribution of disease sources to be detected is obtained. Disease invasion risk assessments are conducted for several passable areas within disease-free zones, and a disease invasion risk heat map is constructed.

[0008] With quarantine resources as a constraint, the initial deployment of quarantine interception points is carried out based on the aforementioned heat map of disease invasion risk, and an initial interception point distribution network is generated for disease detection.

[0009] The system monitors and acquires quarantine information of the initial interception point distribution network within a preset control period, generates a quarantine information sequence topology, and identifies positive transmission paths and risk transfer trends.

[0010] Based on big data retrieval, the current epidemic source distribution information is obtained. Combined with the quarantine information sequence topology, positive transmission path and risk transfer trend, the quarantine interception points in the next control cycle are deployed and optimized to generate the optimal interception point distribution network, and the epidemic detection in the next control cycle is performed.

[0011] Secondly, the present invention provides a system for deploying quarantine and interception points for wild animals in disease-free areas, comprising:

[0012] The risk assessment module is used to retrieve the distribution information of the source of the disease to be detected based on big data retrieval, conduct disease invasion risk assessment for several passable areas in the disease-free zone, and construct a disease invasion risk heat map.

[0013] The initial deployment module is used to initially deploy quarantine interception points based on the disease invasion risk heat map, with quarantine resource constraints as the limit, and generate an initial interception point distribution network for disease detection.

[0014] The monitoring and identification module is used to monitor and acquire the quarantine information of the initial interception point distribution network within a preset control period, generate the quarantine information sequence topology, and identify the positive transmission path and risk transfer trend.

[0015] The optimization deployment module is used to obtain the current epidemic source distribution information based on big data retrieval, and optimize the deployment of quarantine interception points in the next control cycle by combining the quarantine information sequence topology, positive transmission path and risk transfer trend, generate the optimal interception point distribution network, and perform epidemic detection in the next control cycle.

[0016] The beneficial effects of this invention are:

[0017] Compared to existing technologies, this invention first integrates multi-source big data to construct an epidemic source distribution layer. Combining the geometric features of passable areas, the distribution of epidemic sources, and spatial connectivity, it quantitatively assesses the disease invasion risk index of each channel, forming a boundary risk heat map. This solves the problem of strong subjectivity in risk assessment caused by reliance on manual experience in traditional methods. Secondly, using the total amount of quarantine resources as a hard constraint, it calculates a dynamic risk index threshold based on expected risk contribution values ​​and resource carrying capacity. All-weather fixed interception points are configured for high-risk channels, while semi-fixed points or mobile patrols are configured for medium- and low-risk channels, achieving precise deployment of quarantine resources. Thirdly, it constructs a time-series topology network with interception points as nodes and detection rate and positive timestamps as attributes. Through statistical analysis of positive transmission frequency and detection rate trends within continuous periods, it automatically identifies positive transmission paths and risk transfer directions. Finally, it integrates historical quarantine feedback with the latest epidemic source distribution as an optimization basis, iteratively optimizing the interception point deployment scheme with the goal of minimizing the objective function, forming a closed-loop adjustment mechanism of risk identification, resource allocation, execution monitoring, and dynamic optimization. Attached Figure Description

[0018] Figure 1 A flowchart illustrating the method for setting up wildlife quarantine interception points in disease-free areas provided by the present invention;

[0019] Figure 2 A logical diagram illustrating the method for deploying wildlife quarantine interception points in disease-free areas provided by this invention;

[0020] Figure 3 This is a schematic diagram of the structure of the wildlife quarantine and interception point deployment system for disease-free areas provided by the present invention.

[0021] In the attached diagram, the components represented by each number are as follows:

[0022] Risk assessment module 11, initial deployment module 12, monitoring and identification module 13, and optimized deployment module 14. Detailed Implementation

[0023] Example 1, as Figure 1 , Figure 2 As shown, this embodiment of the invention provides a method for setting up wildlife quarantine interception points in disease-free areas, including:

[0024] S10: Based on big data retrieval, obtain the epidemic source distribution information of the disease to be detected, conduct disease invasion risk assessment for several passable areas in the disease-free area, and construct a disease invasion risk heat map;

[0025] Multiple access routes exist along the boundaries of disease-free zones, accessible to wild animals and transport vehicles. These include national highways, provincial highways, county roads, township roads, forest trails, and seasonal waterways. The risk of disease invasion varies significantly across these routes, with routes closer to historical outbreak sites posing a higher risk and those farther away being lower. Applying a uniform defense strategy to all routes could lead to blind spots in high-risk areas due to insufficient resources, while over-defense in low-risk areas could result in wasted resources. Therefore, an independent disease invasion risk assessment is necessary for each accessible route along the boundaries of disease-free zones.

[0026] This step utilizes big data retrieval technology to collect information on the distribution of disease sources for detection from multiple data sources. The diseases to be detected refer to specific wildlife diseases requiring focused prevention in disease-free areas, such as African swine fever, foot-and-mouth disease, and highly pathogenic avian influenza. The disease source distribution information includes historical outbreak location data, host wildlife distribution data, real-time epidemic reports from surrounding areas, and environmental remote sensing data.

[0027] Based on this, due to the significant differences in the spatial location, distance from the source of infection, and surrounding environmental characteristics of different passable areas, the degree of disease invasion risk faced by each passage varies. Therefore, it is necessary to conduct disease invasion risk assessments on several passable areas within the disease-free zone to quantify the potential threat level of each passage and construct a disease invasion risk heat map to intuitively display the risk distribution at various locations on the boundary of the disease-free zone, providing a spatially visualized decision-making basis for the subsequent scientific deployment of quarantine interception points.

[0028] Specifically, based on big data retrieval, information on the distribution of disease sources to be detected is obtained. Disease invasion risk assessments are then conducted for several passable areas within disease-free zones, and a disease invasion risk heat map is constructed, including:

[0029] Data on historical outbreak locations of the disease to be detected, distribution data of host wild animals, real-time epidemic reports of surrounding areas, and environmental remote sensing data are collected from multiple data sources using big data retrieval technology. The multiple data sources are spatiotemporally aligned and fused to generate an epidemic source distribution layer covering the disease-free area and surrounding areas. The severity level of the disease at each epidemic source point is marked in the epidemic source distribution layer.

[0030] Each national highway, provincial highway, county road, township road, forest trail, and passable seasonal waterway on the boundary of the epidemic-free zone is treated as a passable area, and the geometric path and endpoint coordinates of each passable area are extracted.

[0031] Based on the number of epidemic foci along the path in each passable area, the severity level of the disease at each epidemic foci, and the spatial connectivity between the passable area and the epidemic foci, the disease invasion risk index of the passable area is calculated. The disease invasion risk index is positively correlated with the number of epidemic foci, the severity level of the disease, and the spatial connectivity.

[0032] Based on the disease invasion risk index of all passable areas, a heat map of disease invasion risk of continuously distributed disease-free zone boundaries is generated using spatial interpolation method. Each location point in the heat map corresponds to a risk index value.

[0033] First, data on historical outbreak locations of the disease to be detected, distribution data of host wild animals, real-time epidemic reports of surrounding areas, and environmental remote sensing data are collected from multiple data sources using big data retrieval technology. The multiple data sources are spatiotemporally aligned and fused to generate an epidemic source distribution layer covering the disease-free area and surrounding areas. The severity level of the disease at each epidemic source point is marked in the epidemic source distribution layer.

[0034] Specifically, historical outbreak location data records the specific geographic coordinates and outbreak times of past disease outbreaks. Host wildlife distribution data records the geographic distribution range of wild animal populations that may carry and spread pathogens. Real-time epidemic reporting data for surrounding areas comes from epidemic announcements issued by relevant local animal disease control agencies. Environmental remote sensing data, including vegetation cover, water body distribution, and topographic relief, is used to assess the natural conditions for disease transmission. Spatiotemporal alignment refers to unifying the above data to the same temporal resolution and spatial coordinate system, for example, aligning time in days and spatially in kilometers of grid. Fusion processing refers to overlaying the above multi-source data into the same geographic information layer.

[0035] In the generated epidemic source distribution layer, each epidemic source is labeled with an epidemic severity level based on its epidemic status and activity. For example, areas currently experiencing an outbreak and with frequent host animal activity are designated as Level 1 areas, representing the highest risk; areas that have reported confirmed cases in the past three calendar months but currently have no new cases are designated as Level 2 areas; areas that have historically reported confirmed cases but have no new cases in the past three calendar months are designated as Level 3 areas; and areas that have never reported confirmed cases and have no surrounding epidemic threat are designated as Level 4 areas, representing the lowest risk. The severity of the epidemic decreases sequentially among these four levels.

[0036] Furthermore, each national highway, provincial highway, county road, township road, forest trail, and passable seasonal waterway along the boundary of the disease-free zone is considered a passable area, and the geometric path and endpoint coordinates of each passable area are extracted. National highways, provincial highways, county roads, and township roads represent road networks with different administrative levels; forest trails refer to unpaved roads in forests or mountains that allow passage for wild animals or transport vehicles; and seasonal waterways refer to rivers or streams that form temporary passages during the rainy season. Each passable area corresponds to a specific physical passage, and its geometric path is represented by a broken line or curve connecting a series of latitude and longitude coordinate points. The endpoint coordinates indicate the starting and ending points of the passage into the disease-free zone. This information is extracted for subsequent calculations of spatial connectivity strength.

[0037] Next, based on the number of epidemic foci along the path in each passable area, the severity level of the disease at each epidemic foci, and the spatial connectivity between the passable area and the epidemic foci, the disease invasion risk index of the passable area is calculated.

[0038] The number of disease foci refers to the total number of disease outbreak points or host distribution points located within a certain buffer zone on both sides of the center line of the geometric path of the passable area. It is used to characterize the density of disease threats around the passage. The more disease foci there are, the greater the risk of multiple points of entry into the passage. The buffer zone refers to the width of the strip-shaped area extending to both sides of the center line of the geometric path of the passable area. It is used to define which disease foci are considered to have a spatial relationship with the passable area. It can be set comprehensively based on the maximum migration distance of the disease-carrying animals within their daily activity range and the actual terrain features of the disease-free zone boundary. For example, the buffer zone can be set to 10 kilometers, meaning that disease foci within a strip-shaped area extending 10 kilometers on both sides of the geometric path center line are included in the count.

[0039] The severity level of each outbreak point is a quantitative classification based on the current epidemic status and host animal activity. It characterizes the potential intensity of pathogen export from the outbreak point; a higher severity level indicates an active outbreak and a greater probability of pathogen spread. Spatial connectivity strength between the traversable area and the outbreak point refers to the connectivity probability between the outbreak point and the traversable area based on the actual terrain and road network. It characterizes the ease with which pathogens from the outbreak point can spread along the geographical space to this pathway. Greater spatial connectivity strength indicates a convenient animal movement path or environmental connection between the outbreak point and the pathway, and a higher risk of pathogen importation.

[0040] The calculation steps for the spatial connectivity strength between passable areas and the source of the epidemic include:

[0041] For each passable area and each epidemic source point, the shortest path algorithm in graph theory is used to calculate the shortest travel distance between the geometric path centerline of the passable area and the epidemic source point. The reciprocal of the shortest travel distance is used as the spatial connectivity strength value between the passable area and the epidemic source point. The smaller the shortest travel distance, the larger the spatial connectivity strength value.

[0042] When the passable area directly passes through the area where the epidemic source is located, the shortest travel distance is set to a preset minimum positive threshold. When the passable area and the epidemic source are blocked by an insurmountable geographical barrier, the spatial connectivity strength value is set to zero.

[0043] First, for each passable area and each epidemic source, the shortest path algorithm in graph theory is used to calculate the shortest travel distance between the geometric centerline of the passable area and the epidemic source. The shortest path algorithm in graph theory abstracts passable paths such as road networks and terrain passages in geographic space into a graph structure composed of nodes and edges. Nodes in the graph represent road intersections or geographic key points, and edges represent passable road segments between nodes. Each edge is assigned a weight, which can be the length of the road segment, travel time, or travel difficulty. Specifically, in one optional embodiment, the node closest to the epidemic source on the geometric centerline of the passable area is taken as the starting point, and the nearest network node where the projection of the epidemic source in geographic space is located is taken as the ending point. Dijkstra's algorithm or A* algorithm is used to search for the path with the minimum cumulative weight from the starting point to the ending point, and this cumulative weight is taken as the shortest travel distance. This shortest distance reflects the actual ease of travel between the epidemic source and the passable area; the shorter the distance, the easier it is to connect the two.

[0044] Furthermore, the reciprocal of the shortest travel distance is used as the spatial connectivity strength between the passable area and the source of the epidemic; that is, spatial connectivity strength equals 1 divided by the shortest travel distance. This calculation means that when the shortest travel distance approaches 0, the spatial connectivity strength approaches infinity, indicating that the two almost overlap and have extremely strong connectivity; as the shortest travel distance increases, the spatial connectivity strength decreases inversely, indicating that connectivity gradually weakens. Through this reciprocal transformation, the spatial connectivity strength value and the shortest travel distance are inversely related, with the strength value increasing as the distance decreases.

[0045] Furthermore, when a passable area directly passes through the area containing the epidemic source, the shortest travel distance is set to a preset minimum positive threshold. This minimum positive threshold is a pre-set, extremely small positive value, such as 0.1 meters. Its purpose is to ensure that the spatial connectivity strength value reaches a preset upper limit without generating an infinitely large calculation result. When a passable area directly passes through the area containing the epidemic source, there is almost no distance between the two. In this case, the shortest travel distance is set to this minimum positive threshold, and the corresponding spatial connectivity strength is equal to 1 divided by this threshold. For example, 1 divided by 0.1 equals 10, which serves as the upper limit of the spatial connectivity strength.

[0046] Furthermore, when an insurmountable geographical barrier separates the passable area from the source of infection, the spatial connectivity strength is set to zero. Insurmountable geographical barriers include large bodies of water such as lakes and oceans, towering mountains such as insurmountable ridges, restricted areas, and core areas of nature reserves—obstacles that no animals or vehicles can cross. If the shortest path algorithm finds no connecting path between the start and end points in the above graph structure, or if the calculated shortest travel distance exceeds a preset travel distance limit, such as 100 kilometers, it is determined that the area is blocked by an insurmountable geographical barrier. In this case, the spatial connectivity strength is set to 0, indicating that there is no effective pathogen transmission path between the source of infection and the passable area. Through the above calculation method, the contribution of each source of infection to the pathogen input risk of each passable area can be quantitatively assessed.

[0047] Specifically, the formula for calculating the disease invasion risk index can be expressed as follows: The disease invasion risk index equals the weight of the number of epidemic foci multiplied by the number of epidemic foci, plus the weight of the severity multiplied by the value corresponding to the severity level of the disease, plus the weight of the spatial connectivity multiplied by the value of the spatial connectivity. The sum of the weight coefficients of the three factors is 1, for example, they can be set to 0.3, 0.4, and 0.3 respectively, and the specific values ​​can be adjusted according to actual prevention and control needs. The disease invasion risk index is positively correlated with the number of epidemic foci, the severity level of the disease, and the spatial connectivity. That is, the more epidemic foci the path passes through, the higher the risk index; the higher the severity level of the disease at the epidemic foci, the higher the risk index; and the greater the spatial connectivity between the passable area and the epidemic foci, the higher the risk index.

[0048] Furthermore, based on the disease invasion risk index of all passable areas, a heat map of disease invasion risk of continuously distributed disease-free zone boundaries is generated using spatial interpolation methods.

[0049] Spatial interpolation is a mathematical method that estimates the risk indices of other unknown locations within the same spatial range based on observed values ​​of known discrete locations. On the boundary of a disease-free zone, the passable area consists of discretely distributed channels, each with a known disease invasion risk index. However, between two channels on the disease-free zone boundary, there are numerous continuous locations with unknown risk indices. The purpose of spatial interpolation is to use the known channel risk indices to calculate the disease invasion risk index for all locations on the boundary, thereby obtaining a continuous risk curve. The basic assumption of spatial interpolation is that points spatially closer together have more similar attribute values, while points farther apart have greater differences in attribute values.

[0050] In an alternative embodiment, the spatial interpolation method that can be used is inverse distance weighted interpolation.

[0051] Specifically, the inverse distance weighted interpolation is performed as follows: First, the disease-free zone boundary is discretized into a series of equally spaced interpolation points, with the distance between adjacent points set to 100 meters. For each interpolation point, several known channel points closest to it on its front and back sides are selected as reference points. The number of reference points can be set to four: two known channel points on the left and two on the right. Second, the spatial distance from the interpolation point to each reference point is calculated. Spatial distance refers to the actual length along the disease-free zone boundary curve. Third, the weighting coefficient for each reference point is calculated. The weighting coefficient is equal to the square of the reciprocal of the spatial distance from the interpolation point to that reference point, divided by the sum of the squares of the reciprocals of the spatial distances of all reference points. The closer the reference point, the larger its square reciprocal, and the higher the weighting coefficient; the farther the reference point, the lower the weighting coefficient. Fourth, the disease invasion risk index of each reference point is multiplied by the corresponding weighting coefficient, and the products are summed to obtain the risk index estimate for that interpolation point. By iterating through all interpolation points, the disease invasion risk index is obtained for each location point on the boundary of the disease-free zone.

[0052] After spatial interpolation, each location point on the boundary of the disease-free zone corresponds to a disease invasion risk index. Arranging all disease invasion risk index values ​​in spatial order and mapping them to a color gradient according to their magnitude yields a disease invasion risk heatmap. In this heatmap, locations with higher disease invasion risk index values ​​are represented by darker or warmer colors, such as red, while locations with lower values ​​are represented by lighter or cooler colors, such as blue. This heatmap allows managers to visually identify high-risk and low-risk sections on the boundary of the disease-free zone. High-risk sections are represented by continuous dark or warm-colored bands on the heatmap, while low-risk sections are represented by continuous light or cool-colored bands. This disease invasion risk heatmap provides a spatially visualized quantitative basis for the differentiated deployment of subsequent quarantine interception points.

[0053] S20: With quarantine resources as a constraint, the initial layout of quarantine interception points is carried out according to the heat map of disease invasion risk, and an initial interception point distribution network is generated for disease detection.

[0054] Furthermore, after obtaining a heat map of disease invasion risk at the boundaries of disease-free zones, quarantine interception points need to be established at the actual boundaries. The deployment of these interception points is constrained by the total amount of quarantine resources, including the maximum number of interception points that can be deployed, the number of testing devices that can be configured at each interception point, the number of quarantine personnel that can be configured at each interception point, and the maximum daily working hours for each interception point. These resources are allocated uniformly by the disease-free zone management department according to budget and manpower, and are therefore limited resources. Thus, it is impossible to deploy fixed interception points at every risk location; differentiated resource allocation is necessary based on the level of risk index.

[0055] This step, based on the disease invasion risk heat map and constrained by quarantine resources, establishes an initial network of quarantine interception points. This network covers all passable areas along the boundaries of disease-free zones, with high-risk passages equipped with high-intensity interception resources and medium- and low-risk passages equipped with interception resources commensurate with their risk levels. Once formed, the initial network is put into operation to detect diseases in wild animals and transport vehicles crossing the boundaries of disease-free zones. This initial network serves as a baseline for subsequent dynamic optimization, performing disease detection tasks during the first control cycle and recording quarantine information for later analysis.

[0056] Specifically, constrained by quarantine resources, the initial deployment of quarantine interception points is based on the aforementioned disease invasion risk heat map, generating an initial interception point distribution network for disease detection, including:

[0057] Obtain data on the total amount of quarantine resources available in disease-free areas. The total amount of quarantine resources includes the upper limit of the number of interception points that can be deployed, the number of sets of testing equipment that can be configured at each interception point, the number of quarantine personnel that can be configured at each interception point, and the maximum daily working hours of each interception point.

[0058] Based on the total quarantine resource data, a risk index threshold is set, and areas in the disease invasion risk heat map with a risk index greater than the risk index threshold are identified as a set of high-risk passable areas, while areas in the disease invasion risk heat map with a risk index less than the risk index threshold are identified as a set of medium- and low-risk passable areas.

[0059] Assign a fixed interception point to each passable area in the set of high-risk passable areas, and configure 24-hour monitoring resources at the fixed interception point;

[0060] The remaining quarantine resources are allocated to the passable areas in the set of medium- and low-risk passable areas in descending order of risk index. For each allocated passable area, a semi-fixed interception point, a mobile patrol interception point, or no fixed interception point is set up.

[0061] Among them, setting a risk index threshold based on the total quarantine resource data includes:

[0062] Obtain the expected animal flow data for each passable area within a unit of time, and calculate the expected risk contribution value of each passable area based on the product of the disease invasion risk index of each passable area and the expected animal flow data.

[0063] All passable areas are sorted in descending order of expected risk contribution value to generate a first candidate sequence. Passable areas are selected sequentially from the first candidate sequence, and the expected animal flow data of each selected passable area are accumulated to obtain the cumulative expected flow value.

[0064] Calculate the first product, which is equal to the maximum number of deployable interception points multiplied by the maximum daily working hours of each interception point;

[0065] Calculate the second product, which is equal to the first product multiplied by the number of quarantine personnel that can be configured at each interception point, and then multiplied by the average unit detection rate;

[0066] When the cumulative expected circulation value reaches or exceeds the second product for the first time, the selection of passable areas is stopped, and the disease invasion risk index corresponding to the last passable area selected at the time of stopping is set as the risk index threshold.

[0067] If the cumulative expected circulation value still does not reach the second product after all passable areas have been selected, the lowest disease invasion risk index in all passable areas is set as the risk index threshold.

[0068] First, obtain data on the total available quarantine resources in disease-free zones. This data includes the maximum number of interception points that can be deployed, for example, the maximum number of interception points that a disease-free zone management department can operate simultaneously is 20; the number of testing devices that can be configured at each interception point, for example, each fixed interception point can be configured with one polymerase chain reaction (PCR) testing device and two sets of rapid test strips; the number of quarantine personnel that can be configured at each interception point, for example, each fixed interception point can be configured with 4 quarantine personnel and 2 joint law enforcement personnel; and the maximum daily working hours for each interception point, for example, fixed interception points can be configured for 24-hour continuous duty, and semi-fixed interception points can be configured for 12-hour daily duty.

[0069] Secondly, a risk index threshold is set based on the total amount of quarantine resources. This risk index threshold is used to distinguish between high-risk and medium-to-low-risk passable areas. The setting of the risk index threshold is based on the matching relationship between the expected risk contribution value and the carrying capacity of quarantine resources.

[0070] Specifically, the first step is to obtain the expected animal flow data for each passable area within a unit of time. This expected animal flow data can be obtained based on historical data analysis. In one optional embodiment, the specific data collection method is as follows: collect historical animal flow statistics for the same period in each passable area along the boundary of the disease-free zone. This historical data includes the average daily number of animals passing through in the same month or season over the past three calendar years. Combined with seasonal migration data and wildlife habitat distribution data in surrounding areas, the historical data is corrected to obtain the predicted animal flow for each passable area within the current control period. When historical flow statistics are lacking for a certain passable area, the average flow data from adjacent passable areas with similar geographical environments is used as the expected animal flow data for that area. The disease invasion risk index for each passable area is multiplied by the expected animal flow data to calculate the expected risk contribution value for each passable area. This expected risk contribution value reflects both the risk intensity and the scale of passage through a corridor, serving as a comprehensive quantitative indicator for high-risk corridors.

[0071] Then, all passable areas are sorted in descending order of expected risk contribution value to generate the first candidate sequence. Passable areas are selected sequentially from the first candidate sequence, and the expected animal flow data for each selected passable area is accumulated to obtain the cumulative expected flow value. Simultaneously, the first product is calculated: the first product equals the maximum number of deployable interception points multiplied by the maximum daily working hours of each interception point. The second product is calculated: the second product equals the first product multiplied by the number of quarantine personnel that can be configured at each interception point, and then multiplied by the average unit detection rate. The average unit detection rate is calculated by obtaining the number of target animals that each quarantine personnel can test per unit time, and calculating the average unit detection rate of all quarantine personnel. For example, if a quarantine personnel can complete the sampling and rapid testing of 10 wild animals per hour, then the average unit detection rate is 10 animals per hour.

[0072] When the cumulative expected flow rate first reaches or exceeds the second product, the selection of passable areas stops, and the disease invasion risk index corresponding to the last selected passable area at the time of stopping selection is set as the risk index threshold. The second product represents the maximum testing and processing capacity that a disease-free area can complete per unit time under the current quarantine resource constraints. The cumulative expected flow rate represents the total expected flow rate of the selected passable area set. When the total expected flow rate reaches the maximum processing capacity of quarantine resources, the remaining passable areas cannot obtain fixed interception point allocation due to insufficient resources; therefore, the risk index of the last selected channel is used as the segmentation threshold.

[0073] Furthermore, if the cumulative expected circulation value does not reach the second product after all passable areas have been selected, it indicates that quarantine resources are sufficient. In this case, the lowest disease invasion risk index in all passable areas is set as the risk index threshold, so that all areas are included in the high-risk set.

[0074] Furthermore, based on the set risk index threshold, areas in the disease invasion risk heat map with a risk index greater than the threshold are identified as high-risk passable areas, while areas with a risk index less than the threshold are identified as medium- and low-risk passable areas.

[0075] Next, a fixed interception point is assigned to each passable area in the set of high-risk passable areas, and 24-hour on-duty resources are set up at the fixed interception point, including polymerase chain reaction testing equipment, rapid test strips, quarantine personnel and joint law enforcement personnel, to ensure that wild animals or transport vehicles passing through the passage at any time can be intercepted and inspected.

[0076] Meanwhile, the remaining quarantine resources will be allocated to passable areas within the set of medium- and low-risk passable areas in descending order of risk index. Specifically, for medium-risk areas, semi-fixed interception points or mobile patrol interception points will be set up, with resources allocated for 12-hour daily duty and rapid test strips; for low-risk areas, no fixed interception points will be set up, and they will only be included in the coverage of periodic random checks or remote video surveillance.

[0077] In summary, through the above differentiated deployment, each allocated passable area can obtain the type of interception resources that matches its risk level and remaining resources, ultimately generating an initial interception point distribution network for actual disease detection operations.

[0078] S30: Monitor and acquire quarantine information of the initial interception point distribution network within a preset control period, generate quarantine information sequence topology, and identify positive transmission paths and risk transfer trends;

[0079] Furthermore, after the initial interception point distribution network is put into operation, its quarantine effectiveness needs to be continuously monitored. Because the activity range of wild animals and the distribution of disease sources change dynamically over time, the initial deployment plan may not be able to continuously adapt to the subsequent disease transmission trend. Therefore, this step identifies the spatiotemporal patterns of disease transmission by monitoring the quarantine information of the initial interception point distribution network within a preset control period, providing data support for the dynamic optimization of subsequent interception point deployment.

[0080] The preset control cycle is a fixed time interval, determined based on the seasonal migration patterns of wild animals, the length of the disease incubation period, and the frequency of quarantine resource updates. For example, a control cycle might be every 7 days or every 30 days. At the beginning of each cycle, the quarantine information record cache at all interception points is reset. During the cycle, for each interception point in the initial interception point distribution network, the total number of target animals inspected within the cycle, the number of positive animals confirmed by laboratory testing within the cycle, and the timestamp of each positive animal detection are recorded. A quarantine information sequence topology with time attributes is constructed, using interception points as nodes and the physical connections between passable areas as edges. Based on this, the potential transmission direction is determined by the order of positive detection timestamps, stable positive transmission paths are identified by the frequency statistics of transmission directions over multiple cycles, and risk-increasing and risk-decreasing nodes are identified by the continuous multi-cycle changes in the detection rate at each interception point. Ultimately, the positive transmission path and risk transfer trend are obtained.

[0081] Specifically, positive transmission routes are used to reveal the specific channels through which the disease spreads from high-risk areas to adjacent areas, while risk transfer trends reveal the spatial migration direction of the overall disease risk. This information will serve as a key input for optimizing the deployment of quarantine interception points in the next control cycle, guiding quarantine resources to be allocated towards areas upstream of transmission routes and areas with rising risk.

[0082] Specifically, monitoring and acquiring quarantine information of the initial interception point distribution network within a preset control period, and generating a quarantine information sequence topology, including:

[0083] Set a fixed time interval as the preset control cycle, and reset the quarantine information record cache of all interception points at the beginning of each preset control cycle;

[0084] Within the preset control period, quarantine information data in three dimensions is recorded for each interception point in the initial interception point distribution network. The three dimensions include the total number of target animals inspected by the interception point within the period, the number of positive animals confirmed by laboratory testing by the interception point within the period, and the timestamp of each positive animal detected by the interception point within the period.

[0085] The detection rate of the interception point within the cycle is calculated based on the total number of target animals inspected at the interception point within the cycle and the number of positive animals confirmed at the interception point within the cycle.

[0086] Each interception point is taken as a node in the topology network, the physical connection between passable areas is taken as the edge in the topology network, and the detection rate value of each node is arranged in chronological order to form a time series, and the detection timestamp of each positive animal is taken as the node's attribute data. A topology network structure with time attributes is constructed as the quarantine information sequence topology.

[0087] First, a fixed time interval is set as the preset control cycle, and the quarantine information record cache of all interception points is reset at the beginning of each preset control cycle. The preset control cycle is the time unit for quarantine data statistics and risk assessment, and can be set comprehensively based on the seasonal migration patterns of wild animals, the length of disease incubation periods, and the update frequency of quarantine resource allocation. For example, it can be set as a control cycle every 7 days or every 30 days. The cache is reset at the beginning of the cycle to ensure that the quarantine information of each cycle is counted independently and to avoid data confusion between different cycles.

[0088] Secondly, within the preset control period, quarantine information data in three dimensions is recorded for each interception point in the initial interception point distribution network. The first dimension is the total number of target animals inspected by the interception point within the period, i.e., the total number of times the interception point inspects all passing wild animals or transport vehicles within this control period, counted in units of first inspection or vehicle inspection. The second dimension is the number of positive animals confirmed by laboratory testing by the interception point within the period, i.e., the number of animals confirmed as positive after sampling and screening by laboratory polymerase chain reaction testing or rapid test strips, i.e., carrying the pathogen to be tested. The third dimension is the timestamp of each positive animal detected by the interception point within the period, i.e., the specific time point corresponding to each confirmed positive result, with accuracy down to the hour or minute.

[0089] Next, the detection rate of the interception point within the cycle is calculated based on the total number of target animals inspected and the number of positive animals confirmed by the interception point within the cycle. Specifically, the detection rate is calculated as follows: the detection rate equals the number of positive animals confirmed within the cycle divided by the total number of target animals inspected within the cycle. For example, if an interception point inspects 500 wild animals in a control cycle, and 5 of them are confirmed positive by laboratory testing, then the detection rate of that interception point is 2%, or 0.02. The detection rate reflects the interception efficiency of the interception point in intercepting the disease within this cycle. A higher detection rate indicates a greater risk of disease transmission to the area where the interception point is located, or a higher sensitivity of interception and detection.

[0090] Furthermore, each interception point is treated as a node in the topological network, and the physical connections between passable areas are represented as edges in the topological network. These physical connections refer to whether two passable areas are adjacent on the boundary of the disease-free zone, or whether they are connected by roads, forest trails, or waterways. Adjacent passable areas are connected by an edge in the topological network; non-adjacent areas are not directly connected. The detection rate values ​​of each node are arranged in chronological order to form a time series, along with the detection timestamp for each positive animal detected at each node, serving as the node's attribute data. The detection rate time series records the evolution trend of the detection rate at each interception point within different control cycles; for example, the detection rate was 1% in the first cycle, 2% in the second cycle, and 3% in the third cycle, showing an upward trend. The detection timestamp records the specific time of each positive animal detection at the interception point, which can be used to analyze the chronological order of detection events between different interception points.

[0091] By combining the information from the nodes and edges mentioned above, a topological network structure with temporal attributes can be constructed as the quarantine information sequence topology. This quarantine information sequence topology includes both spatial adjacency relationships and temporal changes in detection rates and the order of positive events, providing a structured data foundation for identifying subsequent positive transmission paths and risk transfer trends.

[0092] Furthermore, positive transmission routes and risk transfer trends were identified, including:

[0093] In the quarantine information sequence topology, for two interception points directly connected by a connecting edge, the interception point with the earlier detection timestamp is recorded as the source interception point, and the interception point with the later detection timestamp is recorded as the target interception point. The connecting edge between the source interception point and the target interception point is marked as the potential positive transmission direction edge.

[0094] The frequency of the potential positive propagation direction edge is counted within a series of preset control cycles. When the frequency exceeds a preset frequency threshold, it is determined that there is a stable positive propagation path from the source interception point to the target interception point, which is then used as the output result of the positive propagation path.

[0095] When the detection rate of a certain interception point shows a monotonically increasing trend within three consecutive preset control cycles, it is marked as a risk-rising node.

[0096] When the detection rate of an interception point shows a monotonically decreasing trend over three consecutive preset control cycles and the detection rate drops below the preset lower limit threshold, it is marked as a risk decline node.

[0097] The direction of change in the distribution area of ​​the risk-increasing nodes and the direction of change in the distribution area of ​​the risk-decreasing nodes are used as the output results of the risk transfer trend.

[0098] First, in the quarantine information sequence topology, for two interception points directly connected by a connecting edge, the interception point with the earlier detection timestamp is designated as the source interception point, and the interception point with the later detection timestamp is designated as the target interception point. The connecting edge between the source and target interception points is marked as the potential positive transmission direction edge. Specifically, the quarantine information sequence topology records the detection timestamp of each interception point when it detects a positive animal. For two spatially adjacent interception points, if the detection timestamp of interception point A is earlier than that of interception point B, and there is a passable road connecting the two interception points, then the disease may spread from the area where interception point A is located to the area where interception point B is located. Therefore, the interception point with the earlier detection is designated as the potential source of transmission, and the interception point with the later detection is designated as the potential target of transmission, and the transmission direction is marked on the edge between the two nodes. For example, if interception point A detects a positive animal on day 3 and interception point B detects a positive animal on day 5, then the edge direction is marked as from A to B.

[0099] Secondly, the frequency of potential positive transmission directions occurring within multiple consecutive pre-set control periods is statistically analyzed. When the frequency exceeds a pre-set frequency threshold, a stable positive transmission path from the source interception point to the target interception point is determined, and this is used as the output result of the positive transmission path. Specifically, the timing of a single detection may be random and insufficient to determine a stable transmission path. Therefore, it is necessary to statistically analyze whether the transmission direction is consistent within multiple consecutive control periods. The pre-set frequency threshold represents the minimum number of occurrences required to determine that a stable positive transmission direction exists between two interception points. It is set based on the confidence requirements for disease transmission direction determination in disease-free areas and the number of consecutive observation periods. For example, when observing for 3 consecutive control periods, the frequency threshold can be set to 2, meaning that a stable transmission path is determined when the same transmission direction occurs 2 or more times in 3 periods; when observing for 5 consecutive control periods, the frequency threshold can be set to 3, meaning that a stable transmission path is determined when the same transmission direction occurs 3 or more times in 5 periods.

[0100] For example, a continuous observation period of 30 days is set. If, within these 3 periods, the propagation direction from source interception point A to target interception point B occurs 2 or more times, exceeding a preset frequency threshold of 2 times, then a stable positive propagation path from A to B is determined to exist. This stable positive propagation path indicates that the disease is continuously spreading along this path, requiring focused prevention and control measures in subsequent operations.

[0101] Furthermore, when the detection rate at a certain interception point shows a monotonically increasing trend over three consecutive preset control periods, it is marked as a risk-increasing node. A monotonically increasing detection rate means that the detection rate in the current period is greater than the detection rate in the previous period, and the detection rate in the previous period is greater than the detection rates of the two periods prior; that is, the detection rates over the three periods form a strictly increasing sequence. For example, if the detection rate is 1% in the first period, 2% in the second period, and 3% in the third period, it conforms to a monotonically increasing trend. A continuously rising detection rate indicates that the risk of disease transmission to the area where the interception point is located is gradually increasing, requiring increased quarantine resource allocation or strengthened interception efforts.

[0102] Furthermore, when the detection rate at an interception point exhibits a monotonically decreasing trend over three consecutive preset control periods, and the detection rate falls below a preset lower threshold, it is marked as a risk reduction node. A monotonically decreasing trend means that the detection rate in the current period is lower than the detection rate in the previous period, and the detection rate in the previous period is lower than the detection rates of the two periods prior; that is, the detection rates across the three periods follow a strictly decreasing sequence. For example, if the detection rate is 3% in the first period, 2% in the second period, and 1% in the third period, it conforms to a monotonically decreasing trend. Simultaneously, the detection rate in the third period must also be lower than a preset lower threshold. This lower threshold represents the upper limit of the detection rate required to determine that the risk of disease transmission into the area where the interception point is located has been reduced to an acceptable level. It is set comprehensively based on the tolerance of disease-free areas for disease risk and the optimization goals of quarantine resources; for example, it can be set to 0.5%. A continuously decreasing detection rate, reaching an extremely low level, indicates that the risk of disease transmission into the area where the interception point is located has been significantly reduced, and quarantine resource allocation or interception frequency can be appropriately reduced.

[0103] Finally, the changing directions of the distribution areas of risk-increasing nodes and risk-decreasing nodes are used as the output of the risk transfer trend. Specifically, risk-increasing and risk-decreasing nodes are not randomly distributed spatially, but exhibit certain migration patterns. The changes in the distribution areas of risk-increasing nodes within the current control period compared to the previous period are statistically analyzed, including directional characteristics such as shifting from east to west, from north to south, or spreading outward from the central area. Similarly, the changing directions of the distribution areas of risk-decreasing nodes are statistically analyzed. The combined directions of both are then used to output the risk transfer trend.

[0104] For example, if the risk-increasing nodes are mainly concentrated on the western border of the disease-free zone, and the risk-decreasing nodes are mainly distributed on the eastern border, then the risk transfer trend indicates that the disease threat is moving westward, and defenses should be strengthened on the western border. The output of this risk transfer trend can provide a spatial decision-making basis for optimizing interception points in the next control cycle.

[0105] S40: Based on big data retrieval, obtain the current epidemic source distribution information, combine the quarantine information sequence topology, positive transmission path and risk transfer trend to optimize the deployment of quarantine interception points in the next control cycle, generate the optimal interception point distribution network, and execute the epidemic detection in the next control cycle.

[0106] Finally, after completing the monitoring of quarantine information and identification of transmission patterns for a pre-set control cycle, it is necessary to optimize and adjust the deployment plan of quarantine intercept points for the next control cycle. The initial intercept point distribution network is a static plan built based on historical epidemic source distribution information, while the distribution of epidemic sources itself changes dynamically over time, and the migration paths of wild animals and the direction of disease transmission are constantly evolving. If the initial plan is continued, there may be situations where the risk in the original high-risk areas has decreased but still occupies a large amount of resources, while new high-risk areas have weak interception capabilities due to insufficient resources. Therefore, this step integrates the current epidemic source distribution information, historical quarantine information, and identified transmission patterns to form a closed-loop dynamic optimization mechanism.

[0107] Specifically, based on big data retrieval to obtain current epidemic source distribution information, and combined with the quarantine information sequence topology, positive transmission paths, and risk transfer trends, the deployment of quarantine interception points in the next control cycle is optimized to generate an optimal interception point distribution network, including:

[0108] Based on big data retrieval, information on the current distribution of epidemic sources is obtained, and a heat map of the current risk of disease invasion is constructed.

[0109] Based on the current heat map of disease invasion risk, the topology of quarantine information sequence, positive transmission paths, and risk transfer trends, determine the objective function for deployment optimization.

[0110] Based on the objective function, and constrained by quarantine resources, an optimization algorithm is used to search the solution space of the candidate interception point distribution network to generate multiple candidate interception point distribution networks. The objective function is then numerically calculated for each candidate interception point distribution network, and the candidate interception point distribution network with the smallest objective function value is selected as the optimal interception point distribution network.

[0111] First, based on big data retrieval, current epidemic source distribution information is obtained to construct a heat map of current disease invasion risk. The current epidemic source distribution information is collected using the same methods as the initial stage, including obtaining the latest historical outbreak location data, host wildlife distribution data, real-time epidemic report data from surrounding areas, and environmental remote sensing data from multiple data sources. After spatiotemporal alignment and fusion processing, an epidemic source distribution layer covering the disease-free zone and surrounding areas is generated. Second, following the same risk assessment method, the disease invasion risk index of each passable area on the boundary of the disease-free zone is calculated, and then spatial interpolation is used to generate a heat map of disease invasion risk before the start of the current control period. This current disease invasion risk heat map reflects the static risk distribution up to the current moment.

[0112] Furthermore, based on the current heatmap of disease invasion risk, the topology of quarantine information sequences, positive transmission paths, and risk transfer trends, an objective function for deployment optimization is determined. The current heatmap of disease invasion risk provides static risk information based on the latest distribution of disease sources; the topology of quarantine information sequences provides interception effectiveness data for each interception point over historical periods, such as the detection rate time series; positive transmission paths provide specific channels for the spread of disease from high-risk areas to adjacent areas, indicating the directions that need to be focused on interception; and risk transfer trends provide the spatial migration direction of the overall disease risk, indicating the direction in which resources should follow the migration. The design of the objective function requires integrating the above multi-source information into a quantifiable evaluation index to measure the comprehensive quarantine effect of a candidate interception point distribution network.

[0113] Specifically, the steps to determine the objective function for layout optimization include:

[0114] The product of the risk index of each passable area in the current disease invasion risk heat map and the expected animal flow data is used as the expected loss value of the passable area when no interception point is set up.

[0115] The historical detection rate of each interception point in the quarantine information sequence topology is multiplied by the corresponding transmission impact factor to obtain the historical interception effectiveness value of the interception point. The calculation process for the transmission impact factor of each interception point includes: identifying all positive transmission paths in the quarantine information sequence topology that target the current interception point; for each identified positive transmission path, counting the frequency of its occurrence within multiple consecutive preset control cycles, and summing the frequencies of all identified positive transmission paths to obtain the total pointing frequency of the current interception point; adding one to the total pointing frequency and taking the logarithm to the base 10, using the resulting logarithmic value as the transmission impact factor of the current interception point; if the current interception point is not the target interception point of any positive transmission path, the total pointing frequency is zero, and the transmission impact factor is zero.

[0116] The dynamic risk adjustment coefficient is obtained by weighting and combining the rate of change of the number of risk-increasing nodes and the rate of change of the number of risk-decreasing nodes in the risk transfer trend.

[0117] Calculate a first sum, which is equal to the sum of the expected loss values ​​of all passable areas on the boundary of the disease-free zone;

[0118] Calculate the second sum, which is equal to the sum of the historical interception effectiveness values ​​of all proposed interception points;

[0119] Multiply the second sum by the dynamic risk adjustment coefficient to obtain the adjusted total interception effectiveness;

[0120] Subtracting the adjusted sum of interception effectiveness from the first sum yields the value of the objective function, wherein a smaller value of the objective function indicates a better quarantine effect of the corresponding candidate interception point distribution network.

[0121] The first step is to multiply the risk index of each passable area in the current disease invasion risk heatmap by the expected animal flow data. This product is then used as the expected loss value for the passable area without any interception points. The risk index reflects the probability of disease invasion through the passage, while the expected animal flow data reflects the scale of animal movement through the passage. The product of the two represents the expected number of positive animals that might flow into the passage if no interception points are set up. A higher expected loss value indicates a higher level of risk exposure for the passage without any safeguards.

[0122] The second step is to multiply the historical detection rate of each interception point in the quarantine information sequence topology with the corresponding transmission impact factor to obtain the historical interception effectiveness value of the interception point.

[0123] The historical detection rate reflects the actual ability of the interception point to intercept positive animals during past control cycles. The transmission impact factor quantifies the importance of the interception point in the disease transmission network. Its calculation process is as follows: First, identify all positive transmission paths in the quarantine information sequence topology that target the current interception point; these are stable transmission channels from other interception points to the current interception point. Second, for each identified positive transmission path, count the frequency of its occurrence over multiple consecutive preset control cycles, and sum the frequencies of all identified positive transmission paths to obtain the total frequency of the current interception point. The larger the total frequency of the current interception point, the more numerous and stable the positive transmission channels converging from different directions to the current interception point, and the stronger the pivotal role of the current interception point in blocking the spread of the disease. Then, add one to the total frequency of the current interception point and take the logarithm to the base 10; the resulting logarithmic value is used as the transmission impact factor of the current interception point. Using a logarithmic function can compress the order-of-magnitude differences in the total frequency of the current interception point, avoiding the transmission impact factor from excessively dominating the objective function in extremely high-value scenarios.

[0124] Furthermore, if the current intercept point is not the target intercept point for any positive transmission path, the sum of the pointing frequencies is zero, and the transmission impact factor is zero.

[0125] Specifically, the historical detection rate is multiplied by the transmission impact factor to obtain the historical interception effectiveness value. This historical interception effectiveness value is used to characterize the actual contribution of the interception point to blocking the positive transmission path within the historical control period. The larger the value, the better the historical performance of the interception point in intercepting positive animals, and the more critical the hub position in the disease transmission network.

[0126] The third step involves weighting and combining the rate of change in the number of risk-rising nodes and the rate of change in the number of risk-falling nodes in the risk transfer trend to obtain the dynamic risk adjustment coefficient. The rate of change in the number of risk-rising nodes equals the number of risk-rising nodes in the current period divided by the number of risk-rising nodes in the previous period, minus one; a positive value indicates an increase in the number of risk-rising nodes. The rate of change in the number of risk-falling nodes equals the number of risk-falling nodes in the current period divided by the number of risk-falling nodes in the previous period, minus one; a negative value indicates a decrease in the number of risk-falling nodes. The two rates are then weighted and summed according to preset weights, and then one is added to obtain the dynamic risk adjustment coefficient. The purpose of adding one is to ensure that the coefficient has a baseline value of one under stable epidemic conditions. When the number of risk-rising nodes increases and the number of risk-falling nodes decreases, the dynamic risk adjustment coefficient is greater than one, indicating that the epidemic is spreading and the weight of interception effectiveness needs to be increased; when the number of risk-rising nodes decreases and the number of risk-falling nodes increases, the dynamic risk adjustment coefficient is less than one, indicating that the epidemic is converging and the weight of interception effectiveness can be appropriately reduced.

[0127] The fourth step is to calculate the first sum, which equals the sum of the expected losses of all passable areas on the boundary of the disease-free zone. This first sum represents the total risk exposure faced by the entire boundary in the extreme case where no interception points are set up, and serves as the basic reference value for the objective function.

[0128] The fifth step is to calculate the second sum, which is equal to the sum of the historical interception effectiveness values ​​of all proposed interception points. This second sum represents the total interception capability that all interception points in the candidate interception point distribution network can provide.

[0129] The sixth step is to multiply the second sum by the dynamic risk adjustment coefficient to obtain the adjusted total interception effectiveness. The adjusted total interception effectiveness can dynamically adjust the weight of interception capabilities according to the development trend of the epidemic, amplifying the importance of interception capabilities when the epidemic is spreading and reducing the importance of interception capabilities when the epidemic is converging.

[0130] The seventh step is to subtract the adjusted sum of interception effectiveness from the first sum to obtain the value of the objective function. The objective function quantifies the net intrusion risk at the boundary of the disease-free zone after the deployment of interception points, i.e., the total expected loss minus the interception benefit. The smaller the value of the objective function, the more risk the corresponding candidate interception point distribution network reduces under the constraint of limited quarantine resources, the stronger its ability to reduce total risk, and the better the quarantine effect. Through the design of the above objective function, static disease intrusion risk, historical interception effectiveness data, and dynamic epidemic trends are uniformly incorporated into the optimization evaluation system, enabling the optimization algorithm to select the interception point deployment scheme that best suits the current epidemic situation amidst dynamic spatiotemporal changes.

[0131] Finally, based on the objective function and constrained by quarantine resources, an optimization algorithm is used to search the solution space of the candidate interception point distribution network, generating multiple candidate interception point distribution networks. The objective function is then numerically calculated for each candidate interception point distribution network, and the candidate interception point distribution network with the smallest objective function value is selected as the optimal interception point distribution network.

[0132] Specifically, the steps include:

[0133] Each passable area is encoded as an individual, including whether an interception point is set up and the type of the interception point. The individual uses a hybrid encoding method of binary vector and integer vector, where the binary vector represents whether an interception point is set up and the integer vector represents the interception point type number.

[0134] Initialize a population containing a preset number of individuals, each individual corresponding to a candidate interception point distribution network, and the total quarantine resources consumed by each individual do not exceed the upper limit of the total quarantine resource data.

[0135] The population is iteratively optimized using a genetic algorithm, with selection, crossover, and mutation operations performed in each generation to generate a new generation of population.

[0136] In each iteration, the objective function of the candidate interception point distribution network corresponding to each individual in the new generation population is calculated. When the number of iterations reaches the preset maximum number of iterations, the iteration stops.

[0137] All individuals in the current population at the time of stopping iteration are distributed as multiple candidate interception point distribution networks, and the candidate interception point distribution network corresponding to the individual with the smallest objective function value is taken as the optimal interception point distribution network.

[0138] The specific explanation is as follows:

[0139] First, each passable area is encoded as an individual, specifying whether an interception point is set up and the type of interception point. This individual uses a hybrid encoding method combining binary and integer vectors. Each bit in the binary vector corresponds to a passable area; a bit of 1 indicates an interception point is set up in that area, and 0 indicates no interception point is set up. Each integer value in the integer vector represents the type number of the corresponding interception point; for example, number 1 represents a fixed interception point, number 2 represents a semi-fixed interception point, number 3 represents a mobile patrol interception point, and number 0 represents no interception point. The binary and integer vectors work together: a 1 in the binary vector indicates that an interception point needs to be assigned to that area, while the integer vector specifies the specific type of interception point.

[0140] Secondly, the population is initialized. The population contains a preset number of individuals, which is set based on the total number of passable areas and the required optimization accuracy; for example, it can be set to 100. Each individual corresponds to a candidate interception point distribution network, which is the complete code of the interception point deployment scheme for all passable areas along the entire disease-free zone boundary. When randomly generating individuals, it is necessary to ensure that the total quarantine resources consumed by each individual do not exceed the upper limit of the total quarantine resource data. The total quarantine resource data includes the upper limit of the number of deployable interception points, the number of testing equipment sets that can be configured at each interception point, the number of quarantine personnel that can be configured at each interception point, and the maximum daily working hours of each interception point. Fixed interception points consume more resources, semi-fixed interception points consume less, and mobile patrol interception points consume the least. If the number of fixed interception points in an individual is too high, causing the total resource consumption to exceed the upper limit, the individual needs to be regenerated or corrected until the constraints are met.

[0141] Furthermore, a genetic algorithm is used to iteratively optimize the population. In each generation, selection, crossover, and mutation operations are performed to generate a new generation. The selection operation evaluates the fitness of each individual based on its objective function value; individuals with smaller objective function values ​​indicate better quarantine effectiveness of their corresponding interception point deployment scheme and higher fitness. Optionally, roulette wheel or tournament selection methods can be used to select individuals with high fitness from the current population as parents. The crossover operation, with a preset crossover probability (e.g., 0.8), randomly selects two individuals from the chosen parents, swaps portions of their encoding sequences, and generates two new offspring individuals. The mutation operation, with a preset mutation probability (e.g., 0.05), randomly flips or changes individual bits or integer values ​​in the encoding sequence of the offspring individuals to maintain population diversity and prevent the algorithm from prematurely getting trapped in local optima. Through these operations, a new generation population with the same size as the parent population is generated.

[0142] In each iteration, the objective function of the candidate interception point distribution network corresponding to each individual in the new generation population is calculated. Iteration stops when the preset maximum number of iterations is reached. The maximum number of iterations is determined by a combination of the number of passable regions on the boundary of the disease-free zone and the length of the optimization time window; for example, it can be set to 200 generations. If the number of passable regions is large, the complexity of the solution space is high, requiring an increase in the maximum number of iterations to ensure sufficient algorithm convergence; if the number of passable regions is small, the maximum number of iterations can be appropriately reduced to improve computational efficiency.

[0143] Finally, all individuals in the current population at the point of stopping iteration are distributed as multiple candidate interception point networks, and the candidate interception point network corresponding to the individual with the smallest objective function value is selected as the optimal interception point distribution network. The optimal interception point distribution network, optimized by the genetic algorithm, is the deployment scheme that minimizes net intrusion risk under quarantine resource constraints, and is used for disease detection in the next control cycle. Through iterative search using the genetic algorithm, an approximate optimal solution can be efficiently found in the vast solution space, avoiding the computational explosion problem caused by exhaustive enumeration.

[0144] Specifically, the obtained optimal interception point distribution network is a candidate scheme that minimizes the objective function value under quarantine resource constraints after iterative search using a genetic algorithm. It represents the optimal configuration result of interception point types in each passable area on the boundary of the epidemic-free zone within the next control cycle. This optimal interception point distribution network is set as the initial interception point distribution network for the next preset control cycle, used to execute a new round of disease detection tasks. Simultaneously, at the end of the next preset control cycle, the system again executes the steps of monitoring and acquiring quarantine information, generating quarantine information sequence topology, identifying positive transmission paths and risk transfer trends, and optimizing the deployment based on the current epidemic source distribution information and quarantine feedback. Repeating this process ensures that the interception point deployment scheme for each cycle is dynamically updated based on the quarantine feedback from the previous cycle and the latest epidemic source distribution information, forming a continuous learning, cycle-by-cycle convergence iterative optimization closed loop. Through this closed-loop mechanism, the interception point deployment strategy can continuously evolve with changes in the epidemic situation, the efficiency of quarantine resource allocation improves cycle by cycle, and the protective capabilities of the epidemic-free zone are continuously enhanced in long-term operation.

[0145] In summary, the embodiments of this application have at least the following technical effects:

[0146] This invention first utilizes big data retrieval technology to collect historical outbreak locations, host wildlife distribution, surrounding epidemic reports, and environmental remote sensing data from multiple data sources. This constructs an epidemic source distribution layer covering disease-free areas and surrounding regions. By combining the geometric paths of passable areas, the number of epidemic sources, the severity of the disease, and spatial connectivity, a refined quantitative assessment of the risk of disease invasion is achieved. Secondly, constrained by the total amount of quarantine resources, and based on the risk index and expected animal flow in each passable area in the disease invasion risk heatmap, a risk index threshold is dynamically set by ranking expected risk contribution values ​​and matching resource capacity. This enables differentiated resource allocation between all-weather fixed interception points in high-risk areas and semi-fixed or mobile patrol interception points in medium- and low-risk areas, improving the efficiency of quarantine resource utilization. Thirdly, by monitoring quarantine information such as the detection rate and positive detection timestamps of each interception point within a preset control period, a time-attributed quarantine information sequence topology is constructed. Based on the frequency and detection rate trends of positive transmission directions within continuous periods, stable positive transmission paths and risk transfer trends are identified.

[0147] Finally, by combining the current distribution information of the epidemic source with the dynamic feedback of historical quarantine information, the distribution of interception points in the next control cycle is optimized with the goal of achieving the best quarantine effect. This forms a closed-loop decision-making system from risk identification, resource allocation, execution monitoring to dynamic optimization, which solves the problem of the disconnect between traditional static deployment schemes and real-time risks.

[0148] Example 2, as Figure 3As shown, based on the same inventive concept as the method for deploying wildlife quarantine interception points in disease-free areas provided in Embodiment 1, this embodiment of the invention also provides a system for deploying wildlife quarantine interception points in disease-free areas, including:

[0149] Risk assessment module 11 is used to retrieve the distribution information of the source of the disease to be detected based on big data retrieval, conduct disease invasion risk assessment for several passable areas in the disease-free area, and construct a disease invasion risk heat map.

[0150] The initial deployment module 12 is used to initially deploy quarantine interception points based on the disease invasion risk heat map, with quarantine resource constraints as the limit, and generate an initial interception point distribution network for disease detection.

[0151] The monitoring and identification module 13 is used to monitor and acquire the quarantine information of the initial interception point distribution network within a preset control period, generate the quarantine information sequence topology, and identify the positive transmission path and risk transfer trend.

[0152] The optimization deployment module 14 is used to obtain the current epidemic source distribution information based on big data retrieval, and optimize the deployment of quarantine interception points in the next control cycle by combining the quarantine information sequence topology, positive transmission path and risk transfer trend, generate the optimal interception point distribution network, and perform epidemic detection in the next control cycle.

[0153] Specifically, the risk assessment module 11 is used for:

[0154] Specifically, based on big data retrieval, information on the distribution of disease sources to be detected is obtained. Disease invasion risk assessments are then conducted for several passable areas within disease-free zones, and a disease invasion risk heat map is constructed, including:

[0155] Data on historical outbreak locations of the disease to be detected, distribution data of host wild animals, real-time epidemic reports of surrounding areas, and environmental remote sensing data are collected from multiple data sources using big data retrieval technology. The multiple data sources are spatiotemporally aligned and fused to generate an epidemic source distribution layer covering the disease-free area and surrounding areas. The severity level of the disease at each epidemic source point is marked in the epidemic source distribution layer.

[0156] Each national highway, provincial highway, county road, township road, forest trail, and passable seasonal waterway on the boundary of the epidemic-free zone is treated as a passable area, and the geometric path and endpoint coordinates of each passable area are extracted.

[0157] Based on the number of epidemic foci along the path in each passable area, the severity level of the disease at each epidemic foci, and the spatial connectivity between the passable area and the epidemic foci, the disease invasion risk index of the passable area is calculated. The disease invasion risk index is positively correlated with the number of epidemic foci, the severity level of the disease, and the spatial connectivity.

[0158] Based on the disease invasion risk index of all passable areas, a heat map of disease invasion risk of continuously distributed disease-free zone boundaries is generated using spatial interpolation method. Each location point in the heat map corresponds to a risk index value.

[0159] The calculation steps for the spatial connectivity strength between passable areas and the source of the epidemic include:

[0160] For each passable area and each epidemic source point, the shortest path algorithm in graph theory is used to calculate the shortest travel distance between the geometric path centerline of the passable area and the epidemic source point. The reciprocal of the shortest travel distance is used as the spatial connectivity strength value between the passable area and the epidemic source point. The smaller the shortest travel distance, the larger the spatial connectivity strength value.

[0161] When the passable area directly passes through the area where the epidemic source is located, the shortest travel distance is set to a preset minimum positive threshold. When the passable area and the epidemic source are blocked by an insurmountable geographical barrier, the spatial connectivity strength value is set to zero.

[0162] The initial deployment module 12 is specifically used for:

[0163] Specifically, constrained by quarantine resources, the initial deployment of quarantine interception points is based on the aforementioned disease invasion risk heat map, generating an initial interception point distribution network for disease detection, including:

[0164] Obtain data on the total amount of quarantine resources available in disease-free areas. The total amount of quarantine resources includes the upper limit of the number of interception points that can be deployed, the number of sets of testing equipment that can be configured at each interception point, the number of quarantine personnel that can be configured at each interception point, and the maximum daily working hours of each interception point.

[0165] Based on the total quarantine resource data, a risk index threshold is set, and areas in the disease invasion risk heat map with a risk index greater than the risk index threshold are identified as a set of high-risk passable areas, while areas in the disease invasion risk heat map with a risk index less than the risk index threshold are identified as a set of medium- and low-risk passable areas.

[0166] Assign a fixed interception point to each passable area in the set of high-risk passable areas, and configure 24-hour monitoring resources at the fixed interception point;

[0167] The remaining quarantine resources are allocated to the passable areas in the set of medium- and low-risk passable areas in descending order of risk index. For each allocated passable area, a semi-fixed interception point, a mobile patrol interception point, or no fixed interception point is set up.

[0168] Among them, setting a risk index threshold based on the total quarantine resource data includes:

[0169] Obtain the expected animal flow data for each passable area within a unit of time, and calculate the expected risk contribution value of each passable area based on the product of the disease invasion risk index of each passable area and the expected animal flow data.

[0170] All passable areas are sorted in descending order of expected risk contribution value to generate a first candidate sequence. Passable areas are selected sequentially from the first candidate sequence, and the expected animal flow data of each selected passable area are accumulated to obtain the cumulative expected flow value.

[0171] Calculate the first product, which is equal to the maximum number of deployable interception points multiplied by the maximum daily working hours of each interception point;

[0172] Calculate the second product, which is equal to the first product multiplied by the number of quarantine personnel that can be configured at each interception point, and then multiplied by the average unit detection rate;

[0173] When the cumulative expected circulation value reaches or exceeds the second product for the first time, the selection of passable areas is stopped, and the disease invasion risk index corresponding to the last passable area selected at the time of stopping is set as the risk index threshold.

[0174] If the cumulative expected circulation value still does not reach the second product after all passable areas have been selected, the lowest disease invasion risk index in all passable areas is set as the risk index threshold.

[0175] The monitoring and identification module 13 is specifically used for:

[0176] Specifically, monitoring and acquiring quarantine information of the initial interception point distribution network within a preset control period, and generating a quarantine information sequence topology, including:

[0177] Set a fixed time interval as the preset control cycle, and reset the quarantine information record cache of all interception points at the beginning of each preset control cycle;

[0178] Within the preset control period, quarantine information data in three dimensions is recorded for each interception point in the initial interception point distribution network. The three dimensions include the total number of target animals inspected by the interception point within the period, the number of positive animals confirmed by laboratory testing by the interception point within the period, and the timestamp of each positive animal detected by the interception point within the period.

[0179] The detection rate of the interception point within the cycle is calculated based on the total number of target animals inspected at the interception point within the cycle and the number of positive animals confirmed at the interception point within the cycle.

[0180] Each interception point is taken as a node in the topology network, the physical connection between passable areas is taken as the edge in the topology network, and the detection rate value of each node is arranged in chronological order to form a time series, and the detection timestamp of each positive animal detected at each node is taken as the node's attribute data. A topology network structure with time attributes is constructed as the quarantine information sequence topology.

[0181] Furthermore, positive transmission routes and risk transfer trends were identified, including:

[0182] In the quarantine information sequence topology, for two interception points directly connected by a connecting edge, the interception point with the earlier detection timestamp is recorded as the source interception point, and the interception point with the later detection timestamp is recorded as the target interception point. The connecting edge between the source interception point and the target interception point is marked as the potential positive transmission direction edge.

[0183] The frequency of the potential positive propagation direction edge is counted within a series of preset control cycles. When the frequency exceeds a preset frequency threshold, it is determined that there is a stable positive propagation path from the source interception point to the target interception point, which is then used as the output result of the positive propagation path.

[0184] When the detection rate of a certain interception point shows a monotonically increasing trend within three consecutive preset control cycles, it is marked as a risk-rising node.

[0185] When the detection rate of an interception point shows a monotonically decreasing trend over three consecutive preset control cycles and the detection rate drops below the preset lower limit threshold, it is marked as a risk decline node.

[0186] The direction of change in the distribution area of ​​the risk-increasing nodes and the direction of change in the distribution area of ​​the risk-decreasing nodes are used as the output results of the risk transfer trend.

[0187] The optimized layout module 14 is specifically used for:

[0188] Specifically, based on big data retrieval to obtain current epidemic source distribution information, and combined with the quarantine information sequence topology, positive transmission paths, and risk transfer trends, the deployment of quarantine interception points in the next control cycle is optimized to generate an optimal interception point distribution network, including:

[0189] Based on big data retrieval, information on the current distribution of epidemic sources is obtained, and a heat map of the current risk of disease invasion is constructed.

[0190] Based on the current heat map of disease invasion risk, the topology of quarantine information sequence, positive transmission paths, and risk transfer trends, determine the objective function for deployment optimization.

[0191] Based on the objective function, and constrained by quarantine resources, an optimization algorithm is used to search the solution space of the candidate interception point distribution network to generate multiple candidate interception point distribution networks. The objective function is then numerically calculated for each candidate interception point distribution network, and the candidate interception point distribution network with the smallest objective function value is selected as the optimal interception point distribution network.

[0192] The steps for determining the objective function for layout optimization include:

[0193] The product of the risk index of each passable area in the current disease invasion risk heat map and the expected animal flow data is used as the expected loss value of the passable area when no interception point is set up.

[0194] The historical detection rate of each interception point in the quarantine information sequence topology is multiplied by the corresponding transmission impact factor to obtain the historical interception effectiveness value of the interception point. The calculation process for the transmission impact factor of each interception point includes: identifying all positive transmission paths in the quarantine information sequence topology that target the current interception point; for each identified positive transmission path, counting the frequency of its occurrence within multiple consecutive preset control cycles, and summing the frequencies of all identified positive transmission paths to obtain the total pointing frequency of the current interception point; adding one to the total pointing frequency and taking the logarithm to the base 10, using the resulting logarithmic value as the transmission impact factor of the current interception point; if the current interception point is not the target interception point of any positive transmission path, the total pointing frequency is zero, and the transmission impact factor is zero.

[0195] The dynamic risk adjustment coefficient is obtained by weighting and combining the rate of change of the number of risk-increasing nodes and the rate of change of the number of risk-decreasing nodes in the risk transfer trend.

[0196] Calculate a first sum, which is equal to the sum of the expected loss values ​​of all passable areas on the boundary of the disease-free zone;

[0197] Calculate the second sum, which is equal to the sum of the historical interception effectiveness values ​​of all proposed interception points;

[0198] Multiply the second sum by the dynamic risk adjustment coefficient to obtain the adjusted total interception effectiveness;

[0199] Subtracting the adjusted sum of interception effectiveness from the first sum yields the value of the objective function, wherein a smaller value of the objective function indicates a better quarantine effect of the corresponding candidate interception point distribution network.

[0200] Specifically, based on the objective function and constrained by quarantine resources, an optimization algorithm is used to search the solution space of the candidate interception point distribution network, generating multiple candidate interception point distribution networks. The objective function is then numerically calculated for each candidate interception point distribution network, including:

[0201] Each passable area is encoded as an individual, including whether an interception point is set up and the type of the interception point. The individual uses a hybrid encoding method of binary vector and integer vector, where the binary vector represents whether an interception point is set up and the integer vector represents the interception point type number.

[0202] Initialize a population containing a preset number of individuals, each individual corresponding to a candidate interception point distribution network, and the total quarantine resources consumed by each individual do not exceed the upper limit of the total quarantine resource data.

[0203] The population is iteratively optimized using a genetic algorithm, with selection, crossover, and mutation operations performed in each generation to generate a new generation of population.

[0204] In each iteration, the objective function of the candidate interception point distribution network corresponding to each individual in the new generation population is calculated. When the number of iterations reaches the preset maximum number of iterations, the iteration stops.

[0205] All individuals in the current population at the time of stopping iteration are distributed as multiple candidate interception point distribution networks, and the candidate interception point distribution network corresponding to the individual with the smallest objective function value is taken as the optimal interception point distribution network.

Claims

1. A method for setting up wildlife quarantine interception points in disease-free areas, characterized in that the method... include: Based on big data retrieval, information on the distribution of disease sources to be detected is obtained. Disease invasion risk assessments are conducted for several passable areas within disease-free zones, and a disease invasion risk heat map is constructed. With quarantine resources as a constraint, the initial deployment of quarantine interception points is carried out based on the aforementioned heat map of disease invasion risk, and an initial interception point distribution network is generated for disease detection. The system monitors and acquires quarantine information of the initial interception point distribution network within a preset control period, generates a quarantine information sequence topology, and identifies positive transmission paths and risk transfer trends. Based on big data retrieval, the current epidemic source distribution information is obtained. Combined with the quarantine information sequence topology, positive transmission path and risk transfer trend, the quarantine interception points in the next control cycle are deployed and optimized to generate the optimal interception point distribution network, and the epidemic detection in the next control cycle is performed.

2. The method for setting up wildlife quarantine interception points in disease-free areas according to claim 1, characterized in that, Based on big data retrieval, information on the distribution of disease sources to be detected is obtained. Disease invasion risk assessments are conducted for several passable areas within disease-free zones, and a disease invasion risk heat map is constructed, including: Data on historical outbreak locations of the disease to be detected, distribution data of host wild animals, real-time epidemic reports of surrounding areas, and environmental remote sensing data are collected from multiple data sources using big data retrieval technology. The multiple data sources are spatiotemporally aligned and fused to generate an epidemic source distribution layer covering the disease-free area and surrounding areas. The severity level of the disease at each epidemic source point is marked in the epidemic source distribution layer. Each national highway, provincial highway, county road, township road, forest trail, and passable seasonal waterway on the boundary of the epidemic-free zone is treated as a passable area, and the geometric path and endpoint coordinates of each passable area are extracted. Based on the number of epidemic foci along the path in each passable area, the severity level of the disease at each epidemic foci, and the spatial connectivity between the passable area and the epidemic foci, the disease invasion risk index of the passable area is calculated. The disease invasion risk index is positively correlated with the number of epidemic foci, the severity level of the disease, and the spatial connectivity. Based on the disease invasion risk index of all passable areas, a heat map of disease invasion risk of continuously distributed disease-free zone boundaries is generated using spatial interpolation method. Each location point in the heat map corresponds to a risk index value.

3. The method for setting up wildlife quarantine interception points in disease-free areas according to claim 2, characterized in that, The steps for calculating the spatial connectivity strength between passable areas and the source of the epidemic include: For each passable area and each epidemic source point, the shortest path algorithm in graph theory is used to calculate the shortest travel distance between the geometric path centerline of the passable area and the epidemic source point. The reciprocal of the shortest travel distance is used as the spatial connectivity strength value between the passable area and the epidemic source point. The smaller the shortest travel distance, the larger the spatial connectivity strength value. When the passable area directly passes through the area where the epidemic source is located, the shortest travel distance is set to a preset minimum positive threshold. When the passable area and the epidemic source are blocked by an insurmountable geographical barrier, the spatial connectivity strength value is set to zero.

4. The method for setting up wildlife quarantine interception points in disease-free areas according to claim 1, characterized in that, Constrained by quarantine resources, the initial deployment of quarantine interception points is based on the aforementioned disease invasion risk heat map, generating an initial interception point distribution network for disease detection, including: Obtain data on the total amount of quarantine resources available in disease-free areas. The total amount of quarantine resources includes the upper limit of the number of interception points that can be deployed, the number of sets of testing equipment that can be configured at each interception point, the number of quarantine personnel that can be configured at each interception point, and the maximum daily working hours of each interception point. Based on the total quarantine resource data, a risk index threshold is set, and areas in the disease invasion risk heat map with a risk index greater than the risk index threshold are identified as a set of high-risk passable areas, while areas in the disease invasion risk heat map with a risk index less than the risk index threshold are identified as a set of medium- and low-risk passable areas. Assign a fixed interception point to each passable area in the set of high-risk passable areas, and configure 24-hour monitoring resources at the fixed interception point; The remaining quarantine resources are allocated to the passable areas in the set of medium- and low-risk passable areas in descending order of risk index. For each allocated passable area, a semi-fixed interception point, a mobile patrol interception point, or no fixed interception point is set up. Among them, setting a risk index threshold based on the total quarantine resource data includes: Obtain the expected animal flow data for each passable area within a unit of time, and calculate the expected risk contribution value of each passable area based on the product of the disease invasion risk index of each passable area and the expected animal flow data. All passable areas are sorted in descending order of expected risk contribution value to generate a first candidate sequence. Passable areas are selected sequentially from the first candidate sequence, and the expected animal flow data of each selected passable area are accumulated to obtain the cumulative expected flow value. Calculate the first product, which is equal to the maximum number of deployable interception points multiplied by the maximum daily working hours of each interception point; Calculate the second product, which is equal to the first product multiplied by the number of quarantine personnel that can be configured at each interception point, and then multiplied by the average unit detection rate; When the cumulative expected circulation value reaches or exceeds the second product for the first time, the selection of passable areas is stopped, and the disease invasion risk index corresponding to the last passable area selected at the time of stopping is set as the risk index threshold. If the cumulative expected circulation value still does not reach the second product after all passable areas have been selected, the lowest disease invasion risk index in all passable areas is set as the risk index threshold.

5. The method for setting up wildlife quarantine interception points in disease-free areas according to claim 1, characterized in that, Monitoring and acquiring quarantine information of the initial interception point distribution network within a preset control period, generating a quarantine information sequence topology, including: Set a fixed time interval as the preset control cycle, and reset the quarantine information record cache of all interception points at the beginning of each preset control cycle; Within the preset control period, quarantine information data in three dimensions is recorded for each interception point in the initial interception point distribution network. The three dimensions include the total number of target animals inspected by the interception point within the period, the number of positive animals confirmed by laboratory testing by the interception point within the period, and the timestamp of each positive animal detected by the interception point within the period. The detection rate of the interception point within the cycle is calculated based on the total number of target animals inspected at the interception point within the cycle and the number of positive animals confirmed at the interception point within the cycle. Each interception point is taken as a node in the topology network, the physical connection between passable areas is taken as the edge in the topology network, and the detection rate value of each node is arranged in chronological order to form a time series, and the detection timestamp of each positive animal is taken as the node's attribute data. A topology network structure with time attributes is constructed as the quarantine information sequence topology.

6. The method for setting up wildlife quarantine interception points in disease-free areas according to claim 5, characterized in that, Identifying positive transmission routes and risk transfer trends, including: In the quarantine information sequence topology, for two interception points directly connected by a connecting edge, the interception point with the earlier detection timestamp is recorded as the source interception point, and the interception point with the later detection timestamp is recorded as the target interception point. The connecting edge between the source interception point and the target interception point is marked as the potential positive transmission direction edge. The frequency of the potential positive propagation direction edge is counted within a series of preset control cycles. When the frequency exceeds a preset frequency threshold, it is determined that there is a stable positive propagation path from the source interception point to the target interception point, which is then used as the output result of the positive propagation path. When the detection rate of a certain interception point shows a monotonically increasing trend within three consecutive preset control cycles, it is marked as a risk-rising node. When the detection rate of an interception point shows a monotonically decreasing trend over three consecutive preset control cycles and the detection rate drops below the preset lower limit threshold, it is marked as a risk decline node. The direction of change in the distribution area of ​​the risk-increasing nodes and the direction of change in the distribution area of ​​the risk-decreasing nodes are used as the output results of the risk transfer trend.

7. The method for setting up wildlife quarantine interception points in disease-free areas according to claim 1, characterized in that, Based on big data retrieval to obtain current epidemic source distribution information, and combined with the quarantine information sequence topology, positive transmission paths, and risk transfer trends, the deployment of quarantine interception points in the next control cycle is optimized to generate an optimal interception point distribution network, including: Based on big data retrieval, information on the current distribution of epidemic sources is obtained, and a heat map of the current risk of disease invasion is constructed. Based on the current heat map of disease invasion risk, the topology of quarantine information sequence, positive transmission paths, and risk transfer trends, determine the objective function for deployment optimization. Based on the objective function, and constrained by quarantine resources, an optimization algorithm is used to search the solution space of the candidate interception point distribution network to generate multiple candidate interception point distribution networks. The objective function is then numerically calculated for each candidate interception point distribution network, and the candidate interception point distribution network with the smallest objective function value is selected as the optimal interception point distribution network.

8. The method for setting up wildlife quarantine interception points in disease-free areas according to claim 7, characterized in that, The steps to determine the objective function for layout optimization include: The product of the risk index of each passable area in the current disease invasion risk heat map and the expected animal flow data is used as the expected loss value of the passable area when no interception point is set up. The historical detection rate of each interception point in the quarantine information sequence topology is multiplied by the corresponding transmission impact factor to obtain the historical interception effectiveness value of the interception point. The calculation process for the transmission impact factor of each interception point includes: identifying all positive transmission paths in the quarantine information sequence topology that target the current interception point; for each identified positive transmission path, counting the frequency of its occurrence within multiple consecutive preset control cycles, and summing the frequencies of all identified positive transmission paths to obtain the total pointing frequency of the current interception point; adding one to the total pointing frequency and taking the logarithm to the base 10, using the resulting logarithmic value as the transmission impact factor of the current interception point; if the current interception point is not the target interception point of any positive transmission path, the total pointing frequency is zero, and the transmission impact factor is zero. The dynamic risk adjustment coefficient is obtained by weighting and combining the rate of change of the number of risk-increasing nodes and the rate of change of the number of risk-decreasing nodes in the risk transfer trend. Calculate a first sum, which is equal to the sum of the expected loss values ​​of all passable areas on the boundary of the disease-free zone; Calculate the second sum, which is equal to the sum of the historical interception effectiveness values ​​of all proposed interception points; Multiply the second sum by the dynamic risk adjustment coefficient to obtain the adjusted total interception effectiveness; Subtracting the adjusted sum of interception effectiveness from the first sum yields the value of the objective function, wherein a smaller value of the objective function indicates a better quarantine effect of the corresponding candidate interception point distribution network.

9. The method for setting up wildlife quarantine interception points in disease-free areas according to claim 7, characterized in that, Based on the objective function, and constrained by quarantine resources, an optimization algorithm is used to search the solution space of the candidate interception point distribution network, generating multiple candidate interception point distribution networks. The objective function is then numerically calculated for each candidate interception point distribution network, including: Each passable area is encoded as an individual, including whether an interception point is set up and the type of the interception point. The individual uses a hybrid encoding method of binary vector and integer vector, where the binary vector represents whether an interception point is set up and the integer vector represents the interception point type number. Initialize a population containing a preset number of individuals, each individual corresponding to a candidate interception point distribution network, and the total quarantine resources consumed by each individual do not exceed the upper limit of the total quarantine resource data. The population is iteratively optimized using a genetic algorithm, with selection, crossover, and mutation operations performed in each generation to generate a new generation of population. In each iteration, the objective function of the candidate interception point distribution network corresponding to each individual in the new generation population is calculated. When the number of iterations reaches the preset maximum number of iterations, the iteration stops. All individuals in the current population at the time of stopping iteration are distributed as multiple candidate interception point distribution networks, and the candidate interception point distribution network corresponding to the individual with the smallest objective function value is taken as the optimal interception point distribution network.

10. A wildlife quarantine and interception point deployment system for disease-free areas, characterized in that, The method for deploying wildlife quarantine interception points in disease-free areas according to any one of claims 1-9 includes: The risk assessment module is used to retrieve the distribution information of the source of the disease to be detected based on big data retrieval, conduct disease invasion risk assessment for several passable areas in the disease-free zone, and construct a disease invasion risk heat map. The initial deployment module is used to initially deploy quarantine interception points based on the disease invasion risk heat map, with quarantine resource constraints as the limit, and generate an initial interception point distribution network for disease detection. The monitoring and identification module is used to monitor and acquire the quarantine information of the initial interception point distribution network within a preset control period, generate the quarantine information sequence topology, and identify the positive transmission path and risk transfer trend. The optimization deployment module is used to obtain the current epidemic source distribution information based on big data retrieval, and optimize the deployment of quarantine interception points in the next control cycle by combining the quarantine information sequence topology, positive transmission path and risk transfer trend, generate the optimal interception point distribution network, and perform epidemic detection in the next control cycle.

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