Port animal epidemic disease risk early warning system based on big data analysis

Abstract

The invention relates to the technical field of animal epidemic disease management, in particular to a port animal epidemic disease risk early warning system based on big data analysis, which comprises a regional evaluation unit for acquiring multi-region animal epidemic disease data of a supervision target source; based on the time data of the animal epidemic disease at the epidemic disease site, analyzing an infection risk index in combination with geographic image data; the infection evaluation unit is used for analyzing and calculating infection risk indexes of the epidemic disease point locations relative to the coordinates of the origin; when the infection risk index is smaller than or equal to a preset threshold value, the epidemic disease point location is removed; the source risk assessment unit is used for screening the epidemic disease point locations based on the epidemic disease types of the epidemic disease point locations and the species types of the supervision targets, recording the epidemic disease point locations reserved after screening as epidemic disease influence points, and calculating a source risk assessment value based on an infection risk index and an infection risk index; and the quarantine early warning unit is used for carrying out epidemic disease inspection when the source risk assessment value is greater than or equal to a preset source risk threshold value.

Description

Technical Field

[0001] This invention relates to the field of animal disease management technology, specifically to a port animal disease risk early warning system based on big data analysis. Background Technology

[0002] With the rapid development of global trade and the increasing frequency of people-to-people exchanges, the scale and speed of cross-border circulation of animals and animal products have reached unprecedented levels. While this has promoted economic development, it has also greatly increased the risk of the introduction and spread of foreign animal diseases. The cross-border spread of major animal diseases such as African swine fever, highly pathogenic avian influenza, and foot-and-mouth disease not only deals a devastating blow to the livestock industry and causes huge economic losses, but may also threaten food safety, public health security, and even ecological and environmental security.

[0003] Current technologies for preventing animal diseases mainly rely on on-site inspections, document review, and reporting of known outbreaks. However, they are not timely or forward-looking enough in responding to potential risks, emerging outbreaks, or dynamic changes in the epidemic situation at the source. Furthermore, the focus of disease prevention work is mainly concentrated in the final stage of cross-border circulation, making it difficult to reasonably assess the risk of animal disease infection in the early stages of cross-border circulation, such as before animals and animal products have left their place of origin. This leads to delays in disease prevention work and causes unnecessary economic losses. Summary of the Invention

[0004] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a port animal disease risk early warning system based on big data analysis, which can effectively solve the problem of lack of animal disease infection risk assessment in the early stage of cross-border circulation in the existing technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution: This invention provides a port animal disease risk early warning system based on big data analysis, comprising at least: The assessment unit by region records the imported animals or animal products under its supervision as the supervision targets, obtains animal disease data from multiple regions in the countries of origin of the supervision targets based on big data, and records the locations where animal diseases exist as disease sites; Based on the temporal data of animal diseases at disease sites, combined with geographic image data, the infection risk index of disease sites is analyzed. The infectious disease assessment unit analyzes and calculates the infectious disease risk index of each disease location relative to the coordinate origin based on the straight-line distance between each disease location and the coordinate origin, as well as traffic conditions, including the road width and route distance of the direct route between the disease location and the coordinate origin. When the infection risk index is less than or equal to a preset threshold, the location with the disease will be removed. The source risk assessment unit records the disease sites that have been screened and retained as disease impact points, and calculates the source risk assessment value based on the infection risk index and transmission risk index of each disease impact point. The quarantine early warning unit generates a source quarantine early warning signal and conducts disease inspection when the source risk assessment value is greater than or equal to the preset source risk threshold. The quarantine sequence is planned based on the infection risk index and the transmission risk index of each disease-affected point.

[0006] Furthermore, the infection risk index is calculated as follows: The time of first detection of the disease at an epidemic site is recorded as the discovery time, and the time of death of the first fatal case at the epidemic site is recorded as the moment of death. The infection risk index is calculated by combining the disease's disease cycle, discovery time, and current time. The infection risk index is used to indirectly assess the severity of animal disease outbreaks at the disease site. The calculation formula is as follows: ; in: Indicates the time of death, and T represents the disease cycle of the disease type corresponding to the disease location. Indicates the current time. Indicates the time of discovery. This indicates that there is no point of death. k represents the hazard level of the corresponding disease type, which is divided into Class I, Class II, and Class III diseases.

[0007] Furthermore, when multiple diseases exist at the same disease site, the infection risk index corresponding to each disease is calculated separately and summed to obtain the infection risk index corresponding to that disease site.

[0008] Furthermore, the calculation process for the infection risk index is as follows: A Cartesian coordinate system is constructed with the origin coordinates of the regulatory target as the origin, and multiple disease points are plotted in the Cartesian coordinate system; A single-point disease linear propagation model is constructed based on the Euclidean distance between the disease location and the origin of the coordinate system, and the linear propagation parameters are calculated through the single-point disease linear propagation model. A single-point disease transmission route model is constructed based on traffic hub data between the disease location and the coordinate origin, as well as road data of direct routes. The path transmission parameters are calculated using the single-point disease transmission route model. The linear propagation parameters and path propagation parameters of the same disease location are normalized and summed to calculate the infection risk index of that disease location.

[0009] Furthermore, the specific formula for the single-point linear transmission model of the disease is as follows: ; in: R(P1) represents the linear propagation parameters; d represents the Euclidean distance between the disease location and the origin of the coordinate system; λ is the preset attenuation coefficient.

[0010] Furthermore, the path propagation parameters are calculated as follows: In a Cartesian coordinate system, draw the smallest circular region passing through the epidemic location and the origin, denoted as the analysis region. Denote the diameter of the analysis region as the analysis diameter. Obtain all transportation hubs within the analysis region and calculate the average daily passenger flow for each hub, denoted as _____. Where n is the index of the transportation hub, and the distance of each transportation hub from the origin is denoted as . ; Obtain the shortest direct route connecting the epidemic sites and the origin within the analysis area. Obtain the total length of roads with different numbers of lanes along the shortest direct route. Divide the shortest direct route into multiple road segments according to the number of road lanes. Where m is the sequence number of the road segment passed through, the road segment passed through The corresponding number of lanes is denoted as The number of lanes is The total length of the route is denoted as ; When the analyzed diameter is less than or equal to a preset diameter threshold, a single-point disease transmission route model is constructed. The specific model formula is as follows: The path propagation parameters R(P2) are calculated, where All are preset baseline ratios; When the analysis diameter is greater than the preset diameter threshold, the path propagation parameter R(P2) is set to 0.

[0011] Furthermore, a natural barrier blocking coefficient is introduced, and the corrected linear propagation parameters are obtained by subtracting the natural barrier blocking coefficient from the linear propagation parameters. The calculation process of the natural barrier blocking coefficient is as follows: The natural barrier barrier coefficient is calculated by dividing the vegetation barrier coefficient, river barrier coefficient, and terrain barrier coefficient by the corresponding preset species threshold, converting them into dimensionless constants, and then multiplying them by the corresponding weight coefficients.

[0012] Furthermore, the processes for obtaining the vegetation barrier coefficient, river barrier coefficient, and topographic barrier coefficient are as follows: Draw elevation contour lines in a plane rectangular coordinate system, with the elevation difference between the contour lines being a preset value; Draw a dashed line representing the river in a Cartesian coordinate system. The thickness of the dashed line segment is proportional to the width of the river, and the ratio of the width of the dashed line segment to the width of the river is a preset value. In a Cartesian coordinate system, a grayscale region representing the vegetation cover area is drawn. The grayscale value of the grayscale region is proportional to the average vegetation thickness in the region. In a Cartesian coordinate system, a straight line connecting the disease location to the origin is drawn and denoted as a Euclidean line. The gray area traversed by the Euclidean line is denoted as the pathway region, and the pathway region is denoted as... Where j is the index of the region being traversed, the Euclidean line is placed within the traversed region. The length is denoted as , will pass through the area The corresponding grayscale value is denoted as ,pass The vegetation barrier coefficient is calculated using the formula. Find the intersection points of the Euclidean straight line and the dashed river line, and calculate the sum of the dashed river line widths corresponding to all intersection points. This sum is denoted as the river barrier coefficient. The number of contour lines intersecting the Euclidean straight line is recorded as the terrain barrier coefficient. .

[0013] Furthermore, the quarantine sequence planning process is as follows: For each disease-affected point, an affected disease set is constructed, which includes multiple confirmed disease types. Confirmed disease types refer to the types of diseases for which there are currently infected cases at the disease-affected point. Based on the infection risk index and transmission risk index corresponding to the disease-affected point, the quarantine priority value corresponding to each disease-affected point is calculated, and the priority order of each affected disease set is determined according to the size of the quarantine priority value. For any confirmed disease type within an affected disease set, each type is tested individually. The testing order for confirmed disease types within the same affected disease set is arranged according to the order of Class I, Class II, and Class III diseases. Duplicate confirmed disease types in different affected disease sets are tested only once. in: The quarantine priority value is obtained by multiplying the infection risk index and the transmission risk index by the corresponding priority weight coefficients, and the priority weight coefficients are preset values.

[0014] Furthermore, the process of screening disease sites is as follows: Obtain the types of animal diseases that the regulatory target is susceptible to and construct a target susceptibility set. Obtain all types of diseases currently existing at disease sites and construct an outbreak disease set. When the intersection of the outbreak disease set corresponding to a disease site and the target susceptibility set is an empty set, remove the corresponding disease site.

[0015] The technical solution provided by this invention has the following advantages compared with the known prior art: This invention can comprehensively assess the risk of animal disease infection at the regulated target by analyzing animal disease outbreak data from multiple outbreak sites in the source region of the regulated target. The comprehensive assessment includes the danger level of surrounding animal diseases and the risk of transmission of surrounding animal diseases. This allows the calculated source risk assessment value to reflect whether the regulated target has a risk of disease infection, helping staff to take appropriate quarantine and prevention measures in a timely manner. In addition, by clarifying the quarantine priority of different disease-affected points and the testing order of confirmed disease types within the same disease-affected point, the invention can rationally plan the quarantine process and the order of quarantine operations when a risk of disease infection is found at the regulated target, thereby improving the detection efficiency of animal diseases and minimizing the adverse effects of animal disease infection at the regulated target. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0017] Figure 1 This is an overall module block diagram of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0019] The present invention will be further described below with reference to embodiments.

[0020] See Figure 1 A port animal disease risk early warning system based on big data analysis analyzes the disease risk of imported animals and animal products from their country of origin, generates timely animal disease risk warnings, and takes corresponding disease prevention and control measures, such as mass culling or centralized destruction, including at least: The local assessment unit records the imported animals or animal products under its supervision as the supervision targets, obtains the origin coordinates of the supervision targets, that is, the coordinates of the farms or ranches to which the imported animals or animal products belong, and calculates the infection risk index of each locality based on the animal disease situation in the country to which the origin coordinates belong. in: The country of origin is marked as the country of origin, and a geographical image of the country of origin is obtained. Animal disease data of the country of origin is extracted based on big data. The animal disease data includes the type of disease, the coordinates of occurrence, the time of discovery, and the number of cases. The infection risk index of multiple locations in the country of origin is analyzed in combination with the geographical image data.

[0021] It should be noted that the steps for obtaining animal disease data from the source country based on big data include, but are not limited to, collecting official disease reports from the importing country, data from the World Organisation for Animal Health (OIE), and satellite remote sensing data (such as climate and geographic information).

[0022] Specifically, the infection risk index is calculated as follows: The time of first detection of the disease at an epidemic site is recorded as the discovery time, and the time of death of the first fatal case at the epidemic site is recorded as the moment of death. The infection risk index is calculated by combining the disease's disease cycle, discovery time, and current time. The infection risk index is used to indirectly assess the severity of animal disease outbreaks at the disease site. The calculation formula is as follows: ,in Indicates the time of death, and T represents the disease cycle of the disease type corresponding to the disease location. Indicates the current time. Indicates the time of discovery. This indicates that there is no fatal moment (i.e., no fatal cases have been found). k is the hazard category level of the corresponding disease type, reflecting the degree of harm of animal diseases to livestock production and human health (refer to the animal disease classification in the Animal Epidemic Prevention Law, specifically Class I, Class II, and Class III diseases, i.e., k=1, 2, 3). It should be noted that a higher infection risk index means that the scale of the animal disease outbreak at that location is larger, the harm caused by the animal disease is greater, and the resulting infection risk is greater. Generally speaking, if a large-scale animal disease infection occurs in an area, the probability of indirect or route infection to animals in the surrounding area will increase accordingly, thus increasing the risk of animal disease infection in neighboring areas.

[0023] When multiple diseases exist at the same disease site, the infection risk index corresponding to each disease is calculated separately and summed to obtain the infection risk index corresponding to that disease site.

[0024] The infectious disease assessment unit analyzes the infectious disease risk index of each disease location relative to the origin coordinates based on the straight-line distance between each disease location and the coordinate origin, as well as traffic conditions, including the road width and route distance of the direct route between the disease location and the coordinate origin. When the infectious disease risk index is less than or equal to a preset threshold, the disease location is removed. Specifically, the calculation process for the infection risk index is as follows: A Cartesian coordinate system is constructed with the origin coordinates as the origin. Multiple disease occurrence coordinates are plotted within this system, and disease types are labeled. The types of animal diseases susceptible to the target are obtained, and a target susceptibility set is constructed. All currently existing disease types at each disease occurrence point are obtained, and a disease occurrence set is constructed. When the intersection of the disease occurrence set and the target susceptibility set is empty, the corresponding disease occurrence point is removed. Different animals are susceptible to different animal diseases, and the animal diseases occurring at a disease occurrence point may not be within the target's infection catalog. Therefore, disease occurrence points unrelated to the target (meaning the animal diseases occurring at that point are not infectious to the target) need to be removed. By removing irrelevant disease information, data cleaning is achieved, enabling targeted disease risk assessment of the target. Construct a single-point linear transmission model for the disease; the specific formula for the model is as follows: The linear propagation parameter R(P1) is calculated, where d represents the Euclidean distance (i.e., straight-line distance) between the disease location and the origin of the coordinate system, and λ is a preset attenuation coefficient (taken as 0.2 / km in a specific embodiment). It should be noted that the linear propagation parameter reflects the direct impact of the disease outbreak location on the coordinates of the place of origin. This impact comes from the natural transmission route of the disease (usually the transmission route of wild animals carrying the virus in the natural environment or the airborne transmission route, which are closely related to Euclidean distance). When the linear propagation parameter is larger, it means that after the disease breaks out at the outbreak location, the risk of transmission and infection to animals at the coordinates of the place of origin is higher.

[0025] Furthermore, a natural barrier resistance coefficient is introduced, and the linear propagation parameters are subtracted from the natural barrier resistance coefficient to correct the model output. The calculation process of the natural barrier resistance coefficient is as follows: Elevation contour lines are drawn in a Cartesian coordinate system. The elevation difference between contour lines is a preset value (in a specific embodiment, this value is 50m, meaning the elevation difference between two adjacent contour lines is 50m). Dashed river lines are drawn in the Cartesian coordinate system to represent the river's flow direction and width. The thickness of the dashed line segment is proportional to the river's width, and the ratio of the dashed line segment's width to the river's width is a preset value. The river's width can be calculated from the thickness of the dashed line segment. Grayscale regions with different grayscale values ​​are drawn in the Cartesian coordinate system. The grayscale value of a grayscale region is proportional to the average vegetation thickness within that region, thus distinguishing regions with different vegetation thicknesses. In a Cartesian coordinate system, a straight line connecting the disease location to the origin is drawn and denoted as a Euclidean line. The gray area traversed by the Euclidean line is denoted as the pathway region, and the pathway region is denoted as... Where j is the index of the region being traversed, the Euclidean line is placed within the traversed region. The length is denoted as , will pass through the area The corresponding grayscale value is denoted as ,pass The vegetation barrier coefficient is calculated using the formula. Find the intersection points of the Euclidean straight line and the dashed river line, and calculate the sum of the dashed river line widths corresponding to all intersection points. This sum is denoted as the river barrier coefficient. The number of contour lines intersecting the Euclidean straight line is recorded as the terrain barrier coefficient. ; The natural barrier barrier coefficient is calculated by dividing the vegetation barrier coefficient, river barrier coefficient, and terrain barrier coefficient by the corresponding preset species threshold, converting them into dimensionless constants, and then multiplying them by the corresponding weight coefficients.

[0026] It should be noted that the natural barrier barrier coefficient is used to correct the linear propagation impact parameter, thereby further accurately assessing the risk index of natural transmission infection based on the geographical conditions between two points when outputting the linear propagation impact parameter. This value can introduce the natural barrier impact scenario on the basis of relying solely on Euclidean distance for natural transmission risk assessment, especially hills, vegetation and rivers that hinder the spread of the disease. These natural environmental factors will greatly increase the difficulty of the disease spreading through natural routes.

[0027] In a Cartesian coordinate system, draw the smallest circular region passing through the epidemic location and the origin, denoted as the analysis region. Denote the diameter of the analysis region as the analysis diameter. Obtain all transportation hubs within the analysis region and calculate the average daily passenger flow for each hub, denoted as _____. Where n is the index of the transportation hub, which refers to passenger flow points such as high-speed rail stations, bus stations, and train stations. The distance of each transportation hub from the origin is denoted as . Obtain the shortest direct route connecting the epidemic site and the origin within the analysis area. Calculate the length of roads at each level along the shortest direct route, where road level equals the number of lanes. Divide the shortest direct route into multiple segments according to road level. Where m is the sequence number of the road segment passed through, the road segment passed through The corresponding number of lanes is denoted as The number of lanes is The total length of the route is denoted as (The lengths of road segments with the same number of lanes are calculated together). When the analyzed diameter is less than or equal to a preset diameter threshold, a single-point disease transmission route model is constructed. The specific model formula is as follows: The path propagation parameters R(P2) are calculated, where All are preset benchmark ratios used to convert the formula output results into dimensionless values. When the analysis diameter is greater than the preset diameter threshold (at this time, the distance between the two points is too far, and the possibility of propagation through traffic activities is extremely low and can be ignored), the path propagation parameter R(P2) is set to 0. The linear propagation parameters and path propagation parameters of the same disease location are normalized and summed to calculate the infection risk index of that disease location.

[0028] The source risk assessment unit records the disease sites retained after screening as disease impact points, obtains the infection risk index and transmission risk index corresponding to each disease impact point, and multiplies them to obtain the single-point risk value of each disease impact point. The sum of the single-point risk values ​​of multiple disease impact points is calculated to obtain the source risk assessment value (the source risk assessment value is obtained by superimposing the single-point risk values ​​of multiple disease impact points, thus comprehensively reflecting the degree of infection impact risk of all disease impact points on the origin of the regulated target).

[0029] It should be noted that the calculation of the source risk index is influenced by the distribution and incidence of diseases around the place of origin. It can comprehensively reflect the degree of risk of the regulated target being affected by diseases in its place of origin. The higher the source risk assessment value, the greater the impact of diseases in its place of origin on the regulated target. The dependent variables of the source risk index include the scale of disease infection around the coordinates of the place of origin, the risk level of disease infection, and the distance of the disease occurrence location. When any one or more of these values ​​cause an increase in the source risk index, it means that the disease risk in the place of origin of the regulated target has increased.

[0030] The quarantine early warning unit generates a source quarantine early warning signal when the source risk assessment value is greater than or equal to the preset source risk threshold. This means that the monitored target has a risk of infection from diseases in the vicinity of the place of origin, so epidemic prevention inspection is required. Through a series of epidemic prevention inspection measures, the potential disease infection of the monitored target can be discovered, such as isolation and observation, biological sampling and infrared analysis, so as to prevent the outflow or inflow of diseased targets at the source.

[0031] Furthermore, an affected disease set is constructed for each affected point. The affected disease set includes multiple confirmed disease types, which refer to the types of diseases for which there are currently infected cases at the affected point. Based on the infection risk index and the transmission risk index corresponding to the affected point, the quarantine priority value corresponding to each affected point is calculated. The quarantine priority value is obtained by multiplying the infection risk index and the transmission risk index by the corresponding priority weight coefficients and summing them. The priority weight coefficients are preset values. The priority order of each affected disease set is determined according to the quarantine priority value (from largest to smallest). Confirmed disease types in any affected disease set are tested one by one. The testing order of confirmed disease types in the same affected disease set is sorted according to the order of Class I, Class II, and Class III diseases. Confirmed disease types that are repeated in different affected disease sets are tested only once.

[0032] It is important to note that calculating quarantine priority values ​​firstly allows us to determine the relative importance of different disease-affected sites to the monitored targets. A higher priority value indicates a greater risk of infection to the monitored targets from that site, necessitating priority testing for confirmed diseases within that site. This improves the detection efficiency of potential diseases. Furthermore, the testing order for confirmed diseases within the same affected site is based on their hazard level, helping quarantine personnel identify potentially more dangerous diseases earlier and reducing the risk of major animal disease outbreaks. By clearly defining the quarantine priorities for different disease-affected sites and the testing order for confirmed diseases within the same affected site, we can rationally plan quarantine procedures and operational sequences when a risk of disease infection is detected at the monitored targets, improving the detection efficiency of animal diseases and minimizing the adverse effects of animal disease infection at the monitored targets.

[0033] It is worth noting that this invention can not only conduct origin disease infection risk assessment for imported regulatory targets, but also be applied to disease infection risk assessment during export. In other words, the early warning system in this invention can be applied to both imported animal disease prevention and control and exported animal disease prevention and control.

[0034] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the above method.

[0035] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method.

[0036] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present invention.

Claims

1. A port animal disease risk early warning system based on big data analysis, characterized in that, include: The assessment unit by region records the imported animals or animal products under its supervision as the supervision targets, obtains animal disease data from multiple regions in the countries of origin of the supervision targets based on big data, and records the locations where animal diseases exist as disease sites; Based on the temporal data of animal diseases at disease sites, combined with geographic image data, the infection risk index of disease sites is analyzed. The infectious disease assessment unit analyzes and calculates the infectious disease risk index of each disease location relative to the coordinate origin based on the straight-line distance between each disease location and the coordinate origin, as well as traffic conditions, including the road width and route distance of the direct route between the disease location and the coordinate origin. When the infection risk index is less than or equal to a preset threshold, the location with the disease will be removed. The source risk assessment unit screens disease sites based on the types of diseases and species types of regulatory targets. The disease sites that are retained after screening are recorded as disease impact points. The source risk assessment value is calculated based on the infection risk index and transmission risk index of each disease impact point. The quarantine early warning unit generates a source quarantine early warning signal and conducts disease inspection when the source risk assessment value is greater than or equal to the preset source risk threshold. The quarantine sequence is planned based on the infection risk index and the transmission risk index of each disease-affected point.

2. The port animal disease risk early warning system based on big data analysis according to claim 1, characterized in that, The infection risk index is calculated as follows: The time of first detection of the disease at an epidemic site is recorded as the discovery time, and the time of death of the first fatal case at the epidemic site is recorded as the moment of death. The infection risk index is calculated by combining the disease's disease cycle, discovery time, and current time. The infection risk index is used to indirectly assess the severity of animal disease outbreaks at the disease site. The calculation formula is as follows: ; in: Indicates the time of death, and T represents the disease cycle of the disease type corresponding to the disease location. Indicates the current time. Indicates the time of discovery. This indicates that there is no point of death. k represents the hazard level of the corresponding disease type, which is divided into Class I, Class II, and Class III diseases.

3. The port animal disease risk early warning system based on big data analysis according to claim 2, characterized in that, When multiple diseases exist at the same disease site, the infection risk index corresponding to each disease is calculated separately and summed to obtain the infection risk index corresponding to that disease site.

4. The port animal disease risk early warning system based on big data analysis according to claim 1, characterized in that, The calculation process for the infection risk index is as follows: A Cartesian coordinate system is constructed with the origin coordinates of the regulatory target as the origin, and multiple disease points are plotted in the Cartesian coordinate system; A single-point disease linear propagation model is constructed based on the Euclidean distance between the disease location and the origin of the coordinate system, and the linear propagation parameters are calculated through the single-point disease linear propagation model. A single-point disease transmission route model is constructed based on traffic hub data between the disease location and the coordinate origin, as well as road data of direct routes. The path transmission parameters are calculated using the single-point disease transmission route model. The linear propagation parameters and path propagation parameters of the same disease location are normalized and summed to calculate the infection risk index of that disease location.

5. The port animal disease risk early warning system based on big data analysis according to claim 4, characterized in that, The specific formula for the single-point linear transmission model of an epidemic is as follows: ； in: R(P1) represents the linear propagation parameters; d represents the Euclidean distance between the disease location and the origin of the coordinate system; λ is the preset attenuation coefficient.

6. The port animal disease risk early warning system based on big data analysis according to claim 5, characterized in that, The path propagation parameters are calculated as follows: In a Cartesian coordinate system, draw the smallest circular region passing through the epidemic location and the origin, denoted as the analysis region. Denote the diameter of the analysis region as the analysis diameter. Obtain all transportation hubs within the analysis region and calculate the average daily passenger flow for each hub, denoted as _____. Where n is the index of the transportation hub, and the distance of each transportation hub from the origin is denoted as . ; Obtain the shortest direct route connecting the epidemic sites and the origin within the analysis area. Obtain the total length of roads with different numbers of lanes along the shortest direct route. Divide the shortest direct route into multiple road segments according to the number of road lanes. Where m is the sequence number of the road segment passed through, the road segment passed through The corresponding number of lanes is denoted as The number of lanes is The total length of the route is denoted as ; When the analyzed diameter is less than or equal to a preset diameter threshold, a single-point disease transmission route model is constructed. The specific model formula is as follows: The path propagation parameters R(P2) are calculated, where All are preset baseline ratios; When the analysis diameter is greater than the preset diameter threshold, the path propagation parameter R(P2) is set to 0.

7. The port animal disease risk early warning system based on big data analysis according to claim 5, characterized in that, By introducing a natural barrier blocking coefficient, the corrected linear propagation parameters are obtained by subtracting the natural barrier blocking coefficient from the linear propagation parameters. The calculation process for the natural barrier blocking coefficient is as follows: The natural barrier barrier coefficient is calculated by dividing the vegetation barrier coefficient, river barrier coefficient, and terrain barrier coefficient by the corresponding preset species threshold, converting them into dimensionless constants, and then multiplying them by the corresponding weight coefficients.

8. The port animal disease risk early warning system based on big data analysis according to claim 7, characterized in that, The process for obtaining the vegetation barrier coefficient, river barrier coefficient, and topographic barrier coefficient is as follows: Draw elevation contour lines in a plane rectangular coordinate system, with the elevation difference between the contour lines being a preset value; Draw a dashed line representing the river in a Cartesian coordinate system. The thickness of the dashed line segment is proportional to the width of the river, and the ratio of the width of the dashed line segment to the width of the river is a preset value. In a Cartesian coordinate system, a grayscale region representing the vegetation cover area is drawn. The grayscale value of the grayscale region is proportional to the average vegetation thickness in the region. In a Cartesian coordinate system, a straight line connecting the disease location to the origin is drawn and denoted as a Euclidean line. The gray area traversed by the Euclidean line is denoted as the pathway region, and the pathway region is denoted as... Where j is the index of the region being traversed, the Euclidean line is placed within the traversed region. The length is denoted as , will pass through the area The corresponding grayscale value is denoted as ,pass The vegetation barrier coefficient is calculated using the formula. Find the intersection points of the Euclidean straight line and the dashed river line, and calculate the sum of the dashed river line widths corresponding to all intersection points. This sum is denoted as the river barrier coefficient. The number of contour lines intersecting the Euclidean straight line is recorded as the terrain barrier coefficient. .

9. The port animal disease risk early warning system based on big data analysis according to claim 1, characterized in that, The quarantine sequence planning process is as follows: For each disease-affected point, an affected disease set is constructed, which includes multiple confirmed disease types. Confirmed disease types refer to the types of diseases for which there are currently infected cases at the disease-affected point. Based on the infection risk index and transmission risk index corresponding to the disease-affected point, the quarantine priority value corresponding to each disease-affected point is calculated, and the priority order of each affected disease set is determined according to the size of the quarantine priority value. For any confirmed disease type within an affected disease set, each type is tested individually. The testing order for confirmed disease types within the same affected disease set is arranged according to the order of Class I, Class II, and Class III diseases. Duplicate confirmed disease types in different affected disease sets are tested only once. in: The quarantine priority value is obtained by multiplying the infection risk index and the transmission risk index by the corresponding priority weight coefficients, and the priority weight coefficients are preset values.

10. The port animal disease risk early warning system based on big data analysis according to claim 4, characterized in that, The process of screening disease sites is as follows: Obtain the types of animal diseases that the regulatory target is susceptible to and construct a target susceptibility set. Obtain all types of diseases currently existing at disease sites and construct an outbreak disease set. When the intersection of the outbreak disease set corresponding to a disease site and the target susceptibility set is an empty set, remove the corresponding disease site.

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