An animal disease prevention and control system based on intelligent AI

Abstract

The present invention discloses an animal disease prevention and control system based on intelligent AI, which relates to the technical field of disease prevention and control. By acquiring the corresponding animal medical treatment information in each animal hospital, the animal medical treatment information of animal hospitals distributed in different regions is summarized, and then the occurrence of various animal diseases is statistically analyzed based on the summarized information. When it is found that the number of a certain animal disease type exceeds a preset range, the number of the animal disease type at the corresponding monitoring node is further refined, and a secondary analysis is performed to determine whether there is an outbreak risk. If there is an outbreak risk, a corresponding monitoring range is set according to the monitoring node generated by the animal disease type, and a disease prevention and control point is set within the monitoring range, thereby achieving targeted and effective prevention and control of animal diseases.

Description

Technical Field

[0001] The present invention relates to the technical field of disease prevention and control, and specifically to an animal disease prevention and control system based on intelligent AI. Background Art

[0002] Epidemic disease is an epidemic infectious disease with strong infectivity. Under certain etiologies, the disorder of self-stabilization regulation leads to abnormal life activities, which triggers a series of metabolic, functional and structural changes. Normal life activities are restricted or destroyed. The outcome can be recovery or long-term survival, or even death. Epidemics are often transmitted from animals to humans. Therefore, the early warning equipment for epidemic transmission monitors objects that may cause transmission before the epidemic occurs, playing a preventive warning role.

[0003] Animal clinics or animal hospitals distributed in various regions usually come into contact with a large number of animals with diseases. How to use the animal medical information of these animal clinics or hospitals to realize timely monitoring and prevention of whether there is a risk of outbreak of animal diseases is a problem we need to solve. To this end, we now provide an animal disease prevention and control system based on intelligent AI. Summary of the invention

[0004] The purpose of the present invention is to provide an animal disease prevention and control system based on intelligent AI.

[0005] The object of the present invention can be achieved by the following technical solutions: an animal disease prevention and control system based on intelligent AI, comprising a monitoring center, wherein the monitoring center comprises an information input module, an information processing module, an information analysis module and a prevention and control strategy formulation module;

[0006] The information input module includes a registration unit and an information input unit, through which the basic information of each monitoring node is registered; the information input unit is used to input the animal medical information of the monitoring node, and the animal medical information includes animal disease type, medical start time type and medical end time;

[0007] The information processing module is used to process the animal medical information recorded by each monitoring node to obtain the outbreak risk coefficient of the animal disease type;

[0008] The information analysis module is used to determine whether there is an outbreak risk of the corresponding animal disease according to the outbreak risk coefficient of the corresponding animal disease of each monitoring node obtained, and generate a corresponding strategy formulation instruction;

[0009] The prevention and control strategy formulation module is used to formulate corresponding animal disease prevention and control strategies according to the obtained strategy formulation instructions.

[0010] Furthermore, the process of registering the monitoring node includes:

[0011] Enter the basic information of the monitoring node into the registration unit, and upload the entered basic information of the monitoring node to the monitoring center, which will review the basic information of the monitoring node; it should be noted that the basic information of the monitoring node includes the name, geographical location and person in charge information, and the person in charge information includes the name, age, gender and real-name authenticated mobile phone number;

[0012] After the review is passed, the corresponding login account and login password are generated and sent to the person in charge of the monitoring node;

[0013] After the person in charge of the monitoring node inputs the obtained login account and login password into the information entry unit, the information entry authority of the information entry unit is activated.

[0014] Furthermore, the information processing module is used to process the animal medical information recorded by each monitoring node, and the process of obtaining the outbreak risk coefficient of the animal disease type includes:

[0015] Generate a corresponding animal information set based on the animal disease type entered by each monitoring node, and aggregate the corresponding animal medical information into the animal information set;

[0016] According to the animal disease types in each animal information set, a corresponding two-dimensional coordinate system of time and quantity is established;

[0017] Set the statistical period T, and generate corresponding statistical nodes in the two-dimensional coordinate system according to the statistical period, obtain the number of animal diseases in each statistical period, record it as K, and map it to the corresponding statistical node;

[0018] A first disease quantity threshold K0 is set, and a corresponding disease quantity threshold line is set in the two-dimensional coordinate system according to the set first disease quantity threshold K0;

[0019] Mark the statistical node where the number of animal diseases K>k0, and obtain the distribution of the animal disease type by taking the number of the animal disease type corresponding to the statistical node;

[0020] The number of animal disease types in the monitoring nodes where the animal disease type is distributed is recorded as DS j ;

[0021] Set the second animal disease quantity threshold K1;

[0022] Determine whether the number DS of the animal disease type exists in the monitoring node i ≥K1;

[0023] If it does not exist, it means there is no abnormality. If it does exist, the corresponding monitoring node will be marked as a risk outbreak node;

[0024] Get the start time and end time of each animal disease in the outbreak node, sort the animal diseases according to their start time, and label them according to the sorting results, denoted as k, where k = 1, 2, ..., DS j ;

[0025] Obtain the crossover duration between the animal disease labeled k and other animal diseases in turn, and obtain the sum of the crossover durations between each animal disease and other animal diseases, recorded as T k ;

[0026] According to the sum of the crossover time between each animal disease and other animal diseases, the outbreak risk coefficient Q of the animal disease type is obtained.

[0027] Furthermore, the process of the information analysis module determining whether there is a risk of an outbreak of a corresponding animal disease includes:

[0028] An outbreak risk coefficient threshold range (Q0, Q1) is set, and the outbreak risk coefficient Q of the obtained animal disease is compared with the set outbreak risk coefficient threshold range, and whether the animal disease has an outbreak risk is determined according to the comparison result, and a corresponding strategy formulation instruction is generated, wherein the strategy formulation instruction includes a node monitoring strategy formulation instruction and an animal disease prevention and control strategy formulation instruction;

[0029] If Q≤Q0, it means that there is no risk of outbreak of animal disease;

[0030] If Q∈(Q0, Q1), the animal disease and the corresponding monitoring node are marked as animal disease continuous monitoring points, and node monitoring strategy formulation instructions are generated;

[0031] If Q ≥ Q0, it means that there is a risk of an outbreak of the animal disease, and an instruction for formulating an animal disease prevention and control strategy is generated;

[0032] The generated policy formulation instructions are sent to the prevention and control policy formulation module.

[0033] Furthermore, the process of the prevention and control strategy formulation module formulating a corresponding animal disease prevention and control strategy according to the obtained strategy formulation instruction includes:

[0034] When a node monitoring strategy formulation instruction is received, the current statistical period is used as the base period, the number and distribution of the animal disease type in the next statistical period are obtained, and the two statistical periods are accumulated as a new statistical period, and the risk outbreak coefficient of the animal disease type at each monitoring node is obtained. The number of animal disease continuous monitoring points is obtained based on the obtained risk outbreak coefficient of the animal disease type at each monitoring node. If the number becomes 0, it means that there is no outbreak risk for the animal disease type; if the number does not change, continuous monitoring is carried out; if the number increases, an animal disease prevention and control strategy formulation instruction is generated;

[0035] When receiving the node monitoring strategy formulation instruction, take the monitoring node as the center point and obtain whether the animal disease type exists in other monitoring nodes. If so, mark the corresponding monitoring node as a radiation point;

[0036] Get the monitoring node farthest from the center point, get the distance between the center point and the farthest monitoring node, take the center point as the center and the distance between the center point and the farthest monitoring node as the radius as the monitoring range, and set several disease prevention and control points within the monitoring range.

[0037] Furthermore, the continuous monitoring is performed by accumulating the current statistical period and the next statistical period as a new statistical period for monitoring.

[0038] Furthermore, the number of the disease prevention and control points is linearly related to the radius length of the monitoring range.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows: by obtaining the corresponding animal medical information in each animal hospital, the animal medical information of animal hospitals distributed in different regions is summarized, and then the occurrence of various animal diseases is statistically analyzed based on the summarized information. When it is found that the number of a certain animal disease type exceeds the preset range, the number of the animal disease type at the corresponding monitoring node is further refined, and a secondary analysis is performed to determine whether there is an outbreak risk. If there is an outbreak risk, the corresponding monitoring range is set according to the monitoring node generated by the animal disease type, and disease prevention and control points are set within the monitoring range, thereby achieving targeted and effective prevention and control of animal diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0041] Figure 1It is a flow chart of the present invention. DETAILED DESCRIPTION

[0042] like Figure 1 As shown, an animal disease prevention and control system based on intelligent AI includes a monitoring center, which includes an information input module, an information processing module, an information analysis module, and a prevention and control strategy formulation module;

[0043] The information entry module includes a registration unit and an information entry unit, and the basic information of each monitoring node is registered through the registration unit;

[0044] It should be further explained that, in the specific implementation process, the process of registering the monitoring nodes includes:

[0045] Enter the basic information of the monitoring node into the registration unit, and upload the entered basic information of the monitoring node to the monitoring center, which will review the basic information of the monitoring node; it should be noted that the basic information of the monitoring node includes the name of the animal hospital, the geographical location and the person in charge information, and the person in charge information includes the name, age, gender and real-name authenticated mobile phone number;

[0046] After the review is passed, the corresponding login account and login password are generated and sent to the person in charge of the monitoring node;

[0047] The person in charge of the monitoring node enters the obtained login account and login password into the information entry unit, and activates the information entry authority of the information entry unit;

[0048] After activating the information entry authority, the information entry unit is used to enter the animal medical information of the monitoring node, and the animal medical information includes the animal disease type, the start time type of the medical treatment, and the end time of the medical treatment.

[0049] The information processing module is used to process the animal medical information recorded by each monitoring node to obtain the outbreak risk coefficient of the animal disease type. The specific process includes:

[0050] Each monitoring node is labeled as i, where i=1, 2, ..., n, n is an integer, and n>0;

[0051] Generate a corresponding animal information set based on the animal disease type entered by each monitoring node, and aggregate the corresponding animal medical information into the animal information set;

[0052] According to the animal disease types in each animal information set, a corresponding two-dimensional coordinate system of time and quantity is established;

[0053] Set the statistical period T, and generate corresponding statistical nodes in the two-dimensional coordinate system according to the statistical period, obtain the number of animal diseases in each statistical period, record it as K, and map it to the corresponding statistical node; it should be further explained that in the specific implementation process, the number of animal diseases is calculated from the start time of the consultation, and the end time of the consultation is eliminated. For example:

[0054] Assume that the initial number of animal diseases K = 0. After the monitoring node enters the animal medical information, the start time of the medical treatment is t1, and the end time of the medical treatment is t2. There are statistical nodes m1, m2 and m3 in the time period t1-t2, and the statistical node m4 is after t2.

[0055] Then K of statistical node m1 is 1, K of statistical node m2 is 1, K of statistical node m3 is 1, and K of statistical node m4 is 0;

[0056] A first disease quantity threshold K0 is set, and a corresponding disease quantity threshold line is set in a two-dimensional coordinate system according to the set first disease quantity threshold K0; it should be further explained that, in a specific implementation process, the disease quantity threshold line may be different according to different animal disease types;

[0057] Mark the statistical node where the number of animal diseases K>k0, and obtain the distribution of the animal disease type by taking the number of the animal disease type corresponding to the statistical node;

[0058] The monitoring nodes where the animal disease type is distributed are numbered respectively, denoted as j, where j = 1, 2, ..., m, m is an integer, and m ≥ 1;

[0059] Then the number of animal disease types in the monitoring node labeled j is DS j ;

[0060] Set the second animal disease quantity threshold K1;

[0061] Determine whether the number DS of the animal disease type exists in the monitoring node i ≥K1;

[0062] If it does not exist, it means there is no abnormality. If it does exist, the corresponding monitoring node will be marked as a risk outbreak node;

[0063] Get the start time and end time of each animal disease in the outbreak node, sort the animal diseases according to their start time, and label them according to the sorting results, denoted as k, where k = 1, 2, ..., DS j ;

[0064] Obtain the crossover duration between the animal disease labeled k and other animal diseases in turn, and obtain the sum of the crossover durations between each animal disease and other animal diseases, recorded as T k ; It should be further explained that, in the specific implementation process, the crossover time is specifically: the time period from the start time to the end time of the animal disease treatment is overlapped with the time period from the start time to the end time of the treatment of another animal disease. For example:

[0065] Set the start time of animal disease A to t1 and the end time to t2; set the start time of animal disease B to t3 and the end time to t4, where t1<t3<t2, t4>t2, and the crossover time is the time period from t3 to t2;

[0066] According to the sum of the crossover time between each animal disease and other animal diseases, the outbreak risk coefficient of the animal disease type is obtained, which is recorded as Q.

[0067] Among them, CT k is the duration from the start time to the end time of the animal disease visit of k. For example, after the monitoring node enters the animal visit information, the start time of the visit is t1 and the end time of the visit is t2, then CT k is the duration from t1 to t2.

[0068] The information analysis module is used to determine whether there is an outbreak risk of the corresponding animal disease according to the outbreak risk coefficient of the corresponding animal disease of each monitoring node obtained. The specific process includes:

[0069] An outbreak risk coefficient threshold range (Q0, Q1) is set, and the outbreak risk coefficient Q of the obtained animal disease is compared with the set outbreak risk coefficient threshold range, and whether the animal disease has an outbreak risk is determined according to the comparison result, and a corresponding strategy formulation instruction is generated, wherein the strategy formulation instruction includes a node monitoring strategy formulation instruction and an animal disease prevention and control strategy formulation instruction;

[0070] If Q≤Q0, it means that there is no risk of outbreak of animal disease;

[0071] If Q∈(Q0, Q1), the animal disease and the corresponding monitoring node are marked as animal disease continuous monitoring points, and node monitoring strategy formulation instructions are generated;

[0072] If Q ≥ Q0, it means that there is a risk of an outbreak of the animal disease, and an instruction for formulating an animal disease prevention and control strategy is generated;

[0073] The generated policy formulation instructions are sent to the prevention and control policy formulation module.

[0074] The prevention and control strategy formulation module is used to formulate a corresponding animal disease prevention and control strategy according to the obtained strategy formulation instruction. The specific process includes:

[0075] When a node monitoring strategy formulation instruction is received, the current statistical period is used as the base period, the number and distribution of the animal disease type in the next statistical period are obtained, and the two statistical periods are accumulated as a new statistical period, and the risk outbreak coefficient of the animal disease type at each monitoring node is obtained. The number of animal disease continuous monitoring points is obtained according to the obtained risk outbreak coefficient of the animal disease type at each monitoring node. If the number becomes 0, it means that there is no outbreak risk of the animal disease type; if the number does not change, continuous monitoring is carried out, and if the number increases, an animal disease prevention and control strategy formulation instruction is generated; it should be further explained that in the specific implementation process, the continuous monitoring is still based on the accumulation of the current statistical period and the next statistical period as a new statistical period for monitoring;

[0076] When receiving the node monitoring strategy formulation instruction, take the monitoring node as the center point and obtain whether the animal disease type exists in other monitoring nodes. If so, mark the corresponding monitoring node as a radiation point;

[0077] Obtain the radiation point farthest from the center point, obtain the distance between the center point and the farthest radiation point, take the center point as the center and the distance between the center point and the farthest radiation point as the radius as the monitoring range, and set several disease prevention and control points within the monitoring range. The number of disease prevention and control points is linearly related to the radius of the monitoring range.

[0078] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An animal disease prevention and control system based on intelligent AI, including a monitoring center, characterized in that: The monitoring center includes an information input module, an information processing module, an information analysis module and a prevention and control strategy formulation module; The information input module includes a registration unit and an information input unit, through which the basic information of each monitoring node is registered; the information input unit is used to input the animal medical information of the monitoring node, and the animal medical information includes animal disease type, medical start time type and medical end time; The information processing module is used to process the animal medical information recorded by each monitoring node to obtain the outbreak risk coefficient of the animal disease type; The information analysis module is used to determine whether there is an outbreak risk of the corresponding animal disease according to the outbreak risk coefficient of the corresponding animal disease of each monitoring node obtained, and generate a corresponding strategy formulation instruction; The prevention and control strategy formulation module is used to formulate corresponding animal disease prevention and control strategies according to the obtained strategy formulation instructions; The process of registering a monitoring node includes: Enter the basic information of the monitoring node into the registration unit, and upload the entered basic information of the monitoring node to the monitoring center, which will review the basic information of the monitoring node; it should be noted that the basic information of the monitoring node includes the name, geographical location and person in charge information, and the person in charge information includes the name, age, gender and real-name authenticated mobile phone number; After the review is passed, the corresponding login account and login password are generated and sent to the person in charge of the monitoring node; The person in charge of the monitoring node enters the obtained login account and login password into the information entry unit, and activates the information entry authority of the information entry unit; The information processing module is used to process the animal medical information recorded by each monitoring node, and the process of obtaining the outbreak risk coefficient of the animal disease type includes: Generate a corresponding animal information set based on the animal disease type entered by each monitoring node, and aggregate the corresponding animal medical information into the animal information set; According to the animal disease types in each animal information set, a corresponding two-dimensional coordinate system of time and quantity is established; Set the statistical period T, and generate corresponding statistical nodes in the two-dimensional coordinate system according to the statistical period, obtain the number of animal diseases in each statistical period, record it as K, and map it to the corresponding statistical node; A first disease quantity threshold K0 is set, and a corresponding disease quantity threshold line is set in the two-dimensional coordinate system according to the set first disease quantity threshold K0; Mark the statistical node where the number of animal diseases K>k0, and obtain the distribution of the animal disease type by taking the number of the animal disease type corresponding to the statistical node; The number of animal disease types in the monitoring nodes where the animal disease type is distributed is recorded as DS j ; Set the second animal disease quantity threshold K1; Determine whether the number DS of the animal disease type exists in the monitoring node i ≥K1; If it does not exist, it means there is no abnormality. If it does exist, the corresponding monitoring node will be marked as a risk outbreak node; Get the start time and end time of each animal disease in the outbreak node, sort the animal diseases according to their start time, and label them according to the sorting results, denoted as k, where k = 1, 2, ..., DS j ; Obtain the crossover duration between the animal disease labeled k and other animal diseases in turn, and obtain the sum of the crossover durations between each animal disease and other animal diseases, recorded as T k ; According to the sum of the crossover time between each animal disease and other animal diseases, the outbreak risk coefficient Q of the animal disease type is obtained; The process of the information analysis module determining whether there is a risk of an outbreak of a corresponding animal disease includes: An outbreak risk coefficient threshold range (Q0, Q1) is set, and the outbreak risk coefficient Q of the obtained animal disease is compared with the set outbreak risk coefficient threshold range, and whether the animal disease has an outbreak risk is determined according to the comparison result, and a corresponding strategy formulation instruction is generated, wherein the strategy formulation instruction includes a node monitoring strategy formulation instruction and an animal disease prevention and control strategy formulation instruction; If Q≤Q0, it means that there is no risk of outbreak of animal disease; If Q∈(Q0, Q1), the animal disease and the corresponding monitoring node are marked as animal disease continuous monitoring points, and node monitoring strategy formulation instructions are generated; If Q ≥ Q0, it means that there is a risk of an outbreak of the animal disease, and an instruction for formulating an animal disease prevention and control strategy is generated; Send the generated strategy formulation instructions to the prevention and control strategy formulation module; The process of the prevention and control strategy formulation module formulating a corresponding animal disease prevention and control strategy according to the obtained strategy formulation instruction includes: When a node monitoring strategy formulation instruction is received, the current statistical period is used as the base period, the number and distribution of the animal disease type in the next statistical period are obtained, and the two statistical periods are accumulated as a new statistical period, and the risk outbreak coefficient of the animal disease type at each monitoring node is obtained. The number of animal disease continuous monitoring points is obtained based on the obtained risk outbreak coefficient of the animal disease type at each monitoring node. If the number becomes 0, it means that there is no outbreak risk for the animal disease type; if the number does not change, continuous monitoring is carried out; if the number increases, an animal disease prevention and control strategy formulation instruction is generated; When receiving the node monitoring strategy formulation instruction, take the monitoring node as the center point and obtain whether the animal disease type exists in other monitoring nodes. If so, mark the corresponding monitoring node as a radiation point; Get the monitoring node farthest from the center point, get the distance between the center point and the farthest monitoring node, take the center point as the center and the distance between the center point and the farthest monitoring node as the radius as the monitoring range, and set several disease prevention and control points within the monitoring range; The continuous monitoring is to accumulate the current statistical cycle and the next statistical cycle as a new statistical cycle for monitoring; The number of the disease prevention and control points is linearly related to the radius length of the monitoring range.

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