Intelligent epidemic prevention method for livestock breeding

Abstract

The invention discloses an intelligent epidemic prevention method for livestock breeding, and belongs to the technical field of livestock breeding management, and the method comprises the steps: obtaining the feature data of an animal, generating an animal health state index, and grading the real-time epidemic prevention demands of the current livestock breeding; acquiring personnel data, social data and environmental data of a livestock breeding area, establishing an epidemic prevention demand prediction model, and generating an epidemic situation risk prediction index; according to the epidemic situation risk prediction index and the animal health state index, generating an epidemic prevention demand prediction index, and performing risk level warning on the current livestock breeding area; the animal health state can be monitored in real time, abnormal conditions can be found in time, risk level warning is carried out on the current livestock breeding area according to the animal health state index and the epidemic situation risk prediction index, the accuracy and timeliness of epidemic prevention are improved, early warning can be carried out on the epidemic prevention work of livestock breeding in advance, and the working efficiency is improved. The risk of epidemic outbreak is reduced, so that the stable development of the livestock breeding industry is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of livestock breeding management, and particularly relates to an intelligent epidemic prevention method for livestock breeding. Background Art

[0002] Livestock breeding is an important part of agricultural production and is of great significance for ensuring the supply of meat products and the development of the agricultural economy. However, animals in the process of livestock breeding are vulnerable to various diseases, so epidemic prevention work is particularly important.

[0003] In traditional livestock breeding epidemic prevention work, it mainly relies on manual observation of animal symptoms, signs, etc. to judge the health status of animals and take corresponding epidemic prevention measures accordingly. Although this method can ensure the health of animals to a certain extent, there are obvious deficiencies.

[0004] First of all, the method of manual observation often has a lag, that is, when obvious symptoms appear in animals, the best treatment time may have been missed. Secondly, manual observation is affected by the experience and subjective judgment of observers, and the accuracy is difficult to guarantee. Finally, with the expansion of the scale of livestock breeding and the increase in breeding density, the method of manual observation has been difficult to meet the large-scale and high-efficiency epidemic prevention requirements. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides an intelligent epidemic prevention method for livestock breeding, which solves the above problems.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent epidemic prevention method for livestock breeding, comprising the following steps:

[0007] Obtain the characteristic data of animals; wherein, the characteristic data includes basic physical signs, behavioral characteristics, and group characteristics;

[0008] According to the basic physical signs, behavioral characteristics, and group characteristics of animals, establish a real-time status evaluation model and generate an animal health status index; wherein, the basic physical signs include body temperature and weight, the behavioral characteristics include movement trajectory, food intake, and excrement status, and the group characteristics include distribution status;

[0009] According to the animal health status index, classify the real-time epidemic prevention requirements of the current livestock breeding; wherein, the classification levels include first-level real-time epidemic prevention, second-level real-time epidemic prevention, and third-level real-time epidemic prevention;

[0010] Obtain personnel data, social data, and environmental data of the livestock breeding area, establish an epidemic prevention demand prediction model, and generate an epidemic risk prediction index; among them, the personnel data includes the personnel flow path and personnel disinfection records, the social data includes the epidemic outbreak location and outbreak time, and the environmental data includes the environmental wind direction and environmental wind speed;

[0011] Generate an epidemic prevention demand prediction index according to the epidemic risk prediction index and the animal health status index;

[0012] Warn the current livestock breeding area about the risk level according to the epidemic prevention demand prediction index; among them, the risk levels include low-risk warning, medium-risk warning, and high-risk warning.

[0013] On the basis of the above technical solutions, the present invention also provides the following alternative technical solutions:

[0014] Further technical solution: The specific method for generating the animal health status index includes the following steps:

[0015] Generate a basic characteristic evaluation value according to the body temperature and weight of the animal;

[0016] Generate a behavioral characteristic evaluation value according to the movement trajectory, food intake, and excrement form of the animal;

[0017] Generate a group characteristic evaluation value according to the distribution state of the animals;

[0018] Establish a real-time status evaluation model according to the basic characteristic evaluation value, behavioral characteristic evaluation value, and group characteristic evaluation value, and generate an animal health status index.

[0019] Further technical solution: The specific method for generating the basic characteristic evaluation value includes:

[0020] Set a detection period, and obtain the body temperature of the animal within the historical detection period;

[0021] Generate a body temperature average value according to the body temperature of the animal within all historical detection periods; among them, the body temperature average value refers to the average value of the body temperature of the animal within all historical detection periods;

[0022] Generate a body temperature change value according to the body temperature of the most recent historical detection period and the body temperature of the current detection period; among them, the body temperature change value refers to the difference between the body temperature of the most recent historical detection period and the body temperature of the current detection period;

[0023] Through the formula:

[0024]

[0025] Generate a body temperature evaluation value T int ;

[0026] In the formula, T ave represents the average body temperature, T stan represents the standard body temperature, T change represents the body temperature change value, T0 represents the body temperature change threshold, and T change > T0;

[0027] Obtain the weight of the current animal and the weights of the animal in multiple-interval historical detection periods;

[0028] Generate a weight decline value based on the weight of the current animal and the weights of the animal in multiple-interval historical detection periods; wherein, the weight decline value refers to the difference between the weight of the current animal and the weights of the animal in multiple-interval historical detection periods; wherein, the weight of the current animal is less than the weights of the animal in multiple-interval historical detection periods;

[0029] Through the formula:

[0030]

[0031] Generate a weight evaluation value F int ;

[0032] In the formula, F change represents the weight decline value, F s represents the weights of the animal in multiple-interval historical detection periods;

[0033] Through the formula:

[0034] J int = T int *(1 + F int )

[0035] Generate a basic feature evaluation value J int ;

[0036] In the formula, T int represents the body temperature evaluation value, F int represents the weight evaluation value.

[0037] Further technical solution: The generation method of the behavior feature evaluation value is specifically:

[0038] According to the movement trajectory of the animal, obtain the movement distance and movement speed of the animal in the historical detection period respectively, and mark them as the historical movement distance and historical movement speed;

[0039] Through the formula:

[0040]

[0041] Generate a movement feature evaluation value M int ;

[0042] In the formula, L i Indicates the current movement distance, L s represents the historical movement distance, v i Indicates the current movement speed, v s It represents the historical movement speed, α and β are weight coefficients, and α+β=1;

[0043] Generate a feeding evaluation value according to the current feeding amount of the animal; wherein the feeding evaluation value refers to the ratio between the current feeding amount and the standard feeding amount;

[0044] Generate an excrement evaluation value according to the excrement form of the animal and the standard excrement form; wherein the excrement evaluation value refers to the ratio between the excrement form of the animal and the standard excrement form;

[0045] The movement characteristic evaluation value, eating evaluation value and excrement evaluation value are weighted to generate a behavior characteristic evaluation value.

[0046] Further technical solution: The group characteristic evaluation value is generated in the following manner:

[0047] Obtain the distribution status of the animal group, establish a three-dimensional space coordinate system based on the space where the animal group is located, substitute the positions of all animals in the animal group into the three-dimensional space coordinate system, and generate the coordinates of the animal;

[0048] Generate an individual spacing value according to the coordinate values of all the animals; wherein the individual spacing value refers to the difference between the coordinate values of two animals;

[0049] By formula:

[0050]

[0051] Generate population characteristic evaluation value Q int ;

[0052] In the formula, D max It represents the group alienation value, D0 represents the maximum distance value of the space where the animal group is located, ε is the animal spacing correction value, and the animal spacing correction value ε is a constant, which is specifically the standard spacing value when animals live in groups.

[0053] Further technical solution: The expression of the real-time status evaluation model is:

[0054]

[0055] In the expression, R represents the animal health index, Q int It represents the evaluation value of group characteristics, B int It represents the behavior characteristic evaluation value, Jint It represents the basic feature evaluation value, and σ represents the proportion range of animals showing abnormalities during the standard livestock period, and 0 ≤ σ ≤ 1.

[0056] Further technical solution: The specific method for generating the epidemic risk prediction index includes the following steps:

[0057] Obtain the personnel flow paths and personnel disinfection records in the livestock breeding area to generate a personnel flow risk factor;

[0058] Obtain the epidemic outbreak locations, outbreak times, environmental wind directions, and environmental wind speeds in the livestock breeding area to generate a social impact factor;

[0059] Establish an epidemic prevention demand prediction model to generate an epidemic risk prediction index.

[0060] Further technical solution: The specific method for generating the personnel flow risk factor is as follows:

[0061] Obtain the flow paths of personnel to generate the number of flow regions when personnel are flowing;

[0062] Generate a flow region evaluation value according to the number of flow regions; among them, the flow region evaluation value refers to the ratio between the number of flow regions and the number threshold;

[0063] Obtain the disinfection records of personnel to generate the disinfection frequency of personnel flowing across regions; among them, the disinfection frequency of personnel flowing across regions refers to the ratio between the number of disinfection times when personnel cross regions and the number of times of crossing regions;

[0064] Generate a cross-region disinfection evaluation value according to the disinfection frequency of personnel flowing across regions; among them, the cross-region disinfection evaluation value refers to the ratio between the disinfection frequency of personnel flowing across regions and the standard disinfection frequency of personnel flowing across regions;

[0065] Through the formula:

[0066]

[0067] Generate the personnel flow risk factor E peo ;

[0068] In the formula, C regi represents the flow region evaluation value, and S disi represents the cross-region disinfection evaluation value;

[0069] Among them, the specific method for generating the social impact factor is as follows:

[0070] Obtain the epidemic outbreak locations around the livestock breeding area;

[0071] Generate a relative distance value of the outbreak point based on the location of the current livestock farming area and the location of the epidemic outbreak;

[0072] Generate a distance evaluation value based on the relative distance value of the outbreak point;

[0073] Generate an included angle between the two locations based on the location of the epidemic outbreak and the location of the current livestock farming area;

[0074] Obtain the wind direction angle of the ambient wind; where the wind direction angle refers to the included angle formed between the straight line formed by the movement of the ambient wind and the reference line;

[0075] Perform a difference operation on the wind direction angle and the included angle between the two locations to generate an angle deviation value;

[0076] Perform a ratio operation on the angle deviation value and the angle deviation threshold to generate an angle evaluation value;

[0077] Obtain the ambient wind speed;

[0078] Through the formula:

[0079]

[0080] Generate a wind-borne transmission evaluation value F spr ;

[0081] In the formula, L two represents the relative distance value of the outbreak point, v wind represents the ambient wind speed, t surv represents the pathogen survival time, T two represents the temperature value in the area between the location of the epidemic outbreak and the location of the current livestock farming area, T surv represents the near-endpoint value of the standard pathogen survival temperature range; where the temperature value T two in the area between the location of the epidemic outbreak and the location of the current livestock farming area is outside the standard pathogen survival temperature range.

[0082] Further technical solution: The expression of the epidemic prevention demand prediction model is specifically:

[0083] X pred = E peo * F spr ;

[0084] In the expression, X pred represents the epidemic risk prediction index, E peo represents the personnel flow risk factor, F spr represents the social impact factor.

[0085] Further technical solution: The specific generation method of the epidemic prevention demand prediction index is:

[0086] Through the formula:

[0087] W dema = R * (1 + X pred );

[0088] Generate the epidemic prevention demand prediction index W dema ;

[0089] In the formula, R represents the animal health status index, and X pred represents the epidemic risk prediction index.

[0090] The present invention provides an intelligent epidemic prevention method for livestock breeding, which has the following beneficial effects compared with the prior art:

[0091] The present invention can monitor the health status of animals in real time, timely detect abnormal situations, and give risk level warnings to the current livestock breeding area according to the animal health status index and the epidemic risk prediction index, which not only improves the accuracy and timeliness of epidemic prevention, but also can give early warnings for the epidemic prevention work of livestock breeding, reduce the risk of epidemic outbreaks, and thus ensure the stable development of the livestock breeding industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 It is a flowchart of an intelligent epidemic prevention method for livestock breeding provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0093] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0094] The following describes the specific implementation of the present invention in detail with reference to specific embodiments.

[0095] Please refer to Figure 1 , an intelligent epidemic prevention method for livestock breeding provided by an embodiment of the present invention, includes the following steps:

[0096] Step 1: Obtain the characteristic data of animals; among them, the characteristic data includes basic physical signs, behavioral characteristics and group characteristics;

[0097] Step 2: Establish a real-time status evaluation model according to the basic physical signs, behavioral characteristics and group characteristics of animals, and generate an animal health status index; among them, the basic physical signs include body temperature and weight, the behavioral characteristics include movement trajectory, food intake and excrement status, and the group characteristics include distribution status;

[0098] Step 3: Classify the real-time epidemic prevention requirements of current livestock farming according to the animal health status index; among them, the classification levels include first-level real-time epidemic prevention, second-level real-time epidemic prevention, and third-level real-time epidemic prevention;

[0099] Step 4: Obtain personnel data, social data, and environmental data of the livestock farming area, establish an epidemic prevention demand prediction model, and generate an epidemic risk prediction index; among them, the personnel data includes personnel movement paths and personnel disinfection records, the social data includes the locations and outbreak times of epidemic outbreaks, and the environmental data includes environmental wind direction and environmental wind speed;

[0100] Step 5: Generate an epidemic prevention demand prediction index according to the epidemic risk prediction index and the animal health status index;

[0101] Step 6: Give a risk level warning to the current livestock farming area according to the epidemic prevention demand prediction index; among them, the risk levels include low-risk warning, medium-risk warning, and high-risk warning.

[0102] As a preferred embodiment of the present invention, the generation method of the animal health status index specifically includes the following steps:

[0103] S21: Generate a basic feature evaluation value according to the body temperature and weight of the animal;

[0104] S22: Generate a behavioral feature evaluation value according to the movement trajectory, food intake, and excrement form of the animal;

[0105] It should be noted that the excrement form can be evaluated in ways such as the moisture content range of the excrement; for example, the moisture content range of the excrement of normal animals is z-x. If the moisture content of the excrement of the current animal is not within this range, it means that the form of the excrement of the current animal is abnormal;

[0106] S23: Generate a population feature evaluation value according to the distribution state of the animals;

[0107] S24: Establish a real-time status evaluation model according to the basic feature evaluation value, behavioral feature evaluation value, and population feature evaluation value, and generate an animal health status index.

[0108] As a preferred embodiment of the present invention, the generation method of the basic feature evaluation value specifically includes:

[0109] Set a detection period and obtain the body temperature of the animal within the historical detection period;

[0110] Generate a body temperature average value according to the body temperature of the animal within all historical detection periods; among them, the body temperature average value refers to the average value of the body temperature of the animal within all historical detection periods;

[0111] Generate a body temperature change value based on the body temperature in the most recent historical detection period and the body temperature in the current detection period; wherein, the body temperature change value refers to the difference between the body temperature in the most recent historical detection period and the body temperature in the current detection period;

[0112] It should be noted that the most recent historical detection period refers to the historical detection period that is closest to the current detection period in terms of time sequence;

[0113] Through the formula:

[0114]

[0115] Generate a body temperature evaluation value T int ;

[0116] In the formula, T ave represents the average body temperature, T stan represents the standard body temperature, T change represents the body temperature change value, T0 represents the body temperature change threshold, and T change > T0;

[0117] It should be noted that if T change < T0, the value of T change - T0 is set to 0; for example, when the value of T0 is 0.5, when T change is less than 0.5, it means that the body temperature change of the current animal does not exceed the change threshold, and it is regarded as normal temperature fluctuation, that is, the value of T change - T0 is 0;

[0118] In addition, the body temperature change threshold refers to the maximum value of the temperature fluctuation of the animal in the standard state;

[0119] Furthermore, obtain the weight of the current animal and the weights of the animal in multiple-interval historical detection periods;

[0120] It should be noted that the multiple-interval historical detection period refers to the historical detection period that is separated by several detection periods; for example, taking the rule of being separated by n detection periods, intermittently obtain the weight of the animal, and a certain historical data of the weight is the weight of the animal in the multiple-interval historical detection period;

[0121] In addition, in this embodiment, the multiple-interval historical detection period refers to the multiple-interval historical detection period that is closest to the detection period corresponding to the weight of the current animal; for example, after obtaining the weight of the current animal, the detection period of obtaining the weight that is closest to the current detection period of obtaining the weight in terms of time is the multiple-interval historical detection period;

[0122] Generate a weight decline value based on the weight of the current animal and the weight of the animal in multiple-interval historical detection periods; wherein, the weight decline value refers to the difference between the weight of the current animal and the weight of the animal in multiple-interval historical detection periods; wherein, the weight of the current animal is less than the weight of the animal in multiple-interval historical detection periods.

[0123] It should be explained that the fact that the weight of the current animal is less than the weight of the animal in multiple-interval historical detection periods means that the animal's weight shows a downward trend; if the weight of the current animal is greater than or equal to the weight of the animal in multiple-interval historical detection periods, it means that the animal's weight does not show a downward trend, and the weight decline value is taken as 0.

[0124] Through the formula:

[0125]

[0126] Generate a weight evaluation value F int ;

[0127] In the formula, F change represents the weight decline value, and F s represents the weight of the animal in multiple-interval historical detection periods.

[0128] Through the formula:

[0129] J int = T int *(1 + F int );

[0130] Generate a basic feature evaluation value J int ;

[0131] In the formula, T int represents the body temperature evaluation value, and F int represents the weight evaluation value.

[0132] As a preferred embodiment of the present invention, the generation method of the behavior feature evaluation value is specifically as follows:

[0133] According to the movement trajectory of the animal, respectively obtain the movement distance and movement speed of the animal within the historical detection period, and mark them as the historical movement distance and historical movement speed respectively.

[0134] It should be explained that both the historical movement distance and the historical movement speed are the averages of historical data.

[0135] Through the formula:

[0136]

[0137] Generate a movement feature evaluation value M int ;

[0138] In the formula, L i represents the current movement distance, and L s represents the historical movement distance, and v i represents the current movement speed, and v s represents the historical movement speed. Both α and β are weight coefficients, and α + β = 1;

[0139] It should be noted that the values of α and β are set by relevant personnel in the field themselves, and the ways of setting their values include but are not limited to the expert consultation method, etc.;

[0140] Generate a feeding evaluation value according to the feeding amount of the current animal; among them, the feeding evaluation value refers to the ratio between the current feeding amount and the standard feeding amount;

[0141] Generate an excrement evaluation value according to the excrement form of the animal and the standard excrement form; among them, the excrement evaluation value refers to the ratio between the excrement form of the animal and the standard excrement form;

[0142] Perform weighted processing on the movement feature evaluation value, the feeding evaluation value, and the excrement evaluation value to generate a behavior feature evaluation value;

[0143] Exemplarily, through the formula:

[0144] B int = M int * a1 + Y int * a2 + K int * a3

[0145] Generate the behavior feature evaluation value B int ;

[0146] In the formula, M int represents the movement feature evaluation value, Y int represents the feeding evaluation value, K int represents the excrement evaluation value. Both a1, a2, and a3 are proportionality coefficients, and a1 + a2 + a3 = 1;

[0147] It should be noted that the values of a1, a2, and a3 are set by relevant personnel in the field themselves, and the ways of setting their values include but are not limited to the analytic hierarchy process, etc.

[0148] As a preferred embodiment of the present invention, the generation method of the group feature evaluation value is specifically as follows:

[0149] Obtain the distribution state of the group where the animal is located, establish a three-dimensional space coordinate system with the space where the animal group is located, substitute the positions of all animals in the animal group into the three-dimensional space coordinate system, and generate the coordinates where the animal is located;

[0150] Generate an individual spacing value according to the coordinate values of all the animals; wherein the individual spacing value refers to the difference between the coordinate values of two animals;

[0151] Specifically, the coordinate values corresponding to the same coordinate axis between the two coordinates are subjected to difference processing to generate a single coordinate axis difference;

[0152] All the individual coordinate axis differences are added together to generate the difference between the coordinate values of the two animals, i.e., the individual spacing value;

[0153] All individual distance values are screened to obtain a group alienation value; wherein the group alienation value refers to the maximum value of all individual distance values;

[0154] By formula:

[0155]

[0156] Generate population characteristic evaluation value Q int ;

[0157] In the formula, D max It represents the alienation value of the group, D0 represents the maximum distance value of the space where the animal group is located, and ε is the correction value of the animal spacing;

[0158] It should be explained that the animal spacing correction value ε is a constant, specifically the standard spacing value when animals live in groups;

[0159] When animals live in groups, there is a normal living range between individual animals according to their living habits, which will produce a certain individual spacing value; if the living habit of animals is to live in groups, the animal spacing correction value ε is 0.

[0160] As a preferred embodiment of the present invention, the expression of the real-time status assessment model is:

[0161]

[0162] In the expression, R represents the animal health index, Q int It represents the evaluation value of group characteristics, B int It represents the behavior characteristic evaluation value, J int It represents the basic characteristic evaluation value, σ represents the proportion of animals with abnormalities during the standard animal husbandry period, and 0≤σ≤1;

[0163] It should be noted that during animal husbandry, although scientific management and protection measures are taken to ensure the health and production performance of animals, it does not mean that all animals can avoid abnormal situations; abnormal situations cannot be completely prevented, and due to the influence of various factors, some health problems or even deaths may occur to animals during the breeding process; for example, during animal husbandry under the standard feeding procedure, if the mortality rate of animals is b, then the proportion range σ of abnormal animals during the standard animal husbandry period can take the value of b.

[0164] As a preferred embodiment of the present invention, the specific method for grading the real-time epidemic prevention requirements for current animal husbandry is as follows:

[0165] Compare the animal health status index with the set range of the animal health status index; wherein, the set range of the animal health status index includes a first threshold and a second threshold, and the first threshold is less than the second threshold;

[0166] When the animal health status index is less than or equal to the first threshold, it indicates that the health status of the current animal is good, and then a third-level real-time epidemic prevention signal is generated; at this time, the smaller the animal health status index, the better the health status of the current animal;

[0167] When receiving the third-level real-time epidemic prevention signal, the relevant personnel in animal husbandry only need to carry out daily work and conduct daily epidemic prevention inspections;

[0168] When the animal health status index is greater than the first threshold and less than the second threshold, it indicates that the health status of the current animal is abnormal, and then a second-level real-time epidemic prevention signal is generated; at this time, the larger the animal health status index, the more abnormal the health status of the current animal;

[0169] When receiving the second-level real-time epidemic prevention signal, the relevant personnel in animal husbandry need to carry out epidemic prevention work;

[0170] Among them, the epidemic prevention work includes disinfection, isolating abnormal animals, etc.;

[0171] When the animal health status index is greater than or equal to the second threshold, it indicates that the health status of the current animal is poor, and then a first-level real-time epidemic prevention signal is generated; at this time, the larger the animal health status index, the poorer the health status of the current animal;

[0172] When receiving the first-level real-time epidemic prevention signal, the relevant personnel in animal husbandry need to carry out comprehensive epidemic prevention work and, if necessary, conduct a full isolation of the animal husbandry area.

[0173] As a preferred embodiment of the present invention, the specific method for generating the epidemic risk prediction index specifically includes the following steps:

[0174] S41: Obtain the personnel flow paths and personnel disinfection records in the livestock farming area, and generate personnel flow risk factors;

[0175] S42: Obtain the epidemic outbreak locations, outbreak times, environmental wind directions, and environmental wind speeds in the livestock farming area, and generate social impact factors;

[0176] S43: Establish an epidemic prevention demand prediction model and generate an epidemic risk prediction index.

[0177] As a preferred embodiment of the present invention, the generation method of the personnel flow risk factor is specifically as follows:

[0178] Obtain the flow paths of personnel and generate the number of flow regions when personnel are flowing;

[0179] Generate a flow region evaluation value according to the number of flow regions; wherein, the flow region evaluation value refers to the ratio between the number of flow regions and the number threshold;

[0180] Furthermore, obtain the disinfection records of personnel and generate the disinfection frequency of personnel's cross-regional flow; wherein, the disinfection frequency of personnel's cross-regional flow refers to the ratio between the number of disinfection times when personnel cross regions and the number of cross-regional times;

[0181] Generate a cross-regional disinfection evaluation value according to the disinfection frequency of personnel's cross-regional flow; wherein, the cross-regional disinfection evaluation value refers to the ratio between the disinfection frequency of personnel's cross-regional flow and the standard disinfection frequency of personnel's cross-regional flow;

[0182] Through the formula:

[0183]

[0184] Generate the personnel flow risk factor E peo ;

[0185] In the formula, C regi represents the flow region evaluation value, and S disi represents the cross-regional disinfection evaluation value.

[0186] As a preferred embodiment of the present invention, the generation method of the social impact factor is specifically as follows:

[0187] Obtain the epidemic outbreak locations around the livestock farming area;

[0188] Generate a relative distance value of the outbreak point according to the location of the current livestock farming area and the epidemic outbreak locations;

[0189] Generate a distance evaluation value according to the relative distance value of the outbreak point;

[0190] Further, according to the location of the epidemic outbreak and the location of the current livestock farming area, the included angle between the two locations is generated;

[0191] It should be noted that the included angle between the two locations refers to the angle formed by the straight line between the location of the epidemic outbreak and the location of the current livestock farming area and the reference line, that is, the included angle between the two locations; in addition, the reference line can be a straight line with a fixed angle such as longitude and latitude;

[0192] Obtain the wind direction angle of the ambient wind; where the wind direction angle refers to the included angle formed by the straight line formed by the movement of the ambient wind and the reference line;

[0193] Perform a difference operation on the wind direction angle and the included angle between the two locations to generate an angle deviation value;

[0194] Perform a ratio operation on the angle deviation value and the angle deviation threshold to generate an angle evaluation value;

[0195] Obtain the ambient wind speed;

[0196] Through the formula:

[0197]

[0198] Generate a wind-borne transmission evaluation value F spr ;

[0199] In the formula, L two represents the relative distance value of the outbreak point, v wind represents the ambient wind speed, t surv represents the pathogen survival time, T two represents the temperature value of the area between the location of the epidemic outbreak and the location of the current livestock farming area, T surv represents the near-end value of the standard pathogen survival temperature range; among them, the temperature value T of the area between the location of the epidemic outbreak and the location of the current livestock farming area two is outside the standard pathogen survival temperature range;

[0200] It should be noted that if the temperature value T of the area between the location of the epidemic outbreak and the location of the current livestock farming area two is within the standard pathogen survival temperature range, then the value of T two -T surv is 0.

[0201] As a preferred embodiment of the present invention, the expression of the epidemic prevention demand prediction model is specifically:

[0202] X pred =E peo *F spr ;

[0203] In the expression, X pred represents the epidemic risk prediction index, and E peo represents the personnel flow risk factor, and F spr represents the social impact factor.

[0204] As a preferred embodiment of the present invention, the generation method of the epidemic prevention demand prediction index is specifically as follows:

[0205] Through the formula:

[0206] W dema = R * (1 + X pred );

[0207] Generate the epidemic prevention demand prediction index W dema ;

[0208] In the formula, R represents the animal health status index, and X pred represents the epidemic risk prediction index.

[0209] As a preferred embodiment of the present invention, the method for warning the risk level of the current livestock and poultry breeding area is specifically as follows:

[0210] Compare the epidemic prevention demand prediction index with the set range of the epidemic prevention demand prediction index, where the set range of the epidemic prevention demand prediction index includes a first warning value and a second warning value;

[0211] When the epidemic prevention demand prediction index is less than or equal to the first warning value, a low - risk warning is given to the current livestock and poultry breeding area;

[0212] When the epidemic prevention demand prediction index is greater than the first warning value and less than the second warning value, a medium - risk warning is given to the current livestock and poultry breeding area;

[0213] When the epidemic prevention demand prediction index is greater than or equal to the second warning value, a high - risk warning is given to the current livestock and poultry breeding area.

[0214] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent epidemic prevention method for livestock farming, characterized in that, It includes the following steps: Obtain the characteristic data of the animals; among them, the characteristic data includes basic physical signs, behavioral characteristics, and group characteristics; According to the basic physical signs, behavioral characteristics, and group characteristics of the animals, establish a real-time status evaluation model and generate an animal health status index; among them, the basic physical signs include body temperature and weight, the behavioral characteristics include movement trajectory, food intake, and excrement status, and the group characteristics include distribution status; According to the animal health status index, classify the real-time epidemic prevention requirements of the current livestock farming; among them, the classification levels include first-level real-time epidemic prevention, second-level real-time epidemic prevention, and third-level real-time epidemic prevention; Obtain the personnel data, social data, and environmental data of the livestock farming area, establish an epidemic prevention demand prediction model, and generate an epidemic risk prediction index; among them, the personnel data includes personnel movement paths and personnel disinfection records, the social data includes the locations and outbreak times of the epidemic outbreaks, and the environmental data includes environmental wind direction and environmental wind speed; Generate an epidemic prevention demand prediction index according to the epidemic risk prediction index and the animal health status index; Give a risk level warning to the current livestock farming area according to the epidemic prevention demand prediction index; among them, the risk levels include low-risk warning, medium-risk warning, and high-risk warning.

2. The intelligent epidemic prevention method for livestock farming according to claim 1, characterized in that The specific generation method of the animal health status index specifically includes the following steps: Generate a basic characteristic evaluation value according to the body temperature and weight of the animal; Generate a behavioral characteristic evaluation value according to the movement trajectory, food intake, and excrement form of the animal; Generate a group characteristic evaluation value according to the distribution status of the animals; Establish a real-time status evaluation model according to the basic characteristic evaluation value, behavioral characteristic evaluation value, and group characteristic evaluation value, and generate an animal health status index.

3. The intelligent epidemic prevention method for livestock breeding according to claim 2, wherein, The specific generation method of the basic characteristic evaluation value specifically includes: Set a detection period and obtain the body temperature of the animal within the historical detection period; Generate a body temperature average value according to the body temperatures of the animal within all historical detection periods; among them, the body temperature average value refers to the average value of the body temperatures of the animal within all historical detection periods; Generate a body temperature change value according to the body temperature of the most recent historical detection period and the body temperature of the current detection period; among them, the body temperature change value refers to the difference between the body temperature of the most recent historical detection period and the body temperature of the current detection period; Through the formula: Generate the body temperature assessment value T int ; In the formula, T ave represents the average body temperature, T stan represents the standard body temperature, T change represents the body temperature change value, T0 represents the body temperature change threshold, and T change > T0; Obtain the weight of the current animal and the weights of the animals in multiple-interval historical detection periods; Generate a weight drop value according to the weight of the current animal and the weights of the animals in multiple-interval historical detection periods; among them, the weight drop value refers to the difference between the weight of the current animal and the weights of the animals in multiple-interval historical detection periods; among them, the weight of the current animal is less than the weights of the animals in multiple-interval historical detection periods; Through the formula: Generate a body weight assessment value F int ; In the formula, F change represents the weight regression value, and F s represents the weights of animals in multiple spaced historical detection periods; Through the formula: J int = T int *(1 + F int ); Generate the basic feature evaluation value J int ; In the formula, T int represents the body temperature assessment value, and F int represents the body weight assessment value.

4. The intelligent epidemic prevention method for livestock farming according to claim 2, characterized in that, The specific generation method of the behavioral characteristic evaluation value is as follows: According to the movement trajectory of the animal, respectively obtain the movement distance and movement speed of the animal within the historical detection period, and mark them as the historical movement distance and historical movement speed respectively; Through the formula: Generate a motion feature evaluation value M int ; In the formula, L i represents the current moving distance, and L s represents the historical moving distance, v i represents the current moving speed, and v s represents the historical moving speed. Both α and β are weighting coefficients, and α + β = 1; Generate a food intake evaluation value according to the food intake of the current animal; among them, the food intake evaluation value refers to the ratio between the current food intake and the standard food intake; Generate an excrement evaluation value based on the excrement form of the animal and the standard excrement form; wherein, the excrement evaluation value refers to the ratio between the excrement form of the animal and the standard excrement form. Perform weighted processing on the motion feature evaluation value, the feeding evaluation value, and the excrement evaluation value to generate a behavior feature evaluation value.

5. The intelligent epidemic prevention method for livestock farming according to claim 1, wherein The specific generation method of the group feature evaluation value is as follows: Obtain the distribution state of the group where the animal is located, establish a three-dimensional space coordinate system with the space where the animal group is located, and substitute the positions of all animals in the animal group into the three-dimensional space coordinate system to generate the coordinates where the animal is located. Generate an individual spacing value according to the coordinate values of the coordinates where all animals are located; wherein, the individual spacing value refers to the difference between the coordinate values of the coordinates where two animals are located. Through the formula: Generate the group characteristic evaluation value Q int ; In the formula, D max represents the group distance value, D0 represents the maximum distance value of the space where the animal group is located, ε is the animal spacing correction value, and the animal spacing correction value ε is a constant, specifically the standard spacing value when animals live in groups.

6. The intelligent epidemic prevention method for livestock breeding according to claim 2, wherein, The expression of the real-time state evaluation model is: In the expression, R represents the animal health status index, and Q int represents the group characteristic evaluation value, and B int represents the behavior characteristic evaluation value, and J int represents the basic characteristic evaluation value. σ represents the proportion range of animals with abnormalities during the standard livestock period, and 0 ≤ σ ≤ 1.

7. The intelligent epidemic prevention method for livestock farming according to claim 1, characterized in that, The specific generation method of the epidemic risk prediction index specifically includes the following steps: Obtain the personnel flow path and personnel disinfection records in the livestock breeding area to generate a personnel flow risk factor. Obtain the epidemic outbreak location, outbreak time, environmental wind direction, and environmental wind speed in the livestock breeding area to generate a social impact factor. Establish an epidemic prevention demand prediction model to generate an epidemic risk prediction index.

8. The intelligent epidemic prevention method for livestock breeding according to claim 7, characterized in that, The specific generation method of the personnel flow risk factor is as follows: Obtain the flow path of the personnel to generate the number of flow regions when the personnel are flowing. Generate a flow region evaluation value according to the number of flow regions; wherein, the flow region evaluation value refers to the ratio between the number of flow regions and the number threshold. Obtain the disinfection records of the personnel to generate the disinfection frequency of the personnel's cross-regional flow; wherein, the disinfection frequency of the personnel's cross-regional flow refers to the ratio between the number of disinfection times when the personnel cross regions and the number of cross-regional times. Generate a cross-regional disinfection evaluation value according to the disinfection frequency of the personnel's cross-regional flow; wherein, the cross-regional disinfection evaluation value refers to the ratio between the disinfection frequency of the personnel's cross-regional flow and the standard disinfection frequency of the personnel's cross-regional flow. Through the formula: Generate the personnel flow risk factor E peo ; In the formula, C regi represents the evaluation value of the flow area, and S disi represents the evaluation value of cross-area disinfection; Among them, the specific generation method of the social impact factor is as follows: Obtain the epidemic outbreak locations around the livestock breeding area. Generate a relative distance value of the outbreak point according to the location of the current livestock breeding area and the epidemic outbreak location. Generate a distance evaluation value according to the relative distance value of the outbreak point. Generate an included angle angle between the two places according to the epidemic outbreak location and the location of the current livestock breeding area. Obtain the wind direction angle of the environmental wind; wherein, the wind direction angle refers to the included angle formed between the straight line formed by the movement of the environmental wind and the reference line. Perform a difference process on the wind direction angle and the included angle angle between the two places to generate an angle deviation value. Perform a ratio process on the angle deviation value and the angle deviation threshold to generate an angle evaluation value. Obtain the environmental wind speed. Through the formula: Generate the wind-dispersion evaluation value F spr ; In the formula, L two represents the relative distance value of the outbreak point, v wind represents the ambient wind speed, t surv represents the pathogen survival time, T two represents the temperature value of the area between the location of the epidemic outbreak and the current livestock farming area, T surv represents the near-endpoint value of the standard pathogen survival temperature range; among them, the temperature value T two of the area between the location of the epidemic outbreak and the current livestock farming area is outside the standard pathogen survival temperature range.

9. The intelligent epidemic prevention method for livestock breeding according to claim 1, wherein The specific expression of the epidemic prevention demand prediction model is: X pred = E peo * F spr ; In the expression, X pred represents the epidemic risk prediction index, E peo represents the risk factor of personnel flow, F spr represents the social impact factor.

10. The intelligent epidemic prevention method for livestock farming according to claim 1, characterized in that, The specific generation method of the epidemic prevention demand prediction index is: Through the formula: W dema = R * (1 + X pred ); Generate the epidemic prevention demand prediction index W dema ; In the formula, R represents the animal health status index, and X pred represents the epidemic risk prediction index.

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