Intelligent epidemic prevention system for animal husbandry and veterinary medicine

Abstract

The invention relates to the technical field of data processing, in particular to an intelligent epidemic prevention system for animal husbandry and veterinary, which comprises a pathogen survival probability acquisition module, a cold and heat irritability acquisition module, an epidemic situation risk occurrence probability acquisition module, a health condition acquisition module and an epidemic outbreak risk acquisition module, firstly, the pathogen survival probability of the livestock environment is determined, and the cold and heat irritability of animals in the livestock environment is obtained based on the fluctuation of temperature data of the livestock environment, so that the epidemic risk occurrence probability of the animals in the livestock environment is obtained in combination with the air quality of the livestock environment; analyzing the occurrence probability of the epidemic situation risk and the animal physiological parameters to obtain the health condition of the animal, and finally obtaining each aggregation area according to the aggregation condition of the animal in the animal husbandry environment, thereby obtaining the epidemic situation outbreak risk of the animal husbandry environment based on the health condition of the animal in each aggregation area. According to the invention, the accuracy of the monitoring result of the epidemic outbreak risk can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to an intelligent epidemic prevention system for livestock veterinarians. Background Art

[0002] During the process of veterinarians monitoring livestock diseases, the livestock environment (i.e., the farm) is a habitat for the breeding and reproduction of viruses, and the poor state of the environment will exacerbate the reproduction of viruses, resulting in a relatively high disease risk among the livestock population. Therefore, it is crucial to monitor the outbreak risk of animal diseases in the livestock environment. Currently, the outbreak risk is mainly obtained by monitoring the temperature and humidity data in the livestock environment. This monitoring method considers fewer factors, and the obtained monitoring results of the outbreak risk are somewhat one-sided, thus reducing the accuracy of the monitoring results of the outbreak risk. Summary of the Invention

[0003] In order to solve the technical problem of the low accuracy of the existing monitoring results of the outbreak risk in farms, the purpose of the present invention is to provide an intelligent epidemic prevention system for livestock veterinarians, and the specific technical solutions adopted are as follows: In the first aspect of the present invention, an intelligent epidemic prevention system for livestock veterinarians is provided, including: A pathogen survival probability acquisition module, configured to determine the pathogen survival probability of the livestock environment, and the pathogen survival probability is obtained from the ventilation efficiency and temperature and humidity data of the livestock environment; A heat and cold stress acquisition module, configured to obtain the heat and cold stress of animals in the livestock environment based on the fluctuation of the temperature data in the livestock environment; An epidemic risk occurrence probability acquisition module, configured to obtain the epidemic risk occurrence probability of animals in the livestock environment according to the pathogen survival probability, heat and cold stress, and air quality in the livestock environment; the epidemic risk occurrence probability is directly proportional to the pathogen survival probability and heat and cold stress, and inversely proportional to the air quality; A health status acquisition module, configured to analyze the epidemic risk occurrence probability and animal physiological parameters to obtain the health status of animals; An epidemic outbreak risk acquisition module, configured to obtain the epidemic outbreak risk of the livestock environment based on the health status of animals in each aggregation area; each aggregation area is obtained from the aggregation situation of animals in the livestock environment.

[0004] In an exemplary embodiment, the process of obtaining the pathogen survival probability includes: Obtaining the mean value of the first data during the monitoring time period, and the proportion of the data volume in the first data that is higher than the mean value; the first data is one of the temperature data and the humidity data; Based on the mean value and the proportion of the data volume, an influence index of the first data on the pathogen survival probability is obtained; the influence index is directly proportional to the mean value and the data volume proportion. Based on the influence indexes of the ventilation efficiency, the temperature data, and the humidity data, the pathogen survival probability is obtained. The pathogen survival probability is inversely proportional to the ventilation efficiency and directly proportional to the influence indexes of the temperature data and the humidity data.

[0005] In an exemplary embodiment, the process of obtaining the ventilation efficiency includes: Obtaining the maximum value of the harmful gas concentration in the livestock environment during the monitoring period; Respectively obtaining the concentration rising data segment and the concentration falling data segment adjacent to the maximum value of the harmful gas concentration; Based on the concentration rising rate and the concentration falling rate of each maximum value of the harmful gas concentration, a ventilation efficiency reference index corresponding to each maximum value of the harmful gas concentration is obtained. The ventilation efficiency reference index is inversely proportional to the concentration rising rate and the value of the maximum harmful gas concentration, and directly proportional to the concentration falling rate. The concentration rising rate is the data change rate of the concentration rising data segment, and the concentration falling rate is the data change rate of the concentration falling data segment; Fusing the ventilation efficiency reference indexes corresponding to all the maximum values of the harmful gas concentration to obtain the ventilation efficiency.

[0006] In an exemplary embodiment, the process of obtaining the heat and cold stress includes: Obtaining the temperature difference between each maximum temperature value and the adjacent minimum temperature value in the temperature data during the monitoring period, as well as the time interval; Based on the temperature difference and the time interval, the degree of temperature change between heat and cold corresponding to each maximum temperature value is obtained. The degree of temperature change between heat and cold is directly proportional to the temperature difference and inversely proportional to the time interval; Fusing the degrees of temperature change between heat and cold corresponding to all the maximum temperature values to obtain the heat and cold stress.

[0007] In an exemplary embodiment, the animal physiological parameters include the animal's exercise amount and body temperature; The process of obtaining the health status includes: Obtaining the correlation between the animal's exercise amount and body temperature, as well as the degree of the animal's body temperature fever; Based on the probability of the occurrence of the epidemic risk, the correlation, and the degree of body temperature fever, the health status of the animal is obtained. The health status is inversely proportional to the probability of the occurrence of the epidemic risk and the degree of body temperature fever, and directly proportional to the correlation.

[0008] In an exemplary embodiment, the process of obtaining the correlation between the exercise amount and the body temperature includes: Obtain the exercise volume curve and body temperature curve of the animal within the monitoring time period; Obtain the similarity between the exercise volume curve and the body temperature curve as the correlation between the exercise volume and the body temperature.

[0009] In an exemplary embodiment, the process of obtaining the degree of body temperature fever includes: obtaining the proportion of the amount of body temperature data higher than the upper limit of the normal body temperature in the body temperature data of the animal within the monitoring time period as the degree of body temperature fever.

[0010] In an exemplary embodiment, the process of obtaining the risk of epidemic outbreak includes: Obtain the number of abnormal animals with a health condition lower than the preset health condition threshold in each gathering area; According to the total number of animals, the number of abnormal animals, and the average health condition of the animals in each gathering area, obtain the risk of epidemic outbreak in each gathering area; the risk of epidemic outbreak in the epidemic area is proportional to the total number of animals and the number of abnormal animals, and inversely proportional to the average health condition; Fuse the risks of epidemic outbreak in each gathering area to obtain the risk of epidemic outbreak in the livestock environment.

[0011] In an exemplary embodiment, the intelligent epidemic prevention system further includes a ventilation adjustment module for increasing the ventilation power of the livestock environment when the risk of epidemic outbreak in the livestock environment is greater than the preset epidemic outbreak risk threshold.

[0012] In an exemplary embodiment, the process of obtaining the air quality includes: obtaining the average concentration of harmful gases in the livestock environment within the monitoring time period, and performing negative correlation on the average concentration of harmful gases to obtain the air quality.

[0013] The present invention has the following beneficial effects: Compared with the existing method of obtaining the risk of epidemic outbreak only based on the temperature and humidity data in the livestock environment, the present invention not only considers the temperature and humidity in the livestock environment, but also considers the impact of temperature changes in the livestock environment on the cold and heat stress of animals, and the impact of the air quality in the livestock environment on the probability of the occurrence of the epidemic risk. More importantly, it also combines the impact of animal physiological parameters and the aggregation situation of animals in the livestock environment on the risk of epidemic outbreak. The factors considered are more comprehensive, thereby improving the comprehensiveness of the factors considered in the monitoring of the risk of epidemic outbreak, and further improving the accuracy of the monitoring results of the risk of epidemic outbreak. Description of the Drawings

[0014] Figure 1 is a schematic structural diagram of an intelligent epidemic prevention system for livestock veterinarians provided by an embodiment of the present invention; Figure 2It is the flowchart of the steps corresponding to each module of an intelligent epidemic prevention system for animal husbandry and veterinary medicine provided by an embodiment of the present invention; Figure 3 It is the flowchart for obtaining the ventilation efficiency provided by an embodiment of the present invention; Figure 4 It is the flowchart for obtaining the pathogen survival probability provided by an embodiment of the present invention; Figure 5 It is the flowchart for obtaining the cold and heat stress provided by an embodiment of the present invention; Figure 6 It is the flowchart for obtaining the health status provided by an embodiment of the present invention; Figure 7 It is the flowchart for obtaining the risk of epidemic outbreak provided by an embodiment of the present invention. Detailed implementation manners

[0015] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features and effects of the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0016] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. All data information collected in this application is obtained with full consent and authorization, and the collection, use and processing of relevant information need to comply with the relevant laws, regulations and standards of relevant countries and regions.

[0017] This embodiment provides an intelligent epidemic prevention system for animal husbandry and veterinary medicine, as Figure 1 shown, including a pathogen survival probability acquisition module, a cold and heat stress acquisition module, an epidemic risk occurrence probability acquisition module, a health status acquisition module, and an epidemic outbreak risk acquisition module. Each module can be a software module, which is essentially the corresponding method step; it can also be a hardware module, and the executed method step is configured in the hardware module so that the hardware module realizes the corresponding function. Correspondingly, the intelligent epidemic prevention system can be a software system, configured in relevant processors, computer hosts, and relevant veterinary supervision platforms; it can also be a hardware system, such as a server, a computer host, etc. This embodiment does not limit the specific configuration manners of each module and the intelligent epidemic prevention system.

[0018] As Figure 2 shown, the method steps corresponding to each module are as follows: A pathogen survival probability acquisition module, which is used to determine the pathogen survival probability of the livestock environment, and the pathogen survival probability is obtained from the ventilation efficiency and temperature and humidity data of the livestock environment; A heat and cold stress acquisition module, which is used to obtain the heat and cold stress of animals in the livestock environment based on the fluctuation of the temperature data in the livestock environment; An epidemic risk occurrence probability acquisition module, which is used to obtain the epidemic risk occurrence probability of animals in the livestock environment according to the pathogen survival probability, heat and cold stress, and air quality of the livestock environment; the epidemic risk occurrence probability is directly proportional to the pathogen survival probability and heat and cold stress, and inversely proportional to the air quality; A health status acquisition module, which is used to analyze the epidemic risk occurrence probability and animal physiological parameters to obtain the health status of the animals; An epidemic outbreak risk acquisition module, which is used to obtain the epidemic outbreak risk of the livestock environment based on the health status of animals in each aggregation area; each aggregation area is obtained from the aggregation situation of animals in the livestock environment.

[0019] The following combines the drawings to illustrate the specific implementation process of each module.

[0020] A pathogen survival probability acquisition module, which is used to determine the pathogen survival probability of the livestock environment, and the pathogen survival probability is obtained from the ventilation efficiency and temperature and humidity data of the livestock environment.

[0021] In this embodiment, the livestock environment is the breeding environment of the animals being raised, specifically a farm, and further an indoor farm. The animals raised in the farm are of the same kind, taking the most common pigs as an example. When the situation in the farm deteriorates, it may cause diseases in the pig group. Therefore, it is necessary to analyze various environmental parameters in the farm in real time to judge the risk of an epidemic.

[0022] Temperature sensors and humidity sensors are arranged in the farm to detect the temperature data and humidity data of the farm. A ventilation system, such as a ventilation fan and other equipment, is also arranged in the farm to realize the ventilation of the farm. A harmful gas concentration sensor is also arranged in the farm to detect the concentration of harmful gases in the farm. Since the most common harmful gas in the farm and the one that causes relatively great damage to the animals' bodies is ammonia, the harmful gas concentration sensor in this embodiment is an ammonia concentration sensor, and the obtained harmful gas concentration is the ammonia concentration.

[0023] Preset a monitoring time period, the length of which is set according to actual needs, such as 20 minutes. Moreover, the end time of the monitoring time period can be the current time to achieve real-time monitoring of the risk of epidemic outbreaks based on the data information within the monitoring time period. In this embodiment, the sampling frequency of each sensor is set according to the actual situation, such as once per second. Then, there are multiple sampling moments within the monitoring time period. In addition, the sampling frequencies of each sensor are the same and data is collected synchronously.

[0024] Taking pigs as an example, when the breeding farm is in a state of high temperature and high humidity, it will accelerate the survival of various pathogens (such as foot-and-mouth disease virus and Erysipelothrix rhusiopathiae) in the livestock environment. And when the ventilation of the breeding farm is poor, it will cause the retention of various harmful gases and dust, thus increasing the spread of various pathogens. Therefore, first obtain the ventilation efficiency of the livestock environment during the monitoring time period. The ventilation efficiency is essentially the ventilation condition of the breeding farm. The better the ventilation condition, the lower the survival probability of pathogens in the breeding farm. In an exemplary embodiment, as Figure 3 shown, the following gives a specific process for obtaining the ventilation efficiency: Step 1-1: Obtain the maximum value of the harmful gas concentration in the harmful gas concentration of the livestock environment during the monitoring time period.

[0025] The harmful gas concentration sensor collects the harmful gas concentration in the breeding farm according to the sampling frequency, obtains the harmful gas concentration at each sampling moment, and according to the time sequence, obtains the time sequence of the harmful gas concentration in the breeding farm during the monitoring time period. The time sequence of the harmful gas concentration can also be curve-fitted to obtain the harmful gas concentration change curve. Then obtain each maximum value of the harmful gas concentration in the time sequence of the harmful gas concentration.

[0026] Step 1-2: Respectively obtain the concentration rising data segment and the concentration falling data segment adjacent to the maximum value of the harmful gas concentration.

[0027] It should be understood that in the time series of harmful gas concentrations, the maximum values of harmful gas concentrations are all larger than the values of the harmful gas concentration data on both sides. For any maximum value of harmful gas concentration, obtain the concentration rising data segment and the concentration falling data segment adjacent to the maximum value of harmful gas concentration. Since the data change situation of the data segment before the maximum value of harmful gas concentration in the time series of harmful gas concentrations is that the harmful gas concentration gradually rises, then, obtain the data segment before the maximum value of harmful gas concentration and adjacent to the maximum value of harmful gas concentration as the concentration rising data segment; and obtain the data segment after the maximum value of harmful gas concentration and adjacent to the maximum value of harmful gas concentration as the concentration falling data segment. In this embodiment, the amount of data included in the concentration rising data segment and the concentration falling data segment is set according to actual needs, and the amount of data included needs to satisfy that both the concentration rising data segment and the concentration falling data segment are data segments with monotonic changes, such as 5 data. In addition, for the maximum values of harmful gas concentration near the edge of the time series of harmful gas concentrations, the amount of data included in the corresponding concentration rising data segment and concentration falling data segment may not meet the requirements, then, these maximum values of harmful gas concentration can be discarded and not participate in the subsequent acquisition of ventilation efficiency.

[0028] Step 1-3: Obtain the ventilation efficiency reference index corresponding to each maximum value of harmful gas concentration according to the concentration rising rate and the concentration falling rate of each maximum value of harmful gas concentration.

[0029] For any maximum value of harmful gas concentration, obtain the data change rate of the concentration rising data segment of the maximum value of harmful gas concentration as the concentration rising rate. In an exemplary embodiment, obtain the slope of each data point in the curve segment corresponding to the concentration rising data segment, and then calculate the average slope as the concentration rising rate. Similarly, obtain the data change rate of the concentration falling data segment of the maximum value of harmful gas concentration as the concentration falling rate. In an exemplary embodiment, obtain the slope of each data point in the curve segment corresponding to the concentration falling data segment, and then calculate the absolute value of the average slope as the concentration falling rate.

[0030] When the ventilation condition in the farm is good, the harmful gas concentration cannot rise to a high level, and after rising, it will also drop quickly due to good ventilation. Therefore, the better the ventilation efficiency in the farm, the smaller the concentration rising rate of the concentration rising data segment before the maximum value of harmful gas concentration, and the larger the concentration falling rate of the concentration falling data segment after the maximum value of harmful gas concentration. Moreover, the better the ventilation efficiency in the farm, the less there is a large harmful gas concentration, then, the better the ventilation efficiency in the farm, the smaller the value of the maximum value of harmful gas concentration. Therefore, the ventilation efficiency reference index of the maximum value of harmful gas concentration is inversely proportional to the concentration rising rate and the value of the maximum value of harmful gas concentration, and is directly proportional to the concentration falling rate.

[0031] In an exemplary embodiment, a specific quantification method for the ventilation efficiency reference index of the maximum harmful gas concentration is given as follows: ; Wherein, is the ventilation efficiency reference index of the j-th maximum harmful gas concentration, is the value of the -th maximum harmful gas concentration, is the concentration decrease rate corresponding to the j-th maximum harmful gas concentration, is the concentration increase rate corresponding to the j-th maximum harmful gas concentration.

[0032] represents a normalization function. The normalization method here can be: obtain the maximum and minimum values in the corresponding to all the maximum harmful gas concentrations, and then use the maximum-minimum normalization method to normalize the of the j-th maximum harmful gas concentration so that its numerical range is within [0, 1] for subsequent ventilation efficiency calculation.

[0033] Step 1-4: Integrate the ventilation efficiency reference indexes corresponding to all the maximum harmful gas concentrations to obtain the ventilation efficiency.

[0034] Integrate the ventilation efficiency reference indexes corresponding to all the maximum harmful gas concentrations. In an exemplary embodiment, calculate the average value of the ventilation efficiency reference indexes corresponding to all the maximum harmful gas concentrations as the ventilation efficiency.

[0035] Since the ventilation efficiency of the farm and the temperature and humidity data of the farm determine the survival probability of pathogens in the farm. Therefore, obtain the survival probability of pathogens in the farm according to the ventilation efficiency of the farm and the temperature and humidity data of the farm. In an exemplary embodiment, as Figure 4 shown, a specific acquisition process of the pathogen survival probability is given: Step 1-5: Obtain the mean value of the first data during the monitoring period and the proportion of the data volume in the first data that is higher than the mean value.

[0036] Based on the temperature sensor, humidity sensor and the preset sensor sampling frequency, the time series of temperature data and the time series of humidity data of the farm can be obtained during the monitoring period.

[0037] For the sake of convenience of explanation, assume that the first data is one of the temperature data and the humidity data. Obtain the mean value of the first data during the monitoring period and the proportion of the data volume in the first data that is higher than the mean value.

[0038] Specifically: for temperature data, obtain the average temperature of the time series of temperature data within the monitored time period. Then, the magnitude relationship between each temperature data in the temperature data time series and the average temperature can be obtained, so as to obtain the temperature data higher than the average temperature, and then obtain the quantity of the temperature data higher than the average temperature. Then, obtain the ratio of this quantity to the total quantity of temperature data in the temperature data time series as the proportion of the quantity of temperature data higher than the average temperature; similarly, for humidity data, obtain the average humidity of the time series of humidity data within the monitored time period. Then, the magnitude relationship between each humidity data in the humidity data time series and the average humidity can be obtained, so as to obtain the humidity data higher than the average humidity, and then obtain the quantity of the humidity data higher than the average humidity. Then, obtain the ratio of this quantity to the total quantity of humidity data in the humidity data time series as the proportion of the quantity of humidity data higher than the average humidity.

[0039] Steps 1 - 6: Obtain the influence index of the first data on the survival probability of pathogens according to the average value and the proportion of the quantity.

[0040] Since the higher the temperature, the higher the survival probability of pathogens, and the higher the humidity, the higher the survival probability of pathogens. Also, since the average value of the first data and the proportion of the quantity of data higher than the average value in the first data represent the data levels of temperature data and humidity data, the higher the average value of the first data and the proportion of the quantity of data higher than the average value in the first data, the higher the data levels of temperature data and humidity data, and the higher the survival probability of pathogens. Then, the influence index of the first data on the survival probability of pathogens is proportional to the average value of the first data and the proportion of the quantity of data higher than the average value in the first data. Moreover, the higher the influence index on the survival probability of pathogens, the higher the survival probability of pathogens.

[0041] Correspondingly, the influence index of temperature data is obtained from the average temperature and the proportion of the quantity of temperature data higher than the average temperature; the influence index of humidity data is obtained from the average humidity and the proportion of the quantity of humidity data higher than the average humidity.

[0042] In an exemplary embodiment, a specific quantification formula for the influence index of the first data on the survival probability of pathogens is given as follows: ; Wherein, represents the influence index of the i-th type of data on the survival probability of pathogens, i is equal to 1 or 2. When i is equal to 1, the i-th type of data is temperature data; when i is equal to 2, the i-th type of data is humidity data; is the proportion of the quantity of data higher than the average value of the i-th type of data in the i-th type of data, is the total quantity of the i-th type of data, represents the proportion of the quantity of data higher than the average value in the i-th type of data; is the mean value of the i-th type of data, represents the maximum allowable value of the i-th type of data. When the i-th type of data is temperature data, the maximum allowable value is the upper limit of the temperature that may occur in the farm; when the i-th type of data is humidity data, the maximum allowable value is the upper limit of the humidity that may occur in the farm.

[0043] Step 1-7: Obtain the pathogen survival probability based on the influence indexes of ventilation efficiency, temperature data, and humidity data.

[0044] Comprehensively analyze the pathogen survival probability from two aspects: ventilation efficiency and the influence indexes of temperature data and humidity data to obtain the pathogen survival probability. Since the higher the ventilation efficiency, the lower the pathogen survival probability; the higher the influence indexes of temperature data and humidity data, it indicates that the temperature and humidity in the farm are at a relatively high level, and the pathogen survival probability in the farm is higher. Therefore, the pathogen survival probability is inversely proportional to the ventilation efficiency and directly proportional to the influence indexes of temperature data and humidity data.

[0045] In an exemplary embodiment, a specific quantification method of the pathogen survival probability is given as follows: ; wherein, represents the pathogen survival probability, represents the ventilation efficiency.

[0046] The cold and heat stress acquisition module is used to obtain the cold and heat stress of animals in the livestock environment based on the fluctuation of temperature data in the livestock environment.

[0047] An increase in the pathogen survival probability of animals in the farm will lead to an increased risk of an epidemic. And when the environment of the farm is relatively poor, it may affect the animals themselves and thus increase the probability of an epidemic. Therefore, it is necessary to analyze the impact of the farm environment on the animals themselves. When some environmental parameters of the farm are not suitable for the animals to live, it may affect the animals themselves and thus increase the epidemic risk.

[0048] When the temperature in the farm changes too fast, it may cause cold and heat stress in animals, resulting in an increase in cortisol levels, which inhibits lymphocyte activity, causing the antibody titer of the plague vaccine to decrease by 20% - 30%, leading to an increased epidemic risk in the farm. Because it is necessary to judge whether there is cold and heat stress according to the temperature fluctuation in the farm.

[0049] In an exemplary embodiment, as Figure 5 shown, a specific acquisition process of cold and heat stress is given as follows: Step 2-1: Obtain the temperature difference between each temperature maximum value and the adjacent temperature minimum value in the temperature data within the monitoring time period, as well as the time interval.

[0050] Each temperature maximum value in the temperature data (i.e., the temperature data time series) within the monitoring time period is obtained. Then, the temperature minimum value adjacent to each temperature maximum value is obtained. In this embodiment, the temperature minimum value adjacent to each temperature maximum value is specifically the temperature minimum value adjacent to each temperature maximum value. For a temperature maximum value that does not have an adjacent temperature minimum value, the subsequent cold and heat stress calculation is not performed.

[0051] The temperature difference between each temperature maximum value and the next adjacent temperature minimum value, and the time interval between each temperature maximum value and the next adjacent temperature minimum value are obtained.

[0052] Step 2-2: Based on the temperature difference and time interval, obtain the degree of temperature change corresponding to each temperature maximum value.

[0053] The greater the temperature difference between each temperature maximum and the next temperature minimum, the greater the temperature difference, the more drastic the fluctuation of temperature data, and accordingly, the stronger the cold and heat stress caused by temperature fluctuation on animals. The shorter the time interval between each temperature maximum and the next temperature minimum, the faster the temperature drops from high temperature to low temperature, the more drastic the temperature fluctuation, and accordingly, the stronger the cold and heat stress caused by temperature fluctuation on animals.

[0054] Based on the temperature difference and time interval, the degree of temperature change corresponding to each temperature maximum is obtained. The degree of temperature change is proportional to the temperature difference and inversely proportional to the time interval.

[0055] In an exemplary embodiment, a specific quantitative method for determining the degree of temperature change is given as follows: ; in, For the The degree of temperature change corresponding to the maximum temperature value is For the The time interval between a temperature maximum and the next adjacent minimum point, For the The temperature difference between a temperature maximum and the next adjacent temperature minimum.

[0056] The normalization method here can be: Get the corresponding values ​​of all temperature maxima The maximum and minimum values ​​in the , and then the maximum and minimum values ​​are normalized to the first The temperature maximum Normalize it so that its numerical range is within [0, 1], which is convenient for subsequent calculation of cold and heat stress.

[0057] Step 2-3: Integrate the temperature cold and heat change degrees corresponding to all temperature maxima to obtain the cold and heat stress.

[0058] Integrate the temperature cold and heat change degrees corresponding to all temperature maxima. In an exemplary embodiment, here calculate the average value of the temperature cold and heat change degrees corresponding to all temperature maxima as the required cold and heat stress.

[0059] The epidemic risk occurrence probability acquisition module is used to obtain the epidemic risk occurrence probability of animals in the livestock environment according to the pathogen survival probability, cold and heat stress, and the air quality of the livestock environment.

[0060] When the ammonia concentration is relatively high, it may damage the cilia of the animal's respiratory tract, reduce the mucosal immune barrier function, and cause animals to be more prone to epidemic risks. Therefore, obtain the air quality according to the harmful gas concentration. Specifically: obtain the average value of the harmful gas concentration in the farm during the monitoring period, and perform negative correlation on the obtained average value of the harmful gas concentration to obtain the air quality. In an exemplary embodiment, the negative correlation here is specifically negative correlation normalization, and the negative correlation normalization method adopted can be: exp(-y), where y is the data that needs to be negatively correlated normalized, and exp is the exponential function with the natural constant e as the base.

[0061] Since the higher the pathogen survival probability, the higher the epidemic risk occurrence probability of animals in the farm; the stronger the cold and heat stress, the higher the epidemic risk occurrence probability of animals in the farm; the worse the air quality of the farm, the higher the epidemic risk occurrence probability of animals in the farm. Therefore, the epidemic risk occurrence probability is directly proportional to the pathogen survival probability and cold and heat stress, and inversely proportional to the air quality. According to the pathogen survival probability, cold and heat stress, and the air quality of the livestock environment, the epidemic risk occurrence probability can be obtained.

[0062] In an exemplary embodiment, a specific quantification method of the epidemic risk occurrence probability is given as follows: ; where represents the epidemic risk occurrence probability, represents the cold and heat stress, represents the air quality. Here, the method of averaging the three indicators is adopted, that is, the method of weighted summation with equal weights is used to integrate these three indicators to obtain the epidemic risk occurrence probability.

[0063] The health status acquisition module is used to analyze the epidemic risk occurrence probability and animal physiological parameters to obtain the health status of the animals.

[0064] When there is a risk probability of an epidemic in an animal, if there are also some adverse reactions in the physical state of the animal, it will affect the health of the animal. Therefore, it is necessary to monitor the physiological parameters of the animal. Animal physiological parameters include the amount of exercise and body temperature of the animal. Accordingly, it is necessary to provide a motion sensor and a body temperature sensor on the animal's body.

[0065] Since there are multiple animals in the farm, any one animal will be taken as an example for illustration below. In an exemplary embodiment, a smart ear tag is installed on the animal's ear. The smart ear tag is built-in with a body temperature sensor and a heart rate sensor, which can obtain the body temperature and heart rate data of the animal in real time. Moreover, a locator is also built-in in the smart ear tag for obtaining the position information of the animal in real time, so as to obtain the motion state and the amount of exercise data of the animal. It should be understood that the acquisition frequencies of the various sensors built-in in the smart ear tag can be the same as and synchronized with the acquisition frequencies of the various environmental sensors in the above text.

[0066] When there is a risk of an epidemic in the farm, animals with better physical conditions usually have better resistance and can better resist the epidemic, while animals with weaker bodies may have abnormal physical conditions due to the poor environment. Moreover, when the animal is relatively healthy, the physiological state and motion state of the animal will be relatively normal, the body temperature of the animal will be within the normal range and will be more excited, and the amount of exercise will be in a normal to high state. When the animal does not perform strenuous exercise, the body temperature of a healthy animal will be within the normal range and relatively low. If the body temperature of the animal is also high when it is not moving, the animal may be in a state of persistent fever at this time, which will inhibit the activity of lymphocytes and cause the antibody effect to decrease, and the health of the animal is poor. Therefore, the health of the animal is related to the probability of the epidemic risk in the farm and the animal physiological parameters. In an exemplary embodiment, as Figure 6 shown, a specific acquisition process of the health status is given as follows: Step 4-1: Obtain the correlation between the amount of exercise and body temperature of the animal, and the degree of body temperature fever of the animal.

[0067] The health status is related to the correlation between the amount of exercise and body temperature of the animal, and the degree of body temperature fever of the animal. Therefore, it is necessary to obtain the correlation between the amount of exercise and body temperature of the animal, and the degree of body temperature fever of the animal.

[0068] Among them, the process of obtaining the correlation between the exercise amount and body temperature of an animal is specifically as follows: Based on the intelligent ear tag, the exercise amount curve and body temperature curve of the animal within the monitoring time period are obtained. Among them, the process of obtaining the exercise amount curve is: obtaining the moving distance of the animal at each sampling moment in the intelligent ear tag, using the moving distance to represent the exercise amount, and the greater the moving distance, the greater the exercise amount. The moving distance is the distance between the position of the animal at each sampling moment and the position of the animal at the previous sampling moment (the distance at the first sampling moment is 0). The exercise amount curve is fitted according to the moving distance of the animal at each sampling moment within the monitoring time period. The process of obtaining the body temperature curve is: obtaining the body temperature data of the animal at each sampling moment within the monitoring time period and fitting it into a body temperature curve.

[0069] The similarity between the exercise amount curve and the body temperature curve is obtained as the correlation between the exercise amount and the body temperature. Among them, the similarity can be the Pearson correlation coefficient, cosine similarity, etc. In this embodiment, the Pearson correlation coefficient is taken as an example, that is, the Pearson correlation coefficient between the exercise amount curve and the body temperature curve is obtained, and then the Pearson correlation coefficient is normalized, and the obtained result is the correlation between the exercise amount and the body temperature. Since the value range of the Pearson correlation coefficient is [-1, 1], the method of normalizing the Pearson correlation coefficient can be: calculating the sum value when the Pearson correlation coefficient is the value 1, and then dividing the sum value by 2.

[0070] Under normal circumstances, the correlation between the exercise amount and body temperature of an animal is relatively high. Within a certain range, the greater the exercise amount, the greater the increase in body temperature. Correspondingly, the worse the health condition of the animal, the lower the correlation between the exercise amount and body temperature.

[0071] The degree of body temperature fever characterizes the severity of the animal's body temperature fever. In an exemplary embodiment, the process of obtaining the degree of body temperature fever is: first determining the upper limit value of the normal body temperature of the animal. Under normal circumstances, for pigs, the upper limit value of the normal body temperature can be set to 39.0 °C. Comparing the body temperature data of the animal at each sampling moment within the monitoring time period with the upper limit value of the normal body temperature, obtaining the amount of body temperature data higher than the upper limit value of the normal body temperature in the body temperature data of the animal within the monitoring time period, and then performing a division operation with the total number of the body temperature data of the animal within the monitoring time period. The obtained result is the proportion of the body temperature data higher than the upper limit value of the normal body temperature in the body temperature data of the animal within the monitoring time period, and this proportion of the body temperature data is used as the degree of body temperature fever. Therefore, the higher the proportion of the body temperature data, the more body temperature data with too high body temperature appears within the monitoring time period, and the more severe the degree of body temperature fever.

[0072] Step 4-2: Obtain the health condition of the animal according to the occurrence probability of the epidemic risk, the correlation, and the degree of body temperature fever.

[0073] Comprehensively analyze the health status of animals from three aspects: the probability of the emergence of the epidemic risk, the correlation between the amount of exercise and body temperature, and the degree of body temperature fever. Among them, the higher the probability of the emergence of the epidemic risk, the more serious the impact of the epidemic on the health status of animals, and the worse the health status of animals; the higher the correlation between the amount of exercise and body temperature, the better the health status of animals; the higher the degree of body temperature fever, the worse the health status of animals. Therefore, the health status is inversely proportional to the probability of the emergence of the epidemic risk and the degree of body temperature fever, and directly proportional to the correlation between the amount of exercise and body temperature.

[0074] In an exemplary embodiment, a specific quantification method of the health status is given as follows: ; Wherein, is the health status of the s-th animal, is the correlation between the amount of exercise and body temperature of the s-th animal, is the degree of body temperature fever of the s-th pig. Here, an average method for the three indicators is adopted, that is, a weighted summation method with equal weights is used to fuse these three indicators to obtain the health status.

[0075] The above operations obtain the health status of each animal in the farm, among which there may be some animals with better health status and some animals with worse health status. However, when the animals with poor health status gather together, there may be a certain risk of epidemic outbreak at this time.

[0076] The epidemic outbreak risk acquisition module is used to obtain the epidemic outbreak risk of the livestock environment based on the health status of animals in each aggregation area; each aggregation area is obtained from the aggregation situation of animals in the livestock environment.

[0077] Animals will gather in the farm. Then, the intelligent ear tags are used to obtain the position information of each animal in the farm, so as to obtain multiple aggregation areas according to the aggregation situation of animals in the farm. In an exemplary embodiment, the aggregation situation of animals in the farm can be clustered based on the existing clustering algorithm to obtain multiple clusters, and each cluster represents each aggregation area. For example: clustering is performed using the K-means clustering algorithm based on the distance between any two animals, where the value of K needs to be determined in advance, and the value of K is set according to the clustering requirements.

[0078] Then, based on the health status of animals in each aggregation area, the epidemic outbreak risk of the farm is obtained. In an exemplary embodiment, as Figure 7 shown, a specific acquisition process of the epidemic outbreak risk is given as follows: Step 5-1: Obtain the number of abnormal animals with a health status lower than the preset health status threshold in each aggregation area.

[0079] Analyze any aggregation area, which includes multiple animals. Preset a health status threshold, the value range of the preset health status threshold is 0 - 1, and the specific value of the preset health status threshold is set according to the actual situation. The preset health status threshold is used to distinguish between low or high health status. Then, if a safer health status judgment logic is required, the preset health status threshold can be set slightly larger, so that the health status of more animals meets the judgment logic. In this embodiment, the preset health status threshold is taken as an example of 0.6.

[0080] Compare the health status of each animal in the aggregation area with the preset health status threshold, obtain the animals corresponding to the health status lower than the preset health status threshold, and these animals are defined as abnormal animals, so as to obtain the number of abnormal animals. At the same time, obtain the total number of animals in the aggregation area.

[0081] Step 5-2: Obtain the epidemic area outbreak risk of each aggregation area according to the total number of animals, the number of abnormal animals, and the average health status of animals in each aggregation area.

[0082] Calculate the average value of the health status of each animal in the aggregation area to obtain the average health status of the aggregation area.

[0083] The epidemic area outbreak risk of the aggregation area is affected by the total number of animals, the number of abnormal animals, and the average health status in the aggregation area. Among them, the more the total number of animals in the aggregation area, the higher the risk of epidemic outbreak in the aggregation area; the more the number of abnormal animals in the aggregation area, it means that there are more animals with poor health status in the aggregation area. Then, the higher the risk of epidemic outbreak in the aggregation area. Moreover, when the total number of animals and the number of abnormal animals in the aggregation area are more, it indicates that the animals with lower health status in the aggregation area will affect each other and continue to spread outwards. Therefore, the higher the risk of epidemic outbreak. The better the average health status of the aggregation area, the better the overall health status of the animals in the aggregation area, and the lower the risk of epidemic outbreak in the aggregation area. Therefore, the epidemic area outbreak risk is proportional to the total number of animals and the number of abnormal animals, and inversely proportional to the average health status.

[0084] In an exemplary embodiment, in order to facilitate the specific quantification of the epidemic area outbreak risk, it is necessary to normalize the total number of animals and the number of abnormal animals in the aggregation area. The normalization method here can be: calculate the sum value of the total number of animals and the number of abnormal animals in each aggregation area, determine the maximum value and the minimum value from the sum values corresponding to each aggregation area, and then use the maximum-minimum normalization method to normalize the sum value of the total number of animals and the number of abnormal animals in the aggregation area.

[0085] A specific quantification process for the outbreak risk of the epidemic area in the aggregation area is given as follows: ; Among them, is the outbreak risk of the epidemic area in the c-th aggregation area, is the average health status of the c-th aggregation area, is the normalized result of the sum of the total number of animals and the number of abnormal animals in the c-th aggregation area. It should be noted that the value of the average health status can be undoubtedly determined to be a normalized value according to the calculation method of the health status of each animal.

[0086] By using the above method, the outbreak risks of the epidemic areas in each aggregation area are obtained.

[0087] Step 5-3: Integrate the outbreak risks of the epidemic areas in each aggregation area to obtain the outbreak risk of the livestock environment.

[0088] In an exemplary embodiment, when integrating the outbreak risks of the epidemic areas in each aggregation area, calculate the average value of the outbreak risks of the epidemic areas in all aggregation areas, and the obtained result is the outbreak risk of the farm.

[0089] When the ventilation condition in the farm is poor, various pathogens breed, which in turn leads to problems with the health status of the animals. And when the animals have health problems, it will continuously affect the health of other surrounding animals, resulting in the aggregation of unhealthy animals, and thus the farm has a relatively high outbreak risk.

[0090] After obtaining the outbreak risk of the farm, the veterinarian can monitor the outbreak risk of the farm in real time and take relevant solutions.

[0091] In an exemplary embodiment, after obtaining the outbreak risk of the farm, the intelligent epidemic prevention system further includes a ventilation adjustment module.

[0092] In this embodiment, a preset outbreak risk threshold is set. The numerical range of the preset outbreak risk threshold is 0-1, and the specific value of the preset outbreak risk threshold is set according to the actual situation. The preset outbreak risk threshold is used to determine whether the outbreak risk of the farm is at a high or low level. Then, if a safer monitoring logic is required, the preset outbreak risk threshold can be set slightly smaller. In this embodiment, the preset outbreak risk threshold is taken as an example of 0.5.

[0093] The ventilation adjustment module is used to: compare the obtained epidemic outbreak risk of the farm with the preset epidemic outbreak risk threshold. When the epidemic outbreak risk is greater than the preset epidemic outbreak risk threshold, it indicates that the epidemic outbreak risk of the farm is relatively high and the ventilation situation of the farm needs to be improved. Then, on the basis of the existing ventilation power, increase the ventilation power of the farm, so as to improve the ventilation situation of the farm, optimize the air circulation in the farm, and further reduce the accumulation of pathogens. It should be understood that the increase amplitude of the ventilation power of the farm is set according to the actual situation. For example, it can be increased by a fixed value (such as increasing by 30% on the basis of the original ventilation power), or it can be increased linearly. For example, the greater the epidemic outbreak risk, the greater the increase amplitude of the ventilation power.

[0094] In addition, during the process of increasing the ventilation power, animals with health problems can also be isolated and controlled to prevent the further spread of diseases, so as to achieve the purpose of epidemic prevention and control.

[0095] It should be noted that: the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0096] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

Claims

1. An intelligent epidemic prevention system for animal husbandry and veterinary medicine, characterized in that: include: The pathogen survival probability acquisition module is used to determine the pathogen survival probability in the livestock environment. The pathogen survival probability is obtained from the ventilation efficiency and temperature and humidity data of the livestock environment. A heat stress acquisition module is used to obtain the heat stress of animals in the livestock environment based on the fluctuation of temperature data of the livestock environment; The module for obtaining the probability of occurrence of epidemic risks is used to obtain the probability of occurrence of epidemic risks of animals in the animal husbandry environment based on the survival probability of pathogens, cold and heat stress and the air quality of the animal husbandry environment; The probability of epidemic risk is directly proportional to the survival probability of pathogens and cold and heat stress, and inversely proportional to air quality; The health status acquisition module is used to analyze the probability of epidemic risk and animal physiological parameters to obtain the health status of animals; The epidemic outbreak risk acquisition module is used to obtain the epidemic outbreak risk of the animal husbandry environment based on the health status of animals in each gathering area; each gathering area is obtained by the gathering situation of animals in the animal husbandry environment.

2. The intelligent epidemic prevention system for animal husbandry and veterinary medicine as claimed in claim 1, characterized in that: The process of obtaining the pathogen survival probability includes: Obtaining an average value of first data within a monitoring period, and a proportion of data in the first data that is higher than the average value; the first data is one of temperature data and humidity data; According to the mean and the data volume ratio, an impact index of the first data on the survival probability of the pathogen is obtained; the impact index is proportional to the mean and the data volume ratio; According to the influencing indicators of ventilation efficiency, temperature data and humidity data, the survival probability of pathogens is obtained. The survival probability of pathogens is inversely proportional to the ventilation efficiency and directly proportional to the influencing indicators of temperature data and humidity data.

3. An intelligent epidemic prevention system for animal husbandry and veterinary medicine as claimed in claim 1 or 2, characterized in that: The process of obtaining the ventilation efficiency includes: Obtain the maximum value of harmful gas concentration among the harmful gas concentrations in the livestock environment during the monitoring period; Respectively obtain the concentration increase data segment and the concentration decrease data segment adjacent to the maximum value of the harmful gas concentration; According to the concentration rising rate and concentration falling rate of each harmful gas concentration maximum value, the ventilation efficiency reference index corresponding to each harmful gas concentration maximum value is obtained; the ventilation efficiency reference index is inversely proportional to the concentration rising rate and the value of the harmful gas concentration maximum value, and is proportional to the concentration falling rate; the concentration rising rate is the data change rate of the concentration rising data segment, and the concentration falling rate is the data change rate of the concentration falling data segment; The ventilation efficiency is obtained by integrating the ventilation efficiency reference indicators corresponding to the maximum values ​​of harmful gas concentrations.

4. The intelligent epidemic prevention system for animal husbandry and veterinary medicine as claimed in claim 1, characterized in that: The process of obtaining the cold and heat stress includes: Obtain the temperature difference between each temperature maximum value and the adjacent temperature minimum value in the temperature data within the monitoring time period, as well as the time interval; Based on the temperature difference and the time interval, the degree of temperature change corresponding to each temperature maximum is obtained; the degree of temperature change is proportional to the temperature difference and inversely proportional to the time interval; The degree of temperature change corresponding to all temperature maxima is integrated to obtain the heat and cold stress.

5. The intelligent epidemic prevention system for animal husbandry and veterinary medicine as claimed in claim 1, characterized in that: The animal physiological parameters include the animal's exercise volume and body temperature; The process of obtaining the health status includes: Obtain the correlation between the animal's exercise volume and body temperature, as well as the animal's fever level; The health status of the animal is obtained based on the probability of occurrence of the epidemic risk, the correlation and the degree of body temperature and fever; the health status is inversely proportional to the probability of occurrence of the epidemic risk and the degree of body temperature and fever, and directly proportional to the correlation.

6. An intelligent epidemic prevention system for animal husbandry and veterinary medicine as claimed in claim 5, characterized in that: The process of obtaining the correlation between the amount of exercise and body temperature includes: Obtain the animal's exercise volume curve and body temperature curve during the monitoring period; The similarity between the exercise amount curve and the body temperature curve is obtained as the correlation between the exercise amount and the body temperature.

7. The intelligent epidemic prevention system for animal husbandry and veterinary medicine as claimed in claim 5, characterized in that: The process of obtaining the fever degree includes: obtaining the proportion of the body temperature data of the animal in the monitoring period that is higher than the upper limit of the normal body temperature as the fever degree.

8. The intelligent epidemic prevention system for animal husbandry and veterinary medicine as claimed in claim 1, characterized in that: The process of obtaining the outbreak risk includes: Obtain the number of abnormal animals in each gathering area whose health status is lower than a preset health status threshold; Based on the total number of animals, the number of abnormal animals and the average health status of animals in each gathering area, the outbreak risk of the epidemic area in each gathering area is obtained; the outbreak risk of the epidemic area is proportional to the total number of animals and the number of abnormal animals, and inversely proportional to the average health status; The outbreak risks of the epidemic areas in various clustered areas are integrated to obtain the outbreak risks of the epidemic in the livestock environment.

9. The intelligent epidemic prevention system for animal husbandry and veterinary medicine as claimed in claim 1, characterized in that: The intelligent epidemic prevention system also includes a ventilation adjustment module, which is used to increase the ventilation power of the livestock environment when the epidemic outbreak risk of the livestock environment is greater than a preset epidemic outbreak risk threshold.

10. The intelligent epidemic prevention system for animal husbandry and veterinary medicine as claimed in claim 1, characterized in that: The process of obtaining the air quality includes: obtaining the mean value of the harmful gas concentration in the livestock environment within the monitoring period, and negatively correlating the mean value of the harmful gas concentration to obtain the air quality.

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