Intelligent livestock health management and disease early warning system

Abstract

The invention, which relates to the technical field of animal husbandry livestock management, discloses an intelligent livestock health management and disease early warning system comprising a data acquisition module, a data storage module, a data processing module and a multi-stage early warning module. The data acquisition module is used for installing a sensor on a bred livestock body and shooting the livestock by using a camera, and the data acquisition module comprises a data comparison unit and a temporary storage unit. The data comparison unit matches the livestock data acquired by the sensor with the data in the temporary storage unit, and when the data comparison unit detects that the livestock is abnormal, the data acquisition module transmits the data in the temporary storage unit to the data processing module; and the data processing module performs quick response according to the data transmitted in advance, so that the response speed of the early warning system to livestock management is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of animal husbandry and livestock management, and in particular to an intelligent animal husbandry health management and disease early warning system. Background Art

[0002] With the continuous improvement of science and technology, animal husbandry has gradually tended to use technologies such as the Internet of Things, electronic information systems and artificial intelligence to carry out modern, large-scale and intensive breeding. In traditional livestock farming, disease warning mainly relies on the experience observation of breeders and some basic clinical symptoms. However, this method has great limitations. First of all, relying on experience observation often has a lag. Breeders usually only realize that there may be health problems when the animals show obvious symptoms of the disease, and the disease may have developed to a certain extent at this time. For example, when livestock show symptoms such as loss of appetite and mental depression, the disease may have been lurking in the body for a period of time, and the best time for treatment has been missed. This not only increases the difficulty and cost of treatment, but may also cause the disease to spread rapidly in the herd, bringing huge losses to the entire farm. Secondly, the traditional clinical symptom judgment lacks accuracy and comprehensiveness. Different diseases may have similar symptoms. It is difficult to accurately distinguish the specific type of disease by naked eye observation and simple judgment. For example, the fever, cough, movement and other symptoms of livestock may be caused by a variety of diseases, such as influenza, pneumonia, etc. It is difficult to determine the specific cause based on these symptoms alone, so it is impossible to take targeted treatment measures. In addition, the traditional disease early warning method is difficult to predict potential disease risks in advance. In the process of livestock breeding, environmental factors, feeding management, etc. may affect the health of animals, but traditional methods are difficult to detect these potential risk factors in time;

[0003] Although diseases can be managed and warned under current intelligent management, the existing livestock management and disease warning systems are affected by their response speed, which may lead to subsequent users being unable to isolate livestock in a timely manner. For this reason, we propose an intelligent livestock health management and disease warning system. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent livestock health management and disease early warning system.

[0005] In order to solve the problems raised in the above background technology, the present invention provides the following technical solutions: an intelligent livestock health management and disease early warning system, the early warning system includes a data acquisition module, a data storage module, a data processing module and a multi-level early warning module;

[0006] The data acquisition module is configured by installing sensors on livestock and photographing the livestock using a camera. The data acquisition module includes a data comparison unit and a temporary storage unit. The temporary storage unit is used to store the normal body data range of livestock data and 24-hour livestock movement and body data. The data comparison unit is used to match the data monitored by the sensor with the livestock data in real time. When the monitored data is within the normal range, the data acquisition module stops transmitting data to the data processing module. When the storage time of the data in the temporary storage unit reaches 24 hours, the temporary storage unit transmits the livestock data to the data processing module. After the transmission is completed, the stored data in the temporary storage unit is cleared. When the monitored data exceeds the normal range, the data acquisition module transmits the livestock data in the temporary storage unit to the data processing module.

[0007] The data storage module is used to store livestock management and disease data;

[0008] The data processing module processes the data transmitted by the data acquisition module, transmits the movement data of the livestock to the virtual three-dimensional model, and uses the virtual three-dimensional model to record the positions of the livestock in different time periods. The data processing module retrieves the livestock management data in the data storage module, and uses the livestock management data to perform targeted management on the livestock in different movement paths. When the data processing module receives abnormal data, the data processing module retrieves the data in the temporary storage unit on other livestock, and updates the data to the virtual three-dimensional model;

[0009] The multi-level warning module analyzes the contact between livestock through a virtual three-dimensional model, issues warnings for livestock with diseases and livestock that have been in contact with the livestock, and isolates and inspects the livestock that have been in contact with the livestock according to the diseases of the livestock.

[0010] As a further solution of the present invention, the data acquisition module numbers different livestock, so that the data processing module classifies the data according to the different numbers, and marks the different livestock as Q1, Q2, ..., Q n ,The data storage module performs classified storage according to different livestock numbers.

[0011] As a further solution of the present invention: the data processing module includes an abnormal marking unit. When the data processing module receives abnormal data, the abnormal marking unit marks the livestock with different marks as abnormal according to the data acquisition module. The abnormal marking unit classifies the livestock numbers with abnormal marks and unmarked livestock according to a formula. The specific formula is as follows:

[0012]

[0013] When (x i ,yi )-(x i ,y)>L max When , the formula is judged as 0, and the livestock number at the current time is not marked;

[0014] When (x i ,y i )-(x i ,y)≤L max When , the formula is judged as 1, and the livestock number at the current time is marked as abnormal;

[0015] Where T represents the classification data of livestock numbers marked as abnormal, s represents the maximum threshold time of t, Q li (x i ,y i ) represents the location data of livestock numbered l at the i-th time, Xt(x i ,y) represents the location data of abnormal livestock at time t, L Max The threshold data representing the maximum distance is then used to mark other livestock according to the abnormal livestock movement path.

[0016] As a further solution of the present invention: the data processing module obtains the danger data of abnormal livestock according to a formula, and the specific formula is as follows:

[0017]

[0018] Among them, PQ l Indicates abnormal risk data of livestock numbered l, Q lu It indicates the contact time between livestock number 1 and abnormal number, and then manages the livestock according to the risk data of different livestock numbers.

[0019] As a further solution of the present invention: a health management unit is set in the data storage module, and the health management unit is used to generate health status tables for different livestock, manage and treat diseases according to the health status of the livestock, and set a cloud server in the data storage module, so that the APP and the client are communicated with the cloud server, and the user views the management data through the APP and the client and the cloud server.

[0020] As a further solution of the present invention: the health management unit generates livestock health data through a formula, and the specific formula is as follows:

[0021]

[0022] Among them, R represents the health data of livestock, f1, f2, ..., f n They represent various physical data of livestock, and F represents the total physical data of livestock under normal conditions.

[0023] As a further solution of the present invention: the health management unit processes livestock according to livestock health data and livestock risk data, detects abnormal livestock through users, and then the data processing module generates a treatment plan for the infected livestock according to the diseases of the abnormal livestock.

[0024] As a further solution of the present invention: the data processing module retrieves corresponding basic management data by judging the livestock data, analyzes the corresponding basic management data by using a neural network, and generates a management plan based on the basic management data;

[0025] When R·e1+PQ l e2 ≥ Z max When the multi-level warning module generates a first-level warning instruction, the first-level warning instruction is transmitted to the APP and the client, and the user isolates and treats the livestock according to the abnormal number;

[0026] When Z max >R·e1+PQ l e2 ≥ Z min When the multi-level warning module generates a second-level warning instruction, the user isolates the livestock according to the management plan generated by the health management;

[0027] When Z min >R·e1+PQ l When the value is e2, the multi-level warning module generates a level 3 warning instruction, and the user quarantines and observes the livestock;

[0028] Among them, e1 and e2 represent the weight coefficients of livestock health and livestock danger respectively, Z min represents the maximum danger threshold in the early warning system, Z max Indicates the minimum danger threshold in the early warning system.

[0029] By adopting the above technical solution, compared with the prior art, the beneficial effects of the present invention are:

[0030] 1. The present invention installs a sensor on the livestock, and then uses a data comparison unit to cooperate with a temporary storage unit, so that the data comparison unit matches the livestock data collected by the sensor with the data in the temporary storage unit. When the data comparison unit detects an abnormality of the livestock, the data collection module transmits the data in the temporary storage unit to the data processing module, and the data processing module responds quickly according to the data transmitted in advance, thereby improving the response speed of the early warning system for livestock management;

[0031] 2. The present invention marks different livestock through the abnormal marking unit, so that the early warning system is convenient for quickly marking livestock that may be infected, so that users can subsequently isolate and treat livestock that may be infected;

[0032] 3. The present invention analyzes different livestock numbers through a data processing module to obtain the infection probability of individual livestock. After analyzing the infection probability of different livestock, the user logs in to the APP and the client to classify the infection probability of different livestock, so that the user can carry out targeted treatment according to the size of the infection probability of different livestock;

[0033] 4. The present invention cooperates with the health management unit and the multi-level early warning module, so that the user can isolate livestock in different situations according to needs, and the health management unit generates corresponding management plans, thereby achieving the effect of the early warning system managing livestock in different situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of the early warning system in an embodiment of the present invention;

[0035] Figure 2 This is a flow chart of the data acquisition module in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] Embodiment 1:

[0038] Please see attached Figure 1 -Attached Figure 2 , the present invention provides an intelligent livestock health management and disease early warning system, the early warning system includes a data acquisition module, a data storage module, a data processing module and a multi-level early warning module;

[0039] The data acquisition module installs sensors on the livestock and uses a camera to shoot the livestock. The data acquisition module includes a data comparison unit and a temporary storage unit. The temporary storage unit is used to store the normal body data range of the livestock data and the livestock movement and body data for 24 hours. The data comparison unit is used to match the data monitored by the sensor with the livestock data in real time. When the monitoring data belongs to the normal range, the data acquisition module stops transmitting data to the data processing module. After the storage time of the data in the temporary storage unit reaches 24 hours, the temporary storage unit transmits the livestock data to the data processing module. After the transmission is completed, the storage data in the temporary storage unit is cleared. When the monitoring data exceeds the normal range, the data acquisition module transmits the livestock data in the temporary storage unit to the data processing module.

[0040] The data storage module is used to store livestock management and disease data;

[0041] The data processing module processes the data transmitted by the data acquisition module, transmits the movement data of the livestock to the virtual three-dimensional model, uses the virtual three-dimensional model to record the positions of the livestock in different time periods, retrieves the livestock management data in the data storage module, and uses the livestock management data to perform targeted management on the livestock in different movement paths. When the data processing module receives abnormal data, the data processing module retrieves the data in the temporary storage unit on other livestock, and updates the data to the virtual three-dimensional model;

[0042] The multi-level early warning module analyzes the contact between livestock through a virtual three-dimensional model, issues early warnings for livestock with diseases and livestock that have been in contact with them, and isolates and inspects livestock that have been in contact with them based on their diseases.

[0043] Specific work flow: By installing sensors on pigs, cattle and sheep, different positioning methods and transmission methods are used for pigs, cattle and sheep according to the breeding area. When the breeding area is too large, cattle and sheep can be positioned and data can be transmitted through GPS and wireless networks. When the breeding area is small, pigs, cattle and sheep in the farm can be positioned and transmitted through low-power wide area networks. The data acquisition module is used to collect data on livestock in real time, such as body temperature, environment, exercise volume and blood oxygen for monitoring. At the same time, the health status of each individual livestock is detected in real time through the data comparison unit and the temporary storage unit. Then, the data storage module and the data processing module cooperate with each other to monitor each livestock in real time. The data processing module is used to generate health management plans for different livestock. The multi-level early warning module is used to isolate and treat sick livestock. By obtaining the movement path of livestock, it is easy to quickly find some livestock that may be infected, so that users can easily isolate livestock that may be infected. The multi-level early warning module is used to issue early warnings based on the disease status of livestock.

[0044] Furthermore, by installing sensors on livestock, and then using the data comparison unit and the temporary storage unit to cooperate with each other, the data comparison unit matches the livestock data collected by the sensor with the data in the temporary storage unit. When the data comparison unit detects an abnormality in the livestock, the data collection module transmits the data in the temporary storage unit to the data processing module, and the data processing module responds quickly according to the data transmitted in advance, thereby improving the response speed of the early warning system for livestock management.

[0045] Embodiment 2:

[0046] Based on the first embodiment, please refer to the attached Figure 1 -Attached Figure 2 As shown, the data collection module numbers different livestock, so that the data processing module classifies the data according to the different numbers and marks the different livestock as Q1, Q2, ..., Q n The data storage module classifies and stores different livestock numbers. The data processing module includes an abnormal marking unit. When the data processing module receives abnormal data, the abnormal marking unit marks the livestock with different marks according to the data acquisition module. The abnormal marking unit classifies the livestock numbers with abnormal marks and unmarked livestock numbers through a formula. The specific formula is as follows:

[0047]

[0048] When (x i ,y i )-(x i ,y)>L max When , the formula is judged as 0, and the livestock number at the current time is not marked;

[0049] When (x i ,y i )-(x i ,y)≤L max When , the formula is judged as 1, and the livestock number at the current time is marked as abnormal;

[0050] Where T represents the classification data of livestock numbers marked as abnormal, s represents the maximum threshold time of t, Q li (x i ,y i ) represents the location data of livestock numbered l at the i-th time, Xt(x i ,y) represents the location data of abnormal livestock at time t, L Max The threshold data representing the maximum distance is then used to mark other livestock according to the abnormal livestock movement path.

[0051] Specific workflow: The data collection module numbers different livestock and marks them as Q1, Q2, ..., Q n In this way, different livestock individuals can be clearly distinguished, which is convenient for subsequent data classification and processing. When the data processing module receives abnormal data, it triggers the abnormal marking unit to start working. The abnormal marking unit first determines the livestock number corresponding to the abnormal data, and sets the livestock number as Q m (m and l are specific integers, representing a specific livestock individual), m As the center, determine its position data at time t as Xt(x i ,y), where the location data can be obtained through sensors installed on livestock, and then all livestock numbers are judged and marked through formulas, and other livestock are marked according to the movement paths of livestock with abnormal numbers. For example, if the movement paths of livestock marked as abnormal pass through certain specific areas within a period of time, other livestock in these areas may also be marked as potential risk objects for further observation and inspection.

[0052] Furthermore, different livestock are marked through the abnormal marking unit, so that the early warning system can quickly mark livestock that may be infected, allowing users to isolate and treat the potentially infected livestock later.

[0053] Embodiment three:

[0054] Based on the second embodiment, please refer to the attached Figure 1 -Attached Figure 2 As shown, the data processing module obtains the dangerous data of abnormal livestock according to the formula, and the specific formula is as follows:

[0055]

[0056] Among them, PQ l Indicates abnormal risk data of livestock numbered l, Q lu It indicates the contact time between livestock number 1 and abnormal number, and then manages livestock according to the dangerous data of different livestock numbers. A health management unit is set in the data storage module. The health management unit is used to generate health status tables for different livestock, manage and treat diseases according to the health status of livestock, and set a cloud server in the data storage module so that APP and client communicate with the cloud server, and users view the management data through APP and client and cloud server.

[0057] Specific workflow: The data in the early warning system is retrieved by the user logging into the APP and the client. After the user logs into the APP and the client, the retrieval instruction is transmitted to the data processing module, and then the data processing module retrieves the data in each data acquisition module and updates the livestock movement data in the data processing module.

[0058] Furthermore, different livestock numbers are analyzed through the data processing module to obtain the infection probability of individual livestock. After analyzing the infection probability of different livestock, the user logs in to the APP and the client to classify the infection probabilities of different livestock, so that the user can carry out targeted treatment according to the size of the infection probability of different livestock.

[0059] Embodiment 4:

[0060] Based on the third embodiment, please refer to the attached Figure 1 -Attached Figure 2 As shown, the health management unit generates livestock health data through a formula, the specific formula is as follows:

[0061]

[0062] Among them, R represents the health data of livestock, f1, f2, ..., f n They represent various physical data of livestock, F represents the total data of livestock under normal conditions, the health management unit processes livestock according to livestock health data and livestock risk data, detects abnormal livestock through users, and then the data processing module generates a treatment plan for the infected livestock according to the diseases of abnormal livestock, the data processing module retrieves the corresponding basic management data through the judgment of livestock data, uses neural network to analyze the corresponding basic management data, and generates a management plan based on the basic management data;

[0063] When R·e1+PQ l e2 ≥ Z max When the multi-level warning module generates a first-level warning instruction, the first-level warning instruction is transmitted to the APP and the client, and the user isolates and treats the livestock according to the abnormal number;

[0064] When Z max >R·e1+PQ l e2 ≥ Z min When the multi-level warning module generates a second-level warning instruction, the user isolates the livestock according to the management plan generated by the health management;

[0065] When Z min >R·e1+PQ l When the value is e2, the multi-level warning module generates a level 3 warning instruction, and the user quarantines and observes the livestock;

[0066] Among them, e1 and e2 represent the weight coefficients of livestock health and livestock danger respectively, Z min represents the maximum danger threshold in the early warning system, Z max Indicates the minimum danger threshold in the early warning system.

[0067] Specific workflow: Users detect abnormal livestock, which may be achieved by observing abnormal performance such as livestock behavior and appearance, or by conducting further inspections in combination with the system's early warning information. The data processing module generates a treatment plan based on the disease conditions of abnormal livestock: The data processing module determines the disease type of abnormal livestock by analyzing livestock data, and retrieves corresponding basic management data, which may include historical disease treatment cases, expert knowledge, etc. The basic management data is analyzed using a neural network. The neural network can establish a relationship model between disease characteristics and treatment plans by learning a large amount of historical data, and generates a treatment plan for abnormal livestock based on the analysis results. After receiving the early warning instruction, the user takes corresponding measures according to different early warning levels: For the first-level early warning, the abnormal livestock is immediately isolated and treated, and emergency medical measures are taken to prevent the further spread and deterioration of the disease. For the second-level early warning, the livestock is isolated according to the management plan, which may include adjusting the feeding environment, strengthening monitoring and other measures. For the third-level early warning, the livestock is isolated and observed, and changes in their health status are closely monitored. If necessary, further inspections and diagnoses are carried out.

[0068] Furthermore, through the cooperation between the health management unit and the multi-level early warning module, the user can isolate livestock in different situations according to needs, and the health management unit generates corresponding management plans, so that the early warning system can manage livestock in different situations.

[0069] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An intelligent livestock health management and disease early warning system, characterized by: The early warning system includes a data acquisition module, a data storage module, a data processing module and a multi-level early warning module; The data acquisition module is configured by installing sensors on livestock and photographing the livestock using a camera. The data acquisition module includes a data comparison unit and a temporary storage unit. The temporary storage unit is used to store the normal body data range of the livestock and the livestock movement and body data for 24 hours. The data comparison unit is used to match the data monitored by the sensor with the livestock data in real time. When the monitoring data is within the normal range, the data acquisition module stops transmitting data to the data processing module. When the storage time of the data in the temporary storage unit reaches 24 hours, the temporary storage unit transmits the livestock data to the data processing module. After the transmission is completed, the storage data in the temporary storage unit is cleared. When the monitoring data exceeds the normal range, the data acquisition module transmits the livestock data in the temporary storage unit to the data processing module. The data storage module is used to store livestock management and disease data; The data processing module processes the data transmitted by the data acquisition module, transmits the movement data of the livestock to the virtual three-dimensional model, and uses the virtual three-dimensional model to record the positions of the livestock in different time periods. The data processing module retrieves the livestock management data in the data storage module, and uses the livestock management data to perform targeted management on the livestock in different movement paths. When the data processing module receives abnormal data, the data processing module retrieves the data in the temporary storage unit on other livestock, and updates the data to the virtual three-dimensional model; The multi-level warning module analyzes the contact between livestock through a virtual three-dimensional model, issues warnings for livestock with diseases and livestock that have been in contact with them, inspects the livestock in contact according to the diseases of the livestock, and isolates and treats the livestock if they have infectious diseases.

2. The intelligent livestock health management and disease early warning system according to claim 1 is characterized by: The data collection module numbers different livestock, so that the data processing module classifies the data according to the different numbers, and marks the different livestock as Q1, Q2, ..., Q n ,The data storage module classifies and stores different livestock numbers.

3. The intelligent livestock health management and disease early warning system according to claim 2 is characterized by: The data processing module includes an abnormal marking unit. When the data processing module receives abnormal data, the abnormal marking unit marks the livestock with different marks as abnormal according to the data acquisition module. The abnormal marking unit classifies the livestock numbers with abnormal marks and those without marks by a formula. The specific formula is as follows: When (x i ,y i )-(x i ,y)>L max When , the formula is judged as 0, and the livestock number at the current time is not marked; When (x i ,y i )-(x i ,y)≤L max When , the formula is judged as 1, and the livestock number at the current time is marked as abnormal; Where T represents the classification data of livestock numbers marked as abnormal, s represents the maximum threshold time of t, Q li (x i ,y i ) represents the location data of livestock numbered l at the i-th time, Xt(x i ,y) represents the location data of abnormal livestock at time t, L Max The threshold data representing the maximum distance is then used to mark other livestock according to the abnormal livestock movement path.

4. The intelligent livestock health management and disease early warning system according to claim 3 is characterized by: The data processing module obtains the dangerous data of abnormal livestock according to the formula, and the specific formula is as follows: Among them, PQ l Indicates abnormal risk data of livestock numbered l, Q lu It indicates the contact time between livestock number 1 and abnormal number, and then manages the livestock according to the risk data of different livestock numbers.

5. The intelligent livestock health management and disease early warning system according to claim 4 is characterized by: A health management unit is provided in the data storage module, and the health management unit is used to generate a health status table for different livestock, and manage and treat diseases according to the health status of the livestock. A cloud server is provided in the data storage module, so that the APP and the client communicate with the cloud server, and the user views the management data through the APP and the client and the cloud server.

6. The intelligent livestock health management and disease early warning system according to claim 5 is characterized by: The health management unit generates livestock health data through a formula, and the specific formula is as follows: Among them, R represents the health data of livestock, f1, f2, ..., f n They represent various physical data of livestock, and F represents the total physical data of livestock under normal conditions.

7. The intelligent livestock health management and disease early warning system according to claim 6 is characterized by: The health management unit processes the livestock according to the livestock health data and livestock risk data, detects abnormal livestock through users, and then the data processing module generates a treatment plan for the infected livestock according to the diseases of the abnormal livestock.

8. The intelligent livestock health management and disease early warning system according to claim 7 is characterized by: The data processing module retrieves corresponding basic management data by judging the livestock data, analyzes the corresponding basic management data using a neural network, and generates a management plan based on the basic management data; When R·e1+PQ l e2 ≥ Z max When the multi-level warning module generates a first-level warning instruction, the first-level warning instruction is transmitted to the APP and the client, and the user isolates and treats the livestock according to the abnormal number; When Z max >R·e1+PQ l e2 ≥ Z min When the multi-level warning module generates a second-level warning instruction, the user isolates the livestock according to the management plan generated by the health management; When Z min >R·e1+PQ l When the value is e2, the multi-level warning module generates a level 3 warning instruction, and the user quarantines and observes the livestock; Among them, e1 and e2 represent the weight coefficients of livestock health and livestock danger respectively, Z min represents the maximum danger threshold in the early warning system, Z max Indicates the minimum danger threshold in the early warning system.

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