Animal epidemic prevention processing method and system based on block chain

Abstract

The invention discloses an animal epidemic prevention processing method and system based on a block chain, and the method comprises the steps: building a farm basic database, formulating an epidemic prevention time table in combination with animal variety characteristics and geographic position climate conditions, and determining epidemic prevention time nodes of a farm; the method comprises the following steps: acquiring farm environment data, animal health data and epidemic prevention operation records in real time through Internet of Things equipment, and generating an epidemic prevention execution data stream; comparing the epidemic prevention execution data stream with a preset epidemic prevention measure execution list, and judging the implementation condition of epidemic prevention measures by setting a threshold value and a rule; according to the epidemic prevention execution data flow and the implementation condition of the epidemic prevention measures, a random forest algorithm is adopted to analyze the epidemic prevention effect, and an epidemic prevention effect evaluation report is generated; and an epidemic prevention effect evaluation report and epidemic situation early warning information are encrypted, stored and shared through a block chain, and data support is provided for epidemic prevention decision making. According to the invention, intelligence, precision and traceability of epidemic prevention management of the farm are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of information processing, and in particular, to an animal epidemic prevention processing method and system based on blockchain. Background Art

[0002] In the process of animal breeding, due to factors such as scattered geographical locations of farms, diverse types of raised animals, and uneven breeding management levels, the problems of great difficulty and low efficiency in animal epidemic prevention and control have become increasingly prominent.

[0003] Traditional animal epidemic prevention models mainly rely on manual inspections and regular immunizations, and there are problems such as inconsistent epidemic prevention times, ineffective implementation of epidemic prevention measures, and difficulty in evaluating epidemic prevention effects. At the same time, there is a lack of effective information sharing and collaboration mechanisms among farms, resulting in untimely and inaccurate transmission of epidemic information, increasing the risk of epidemic spread. In addition, there are no effective management and analysis means for the massive data generated during the epidemic prevention process, the authenticity and security of the data are difficult to guarantee, and epidemic prevention decisions lack data support.

[0004] Therefore, there is an urgent need for a new animal epidemic prevention model that can effectively integrate multiple parties such as farms and epidemic prevention stations, realize the credibility, controllability, data security sharing, and intelligent analysis and decision-making of the entire epidemic prevention process, so as to improve the efficiency of animal epidemic prevention and control and ensure the healthy and sustainable development of the livestock industry. Summary of the Invention

[0005] To solve the technical problems existing in the above-mentioned prior art, the present invention proposes an animal epidemic prevention processing method and system based on blockchain, which realizes the intelligence, precision, and traceability of farm epidemic prevention management, improves the epidemic prevention efficiency and decision-making level, and provides strong support for the healthy and sustainable development of the livestock industry.

[0006] On the one hand, to achieve the above object, the present invention provides an animal epidemic prevention processing method based on blockchain, including:

[0007] Establish a basic database of the farm, and based on the basic database of the farm, combine the characteristics of animal species and geographical location and climatic conditions to formulate an epidemic prevention schedule and determine the epidemic prevention time nodes of the farm;

[0008] Real-time collect the environmental data, animal health data, and epidemic prevention operation records of the farm through Internet of Things devices to generate an epidemic prevention execution data stream;

[0009] Compare the epidemic prevention execution data stream with a preset epidemic prevention measure execution list, and judge the implementation situation of the epidemic prevention measures by setting thresholds and rules;

[0010] According to the epidemic prevention execution data stream and the implementation situation of the epidemic prevention measures, use the random forest algorithm to analyze the epidemic prevention effect and generate an epidemic prevention effect evaluation report;

[0011] The epidemic prevention effect evaluation report and epidemic warning information are encrypted and stored and shared through the blockchain, providing data support for epidemic prevention decision-making.

[0012] On the other hand, to achieve the above object, the present invention also provides an animal epidemic prevention processing system based on the blockchain, including:

[0013] A basic database construction module for constructing a basic database of the farm;

[0014] An epidemic prevention schedule formulation module for formulating an epidemic prevention schedule according to the basic database of the farm, combining the characteristics of animal species and the geographical location and climatic conditions, and determining the epidemic prevention time nodes of each farm;

[0015] A data collection module for real-time collecting farm environment data, animal health data and epidemic prevention operation records through Internet of Things devices to generate an epidemic prevention execution data stream;

[0016] An epidemic prevention measure comparison module for comparing the epidemic prevention execution data stream with a preset epidemic prevention measure execution list, and judging the implementation of epidemic prevention measures by setting thresholds and rules;

[0017] An epidemic prevention effect analysis module for analyzing the epidemic prevention effect by using the random forest algorithm according to the epidemic prevention execution data stream and the implementation of epidemic prevention measures, and generating an epidemic prevention effect evaluation report;

[0018] A data encryption storage module for encrypting and storing and sharing the epidemic prevention effect evaluation report and epidemic warning information through blockchain technology.

[0019] Compared with the prior art, the present invention has the following advantages and technical effects:

[0020] The present invention establishes a basic database of the farm, formulates a unified epidemic prevention schedule by combining animal characteristics and geographical and climatic conditions. Real-time collects environment, animal health and epidemic prevention operation data through Internet of Things devices to form an epidemic prevention execution data stream. Compares the execution data with a preset list to judge the implementation of epidemic prevention measures. Analyzes the epidemic prevention effect by using the random forest algorithm to generate an evaluation report. Finally, uses blockchain technology to encrypt and store and share epidemic prevention data, evaluation reports and warning information to ensure the authenticity and security of the data. The present invention realizes the intelligentization, precision and traceability of farm epidemic prevention management, improves the epidemic prevention efficiency and decision-making level, and provides strong support for the healthy and sustainable development of the livestock industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which form a part of this application, are used to provide a further understanding of this application. The schematic embodiments and descriptions thereof of this application are used to explain this application and do not constitute an improper limitation to this application. In the accompanying drawings:

[0022] Figure 1 It is a flowchart of a blockchain-based animal epidemic prevention and control method according to an embodiment of the present invention;

[0023] Figure 2 It is a schematic structural diagram of a blockchain-based animal epidemic prevention and control system according to an embodiment of the present invention. Detailed implementation manners

[0024] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will refer to the accompanying drawings and combine the embodiments to detail this application.

[0025] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0026] The present invention proposes a blockchain-based animal epidemic prevention and control method, as Figure 1 , including:

[0027] Establish a basic database of the farm. According to the basic database of the farm, combine the characteristics of animal species and the geographical location and climate conditions to formulate an epidemic prevention schedule and determine the epidemic prevention time nodes of the farm;

[0028] Real-time collect the environmental data, animal health data and epidemic prevention operation records of the farm through Internet of Things devices to generate an epidemic prevention execution data stream;

[0029] Compare the epidemic prevention execution data stream with a preset list of epidemic prevention measures execution, and judge the implementation of the epidemic prevention measures by setting thresholds and rules;

[0030] According to the epidemic prevention execution data stream and the implementation of the epidemic prevention measures, use the random forest algorithm to analyze the epidemic prevention effect and generate an epidemic prevention effect evaluation report;

[0031] Encrypt and store and share the epidemic prevention effect evaluation report and the epidemic warning information through the blockchain to provide data support for epidemic prevention decision-making.

[0032] Further, establishing a basic database of the farm includes:

[0033] Obtain the geographical location information of the farm and determine the specific location coordinates of the farm;

[0034] Obtain the information on the types of animals in the farm and determine the types of animals raised in the farm;

[0035] Obtain the information on the number of animals in the farm and determine the numbers of different types of animals;

[0036] Classify and organize the geographical location, types of animals, and number information of the said farm and establish a structured data record;

[0037] Import the said structured data record into the database system and establish the basic database of the said farm.

[0038] Specifically, obtain the geographical location information of the farm and determine the specific location coordinates of the farm through GPS positioning or map marking, etc. Obtain the information on the types of animals in the farm and determine the types of animals raised in the farm through on-site investigation or materials provided by the farm. Obtain the information on the number of animals in the farm and determine the numbers of various animals through on-site counting or statistical data provided by the farm. Classify and organize the geographical location, types of animals, and number information of the farm obtained and establish a structured data record. Import the classified and organized farm information into the database system and establish the basic information database of the farm. Conduct data analysis using statistical methods based on the data of the types and numbers of animals in the farm to obtain statistical indicators such as the inventory quantity and inventory density of various animals. Conduct regional division of the farm using a spatial clustering algorithm based on the geographical location data of the farm and determine the spatial distribution characteristics of the farms in different regions.

[0039] Collect the longitude and latitude coordinates on-site through a GPS positioning device or mark them using satellite maps. For example, a pig farm is located at 30°15'36" N latitude and 120°10'48" E longitude. This precise positioning helps with subsequent spatial analysis and regional planning. The acquisition of animal species information usually requires on-site investigations or referring to the materials provided by the farm. A typical farm may raise multiple types of animals, such as live pigs, broiler chickens, laying hens, etc. Accurately mastering the animal species is crucial for disease prevention and control and production management. The animal quantity information can be obtained through on-site counting or the statistical data provided by the farm. For example, a certain farm may raise 5,000 live pigs and 20,000 broiler chickens. These data reflect the production scale and operating conditions of the farm. Classify and organize the information obtained, and establish a structured data record. It can be organized according to dimensions such as geographical location, animal species, quantity, etc., to form a data structure that is easy to query and analyze. This structured data is convenient for importing into a database system to establish a basic information database for the farm. Based on the animal species and quantity data of the farm, statistical analysis can be carried out. For example, calculate indicators such as the inventory quantity and inventory density of various animals. The inventory density of live pigs in a certain area may be 100 heads per square kilometer, and this indicator can be used to evaluate the breeding intensity and environmental carrying capacity. Using the geographical location data of the farm, spatial clustering algorithms can be used for regional division. K-means clustering is one of the commonly used methods, which can divide the farms into several groups according to the degree of geographical proximity. This division helps to understand the spatial distribution characteristics of the aquaculture industry and provides a basis for regional planning and epidemic prevention and control. Through these steps, a comprehensive farm information system can be constructed. This system not only records the basic data but also supports in-depth statistical analysis and spatial analysis.

[0040] Furthermore, determine the epidemic prevention time nodes of the farm, including:

[0041] According to the animal species and geographical location information in the basic database of the farm, obtain the animal characteristics and climate condition data of each farm;

[0042] Through cluster analysis of the animal species characteristic data, divide the animal species with similar characteristics into several categories, and formulate a unified epidemic prevention schedule for each animal category;

[0043] According to the geographical location of the farm, obtain the historical climate condition data of this location, and through time series analysis, predict the climate change trend in this area within a preset time period;

[0044] Match the epidemic prevention schedule of the animal species with the climate prediction data of the geographical location of the farm to obtain the epidemic prevention time nodes suitable for different farms.

[0045] Specifically, the animal characteristics and climate condition data of each farm are obtained according to the animal species and geographical location information in the basic database of the farm. By clustering the animal species characteristic data, the animal species with similar characteristics are divided into several categories, and a unified epidemic prevention schedule is formulated for each category of animal species. According to the geographical location of the farm, the historical climate condition data of the location is obtained, and the climate change trend of the region in the future is predicted through time series analysis. The epidemic prevention schedule of the animal species is matched with the climate forecast data of the geographical location of the farm to obtain the epidemic prevention time node suitable for each farm. The decision tree algorithm is adopted, and the judgment rules of the epidemic prevention time node are generated with animal species, climate conditions, etc. as decision factors. The judgment rules are applied to the actual situation of each farm to automatically determine the specific epidemic prevention time arrangement of the farm. The epidemic prevention schedule and epidemic prevention nodes are visualized for easy viewing and execution by farm managers, and the epidemic prevention reminder information is pushed to relevant personnel to ensure that the epidemic prevention work is carried out on time.

[0046] Obtain animal characteristic data, such as the growth cycle of pigs and the egg-laying period of chickens, which affect the timing of epidemic prevention. Climate condition data include temperature, humidity, precipitation, etc., which are closely related to the spread of diseases. Cluster analysis can classify animals with similar characteristics, such as large mammals such as pigs and cattle into one category and poultry such as chickens and ducks into another category. A unified epidemic prevention schedule is formulated for each type of animal, such as large mammals are vaccinated against foot-and-mouth disease once a quarter, and poultry are tested for avian influenza once a month. Time series analysis predicts climate change trends, such as the average summer temperature in a certain area is rising year by year, and humidity is increasing, which may lead to an increase in the risk of certain infectious diseases. Match the epidemic prevention schedule with climate forecasts, such as increasing the frequency of disinfection in months with higher humidity and carrying out vaccination in advance in seasons with higher temperatures. The decision tree algorithm generates epidemic prevention time node judgment rules. Construct a judgment tree with animal species, age, weight, ambient temperature, etc. as decision factors. For example, if it is a growing pig and the ambient temperature exceeds 30°C, it will be vaccinated one week in advance. Apply these rules to each farm to automatically generate specific epidemic prevention arrangements. Visually display the epidemic prevention timetable, such as using a Gantt chart to display the time schedule of various epidemic prevention tasks, with different colors representing different types of epidemic prevention measures. Push epidemic prevention reminders, such as through text messages, App notifications, etc., to remind managers of upcoming epidemic prevention tasks.

[0047] Furthermore, after the epidemic prevention execution data flow is generated, it includes:

[0048] Preprocessing the epidemic prevention execution data stream, removing abnormal data, and obtaining standardized epidemic prevention execution data;

[0049] According to the preset environmental parameter thresholds and animal health indicator thresholds, determine whether the current farm environment is suitable and whether the animal health status is normal. If the thresholds are exceeded, trigger an alarm;

[0050] Use machine learning methods to analyze the standardized epidemic prevention execution data to obtain an evaluation result of the current situation of epidemic prevention management on the farm;

[0051] Conduct a correlation analysis on the farm environment monitoring data, animal health monitoring data, and the evaluation result of the current situation of epidemic prevention management to identify the risk points of epidemic prevention management existing on the farm;

[0052] Automatically generate optimization suggestions for epidemic prevention management for the identified risk points of epidemic prevention management and push them to the farm management personnel;

[0053] Continuously track the improvement of epidemic prevention management on the farm. By comparing historical data and current data, evaluate the implementation effect of the optimization suggestions for epidemic prevention management to form a closed loop of epidemic prevention management.

[0054] Specifically, environmental data such as temperature, humidity, ammonia concentration, etc., and animal health data such as body temperature, heart rate, activity level, etc. are collected in real time. These data constitute the epidemic prevention execution data stream, providing a basis for subsequent analysis. For example, the temperature sensor in a pig farm records the temperature once an hour, and the body temperature detection device measures the body temperature of each pig every day. Data preprocessing is a key step to ensure the quality of analysis. Abnormal data may result from equipment failures or human errors and need to be identified and removed through statistical methods. For example, the humidity data in a chicken farm suddenly jumps from the normal 60% to 99%, which may be an abnormal value caused by a sensor failure and should be removed. Preset thresholds are used to detect potential problems in a timely manner. For example, when the temperature in the chicken coop exceeds 35°C or is lower than 15°C, an alarm is triggered to remind the management to take measures. Similarly, if the body temperature of multiple chickens is found to exceed 42°C, the system will issue an epidemic warning. Machine learning algorithms such as support vector machines or random forests can be used to analyze the epidemic prevention execution data and evaluate the current situation of epidemic prevention management. The algorithms can learn the patterns in the historical data, such as the correlation between certain combinations of environmental parameters and disease outbreaks, so as to evaluate the current situation. Association analysis helps to identify the risk points of epidemic prevention management. For example, through analysis, it is found that when the temperature drops suddenly in a duck farm and the breeding environment is not adjusted in time, the incidence of avian influenza will increase significantly. This association can help the management take preventive measures in advance. Based on the identified risk points, the system can automatically generate optimization suggestions. For example, it is recommended to turn on the heating equipment 24 hours before the temperature drops suddenly and increase the disinfection frequency. These suggestions are pushed to the management through a mobile application to ensure timely implementation. Continuous tracking and evaluation are the key to forming a closed-loop epidemic prevention management. For example, after implementing a new disinfection plan, the system will compare the disease incidence before and after implementation. If the new plan reduces the disease incidence by 20%, it proves that its effect is significant. This continuous evaluation and improvement process helps to continuously optimize the epidemic prevention management strategy and improve the overall epidemic prevention level of the farm. Through this series of steps, the farm can establish a data-driven intelligent epidemic prevention system. This not only improves the epidemic prevention efficiency but also reduces the risk of human judgment errors. In the long run, this method can significantly improve the production efficiency and animal welfare of the farm, providing strong support for the sustainable development of the aquaculture industry.

[0055] Furthermore, identify the risk points of epidemic prevention management existing in the farm, including:

[0056] Obtain the environmental monitoring data of the farm, including temperature, humidity, and air quality, and use the environmental monitoring data of the farm as the basis for judging whether the farm environment is suitable for animal growth;

[0057] Obtain the animal health monitoring data, including the growth situation, immune status, disease occurrence situation, etc. of the animals, and use the animal health monitoring data as an indicator for evaluating the animal health status;

[0058] Obtain the evaluation results of the epidemic prevention management status of the farm, including the situation of epidemic prevention facilities, the implementation of epidemic prevention systems, and personnel training, and use the evaluation results of the epidemic prevention management status of the farm as the basis for judging the level of epidemic prevention management;

[0059] Perform correlation analysis on the environmental monitoring data, animal health monitoring data, and the evaluation results of the epidemic prevention management status of the farm, and use data mining algorithms to identify the correlation relationships between various indicators;

[0060] According to the correlation analysis results, determine the weak links and risk points in the epidemic prevention management of the farm. For the identified risk points, use expert system technology and combine with a preset epidemic prevention management knowledge base to generate suggestions for epidemic prevention management measures, and provide the suggestions for epidemic prevention management measures to the farm management personnel.

[0061] Specifically, obtain the environmental monitoring data of the farm, including index data such as temperature, humidity, and air quality, and use it as one of the bases for judging whether the farm environment is suitable for animal growth. Obtain the animal health monitoring data, including the growth situation, immune status, and disease occurrence of animals, and use it as an important indicator for evaluating the animal health status. Obtain the evaluation results of the epidemic prevention management status of the farm, including the situation of epidemic prevention facilities, the implementation of epidemic prevention systems, and personnel training, and use it as the basis for judging the level of epidemic prevention management. Perform correlation analysis on the environmental monitoring data, animal health monitoring data, and the evaluation results of the epidemic prevention management status of the farm, and use data mining algorithms, such as association rule mining algorithms, to identify the correlation relationships between various indicators. According to the correlation analysis results, determine the weak links and risk points in the epidemic prevention management of the farm, such as unqualified environmental conditions, low animal immunity, and ineffective implementation of epidemic prevention systems. For the identified risk points, use expert system technology and combine with a preset epidemic prevention management knowledge base to generate targeted suggestions for epidemic prevention management measures, such as improving environmental conditions, strengthening animal immunity, and improving epidemic prevention systems. Provide the generated suggestions for epidemic prevention management measures to the farm management personnel, and through visualization technologies, such as dashboards and reports, intuitively display the current situation and improvement direction of the farm's epidemic prevention management, providing decision-making support for the farm to optimize epidemic prevention management.

[0062] Furthermore, compare the epidemic prevention execution data stream with a preset list of epidemic prevention measures to be implemented, and judge the implementation situation of epidemic prevention measures by setting thresholds and rules, including:

[0063] For the epidemic prevention execution data stream and the list of epidemic prevention measures to be implemented, use a text similarity algorithm for comparison to obtain a similarity score;

[0064] Judge whether the similarity score exceeds the threshold according to a preset threshold. If the similarity score exceeds the threshold, it is determined that the epidemic prevention measures are implemented in place. If the similarity score does not exceed the threshold, it is determined that the epidemic prevention measures are not implemented in place;

[0065] Judge the implementation degree of the overall epidemic prevention measures by integrating the implementation situations of several epidemic prevention measures through set rules;

[0066] Output a report on the implementation situation of the epidemic prevention measures according to the judgment result.

[0067] Specifically, when judging the implementation situation of epidemic prevention measures, the importance of different measures needs to be considered. Weights can be assigned to different epidemic prevention measures. For example, vaccination may be more important than daily disinfection, so a higher weight can be given. By means of weighted average, the implementation degree of the overall epidemic prevention measures can be obtained. The generation of the implementation situation report is the last and most important output of the whole process. This report should not only list the implementation situations of various measures, but also contain analysis and suggestions. For example, if it is found that an important measure fails to be implemented continuously for many times, the report should highlight and analyze the possible reasons, such as complex operation procedures and insufficient personnel training, and give corresponding improvement suggestions. This data-driven epidemic prevention management method has many advantages. First of all, it improves the accuracy and efficiency of epidemic prevention management and reduces the subjectivity of human judgment. Secondly, through continuous data collection and analysis, weak links in epidemic prevention management can be found in a timely manner, which helps to continuously optimize epidemic prevention strategies. Finally, this method can also provide an important basis for epidemic warning. By analyzing historical data, a prediction model can be established to identify potential epidemic risks in advance. However, some problems also need to be noted in the implementation process. For example, ensuring data quality is crucial, and data collection devices need to be calibrated and maintained regularly. In addition, the special situations of different farms should be considered, and the judgment criteria and weights should be adjusted appropriately to ensure the universality and effectiveness of the system. Through continuous practice and optimization, this intelligent epidemic prevention management system will provide strong support for the healthy development of the aquaculture industry.

[0068] Furthermore, generate an epidemic prevention effect evaluation report, including:

[0069] Obtain the epidemic prevention execution data stream and the data on the implementation situation of epidemic prevention measures, preprocess the data, and obtain a standardized data set;

[0070] According to the epidemic prevention effect evaluation index system, select relevant features from the standardized data set to construct a training data set for a random forest model;

[0071] Use the random forest algorithm to train the training data set, optimize the model parameters through the cross-validation method, and obtain the optimal random forest model;

[0072] Input the real-time data of the epidemic prevention execution data stream and the implementation status of epidemic prevention measures into the optimal random forest model to predict and evaluate the epidemic prevention effect, and output the prediction result;

[0073] According to the prediction result, judge whether the epidemic prevention effect reaches the expected goal. If not, analyze the key factors affecting the epidemic prevention effect. For the key factors affecting the epidemic prevention effect, put forward suggestions for improving epidemic prevention measures, and feedback the suggestions to relevant departments to optimize the epidemic prevention execution plan;

[0074] Generate the epidemic prevention effect evaluation report according to the evaluation result of the random forest model and the improvement suggestions.

[0075] Specifically, in the evaluation of the epidemic prevention effect, the random forest can effectively process multi-dimensional features and give the importance ranking of each feature. For example, the model may find that in a certain area, the vaccination rate has the greatest impact on the epidemic prevention effect, followed by the nucleic acid testing coverage rate. Cross-validation is an important method for optimizing model parameters. By dividing the data set into multiple subsets and training and validating repeatedly, the best combination of model parameters can be found. In the evaluation of the epidemic prevention effect, parameters such as the depth of the decision tree and the number of feature selections may need to be adjusted. Through cross-validation, overfitting of the model can be avoided and its generalization ability on new data can be improved. Inputting real-time data into the trained random forest model can dynamically evaluate the epidemic prevention effect. For example, the model may predict the change trend of the number of new cases in the next week under the current epidemic prevention measures. If the prediction result shows that the epidemic may rebound, the prevention and control strategy needs to be adjusted in time. Analyzing the key factors affecting the epidemic prevention effect is an important basis for optimizing epidemic prevention measures. The random forest model can give the contribution degree of each feature to the prediction result. For example, if the model finds that the poor epidemic prevention effect in a certain area is mainly due to the low nucleic acid testing coverage rate, it can be recommended to increase the number of testing points and improve the testing frequency, etc. The improvement suggestions put forward according to the model evaluation results need to consider practical feasibility. For example, if the model suggests increasing the vaccination rate, relevant departments can formulate targeted publicity strategies or take convenient measures such as setting up temporary vaccination points. The implementation effect of these suggestions can be verified through continuous monitoring and model prediction. The epidemic prevention effect evaluation report is an important tool for providing support for decision-making. The report should include quantitative indicators of the current epidemic prevention effect, future trend prediction, analysis of key influencing factors, and specific improvement suggestions. Such a report can help decision-makers quickly understand the epidemic prevention situation, make scientific and timely decisions, and thus more effectively control the spread of the epidemic.

[0076] Furthermore, encrypt and store and share the epidemic prevention effect evaluation report and epidemic warning information through blockchain, including:

[0077] Pre-process the epidemic prevention data, and use machine learning algorithms to model and analyze the epidemic prevention data based on the preset epidemic warning indicator system to generate epidemic warning information;

[0078] Format the epidemic prevention effect evaluation report and epidemic warning information according to the preset data standard, generate data fingerprints through hash algorithm, and encrypt and store them using blockchain technology;

[0079] Establish a blockchain-based epidemic prevention data sharing mechanism, connect to the data interfaces of relevant departments, control data access rights through smart contracts, and realize secure data sharing across departments and regions.

[0080] Specifically, by preprocessing the epidemic prevention-related data, identifying and processing missing values ​​and outliers, the data quality and reliability are improved, laying the foundation for subsequent analysis. According to the preset epidemic warning indicator system, machine learning algorithms such as support vector machines or random forests are used to model and analyze the epidemic prevention data, monitor the epidemic risk in real time, and generate epidemic warning information in a timely manner. The epidemic prevention data, epidemic warning information, etc. are formatted according to certain data standards, and data fingerprints are generated through hash algorithms. Blockchain technology is used for encrypted storage to ensure the integrity and non-tamperability of the data. A blockchain-based epidemic prevention data sharing mechanism is established, which connects to the data interfaces of relevant departments, controls data access rights through smart contracts, realizes cross-departmental and cross-regional secure data sharing, and improves the efficiency of collaborative epidemic prevention. The characteristics of blockchain such as non-tamperability and traceability are used to record and store the entire process of epidemic prevention data and epidemic prevention decision-making processes, ensure that the decision-making process is open and transparent, accept social supervision, and improve the credibility of epidemic prevention work. The blockchain-based epidemic prevention data platform is interconnected with various business systems, connecting the processes of epidemic monitoring and early warning, data analysis, and epidemic prevention decision-making to form a closed loop, realizing full-process visual management of data flow and business flow, and improving the accuracy and timeliness of epidemic prevention.

[0081] This embodiment also provides an animal epidemic prevention and control system based on blockchain, such as Figure 2 ,include:

[0082] Basic database construction module, used to build the basic database of the farm;

[0083] The epidemic prevention schedule formulation module is used to formulate an epidemic prevention schedule based on the basic database of the farms, combined with the characteristics of animal species and the geographical location and climatic conditions, and determine the epidemic prevention time nodes for each farm;

[0084] The data collection module is used to collect farm environmental data, animal health data and epidemic prevention operation records in real time through IoT devices to generate epidemic prevention execution data streams;

[0085] An epidemic prevention measure comparison module, which is used to compare the epidemic prevention execution data stream with a preset list of epidemic prevention measure executions, and judge the implementation of epidemic prevention measures by setting thresholds and rules;

[0086] An epidemic prevention effect analysis module, which is used to analyze the epidemic prevention effect by using the random forest algorithm according to the epidemic prevention execution data stream and the implementation of epidemic prevention measures, and generate an epidemic prevention effect evaluation report;

[0087] A data encryption storage module, which is used to encrypt and store and share the epidemic prevention effect evaluation report and epidemic warning information through blockchain technology.

[0088] The above is only a preferred specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A blockchain-based animal epidemic prevention method, characterized in that: include: Establish a basic database of breeding farms, formulate an epidemic prevention schedule based on the basic database of breeding farms, combined with the characteristics of animal species and geographical location and climatic conditions, and determine the epidemic prevention time nodes of the breeding farms; Collect farm environmental data, animal health data and epidemic prevention operation records in real time through IoT devices to generate epidemic prevention execution data streams; Compare the epidemic prevention execution data flow with the preset epidemic prevention measures execution list, and determine the implementation of epidemic prevention measures by setting thresholds and rules; According to the epidemic prevention execution data flow and the implementation of the epidemic prevention measures, a random forest algorithm is used to analyze the epidemic prevention effect and generate an epidemic prevention effect evaluation report; The epidemic prevention effectiveness evaluation report and epidemic warning information are encrypted, stored and shared through blockchain to provide data support for epidemic prevention decisions.

2. The animal epidemic prevention method based on blockchain according to claim 1 is characterized in that: Establish a basic database of farms, including: Obtain the geographical location information of the farm and determine the specific location coordinates of the farm; Obtain information about animal species in the farm and determine the types of animals raised in the farm; Obtain information on the number of animals on the farm and determine the number of different animal species; Classify and organize the geographical location, animal species and quantity information of the breeding farm to establish a structured data record; The structured data records are imported into a database system to establish the farm basic database.

3. The animal epidemic prevention method based on blockchain according to claim 2 is characterized in that: Determine the epidemic prevention time nodes of the farm, including: Obtaining animal characteristics and climate condition data for each farm based on the animal species and geographic location information in the farm basic database; By clustering the data on animal species characteristics, animal species with similar characteristics are divided into several categories, and a unified epidemic prevention schedule is formulated for each animal category; According to the geographical location of the farm, obtain the historical climate conditions data of the geographical location, and predict the climate change trend of the area within the preset time period through time series analysis; The epidemic prevention schedule for each animal species is matched with the climate forecast data for the geographical location of the farm to obtain the epidemic prevention time nodes suitable for different farms.

4. The animal epidemic prevention method based on blockchain according to claim 1 is characterized in that: After the epidemic prevention execution data flow is generated, it includes: Preprocessing the epidemic prevention execution data stream, removing abnormal data, and obtaining standardized epidemic prevention execution data; According to the preset environmental parameter thresholds and animal health index thresholds, it is determined whether the current farm environment is suitable and whether the animal health status is normal. If the thresholds are exceeded, an early warning is triggered; The standardized epidemic prevention execution data is analyzed using a machine learning method to obtain an evaluation result of the epidemic prevention management status of the farm; Conduct correlation analysis on the farm environmental monitoring data, animal health monitoring data and the epidemic prevention management status assessment results to identify the epidemic prevention management risk points in the farm; Automatically generate optimization suggestions for epidemic prevention management based on the epidemic prevention management risk points and push them to farm managers; Continue to track the improvement of epidemic prevention management in the farms, evaluate the implementation effect of epidemic prevention management optimization suggestions by comparing historical data with current data, and form a closed loop of epidemic prevention management.

5. The animal epidemic prevention method based on blockchain according to claim 4 is characterized in that: Identify the epidemic prevention and management risk points of the farm, including: Obtaining farm environmental monitoring data, including temperature, humidity, and air quality, and using the farm environmental monitoring data as a basis for determining whether the farm environment is suitable for animal growth; Obtain animal health monitoring data, including animal growth, immune status, disease occurrence, etc., and use the animal health monitoring data as an indicator for assessing animal health status; Obtain the results of the current status assessment of epidemic prevention management of the farm, including the status of epidemic prevention facilities, the implementation of epidemic prevention systems, and personnel training, and use the results of the current status assessment of epidemic prevention management of the farm as the basis for judging the level of epidemic prevention management; Conduct correlation analysis on the farm environment monitoring data, the animal health monitoring data and the epidemic prevention management status assessment results, and use data mining algorithms to identify the correlation between the various indicators; According to the results of correlation analysis, the weak links and risk points in the epidemic prevention management of the farms are determined. For the identified risk points, expert system technology is used, combined with the preset epidemic prevention management knowledge base, to generate epidemic prevention management measures suggestions, and the epidemic prevention management measures suggestions are provided to the farm managers.

6. The animal epidemic prevention method based on blockchain according to claim 1 is characterized in that: Compare the epidemic prevention execution data flow with the preset epidemic prevention measures execution list, and determine the implementation of epidemic prevention measures by setting thresholds and rules, including: A text similarity algorithm is used to compare the epidemic prevention execution data flow and the epidemic prevention measures execution list to obtain a similarity score; According to a preset threshold, determine whether the similarity score exceeds the threshold; if the similarity score exceeds the threshold, determine that the epidemic prevention measures are in place; if the similarity score does not exceed the threshold, determine that the epidemic prevention measures are not in place; Through the set rules, the implementation of several epidemic prevention measures is comprehensively considered to judge the overall implementation degree of epidemic prevention measures; Based on the judgment results, output a report on the implementation of epidemic prevention measures.

7. The animal epidemic prevention method based on blockchain according to claim 1 is characterized in that: Generate the epidemic prevention effect evaluation report, including: Obtain epidemic prevention execution data flow and epidemic prevention measures implementation data, pre-process the data, and obtain a standardized data set; According to the epidemic prevention effect evaluation indicator system, relevant features are selected from the normalized data set to construct a training data set for the random forest model; The training data set is trained using a random forest algorithm, and model parameters are optimized using a cross-validation method to obtain an optimal random forest model; Input the epidemic prevention execution data flow and the real-time data of the implementation of epidemic prevention measures into the optimal random forest model, predict and evaluate the epidemic prevention effect, and output the prediction result; Based on the prediction results, determine whether the epidemic prevention effect has reached the expected goal. If not, analyze the key factors affecting the epidemic prevention effect, propose suggestions for improving epidemic prevention measures based on the key factors affecting the epidemic prevention effect, and feed back the suggestions to relevant departments to optimize the epidemic prevention implementation plan; Based on the evaluation results and improvement suggestions of the random forest model, the epidemic prevention effectiveness evaluation report is generated.

8. The animal epidemic prevention method based on blockchain according to claim 1 is characterized in that: The epidemic prevention effect evaluation report and epidemic warning information are encrypted, stored and shared through blockchain, including: Pre-process the epidemic prevention data, and use machine learning algorithms to model and analyze the epidemic prevention data based on the preset epidemic warning indicator system to generate epidemic warning information; Format the epidemic prevention effect evaluation report and epidemic warning information according to the preset data standard, generate data fingerprints through hash algorithm, and encrypt and store them using blockchain technology; Establish a blockchain-based epidemic prevention data sharing mechanism, connect to the data interfaces of relevant departments, control data access rights through smart contracts, and realize secure data sharing across departments and regions.

9. An animal epidemic prevention and treatment system based on blockchain, characterized in that: include: Basic database construction module, used to build the basic database of the farm; The epidemic prevention schedule formulation module is used to formulate an epidemic prevention schedule based on the basic database of the farms, combined with the characteristics of animal species and the geographical location and climatic conditions, and determine the epidemic prevention time nodes for each farm; The data collection module is used to collect farm environmental data, animal health data and epidemic prevention operation records in real time through IoT devices to generate epidemic prevention execution data streams; The epidemic prevention measures comparison module is used to compare the epidemic prevention execution data flow with the preset epidemic prevention measures execution list, and determine the implementation of epidemic prevention measures by setting thresholds and rules; An epidemic prevention effect analysis module, used to analyze the epidemic prevention effect using a random forest algorithm according to the epidemic prevention execution data flow and the implementation of the epidemic prevention measures, and generate an epidemic prevention effect evaluation report; The data encryption storage module is used to encrypt, store and share the epidemic prevention effect evaluation report and epidemic warning information through blockchain technology.

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