Animal epidemic disease epidemiological survey data analysis and processing system

Abstract

The invention relates to the technical field of animal epidemic disease epidemiological survey data analysis and processing systems, and discloses an animal epidemic disease epidemiological survey data analysis and processing system. The exploration data warehouse is used for reading survey data of animal epidemic situation epidemiology, the model library is used for storing various calculation models and algorithm formulas, and the component library is used for storing customer division data, customer loss data and customer product data of different industries. The mining algorithm library is used for performing mining calculation on data according to information of the model library, the component library and the exploration data warehouse and importing a calculation result into the survey data client, the knowledge storage end is used for receiving and storing the data imported by the data mining algorithm library, and the survey data client is used for reading the data of the knowledge storage end for query. The problems that a traditional data analysis and processing system is complex in architecture design structure and low in efficiency are solved.

Description

Technical Field

[0001] This invention relates to the technical field of data analysis and processing systems for animal disease epidemiological surveys, specifically to a data analysis and processing system for animal disease epidemiological surveys. Background Technology

[0002] Data processing refers to the process of analyzing and summarizing large amounts of collected data using appropriate statistical analysis methods, extracting useful information, and forming conclusions through detailed research and generalization. This process also supports the quality management system. In practice, data analysis and processing can help people make judgments so that appropriate actions can be taken. The mathematical foundations of data analysis and processing were established in the early 20th century, but it was not until the advent of computers that practical operation became possible and data analysis was widely adopted. Data analysis is a product of the combination of mathematics and computer science.

[0003] Current data analysis and processing faces challenges such as large data volumes, diverse structural forms, and real-time requirements. These challenges increase the difficulty of data analysis and integration. Traditional data analysis and processing systems suffer from complex architectures, low efficiency, and a lack of specificity. In addition, utilizing a main processor can improve the convenience and efficiency of querying and retrieving knowledge data. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] To address the shortcomings of existing technologies, this invention provides a data analysis and processing system for animal disease epidemiological surveys. This system boasts advantages such as simple data analysis structure, convenient operation, and high efficiency, solving the problems of complex architecture, low efficiency, lack of specificity, and inconvenient data querying in traditional data analysis and processing systems.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, the present invention provides the following technical solution: a system for analyzing and processing data from animal disease epidemiological surveys, comprising the following steps:

[0008] 1) Application process of intelligent data acquisition terminal;

[0009] 2) Regional nodes;

[0010] 3) Sample extraction and allocation module;

[0011] 4) Data analysis module;

[0012] 5) Data analysis and processing system workflow;

[0013] Preferably, the intelligent data acquisition terminal application process first prepares a main processor using a Corterx-M3 processor chip. The main processor is connected to a storage chip and at least one communication interface circuit. The storage chip embeds a real-time in-memory database, which provides an I / O software interface for exchanging data with a relational database stored on an external storage chip. Simultaneously, the in-memory database transaction management must be based on priority and consider timing constraints. The data files of the in-memory database are stored in the form of index files, and the records are stored using pointers. An SQL syntax analyzer is used to define, revoke, and modify data schemas, query data, add, delete, and modify data, and control data access permissions. A transaction scheduler is used to implement priority allocation, overload management, concurrency control, and real-time transaction scheduling for real-time transaction management, thereby ensuring data consistency and integrity. Security mechanisms are used to implement registration confidentiality, access level confidentiality, and data confidentiality. Backup records or log management are used for mechanism recovery.

[0014] Preferably, the main processor is further connected to a meter protocol card for data acquisition and parsing, an RS232 interface or infrared interface for updating the meter protocol card parameters, an LCD display, an information input device, and a USB interface. These functions are the same as those of the meter protocol card used for data acquisition and parsing, thereby enabling the data acquisition and parsing process to be separated from the main processor, greatly reducing the CPU load. The meter protocol card can be manually updated or its parameters changed via the RS232 interface or infrared interface.

[0015] Preferably, the regional node includes the following steps:

[0016] 1) The qualitative judgment results of regional nodes on the epidemic event determine the type of epidemic.

[0017] 2) Analyze the transmission characteristics of the disease based on its type, identify the risk factors of the disease, and classify the risk factors according to their degree of harm, including Class I risk factors and Class II risk factors.

[0018] 3) Based on the collected basic information on the epidemic event, extract all risk factors from the basic information on the epidemic event, and count the number of Class I risk factors and Class II risk factors in the basic information on the epidemic event.

[0019] 4) Develop comprehensive risk assessment criteria, classify risk levels according to the number of risk factors at different levels, and obtain the assessment results of the epidemic risk of disease events.

[0020] Preferably, the sample extraction and allocation module includes the following steps:

[0021] 1) Simple random sampling: The data is calculated as a percentage based on the given sample size, and then randomly selected from the population with or without replacement. For cases where the relevant costs are known, the sample size is automatically calculated based on the cost relationship before random sampling is performed.

[0022] 2) Stratified sampling: Combining stratified data and sample size in each stratum, random sampling is performed in each stratum.

[0023] 3) Paired data for two-sample tests: Paired data for significance tests are randomly assigned to a processing method.

[0024] 4) Two-sample test for grouped data: For grouped data for significance testing, the total sample is randomly assigned to two different groups according to the required sample size for each group.

[0025] Preferably, the data analysis module includes the following steps:

[0026] 1) Descriptive statistics: Descriptive statistics on epidemiological data enable researchers to have a holistic understanding of the epidemiological data in terms of central tendency, dispersion, etc., and to more vividly describe the information contained in the data by creating various charts.

[0027] 2) Simple random sampling and stratified sampling: Analyze the epidemiological data collected by simple random sampling and stratified sampling methods to estimate the population mean or population rate of the population to which the data belongs. The analysis results can be used to understand the disease situation reflected by the epidemiological data, thereby guiding the prevention and control of diseases and providing a basis for animal epidemiology research.

[0028] 3) Significance test of differences: Perform a significance test on the sampling survey data.

[0029] Preferably, the data analysis and processing system process is first divided into an information system, a data mining application server, and a survey data client. The information system is used to collect and process industry data based on user-preset conditions and access the system via a bus. The data mining application server is used to extract, transform, and load data based on the user-preset industry data and import the data mining results into the survey data client. The survey data client is used to provide users with the final data after analysis and processing. The data mining application server includes an exploratory data warehouse, a data mining algorithm library, a model library, and a component library.

[0030] Preferably, the survey data client includes a client application terminal and a knowledge storage terminal. The data warehouse is used to read data from animal epidemic epidemiological surveys. The model library is used to store various calculation models and algorithm formulas. The component library is used to store customer segmentation data, customer churn data, and customer product data from different industries. The data mining algorithm library is used to perform data mining calculations based on the information from the model library, component library, and data warehouse, and import the calculation results into the survey data client. The knowledge storage terminal is used to receive and store the data imported by the data mining algorithm library. The survey data client is used to read the data from the knowledge storage terminal and provide it for querying.

[0031] Another technical problem to be solved by the present invention is to provide a system for analyzing and processing data from animal disease epidemiological surveys, comprising the following steps:

[0032] 1) Application process of intelligent data acquisition terminal;

[0033] 2) Regional nodes;

[0034] 3) Sample extraction and allocation module;

[0035] 4) Data analysis module;

[0036] 5) Data analysis and processing system workflow;

[0037] (III) Beneficial Effects

[0038] Compared with the prior art, the present invention provides a data analysis and processing system for animal disease epidemiological surveys, which has the following beneficial effects:

[0039] 1. This data analysis and processing system for animal disease epidemiology surveys includes a survey data client (client application terminal and knowledge storage terminal), an exploratory data warehouse for reading survey data on animal disease epidemiology, a model library for storing various calculation models and algorithm formulas, a component library for storing customer segmentation data, customer churn data, and customer product data from different industries, a data mining algorithm library for performing data mining calculations based on information from the model library, component library, and exploratory data warehouse, and importing the calculation results into the survey data client, and a knowledge storage terminal for receiving and storing data imported from the data mining algorithm library. The survey data client reads data from the knowledge storage terminal and provides query functionality. This system solves the problems of complex architecture and low efficiency in traditional data analysis and processing systems.

[0040] 2. This data analysis and processing system for animal disease epidemiological surveys, through an intelligent data acquisition terminal application process, first prepares a main processor using a Corterx-M3 processor chip. The main processor is connected to a storage chip and at least one communication interface circuit. The storage chip embeds a real-time in-memory database, which provides an I / O software interface for exchanging data with a relational database stored on an external storage chip. Simultaneously, the in-memory database transaction management must adopt a priority-based approach considering timing constraints. The data files of the in-memory database are stored in the form of index files, and records are stored using pointers. An SQL syntax analyzer is used for defining, undoing, and modifying data schemas, querying data, adding, deleting, and modifying data, and controlling data access permissions. A transaction scheduler is used to implement real-time transaction management, including priority allocation, overload management, concurrency control, and real-time transaction scheduling, thereby ensuring data consistency and integrity. Security mechanisms are employed to achieve registration confidentiality, access level confidentiality, and data confidentiality. Backup records or log management are used for recovery mechanisms, improving the convenience and efficiency of data querying and management. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the main processor flow proposed in this invention;

[0042] Figure 2 This is a schematic diagram of the sample extraction and allocation process proposed in this invention;

[0043] Figure 3 This is a schematic diagram of the data analysis process proposed in this invention;

[0044] Figure 4 This is a schematic diagram of the difference significance test process proposed in this invention;

[0045] Figure 5 This is a schematic diagram of the data information application server proposed in this invention;

[0046] Figure 6 This is a schematic diagram of the steps proposed in this invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Example 1: A data analysis and processing system for animal disease epidemiological surveys, comprising the following steps:

[0049] 1) Intelligent data acquisition terminal application process: The intelligent data acquisition terminal application process first prepares a main processor using a Corterx-M3 processor chip. The main processor is connected to a storage chip and at least one communication interface circuit. The storage chip embeds a real-time in-memory database, which provides an I / O software interface for exchanging data with a relational database stored on an external storage chip. Simultaneously, the in-memory database transaction management must adopt a priority-based approach considering timing constraints. The data files of the in-memory database are stored in the form of index files, and records are stored using pointers. Furthermore, an SQL syntax analyzer is used for defining, revoking, and modifying data schemas, querying data, adding, deleting, and modifying data, and controlling data access permissions. A transaction scheduler is employed. It is responsible for prioritizing, managing overload, controlling concurrency, and scheduling real-time transactions to ensure data consistency and integrity. It employs security mechanisms for registration confidentiality, access level confidentiality, and data confidentiality, and uses backup records or log management for recovery. The main processor is also connected to a metering protocol card for data acquisition and parsing, an RS232 or infrared interface for updating metering protocol card parameters, an LCD display, an information input device, and a USB interface. These functions are related to the metering protocol card used for data acquisition and parsing, thus enabling the data acquisition and parsing process to be independent of the main processor, greatly reducing the CPU load. The metering protocol card can be manually updated or its parameters changed via the RS232 or infrared interface.

[0050] 2) Regional nodes, wherein the regional nodes include the following steps:

[0051] Step 1: The regional nodes determine the type of disease by analyzing the qualitative assessment results of the epidemic event.

[0052] Step 2: Analyze the transmission characteristics of the disease based on its type, identify the risk factors, and classify the risk factors according to their severity, including Class I and Class II risk factors.

[0053] Step 3: Based on the collected basic information on the epidemic event, extract all risk factors from the basic information on the epidemic event, and count the number of Class I risk factors and Class II risk factors in the basic information on the epidemic event.

[0054] Step 4: Develop comprehensive risk assessment criteria, classify risk levels according to the number of risk factors at different levels, and obtain the assessment results of the epidemic risk of disease events.

[0055] 3) Sample extraction and allocation module, the sample extraction and allocation module includes the following steps:

[0056] Step 1: Simple Random Sampling: Calculate the data as a percentage based on the given sample size, and then randomly select from the population with or without replacement. For cases where the relevant costs are known, the sample size is automatically calculated based on the cost relationship before random sampling.

[0057] Step 2: Stratified sampling: Based on the stratified data and the sample size of each stratum, random sampling is performed in each stratum.

[0058] Step 3: Paired data for two-sample test: Randomly assign a processing method to the paired data for significance test.

[0059] Step 4: Two-sample test for grouped data: For grouped data for significance testing, the total sample is randomly assigned to two different groups according to the required sample size for each group.

[0060] 4) Data analysis module, which includes the following steps:

[0061] Step 1: Descriptive statistics. Descriptive statistics of epidemiological data enable researchers to gain an overall understanding of the data in terms of central tendency, dispersion, etc., and to more vividly describe the information contained in the data by creating various charts and graphs.

[0062] Step Two: Simple Random Sampling and Stratified Sampling. Analyze the epidemiological data collected using simple random sampling and stratified sampling methods to estimate the population mean or population rate of the population to which the data belongs. The analysis results help to understand the disease situation reflected in the epidemiological data, thereby guiding disease prevention and control work and providing a basis for animal epidemiological research.

[0063] Step 3: Significance test of differences. Perform a significance test on the sampling survey data.

[0064] 5) Data Analysis and Processing System Flow: The data analysis and processing system flow is first divided into an information system, a data mining application server, and a survey data client. The information system is used to collect and process industry data based on user-preset conditions, and the data is accessed through a bus. The data mining application server is used to extract, transform, and load data based on the user-preset industry data, and import the data mining results into the survey data client. The survey data client is used to provide users with the final data after analysis and processing. The data mining application server includes an exploratory data warehouse, a data mining algorithm library, a model library, and a component library. The survey data client includes a client application terminal and a knowledge storage terminal. The exploratory data warehouse is used to read data from animal epidemic epidemiological surveys. The model library is used to store various calculation models and algorithm formulas. The component library is used to store customer segmentation data, customer churn data, and customer product data for different industries. The mining algorithm library is used to perform mining calculations on the data based on the information from the model library, component library, and exploratory data warehouse, and import the calculation results into the survey data client. The knowledge storage terminal is used to receive and store the data imported by the data mining algorithm library. The survey data client is used to read the data from the knowledge storage terminal and query it.

[0065] The beneficial effects of this invention are as follows: Firstly, through the survey data client, which includes a client application terminal and a knowledge storage terminal, an exploratory data warehouse is used to read data from animal epidemic epidemiological surveys; a model library is used to store various calculation models and algorithm formulas; a component library is used to store customer segmentation data, customer churn data, and customer product data from different industries; a data mining algorithm library is used to perform data mining calculations based on information from the model library, component library, and exploratory data warehouse, and import the calculation results into the survey data client; the knowledge storage terminal is used to receive and store the data imported from the data mining algorithm library; and the survey data client is used to read and query the data from the knowledge storage terminal. This solves the problems of complex architecture and low efficiency in traditional data analysis and processing systems. Then, through the intelligent acquisition terminal application process, a main processor using a Corterx-M3 processor chip is first prepared, with the main processor connected to a storage chip and... At least one communication interface circuit is included. The memory chip embeds a real-time in-memory database, which provides an I / O software interface for exchanging data with a relational database stored on an external memory chip. Transaction management for the in-memory database must be priority-based and time-considered. Data files in the in-memory database are stored as index files, and records are stored using pointers. An SQL parser is used to define, revoke, and modify data schemas, query data, add, delete, and modify data, and control data access permissions. A transaction scheduler is responsible for prioritizing, managing overload, controlling concurrency, and scheduling real-time transactions to ensure data consistency and integrity. Security mechanisms are employed to ensure registration confidentiality, access level confidentiality, and data confidentiality. Backup records or log management are used for recovery, improving the convenience and efficiency of data querying and management.

[0066] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A system for analyzing and processing data from animal disease epidemiological surveys, characterized in that, Includes the following steps: 1) Application process of intelligent data acquisition terminal; 2) Regional nodes; 3) Sample extraction and allocation module; 4) Data analysis module; 5) Data analysis and processing system workflow.

2. The system for analyzing and processing data from animal disease epidemiological surveys according to claim 1, characterized in that, The intelligent data acquisition terminal application process first prepares a main processor using a Corterx-M3 processor chip. The main processor is connected to a storage chip and at least one communication interface circuit. The storage chip embeds a real-time in-memory database, which provides an I / O software interface for exchanging data with a relational database stored on an external storage chip. Simultaneously, the in-memory database transaction management must be based on priority and consider timing constraints. The data files of the in-memory database are stored in the form of index files, and records are stored using pointers. An SQL syntax analyzer is used to define, revoke, and modify data schemas, query data, add, delete, and modify data, and control data access permissions. A transaction scheduler is used to implement priority allocation, overload management, concurrency control, and real-time transaction scheduling for real-time transaction management, thereby ensuring data consistency and integrity. Security mechanisms are used to implement registration confidentiality, access level confidentiality, and data confidentiality. Backup records or log management are used for mechanism recovery.

3. The system for analyzing and processing data from animal disease epidemiological surveys according to claim 2, characterized in that, The main processor is also connected to a meter protocol card for data acquisition and parsing, an RS232 interface or infrared interface for updating the meter protocol card parameters, an LCD display, an information input device, and a USB interface. These functions are the same as those of the meter protocol card used for data acquisition and parsing, thereby enabling the data acquisition and parsing process to be separated from the main processor, greatly reducing the CPU load. The meter protocol card can be manually updated or its parameters changed through the RS232 interface or infrared interface.

4. The system for analyzing and processing data from animal disease epidemiological surveys according to claim 1, characterized in that, The regional node includes the following steps: 1) The qualitative judgment results of regional nodes on the epidemic event determine the type of epidemic. 2) Analyze the transmission characteristics of the disease based on its type, identify the risk factors of the disease, and classify the risk factors according to their degree of harm, including Class I risk factors and Class II risk factors. 3) Based on the collected basic information on the epidemic event, extract all risk factors from the basic information on the epidemic event, and count the number of Class I risk factors and Class II risk factors in the basic information on the epidemic event. 4) Develop comprehensive risk assessment criteria, classify risk levels according to the number of risk factors at different levels, and obtain the assessment results of the epidemic risk of disease events.

5. The system for analyzing and processing data from animal disease epidemiological surveys according to claim 1, characterized in that, The sample extraction and allocation module includes the following steps: 1) Simple random sampling: The data is calculated as a percentage based on the given sample size, and then randomly selected from the population with or without replacement. For cases where the relevant costs are known, the sample size is automatically calculated based on the cost relationship before random sampling is performed. 2) Stratified sampling: Combining stratified data and sample size in each stratum, random sampling is performed in each stratum. 3) Paired data for two-sample tests: Paired data for significance tests are randomly assigned to a processing method. 4) Two-sample test for grouped data: For grouped data for significance testing, the total sample is randomly assigned to two different groups according to the required sample size for each group.

6. The system for analyzing and processing data from animal disease epidemiological surveys according to claim 1, characterized in that, The data analysis module includes the following steps: 1) Descriptive statistics: Descriptive statistics on epidemiological data enable researchers to have a holistic understanding of the epidemiological data in terms of central tendency, dispersion, etc., and to more vividly describe the information contained in the data by creating various charts. 2) Simple random sampling and stratified sampling: Analyze the epidemiological data collected by simple random sampling and stratified sampling methods to estimate the population mean or population rate of the population to which the data belongs. The analysis results can be used to understand the disease situation reflected by the epidemiological data, thereby guiding the prevention and control of diseases and providing a basis for animal epidemiology research. 3) Significance test of differences: Perform a significance test on the sampling survey data.

7. The system for analyzing and processing data from animal disease epidemiological surveys according to claim 1, characterized in that, The data analysis and processing system workflow is first divided into an information system, a data mining application server, and a survey data client. The information system is used to collect and process industry data based on user-preset conditions, and the data is accessed through a bus. The data mining application server is used to extract, transform, and load data based on the user-preset industry data, and import the data mining results into the survey data client. The survey data client is used to provide users with the final data after analysis and processing. The data mining application server includes an exploratory data warehouse, a data mining algorithm library, a model library, and a component library.

8. A data analysis and processing system for animal disease epidemiological surveys according to claim 7, characterized in that, The survey data client includes a client application and a knowledge storage terminal. The data warehouse is used to read survey data for animal epidemic epidemiology. The model library is used to store various calculation models and algorithm formulas. The component library is used to store customer segmentation data, customer churn data, and customer product data for different industries. The data mining algorithm library is used to perform data mining calculations based on the information from the model library, component library, and data warehouse, and import the calculation results into the survey data client. The knowledge storage terminal is used to receive and store the data imported by the data mining algorithm library. The survey data client is used to read the data from the knowledge storage terminal and provide it for querying.

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