Distributed storage method and system for multi-source heterogeneous infection source data

Abstract

The invention discloses a distributed storage method and system for multi-source heterogeneous infection source data, relates to the technical field of infection source data, and aims to optimize the storage efficiency of infectious disease data by dynamically adjusting a storage strategy. According to the system, efficient storage space utilization is achieved by evaluating historical and real-time data, storage resources are preferentially allocated for high-risk areas, and data redundancy is reduced. The method not only improves the flexibility of storage management, but also provides powerful data support for public health decision making.

Description

Technical Field

[0001] The present invention relates to the technical field of infectious source data, and in particular to a distributed storage method and system for multi-source heterogeneous infectious source data. Background Art

[0002] In recent years, the frequent outbreaks of infectious diseases and the emergence of new diseases around the world, such as monkeypox virus, Ebola, and Zika virus, have posed serious challenges to public health security. These diseases often spread across regions, have a wide range of impacts, and have a significant impact on human health and social economy. When responding to infectious disease outbreaks, only by timely obtaining and analyzing the latest relevant data can we effectively assess risks and formulate response measures. Therefore, establishing an efficient distributed storage and analysis mechanism is the key to improving public health emergency response capabilities.

[0003] Existing technologies such as the infectious disease early warning method and infectious disease data blockchain system disclosed in the invention patent application with announcement number CN116913453A belong to the field of infectious disease prevention and control technology. In order to solve the problem that the existing infectious disease early warning mainly relies on doctors for diagnosis and identification, doctors misdiagnose or miss diagnoses, and disease prevention and control agencies make misjudgments after reporting, which easily leads to difficulty in effective prevention and control before the spread of infectious diseases; the present invention performs regional early warning zoning, builds an early warning blockchain in each early warning block to report patient data, and the early warning monitoring platform performs qualitative calculation and monitoring of infectious diseases on all patient data based on the early warning blockchain, determines the infectious disease patients in different early warning blocks and the entire city, and issues infectious disease epidemic early warning notifications to the nodes in each early warning block based on the early warning blockchain, realizes information exchange between nodes, and carries out corresponding epidemic prevention and control in a targeted manner for the early warning block to which the infectious disease patient belongs or the entire city, thereby ensuring the timeliness of infectious disease epidemic prevention and control.

[0004] In view of the above scheme, the applicant of the present invention has found that the above technology has at least the following technical problems: 1. In the absence of distributed storage, the infectious disease source data of various regions may be stored in isolation in the existing technology, making it difficult to effectively integrate and analyze. Specifically, the differences between regions may not be fully taken into account, resulting in inaccurate data evaluation. At the same time, the acquisition and update of real-time data may be delayed, affecting the timely analysis and response to the development of the epidemic. In the absence of flexible data storage and access mechanisms, the setting of evaluation time points may be restricted and cannot be flexibly adjusted according to changes in the epidemic, resulting in lags and inaccuracies in the evaluation results. In the face of public health emergencies, the lack of distributed storage methods will limit the flexibility of data management and will not be able to quickly adapt to the needs of changes in the epidemic, affecting the timeliness and effectiveness of decision-making.

[0005] 2. Existing technologies do not have the support of distributed storage, and the data sharing process may be restricted, resulting in the inability to effectively share important infectious disease source data between regions, affecting cross-regional collaboration and research. The storage and adjustment of shared data may become complicated and time-consuming, making it difficult to achieve dynamic updates and maintenance, resulting in the data in the shared area being outdated or inaccurate, and may not be able to fully reflect the development of the epidemic in various regions. The lack of comprehensive consideration of multi-source data leads to a lack of necessary scientific basis for decision-making. The isolation of data and the lack of sharing mechanisms will limit the breadth and depth of scientific research. Researchers cannot obtain comprehensive and accurate data support, affecting their in-depth understanding of infectious diseases and the formulation of response strategies. Summary of the invention

[0006] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a distributed storage method and system for multi-source heterogeneous infectious source data.

[0007] To solve the above technical problems, the present invention adopts the following technical solutions: In the first aspect, the present invention provides a distributed storage method for multi-source heterogeneous infectious source data, including: Step 1, analysis of the initial storage strategy: obtaining the basic characteristics corresponding to each type of infectious source data in each region in the historical period in the target storage platform, thereby analyzing and obtaining the basic characteristic evaluation coefficients corresponding to each type of infectious source data in each region, and then processing the initial storage strategy for each type of infectious source data in each region.

[0008] Step 2. Acquisition of real-time development assessment coefficients: After the initial storage strategy processing of each type of infectious disease source data in each region is completed, several assessment time points are set to collect the real-time development characteristics corresponding to each type of infectious disease in each region at each assessment time point, and then analyze and obtain the real-time development assessment coefficients corresponding to each type of infectious disease in each region at each assessment time point.

[0009] Step 3. Analysis of real-time shared storage: Based on the real-time development assessment coefficients corresponding to each type of infectious disease in each region at each assessment time point, assess whether the data on the source of each type of infectious disease in each region at each assessment time point need to be shared for research, and store the data on the source of each type of infectious disease in each region that need to be shared for research at each assessment time point in the shared area.

[0010] Step 4. Adjustment of shared data storage: Obtain the real-time development characteristics of each type of infectious disease in each region at each evaluation time point in the shared area, and then evaluate the impact of each type of infectious disease in each region at each evaluation time point in the shared storage, and adjust the source data of each type of infectious disease in each region at each evaluation time point in the shared storage.

[0011] Preferably, the initial storage strategy for each type of infectious source data in each region is processed, and the specific analysis process is as follows: S1. The target storage platform is divided into several storage nodes, and each region corresponds to a storage node. At the same time, three storage levels are set in each storage node, and the three storage levels include: basic level, intermediate level and advanced level.

[0012] S2. Compare the basic characteristic evaluation coefficients corresponding to each type of infectious disease source data in each region with the basic characteristic evaluation coefficient intervals corresponding to each storage level in the corresponding storage node in the database. If the basic characteristic evaluation coefficient corresponding to a certain type of infectious disease source data in a certain region is within the basic characteristic evaluation coefficient interval corresponding to a certain storage level in the corresponding storage node in the database, then store the infectious disease source data of that type in the region in the storage level in the corresponding storage node. In this way, the initial storage strategy is applied to the infectious disease source data of each type in each region.

[0013] Preferably, the evaluation of whether the data of each type of infectious disease source in each region at each evaluation time point needs to be shared is specifically carried out as follows: the real-time development assessment coefficient corresponding to each type of infectious disease in each region at each evaluation time point is compared with the real-time development assessment coefficient corresponding to the set standard infectious disease. If the real-time development assessment coefficient corresponding to a certain type of infectious disease in a certain region at a certain evaluation time point is greater than or equal to the real-time development assessment coefficient corresponding to the set standard infectious disease, then it is evaluated that the data of the infectious disease source of this type in the region at this evaluation time point needs to be shared. If the real-time development assessment coefficient corresponding to a certain type of infectious disease in a certain region at a certain evaluation time point is less than the real-time development assessment coefficient corresponding to the set standard infectious disease, then it is evaluated that the data of the infectious disease source of this type in the region at this evaluation time point does not need to be shared. In this way, it is evaluated whether the data of each type of infectious disease source in each region at each evaluation time point needs to be shared.

[0014] Preferably, the evaluation shared storage corresponds to the degree of impact of each type of infectious disease in each region at each evaluation time point, and the specific evaluation process is as follows: Q1. The real-time development characteristics corresponding to each type of infectious disease in each region at each evaluation time point in the shared area are obtained according to the method of obtaining the real-time development evaluation coefficient in step 2, and then the real-time development characteristics corresponding to each type of infectious disease in each region at each evaluation time point in the shared area are obtained.

[0015] Q2. Compare the real-time development characteristics of each type of infectious disease in each region at each evaluation time point in the shared area with the real-time development characteristics of each impact level in the database. If the real-time development characteristics of a certain type of infectious disease in a certain region at a certain evaluation time point in the shared area are within the real-time development characteristics corresponding to a certain impact level in the database, then the impact level in the evaluation database is the impact level corresponding to the type of infectious disease in the region at the evaluation time point in the shared storage. The impact levels include low risk, medium risk and high risk. In this way, the impact levels of each type of infectious disease in each region at each evaluation time point in the shared storage are evaluated.

[0016] Preferably, the data of each type of infectious disease source in each region at each evaluation time point in the shared storage is adjusted, and the specific adjustment process is as follows: W1. If the impact level corresponding to a certain type of infectious disease in a certain region at a certain evaluation time point in the shared storage is low risk, the infectious disease source data corresponding to the type of infectious disease in the region at the evaluation time point in the shared storage is marked as "low-risk data". At the same time, a feedback alarm is given to the infectious disease source data corresponding to the type of infectious disease in the region at the evaluation time point in the shared storage, and the infectious disease source data corresponding to the type of infectious disease in the region at the evaluation time point in the shared storage is removed from the shared storage, and stored in the storage layer in the corresponding storage node where the original infectious disease source data is stored.

[0017] W2. If the impact level of a certain type of infectious disease in a certain area at a certain evaluation time point in the shared storage is medium risk, the infectious disease source data corresponding to the type of infectious disease in the area at the evaluation time point in the shared storage shall be marked as "medium risk data". At the same time, a feedback alarm shall be given to the infectious disease source data corresponding to the type of infectious disease in the area at the evaluation time point in the shared storage, and the storage time of the infectious disease source data corresponding to the type of infectious disease in the area at the evaluation time point in the shared storage shall be set to the standard storage time. When the storage time of the infectious disease source data corresponding to the type of infectious disease in the area at the evaluation time point in the shared storage reaches the standard storage time, the infectious disease source data corresponding to the type of infectious disease in the area at the evaluation time point in the shared storage shall be removed from the shared storage, and stored in the storage layer in the corresponding storage node of the original infectious disease source data.

[0018] W2. If the impact level of a certain type of infectious disease in a certain region at a certain evaluation time point in the shared storage is medium risk, the infectious disease source data corresponding to the type of infectious disease in the region at the evaluation time point in the shared storage shall be marked as "high-risk data", and the infectious disease source data corresponding to the type of infectious disease in the region at the evaluation time point in the shared storage shall continue to be stored in the shared storage, and the storage period shall be unlimited.

[0019] In a second aspect, the present invention provides a distributed storage system for multi-source heterogeneous infectious source data, including: an initial storage strategy analysis module: used to obtain the basic characteristics corresponding to each type of infectious source data in each region in the historical period in the target storage platform, so as to analyze and obtain the basic characteristic evaluation coefficients corresponding to each type of infectious source data in each region, and then perform initial storage strategy processing on each type of infectious source data in each region.

[0020] Real-time development assessment coefficient acquisition module: used to set several evaluation time points after the initial storage strategy processing of various types of infectious disease source data in various regions is completed, so as to collect the real-time development characteristics corresponding to various types of infectious diseases in various regions at each evaluation time point, and then analyze and obtain the real-time development assessment coefficients corresponding to various types of infectious diseases in various regions at each evaluation time point.

[0021] Real-time shared storage and analysis module: used to evaluate whether the data of various types of infectious disease sources in various regions at each assessment time point need to be shared and studied based on the real-time development assessment coefficients corresponding to various types of infectious diseases in various regions at each assessment time point, and store the data of various types of infectious disease sources in various regions that need to be shared and studied at each assessment time point in the shared area.

[0022] Shared data storage adjustment module: used to obtain the real-time development characteristics of each type of infectious disease in each region at each evaluation time point in the shared area, and then evaluate the impact level of each type of infectious disease in each region at each evaluation time point in the shared storage, and adjust the source data of each type of infectious disease in each region at each evaluation time point in the shared storage.

[0023] The beneficial effects of the present invention are: 1. In the embodiment of the present invention, the system can deeply mine the basic characteristics of infectious disease source data in various regions in the historical cycle. This process enables the storage platform to formulate adaptive storage solutions for different types of infectious diseases and achieve higher storage utilization. With the help of characteristic evaluation coefficients, the system can effectively identify and prioritize high-risk areas and types, thereby reducing data redundancy, which not only saves storage space, but also reduces operation and maintenance costs, and evaluates the development trend of infectious diseases in various regions in real time, providing a dynamic adjustment basis for data storage strategies. The real-time development evaluation coefficients obtained by each region at a fixed time point enable the system to quickly respond to changes in the spread of infectious diseases and adjust the allocation of storage resources in a timely manner. For example, when an epidemic situation in a certain area shows an upward trend, the system can immediately increase the storage capacity and attention of the data in that area, thereby strengthening the monitoring and management of the epidemic situation in that area.

[0024] 2. In the embodiment of the present invention, by analyzing the data at each evaluation time point, the system can screen out the data sets that need to be shared for research. Such a mechanism provides support for cross-regional public health collaboration, enabling researchers and public health workers in various regions to collaborate based on the latest and most relevant data. The timeliness and effectiveness of shared data are crucial for early warning, response and control of epidemics, and help to form a comprehensive and coordinated public health strategy, so that public health decision makers can quickly grasp the dynamic changes of infectious diseases in various regions and formulate response measures based on data-driven analysis results. Real-time development assessment coefficients not only help identify different behavioral characteristics and transmission patterns, but also provide a scientific basis for policy making, thereby improving the accuracy and effectiveness of decision-making and ultimately safeguarding public health.

[0025] 3. In the embodiments of the present invention, by evaluating the degree of impact of shared data, the system can allocate public health resources more reasonably. For example, when the risk of infectious disease outbreaks in a certain area increases, more epidemic prevention materials, personnel and technical support can be allocated to the area in a timely manner to ensure that the risk areas can be effectively prevented, controlled and managed. This flexible resource allocation promotes the efficiency and accuracy of public health services, including the design of distributed storage technology, so that the system has good scalability and can adapt to the growth of infectious disease data and changes in storage requirements in the future. This flexible architecture allows data nodes to be added dynamically to cope with the increasing real-time data input. With the development of technology, emerging data analysis tools and algorithms in the future can also be easily integrated into existing systems to provide more accurate services for public health. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 The present invention is a flowchart of the steps for implementing the method.

[0028] Figure 2 It is a schematic diagram of the connection of the system modules of the present invention. DETAILED DESCRIPTION

[0029] 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.

[0030] Embodiments of the present invention Figure 1 As shown, a distributed storage method for multi-source heterogeneous infectious source data includes: Step 1, analysis of the initial storage strategy: obtaining the basic characteristics corresponding to each type of infectious source data in each region in the historical period in the target storage platform, thereby analyzing and obtaining the basic characteristic evaluation coefficients corresponding to each type of infectious source data in each region, and then processing the initial storage strategy for each type of infectious source data in each region.

[0031] It should be noted that various types of infectious disease source data include data on pathogenic microorganisms, wild animals, livestock and poultry, vectors, patients, and online epidemics.

[0032] In a specific embodiment, the basic characteristics include the number of data sources, the amount of data corresponding to each type of data source, and the data growth rate.

[0033] It should be noted that access the target storage platform, use API, database query or data export function to obtain historical data, count the number of sources of each type of infectious source data, count the amount of data of each type, and achieve this by counting the number of records of each type of data. Select the time period for analysis, record the initial value of a certain type of infectious source in the region at the beginning of the time period and the ending value at the end, and use the following formula to calculate the change (growth): Growth = End value − Initial value = End value, calculate the growth rate, and based on the growth and initial value, use the following formula to calculate the growth rate: Growth rate = (Initial value plus growth) multiplied by 100%.

[0034] In another specific embodiment, the analysis obtains the basic characteristic evaluation coefficients corresponding to each type of infectious disease source data in each region. The specific analysis process is as follows: the number of data sources corresponding to each type of infectious disease source data in each region, the data volume corresponding to each type of data source, and the data growth rate are respectively recorded as , and ,in, Indicates the number corresponding to each region. , u is any integer greater than 2, Indicates the number corresponding to each type of infectious disease, , n is any integer greater than 2, Indicates the number corresponding to each type of data source, , t is any integer greater than 2, substitute it into the calculation formula The basic characteristic evaluation coefficients corresponding to the data of various types of infectious disease sources in each region are obtained. ,in, , , They are the number of standard data sources corresponding to the set infectious disease source data, the amount of standard data corresponding to the type of data source, and the standard data growth rate. , , They are respectively the weight factor corresponding to the number of data sources of infectious disease source data, the weight factor corresponding to the amount of data from the type data source, and the weight factor corresponding to the data growth rate.

[0035] It should be noted that , , Both are greater than 0 and less than 1.

[0036] It should also be noted that through the summary of a large amount of research data and experimental data, the standard data source quantity corresponding to the infectious disease source data, the standard data volume corresponding to the type data source, and the standard data growth rate are set according to professional institutions and research institutions. At the same time, based on the professional knowledge and research basis of experts in the field, they are discussed and confirmed with industry organizations or professional institutions. Experts set the weight factors corresponding to the number of infectious disease source data sources, the weight factors corresponding to the data volume of the type data source, and the weight factors corresponding to the data growth rate based on their own experience and knowledge.

[0037] It needs to be explained again that the number of standard data sources, the standard data volume corresponding to the type data source, and the standard data growth rate are the data source number threshold, the data volume threshold corresponding to the type data source, and the data growth rate threshold, respectively. For example: the number of standard data sources is 10, the standard data volume corresponding to the type data source is 800, and the standard data growth rate is 15.

[0038] In another specific embodiment, the initial storage strategy for each type of infectious source data in each region is processed, and the specific analysis process is as follows: S1. The target storage platform is divided into several storage nodes, and each region corresponds to a storage node. At the same time, three storage levels are set in each storage node, and the three storage levels include: basic level, intermediate level and advanced level.

[0039] S2. Compare the basic characteristic evaluation coefficients corresponding to each type of infectious disease source data in each region with the basic characteristic evaluation coefficient intervals corresponding to each storage level in the corresponding storage node in the database. If the basic characteristic evaluation coefficient corresponding to a certain type of infectious disease source data in a certain region is within the basic characteristic evaluation coefficient interval corresponding to a certain storage level in the corresponding storage node in the database, then store the infectious disease source data of that type in the region in the storage level in the corresponding storage node. In this way, the initial storage strategy is applied to the infectious disease source data of each type in each region.

[0040] Step 2. Acquisition of real-time development assessment coefficients: After the initial storage strategy processing of each type of infectious disease source data in each region is completed, several assessment time points are set to collect the real-time development characteristics corresponding to each type of infectious disease in each region at each assessment time point, and then analyze and obtain the real-time development assessment coefficients corresponding to each type of infectious disease in each region at each assessment time point.

[0041] In a specific embodiment, the real-time development characteristics include recurrence rate, mutation frequency, and treatment response duration corresponding to each mutation.

[0042] It should be noted that the recurrence rate refers to the proportion of patients who have been cured and have the same disease reappear within a certain period of time. Collect patient data from various regions, including the time of initial infection, the time of cure, and the time of recurrence. Data sources may include medical records from medical institutions, epidemic reports from public health departments, etc. Calculation method: Set an evaluation time point and count the number of patients who relapse among patients who have been cured before each time point. Use the following formula to calculate the recurrence rate: Recurrence rate: (number of relapsed patients total number of cured patients) × 100% Recurrence rate = (total number of cured patients number of relapsed patients) × 100%, Mutation frequency refers to the proportion of pathogens, such as viruses and bacteria, that mutate within a certain period of time. Collect pathogen gene sequencing data, especially monitoring data for known variants. Data sources may include public health laboratories, genome research centers, etc. Set an evaluation time point and count the number of samples that mutate in samples collected before that time point. Use the following formula to calculate the mutation frequency: Mutation frequency: (number of variant samples total number of samples) × 100% Mutation frequency = (total number of variant samples number) × 100%, Treatment response time refers to the time required for patients to improve symptoms or clear the virus after receiving a specific treatment. Collect the patient's treatment records, including the start time of treatment, the time of symptom improvement, and the time of virus test results. Data sources can include the hospital's electronic medical record system, clinical trial records, etc. Calculation method: For each variation, count the time interval of symptom improvement in patients receiving a specific treatment. Use the following formula to calculate the treatment response time: Treatment response time = symptom improvement time - treatment start time Treatment response time = symptom improvement time - treatment start time.

[0043] In another specific embodiment, the analysis obtains the real-time development evaluation coefficient corresponding to each type of infectious disease in each region at each evaluation time point. The specific analysis process is as follows: the recurrence rate, mutation frequency and treatment response time corresponding to each type of infectious disease in each region are recorded as , and ,in, Indicates the number corresponding to each region. , u is any integer greater than 2, Indicates the number corresponding to each type of infectious disease, , n is any integer greater than 2, Indicates the number corresponding to each mutation, , m is any integer greater than 2, substitute it into the calculation formula The real-time development assessment coefficients corresponding to each type of infectious disease in each region at each assessment time point are obtained. ,in, , , They are the standard recurrence rate, standard mutation frequency, and standard treatment response time corresponding to the set infectious disease. , , They are respectively the weight factor corresponding to the recurrence rate of the infectious disease incidence, the weight factor corresponding to the mutation frequency, and the weight factor corresponding to the treatment response time corresponding to the mutation.

[0044] It should be noted that , , Both are greater than 0 and less than 1.

[0045] It should also be noted that through the summary of a large amount of research data and experimental data, the standard recurrence rate, standard mutation frequency, and standard treatment response time corresponding to the mutation of infectious diseases are set by professional institutions and research institutions. At the same time, based on the professional knowledge and research basis of experts in the field, they are discussed and confirmed with industry organizations or professional institutions. Experts set the weight factors corresponding to the recurrence rate of infectious disease incidence, the weight factors corresponding to the mutation frequency, and the weight factors corresponding to the treatment response time corresponding to the mutation based on their own experience and knowledge.

[0046] It should be noted again that the standard recurrence rate, standard mutation frequency, and standard treatment response time corresponding to the mutation are the recurrence rate threshold, mutation frequency threshold, and treatment response time threshold corresponding to the mutation, respectively. For example, the standard recurrence rate is 30, the standard mutation frequency is 5, and the standard treatment response time corresponding to the mutation is 6.

[0047] Step 3. Analysis of real-time shared storage: Based on the real-time development assessment coefficients corresponding to each type of infectious disease in each region at each assessment time point, assess whether the data on the source of each type of infectious disease in each region at each assessment time point need to be shared for research, and store the data on the source of each type of infectious disease in each region that need to be shared for research at each assessment time point in the shared area.

[0048] In a specific embodiment, the evaluation of whether the source data of each type of infectious disease in each region at each evaluation time point needs to be shared is performed as follows: the real-time development evaluation coefficient corresponding to each type of infectious disease in each region at each evaluation time point is compared with the real-time development evaluation coefficient corresponding to the set standard infectious disease. If the real-time development evaluation coefficient corresponding to a certain type of infectious disease in a certain region at a certain evaluation time point is greater than or equal to the real-time development evaluation coefficient corresponding to the set standard infectious disease, then it is evaluated that the source data of this type of infectious disease in the region at that evaluation time point needs to be shared. If the real-time development evaluation coefficient corresponding to a certain type of infectious disease in a certain region at a certain evaluation time point is less than the real-time development evaluation coefficient corresponding to the set standard infectious disease, then it is evaluated that the source data of this type of infectious disease in the region at that evaluation time point does not need to be shared. In this way, it is evaluated whether the source data of each type of infectious disease in each region at each evaluation time point needs to be shared.

[0049] In an embodiment of the present invention, by analyzing the data at each evaluation time point, the system can screen out data sets that need to be shared for research. Such a mechanism provides support for cross-regional public health collaboration, enabling researchers and public health workers in various regions to collaborate based on the latest and most relevant data. The timeliness and effectiveness of shared data are crucial for early warning, response, and control of epidemics, and help to form a comprehensive and collaborative public health strategy, so that public health decision makers can quickly grasp the dynamic changes of infectious diseases in various regions and formulate response measures based on data-driven analysis results. Real-time development assessment coefficients not only help identify different behavioral characteristics and transmission patterns, but also provide a scientific basis for policy making, thereby improving the accuracy and effectiveness of decision-making and ultimately safeguarding public health.

[0050] Step 4. Adjustment of shared data storage: Obtain the real-time development characteristics of each type of infectious disease in each region at each evaluation time point in the shared area, and then evaluate the impact of each type of infectious disease in each region at each evaluation time point in the shared storage, and adjust the source data of each type of infectious disease in each region at each evaluation time point in the shared storage.

[0051] In a specific embodiment, the evaluation of the impact degree corresponding to each type of infectious disease in each region at each evaluation time point in the shared storage is specifically evaluated as follows: Q1. The real-time development characteristics corresponding to each type of infectious disease in each region at each evaluation time point in the shared area are obtained according to the method of obtaining the real-time development evaluation coefficient in step 2, and then the real-time development characteristics corresponding to each type of infectious disease in each region at each evaluation time point in the shared area are obtained.

[0052] Q2. Compare the real-time development characteristics of each type of infectious disease in each region at each evaluation time point in the shared area with the real-time development characteristics of each impact level in the database. If the real-time development characteristics of a certain type of infectious disease in a certain region at a certain evaluation time point in the shared area are within the real-time development characteristics corresponding to a certain impact level in the database, then the impact level in the evaluation database is the impact level corresponding to the type of infectious disease in the region at the evaluation time point in the shared storage. The impact levels include low risk, medium risk and high risk. In this way, the impact levels of each type of infectious disease in each region at each evaluation time point in the shared storage are evaluated.

[0053] In another specific embodiment, the data of each type of infectious disease source in each region at each evaluation time point in the shared storage is adjusted, and the specific adjustment process is as follows: W1. If the impact level corresponding to a certain type of infectious disease in a certain region at a certain evaluation time point in the shared storage is low risk, the infectious disease source data corresponding to the type of infectious disease in the region at the evaluation time point in the shared storage is marked as "low-risk data". At the same time, a feedback alarm is given to the infectious disease source data corresponding to the type of infectious disease in the region at the evaluation time point in the shared storage, and the infectious disease source data corresponding to the type of infectious disease in the region at the evaluation time point in the shared storage is removed from the shared storage, and stored in the storage layer in the corresponding storage node of the original infectious disease source data.

[0054] W2. If the impact level of a certain type of infectious disease in a certain area at a certain evaluation time point in the shared storage is medium risk, the infectious disease source data corresponding to the type of infectious disease in the area at the evaluation time point in the shared storage shall be marked as "medium risk data". At the same time, a feedback alarm shall be given to the infectious disease source data corresponding to the type of infectious disease in the area at the evaluation time point in the shared storage, and the storage time of the infectious disease source data corresponding to the type of infectious disease in the area at the evaluation time point in the shared storage shall be set to the standard storage time. When the storage time of the infectious disease source data corresponding to the type of infectious disease in the area at the evaluation time point in the shared storage reaches the standard storage time, the infectious disease source data corresponding to the type of infectious disease in the area at the evaluation time point in the shared storage shall be removed from the shared storage, and stored in the storage layer in the corresponding storage node of the original infectious disease source data.

[0055] W2. If the impact level of a certain type of infectious disease in a certain region at a certain evaluation time point in the shared storage is medium risk, the infectious disease source data corresponding to the type of infectious disease in the region at the evaluation time point in the shared storage shall be marked as "high-risk data", and the infectious disease source data corresponding to the type of infectious disease in the region at the evaluation time point in the shared storage shall continue to be stored in the shared storage, and the storage period shall be unlimited.

[0056] In the embodiment of the present invention, by evaluating the degree of impact of shared data, the system can allocate public health resources more reasonably. For example, when the risk of infectious disease outbreaks in a certain area increases, more epidemic prevention materials, personnel and technical support can be allocated to the area in a timely manner to ensure that the risk areas can be effectively prevented and managed. This flexible resource allocation promotes the efficiency and accuracy of public health services, including the design of distributed storage technology, so that the system has good scalability and can adapt to the growth of infectious disease data and changes in storage requirements in the future. This flexible architecture allows data nodes to be added dynamically to cope with the increasing real-time data input. With the development of technology, emerging data analysis tools and algorithms in the future can also be easily integrated into existing systems to provide more accurate services for public health.

[0057] Embodiments of the present invention Figure 2 As shown, a distributed storage system for multi-source heterogeneous infectious source data includes: an initial storage strategy analysis module, a real-time development evaluation coefficient acquisition module, a real-time shared storage analysis module, a shared data storage adjustment module and a database.

[0058] Initial storage strategy analysis module: used to obtain the basic characteristics corresponding to each type of infectious disease source data in each region in the historical period in the target storage platform, so as to analyze and obtain the basic characteristic evaluation coefficients corresponding to each type of infectious disease source data in each region, and then process the initial storage strategy of each type of infectious disease source data in each region.

[0059] Real-time development assessment coefficient acquisition module: used to set several evaluation time points after the initial storage strategy processing of various types of infectious disease source data in various regions is completed, so as to collect the real-time development characteristics corresponding to various types of infectious diseases in various regions at each evaluation time point, and then analyze and obtain the real-time development assessment coefficients corresponding to various types of infectious diseases in various regions at each evaluation time point.

[0060] Real-time shared storage and analysis module: used to evaluate whether the data of various types of infectious disease sources in various regions at each assessment time point need to be shared and studied based on the real-time development assessment coefficients corresponding to various types of infectious diseases in various regions at each assessment time point, and store the data of various types of infectious disease sources in various regions that need to be shared and studied at each assessment time point in the shared area.

[0061] Shared data storage adjustment module: used to obtain the real-time development characteristics of each type of infectious disease in each region at each evaluation time point in the shared area, and then evaluate the impact level of each type of infectious disease in each region at each evaluation time point in the shared storage, and adjust the source data of each type of infectious disease in each region at each evaluation time point in the shared storage.

[0062] In the embodiment of the present invention, the system can deeply mine the basic characteristics of infectious disease source data in various regions in the historical cycle. This process enables the storage platform to formulate adaptive storage solutions for different types of infectious diseases and achieve higher storage utilization. With the help of characteristic evaluation coefficients, the system can effectively identify and prioritize high-risk areas and types, thereby reducing data redundancy, which not only saves storage space, but also reduces operation and maintenance costs, and evaluates the development trend of infectious diseases in various regions in real time, providing a dynamic adjustment basis for data storage strategies. The real-time development evaluation coefficients obtained by each region at a fixed time point enable the system to quickly respond to changes in the spread of infectious diseases and adjust the allocation of storage resources in a timely manner. For example, when an epidemic situation in a certain area shows an upward trend, the system can immediately increase the storage capacity and attention of the data in that area, thereby strengthening the monitoring and management of the epidemic situation in that area.

[0063] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they shall all fall within the protection scope of the present invention.

Claims

1. A distributed storage method for multi-source heterogeneous infectious source data, characterized in that: include: Obtain the basic characteristics corresponding to each type of infectious disease source data in each region in the historical period in the target storage platform, so as to analyze and obtain the basic characteristic evaluation coefficients corresponding to each type of infectious disease source data in each region, and then process the initial storage strategy for each type of infectious disease source data in each region; Set a number of evaluation time points, so as to collect the real-time development characteristics corresponding to each type of infectious disease in each region at each evaluation time point, and then analyze and obtain the real-time development evaluation coefficient corresponding to each type of infectious disease in each region at each evaluation time point; Evaluate whether the data on the source of each type of infectious disease in each region at each assessment time point need to be shared for research, and store the data on the source of each type of infectious disease in each region that need to be shared for research at each assessment time point in the shared area; Obtain the real-time development characteristics of each type of infectious disease in each region at each evaluation time point in the shared area, and then evaluate the impact of each type of infectious disease in each region at each evaluation time point in the shared storage, and adjust the source data of each type of infectious disease in each region at each evaluation time point in the shared storage.

2. A distributed storage method for multi-source heterogeneous infectious source data as claimed in claim 1, characterized in that: The basic characteristics include the number of data sources, the data volume corresponding to each type of data source, and the data growth rate.

3. A distributed storage method for multi-source heterogeneous infectious source data as claimed in claim 2, characterized in that: The analysis obtained the basic characteristic evaluation coefficients corresponding to each type of infectious disease source data in each region. The specific analysis process is as follows: The number of data sources corresponding to each type of infectious disease source data in each region, the data volume corresponding to each type of data source, and the data growth rate are recorded as , and ,in, Indicates the number corresponding to each region, , u is any integer greater than 2, Indicates the number corresponding to each type of infectious disease, , n is any integer greater than 2, Indicates the number corresponding to each type of data source, , t is any integer greater than 2, substitute it into the calculation formula The basic characteristic evaluation coefficients corresponding to the data of various types of infectious disease sources in each region are obtained. ,in, , , They are the number of standard data sources corresponding to the set infectious disease source data, the amount of standard data corresponding to the type of data source, and the standard data growth rate. , , They are respectively the weight factor corresponding to the number of data sources of infectious disease source data, the weight factor corresponding to the amount of data from the type data source, and the weight factor corresponding to the data growth rate.

4. A distributed storage method for multi-source heterogeneous infectious source data as claimed in claim 3, characterized in that: The initial storage strategy for the data of various types of infectious disease sources in various regions is processed, and the specific analysis process is as follows: S1. Divide the target storage platform into several storage nodes, and each region corresponds to a storage node. At the same time, each storage node is provided with three storage levels, including: basic level, intermediate level and advanced level; S2. Compare the basic characteristic evaluation coefficients corresponding to each type of infectious disease source data in each region with the basic characteristic evaluation coefficient intervals corresponding to each storage level in the corresponding storage node in the database. If the basic characteristic evaluation coefficient corresponding to a certain type of infectious disease source data in a certain region is within the basic characteristic evaluation coefficient interval corresponding to a certain storage level in the corresponding storage node in the database, then store the infectious disease source data of that type in the region in the storage level in the corresponding storage node. In this way, the initial storage strategy is applied to the infectious disease source data of each type in each region.

5. A distributed storage method for multi-source heterogeneous infectious source data as claimed in claim 1, characterized in that: The real-time development characteristics include recurrence rate, mutation frequency and treatment response duration corresponding to each mutation.

6. A distributed storage method for multi-source heterogeneous infectious source data as claimed in claim 5, characterized in that: The analysis obtains the real-time development assessment coefficients corresponding to each type of infectious disease in each region at each assessment time point. The specific analysis process is as follows: The recurrence rate, mutation frequency and treatment response time of each type of infectious disease in each region are recorded as , and ,in, Indicates the number corresponding to each region, , u is any integer greater than 2, Indicates the number corresponding to each type of infectious disease, , n is any integer greater than 2, Indicates the number corresponding to each mutation, , m is any integer greater than 2, substitute it into the calculation formula The real-time development assessment coefficients corresponding to each type of infectious disease in each region at each assessment time point are obtained. ,in, , , They are the standard recurrence rate, standard mutation frequency, and standard treatment response time corresponding to the set infectious disease. , , They are respectively the weight factor corresponding to the recurrence rate of the infectious disease incidence, the weight factor corresponding to the mutation frequency, and the weight factor corresponding to the treatment response time corresponding to the mutation.

7. A distributed storage method for multi-source heterogeneous infectious source data as claimed in claim 6, characterized in that: The specific evaluation process of evaluating whether the source data of various types of infectious diseases in various regions at various evaluation time points need to be shared is as follows: The real-time development assessment coefficient corresponding to each type of infectious disease in each region at each assessment time point is compared with the real-time development assessment coefficient corresponding to the set standard infectious disease. If the real-time development assessment coefficient corresponding to a certain type of infectious disease in a certain region at a certain assessment time point is greater than or equal to the real-time development assessment coefficient corresponding to the set standard infectious disease, then the source data of this type of infectious disease in the region at that assessment time point needs to be shared. If the real-time development assessment coefficient corresponding to a certain type of infectious disease in a certain region at a certain assessment time point is less than the real-time development assessment coefficient corresponding to the set standard infectious disease, then the source data of this type of infectious disease in the region at that assessment time point does not need to be shared. In this way, it is evaluated whether the source data of each type of infectious disease in each region at each assessment time point need to be shared.

8. A distributed storage method for multi-source heterogeneous infectious source data as claimed in claim 7, characterized in that: The evaluation process of the shared storage is as follows: Q1. Obtain the real-time development characteristics of each type of infectious disease in each region at each evaluation time point in the shared area according to the method of obtaining the real-time development evaluation coefficient in step 2, and then obtain the real-time development characteristics of each type of infectious disease in each region at each evaluation time point in the shared area; Q2. Compare the real-time development characteristics of each type of infectious disease in each region at each evaluation time point in the shared area with the real-time development characteristics of each impact level in the database. If the real-time development characteristics of a certain type of infectious disease in a certain region at a certain evaluation time point in the shared area are within the real-time development characteristics corresponding to a certain impact level in the database, then the impact level in the evaluation database is the impact level corresponding to the type of infectious disease in the region at the evaluation time point in the shared storage. The impact levels include low risk, medium risk and high risk. In this way, the impact levels of each type of infectious disease in each region at each evaluation time point in the shared storage are evaluated.

9. A distributed storage method for multi-source heterogeneous infectious source data as claimed in claim 8, characterized in that: The data of various types of infectious disease sources in various regions at various evaluation time points in the shared storage are adjusted. The specific adjustment process is as follows: W1. If the impact level of a certain type of infectious disease in a certain area at a certain evaluation time point in the shared storage is low risk, the infectious disease source data corresponding to the type of infectious disease in the area at the evaluation time point in the shared storage is marked as "low risk data". At the same time, a feedback alarm is given to the infectious disease source data corresponding to the type of infectious disease in the area at the evaluation time point in the shared storage, and the infectious disease source data corresponding to the type of infectious disease in the area at the evaluation time point in the shared storage is removed from the shared storage and stored in the storage layer in the corresponding storage node where the original infectious disease source data is stored; W2. If the impact level of a certain type of infectious disease in a certain region at a certain evaluation time point in the shared storage is medium risk, the infectious disease source data corresponding to the type of infectious disease in the region at the evaluation time point in the shared storage is marked as "medium risk data". At the same time, a feedback alarm is given to the infectious disease source data corresponding to the type of infectious disease in the region at the evaluation time point in the shared storage, and the storage time of the infectious disease source data corresponding to the type of infectious disease in the region at the evaluation time point in the shared storage is set to the standard storage time period. When the storage time of the infectious disease source data corresponding to the type of infectious disease in the region at the evaluation time point in the shared storage reaches the standard storage time period, the infectious disease source data corresponding to the type of infectious disease in the region at the evaluation time point in the shared storage is removed from the shared storage, and stored in the storage layer in the corresponding storage node of the original infectious disease source data; W2. If the impact level of a certain type of infectious disease in a certain region at a certain evaluation time point in the shared storage is medium risk, the infectious disease source data corresponding to the type of infectious disease in the region at the evaluation time point in the shared storage shall be marked as "high-risk data", and the infectious disease source data corresponding to the type of infectious disease in the region at the evaluation time point in the shared storage shall continue to be stored in the shared storage, and the storage period shall be unlimited.

10. A distributed storage system for multi-source heterogeneous infectious source data that executes the distributed storage method for multi-source heterogeneous infectious source data according to any one of claims 1 to 9, characterized in that: include: Initial storage strategy analysis module: used to obtain the basic characteristics of each type of infectious disease source data in each region in the historical period of the target storage platform, so as to analyze and obtain the basic characteristic evaluation coefficients corresponding to each type of infectious disease source data in each region, and then process the initial storage strategy of each type of infectious disease source data in each region; Real-time development assessment coefficient acquisition module: used to set several assessment time points after the initial storage strategy processing of various types of infectious disease source data in various regions is completed, so as to collect the real-time development characteristics corresponding to various types of infectious diseases in various regions at each assessment time point, and then analyze and obtain the real-time development assessment coefficients corresponding to various types of infectious diseases in various regions at each assessment time point; Real-time shared storage analysis module: used to evaluate whether the source data of various types of infectious diseases in various regions at each evaluation time point need to be shared and studied according to the real-time development evaluation coefficients corresponding to various types of infectious diseases in various regions at each evaluation time point, and store the source data of various types of infectious diseases in various regions that need to be shared and studied at each evaluation time point in the shared area; Shared data storage adjustment module: used to obtain the real-time development characteristics of each type of infectious disease in each region at each evaluation time point in the shared area, and then evaluate the impact level of each type of infectious disease in each region at each evaluation time point in the shared storage, and adjust the source data of each type of infectious disease in each region at each evaluation time point in the shared storage.

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