Disease condition analysis system based on biomedical big data

Abstract

The invention relates to the technical field of medical data analysis, and discloses an illness state analysis system based on biomedical big data, which comprises a multi-source data acquisition module, a real-time updating system module, a network transmission security protection module, a preprocessing module, a data analysis module and a data management module, the real-time updating system module is responsible for updating data in real time, the network transmission security protection module is responsible for adding security protection in the data transmission process, the preprocessing module is responsible for data format conversion to ensure that the data format is simple and easy to process, and the data analysis module is responsible for data calculation and analysis. And the data management module issues an improvement instruction to the system according to an analysis result, through mutual cooperation of the modules, the system can display a data security problem in real time, the security problem can be captured in real time, and the process of data analysis and illness state analysis can be ensured to be carried out in a safer environment, so that the confidentiality of doctor-patient information is protected.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data analysis, and in particular to a disease analysis system based on biomedical big data. Background Art

[0002] In the existing biomedical big data system, data privacy issues, data update lag and difficulty in data integration are the three major challenges. First, medical data involves the personal privacy of patients, so the privacy rights of patients need to be strictly protected during collection, storage and use. However, due to the limitations of technical means and management measures, there is a risk of data leakage in actual operations. This risk not only threatens the privacy security of patients, but also leads to a crisis of trust and legal risks. Secondly, medical data needs to be updated in real time to reflect the latest status of patients. However, delays in data transmission and processing, as well as poor information sharing between medical institutions, will lead to data update lags, thus affecting the timeliness and accuracy of disease analysis. This lag will delay diagnosis and treatment, and thus affect the health outcomes of patients. Finally, data integration has become a major problem because different medical institutions use different information systems and data formats. This increases the complexity and difficulty of data analysis and reduces the efficiency and effectiveness of data analysis. The lack of unified data standards and interoperability makes cross-institutional data sharing and collaborative analysis difficult. In response to the above problems, a new data solution is proposed, which aims to solve the problems of data source, data update lag, data security and complexity of data format conversion in disease analysis based on biomedical big data, so as to improve the accuracy and timeliness of data analysis. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention provides a disease analysis system based on biomedical big data, which has the advantages of wide data sources, rapid data update, high data security and simple data format conversion, and solves the problems of data source problems, data update lag, data security problems and complexity of data format conversion existing in the prior art.

[0004] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a disease analysis system based on biomedical big data, comprising a multi-source data acquisition module, a real-time update system module, a network transmission security protection module, a preprocessing module, a data analysis module and a data management module; After the multi-source data acquisition module designs the total data source, it acquires data from multiple data sources. After the pre-processing module processes the data, it classifies and numbers the added data source parts, and transmits the data to the data analysis module through the network to calculate the missing parts of the total data volume of the added data source. ; The real-time update system module is responsible for updating data in real time. After data processing by the preprocessing module, the updated data is classified and numbered, and the data is transmitted to the data analysis module through the network to calculate the average update frequency. ; The network transmission security protection module is responsible for adding security protection during the data transmission process, integrating historical protection data, and classifying and numbering the added security protection information and historical protection data after data processing by the preprocessing module, and transmitting the data to the data analysis module through the network to calculate the security protection effect. ; The preprocessing module is responsible for data format conversion to ensure that the data format is simple and easy to process; The data analysis module is responsible for data calculation and analysis; The data management module issues improvement instructions to the system based on the analysis results.

[0005] Preferably, the multi-source data acquisition module numbers the data volume of all added data sources according to the characteristics of the added data sources, and the data volume of all added data sources is numbered as follows: , , ,… .

[0006] Preferably, the data analysis module calculates the missing portion of the total amount of data of the added data source according to the amount of data of all added data sources. , and its calculation formula is: In the formula, Indicates adding the missing part of the total data volume of the data source. , , ,… Indicates increasing the amount of data in the data source. , , ,… Indicates that the design increases the amount of data in the data source. Indicates the missing part of the data volume of the first added data source and the data volume of the designed added data source. It indicates the missing part of the data volume of the second added data source and the data volume of the designed added data source. It represents the combined part of the missing part of the data volume of the first added data source and the data volume of the designed added data source, and the missing part of the data volume of the second added data source and the data volume of the designed added data source. It represents the missing part of the data volume of the nth added data source and the data volume of the designed added data source. It represents the missing part of the data volume of the i-th added data source and the data volume of the designed added data source, Indicates the number of added data sources.

[0007] Preferably, the total amount of data in the data source is increased to include the missing part Reflects the integrity of the data in the added data source.

[0008] Preferably, the real-time update system module numbers the update frequency of each data source according to the update data characteristics, and the update frequency number of each data source is , , ,… .

[0009] Preferably, the data analysis module calculates the average update frequency according to the update frequency of each data source. , and its calculation formula is: In the formula, represents the average update frequency, , , ,… Indicates the update frequency of each data source, Indicates The update frequency of each data source, Indicates the number of data sources.

[0010] Preferably, the average update frequency The normal range of update frequency is set through the model and historical data to determine whether the update frequency of system data is within the normal range of update frequency.

[0011] Preferably, the security protection information added by the network transmission security protection module and the historical protection data features are used to perform data numbering on the total number of historical data events, the number of historical data security attack events, the total number of data events after adding security protection measures, and the number of data security attack events after adding security protection measures. The total number of historical data events, the number of historical data security attack events, the total number of data events after adding security protection measures, and the number of data security attack events after adding security protection measures are numbered respectively. , , , .

[0012] Preferably, the data analysis module calculates the security protection effect according to the total number of historical data events, the number of historical data security attack events, the total number of data events after adding security protection measures, and the number of data security attack events after adding security protection measures. : In the formula, Indicates the safety protection effect. , , , They represent the total number of historical data events, the number of historical data security attack events, the total number of data events after adding security protection measures, and the number of data security attack events after adding security protection measures. Indicates the probability of data being attacked before security measures are added. Indicates the probability of data being attacked after security protection measures are implemented.

[0013] Preferably, the safety protection effect Under the online monitoring of the system, data security issues can be displayed in real time. When >0, it means that the added safety protection measures are effective.

[0014] Compared with the prior art, the present invention provides a disease analysis system based on biomedical big data, which has the following beneficial effects: 1. The present invention calculates the average update frequency The system sets a normal update frequency range based on historical representative data. Within this range, the system can monitor the performance of each data source and regularly check whether their update frequency remains at a normal level. When the update frequency of a data source deviates from the established normal range, the system will automatically mark it as an abnormal state and immediately notify the technical team for investigation and repair. Through this calculation method, the system can ensure the stable update of the data source and reduce the delays and errors that users encounter when using the system, thereby providing a smoother and more reliable user experience. This monitoring and response mechanism can effectively solve the problem of data update lag, thereby improving the overall performance and stability of the data analysis system.

[0015] 2. The present invention calculates the safety protection effect , which helps to display data security issues in real time and can provide clear judgment criteria for measuring the effectiveness of the current security protection measures. In the online monitoring function of the system, the real-time display capability of security protection effects enables data security issues to be captured immediately. When the indicator is greater than 0, it means that the currently implemented security protection measures are playing an effective role and are solving data security issues. This real-time feedback mechanism can not only enhance the security of the system, but also provide users with instant security confirmation, thereby ensuring that the data analysis and disease analysis process can be carried out in a safer environment to protect the integrity and confidentiality of medical and patient information. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0017] 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.

[0018] See also Figure 1 , a disease analysis system based on biomedical big data, including a multi-source data acquisition module, a real-time update system module, a network transmission security protection module, a preprocessing module, a data analysis module and a data management module; After the multi-source data acquisition module designs the total data source, it acquires data from multiple data sources. After the pre-processing module processes the data, it classifies and numbers the added data source parts, and transmits the data to the data analysis module through the network to calculate the missing parts of the total data volume of the added data source. ; The real-time update system module is responsible for updating data in real time. After data processing by the preprocessing module, the updated data is classified and numbered, and the data is transmitted to the data analysis module through the network to calculate the average update frequency. ; The network transmission security protection module is responsible for adding security protection during the data transmission process, integrating historical protection data, and classifying and numbering the added security protection information and historical protection data after data processing by the preprocessing module, and transmitting the data to the data analysis module through the network to calculate the security protection effect. ; Preprocessing module: responsible for data format conversion to ensure that the data format is simple and easy to process; The data analysis module is responsible for data calculation and analysis; The data management module issues improvement instructions to the system based on the analysis results.

[0019] The multi-source data acquisition module numbers the data volume of all added data sources according to the characteristics of the added data sources. The data volume of all added data sources is numbered as follows: , , ,… .

[0020] The data analysis module calculates the missing part of the total data volume of the added data source based on the data volume of all added data sources. , and its calculation formula is: In the formula, Indicates adding the missing part of the total data volume of the data source. , , ,… Indicates increasing the amount of data in the data source. , , ,… Indicates that the design increases the amount of data in the data source. Indicates the missing part of the data volume of the first added data source and the data volume of the designed added data source. It indicates the missing part of the data volume of the second added data source and the data volume of the designed added data source. It represents the combined part of the missing part of the data volume of the first added data source and the data volume of the designed added data source, and the missing part of the data volume of the second added data source and the data volume of the designed added data source. It represents the missing part of the data volume of the nth added data source and the data volume of the designed added data source. It represents the missing part of the data volume of the i-th added data source and the data volume of the designed added data source, Indicates the number of added data sources.

[0021] Increase the total amount of data in the data source to include the missing part It can reflect the integrity of the data in the added data source. When the total amount of data in the added data source is missing, When it is greater than 0.25, the system will use data interpolation techniques (statistical methods using mean, median and mode) to fill in the missing values, or use machine learning algorithms to predict the missing data for improvement.

[0022] The advantage is: increasing the total amount of data from the data source by calculating the missing part , which can solve the data source problem existing in the prior art. When the system monitoring function detects the missing part of the total data volume of the added data source, When it is greater than 0.25, it means that the data is incomplete or missing, and appropriate interpolation measures need to be taken to provide the system with more accurate and complete data analysis results to support better decision-making processes.

[0023] The real-time update system module numbers the update frequency of each data source according to the update data characteristics. The update frequency number of each data source is , , ,… .

[0024] The data analysis module calculates the average update frequency based on the update frequency of each data source. , and its calculation formula is: In the formula, represents the average update frequency, , , ,… Indicates the update frequency of each data source, Indicates The update frequency of each data source, Indicates the number of data sources.

[0025] Average update frequency The calculation formula is substituted by historical representative data to obtain the representative average update frequency In order to set the normal range of update frequency, the update frequency of each data source is checked regularly in the system's monitoring mode to determine whether it is within the normal range of update frequency. When the update frequency of a data source deviates from the normal range, the system should automatically mark the data source as abnormal and immediately notify the technical team for investigation and repair, including checking the connection, query performance or data quality issues of the data source.

[0026] The advantage is: by calculating the average update frequency The system sets a normal update frequency range based on historical representative data. Within this range, the system can monitor the performance of each data source and regularly check whether their update frequency remains at a normal level. When the update frequency of a data source deviates from the established normal range, the system will automatically mark it as an abnormal state and immediately notify the technical team to investigate and repair (including checking the connection of the data source, query performance or data quality issues). Through this calculation method, the system can ensure the stable update of the data source, reduce the delays and errors that users encounter when using the system, and thus provide a smoother and more reliable user experience. This monitoring and response mechanism can effectively solve the problem of data update lag, thereby improving the overall performance and stability of the data analysis system.

[0027] The security protection information and historical protection data features added by the network transmission security protection module are numbered for the total number of historical data events, the number of historical data security attack events, the total number of data events after adding security protection measures, and the number of data security attack events after adding security protection measures. The total number of historical data events, the number of historical data security attack events, the total number of data events after adding security protection measures, and the number of data security attack events after adding security protection measures are numbered as follows: , , , .

[0028] The data analysis module calculates the security protection effect based on the total number of historical data events, the number of historical data security attack events, the total number of data events after adding security protection measures, and the number of data security attack events after adding security protection measures. : In the formula, Indicates the safety protection effect. , , , They represent the total number of historical data events, the number of historical data security attack events, the total number of data events after adding security protection measures, and the number of data security attack events after adding security protection measures. Indicates the probability of data being attacked before security measures are added. Indicates the probability of data being attacked after security protection measures are implemented.

[0029] Safety protection effect Under the online monitoring of the system, data security issues can be displayed in real time. When >0, it means that the added safety protection measures are effective.

[0030] Advantages: By calculating the safety protection effect , which helps to display data security issues in real time and can provide clear judgment criteria for measuring the effectiveness of the current security protection measures. In the online monitoring function of the system, the real-time display capability of security protection effects enables data security issues to be captured immediately. When the indicator is greater than 0, it means that the currently implemented security protection measures are playing an effective role and are solving data security issues. This real-time feedback mechanism can not only enhance the security of the system, but also provide users with instant security confirmation, thereby ensuring that the data analysis and disease analysis process can be carried out in a safer environment to protect the integrity and confidentiality of medical and patient information.

[0031] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A disease analysis system based on biomedical big data, characterized in that: It includes multi-source data acquisition module, real-time update system module, network transmission security protection module, pre-processing module, data analysis module and data management module; After the multi-source data acquisition module designs the total data source, it acquires data from multiple data sources. After the pre-processing module processes the data, it classifies and numbers the added data source parts, and transmits the data to the data analysis module through the network to calculate the missing parts of the total data volume of the added data source. ; The real-time update system module is responsible for updating data in real time. After data processing by the preprocessing module, the updated data is classified and numbered, and the data is transmitted to the data analysis module through the network to calculate the average update frequency. ; The network transmission security protection module is responsible for adding security protection during the data transmission process, integrating historical protection data, and classifying and numbering the added security protection information and historical protection data after data processing by the preprocessing module, and transmitting the data to the data analysis module through the network to calculate the security protection effect. ; The preprocessing module is responsible for data format conversion to ensure that the data format is simple and easy to process; The data analysis module is responsible for data calculation and analysis; The data management module issues improvement instructions to the system based on the analysis results.

2. The disease analysis system based on biomedical big data according to claim 1, characterized in that: The multi-source data acquisition module numbers the data volume of all added data sources according to the characteristics of the added data sources. The data volume of all added data sources is numbered as follows: , , ,… .

3. The disease analysis system based on biomedical big data according to claim 2, characterized in that: The data analysis module calculates the missing part of the total data volume of the added data source according to the data volume of all added data sources. , and its calculation formula is: In the formula, Indicates the missing part of the total data volume of the data source is increased. , , ,… Indicates increasing the amount of data in the data source. , , ,… Indicates that the design increases the amount of data in the data source. Indicates the missing part of the data volume of the first added data source and the data volume of the designed added data source. It indicates the missing part of the data volume of the second added data source and the data volume of the designed added data source. It represents the combined part of the missing part of the data volume of the first added data source and the data volume of the designed added data source, and the missing part of the data volume of the second added data source and the data volume of the designed added data source. It represents the missing part of the data volume of the nth added data source and the data volume of the designed added data source. It represents the missing part of the data volume of the i-th added data source and the data volume of the designed added data source, Indicates the number of added data sources.

4. The disease analysis system based on biomedical big data according to claim 3, characterized in that: The missing part of the total data volume of the increased data source Reflects the integrity of the data in the added data source.

5. The disease analysis system based on biomedical big data according to claim 1, characterized in that: The real-time update system module numbers the update frequency of each data source according to the update data characteristics. The update frequency number of each data source is , , ,… .

6. The disease analysis system based on biomedical big data according to claim 5, characterized in that: The data analysis module calculates the average update frequency based on the update frequency of each data source , and its calculation formula is: In the formula, represents the average update frequency, , , ,… Indicates the update frequency of each data source, Indicates The update frequency of each data source, Indicates the number of data sources.

7. The disease analysis system based on biomedical big data according to claim 6, characterized in that: The average update frequency The normal range of update frequency is set through the model and historical data to determine whether the update frequency of system data is within the normal range of update frequency.

8. The disease analysis system based on biomedical big data according to claim 1, characterized in that: The security protection information added by the network transmission security protection module and the historical protection data features are used to number the total number of historical data events, the number of historical data security attack events, the total number of data events after adding security protection measures, and the number of data security attack events after adding security protection measures. The total number of historical data events, the number of historical data security attack events, the total number of data events after adding security protection measures, and the number of data security attack events after adding security protection measures are numbered respectively. , , , .

9. The disease analysis system based on biomedical big data according to claim 8, characterized in that: The data analysis module calculates the security protection effect based on the total number of historical data events, the number of historical data security attack events, the total number of data events after adding security protection measures, and the number of data security attack events after adding security protection measures. : In the formula, Indicates the safety protection effect. , , , They represent the total number of historical data events, the number of historical data security attack events, the total number of data events after adding security protection measures, and the number of data security attack events after adding security protection measures. Indicates the probability of data being attacked before security measures are added. Indicates the probability of data being attacked after security protection measures are implemented.

10. The disease analysis system based on biomedical big data according to claim 9, characterized in that: The safety protection effect Under the online monitoring of the system, data security issues can be displayed in real time. When >0, it means that the added safety protection measures are effective.

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