Big data processing method for biological medicine industry development diagnosis

Abstract

The invention discloses a big data processing method for biological medicine industry development diagnosis. The method specifically comprises the following steps: respectively extracting required biological medicine industry multi-source data according to a biological medicine industry development diagnosis index system; formatting the data with different and same format specifications into unified standard specification data according to the biological medicine industry index standardization specification; carrying out layer-by-layer index-by-index iteration diagnosis analysis according to biological medicine industry development diagnosis index system information; the biological medicine industry development diagnosis result is presented to a user through a human-computer interaction interface, and a diagnosis result of a specified level is displayed according to item information selected by the user. According to the method, the problems of data regularization, diagnosis index analysis operation automation, diagnosis result individuation and the like of the biomedical industry are solved, and a feasible solution is provided for intelligent and precise industrial development diagnosis by utilizing multi-source, multi-type and multi-format biomedical industry data element information.

Description

Technical Field

[0001] The present invention relates to the technical field of big data applications, and particularly to a big data processing method for the development diagnosis of strategic emerging industries such as biomedicine. Background Art

[0002] Industrial development refers to the process of the emergence, growth, and evolution of industries, including both the evolution process of individual industries and the evolution process of the overall industry, i.e., the entire national economy; and the evolution process includes both quantitative changes such as the number of enterprises, the output of products or services in a certain industry, and qualitative changes such as the adjustment, change, replacement of the industrial structure, and the dominant position of the industry. Therefore, when evaluating and diagnosing the development status of an industry, a large amount of data is required as support, and these data include source data in multiple aspects.

[0003] In the prior art, for the diagnostic index system and diagnostic operation rules for industrial development, most do not adopt a systematic storage method but are embedded in the system program, resulting in the relative rigidity of the diagnostic index system and diagnostic operation rules and being unable to be adjusted in a timely manner according to the complex conditions of industrial development and changes in the latest application scenarios; moreover, due to the lack of correspondence between diagnostic indicators and multi-source data and their precise storage, even a small change in the diagnostic operation rules often leads to the inability to obtain relevant data in a timely, effective, and accurate manner, resulting in the relative fixation of the diagnostic operation rules and being unable to be adjusted quickly and flexibly.

[0004] In addition, in the overall diagnostic analysis process of industrial development, due to the lack of effective connection of data formats and standard specifications for the entire process such as diagnostic data, diagnostic indicators, diagnostic rules, analysis operations, and diagnostic results, it is usually only possible to implement in steps by manual or semi-manual methods; due to the lack of effective correspondence and storage between the acquisition of multi-source data and the relationship with the indicator system, the relationship between the operation rules and diagnostic analysis, etc., it is impossible to apply information technology means such as computers and big data for highly efficient and intelligent automatic analysis and judgment and result presentation. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a big data processing method for the development diagnosis of the biomedicine industry, so as to solve the defects such as the association between the diagnostic index system and diagnostic data, the inability to adjust the diagnostic rules in real time, and the inability to present the diagnostic results as required in the current development diagnosis process of the biomedicine industry, and provide a solution for the large-scale data automatic operation of the development diagnosis of the biomedicine industry.

[0006] To solve the above technical problems, the technical solutions adopted by the present invention are as follows.

[0007] A big data processing method for the development diagnosis of the biomedicine industry mainly includes the following steps:

[0008] S1. Use the industrial data loading extractor to store the required multi-source data of the biomedical industry in the diagnostic database DDDB according to the information of the biomedical industry development diagnostic index system set in the Diagnostic Index Database DIDB, the relevant database parameter configuration information in the System Parameter Configuration Database PCDB, and the corresponding relationship information between the index names and multi-source data in the Diagnostic Index and Multi-source Data Relationship Database IMDB;

[0009] S2. Use the industrial data formatting and regularization tool to format the non-standardized biomedical industry development diagnostic data to be calculated in the diagnostic database DDDB, which has multiple sources, multiple types, multiple data field formats, multiple data unit standards, and inconsistent data digits, into data with a unified standard specification according to the biomedical industry index standardization specification set in the Diagnostic Index Database DIDB;

[0010] S3. Use the biomedical industry development diagnostic analyzer to read the corresponding data information in the diagnostic database DDDB according to the index hierarchy, index names, and index operation rule information of the biomedical industry development diagnostic index system in the Diagnostic Index Database DIDB, perform diagnostic analysis layer by layer and index by index iteratively, and store the diagnostic results in the Diagnostic Result Database DRDB;

[0011] S4. Use the biomedical industry diagnostic result retrieval and presentation tool to read the relevant information in the Diagnostic Result Database DRDB and the Diagnostic Index Database DIDB, present the biomedical industry development diagnostic results to the user through the human-computer interaction interface, and drill down to display the diagnostic results at the specified level according to the user-selected item information.

[0012] Preferably: Step S1 specifically includes the following contents:

[0013] S11. Load the link address, connection method, database type, and password interface parameters of the database involved in the multi-source data of the biomedical industry from the System Parameter Configuration Database PCDB;

[0014] S12. Read the biomedical industry development diagnostic index information from the Diagnostic Index Database DIDB to form an index data set Z1;

[0015] S13. Load the corresponding information between the diagnostic index and the database and its fields from the Diagnostic Index and Multi-source Data Relationship Database IMDB according to the index data set Z1;

[0016] S14. Read the multi-source data of the biomedical industry according to the database interface parameter information;

[0017] S15. Store the relevant data read in Step S14 into the diagnostic database DDDB.

[0018] Preferably, the databases involved in the multi-source data of the biomedical industry described in step S11 include a biomedical innovation entity database, a biomedical science and technology project database, a biomedical science and technology achievement database, a biomedical policy database, a biomedical standard database, a patent database, a paper database, and a macroeconomic database.

[0019] Preferably, step S2 specifically includes the following:

[0020] S21. Load the standardized specifications of the index name, field type, field length, field format, and field unit of the biomedical industry development diagnosis index from the diagnosis index database DIDB to form an index standard specification data set Z2.

[0021] S22. Let the total number of records in the index standard specification data set Z2 be i.

[0022] S23. Determine whether i>0 is true; if so, jump to step S24 for execution; if not, end.

[0023] S24. Sequentially read the biomedical industry development diagnosis data from the diagnosis database DDDB according to the index name in the index standard specification data set Z2.

[0024] S25. Format the corresponding data sequentially according to the i-th index standard specification.

[0025] S26. Update the latest data after normalization into the diagnosis database DDDB.

[0026] S27. Execute i-1, and jump to step S23 for execution.

[0027] Preferably, step S3 specifically includes the following:

[0028] S31. Load the index hierarchy, index name, and index operation rule information of the biomedical industry development diagnosis index system from the diagnosis index database DIDB.

[0029] S32. Let the number of index levels be x.

[0030] S33. Determine whether x>0 is true; if so, jump to step S34 for execution; if not, end.

[0031] S34. Read the index name corresponding to the x-th layer, and let the number of indexes be j.

[0032] S35. Determine whether j>0 is true; if so, jump to step S36 for execution; if not, jump to step S39 for execution.

[0033] S36. Read the corresponding required data from the diagnostic database DDDB according to the index name and index operation rule information;

[0034] S37. Perform calculations according to the index operation rule information, and store the calculation results in the diagnostic result database DRDB;

[0035] S38. Execute j - 1, and jump to step S35 for execution;

[0036] S39. Read all the index diagnostic result value data corresponding to the x - th layer, perform calculations according to the operation rules corresponding to this layer, and store the calculation results in the corresponding diagnostic result database DRDB;

[0037] S310. Execute x - 1, and jump to step S33 for execution.

[0038] Preferably: Step S4 specifically includes the following content:

[0039] S41. Construct the diagnostic interaction interface template M for the development of the biomedical industry according to the framework structure set by the system, mainly including the diagnostic result visualization display area, the diagnostic result list display area, and the diagnostic rule display area;

[0040] S42. Initialize the parameters, read the total number of diagnostic index levels from the diagnostic index database DIDB and set it as c, set the current index level as y = 0, and the index name mc = NULL;

[0041] S43. According to the interaction interface template M, construct the human - machine interaction interface of the (y + 1) - th level at the bottom of the current y - th level index module;

[0042] S44. From the diagnostic index database DIDB, load the index level and index name information corresponding to the (y + 1) - th level under the index name mc, and form the data set ZM;

[0043] S45. According to the index name information in the data set ZM, read the corresponding index diagnostic rule information from the diagnostic index database DIDB;

[0044] S46. According to the data set ZM and the index diagnostic rule information, form the data set D1;

[0045] S47. Fill the diagnostic rule display area according to the index name and index diagnostic rules, and present it to the user;

[0046] S48. According to the data set ZM, read the corresponding diagnostic result numerical information from the diagnostic result database DRDB;

[0047] S49. According to the data set ZM and the diagnostic result numerical information, form the data set D2;

[0048] S410. Populate the diagnostic result list display area with dataset D2 and present it to the user for selection, and listen for the information ZB passed in by user interaction; at the same time, populate the diagnostic result visualization display area with dataset D2 and present it to the user for selection, and listen for the information ZB passed in by user interaction;

[0049] S411. Determine whether ZB = NULL is true; if so, end; if not, jump to step S412 for execution;

[0050] S412. Extract the hierarchical information y and the information mc of the indicator name according to the information ZB;

[0051] S413. Determine whether y + 1 > c is true; if so, end; if not, jump to step S43 for execution.

[0052] Due to the adoption of the above technical solutions, the technical progress achieved by the present invention is as follows.

[0053] Through the automatic loading, extraction and format standardization of multi-source biopharmaceutical industry data, the present invention has established automatic and effective associations among diagnostic data, diagnostic indicators, diagnostic rules, analysis operations, diagnostic results, etc., achieved intelligent development diagnosis and analysis operations in the biopharmaceutical industry, constructed a modular human-computer interaction interface, provided personalized services for users, unified the diagnostic data format, associated the operation relationships, automated the diagnostic analysis, and personalized the result presentation, providing a feasible solution for the intelligent and precise industrial development diagnosis using multi-source, multi-type and multi-format biopharmaceutical industry data element information.

[0054] The specific remarkable effects are as follows:

[0055] (1) Standardization of diagnostic data. Based on database storage, the present invention has established interface parameters and loading and retrieval rules for multi-source and multi-type data required for the development diagnosis of the biopharmaceutical industry. At the same time, based on the indicator standard specifications, it has further unified the format of non-standardized data to be calculated for the development diagnosis of the biopharmaceutical industry, and preferably solved the problem that the existing processing methods cannot or are unable to perform automatic operations using information processing technology due to the inconsistent format of the basic data in the industrial development diagnosis and analysis.

[0056] (2) Association of operation relationships. Based on the industrial development diagnostic data with a unified format standard and the user-defined operation rules stored in the database, the present invention has established the corresponding relationships among diagnostic data, diagnostic indicator systems, diagnostic analysis and diagnostic results, and clarified the associations between data and indicator operations; compared with the existing technical methods, it has effectively solved the problems of the relationship associations among data, indicators, rules, etc. involved in diagnostic analysis operations and the relative isolation of each link in the industrial development diagnosis process, and made the operation relationships associated.

[0057] (3)Automated diagnostic analysis. Based on the established unified format standard diagnostic data and the association of operation relationships such as data, indicators, and rules, the present invention constructs a diagnostic analysis processing flow and a diagnostic result storage mechanism layer by layer and index by index. Compared with the existing technical methods, it can achieve automatic loading of the data required for the diagnostic analysis of the biomedical industry, automatic recognition of diagnostic rules, and automatic operation of diagnostic analysis, etc., realizing the automation of industrial diagnostic analysis, enhancing the intelligence of the analysis process, and greatly improving the efficiency of industrial diagnostic analysis.

[0058] (4)Personalized result presentation. Based on the data storage and operation system of the hierarchical and named dynamic biomedical industry indicator system, the present invention constructs a human-computer interaction interface with a function area that can be automatically expanded according to user needs, realizing the visualization display and list display of diagnostic results, as well as the hierarchical display of diagnostic rules. Compared with the existing technical methods, it has the characteristics of flexible presentation content, diverse presentation methods, and personalized presentation results, etc., which is conducive to quickly adapting to the complex situations of the development of strategic emerging industries such as biomedicine and the changes in the latest application scenarios. Description of the Drawings

[0059] Figure 1 It is a schematic structural diagram of the human-computer interaction interface described in the present invention;

[0060] Figure 2 It is a structural block diagram of the data analysis processing module described in the present invention;

[0061] Figure 3 It is a big data processing flow chart of the present invention;

[0062] Figure 4 It is a schematic diagram of the data processing flow of the industrial data loading and extractor described in the present invention;

[0063] Figure 5 It is a schematic diagram of the data regularization process of the industrial data formatting and regularizer described in the present invention;

[0064] Figure 6 It is a schematic diagram of the data analysis process of the biomedical industry development diagnostic analyzer described in the present invention;

[0065] Figure 7 It is a schematic diagram of the result presentation process of the biomedical industry diagnostic result retrieval and presenter described in the present invention. Detailed Embodiments

[0066] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments.

[0067] A big data processing method for the development diagnosis of the biomedical industry constructs a relevant data repository, a data processing and analysis module, and a modular and personalized human-computer interaction interface suitable for presenting diagnosis results in the process of big data processing for the development diagnosis of the biomedical industry; stores multi-source data of the biomedical industry through each data repository; conducts diagnostic analysis of the development of the biomedical industry through the data processing and analysis module; and presents the diagnosis results of the biomedical industry in a personalized manner through the human-computer interaction interface.

[0068] The data repository of the present invention is mainly used to store multi-source database interface parameter information, diagnostic index information, diagnostic data information, relationship information between diagnostic indexes and multi-source data, diagnostic result information, etc. of the biomedical industry, and includes the following five databases.

[0069] (1) System Parameter Configuration Database (PCDB): Stores interface parameter information such as link addresses, connection methods, database types, passwords, etc. of databases such as the innovation entity library, science and technology project library, scientific and technological achievement library, patent library, paper library, and macroeconomic database required for the development diagnosis of the biomedical industry.

[0070] (2) Diagnostic Index Database (DIDB): Stores information on the diagnostic index system for the development of the biomedical industry, such as index levels, index names, etc.; information on the data standard specifications of the indexes, such as field types, field lengths, field formats, field units, etc.; and information on index operation rules such as the numerical unit conversion rules between the data fields corresponding to the indexes, index weights, and calculation methods.

[0071] For example: 10,000 yuan: yuan = 1:10,000;

[0072] The value of the industry environment index of the biomedical industry is:

[0073] C 行业环境 = C 生物医药产业增加率 × 0.6 + C 生物医药产业集聚程度 × 0.4;

[0074] The proportion of enterprise R&D personnel in all employees is:

[0075] ∑ Number of enterprise R&D personnel / ∑ Number of enterprise employees.

[0076] (3) Diagnostic Database (DDDB): Stores the data information required for the diagnosis of the development of the biopharmaceutical industry, such as internal expenditure on R&D (in ten thousand yuan), expenditure on product development (in ten thousand yuan), full-time equivalent of R&D personnel (in person-years), number of R&D institutions (in units), number of enterprise research centers (in units), number of patent applications (in pieces), number of valid invention patents (in pieces), number of included papers (in pieces), proportion of industrial R&D expenditure in GDP (in %).

[0077] (4) Indicator and Multi-source Database (IMDB): Stores information such as the corresponding relationship between the indicator names in the Diagnostic Indicator Database (DIDB) and the multi-source data of the biopharmaceutical industry. The multi-source data includes databases such as the biopharmaceutical innovation entity database, science and technology project database, scientific and technological achievement database, patent database, paper database, and macroeconomic database, as well as their fields.

[0078] (5) Diagnostic Results Database (DRDB): Stores information such as the diagnostic results of the development of the biopharmaceutical industry, such as indicator names, indicator values, etc.

[0079] The data processing and analysis module mainly includes an industrial data loading and extractor, an industrial data formatting and regularization device, a biopharmaceutical industry development diagnostic analyzer, and a biopharmaceutical industry diagnostic result retrieval and presentation device, as Figure 2 shown.

[0080] (1) The industrial data loading and extractor stores the required multi-source data of the biopharmaceutical industry in the Diagnostic Database (DDDB) according to the diagnostic indicator system of the development of the biopharmaceutical industry set by the system.

[0081] (2) The industrial data formatting and regularization device formats the non-standardized biopharmaceutical industry development diagnostic data to be calculated in the Diagnostic Database (DDDB), such as multi-source, multi-type, multi-data field formats, multi-data unit standards, and inconsistent data digits, into data with a unified standard specification according to the set biopharmaceutical industry indicator standardization specification.

[0082] (3) The biopharmaceutical industry development diagnostic analyzer conducts diagnostic analysis layer by layer and indicator by indicator according to the information such as the indicator hierarchy, indicator names, and indicator operation rules in the diagnostic indicator system of the biopharmaceutical industry development, and stores the diagnostic results in the Diagnostic Results Database (DRDB).

[0083] (4) The biopharmaceutical industry diagnostic result retrieval and presentation device presents the diagnostic results of the development of the biopharmaceutical industry to the user through the human-computer interaction interface, and drills down to display the diagnostic results at the specified level according to the default or user-selected item information.

[0084] The human - machine interaction interface is for personalized interaction settings between users and system modules, including four main functional areas: the diagnostic result visualization display area, the diagnostic result list display area, the diagnostic rule display area, and the n - th level sub - index diagnostic result visualization display area. As Figure 1 shown.

[0085] (1) Diagnostic result visualization display area: Present the diagnostic results of the biomedical industry development in the form of visualization set by the system. According to the default or user - selected indicators and their hierarchical information, display the indicator names and their scores at the corresponding level and other diagnostic result information of the biomedical industry development.

[0086] (2) Diagnostic result list display area: Present the diagnostic results of the biomedical industry development in an itemized form. According to the default or user - selected indicators and their hierarchical information, display the indicator names and their scores at the corresponding level and other diagnostic result information of the biomedical industry development.

[0087] (3) Diagnostic rule display area: Present the diagnostic rules of the biomedical industry development in an itemized form. According to the default or user - selected indicators and their hierarchical information, display the indicator names and their operation rules at the corresponding level and other diagnostic rule information of the biomedical industry development.

[0088] (4) The n - th level sub - index diagnostic result visualization display area: According to the user's personalized needs, construct the next - level diagnostic result visualization display area, diagnostic result list display area, and diagnostic rule display area, and use them to present the corresponding diagnostic result information.

[0089] Specifically, a big - data processing method for biomedical industry development diagnosis proposed by the present invention has a process as Figure 3 shown, mainly including the following steps.

[0090] S1. Use the industrial data loading extractor to store the required multi - source data of the biomedical industry in the diagnostic database DDDB according to the information of the diagnostic index system for biomedical industry development set in the diagnostic index database DIDB, the relevant database parameter configuration information in the system parameter configuration database PCDB, and the corresponding relationship information between the index names and multi - source data in the diagnostic index and multi - source data relationship database IMDB.

[0091] The specific process of this step is as Figure 4 shown, specifically as follows.

[0092] S11. Load the interface parameters such as the link address, connection method, database type, and password of the database involved in the multi - source data of the biomedical industry from the system parameter configuration database PCDB.

[0093] The databases involved in the multi-source data of the biopharmaceutical industry include a biopharmaceutical innovation entity database, a biopharmaceutical science and technology project database, a biopharmaceutical science and technology achievement database, a biopharmaceutical policy database, a biopharmaceutical standard database, a patent database, a paper database, and a macroeconomic database.

[0094] S12. Read the biopharmaceutical industry development diagnosis index information from the diagnosis index database DIDB to form an index data set Z1.

[0095] S13. According to the index data set Z1, load the corresponding information of the diagnosis index and the database and its fields from the relationship database IMDB between the diagnosis index and multi-source data.

[0096] S14. Read the multi-source data of the biopharmaceutical industry according to the database interface parameter information.

[0097] S15. Store the relevant data read in step S14 into the diagnosis database DDDB.

[0098] S2. Use the industrial data formatting and regularization tool to format the non-standardized biopharmaceutical industry development diagnosis data to be calculated in the diagnosis database DDDB, such as multi-source, multi-type, multi-data field formats, multi-data unit standards, and inconsistent data digits, into data with a unified standard specification according to the biopharmaceutical industry index standardization specification set in the diagnosis index database DIDB.

[0099] The specific process of this step is as Figure 5 shown as follows.

[0100] S21. Load the standardization specifications such as the index name, field type, field length, field format, and field unit of the biopharmaceutical industry development diagnosis index from the diagnosis index database DIDB to form an index standard specification data set Z2.

[0101] S22. Let the total number of records in the index standard specification data set Z2 be i.

[0102] S23. Judge whether i>0 is true; if it is, jump to step S24 to execute; if not, end.

[0103] S24. According to the index name in the index standard specification data set Z2, sequentially read the biopharmaceutical industry development diagnosis data from the diagnosis database DDDB.

[0104] S25. Format the corresponding data sequentially according to the i-th index standardization specification.

[0105] S26. Update the latest data after the normalization process into the diagnosis database DDDB.

[0106] S27. Execute i-1, and jump to step S23 to execute.

[0107] S3. Use the biomedical industry development diagnostic analyzer to read the corresponding data information in the diagnostic database DDDB according to the information such as the index hierarchy, index name, and index operation rules of the biomedical industry development diagnostic index system in the diagnostic index database DIDB, perform diagnostic analysis iteratively layer by layer and index by index, and store the diagnostic results in the diagnostic result database DRDB.

[0108] The specific process of this step is as Figure 6 shown below.

[0109] S31. Load the information such as the index hierarchy, index name, and index operation rules of the biomedical industry development diagnostic index system from the diagnostic index database DIDB.

[0110] S32. Let the number of index levels be x.

[0111] S33. Judge whether x>0 is true; if it is, jump to step S34 to execute; if it is not, end.

[0112] S34. Read the index name corresponding to the xth layer, and let the number of indexes be j.

[0113] S35. Judge whether j>0 is true; if it is, jump to step S36 to execute; if it is not, jump to step S39 to execute.

[0114] S36. Read the required corresponding data from the diagnostic database DDDB according to the information such as the index name and index operation rules.

[0115] S37. Perform operations according to the index operation rule information, and store the operation results in the diagnostic result database DRDB.

[0116] S38. Execute j-1, and jump to step S35 to execute.

[0117] S39. Read all the index diagnostic result value data corresponding to the xth layer, perform operations according to the operation rules corresponding to this layer, and store the operation results in the corresponding diagnostic result database DRDB.

[0118] S310. Execute x-1, and jump to step S33 to execute.

[0119] S4. Use the biomedical industry diagnostic result retrieval and displayer to read the relevant information in the diagnostic result database DRDB and the diagnostic index database DIDB, present the biomedical industry development diagnostic results to the user through the human-computer interaction interface, and drill down to display the diagnostic results of the specified layer according to the user-selected item information.

[0120] The specific process of this step is as Figure 7As shown below, the details are as follows.

[0121] S41. Construct the diagnostic interaction interface template M for the development of the biomedical industry according to the framework structure set by the system, mainly including a diagnostic result visualization display area, a diagnostic result list display area, a diagnostic rule display area, etc.

[0122] S42. Initialize the parameters, read the total number of diagnostic index levels from the diagnostic index database DIDB and set it as c, set the current index level as y = 0, and the index name mc = NULL.

[0123] S43. Based on the interaction interface template M, construct the human-computer interaction interface at the y + 1 level at the bottom of the current y-level index module.

[0124] S44. From the diagnostic index database DIDB, load the information such as the corresponding index level and index name at the y + 1 level under the index name mc to form the data set ZM.

[0125] S45. Based on the index name information in the data set ZM, read the corresponding index diagnostic rule information from the diagnostic index database DIDB.

[0126] S46. According to the information such as the data set ZM and the index diagnostic rules, form the data set D1.

[0127] S47. Fill the diagnostic rule display area according to the index name and index diagnostic rules and present it to the user;

[0128] S48. Based on the data set ZM, read the corresponding diagnostic result numerical information from the diagnostic result database DRDB.

[0129] S49. According to the information such as the data set ZM and the diagnostic result numerical values, form the data set D2.

[0130] S410. Use the data set D2 to fill the diagnostic result list display area and present it to the user for selection, and listen to the information ZB passed in by the user interaction; at the same time, use the data set D2 to fill the diagnostic result visualization display area and present it to the user for selection, and listen to the information ZB passed in by the user interaction.

[0131] S411. Judge whether ZB = NULL is true; if so, end; if not, jump to step S412 for execution.

[0132] S412. Based on the information ZB, extract the level information y, index name mc and other information.

[0133] S413. Judge whether y + 1 > c is true; if so, end; if not, jump to step S43 for execution.

[0134] By establishing an effective association between the diagnostic index system and diagnostic data, the present invention effectively associates information on the diagnostic index system such as the diagnostic index hierarchy and index names with multi-source data required for the development diagnosis of the biopharmaceutical industry, and performs structured storage, which is conducive to timely adjustment of the index system according to the complex situation of industrial development and changes in the latest application scenarios; by establishing the association relationship between the diagnostic rules and the operation data involved, and performing systematic storage, it is conducive to meeting the gradually complex, personalized and dynamic diagnostic rule adjustment requirements, and is conducive to large-scale and automated operations on biopharmaceutical industry data and their relationships of multiple sources, types and formats; by unifying and organically associating the data formats and standards of diagnostic data, diagnostic indexes and diagnostic rules, the present invention solves the whole-process processing of automatic loading and extraction of diagnostic data and format standardization, automatic recognition of diagnostic index systems and rule operations, and personalized display and on-demand presentation of diagnostic results at one time, which is conducive to using relevant information technologies for efficient and intelligent industrial development diagnosis of the biopharmaceutical industry, and greatly improves the efficiency.

Claims

1. A big data processing method for the development diagnosis of the biomedical industry, characterized in that, It mainly includes the following steps: S1. Use the industrial data loading extractor to store the required multi-source data of the biomedical industry in the diagnostic database DDDB according to the information of the biomedical industry development diagnostic index system set in the Diagnostic Index Database DIDB, the relevant database parameter configuration information in the System Parameter Configuration Database PCDB, and the corresponding relationship information between the index names and multi-source data in the Index-Multi-source Data Relationship Database IMDB; S2. Use the industrial data formatting and regularization tool to format the non-standardized biomedical industry development diagnostic data to be calculated in the diagnostic database DDDB, which has multiple sources, multiple types, multiple data field formats, multiple data unit standards, and inconsistent data digits, into data with a unified standard specification according to the biomedical industry index standardization specification set in the Diagnostic Index Database DIDB; S3. Use the biomedical industry development diagnostic analyzer to read the corresponding data information in the diagnostic database DDDB according to the index hierarchy, index names, and index operation rule information of the biomedical industry development diagnostic index system in the Diagnostic Index Database DIDB, perform diagnostic analysis layer by layer and index by index, and store the diagnostic results in the Diagnostic Result Database DRDB; S4. Use the biomedical industry diagnostic result retrieval and presentation tool to read the relevant information in the Diagnostic Result Database DRDB and the Diagnostic Index Database DIDB, present the biomedical industry development diagnostic results to the user through the human-computer interaction interface, and drill down to display the diagnostic results at the specified level according to the user-selected item information.

2. The big data processing method for the development diagnosis in the biomedical industry according to claim 1, wherein, Step S1 specifically includes the following contents: S11. Load the link address, connection method, database type, and password interface parameters of the databases involved in the multi-source data of the biomedical industry from the System Parameter Configuration Database PCDB; S12. Read the biomedical industry development diagnostic index information from the Diagnostic Index Database DIDB to form an index data set Z1; S13. Load the corresponding information between the diagnostic index and the database and its fields from the Index-Multi-source Data Relationship Database IMDB according to the index data set Z1; S14. Read the multi-source data of the biomedical industry according to the database interface parameter information; S15. Store the relevant data read in step S14 in the diagnostic database DDDB.

3. A big data processing method for the development diagnosis in the biomedical industry according to claim 2, characterized in that, The databases involved in the multi-source data of the biomedical industry mentioned in step S11 include the Biomedical Innovation Entity Database, the Biomedical Science and Technology Project Database, the Biomedical Science and Technology Achievement Database, the Biomedical Policy Database, the Biomedical Standard Database, the Patent Database, the Thesis Database, and the Macroeconomic Database.

4. A big data processing method for the development diagnosis of the biomedical industry according to claim 1, characterized in that, Step S2 specifically includes the following contents: S21. Load the standardized specifications of the index names, field types, field lengths, field formats, and field units of the biomedical industry development diagnostic indexes from the Diagnostic Index Database DIDB to form an index standard specification data set Z2; S22. Let the total number of records in the index standard specification data set Z2 be i; S23. Judge whether i>0 is true; if it is, jump to step S24 to execute; if not, end; S24. According to the index names in the index standard specification dataset Z2, sequentially read the biomedical industry development diagnosis data from the diagnosis database DDDB; S25. According to the i-th index standardization specification, sequentially format the corresponding data; S26. Update the latest data after the normalization process into the diagnosis database DDDB; S27. Execute i - 1, and jump to step S23 for execution.

5. A big data processing method for biomedical industry development diagnosis according to claim 1, characterized in that, Step S3 specifically includes the following content: S31. Load the index hierarchy, index names, and index operation rule information of the biomedical industry development diagnosis index system from the diagnosis index database DIDB; S32. Let the number of index levels be x; S33. Determine whether x > 0 is true; if it is, then jump to step S34 for execution; if not, then end; S34. Read the index names corresponding to the x-th layer, and let the number of indexes be j; S35. Determine whether j > 0 is true; if it is, then jump to step S36 for execution; if not, then jump to step S39 for execution; S36. According to the index names and index operation rule information, read the required corresponding data from the diagnosis database DDDB; S37. Perform operations according to the index operation rule information, and store the operation results in the diagnosis result database DRDB; S38. Execute j - 1, and jump to step S35 for execution; S39. Read all the index diagnosis result value data corresponding to the x-th layer, perform operations according to the operation rules corresponding to this layer, and store the operation results in the corresponding diagnosis result database DRDB; S310. Execute x - 1, and jump to step S33 for execution.

6. A big data processing method for the development diagnosis in the biomedical industry according to claim 1, characterized in that, Step S4 specifically includes the following content: S41. Construct the biomedical industry development diagnosis interaction interface template M according to the system-set framework structure, mainly including the diagnosis result visualization display area, the diagnosis result list display area, and the diagnosis rule display area; S42. Initialize the parameters, read the total number of diagnosis index levels from the diagnosis index database DIDB and set it as c, set the current index level as y = 0, and the index name mc = NULL; S43. According to the interaction interface template M, construct the human-computer interaction interface of the (y + 1)-th layer at the bottom of the current y-layer index level module; S44. From the diagnosis index database DIDB, load the index hierarchy and index name information corresponding to the (y + 1)-th layer under the index name mc to form the dataset ZM; S45. According to the index name information in the dataset ZM, read the corresponding index diagnosis rule information from the diagnosis index database DIDB; S46. According to the dataset ZM and the index diagnosis rule information, form the dataset D1; S47. Fill the diagnosis rule display area according to the index names and index diagnosis rules and present it to the user; S48. According to the dataset ZM, read the corresponding diagnosis result numerical information from the diagnosis result database DRDB; S49. According to the dataset ZM and the diagnosis result numerical information, form the dataset D2; S410. Populate the diagnostic result list display area with dataset D2 and present it to the user for selection, and listen for the information ZB passed in by user interaction; at the same time, populate the diagnostic result visualization display area with dataset D2 and present it to the user for selection, and listen for the information ZB passed in by user interaction; S411. Determine whether ZB = NULL is true; if so, end; if not, jump to step S412 for execution; S412. Extract the hierarchical information y and the information mc of the indicator name according to the information ZB; S413. Determine whether y + 1 > c is true; if so, end; if not, jump to step S43 for execution.

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