A big data processing method for development diagnosis of the biomedical industry

Abstract

The application discloses a big data processing method for biological medicine industry development diagnosis, and specifically comprises the following steps: extracting required biological medicine industry multi-source data according to a biological medicine industry development diagnosis index system; formatting data with different formats into unified standard specification data according to biological medicine industry index standardization specifications; performing diagnosis analysis layer by layer and index by index according to biological medicine industry development diagnosis index system information; presenting biological medicine industry development diagnosis results to users through a man-machine interaction interface, and drilling down to display diagnosis results of specified levels according to user selected item information. The application solves the problems of biological medicine industry data regularization, diagnosis index analysis operation automation, diagnosis result presentation personalization and the like, and provides a feasible solution for intelligent and accurate industry development diagnosis by using multi-source, multi-type and multi-format biological medicine industry data element information.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of big data application, and particularly relates to a big data processing method for development diagnosis of strategic emerging industries such as biological medicine. BACKGROUND

[0002] Industrial development refers to the generation, growth and evolution process of an industry, which includes the evolution process of a single industry and the evolution process of the overall industry, i.e., the entire national economy. The evolution process includes the quantitative changes such as the number of enterprises, the product or service output in an industry, and the qualitative changes such as the adjustment, change, replacement and leading position of the industry structure. Therefore, when evaluating and diagnosing the development of an industry, a large amount of data is needed as support, and these data include source data in multiple aspects.

[0003] In the prior art, the diagnosis index system and diagnosis operation rules for industrial development are mostly not stored in a systematic manner, but are embedded in the system program, which leads to the relative solidification of the diagnosis index system and diagnosis operation rules, and the diagnosis index system and diagnosis operation rules cannot be adjusted in time according to the complex conditions of industrial development and the changes in the latest application scenarios. In addition, due to the lack of correspondence between the diagnosis index and the multi-source data and the accurate storage thereof, a small change in the diagnosis operation rules will lead to the inability to obtain the related data in time, effectively and accurately, so that the diagnosis operation rules are relatively fixed and cannot be quickly and flexibly adjusted.

[0004] In addition, in the overall diagnosis and analysis process of industrial development, due to the lack of effective connection of the data formats and standard specifications of the whole process such as diagnosis data, diagnosis index, diagnosis rule, analysis operation and diagnosis result, the manual or semi-manual mode is usually used to implement the process step by step. Due to the lack of effective correspondence and storage between the multi-source data acquisition and the index system, and the correlation between the operation rules and the diagnosis analysis, the computer and big data information technology means cannot be applied to efficiently and intelligently automatically analyze and judge and present the results. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a big data processing method for development diagnosis of biological medicine industry, so as to solve the defects such as the correlation between the diagnosis index system and the diagnosis data, the real-time adjustment of the diagnosis rules, and the on-demand presentation of the diagnosis results in the current development diagnosis process of biological medicine industry, and to provide a solution for large-scale data automatic operation of development diagnosis of biological medicine industry.

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

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

[0008] S1. Using the industry data loading extractor, according to the information of the biological medicine industry development diagnosis index system set in the diagnosis index library DIDB, the related database parameter configuration information in the system parameter configuration library PCDB, and the information of the corresponding relationship between the index name and the multi-source data in the diagnosis index and multi-source data relationship library IMDB, the required biological medicine industry multi-source data is stored in the diagnosis database DDDB;

[0009] S2. Using the industry data formatting regulator, according to the standardization specification of the biological medicine industry index set in the diagnosis index library DIDB, the non-standardized biological medicine industry development diagnosis data to be operated in the diagnosis database DDDB is formatted into data with uniform standard specification, which is multi-source, multi-type, multi-data field format, multi-data unit standard, and inconsistent data bit number;

[0010] S3. Using the biological medicine industry development diagnosis analyzer, according to the index level, index name, and index operation rule information of the biological medicine industry development diagnosis index system in the diagnosis index library DIDB, the corresponding data information in the diagnosis database DDDB is read, and the diagnosis analysis is performed layer by layer and index by index, and the diagnosis result is stored in the diagnosis result library DRDB;

[0011] S4. Using the biological medicine industry diagnosis result calling presenter, reading the related information in the diagnosis result library DRDB and the diagnosis index library DIDB, the biological medicine industry development diagnosis result is presented to the user through the man-machine interface, and according to the user selected item information, the diagnosis result of the specified level is displayed.

[0012] Preferably, step S1 specifically includes the following contents:

[0013] S11. Loading the link address, connection mode, database type, and password interface parameters of the database involved in the biological medicine industry multi-source data from the system parameter configuration library PCDB;

[0014] S12. Reading the biological medicine industry development diagnosis index information from the diagnosis index library DIDB to form an index data set Z1;

[0015] S13. According to the index data set Z1, loading the corresponding information of the diagnosis index and the database and its field from the diagnosis index and multi-source data relationship library IMDB;

[0016] S14. According to the database interface parameter information, reading the biological medicine industry multi-source data;

[0017] S15. Storing the related data read in step S14 into the diagnosis database DDDB.

[0018] Preferably, the databases involved in the multi-source data of the bio-pharmaceutical industry in step S11 include a bio-pharmaceutical innovation subject database, a bio-pharmaceutical technology project database, a bio-pharmaceutical scientific and technological achievement database, a bio-pharmaceutical policy database, a bio-pharmaceutical standard database, a patent database, a paper database, and a macroeconomic database.

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

[0020] S21. Loading the standardized specifications of the index name, field type, field length, field format and field unit of the bio-pharmaceutical industry development diagnosis index from the diagnosis index database DIDB to form an index standard specification dataset Z2;

[0021] S22. Let the total number of records of the index standard specification dataset Z2 be i;

[0022] S23. Determine whether i>0 is true; if yes, jump to step S24 for execution; if no, end;

[0023] S24. According to the index name in the index standard specification dataset Z2, read the bio-pharmaceutical industry development diagnosis data from the diagnosis database DDDB in turn;

[0024] S25. Format the corresponding data in turn according to the i-th index standardization specification;

[0025] S26. Update the latest data after standardization 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 contents:

[0028] S31. Loading the index level, index name, and index operation rule information of the bio-pharmaceutical 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 yes, jump to step S34 for execution; if no, end;

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

[0032] S35. Determine whether j>0 is true; if yes, jump to step S36 for execution; if no, jump to step S39 for execution;

[0033] S36. According to the index name and index operation rule information, read the required corresponding data from the diagnosis database DDDB;

[0034] S37. According to the index operation rule information, perform operation, and store the operation result in the diagnosis result database DRDB;

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

[0036] S39. Read all index diagnosis result value data corresponding to the xth layer, perform operation according to the operation rule corresponding to the layer, and store the operation result in the corresponding diagnosis result database DRDB;

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

[0038] Preferably, step S4 specifically includes the following contents:

[0039] S41. According to the system set framework structure, construct the biological medicine industry development diagnosis interactive interface template M, mainly including a diagnosis result visual display area, a diagnosis result list display area, and a diagnosis rule display area;

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

[0041] S43. According to the interactive interface template M, construct the y+1th level human-computer interactive interface at the bottom of the current yth level index level module;

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

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

[0044] S46. Form a data set D1 according to the data set ZM and the index diagnosis rule information;

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

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

[0047] S49. Form a data set D2 according to the data set ZM and the diagnosis result numerical information;

[0048] S410. Fill the diagnostic result list display area with dataset D2, present to the user for selection, and listen to the information ZB input by the user interaction; at the same time, fill the diagnostic result visualization display area with dataset D2, present to the user for selection, and listen to the information ZB input by the user interaction;

[0049] S411. Determine whether ZB=NULL is true; if yes, end; if no, jump to step S412 for execution;

[0050] S412. According to the information ZB, extract the hierarchical information y and the index name mc information;

[0051] S413. Determine whether y+1>c is true; if yes, end; if no, jump to step S43 for execution.

[0052] Thanks to the above technical solutions, the technical progress achieved by the present application is as follows.

[0053] The present application achieves automatic loading extraction and format standardization of multi-source biological and pharmaceutical industry data, establishes automatic and effective association of diagnostic data, diagnostic indicators, diagnostic rules, analysis and operation, diagnostic results, etc., realizes intelligent development diagnosis and analysis operation of biological and pharmaceutical industry, constructs a modular human-computer interaction interface, provides personalized services for users, makes diagnostic data format unified, operation relationship associated, diagnostic breakdown automated, and result presentation personalized, and provides a feasible solution for intelligent and precise industry development diagnosis using multi-source, multi-type and multi-format biological and pharmaceutical industry data element information.

[0054] The specific and remarkable effects are as follows:

[0055] (1) Diagnostic data standardization. The present application is based on database storage, establishes the interface parameters and loading and calling rules of multi-source and multi-type data required for biological and pharmaceutical industry development diagnosis, and further unifies the format of non-standardized biological and pharmaceutical industry development diagnosis data to be operated based on index standard specification, which better solves the problem that the inconsistent formats of basic data lead to the inability or inability to use information processing technology for automatic operation of industry development diagnosis analysis in the prior art.

[0056] (2) Operation relationship association. The present application establishes the corresponding relationship between diagnostic data, diagnostic index system, diagnostic analysis and diagnostic results based on the unified format standard of industry development diagnosis data and the user-defined operation rules of database storage, and clearly defines the association between data and index operation; compared with the prior art, the present application effectively solves the relationship association of data, index, rules and other involved in diagnostic analysis operation, and the problem of relative isolation of each link in the process of industry development diagnosis, so that the operation relationship is associated.

[0057] (3) Diagnosis resolution automation. The present application is based on the established unified format standard diagnosis data and the association of data, indicators, rules and other operation relations, and constructs a diagnosis analysis processing flow and a diagnosis result storage mechanism layer by layer and indicator by indicator. Compared with the prior art method, the present application can achieve automatic loading of data required for biological and pharmaceutical industry diagnosis analysis, automatic identification of diagnosis rules, and automatic operation of diagnosis analysis, realizes automation of industry diagnosis analysis, enhances the intelligentization of the analysis process, and greatly improves the efficiency of industry diagnosis analysis.

[0058] (4) Result presentation individualization. The present application is based on the dynamic biological and pharmaceutical industry indicator system hierarchy and name data storage, operation system and the like, and constructs a man-machine interface which can automatically expand functional areas according to user needs, realizes visual mode display and list mode display of diagnosis results, and hierarchical display of diagnosis rules. Compared with the prior art method, the present application has the characteristics of flexible presentation content, diversified presentation mode, and individualized presentation result, and is beneficial to quickly adapt to the changes of the complex conditions and the latest application scenarios of the development of strategic emerging industries such as biological and pharmaceutical industries. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 Fig. 1 is a structural schematic diagram of the man-machine interface of the present application;

[0060] Figure 2 Fig. 2 is a structural block diagram of the data analysis processing module of the present application;

[0061] Figure 3 Fig. 3 is a big data processing flowchart of the present application;

[0062] Figure 4 Fig. 4 is a data processing flowchart of the industry data loading extractor of the present application;

[0063] Figure 5 Fig. 5 is a data regularization flowchart of the industry data formatting regularizer of the present application;

[0064] Figure 6 Fig. 6 is a data analysis flowchart of the biological and pharmaceutical industry development diagnosis analyzer of the present application;

[0065] Figure 7 Fig. 7 is a result presentation flowchart of the biological and pharmaceutical industry diagnosis result calling presenter of the present application. DETAILED DESCRIPTION

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

[0067] A big data processing method for biological medicine industry development diagnosis, which constructs relevant data storage library, data processing analysis module and modularized, personalized human-computer interaction interface suitable for diagnosis result display in the big data processing process of biological medicine industry development diagnosis; biological medicine industry multi-source data is stored through each data storage library; biological medicine industry development diagnosis analysis is carried out through the data processing analysis module; the biological medicine industry diagnosis result is presented through the personalized human-computer interaction interface.

[0068] The data storage library of the application is mainly used for storing biological medicine industry multi-source database interface parameter information, diagnosis index information, diagnosis data information, diagnosis index and multi-source data relationship information, diagnosis result information, etc., and contains the following five databases.

[0069] (1) Parameter Configuration Database (PCDB): stores the interface parameter information of the link address, connection mode, database type, password, etc. of the database of the innovation subject library, science and technology project library, scientific and technological achievement library, and patent library, paper library, macroeconomic database, etc. required for biological medicine industry development diagnosis.

[0070] (2) Diagnostic Indicator Database (DIDB): stores biological medicine industry development diagnosis index system information, such as: index level, index name, etc.; index data standard specification information, such as: field type, field length, field format, field unit, etc.; numerical unit conversion rules between data fields corresponding to the index, index weight and calculation method, etc. index operation rule information.

[0071] For example: 10,000 yuan: yuan = 1:10000;

[0072] The biological medicine industry environmental index value 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 to all employees is:

[0075] ∑ enterprise R&D personnel quantity / ∑ enterprise employee quantity.

[0076] (3) Diagnostic Data Database (DDDB): stores the data information required for the diagnosis of the development of the bio-pharmaceutical industry, such as internal expenditure of R&D funds (ten thousand yuan), product development fund expenditure (ten thousand yuan), R&D personnel full-time equivalent (person-year), R&D institution quantity (pieces), enterprise research center quantity (pieces), patent application number (pieces), effective invention patent number (pieces), number of included papers (pieces), and proportion of industrial R&D funds to GDP (%).

[0077] (4) Indicators and Multi-source Databases (IMDB): stores the corresponding relationship between the indicator names in the Diagnostic Indicators Database (DIDB) and the multi-source data corresponding to the bio-pharmaceutical industry, and other information. The multi-source data includes the databases of bio-pharmaceutical innovation subjects, scientific and technological projects, scientific and technological achievements, patents, papers, and macroeconomic databases, and their fields.

[0078] (5) Diagnostic Results Database (DRDB): stores the information of the diagnostic results of the development of the bio-pharmaceutical industry, such as the indicator name and the indicator value.

[0079] The data processing and analysis module mainly includes an industry data loading extractor, an industry data formatting regulator, a bio-pharmaceutical industry development diagnostic analyzer, and a bio-pharmaceutical industry diagnostic result retrieval presenter, as shown in FIG. 1. Figure 2

[0080] (1) The industry data loading extractor stores the required multi-source data of the bio-pharmaceutical industry into the diagnostic database DDDB according to the bio-pharmaceutical industry development diagnostic indicator system set by the system.

[0081] (2) The industry data formatting regulator formats the non-standardized bio-pharmaceutical industry development diagnostic data to be operated into data of uniform standard specification according to the bio-pharmaceutical industry indicator standardization specification set, including multi-source, multi-type, multi-data field format, multi-data unit standard, and inconsistent data bit number.

[0082] (3) The bio-pharmaceutical industry development diagnostic analyzer performs diagnostic analysis layer by layer and indicator by indicator according to the information of the indicator hierarchy, indicator name, and indicator operation rule of the bio-pharmaceutical industry development diagnostic indicator system, and stores the diagnostic results into the diagnostic results database DRDB.

[0083] (4) The bio-pharmaceutical industry diagnostic result retrieval presenter presents the diagnostic results of the development of the bio-pharmaceutical industry to the user through the human-computer interaction interface, and displays the diagnostic results of the specified level according to the default or user-selected item information. ​

[0084] The human-computer interaction interface comprises four main functional areas, i.e., a diagnosis result visual display area, a diagnosis result list display area, a diagnosis rule display area and an n-level sub-index diagnosis result visual display area, in order to meet personalized interaction between a user and a system module, as shown in Figure 1 .

[0085] (1) The diagnosis result visual display area: the biological medicine industry development diagnosis result is displayed according to the index and hierarchical information selected by the user or by default, and the index name and score of the corresponding level and other biological medicine industry development diagnosis result information are displayed according to the visual presentation form set by the system.

[0086] (2) The diagnosis result list display area: the biological medicine industry development diagnosis result is displayed according to the index and hierarchical information selected by the user or by default, and the index name and score of the corresponding level and other biological medicine industry development diagnosis result information are displayed according to the item form.

[0087] (3) The diagnosis rule display area: the biological medicine industry development diagnosis rule is displayed according to the index and hierarchical information selected by the user or by default, and the index name and operation rule of the corresponding level and other biological medicine industry development diagnosis rule information are displayed according to the item form.

[0088] (4) The n-level sub-index diagnosis result visual display area: the diagnosis result visual display area, the diagnosis result list display area and the diagnosis rule display area of the next level are constructed according to the user's personalized needs, and the corresponding diagnosis result information is displayed.

[0089] Specifically, the present application provides a big data processing method for biological medicine industry development diagnosis, and the flow thereof is shown in Figure 3 , and mainly comprises the following steps.

[0090] S1. Using an industry data loading extractor, the biological medicine industry multi-source data required is stored in a diagnosis database DDDB according to the biological medicine industry development diagnosis index system information set in a diagnosis index database DIDB, the related database parameter configuration information in a system parameter configuration database PCDB, and the index name and multi-source data corresponding relationship information in an index and multi-source data relationship database IMDB.

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

[0092] S11. The interface parameters of the biological medicine industry multi-source data involved database, such as the link address, connection mode, database type and password, are loaded from the system parameter configuration database PCDB.

[0093] The database involved in the multi-source data of the bio-pharmaceutical industry includes a bio-pharmaceutical innovation subject database, a bio-pharmaceutical technology project database, a bio-pharmaceutical scientific and technological achievement database, a bio-pharmaceutical policy database, a bio-pharmaceutical standard database, a patent database, a paper database and a macroeconomic database.

[0094] S12. From the diagnosis index database DIDB, read the bio-pharmaceutical industry development diagnosis index information to form an index data set Z1.

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

[0096] S14. According to the database interface parameter information, read the bio-pharmaceutical industry multi-source data.

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

[0098] S2. Using the industry data formatting regulator, according to the bio-pharmaceutical industry index standardization specification set in the diagnosis index database DIDB, format the non-standardized bio-pharmaceutical industry development diagnosis data to be operated in the diagnosis database DDDB into data of unified standard specification, which is of multi-source, multi-type, multi-data field format, multi-data unit standard and inconsistent data bit number.

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

[0100] S21. From the diagnosis index database DIDB, load the index name, field type, field length, field format and field unit of the bio-pharmaceutical industry development diagnosis index standardization specification to form an index standard specification data set Z2.

[0101] S22. Set the total number of records of the index standard specification data set Z2 as i.

[0102] S23. Determine whether i>0 is true; if yes, jump to step S24 for execution; if no, end.

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

[0104] S25. According to the i-th index standardization specification, format the corresponding data in turn.

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

[0106] S27. Execute i-1 and jump to step S23 for execution.

[0107] S3. Using the biological medicine industry development diagnostic analyzer, according to the index level, index name, index operation rule and other information of the biological medicine industry development diagnostic index system in the diagnostic index database DIDB, reading the corresponding data information in the diagnostic database DDDB, iteratively performing diagnostic analysis layer by layer and index by index, and storing the diagnostic results in the diagnostic result database DRDB.

[0108] The specific process of this step is shown in Figure 6 , and is as follows.

[0109] S31. Load the index level, index name, index operation rule and other information of the biological medicine industry development diagnostic index system from the diagnostic index database DIDB.

[0110] S32. Let the index level number be x.

[0111] S33. Determine whether x>0 is true; if yes, jump to step S34 for execution; if no, end.

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

[0113] S35. Determine whether j>0 is true; if yes, jump to step S36 for execution; if no, jump to step S39 for execution.

[0114] S36. According to the index name and index operation rule and other information, read the required corresponding data from the diagnostic database DDDB.

[0115] S37. According to the index operation rule information, perform operation, and store the operation result in the diagnostic result database DRDB.

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

[0117] S39. Read the diagnostic result value data of all indexes corresponding to the xth layer, perform operation according to the operation rule corresponding to the layer, and store the operation result in the corresponding diagnostic result database DRDB.

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

[0119] S4. Using the biological medicine industry diagnostic result presentation device, reading the related information in the diagnostic result database DRDB and the diagnostic index database DIDB, presenting the biological medicine industry development diagnostic result to the user through the human-computer interaction interface, and according to the user selected item information, drilling down to display the diagnostic result of the specified level.

[0120] The specific process of this step is shown in Figure 7As shown, specifically as follows.

[0121] S41. According to the framework structure set by the system, a biological medicine industry development diagnosis interactive interface template M is constructed, mainly including a diagnosis result visual display area, a diagnosis result list display area, and a diagnosis rule display area, etc.

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

[0123] S43. According to the interactive interface template M, the y+1 level human-computer interaction interface is constructed at the bottom of the current y level index level module.

[0124] S44. From the diagnosis index library DIDB, the index level corresponding to the y+1 level under the index name mc and the index name are loaded, and a data set ZM is formed.

[0125] S45. According to the index name information in the data set ZM, the corresponding index diagnosis rule information is read from the diagnosis index library DIDB.

[0126] S46. According to the data set ZM, the index diagnosis rule, etc. Form a data set D1.

[0127] S47. Fill in the diagnosis rule display area according to the index name and the index diagnosis rule, and present it to the user;

[0128] S48. According to the data set ZM, read the corresponding diagnosis result numerical information from the diagnosis result library DRDB.

[0129] S49. According to the data set ZM, the diagnosis result numerical information, etc. Form a data set D2.

[0130] S410. Fill in the diagnosis result list display area with the data set D2 to present to the user for selection, and listen to the information ZB input by the user interaction; at the same time, fill in the diagnosis result visual display area with the data set D2 to present to the user for selection, and listen to the information ZB input by the user interaction.

[0131] S411. Determine whether ZB=NULL is true; if yes, end; if no, jump to step S412.

[0132] S412. According to the information ZB, extract the level information y, the index name mc, etc.

[0133] S413. Determine whether y+1>c is true; if yes, end; if no, jump to step S43.

[0134] The present application establishes effective association between diagnostic index system and diagnostic data, and establishes effective association between index system information such as diagnostic index level and index name and multi-source data required for biological medicine industry development diagnosis, and performs structured storage, which is beneficial to timely adjustment of index system according to complex conditions of industry development and latest application scene changes; the present application establishes association relationship between diagnostic rules and operation data involved in the diagnostic rules, and performs systematic storage, which is beneficial to adjustment requirement of gradually complicated, personalized and dynamic diagnostic rules, and is beneficial to large-scale and automatic operation of biological medicine industry data and relationship thereof in multiple sources, multiple categories and multiple formats; the present application unifies and associates data format and standard specification of diagnostic data, diagnostic index and diagnostic rules, and solves full-process processing such as automatic loading extraction and format standardization of diagnostic data, automatic identification and rule operation of diagnostic index system, personalized display and on-demand presentation of diagnostic result, which is beneficial to efficient and intelligent industry development diagnosis of biological medicine industry by using relevant information technology, and greatly improves efficiency.

Claims

1. A big data processing method for the development diagnosis of the biomedical industry, characterized in that, The method comprises the following steps: S1. Using an industry data loading extractor, according to the information of the bio-pharmaceutical industry development diagnosis index system set in the diagnosis index library DIDB, the related database parameter configuration information in the system parameter configuration library PCDB, and the information of the corresponding relationship between the index name and the multi-source data in the diagnosis index and multi-source data relationship library IMDB, the required bio-pharmaceutical industry multi-source data is stored in the diagnosis database DDDB; S2. Using an industry data formatting regulator, according to the bio-pharmaceutical industry index standardization specification set in the diagnosis index library DIDB, the non-standardized bio-pharmaceutical industry development diagnosis data to be operated in the diagnosis database DDDB is formatted into data of a unified standard specification, which is of multiple sources, multiple types, multiple data field formats, multiple data unit standards, and inconsistent data bit numbers; S3. Using a bio-pharmaceutical industry development diagnosis analyzer, according to the index level, index name, and index operation rule information of the bio-pharmaceutical industry development diagnosis index system in the diagnosis index library DIDB, the corresponding data information in the diagnosis database DDDB is read, diagnosis analysis is iteratively performed layer by layer and index by index, and the diagnosis result is stored in the diagnosis result library DRDB; S4. Using a bio-pharmaceutical industry diagnosis result calling presenter, the related information in the diagnosis result library DRDB and the diagnosis index library DIDB is read, the bio-pharmaceutical industry development diagnosis result is presented to the user through a human-computer interaction interface, and the diagnosis result of a specified level is displayed according to the user-selected item information.

2. The big data processing method for the development diagnosis of the bio-pharmaceutical industry according to claim 1, characterized in that, Step S1 specifically comprises the following contents: S11. Loading the link address, connection mode, database type, and password interface parameter of the database related to the bio-pharmaceutical industry multi-source data from the system parameter configuration library PCDB; S12. Reading the bio-pharmaceutical industry development diagnosis index information from the diagnosis index library DIDB to form an index data set Z1; S13. Loading the corresponding information of the diagnosis index and the database and its field from the diagnosis index and multi-source data relationship library IMDB according to the index data set Z1; S14. Reading the bio-pharmaceutical industry multi-source data according to the database interface parameter information; S15. Storing the related data read in step S14 into the bio-diagnosis database DDDB.

3. The big data processing method for the development diagnosis of the bio-pharmaceutical industry according to claim 2, characterized in that, The database related to the bio-pharmaceutical industry multi-source data in step S11 comprises a bio-pharmaceutical innovation subject library, a bio-pharmaceutical technology project library, a bio-pharmaceutical technology achievement library, a bio-pharmaceutical policy library, a bio-pharmaceutical standard library, a patent library, a paper library, and a macroeconomic database.

4. The big data processing method for the development diagnosis of the bio-pharmaceutical industry according to claim 1, wherein, Step S2 specifically comprises the following contents: S21. Loading the standardization specification of the index name, field type, field length, field format, and field unit of the bio-pharmaceutical industry development diagnosis index from the diagnosis index library DIDB to form an index standard specification data set Z2; S22. Setting the total number of records of the index standard specification data set Z2 as i; S23. Judging whether i>0 is true; if yes, jumping to step S24 for execution; if no, ending; S24. According to the index name in the index standard specification data set Z2, read the bio-pharmaceutical industry development diagnosis data from the diagnosis database DDDB in turn; S25. According to the i-th index standardization specification, format the corresponding data in turn; S26. Update the latest data after standardization processing into the diagnosis database DDDB; S27. Execute i-1, and jump to step S23 for execution.

5. The big data processing method for the development diagnosis of the bio-pharmaceutical industry according to claim 1, wherein, Step S3 specifically includes the following contents: S31. Load the index hierarchy, index name, and index operation rule information of the bio-pharmaceutical industry development diagnosis index system from the diagnosis index database DIDB; S32. Set the index hierarchy number as x; S33. Determine whether x>0 is true; if yes, jump to step S34 for execution; if no, end; S34. Read the index name corresponding to the x-th layer, and set the index number as j; S35. Determine whether j>0 is true; if yes, jump to step S36 for execution; if no, jump to step S39 for execution; S36. According to the index name and index operation rule information, read the required corresponding data from the diagnosis database DDDB; S37. Perform operation according to the index operation rule information, and store the operation result into 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 operation according to the operation rule corresponding to the layer, and store the operation result into the corresponding diagnosis result database DRDB; S310. Execute x-1, and jump to step S33 for execution.

6. The big data processing method for the development diagnosis of the bio-pharmaceutical industry according to claim 1, wherein, Step S4 specifically includes the following contents: S41. Construct the bio-pharmaceutical industry development diagnosis interactive interface template M according to the framework structure set by the system, mainly including the diagnosis result visual display area, the diagnosis result list display area, and the diagnosis rule display area; S42. Initialize parameters, read the total number of diagnosis index hierarchies from the diagnosis index database DIDB and set as c, set the current index hierarchy as y=0, and set the index name mc=NULL; S43. According to the interactive interface template M, construct the y+1-th level human-computer interactive interface at the bottom of the current y-th level index hierarchy module; S44. Load the index hierarchy, index name information corresponding to the y+1-th layer under the index name mc from the diagnosis index database DIDB, and form a data set ZM; S45. According to the index name information in the data set ZM, read the corresponding index diagnosis rule information from the diagnosis index database DIDB; S46. Form a data set D1 according to the data set ZM and the index diagnosis rule information; S47. Fill the diagnosis rule display area according to the index name and the index diagnosis rule, and present to the user; S48. According to the data set ZM, read the corresponding diagnosis result numerical information from the diagnosis result database DRDB; S49. Form a data set D2 according to the data set ZM and the diagnosis result numerical information; S410. Fill the diagnostic result list display area with dataset D2 and present to the user for selection, and listen to the information ZB input by the user interaction; at the same time, fill the diagnostic result visualization display area with dataset D2 and present to the user for selection, and listen to the information ZB input by the user interaction; S411. Determine whether ZB=NULL is true; if yes, end; if no, jump to step S412 for execution; S412. According to the information ZB, extract the hierarchical information y and the index name mc information; S413. Determine whether y+1>c is true; if yes, end; if no, jump to step S43 for execution.

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