CN107918600B

CN107918600B - Report development system and method, storage medium and electronic equipment

Info

Publication number: CN107918600B
Application number: CN201711127508.8A
Authority: CN
Inventors: 王超
Original assignee: Taikang Insurance Group Co Ltd
Current assignee: Taikang Insurance Group Co Ltd
Priority date: 2017-11-15
Filing date: 2017-11-15
Publication date: 2021-11-23
Anticipated expiration: 2037-11-15
Also published as: CN107918600A

Abstract

The invention discloses a report development system and method, a storage medium and electronic equipment, and relates to the technical field of data processing. The report development system comprises: the basic data warehouse is used for integrating the data of all the source systems and storing the integrated data; the system comprises a theme module, a basic data warehouse and a source system, wherein the theme module is used for classifying and storing the common data of different source systems according to business themes based on the basic data warehouse; the analysis module is used for defining data indexes aiming at the data stored by the theme module, maintaining the data indexes into a target dimension table, and inquiring and calculating the data based on a Spark architecture; and the report development module is used for developing reports according to the report requirements provided by the user and by combining the subject module and the analysis module. The invention improves the speed of report development.

Description

Report development system and method, storage medium and electronic equipment

Technical Field

The invention relates to the technical field of data processing, in particular to a report development system, a report development method, a storage medium and electronic equipment.

Background

In the big data era, operational analysis plays a crucial role in the development of an enterprise. Particularly in the medical industry, the analysis and processing results of daily operation data of medical institutions play a great role in supporting the decision of managers, and can effectively improve the utilization rate of scarce medical resources.

With the increasing emphasis on the health care industry, the medical industry has been in a rapidly growing situation, and the medical systems in hospitals, especially in third-level hospitals, are dozens or even hundreds, and generate a large amount of business data every day. The service data is characterized by complex and various types, high generation frequency, large data volume and a lot of redundant data. With the development of internet technology, these data areas tend to be highly centralized, large-scale data centers are gradually established, and the demand for professional systems for storing, processing and displaying mass data is increasingly urgent.

In addition, various business departments in the medical institution may put forward various report requirements, and although the background technical service department has tried to deal with the requirements, the background technical service department still cannot better and quickly deal with the requirements of the business departments. Therefore, a professional medical big data analysis system which can rapidly and efficiently meet the business development is urgently needed.

In view of the above, there is a need for a report development system, a report development method, a storage medium, and an electronic device.

It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present invention and therefore may include information that does not constitute prior art known to a person of ordinary skill in the art.

Disclosure of Invention

An object of the present invention is to provide a report development system, a report development method, a storage medium, and an electronic device, which overcome one or more problems due to limitations and disadvantages of the related art, at least to some extent.

According to an aspect of the present invention, there is provided a report development system including:

the basic data warehouse is used for integrating the data of all the source systems and storing the integrated data;

the system comprises a theme module, a basic data warehouse and a source system, wherein the theme module is used for classifying and storing the common data of different source systems according to business themes based on the basic data warehouse;

the analysis module is used for defining data indexes aiming at the data stored by the theme module, maintaining the data indexes into a target dimension table, and inquiring and calculating the data based on a Spark architecture;

and the report development module is used for developing reports according to the report requirements provided by the user and by combining the subject module and the analysis module.

Optionally, the report development module includes:

the demand acquisition unit is used for acquiring report demands of users;

the data type judging unit is used for determining the report data type according to the report requirement;

the common report development unit is used for integrating the data indexes corresponding to the report data to the user self-service development platform when the data type judgment unit judges that the report data type is common data, so that the user can develop the report by himself;

and the individual report development unit is used for synchronously deploying the report requirements to the development terminal and the user terminal when the data type judgment unit judges that the data type of the report is the individual data, so that developers and users can cooperatively develop the report by combining the theme module and the analysis module.

Optionally, the report development module further includes:

and the data type conversion unit is used for converting the individual data into common data when the occurrence frequency of the individual data is greater than a threshold value.

Optionally, the analysis module comprises:

the Spark calculation unit is used for receiving the query statement, analyzing the query statement to generate a group of RDDs, judging whether the query statement has a sequence instruction or not, and if the sequence instruction does not exist, executing tasks corresponding to the RDDs in parallel and outputting an execution result; and if the sequential instructions exist, executing the tasks corresponding to the RDDs based on the index sequence of the tasks corresponding to the RDDs, and outputting the execution result according to the index sequence.

According to one aspect of the invention, a report development method is provided, which comprises the following steps:

integrating the data of each source system through a basic data warehouse and storing the integrated data;

classifying and storing the common data of different source systems according to business topics based on a basic data warehouse;

defining data indexes of data which are classified and stored according to business topics, maintaining the data indexes into a target dimension table, and inquiring and calculating the data based on a Spark architecture;

and carrying out report development according to report requirements provided by users and by combining data stored according to business subject classification and Spark architecture.

Optionally, the report development according to the report requirement provided by the user and in combination with the data stored in a classified manner according to the business theme and the Spark architecture includes:

acquiring report requirements of a user;

determining the report data type according to the report requirement;

when the report data type is common data, integrating data indexes corresponding to the report data to a user self-service development platform for a user to develop a report by himself;

and when the report data type is the individual data, synchronously deploying the report requirements to the development terminal and the user terminal so that developers and users can cooperatively develop the report by utilizing the data which are classified and stored according to the business theme and the Spark framework.

Optionally, the report development method further includes:

and when the occurrence frequency of the personality data is greater than a threshold value, the personality data is converted into common data.

Optionally, the data query and calculation based on Spark architecture includes:

receiving a query statement and analyzing the query statement to generate a set of RDDs;

judging whether a sequence instruction exists in the query statement or not;

if no sequential instruction exists, executing the tasks corresponding to the RDDs in parallel and outputting an execution result;

and if the sequential instructions exist, executing the tasks corresponding to the RDDs based on the index sequence of the tasks corresponding to the RDDs, and outputting the execution result according to the index sequence.

According to an aspect of the present invention, there is provided a storage medium having stored thereon a computer program which, when executed by a processor, implements the report development method of any of the above.

According to an aspect of the present invention, there is provided an electronic apparatus including:

a processor; and

a memory for storing executable instructions of the processor;

wherein the processor is configured to execute any one of the report development methods described above via execution of the executable instructions.

In the technical solutions provided by some embodiments of the present invention, on one hand, data is integrated and stored by the base data warehouse, so that data collision and redundancy are avoided, and the data access speed can be increased; on the other hand, the data is classified and stored according to the service theme through the theme module, so that the influence of frequent requirement change on the system architecture can be avoided, and the stability of data logic is ensured; on the other hand, in the analysis module, data indexes are defined for data, data standards are standardized, the consistency of the data can be ensured, and in addition, the data query and calculation are carried out through a Spark architecture, so that the response speed of the data query and calculation is greatly improved; in another aspect, in the report development module, report development can be performed according to the report requirements of the user, so that the flexibility of the development process is increased, and the development resources are saved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:

FIG. 1 illustrates an architectural diagram of a data warehouse of some current technology;

FIG. 2 schematically illustrates a block diagram of a report development system according to an exemplary embodiment of the present invention;

FIG. 3 schematically illustrates a block diagram of an analysis module according to an exemplary embodiment of the present invention;

FIG. 4 schematically illustrates a block diagram of a report development module according to an exemplary embodiment of the present invention;

FIG. 5 schematically illustrates a block diagram of another report development module, according to an exemplary embodiment of the present invention;

FIG. 6 schematically illustrates a flow chart of a report development method according to an exemplary embodiment of the present invention;

FIG. 7 schematically illustrates a flow diagram for data query and computation based on a Spark framework in accordance with an exemplary embodiment of the present invention;

FIG. 8 schematically illustrates a flow diagram of a report development process in accordance with an exemplary embodiment of the present invention;

FIG. 9 schematically illustrates a block diagram of an embodiment of a report development architecture, according to an exemplary embodiment of the present invention;

FIG. 10 illustrates a schematic diagram of a storage medium according to an exemplary embodiment of the present invention; and

fig. 11 schematically shows a block diagram of an electronic device according to an exemplary embodiment of the present invention.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.

The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the steps. For example, some steps may be decomposed, and some steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.

The following will describe the content of the present invention by taking report development in a medical system as an example, however, it should be understood that the report development system and method disclosed in the present invention can also be applied to the processes of report development and data query analysis in other fields, which is not particularly limited in the present exemplary embodiment.

In some technologies, medical data is typically stored and analyzed using data warehousing techniques. Currently, data warehouse technology generally adopts a three-layer data structure, specifically, the three-layer data structure may include a business source system, a data warehouse, and an application marketplace. As shown in FIG. 1, in one aspect, the service source system can include, but is not limited to, service source system data 111, service source system data 112, service source system data 113, and service source system data 114, which typically require manual maintenance; on the other hand, data in the business source system may be acquired by data warehouse 120, and data warehouse 120 may perform unified integration on the acquired data, which may be a theme-oriented, integrated, relatively stable data set reflecting historical changes. Through the data warehouse 120, the process that data need to go to different source systems for collection when performing joint analysis can be omitted; in yet another aspect, data warehouses 120 may send their own stored data to

application marts

131, 132, and/or 133 in order to specifically apply the data.

Currently, the traditional transactional data warehouse is usually replaced by an HBase database constructed by a Hadoop (distributed system architecture) platform. The HBase database supports storage analysis of mass data and has good expansibility, and a data analysis and display module can be established on the basis of the HBase database to form an operation statement analysis system. However, the integration, analysis and presentation of medical data still presents the following problems: on one hand, the data warehouse directly exposes the business data to the business reporting system for data analysis, so that the coupling degree of the business data and the display data is high, the data dependency between internal modules of the business reporting system is strong, the calculated data indexes cannot be reused, and the interaction efficiency of the system is reduced; on the other hand, in the process of report display, a user usually expects to obtain a quick response when a report is opened, especially at a mobile end, so that the calculation efficiency is a problem which cannot be ignored in the face of mass data, but the current management report system lacks a special processing calculation module or the efficiency of the calculation module is low; on the other hand, the development of the medical industry is rapid, the operation condition of the enterprise is variable, the business department may be in a hurry to obtain a certain type of report, the current report development mode cannot meet the requirements of the business department, and the time point of the best requirements of the business department may be missed after the technical department completes the report development.

In view of this, the present invention provides a report development system.

FIG. 2 schematically illustrates a block diagram of a report development system in accordance with an exemplary embodiment of the present invention. Referring to fig. 2, the report development system 2 may include a base data warehouse 21, a topic module 23, an analysis module 25, and a report development module 27. Each module in the report development system 2 will be described in detail below.

The basic data warehouse 21 may be configured to integrate data of each source system and store the integrated data.

Stored in each source system is flow data in the process of business development, which may include, but is not limited to, registration, visit, prescription, payment, and drug delivery, taking medical system as an example. In addition to HIS (Hospital Information System), the user may use the physical examination System and/or the financial System at the same time, and there may be a problem of data coding duplication and even collision between these systems.

In an exemplary embodiment of the invention, the base data warehouse 21 may extract data from each source system according to a data model, organize the data according to a third paradigm modeling rule, and then filter and integrate according to data characteristics. The data model may be a model related to actual business data, which is set by a developer, and the data features correspond to the data model. The processing solves the problem of data conflict, the stored data is not increased at the moment, only the storage form is changed, and the storage space is not required to be additionally occupied.

The basic data warehouse 21 can be constructed based on a Hadoop platform, and data subjected to data model integration can be stored through an HBase database. In addition, the MongoDB database can be integrated. Specifically, aiming at the characteristics of business type data, a corresponding data filtering criterion is formulated in advance, the quality of the data is improved, and source system data is converted and stored in an HBase database; aiming at non-relational data, the data can be stored in a MongoDB database, a data structure does not need to be created in advance, rich query can be supported, an index can be created, and an efficient query plan is correspondingly generated.

Data are integrated and stored through the basic data warehouse, data conflict and redundancy are avoided, and data access speed can be improved.

The topic module 23 may be configured to store the common data of different source systems in a classified manner according to business topics based on the basic data warehouse 21.

For medical systems, business topics may include, but are not limited to, customer topics, performance topics, operational topics, gross disease prediction topics, public topics, and the like. The topic module 23 can extract common data of different source systems based on the underlying data warehouse 21, and combine tables or some fields in tables belonging to the same analysis topic in the same data source to provide basic elements for further visualization operation. The business data is stored through the business theme instead of being organized through application or application requirements, and the method has the advantages that the stability of data logic is guaranteed, and even frequent or subversive requirements change, the system architecture is not affected.

It should be understood that the underlying data warehouse 21 differs from the topic module 23 in data storage in that: on one hand, the basic data warehouse 21 is responsible for effectively classifying and storing data of each service source system, and the topic module 23 integrates common data of different source systems on the basis of the basic data warehouse 21 to establish a standard and solve the problems of data repeated processing and data isolated island; on the other hand, the basic data warehouse 21 correspondingly creates database storage for the source system data, and the topic module 23 needs to centrally create different topics through a complete system according to business requirement documents or system architecture design and the like, and perform synchronous processing and deployment; on the other hand, the basic data warehouse 21 and the topic module 23 are relatively isolated in physical storage, the basic data warehouse 21 stores complete data of each source system, the topic module 23 stores common data derived in the business development process, and the common data and the topic module are stored independently, so that data redundancy is avoided, and data access speed is provided.

In addition, the topic module 23 can include, but is not limited to, business objects, business attributes, filters, business topic table relationships, and the like. The business object is a basic element forming a business theme, the business object can be nested with the business object, and except the first-level business object, other business objects can establish new business sub-objects; the service attribute is a basic component element of the service object, is equivalent to a field in a table, and can be newly created, edited and deleted in a service subject equipment area; the filter is a service attribute expression which can be a four-arithmetic operation or SQL expression; and aiming at the relation of the business theme table, forming a theme mart with a logical relation finally through table relation setting according to the fact that the system options have a global relation and a local table relation.

And the analysis module 25 may be configured to define a data index for the data stored in the topic module, maintain the data index in the target dimension table, and perform data query and calculation based on the Spark architecture.

The medical field has various data types and strong data name speciality, and has the condition that a plurality of data meanings are relatively close to each other, so that a data standard needs to be established and the data specification needs to be unified in order to ensure that a manager, a decision maker and front-line business personnel have the same problem.

Analysis module 25 may establish an index definition. Specifically, data indexes may be defined for the data stored in the topic module 23, and still taking the medical system as an example, the data indexes may include, but are not limited to, an index name, an index level, a calculation frequency, an expression, a metering unit, and the like. Subsequently, the data indexes may be maintained in a target dimension table, which may be located in the storage space of the analysis module 25 or in other storage spaces, which is not limited in this exemplary embodiment. In addition, the target dimension table can be subjected to authority control, that is, only personnel with corresponding authority can manage the target dimension table.

By defining data indexes for the data and maintaining the data indexes in the target dimension table, the data standard is standardized, and the consistency of the data can be ensured.

In addition, a MapReduce computing framework provided by Hadoop can analyze mass data, but the processing mode still has the problems of low response speed and incapability of better meeting the requirements of current users. In view of this, referring to fig. 3, the analysis module 25 of the present invention may further include a Spark calculation unit 301.

Specifically, the Spark calculation unit 301 may be configured to receive a query statement and analyze the query statement to generate a set of RDDs (flexible Distributed datasets), determine whether a sequence instruction exists in the query statement, and if the sequence instruction does not exist, execute tasks corresponding to the RDDs in parallel and output an execution result; and if the sequential instructions exist, executing the tasks corresponding to the RDDs based on the index sequence of the tasks corresponding to the RDDs, and outputting the execution result according to the index sequence.

Data query and calculation are carried out through the Spark architecture, and response speed of the data query and calculation is greatly improved.

The report development module 27 may be configured to perform report development according to report requirements provided by the user and by combining the topic module 23 and the analysis module 25.

Referring to fig. 4, the report development module 27 may include a requirement obtaining unit 401, a data type determining unit 403, a common report development unit 405, and an individual report development unit 407, where:

a requirement obtaining unit 401, configured to obtain a report requirement of a user;

the data type determining unit 403 may be configured to determine a report data type according to the report requirement;

the common report development unit 405 may be configured to, when the data type determination unit determines that the report data type is common data, integrate the data index corresponding to the report data onto the user self-service development platform so that the user can develop the report by himself;

the personalized report development unit 407 is configured to, when the data type determination unit determines that the report data type is the personalized data, synchronously deploy the report requirement to the development terminal and the user terminal, so that the developer and the user cooperatively develop the report by combining the theme module and the analysis module.

For the common report development unit 405, since the topic module 23 has classified and stored the common data, and the analysis module 25 establishes the corresponding index definition and calculates the common data through the Spark module, the user can quickly develop the required report by simply screening the data on the self-service platform developed by the developer. In addition, according to the specific using process of the user, on the basis of the semantic model, flexible self-service query of data can be carried out, the data can be analyzed by slicing, drilling, crossing, folding, early warning, sequencing and the like, the user does not need to have professional technology of report development, and the report development can be completed as long as the business requirements are known.

The specific process can include: firstly, data source connection and setting can be carried out, and the data source connection, table adding, semantic layer definition and table relation view are mainly included; subsequently, a business topic can be created; then, a combined analysis and a perspective analysis can be created, the combined analysis can be created only by selecting the service data and the analysis mode required by the user on the interface, and multidimensional analysis operations such as drilling-up, drilling-down, slicing and the like are selected on the basis of the combined analysis to form the perspective analysis; and then, publishing and browsing can be carried out, after the report is created, the report can be published to different browsing terminals in a resource publishing manner by selecting a computer theme, a tablet theme, a mobile phone theme and the like, and resource authorization can be carried out while publishing, so that different permissions are allocated for different users, and the data security is further improved.

Aiming at the individual report development unit 407, the individual report development unit is developed by a developer, a user is matched to confirm and test and verify the requirements, the developer can perform map analysis, instrument analysis, spreadsheet analysis and the like in a joint development module according to the diversified requirements of the user, and the requirements of the user are quickly developed and iterated on the basis of a basic data warehouse 21 by combining a theme module 23 and an analysis module 25.

Referring to fig. 5, compared to the report development module 27, the report development module 51 may further include a data type conversion unit 501 in addition to the requirement acquisition unit 401, the data type determination unit 403, the common report development unit 405, and the individual report development unit 407, where:

the data type converting unit 501 may be configured to convert the personality data into common data when the occurrence frequency of the personality data is greater than a threshold. The threshold value may be set by a developer according to actual business conditions, for example, the threshold value may be 10 times a day, that is, if one sex data appears 10 times a day, the personality data may be converted into common sex data. Therefore, the individual report development unit 407 can be switched to the common report development unit 405, and the user can develop the report by himself.

In the report development system of the exemplary embodiment of the invention, on one hand, data are integrated and stored through the basic data warehouse, so that data conflict and redundancy are avoided, and the data access speed can be improved; on the other hand, the data is classified and stored according to the service theme through the theme module, so that the influence of frequent requirement change on the system architecture can be avoided, and the stability of data logic is ensured; on the other hand, in the analysis module, data indexes are defined for data, data standards are standardized, the consistency of the data can be ensured, and in addition, the data query and calculation are carried out through a Spark architecture, so that the response speed of the data query and calculation is greatly improved; in another aspect, in the report development module, report development can be performed according to the report requirements of the user, so that the flexibility of the development process can be increased, and the development resources can be saved.

Further, the present exemplary embodiment also provides a report development method.

FIG. 6 schematically illustrates a flowchart of a report development method according to an exemplary embodiment of the present invention. Referring to fig. 6, the report development method of the present invention may include the steps of:

and S60, integrating the data of each source system through a basic data warehouse and storing the integrated data.

In an exemplary embodiment of the invention, the base data warehouse may extract data from the source systems according to a data model and organize the data according to a third paradigm modeling rule. Then, filtering and integrating are carried out according to the data characteristics corresponding to the data model, and the integrated data are stored. In addition, the base data warehouse may be built based on a Hadoop platform, and the integrated data may be stored to an HBase database or a MongoDB database.

And S62, classifying and storing the common data of different source systems according to business topics based on a basic data warehouse.

The common data of different source systems can be extracted based on the basic data warehouse, tables belonging to the same analysis subject in the same data source or some fields in the tables are combined together, and basic elements are provided for further visualization operation. The business data is stored through the business theme instead of being organized through application or application requirements, and the method has the advantages that the stability of data logic is guaranteed, and even frequent or subversive requirements change, the system architecture is not affected.

And S64, defining data indexes of the data which are classified and stored according to the business theme, maintaining the data indexes into a target dimension table, and inquiring and calculating the data based on a Spark architecture.

Further, referring to fig. 7, a process of data query and calculation based on Spark architecture of the present invention is illustrated.

In step S701, the user may customize the SQL query statement according to the user' S own needs; in step S703, the SQL query statement may be input into a module corresponding to the Spark architecture; in step S705, the data input interface of the Spark application may be called to send the SQL query statement to the Spark application; in step S707, Spark may parse the SQL query statement to generate a group of RDD sequences, and when generating the group of RDDs, an index table is correspondingly generated to store each Job and its index value, where each RDD corresponds to one Job and represents one operation on data, and after all the jobs are completed, a complete query can be completed; in step S709, jobs are generated, submitted to eventprocessor (an event handler executor), and the Spark interface is called to submit jobs, one for each Task. In step S711, performing syntax analysis to determine whether the SOL query statement submitting Job has Order By (i.e., the above mentioned sequential instruction), if yes, jumping to step S713, and if not, jumping to step S723; in step S713, it is necessary to determine an index value corresponding to the Task; in step S715, according to the determined index values, if the index values are different, the tasks are executed in order from small to large, and if the index values are the same, the tasks are executed in parallel; in step S717, it is determined whether the last executed Task corresponding index value is the next bit of the output result index value, if yes, the step jumps to step S727, and if not, the step jumps to step S719; in step S719, the index value may be temporarily stored in the memory; in step S721, if the output index value is arranged to the index value corresponding to the Task in the memory, the step jumps to step S727; in step S727, directly outputting to the SQL output module of Spark; in addition, in step S723, the respective tasks may be executed in parallel; in step S725, after each Task is completed, notify the Context of Spark; in step S729, the query results may be fed back to the user in succession; in step S731, after all tasks are output, the whole query is finished.

Through the process, the response speed of the query can be greatly improved. In addition, when the SQL query customized by the user is a simple unordered query, huge data volume can be divided into a plurality of tasks to be executed in parallel, the tasks can be immediately output when the condition is met, if the SQL query is a complex ordered query, the SQL query can be output in sequence according to the index value table generated when Job is decomposed, the operation of outputting after all the tasks are finished is not required, data overflow is avoided, and the query response speed can be improved.

And S66, carrying out report development according to report requirements provided by the user and by combining data stored according to the business theme in a classified mode and a Spark framework.

Specifically, firstly, the report requirements of the user can be obtained; then, the report data type can be determined according to the report requirement; and then, when the report data type is judged to be common data, integrating the data indexes corresponding to the report data to a user self-service development platform for a user to develop a report by himself, and when the report data type is judged to be individual data, synchronously deploying report requirements to a development terminal and a user terminal for developers and users to cooperatively develop the report by utilizing the data stored according to the business theme in a classified mode and the Spark framework.

In addition, when the frequency of occurrence of the personality data is greater than a threshold, the personality data may be converted into commonality data. The threshold may be a frequency threshold set by a developer according to historical data or experience, and is not particularly limited in this exemplary embodiment.

The process of report development is explained with reference to fig. 8. In step S801, a report requirement may be obtained; in step S803, the report data type may be determined, and if the report data type is the individual data, the step may jump to step S807, and if the report data type is the common data, the step may jump to step S805; in step S805, self-development may be performed through simple screening (in operation, screening may be implemented in a pulling manner) according to a target dimension table specified by a developer; in step S807, the user may cooperate with the developer to develop and test cooperatively; in step S809, a process of authorizing the report resource may be performed; in step S811, the report resource can be published simultaneously by the PC side and the mobile side. In addition, when the appearance frequency of the personality data is greater than a threshold value, the personality data can be converted into common data.

In the report development method of the exemplary embodiment of the invention, on one hand, data are integrated and stored through the basic data warehouse, so that data conflict and redundancy are avoided, and the data access speed can be improved; on the other hand, the data is classified and stored according to the business theme, so that the influence of frequent demand change on the system architecture can be avoided, and the stability of data logic is ensured; on the other hand, data indexes are defined for the data, data standards are standardized, the consistency of the data can be ensured, and in addition, the response speed of data query and calculation is greatly improved by performing data query and calculation through a Spark architecture; in another aspect, report development can be performed according to report requirements of users, flexibility of a development process is improved, and development resources are saved.

It should be noted that although the various steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that these steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.

FIG. 9 schematically illustrates a block diagram of an embodiment of a report development architecture according to an exemplary embodiment of the present invention. This embodiment is exemplified by a medical system, wherein the architecture may include a unified management platform 90, a source system 91, an Enterprise Data Warehouse (EDW)92, a development platform 93, an application platform 94, a terminal 95, a user 96, and the like. Specifically, the source System 91 may include HIS, EMR (Electronic Medical Record), LIS (Laboratory Information System), PACS (Picture Archiving and Communication System), and the like in the Medical System; the enterprise data warehouse 92 may include a technical interface layer, a standard data layer, a topic module and an analysis module, wherein the topic module may include a data integration layer and a common summary layer, and the analysis module may include a business logic unit and a data calculation unit; the development platform 93 may include a self-development module, a joint development module, an authorization module, and a publishing module; the application platform 94 may include enterprise reports, cockpit, business analytics, etc.; the terminal 95 may include a PC, a cell phone, and other mobile terminals; the user 96 may include a decision layer, a management layer, and a business layer, among others.

According to an embodiment of the invention, aiming at a secondary rehabilitation hospital, firstly, business system data such as HIS, LIS, PACS, EMR, rehabilitation system, long-term care service system and the like in the rehabilitation hospital can be integrated into a data warehouse, and the data volume can reach TB level; next, different theme sub-modules such as outpatient service, hospitalization, physical examination, rehabilitation, long-term care and the like are established on a data warehouse according to the service range and the requirements of a service party in the theme module, more than 500 data indexes are sorted on the basis of the sub-modules to form a target dimension table, and data processing and calculation are performed through a Spark tool. In the embodiment, a total of 15 departments and 100 business personnel develop 94 reports on the report development module, technical personnel develop 39 reports, and the dimensions of the developed report display data are richer than before. In addition, for example, the development of a report needs to spend 4 days of time on demand personnel, designers and developers, and the report development system can complete the development only one day, so that the working efficiency of enterprise staff is improved, the requirement of business development is better supported, and reasonable decision support is conveniently and quickly provided for an enterprise management layer.

In an exemplary embodiment of the present invention, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above section "exemplary methods" of the present description, when said program product is run on the terminal device.

Referring to fig. 10, a program product 1000 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

In an exemplary embodiment of the present invention, there is also provided an electronic device capable of implementing the above method.

As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.

An electronic device 1100 according to this embodiment of the invention is described below with reference to fig. 11. The electronic device 1100 shown in fig. 11 is only an example and should not bring any limitations to the function and the scope of use of the embodiments of the present invention.

As shown in fig. 11, electronic device 1100 is embodied in the form of a general purpose computing device. The components of the electronic device 1100 may include, but are not limited to: the at least one processing unit 1110, the at least one memory unit 1120, a bus 1130 connecting different system components (including the memory unit 1120 and the processing unit 1110), and a display unit 1140.

Wherein the storage unit stores program code that is executable by the processing unit 1110 to cause the processing unit 1110 to perform steps according to various exemplary embodiments of the present invention as described in the above section "exemplary methods" of the present specification. For example, the processing unit 1110 may execute step S60 as shown in fig. 6: integrating the data of each source system through a basic data warehouse and storing the integrated data; step S62: classifying and storing the common data of different source systems according to business topics based on a basic data warehouse; step S64: defining data indexes of data which are classified and stored according to business topics, maintaining the data indexes into a target dimension table, and inquiring and calculating the data based on a Spark architecture; step S66: and carrying out report development according to report requirements provided by users and by combining data stored according to business subject classification and Spark architecture.

The storage unit 1120 may include a readable medium in the form of a volatile memory unit, such as a random access memory unit (RAM)11201 and/or a cache memory unit 11202, and may further include a read only memory unit (ROM) 11203.

Storage unit 1120 may also include a program/utility 11204 having a set (at least one) of program modules 11205, such program modules 11205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 1130 may be representative of one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 1100 may also communicate with one or more external devices 1200 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 1100, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 1100 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 1150. Also, the electronic device 1100 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet) via the network adapter 1160. As shown, the network adapter 1160 communicates with the other modules of the electronic device 1100 over the bus 1130. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 1100, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.

It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is to be limited only by the terms of the appended claims.

Claims

1. A report development system is applied to the field of medical data processing, and comprises:

the basic data warehouse is used for extracting the data of each source system according to a data model to integrate and storing the integrated data;

the theme module is used for classifying and storing the common data of different source systems according to business themes based on the basic data warehouse;

the report development module is used for developing reports according to report requirements provided by users and by combining the subject module and the analysis module;

the report development module comprises:

the demand acquisition unit is used for acquiring report demands of users;

the common report development unit is used for integrating the data indexes corresponding to the report data to a user self-service development platform when the data type judgment unit judges that the report data type is common data, so that a user can develop a report by himself;

and the individual report development unit is used for synchronously deploying the report requirements to a development terminal and a user terminal when the data type judgment unit judges that the data type of the report is individual data, so that a developer and a user can cooperatively develop the report by combining the theme module and the analysis module.

2. The report development system of claim 1, wherein the report development module further comprises:

and the data type conversion unit is used for converting the personal data into common data when the occurrence frequency of the personal data is greater than a threshold value.

3. A report development system according to claim 1, characterized in that said analysis module comprises:

the Spark calculation unit is used for receiving query statements, analyzing the query statements to generate a group of RDDs, judging whether sequence instructions exist in the query statements or not, and if the sequence instructions do not exist, executing tasks corresponding to the RDDs in parallel and outputting execution results; and if the sequence instruction exists, executing the tasks corresponding to the RDDs based on the index sequence of the tasks corresponding to the RDDs, and outputting the execution result according to the index sequence.

4. A report development method is applied to the field of medical data processing, and comprises the following steps:

extracting data of each source system according to a data model through a basic data warehouse for integration and storing the integrated data;

classifying and storing the common data of different source systems according to business topics based on the basic data warehouse;

carrying out report development according to report requirements provided by a user and by combining data stored according to business subject classification and Spark architecture;

the report development according to the report requirement provided by the user and by combining the data classified and stored according to the business theme and the Spark architecture comprises the following steps:

acquiring report requirements of a user;

determining the report data type according to the report requirement;

when the report data type is common data, integrating the data indexes corresponding to the report data to a user self-service development platform for a user to develop a report by himself;

and when the report data type is the individual data, synchronously deploying the report requirements to a development terminal and a user terminal so that developers and users can cooperatively develop the report by utilizing the data which are classified and stored according to the business theme and the Spark framework.

5. The report development method according to claim 4, further comprising:

and when the occurrence frequency of the personality data is greater than a threshold value, converting the personality data into common data.

6. The report development method according to claim 4, wherein the data query and calculation based on Spark architecture comprises:

judging whether a sequence instruction exists in the query statement or not;

and if the sequence instruction exists, executing the tasks corresponding to the RDDs based on the index sequence of the tasks corresponding to the RDDs, and outputting the execution result according to the index sequence.

7. A storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the report development method of any of claims 4 to 6.

8. An electronic device, comprising:

a processor; and

a memory for storing executable instructions of the processor;

wherein the processor is configured to perform the report development method of any of claims 4 to 6 via execution of the executable instructions.