CN111078729B - Medical data tracing method, device, system, storage medium and electronic equipment - Google Patents
Medical data tracing method, device, system, storage medium and electronic equipment Download PDFInfo
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Abstract
The invention relates to a medical data tracing method, a device, a system, a storage medium and electronic equipment, which relate to the technical field of medical large medical data tracing, and the method comprises the following steps: acquiring a first structured query statement, and analyzing the blood-edge relationship of the first structured query statement to obtain the blood-edge relationship; extracting blood margin data from the first structured query sentence to obtain a second structured query sentence; and tracing the medical data to be traced according to the blood relationship and the second structured query statement. The invention improves the efficiency of tracing the medical data to be traced.
Description
Technical Field
The embodiment of the invention relates to the technical field of medical big data processing, in particular to a medical data tracing method, a medical data tracing device, a medical data tracing system, a computer readable storage medium and electronic equipment.
Background
Along with the development of computer and internet technology, it is more and more convenient to build a computer network system, and medical institutions such as hospitals and health care hospitals also utilize the computer network system to carry out informatization management on medical data such as medical records, test results, inspection reports and the like, so that automatic analysis on medical big data can be realized.
In the existing processing link of medical big data analysis, when a data quality control person finds that the data of a certain field in the product application layer data has the problems of unreasonable logic or the data does not meet the quality requirement standard and the like, an engineer needs to check the calculation logic and sample data of all relevant fields in the whole medical data tracing link, so as to find out the cause of the problem and repair the problem existing in the corresponding medical data tracing link.
However, the above scheme has the following drawbacks: because the whole processing link involves a plurality of data and fields, the calculation flow is long, the calculation logic is complex, and the processing flow and business logic change frequently, the tracing flow is complicated, and the tracing efficiency is low.
Therefore, a new medical data tracing method and device are needed to be provided.
It should be noted that the information of the present invention in the above background section is only for enhancing the understanding of the background of the present invention and thus may include information that does not form the prior art that is already known to those of ordinary skill in the art.
Disclosure of Invention
The invention aims to provide a medical data tracing method, a medical data tracing device, a medical data tracing system, a computer readable storage medium and electronic equipment, so that the problem of low tracing efficiency caused by the limitations and defects of related technologies is overcome at least to a certain extent.
According to one aspect of the present disclosure, there is provided a medical data tracing method, including:
acquiring a first structured query statement, and analyzing the blood-edge relationship of the first structured query statement to obtain the blood-edge relationship;
extracting blood margin data from the first structured query sentence to obtain a second structured query sentence;
and tracing the medical data to be traced according to the blood relationship and the second structured query statement.
In one exemplary embodiment of the present disclosure, obtaining the first structured query statement includes:
acquiring basic medical data comprising a field name to be traced, a table name corresponding to the field name to be traced and sample data;
and acquiring a first structured query statement corresponding to the table name according to the basic data.
In one exemplary embodiment of the present disclosure, obtaining the structured query statement corresponding to the table name from the underlying medical data includes:
and acquiring a first structured query statement corresponding to the table name from a data production platform according to the field name to be traced, the table name corresponding to the field name to be traced and the index identifier of the sample data.
In an exemplary embodiment of the present disclosure, performing a blood-edge relationship parsing on the first structured query statement to obtain a blood-edge relationship includes:
analyzing the first structured query statement to obtain a grammar tree, and abstracting each query node and a table name node to be traced in the grammar tree into a query source;
obtaining an output field list of each query source according to the child nodes of each query node, and carrying out recursion analysis on the data source nodes of the child nodes of each query node to obtain child data sources of the child nodes of each query node;
and matching the fields in the output field list of each query source and the child data sources of the child nodes of each query node, and obtaining the blood relationship according to the matching result.
In an exemplary embodiment of the present disclosure, matching fields in the output field list of each query source and child data sources of child nodes of each query node, and obtaining the to-be-blood-edge relationship according to a matching result includes:
performing first association processing on fields in the output field list of each query source with the affiliation and sub-data sources of sub-nodes of each query node to obtain a first association result;
And obtaining the to-be-blood-margin relationship according to each first association result.
In an exemplary embodiment of the present disclosure, performing blood-edge data extraction on the first structured query statement to obtain a second structured query statement includes:
and extracting blood margin data from the first structured query statement to obtain a second structured query statement which comprises source table data corresponding to the sample data and calculation process data from the source table data to the sample data.
In one exemplary embodiment of the present disclosure, extracting blood-edge data from the first structured query statement to obtain a second structured query statement including source table data corresponding to the sample data and calculation process data from the source table data to the sample data includes:
analyzing the first structured query sentence to obtain a grammar tree, and carrying out grammar translation on each grammar node in the grammar tree to obtain a plurality of translation results;
splicing each translation result to obtain a target query statement, and adding splicing function nodes at the tail ends of sub-node expressions corresponding to the sub-nodes of the query node in the target query statement;
And carrying out second association processing on the to-be-traced table name according to the splicing function node, and obtaining second structured query sentences comprising source table data corresponding to the sample data and calculation process data from the source table data to the sample data according to a second association result.
In an exemplary embodiment of the present disclosure, tracing the medical data to be traced according to the blood relationship and the second structured query statement includes:
and generating a data tracing configuration file according to the blood relationship and the second structured query statement, and obtaining data to be traced according to the data tracing configuration file.
In an exemplary embodiment of the present disclosure, obtaining the data to be traced according to the data tracing configuration file includes:
invoking a data calculation engine to calculate the data tracing configuration file to obtain a calculation result;
and screening the calculation result, and obtaining the data to be traced according to the screening result.
In an exemplary embodiment of the present disclosure, the medical data tracing method further includes:
and displaying the data to be traced and the data tracing configuration file so that a manager can locate abnormal data in the data to be traced according to blood relationship in the data tracing configuration file.
According to one aspect of the present disclosure, there is provided a medical data tracing apparatus, comprising:
the blood relationship analysis module is used for acquiring a first structured query statement, and carrying out blood relationship analysis on the first structured query statement to obtain a blood relationship;
the blood margin data extraction module is used for extracting blood margin data from the first structured query statement to obtain a second structured query statement;
and the medical data tracing module is used for tracing the medical data to be traced according to the blood relationship and the second structured query statement.
According to one aspect of the present disclosure, there is provided a medical data tracing system, comprising:
the data production platform is used for generating a first structured query statement;
the medical data tracing platform is connected with the data production platform through a network and is used for acquiring a first structured query statement and analyzing the blood-edge relationship of the first structured query statement to obtain the blood-edge relationship; and
extracting blood margin data from the first structured query sentence to obtain a second structured query sentence; and
and tracing the medical data to be traced according to the blood relationship and the second structured query statement.
In one exemplary embodiment of the present disclosure, the medical data tracing platform includes a blood relationship parser and a blood relationship data extractor;
the blood relationship analyzer is used for analyzing the blood relationship of the first structured query statement to obtain the blood relationship;
the blood margin data extractor is used for extracting blood margin data from the first structured query statement to obtain a second structured query statement.
According to one aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the medical data tracing method of any one of the above.
According to one aspect of the present disclosure, there is provided an electronic device including:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the medical data tracing method of any one of the above via execution of the executable instructions.
According to the medical data tracing method and device provided by the embodiment of the invention, on one hand, the blood-margin relationship is obtained by acquiring the first structured query statement and analyzing the blood-margin relationship of the first structured query statement; then extracting blood margin data from the first structured query sentence to obtain a second structured query sentence; finally tracing the medical data to be traced according to the blood relationship and the second structured query statement, thereby solving the problems of complicated tracing flow due to the fact that the data and fields related to the whole processing link are numerous, the calculation flow is tedious, the calculation logic is complex, and the processing flow and business logic change frequently in the prior art, simplifying the tracing flow of the data, and further improving the tracing efficiency; on the other hand, the blood-edge relationship is obtained by carrying out blood-edge relationship analysis on the first structured query statement; then extracting blood margin data from the first structured query sentence to obtain a second structured query sentence; and finally tracing the medical data to be traced according to the blood relationship and the second structured query statement, thereby improving the accuracy of tracing the medical data to be traced.
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 invention as claimed.
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 evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
Fig. 1 schematically shows a flow chart of a medical data tracing method according to an exemplary embodiment of the invention.
Fig. 2 schematically shows a flowchart of a method for resolving a structured query statement to obtain a blood relationship of a field name to be traced according to an exemplary embodiment of the invention.
FIG. 3 schematically illustrates a method flow diagram for relational extraction of the structured query statement to obtain raw data corresponding to the sample data and computational process data from the raw data to the sample data, according to an example embodiment of the invention.
Fig. 4 schematically shows a block diagram of a medical data tracing system according to an example embodiment of the invention.
Fig. 5 schematically shows a flow chart of another medical data tracing method according to an example embodiment of the invention.
Fig. 6 schematically shows a block diagram of a medical data tracing apparatus according to an exemplary embodiment of the invention.
Fig. 7 schematically illustrates an electronic device for implementing the above-described medical data tracing method 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. However, the exemplary embodiments may be embodied in many 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 the 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 give 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, etc. In other instances, well-known aspects have not been shown or described in detail to avoid obscuring aspects of the invention.
Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof 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 software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
The existing way of locating the medical data problem is: firstly, finding out the name of a problem field to be checked, the table name to which the field belongs and a sample of problem data from a quality control platform; then, according to the name of the table to which the field belongs, an SQL sentence for producing a corresponding table is found on the data production platform; further, analyzing the SQL sentences step by step, writing the SQL sentences for each level of logic, extracting sample data from the database for checking, and finally, finding out the part of which the processing result is inconsistent with the expected effect from the extracted sample data, namely finding out the processing link with the problem.
However, in the above method, on one hand, a large amount of manpower is required, so that a large amount of resource waste is caused; on the other hand, SQL statements need to be written for each level of logic to draw sample data from the database for verification, which results in a problem of low verification rate.
In this example embodiment, a medical data tracing method is provided first, where the method may operate on a server, a server cluster, a cloud server, or the like, or may also operate on a terminal device; of course, those skilled in the art may also operate the method of the present invention on other platforms as required, and this is not a particular limitation in the present exemplary embodiment. Referring to fig. 1, the medical data tracing method may include the steps of:
s110, acquiring a first structured query statement, and analyzing the blood-edge relationship of the first structured query statement to obtain the blood-edge relationship.
And S120, extracting blood-edge data of the first structured query statement to obtain a second structured query statement.
And S130, tracing the medical data to be traced according to the blood relationship and the second structured query statement.
In the medical data tracing method, on one hand, the blood relationship is obtained by acquiring a first structured query statement and analyzing the blood relationship of the first structured query statement; then extracting blood margin data from the first structured query sentence to obtain a second structured query sentence; finally tracing the medical data to be traced according to the blood relationship and the second structured query statement, thereby solving the problems of complicated tracing flow due to the fact that the data and fields related to the whole processing link are numerous, the calculation flow is tedious, the calculation logic is complex, and the processing flow and business logic change frequently in the prior art, simplifying the tracing flow of the data, and further improving the tracing efficiency; on the other hand, the blood-edge relationship is obtained by carrying out blood-edge relationship analysis on the first structured query statement; then extracting blood margin data from the first structured query sentence to obtain a second structured query sentence; and finally tracing the medical data to be traced according to the blood relationship and the second structured query statement, thereby improving the accuracy of tracing the medical data to be traced.
The steps involved in the medical data tracing method according to the exemplary embodiment of the present invention will be explained and described in detail with reference to the accompanying drawings.
First, the related terms mentioned in the exemplary embodiment of the present invention are explained as follows:
medical information system: the electronic computer and the communication equipment are utilized to provide the capability of collecting, storing, processing, extracting and exchanging data of diagnosis and treatment information and administrative management information of patients for all departments belonging to the hospital, and meet the functional requirements of all authorized users.
Primary medical data: medical information systems produce and accumulate data during daily operations.
Application layer data: after complex calculation and processing for many times, the structure is complex and changeable, and the data used for complex scene services such as actual scientific research and management are oriented.
Medical data trace-source link: the whole processing link is formed by connecting a plurality of processing links in series or in parallel.
SQL: the structured query language (Structured Query Language) is called SQL for short, is a special purpose programming language, is a database query and programming language, is applied to each processing link of a medical data traceability link, is an important carrier of processing logic, and allows nesting of SQL sentences, namely, multi-level processing logic can exist in one processing link; SQL statements contain functions such as add, modify, delete, query, etc., and the SQL statements referred to herein are used as query functions (typically, data that is output as a new table and presents the new table after one or more tables are joined, linked, filtered, sub-queried, etc.).
SQL parser: the SQL statement is parsed into a logical structure of a tree of grammar nodes (hereinafter referred to as a grammar tree), each node on the tree representing an operation/computation logic or an indication/constant of the operation/computation or a sub-grammar tree.
Blood relationship resolver: for a given SQL statement, according to the logical structure of SQL, the original table, the original field and the computing process (the source table, the source field and the computing process are collectively called as blood relationship) of each field source of the new table output by SQL are analyzed.
Blood margin data extractor: for a given SQL sentence, a new SQL sentence is reorganized by adding marks or operations on specific nodes according to a grammar tree, and the execution effect of the new SQL sentence is that a field is newly added in the finally output data of each row, the content of the field comprises the whole row records corresponding to all source tables of the data, and if the given SQL comprises sub-queries, the output of the sub-queries is also contained in the field.
The invention takes a quality control platform as a starting point, performs task scheduling and distribution through a task scheduling system and performs data transmission among different systems, takes a production platform as a production task management/data display tool, analyzes SQL sentences through a blood relationship analyzer and a blood relationship data extractor based on SQL analysis, generates corresponding data traceability configuration files, uses a data calculation engine to perform traceability data extraction in a distributed storage system according to data traceability configuration, and returns the extracted data to the production platform to display to engineers so as to help the engineers to improve the efficiency of finding problems.
In step S110, a first structured query statement is obtained, and a blood-edge relationship is resolved for the first structured query statement to obtain a blood-edge relationship.
In this example embodiment, first, basic data information including a field name to be traced (field name to be traced), a table name corresponding to the field name to be traced, and sample data (a sample of problem data) is acquired from a quality control platform, and then a first structured query statement corresponding to the table name is acquired according to the basic data information. Specifically, a first structured query statement corresponding to the table name may be obtained from a data production platform according to the field name to be traced, the table name corresponding to the field name to be traced, and the index identifier of the sample data. Compared with the prior art that the SQL statement for producing the corresponding table is found on the data production platform directly through the table name, the method can reduce the redundancy degree of the SQL statement, reduce the data quantity of the SQL statement and further improve the medical data tracing efficiency.
And then, carrying out blood relationship analysis on the first structured query sentence to obtain the blood relationship. Specifically, referring to fig. 2, the step of parsing the blood-edge relationship of the first structured query term to obtain the blood-edge relationship may include step S210 to step S230, which will be described in detail below.
In step S210, the first structured query sentence is parsed to obtain a syntax tree, and each query node and the table name node to be traced in the syntax tree are abstracted to form a query source.
In step S220, an output field list of each query source is obtained according to the child node of each query node, and a data source node of the child node of each query node is recursively parsed to obtain a child data source of the child node of each query node.
In step S230, the fields in the output field list of each query source and the child data sources of the child nodes of each query node are matched, and the blood-edge relationship is obtained according to the matching result.
In this exemplary embodiment, first, a first association process is performed on fields in an output field list of each query source having a dependency relationship and child data sources of child nodes of each query node to obtain a first association result; and secondly, obtaining the blood relationship according to each first association result.
Hereinafter, the steps S210 to S230 and the steps related thereto will be explained and described.
Firstly, an input SQL sentence (a first structured query sentence) is parsed into a grammar tree through an SQL parser; secondly, recursively traversing a grammar tree by a depth-first algorithm, abstracting each Query (Query node) and Table (Table name node) into a Query source object, abstracting a sub-node Select (expression set node of a Query output field) of the Query into a field list as a column list (output field list) of the Query source, recursively analyzing a sub-node From (data source node) of the Query, and adding the Query or Table in the sub-node to a sub-source list of the current Query source; further, after the Query node is traversed, matching the field of the QuerySource abstracted by the Query node with the data source, namely traversing ColumnList and SubSourceList of the QuerySource and associating the field with the sub data source; and finally, traversing all the nodes, and outputting QuerySource on the root node to obtain all the blood relationship. It should be noted here that, for other types of nodes, the nodes may be translated or ignored as needed, without any deliberate processing. By this method, the processing speed can be increased.
Further, an example of the data structure of the query source object may be as follows:
data source object, abstract out table and query
public class QuerySource{
List<QuerySource>subSources＝new LinkedList<QuerySource>()；
List<Column>columns＝new ArrayList<Column>()；
SourceType type；
Alias name of/table name or sub-query
String name；
V/alias
String alias；
}
In addition, an example of the data structure of the Column object may be as follows:
public class Column{
String content；
String alias；
List<Column>sourceClos；
List<String>functions；
QuerySource table；
}
in step S120, blood-edge data extraction is performed on the first structured query term, so as to obtain a second structured query term.
In this example embodiment, the first structured query statement may be subjected to blood-edge data extraction to obtain a second structured query statement including source table data corresponding to the sample data and calculation process data from the source table data to the sample data.
Specifically, referring to fig. 3, extracting blood-edge data from the first structured query term to obtain a second structured query term including source table data corresponding to the sample data and calculation process data from the source table data to the sample data may include steps S310 to S330, which are described in detail below.
In step S310, the first structured query sentence is parsed to obtain a syntax tree, and each syntax node in the syntax tree is subjected to syntax translation to obtain a plurality of translation results.
In step S320, each translation result is spliced to obtain a target query statement, and a splicing function node is added at the end of a child node expression corresponding to a child node of a query node in the target query statement.
In step S330, a second association process is performed on the table name to be traced according to the splicing function node, and a second structured query statement including source table data corresponding to the sample data and calculation process data from the source table data to the sample data is obtained according to a second association result.
Hereinafter, step S310 to step S330 will be explained and explained. Firstly, an input SQL sentence is parsed into a grammar tree through an SQL parser by a blood-margin data extractor; the SQL parser may be, for example, a parsetriver of Hive, a self-developed parser with a parsing function, or the like, which is not limited in this example. Then recursively traversing the grammar tree by a depth-first algorithm, translating corresponding grammar for each grammar node, and after traversing all nodes, splicing translated results of all nodes to reproduce SQL (target query statement); further, a splicing function node is added at last to the Select expression node, and parameters of the splicing function node are full-field references and customizable separators of all tables after a From node at the same level as the Select node. Specific examples may be as follows:
If the original SQL statement (first structured query statement) is:
“select t1,t2 from table1 left join table2 on table1.col1＝table2.col1”
the new SQL statement (second structured query statement) obtained after conversion is:
“selectt1,t2,concat(”concat_start_tag”,table1.*,”table_split_tag”,table2.*,”concat_end_tag”)from table1 left join table2 on table1.col1＝table2.col1”。
it should be noted here that, for a given SQL statement, some marks or operations (splicing function nodes) are added to specific nodes according to its syntax tree, so as to reconstruct and construct a new SQL statement, in the execution effect of the new SQL statement (target query statement), a field is newly added to the data finally output by each line, the content of this field contains the whole line record corresponding to all source tables (original data/native data) of this data, and if a given SQL contains a sub-query, the output of the sub-query is also contained in this field. And, the implementation method of the splicing function is not limited. By the method, the acquisition speed of the original data and the calculation process data can be improved.
In step S130, tracing the medical data to be traced according to the blood relationship and the second structured query statement.
In this example embodiment, first, a data tracing configuration file is generated according to the blood relationship and the second structured query statement, and data to be traced is obtained according to the data tracing configuration file. The obtaining the data to be traced according to the data tracing configuration file may include: firstly, calling a data calculation engine to calculate the data tracing configuration file to obtain a calculation result; and secondly, screening the calculation result, and obtaining the data to be traced according to the screening result.
In detail, firstly, a data tracing configuration file can be generated according to the basic data information, the blood relationship of the field names to be traced, the original data and the calculation process data, and then the data to be traced is obtained according to the data tracing configuration file. Specifically, firstly, a data calculation engine is called to calculate the data traceability configuration file to obtain a calculation result; and secondly, screening the calculation result, and obtaining the data to be traced according to the screening result. For example, the data tracing configuration file is called by the data calculation engine to calculate and filter, and then the required tracing data is extracted from the filtering result. By the method, the extraction efficiency of the data to be traced can be improved, and meanwhile, the accuracy of the extracted data to be traced can be improved, so that the accuracy of positioning the abnormal data is improved.
Further, in order to facilitate the positioning of the abnormal data, the medical data tracing method further includes: and displaying the data to be traced and the data tracing configuration file so that a manager can position the abnormal data in the data to be traced according to the blood relationship of the field names to be traced in the data tracing configuration file.
By the method, the problem that in the prior art, when searching the problem, whether calculation logic and sample data of all relevant fields meet expectations or not is needed to be checked, so that the workload is high, the execution of a data extraction task is needed to be waited for a long time, and further the processing efficiency is low is solved, so that a manager can position abnormal data in the to-be-traced data according to the blood relationship of field names in the data tracing configuration file, the accuracy of positioning the abnormal data is improved, and meanwhile, the efficiency of positioning the abnormal data is also improved.
The example embodiment of the invention also provides a medical data tracing system. Referring to fig. 4, the medical data tracing system may include a data production platform 420 and a medical data tracing platform 430. Wherein:
a data production platform 420 for generating a first structured query statement;
the medical data tracing platform 430 is connected with the data production platform 420 in a network manner, and is used for acquiring a first structured query statement, and analyzing the blood-edge relationship of the first structured query statement to obtain the blood-edge relationship; extracting blood relationship from the first structured query sentence to obtain a second structured query sentence; and tracing the medical data to be traced according to the blood relationship and the second structured query statement.
Further, the medical data tracing system may further include a data quality control platform 410, which may be connected to the medical data tracing platform 430 through a network, and configured to generate basic data information including a table name to be traced, a field name corresponding to the table name to be traced, and sample data.
Further, the medical data traceability platform 430 may further include a blood relationship analyzer 431 and a blood relationship data extractor 432;
the blood relationship analyzer 431 is configured to analyze the blood relationship of the first structured query term to obtain a blood relationship;
the blood edge data extractor 432 is configured to extract blood edge data from the first structured query term to obtain a second structured query term.
Further, the medical data tracing platform 430 may further include a task scheduling system 433 and a distributed storage system 434, where the task scheduling system 433 may be configured to distribute a data tracing configuration file to the distributed storage system 434, and call the computing engine 435 on the distributed storage system 434 to obtain data to be traced according to the data tracing configuration file.
Finally, the task scheduling system 433 pushes the data to be traced and the data tracing configuration file to the data production platform 420 for visual display.
It should be noted here that the above-mentioned distributed storage system may be, for example, hadoop or Hbase, etc., the task scheduling system may be, for example, azkaban or Oozie, etc., and the data computing engine may be, for example, spark, mapReduce and Storm, etc.
The medical data tracing method according to the exemplary embodiment of the present invention is further explained and illustrated below with reference to fig. 4 and 5. Referring to fig. 5, the medical data tracing method may include the steps of:
step S510, index id three-sample basic information of table names, field names and sample data needing to be traced is obtained from a quality control platform;
step S520, acquiring SQL sentences (first structured query sentences) of the table from the production platform according to the basic information, transmitting the SQL sentences as parameters to a blood relationship analyzer, and acquiring blood relationship of all fields;
step S530, the SQL statement is used as a parameter to be transmitted to a blood edge data extractor, so as to obtain a new SQL (second structured query statement) capable of acquiring all relevant source table data and calculation process data;
step S540, the basic information, the blood relationship and the new SQL are stored as data tracing configuration files, the data tracing configuration files are distributed to a distributed storage system by a task scheduling system, and the configuration files are called by a data calculation engine to calculate, filter and extract required tracing data on the distributed storage system;
In step S550, the task scheduling system pushes the extraction result (to-be-traced data) and the tracing configuration file back to the production platform and displays the result visually, so that the engineer can quickly locate the problem according to the blood relationship between the extraction result and the field.
The medical data tracing method provided by the example embodiment of the invention provides a solution based on an SQL parser, a blood relationship parser and a blood relationship data extractor aiming at the problem of medical data tracing in a complex medical scene. The method of the invention can obtain the following meaningful results: (1) medical data tracing flow is transparent and visible; (2) the cost of human participation is greatly reduced; (3) The tracing method is decoupled from specific business logic, and can be highly multiplexed in different medical data tracing modules; and (4) the efficiency of data problem positioning is greatly improved.
The example embodiment of the invention also provides a medical data tracing device. Referring to fig. 6, the medical data tracing apparatus may include a blood relationship analysis module 610, a blood relationship data extraction module 620, and a medical data tracing module 630. Wherein:
the blood relationship analysis module 610 may be configured to obtain a first structured query statement, and perform blood relationship analysis on the first structured query statement to obtain a blood relationship.
The blood-edge data extraction module 620 may be configured to extract blood-edge data from the first structured query statement to obtain a second structured query statement.
The medical data tracing module 630 may be configured to trace the medical data to be traced according to the blood relationship and the second structured query statement.
In one example embodiment of the present disclosure, obtaining the first structured query statement includes:
acquiring basic medical data comprising a field name to be traced, a table name corresponding to the field name to be traced and sample data; and acquiring a first structured query statement corresponding to the table name according to the basic data.
In one example embodiment of the present disclosure, obtaining the structured query statement corresponding to the table name from the underlying medical data includes:
and acquiring a first structured query statement corresponding to the table name from a data production platform according to the field name to be traced, the table name corresponding to the field name to be traced and the index identifier of the sample data.
In an example embodiment of the present disclosure, performing a blood-edge relationship parsing on the first structured query statement to obtain a blood-edge relationship includes:
Analyzing the first structured query statement to obtain a grammar tree, and abstracting each query node and a table name node to be traced in the grammar tree into a query source; obtaining an output field list of each query source according to the child nodes of each query node, and carrying out recursion analysis on the data source nodes of the child nodes of each query node to obtain child data sources of the child nodes of each query node; and matching the fields in the output field list of each query source and the child data sources of the child nodes of each query node, and obtaining the blood relationship according to the matching result.
In an example embodiment of the present disclosure, matching fields in the output field list of each query source and child data sources of child nodes of each query node, and obtaining the to-be-rimmed relationship according to a matching result includes:
performing first association processing on fields in the output field list of each query source with the affiliation and sub-data sources of sub-nodes of each query node to obtain a first association result;
and obtaining the to-be-blood-margin relationship according to each first association result.
In an example embodiment of the present disclosure, performing blood-edge data extraction on the first structured query statement, obtaining a second structured query statement includes:
And extracting blood margin data from the first structured query statement to obtain a second structured query statement which comprises source table data corresponding to the sample data and calculation process data from the source table data to the sample data.
In one example embodiment of the present disclosure, extracting the blood-edge data from the first structured query statement to obtain a second structured query statement including source table data corresponding to the sample data and calculation process data from the source table data to the sample data includes:
analyzing the first structured query sentence to obtain a grammar tree, and carrying out grammar translation on each grammar node in the grammar tree to obtain a plurality of translation results;
splicing each translation result to obtain a target query statement, and adding splicing function nodes at the tail ends of sub-node expressions corresponding to the sub-nodes of the query node in the target query statement;
and carrying out second association processing on the to-be-traced table name according to the splicing function node, and obtaining second structured query sentences comprising source table data corresponding to the sample data and calculation process data from the source table data to the sample data according to a second association result.
In an example embodiment of the present disclosure, tracing the medical data to be traced according to the blood relationship and the second structured query statement includes:
and generating a data tracing configuration file according to the blood relationship and the second structured query statement, and obtaining data to be traced according to the data tracing configuration file.
In an example embodiment of the present disclosure, obtaining the data to be traced according to the data tracing configuration file includes:
invoking a data calculation engine to calculate the data tracing configuration file to obtain a calculation result;
and screening the calculation result, and obtaining the data to be traced according to the screening result.
In an example embodiment of the present disclosure, the medical data tracing apparatus further includes:
the display module can be used for displaying the data to be traced and the data tracing configuration file, so that a manager can position abnormal data in the data to be traced according to blood relationship in the data tracing configuration file.
The specific details of each module in the medical data tracing device are described in detail in the corresponding medical data tracing method, so that the details are not repeated here.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the invention. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Furthermore, although the steps of the methods of the present invention are depicted in the accompanying drawings in a particular order, this is not required to either imply that the steps must be performed in that particular order, or that all of the illustrated steps be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.
In an exemplary embodiment of the present invention, an electronic device capable of implementing the above method is also provided.
Those skilled in the art will appreciate that the various aspects of the invention may be implemented as a system, method, or program product. Accordingly, aspects of the invention may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
An electronic device 700 according to this embodiment of the invention is described below with reference to fig. 7. The electronic device 700 shown in fig. 7 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 7, the electronic device 700 is embodied in the form of a general purpose computing device. Components of electronic device 700 may include, but are not limited to: the at least one processing unit 710, the at least one memory unit 720, a bus 730 connecting the different system components (including the memory unit 720 and the processing unit 710), and a display unit 740.
Wherein the storage unit stores program code that is executable by the processing unit 710 such that the processing unit 710 performs steps according to various exemplary embodiments of the present invention described in the above-mentioned "exemplary methods" section of the present specification. For example, the processing unit 710 may perform step S110 as shown in fig. 1: acquiring a first structured query statement, and analyzing the blood-edge relationship of the first structured query statement to obtain the blood-edge relationship; s120: extracting blood margin data from the first structured query sentence to obtain a second structured query sentence; step S130: and tracing the medical data to be traced according to the blood relationship and the second structured query statement.
The memory unit 720 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 7201 and/or cache memory 7202, and may further include Read Only Memory (ROM) 7203.
The storage unit 720 may also include a program/utility 7204 having a set (at least one) of program modules 7205, such program modules 7205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The electronic device 700 may also communicate with one or more external devices 800 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 700, and/or any device (e.g., router, modem, etc.) that enables the electronic device 700 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 750. Also, electronic device 700 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through network adapter 760. As shown, network adapter 760 communicates with other modules of electronic device 700 over bus 730. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 700, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present invention may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present invention.
In an exemplary embodiment of the present invention, a computer-readable storage medium having stored thereon a program product capable of implementing the method described above in the present specification is also provided. In some possible embodiments, the various aspects of the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the invention as described in the "exemplary methods" section of this specification, when said program product is run on the terminal device.
A program product for implementing the above-described method according to an embodiment of the present invention 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 thereto, and in this 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. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. 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 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, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, 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., connected via the Internet using an Internet service provider).
Furthermore, the above-described drawings are only schematic illustrations of processes included in the method according to the exemplary embodiment of the present invention, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
Claims (13)
1. A medical data tracing method, comprising:
acquiring a first structured query statement, and analyzing the blood-edge relationship of the first structured query statement to obtain the blood-edge relationship;
analyzing the first structured query sentence to obtain a grammar tree, and carrying out grammar translation on each grammar node in the grammar tree to obtain a plurality of translation results; splicing each translation result to obtain a target query statement, and adding splicing function nodes at the tail ends of sub-node expressions corresponding to the sub-nodes of the query node in the target query statement; performing second association processing on the table names to be traced according to the splicing function nodes, and obtaining second structured query sentences comprising source table data corresponding to sample data and calculation process data from the source table data to the sample data according to second association results;
And tracing the medical data to be traced according to the blood relationship and the second structured query statement.
2. The medical data tracing method of claim 1, wherein obtaining a first structured query statement comprises:
acquiring basic medical data comprising a field name to be traced, a table name corresponding to the field name to be traced and sample data;
and acquiring a first structured query statement corresponding to the table name according to the basic medical data.
3. The medical data tracing method of claim 2, wherein obtaining a structured query statement corresponding to the table name from the underlying medical data comprises:
and acquiring a first structured query statement corresponding to the table name from a data production platform according to the field name to be traced, the table name corresponding to the field name to be traced and the index identifier of the sample data.
4. The medical data tracing method of claim 1, wherein performing a blood relationship analysis on the first structured query statement to obtain a blood relationship comprises:
analyzing the first structured query statement to obtain a grammar tree, and abstracting each query node and a table name node to be traced in the grammar tree into a query source;
Obtaining an output field list of each query source according to the child nodes of each query node, and carrying out recursion analysis on the data source nodes of the child nodes of each query node to obtain child data sources of the child nodes of each query node;
and matching the fields in the output field list of each query source and the child data sources of the child nodes of each query node, and obtaining the blood relationship according to the matching result.
5. The medical data tracing method according to claim 4, wherein matching the fields in the output field list of each query source and the child data sources of the child nodes of each query node, and obtaining the blood relationship according to the matching result comprises:
performing first association processing on fields in the output field list of each query source with the affiliation and sub-data sources of sub-nodes of each query node to obtain a first association result;
and obtaining the blood relationship according to each first association result.
6. The medical data tracing method according to claim 1, wherein tracing the medical data to be traced according to the blood relationship and the second structured query statement comprises:
And generating a data tracing configuration file according to the blood relationship and the second structured query statement, and obtaining data to be traced according to the data tracing configuration file.
7. The medical data tracing method according to claim 6, wherein obtaining the data to be traced according to the data tracing configuration file comprises:
invoking a data calculation engine to calculate the data tracing configuration file to obtain a calculation result;
and screening the calculation result, and obtaining the data to be traced according to the screening result.
8. The medical data tracing method of claim 6, further comprising:
and displaying the data to be traced and the data tracing configuration file so that a manager can locate abnormal data in the data to be traced according to blood relationship in the data tracing configuration file.
9. A medical data traceability device, comprising:
the blood relationship analysis module is used for acquiring a first structured query statement, and carrying out blood relationship analysis on the first structured query statement to obtain a blood relationship;
the blood margin data extraction module is used for analyzing the first structured query statement to obtain a grammar tree, and carrying out grammar translation on each grammar node in the grammar tree to obtain a plurality of translation results; splicing each translation result to obtain a target query statement, and adding splicing function nodes at the tail ends of sub-node expressions corresponding to the sub-nodes of the query node in the target query statement; performing second association processing on the table names to be traced according to the splicing function nodes, and obtaining second structured query sentences comprising source table data corresponding to sample data and calculation process data from the source table data to the sample data according to second association results;
And the medical data tracing module is used for tracing the medical data to be traced according to the blood relationship and the second structured query statement.
10. A medical data tracing system, comprising:
the data production platform is used for generating a first structured query statement;
the medical data tracing platform is connected with the data production platform through a network and is used for acquiring a first structured query statement and analyzing the blood-edge relationship of the first structured query statement to obtain the blood-edge relationship; and
analyzing the first structured query sentence to obtain a grammar tree, and carrying out grammar translation on each grammar node in the grammar tree to obtain a plurality of translation results; splicing each translation result to obtain a target query statement, and adding splicing function nodes at the tail ends of sub-node expressions corresponding to the sub-nodes of the query node in the target query statement; performing second association processing on the table names to be traced according to the splicing function nodes, and obtaining second structured query sentences comprising source table data corresponding to sample data and calculation process data from the source table data to the sample data according to second association results; and
And tracing the medical data to be traced according to the blood relationship and the second structured query statement.
11. The medical data tracing system of claim 10, wherein said medical data tracing platform comprises a blood relationship parser and a blood relationship data extractor;
the blood relationship analyzer is used for analyzing the blood relationship of the first structured query statement to obtain the blood relationship;
the blood margin data extractor is used for extracting blood margin data from the first structured query statement to obtain a second structured query statement.
12. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the medical data tracing method of any one of claims 1-8.
13. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the medical data tracing method of any one of claims 1-8 via execution of the executable instructions.
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