CN116450768A

CN116450768A - Industrial data processing method, device and equipment oriented to low-code development platform

Info

Publication number: CN116450768A
Application number: CN202310671511.5A
Authority: CN
Inventors: 张硕; 范晓; 田春华; 陆薇
Original assignee: Kunshan Industrial Big Data Innovation Center Co ltd
Current assignee: Kunshan Industrial Big Data Innovation Center Co ltd
Priority date: 2023-06-08
Filing date: 2023-06-08
Publication date: 2023-07-18
Anticipated expiration: 2043-06-08
Also published as: CN116450768B

Abstract

The invention provides an industrial data processing method, device and equipment for a low-code development platform, wherein the method comprises the following steps: acquiring an industrial data processing relationship diagram and an association relationship initial set; according to the initial set of association relations, determining the weight coefficient value of each graph vertex in the industrial data processing relation graph; constructing a complete set of association relationships based on the industrial data processing relationship graph and the weight coefficient values; generating a data recommendation scheme according to the complete set of the association relation; the scheme of the invention can help a user to quickly select the potentially suitable data application components and data contents, and improves the hatching accuracy, recall degree and development efficiency of the data application.

Description

Industrial data processing method, device and equipment oriented to low-code development platform

Technical Field

The invention relates to the technical field of industrial data processing, in particular to an industrial data processing method, device and equipment for a low-code development platform.

Background

The traditional industrial data application software development mode is that an industrial enterprise business department brings up business demands, then an enterprise IT department or a third party software technology service provider carries out directional and custom development to match complex application scenes, and a low-code development environment and a platform for industrial data application are developed along with the rising and development of industrial Internet and industrial digital transformation and the continuous development of the software development technology level;

However, in specific applications, business personnel and data analysts of the industry remain a major obstacle in using low code development environments and platforms for industrial data applications:

1) Industrial business personnel have intermittent characteristics when using a low-code development environment and a platform to hatch application software, namely, the application software is used for a plurality of times, possibly after being used for a plurality of weeks or even months, and then is used for a plurality of times, and is not used continuously, so that each time of use, the system function, the meaning of each element of a data model, the current condition of the data model and the change condition of the data content are required to be known again, the (re) starting time of the application development is long, and the development and the hatching of efficient innovative application are very unfavorable;

2) The lack of knowledge of functions of the development environment, data related conditions and the like seriously hinders the popularization and acceptance of the low-code development environment and the platform, and the initial prospect of the low-code development environment and the platform cannot be achieved, so that the high-speed development of industrial business innovation cannot be realized.

Disclosure of Invention

The invention aims to solve the technical problem of providing an industrial data processing method, device and equipment for a low-code development platform, and solves the problem of longer restarting time of application development.

In order to solve the technical problems, the technical scheme of the invention is as follows:

the industrial data processing method facing the low-code development platform comprises the following steps:

acquiring an industrial data processing relationship diagram and an association relationship initial set;

according to the initial set of association relations, determining the weight coefficient value of each graph vertex in the industrial data processing relation graph;

constructing a complete set of association relationships based on the industrial data processing relationship graph and the weight coefficient values;

and generating a data recommendation scheme according to the complete set of the association relation.

Optionally, acquiring the industrial data processing relationship graph includes:

acquiring physical realization library and data processing process information of a field data model; the physical implementation library comprises at least one of an industrial data table, key fields and relationships among the industrial data tables; the data processing process information comprises at least one of data processing task sets, running conditions and statistical information;

generating an initial data processing relation diagram according to the physical realization library of the field data model and the data processing task set;

and updating the initial data processing relation diagram based on the running condition and the statistical information to obtain a data processing relation diagram.

Optionally, obtaining the initial set of association relationships includes:

acquiring user information, service information and log information; the log information comprises at least one of a component theme, a display component type, component configuration information and time;

determining an initial triplet according to the user information, the service information and the log information; the initial triples comprise business classification words, display component types and data model element structure sets;

calculating a time attenuation support coefficient corresponding to the initial triplet;

and generating an association relation initial set according to the initial triplet and the time attenuation support coefficient.

Optionally, determining the weight coefficient value of each graph vertex in the industrial data processing relationship graph according to the initial set of association relationships includes:

determining at least one graph vertex corresponding to the initial triplet in the industrial data processing relation graph according to the initial set of association relations;

and carrying out iterative computation on the graph vertexes according to a preset ordering rule to obtain the weight coefficient value corresponding to each graph vertex.

Optionally, performing iterative computation on the graph vertices according to a preset ordering rule to obtain a weight coefficient value corresponding to each graph vertex, where the weight coefficient value comprises:

By the formula

Calculating to obtain a weight coefficient value corresponding to each graph vertex;

wherein,,for the graph vertex, m is the tuple in the data model element structure set, +.>Is the top of the graph->Corresponding weight coefficient value, ">For the reverse neighbor vertex set, +.>Weight coefficient value obtained for the previous iteration, < ->Is the graph vertex +_ in the industrial data processing relationship graph>To the vertex of the graph->Is included.

Optionally, constructing a complete set of association relationships based on the industrial data processing relationship graph and the weight coefficient values includes:

determining a first intermediate triplet item according to a compatibility relation among preset attribute columns;

grouping and polymerizing the first intermediate triplet item to obtain a second intermediate triplet item;

determining a target weight coefficient value corresponding to each second intermediate triplet item according to the second intermediate triplet item and the weight coefficient value;

and generating a complete set of association relations according to the second intermediate triplet item and the target weight coefficient value.

Optionally, generating a data recommendation scheme according to the complete set of association relations includes:

based on the complete set of association relations, generating a first data recommendation scheme according to the user information;

Or generating recommended service groups according to the user information, and providing a second data recommendation scheme under each recommended service group;

or generating a third data recommendation scheme according to the business vocabulary information input by the user.

The invention also provides an industrial data processing device facing the low-code development platform, which comprises:

the acquisition module is used for acquiring an industrial data processing relationship graph and an association relationship initial set;

the processing module is used for determining the weight coefficient value of each graph vertex in the industrial data processing relationship graph according to the initial set of the association relationship; constructing a complete set of association relationships based on the industrial data processing relationship graph and the weight coefficient values; and generating a data recommendation scheme according to the complete set of the association relation.

The present invention provides a computing device comprising: a processor, a memory storing a computer program which, when executed by the processor, performs the method as described above.

The invention also provides a computer readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method as described above.

The scheme of the invention at least comprises the following beneficial effects:

According to the scheme, the industrial data processing relation diagram and the association relation initial set are obtained; according to the initial set of association relations, determining the weight coefficient value of each graph vertex in the industrial data processing relation graph; constructing a complete set of association relationships based on the industrial data processing relationship graph and the weight coefficient values; generating a data recommendation scheme according to the complete set of the association relation; the problem of longer restarting time of application development is solved, a user can be helped to quickly select potentially suitable data application components and data contents, and the hatching accuracy, recall degree and development efficiency of the data application are improved.

Drawings

FIG. 1 is a flow diagram of an industrial data processing method for a low-code development platform according to an embodiment of the present invention;

FIG. 2 is a flow chart of an industrial data processing method facing a low code development platform in a specific embodiment provided by the invention;

FIG. 3 is a schematic diagram of an industrial data processing system facing a low code development platform in accordance with a specific embodiment of the present invention;

FIG. 4 is a schematic diagram of an industrial data processing device facing a low-code development platform according to an embodiment of the present invention.

Detailed Description

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

The industrial data processing method is preferably oriented to a low-code development platform (such as a data application development platform of industrial data), but is not limited to the low-code development platform, and industrial data application is application services and application software which are developed and oriented to specific business scenes of industrial enterprises by using IT (Internet Technology ) and software technology; the data application development platform of the industrial data can solve the business problem of industrial enterprises. The data application development platform is preferably configured and developed through a low-code development technology and a platform, namely, a mode similar to building blocks is utilized to quickly construct the development and incubation of data application of enterprise-specific applications for users such as business personnel, data analyzers and the like, so that the business innovation sense of the business personnel can be fully released, the traditional software incubation link from the requirement to the design to the development is shortened, the loss of information in the layer-by-layer transmission process is reduced, and the possibility of more business innovations is created;

In order to help a user to quickly select potentially suitable data application components and data contents in an industrial data application-oriented development process, as shown in fig. 1, an embodiment of the present invention proposes an industrial data processing method oriented to a low-code development platform, including:

step 11, acquiring an industrial data processing relationship diagram and an association relationship initial set;

step 12, determining the weight coefficient value of each graph vertex in the industrial data processing relationship graph according to the initial set of the association relationship;

step 13, constructing a complete set of association relations based on the industrial data processing relation graph and the weight coefficient value;

and 14, generating a data recommendation scheme according to the complete set of association relations.

The technical means provided by the application updates the weight coefficient value of each graph vertex in the industrial data processing relationship graph through the initial set of the association relationship to obtain the complete set of the association relationship, generates a data recommendation scheme, and recommends data for a user in the development process of the industrial data application according to the data recommendation scheme.

The industrial data processing process related to the industrial data processing relation diagram comprises convergence, association integration, preprocessing, summary statistics processing, feature generation, analysis model calculation, data processing and generation facing application reading access and the like; business insights (business data) obtained after industrial data processing can be used for users to view browsing, support decisions, etc. The processing process of industrial data processing is continuously carried out by taking continuously accessed data as input, so that the effectiveness of online data application service can be ensured.

In an alternative embodiment of the present invention, in step 11, obtaining an industrial data processing relationship graph includes:

step 11a1, acquiring a physical realization library and data processing process information of a domain data model; the physical implementation library comprises at least one of an industrial data table, key fields and relationships among the industrial data tables; the data processing process information comprises at least one of data processing task sets, running conditions and statistical information;

step 11a2, generating an initial data processing relation diagram according to the physical realization library of the field data model and the data processing task set;

And step 11a3, updating the initial data processing relation diagram based on the running condition and the statistical information to obtain a data processing relation diagram.

In this embodiment, a physical implementation library of a domain data model is obtained, and a model mode corresponding to the physical implementation library is read to realize connection and adaptation with a data system where the domain data model is located, where the model mode includes at least one of a data table, a field, a primary key, an external key association, and an index; the physical implementation library can be an industrial multi-mode data system, a relational database system, a data warehouse system, a data lake system and the like, and the application is not limited by the method; in a preferred implementation example, the physical implementation library comprises at least one of: typical relational databases, data warehouse teraadata, etc., big data ecological Hive (data warehouse tool), data lake Iceberg, hudi, etc.

Based on the model mode, analyzing the field data model to obtain model element information, wherein the model element information comprises: the association relation between the model element set and each element is preferably a grammar-level association relation; in a preferred implementation example, the analysis range of the domain data model is at least one of a data table, a view, a field, an index and a primary key, and the model element information obtained by analysis can be at least one of a set of tables, a set of views, a set of fields on the table, a set of fields on the view and index information. Furthermore, a dependency graph related to the model element can be constructed according to the model element information, and the vertex of the dependency graph is the model element information;

The data processing process information comprises a data processing task set; the data processing task set is a set of related information about a plurality of data processing tasks and can be obtained through adaptation and periodic reading; the related information of the data processing task comprises data input information (namely a data source), output information (namely a data destination), a timing operation period of the task and the like; the relevant information sources of the data processing task can be a data processing and processing task management system (such as a service integration interface), a data analysis task management system, a large data platform metadata system, a data blood edge analysis subsystem, a data influence analysis subsystem and the like, and the application is not limited by the information sources.

The method comprises the steps of taking an analysis result (namely the data table, the view, the field, the index, a primary key and the like) of a physical implementation library of a field data model as a verification basis, taking a vertex set in a dependency graph as an anchor point (vertex) set, taking each data processing task in a data processing task set as an edge, generating an initial data processing relation graph, wherein the initial data processing relation graph takes a set (an element of the set is a table and an attribute column name) as an vertex of identification information (the table and the attribute column name are verified according to the analysis result of the physical implementation library of the field data model), taking data input and output directions of the data processing tasks as directed edges, each vertex in the initial data processing relation graph can be non-communicated, and each edge in the initial data processing relation graph can be attached with tag information, and the tag information comprises running time information (such as a timing running period) of the data processing tasks.

The data processing process information also comprises running conditions and statistical information, wherein the running conditions of the data processing tasks can be obtained through the following processes: adapting and periodically reading a running log of the data processing task and an operating system (namely dynamic running information);

analyzing a dynamic running log of the read data processing task, wherein the running log comprises the data processing amount, the processing time length and the processing rate (throughput) of the data processing task job;

the source of the dynamic operation information of the data processing task operation can be a data processing and processing task execution system (namely a service integration interface), a data analysis task execution system and a system operation log centralized management system, and the application is not limited by the source;

the statistical information in the data processing process information is obtained through periodical adaptation and reading, the statistical information specifically can comprise the data amount in a table, and the statistical information can be from a data storage management system or a data storage engine.

Furthermore, the statistical information can be subjected to read-write statistical analysis to obtain a read-write statistical analysis result, and specifically, the process of obtaining the read-write statistical analysis result comprises the following steps: based on model element information of the field data model, taking statistical information as input, and adapting and periodically reading data read-write operation logs; the data read-write operation log comprises read-write information of a table, and the source of the data read-write operation log can be a data read-write processing execution system (namely a service integration interface), a data query execution system and a system operation log centralized management system, which is not limited in the application;

According to the data read-write operation log, a read-write statistical analysis result in model element information of the field data model is obtained through calculation; the read-write statistics analysis result comprises data amount of each table, table data information (such as data increment of the table), access frequency of each table, table data access statistics, table access association, attribute column access association and the like.

And updating the initial data processing relation diagram based on the running condition and the statistical information, specifically, attaching dynamic running information (i.e. running condition) such as the data processing quantity (the read data quantity of the source vertex of the side, the write data quantity of the target vertex of the side), the processing time length, the processing rate (i.e. throughput) and the like of the latest period corresponding to the task operation on the side of the initial data processing relation diagram, and obtaining the data processing relation diagram.

It should be noted that, the data processing task is obtained by selecting an effective data processing task in a latest time period, where the latest time period may be the latest half year, the latest year, etc., and the effective data processing task refers to a data processing task in a state of being in a non-disabled state in the latest time period, or a data processing task in which a historical running state exists at least once.

In a specific embodiment, acquiring a physical implementation library of the domain data model and data processing process information (including at least one of data processing task set, running condition and statistical information); generating an initial data processing relation diagram according to a physical realization library and a data processing task set of the field data model, wherein the initial data processing relation diagram consists of a table and attribute names, and the identification information corresponding to the vertexes of the initial data processing relation diagram is as follows:the method comprises the steps of carrying out a first treatment on the surface of the I.e. "table", "attribute column name"; the vertexes are connected through directed edges, and the edges are marked (information such as data volume and data increment of a source vertex, data volume and data increment of a target vertex, data processing volume, task timing frequency and the like); and acquiring the running condition and the statistical information, and updating the initial data processing relation diagram to obtain the data processing relation diagram.

In an optional embodiment of the present invention, in step 11, obtaining an initial set of association relationships includes:

step 11b1, obtaining user information, service information and log information; the log information comprises at least one of a component theme, a display component type, component configuration information and time;

step 11b2, determining an initial triplet according to the user information, the service information and the log information; the initial triples comprise business classification words, display component types and data model element structure sets;

Step 11b3, calculating a time attenuation support coefficient corresponding to the initial triplet;

and 11b4, generating an association relation initial set according to the initial triplet and the time attenuation support coefficient.

In the embodiment of the invention, user information, service information and log information are acquired; the user information refers to user and/or user group department information, wherein the user group department information refers to a set of all user information corresponding to departments formed by a preset number of users, the service information refers to service vocabulary field classification, the log information refers to log and configuration information of user selection data and display components, and the log information comprises at least one of component theme, display component type, component configuration information and time;

in an alternative embodiment of the present invention, step 11b2 includes:

step 11b21, performing word segmentation, part-of-speech tagging and subject word extraction processing on the component subjects in the log information to obtain subject words;

step 11b22, obtaining topic service classification words according to the service information and the topic words;

step 11b23, processing the component configuration information in the log information to obtain a data model element structure set The method comprises the steps of carrying out a first treatment on the surface of the The data model element structure set is a triplet set.

Wherein,,is an expression axis variable tuple, +.>Andobtained by the following steps: (1) For the presentation component types of the direct selection data, the presentation component types are directly obtained from component configuration information in log information, and each expression is typically in the specific form: />The method comprises the steps of carrying out a first treatment on the surface of the (2) For the input statement to select the type of the exposed component of the data, the result is obtained by SQL (Structured Query Language, structured query language database) parsing in combination with component configuration information, each expression typically being in the specific form ofAlong with SQL statements, wherein Expr represents an expression.

In an alternative embodiment of the present invention, step 11b3 includes:

for each initial triplet tau, calculating a time attenuation support coefficient corresponding to the initial tripletWherein->The number of occurrences and time decay values in the log and configuration information are monotonic functions of the argument for τ (data selection and presentation).

In a preferred implementation example, the time-decaying support coefficientThe method comprises the following steps:

；

where lambda is the decay rate,for the current time +.>Is the time τ is in the log.

Generating an initial set of association relations according to the initial triples and the time attenuation support coefficients, wherein elements of the initial set of association relations are formed as follows:

form triples (called "association triples") can be described as +.>Wherein g is a service classification word, v is a display component type, M is a data model element structure set, and each triplet tau corresponds to a time attenuation support coefficient ∈>。

In another specific embodiment, determining the initial set of association relationships for yield specifically includes:

s1, acquiring user information, service information and log information; the user information refers to user and/or user group department information, the service information refers to service vocabulary field classification, the log information refers to log and configuration information of user selection data and display components, and the log information comprises at least one of component theme, display component type, component configuration information and time;

s2, performing word segmentation, part-of-speech tagging and subject word extraction processing on the component subjects in the log information to obtain subject words, and obtaining subject business classification words according to the business information and the subject words;

s3, processing the component configuration information in the log information to obtain a data model element structure set The method comprises the steps of carrying out a first treatment on the surface of the The data model element structure set is a triplet set;

wherein,,is an expression axis variable tuple, +.>Andobtained by the following steps: (1) For the display component type of the direct selection data, expressions can be directly obtained from component configuration information, and the specific form of each expression is as follows: />. Such as: to be used forA data model element structure in which the triples of the collection elements are set elements; (2) For the type of the presentation component for selecting data by inputting SQL sentences, the type can be obtained by combining SQL analysis and component configuration information, and the specific form of each expression is as follows: />Along with SQL statements. Wherein Expr represents an expression. Such as: in triplet->Data model element structure that is a collection element.

S4, for each initial triplet tau, calculating a time attenuation support coefficient corresponding to the initial tripletWherein->A monotonic function with independent values of time decay and number of occurrences of τ in (data selection and presentation) log and configuration informationA number;

to be used forFor example, for->Time-decaying support coefficient:

；

s5, generating an initial association relation set according to the initial triplet and the time attenuation support coefficient, wherein elements of the initial association relation set are formed as follows:

；/>；

The 4.5 is the initial tripletTime-decaying support coefficient +.>3.8 is the initial triplet +.>Time-decaying support coefficient +.>。

In an alternative embodiment of the present invention, step 12 includes:

step 121, determining at least one graph vertex corresponding to the initial triplet in the industrial data processing relationship graph according to the initial set of association relationships;

and step 122, carrying out iterative computation on the graph vertexes according to a preset ordering rule to obtain the weight coefficient value corresponding to each graph vertex.

In this embodiment, with the industrial data processing relationship graph as a structure, for each expression axis variable tuple in each initial triplet in the initial set of association relationships, a weight coefficient value on a graph vertex of the industrial data processing relationship graph is calculated, specifically:

for each triplet in the current associated knowledge setCorresponding coefficient->Wherein the initial triplet is:

the method comprises the steps of carrying out a first treatment on the surface of the Each tuple m in m is an expression axis variable tuple, therefore, each initial triplet +.>A set of +.>All elements within the set collectively correspond to a time-decay support factor；

For each expression axis variable tuple is:

；

Wherein,,and->A collection of attribute columns that constitute m;

determining a set of all graph vertices from the industrial data processing relationship graph G is:the set of graph vertices can be denoted +.>The method comprises the steps of carrying out a first treatment on the surface of the Wherein the set of columns satisfying the graph vertex v identification are columns of the same table.

Defining a compatibility relation among preset attributes, and determining the compatibility relation of two attribute columns in different tables to indicate the compatibility of the two attribute columns;

the compatibility relationship is formed by the following conditions:

1) There is a data processing task whose input contains one of the attribute columns and whose output contains the other attribute column;

2) Satisfying the similarity in name;

in a preferred implementation example, the approximation of the name may be obtained by:

the method comprises the following steps: the two attribute columns have the same name and the same type;

the second method is as follows: the names of the two attribute columns are identical and the types of the two attribute column names are identical under different naming rules; for example, names under hump (CamelCase), serpentine (snatecase), serial (kebabase) naming rules are equivalent;

and a third method: the name based on the domain synonym table is identical and the type is the same.

Further, for a collection of graph verticesEvery figure vertex +. >Determining graph vertex->The forward neighbor vertex set of (1) is: />The method comprises the steps of carrying out a first treatment on the surface of the Wherein, the forward neighbor vertex v +.>The identified column set need not be a column of the same table.

In an alternative embodiment of the present invention, step 122 includes:

step 1221, passing through the formula

In this embodiment, the weight coefficient value of each graph vertex μ with respect to m on the industrial data processing relationship graph G is:；

wherein, the reverse neighbor vertex set of the graph vertex mu is:

；

is the weight coefficient value of the previous iteration, and is the weight coefficient value of the previous iterationIf the graph vertex #, then>Its initial value is +.>Otherwise, its initial value is 0;

data flow coefficient values for data processing tasks from v ' ' to μ ' constructed on the industrial data processing relationship graph G;

in one example of an implementation that may be implemented, Equal to the ratio of the data increment to the amount of data in v'; the above formula of weight coefficient values can be applied in an iterative manner for evaluation;

in yet another example, the formula evaluation of the weight coefficient values described above may be used in an iterative fashion in terms of topological ordering, where topological ordering is followed byThe medium graph vertices are preferentially dropped into edges and the selected edge weights are greater such that they are evaluated on the industrial data processing relationship graph G in a manner that ignores edges.

In an alternative embodiment of the present invention, step 13 includes:

step 131, determining a first intermediate triplet item according to a compatibility relation among preset attribute columns;

step 132, performing grouping aggregation processing on the first intermediate triplet item to obtain a second intermediate triplet item;

step 133, determining a target weight coefficient value corresponding to each second intermediate triplet item according to the second intermediate triplet item and the weight coefficient value;

and step 134, generating a complete set of association relations according to the second intermediate triplet item and the target weight coefficient value.

In this embodiment, according to the weight coefficient value of each expression axis variable tuple on the vertex of the graph, an association knowledge triplet (second intermediate triplet item) is iteratively constructed to output a complete set of association relationships, specifically:

Processing the weight coefficient value of each vertex μ on the relational graph G with respect to the expression axis variable tuple m according to the industrial data in step 12The first intermediate triplet is obtained as +.>The corresponding weight coefficient value isWherein->Is a column set on the vertex μ with m having a compatibility relationship between preset attribute columns;

all the first intermediate triples obtained by the above are processed according to the following stepsGrouping aggregation, i.e. merging identical intermediate items +.>Obtaining a second intermediate triplet item; the destination corresponding to the second intermediate tripletThe target weight coefficient value is the sum of the correlation values, i.e. the target weight coefficient value is +.>And generating a complete set of association relations according to the second intermediate triplet item and the target weight coefficient value.

The elements of the obtained complete set of association relations are formed as follows:form of triplet>The method comprises the steps of carrying out a first treatment on the surface of the Each triplet corresponds to a weight coefficient value +.>。

In an alternative embodiment of the present invention, step 14 includes:

14a, generating a first data recommendation scheme according to the user information based on the complete set of association relations;

or, step 14b, generating recommended service packets according to the user information, and providing a second data recommendation scheme under each recommended service packet;

Or, in step 14c, a third data recommendation scheme is generated according to the business vocabulary information input by the user.

In an optional embodiment of the invention, based on the complete set of association relationships, different data recommendation schemes can be generated, and the different data recommendation schemes are applicable to different application scenes;

the first data recommendation scheme in step 14a is preferably a recommendation suggestion of a data selection and data display mode, and specifically includes:

step 14a1, constructing an associated knowledge set according to the user group and the department group (user information); specifically, according to the relationship between the user information and the user group departments (obtained through the user information), dividing the users into different department domains; taking department domains as groups, and constructing an associated knowledge set in the groups; the elements in the associated knowledge set are arranged in reverse order according to the weight coefficient value omega (or the K items with the largest values can be rapidly evaluated);

step 14a2, online generating a recommendation proposal (i.e. a first data recommendation proposal) of data selection and data display modes; specifically, identifying the current user and the user group and department information (namely user information) of the current user, calculating target triples of the K items with the maximum weight coefficient value omega based on the associated knowledge set corresponding to the user group and the department, and outputting the triples in a reverse order; and converting each item label triplet into a specific display component and a corresponding data selection parameter as a first data recommendation scheme of the user.

The second data recommendation scheme in step 14b is preferably a service packet recommendation and a recommendation suggestion for recommending data selection and data display modes under each packet, and specifically includes:

step 14b1, constructing an associated knowledge set according to the user group and the department group (user information); specifically, according to the relationship between the user information and the user group departments (obtained through the user information), dividing the users into different department domains; taking the department domain as a first-level group, and constructing an associated knowledge set in the group; performing second-stage grouping on elements in the associated knowledge set according to the service classification words so as to make the same service classification words be a group;

step 14b2, online generating a service packet recommendation, a data selection under the service packet and a recommendation suggestion of a data display mode (namely a second data recommendation scheme); specifically, identifying the current user and the user group and department information (i.e., user information) of the current user; calculating L items with the maximum secondary grouping weights based on the associated knowledge sets of the user groups and departments, and using the L items as grouping suggestions recommended for the users (namely selecting target business groups); after the user determines the target service group, calculating the target triples of K items with the maximum weight coefficient value omega in the target service group, and outputting the target triples in an inverted order; and converting the target triples into specific presentation components and corresponding data selection parameters as a second data recommendation scheme of the user.

The third data recommendation scheme in step 14c preferably includes data selection and recommendation suggestion of the data display mode, and specifically includes:

step 14c1, constructing an associated knowledge set and an efficient index; specifically, storing the constructed associated knowledge set; creating an efficient index based on the business classification words, and quickly inquiring elements in the efficient index according to the weight coefficient value omega (or quickly evaluating the maximum K items);

step 14c2, generating recommendation suggestions (namely a third data recommendation scheme) of data selection and data display modes on line according to the business vocabulary information input by the user; specifically, identifying business vocabulary information input by a user; according to the input business vocabulary information, carrying out high-efficiency search on business classification word fields in the associated knowledge set (specifically, search word expansion can be carried out based on auxiliary means such as a synonym table and the like), inquiring to obtain target triples of K items with the maximum weight coefficient value omega, and outputting the target triples in an inverted order; and converting each item label triplet into a specific display component and a corresponding data selection parameter as a third data recommendation scheme of the user.

As shown in FIG. 2, in one particular embodiment, the industrial data processing method for the low-code development platform comprises the following steps:

Step 21, obtaining a physical realization library of the field data model, and reading a model mode corresponding to the physical realization library to realize connection and adaptation with a data system where the field data model is located; wherein the physical implementation library comprises at least one of an industrial data table, key fields, and relationships between industrial data tables;

step 22, analyzing (i.e. identifying) the domain data model based on the model mode to obtain model element information, wherein the model element information comprises: the association relation between the model element set and each element is analyzed and identified, and the obtained field data model comprises a table, a view, a field, an index, association and the like;

step 23, reading and collecting a data processing task set from a data processing and analyzing task set (including a task set, input and output of tasks and the like), wherein the data processing task set is a set of related information about a plurality of data processing tasks;

step 24, analyzing the processing links of the data processing task and the attribute column based on the field model element (namely model element information) to obtain an initial data processing relation diagram, specifically, using the analysis result of the physical implementation library of the field data model as a verification basis, using the vertex set in the dependency relation diagram as a vertex set, and using each data processing task in the data processing task set as an edge to generate an initial data processing relation diagram (namely, a data processing link relation diagram based on the field model element);

Step 25, reading and collecting the running condition of the data processing task job from the data processing and analyzing task running log, wherein the historical running condition of the data processing task job comprises success condition and the like;

step 26, collecting statistical information of the data store from the data store;

step 27, carrying out data read-write statistical analysis on the statistical information to obtain a read-write statistical analysis result; the running information related to the model element information comprises data quantity in a table, data increment, table access frequency, table data access statistics, table access association, attribute column access association and the like;

28, analyzing the running condition of the data processing link on the initial data processing relation diagram based on the running condition and the statistical information to obtain an updated data processing relation diagram; the data processing link relation diagram containing the operation condition comprises the data processing quantity, the processing time length, the speed and the like on a processing link;

step 29, collecting user and user grouping/department information (i.e. user information);

step 210, collecting business vocabulary and classifying according to the field (namely business information);

step 211, collecting data selection and display component and service domain information from the log (i.e. user information) of the user selection data and display component;

Step 212, generating an association relation initial set of a time-sensitive data model, a display component and a service domain according to the information of the steps 29 to 211; forming an initial set of association relation related to the service information according to the service information and combining the user information;

step 213, based on the industrial data processing relationship graph and the weight coefficient value, constructing a complete set of association relationship, namely, propagating the business classification and data selection display modes along the industrial data processing relationship graph, expanding the business classification and data model elements and the data display mode relationship items (namely, iteratively updating the second intermediate triplet items) to form the complete set of association relationship;

step 214a, based on the complete set of association relationships, generating a first data recommendation scheme according to user information;

step 214b, generating recommended service groups according to the user information, and providing a second data recommendation scheme under each recommended service group;

step 214c, generating a third data recommendation scheme according to the business vocabulary information input by the user.

As shown in FIG. 3, in one particular embodiment, an industrial data processing system for a low-code development platform includes:

the bottom layer data storage and processing platform; the bottom layer data storage and processing platform comprises a field data model, data processing and processing task information, a data processing task history operation log, a data request statistics log and the like;

A business vocabulary field information module;

the bottom data storage and processing platform and the business vocabulary field information module are used for providing relevant information for the industrial data processing method facing the low-code development platform, the bottom data storage and processing platform can provide a field data model and log information, and the business vocabulary field information module can provide business information.

A model element-oriented analyzer; a domain data model connector; the analyzer facing the model elements reads the model mode corresponding to the physical realization library from the bottom data storage and processing platform through the field data model connector so as to realize connection and adaptation with a data system where the field data model is located; wherein the physical implementation library comprises at least one of an industrial data table, key fields, and relationships between industrial data tables;

a data processing link analysis and generator based on field data model element verification; a data processing task information collector; the data processing link analysis and generation device reads and collects a data processing task set through the data processing task information collector;

the data processing link operation condition analysis, data reading and writing and data statistics analyzer; a historical operating condition collector for data processing operations; a data read-write and data statistics information collector; the data processing link operation condition analysis, data reading and writing and data statistics analyzer respectively reads and collects the operation condition of the data processing task operation and collects the statistical information of data storage through a historical operation condition collector and a data reading and writing and data statistics information collector of the data processing operation; and further carrying out data read-write statistical analysis on the statistical information to obtain a read-write statistical analysis result.

A business vocabulary classification information collector;

an industrial data application and low code development environment for providing user information and user selection data and presentation component logs and configuration information, etc.;

information collectors such as users and groups;

a data selection and presentation history run and configuration collector;

the data selection and display history operation and configuration collector collects service information in service vocabulary field information through the service vocabulary classification information collector, and also collects user information under industrial data application and low-code development environments through information collectors such as users and groups;

an initial knowledge set constructor facing data selection and display; the initial knowledge set constructor facing data selection and display is used for constructing an association relation initial set;

a knowledge set expansion constructor for data selection and display based on the data processing information; the knowledge set expansion constructor is used for determining weight coefficient values of all the graph vertexes in the industrial data processing relation graph according to the initial set of the association relation; constructing a complete set of association relationships based on the industrial data processing relationship graph and the weight coefficient values;

A generator for data selection and display mode recommendation suggestion; the generator is for generating a data recommendation.

The industrial data processing system facing the low-code development platform can utilize the historical experience of data selection and display of users, classify according to the field of business vocabulary, combine the relationship between users and user group departments to form the association relationship between business classification and data selection and display, and consider the condition that the association relationship changes with time to form an initial association knowledge set of data selection and display; forming a processing link relation diagram based on data processing by using model element dependence formed between data processing tasks, model element association dependence reflected in data access operation, information such as data quantity, data increment, operation frequency (transition with time) of the model elements and the like; based on the processing and synthesizing relation of the data, the business classification and data selection display modes are transmitted along the relation diagram to form the expansion association of business classification and data model elements and the data display modes, and the association knowledge set is further expanded and constructed; generating recommendation suggestions of data selection and data display modes according to the users and the business vocabulary information input by the users; generating recommendation suggestions of recently compared popular data selection and display modes according to the information of users, the located grouping departments and the like;

And recommending candidate data tables, attribute columns and display components and parameter values on related data models according to the information of the user, the contextual actions of the user in the data application development process and the data processing condition, so that the user is helped to quickly select potentially suitable data application components and data contents, and the data application hatching accuracy, recall degree and development efficiency are improved.

The embodiment of the invention processes the relation diagram and the initial set of the incidence relation by acquiring the industrial data; according to the initial set of association relations, determining the weight coefficient value of each graph vertex in the industrial data processing relation graph; constructing a complete set of association relationships based on the industrial data processing relationship graph and the weight coefficient values; according to the complete set of association relations, a data recommendation scheme is generated, so that the problem of long restarting time of application development is solved, a user can be helped to quickly select potentially suitable data application components and data contents, and the hatching accuracy, recall degree and development efficiency of the data application are improved.

As shown in fig. 4, an embodiment of the present invention further provides an industrial data processing apparatus 40 facing a low-code development platform, including:

An acquisition module 41, configured to acquire an industrial data processing relationship graph and an initial set of association relationships;

a processing module 42, configured to determine a weight coefficient value of each vertex in the industrial data processing relationship graph according to the initial set of association relationships; constructing a complete set of association relationships based on the industrial data processing relationship graph and the weight coefficient values; and generating a data recommendation scheme according to the complete set of the association relation.

Optionally, obtaining the initial set of association relationships includes:

By the formula

wherein,,for the graph vertex, m is the tuple in the data model element structure set, +.>Is the top of the graph->Corresponding weight coefficient value, ">Is the reverse ofTo the neighbor vertex set, ++>Weight coefficient value obtained for the previous iteration, < ->Is the graph vertex +_ in the industrial data processing relationship graph>To the vertex of the graph->Is included.

It should be noted that, the device is a device corresponding to the above method, and all implementation manners in the above method embodiments are applicable to the embodiment of the device, so that the same technical effects can be achieved.

Embodiments of the present invention provide a computing device comprising: a processor, a memory storing a computer program which, when executed by the processor, performs the method as described above. All the implementation manners in the method embodiment are applicable to the embodiment, and the same technical effect can be achieved.

Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method as described above. All the implementation manners in the method embodiment are applicable to the embodiment, and the same technical effect can be achieved.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.

Furthermore, it should be noted that in the apparatus and method of the present invention, it is apparent that the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. Also, the steps of performing the series of processes described above may naturally be performed in chronological order in the order of description, but are not necessarily performed in chronological order, and some steps may be performed in parallel or independently of each other. It will be appreciated by those of ordinary skill in the art that all or any of the steps or components of the methods and apparatus of the present invention may be implemented in hardware, firmware, software, or a combination thereof in any computing device (including processors, storage media, etc.) or network of computing devices, as would be apparent to one of ordinary skill in the art after reading this description of the invention.

The object of the invention can thus also be achieved by running a program or a set of programs on any computing device. The computing device may be a well-known general purpose device. The object of the invention can thus also be achieved by merely providing a program product containing program code for implementing said method or apparatus. That is, such a program product also constitutes the present invention, and a storage medium storing such a program product also constitutes the present invention. It is apparent that the storage medium may be any known storage medium or any storage medium developed in the future. It should also be noted that in the apparatus and method of the present invention, it is apparent that the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. The steps of executing the series of processes may naturally be executed in chronological order in the order described, but are not necessarily executed in chronological order. Some steps may be performed in parallel or independently of each other.

While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the present invention.

Claims

1. The industrial data processing method for the low-code development platform is characterized by comprising the following steps of:

2. The method for processing industrial data for a low-code development platform according to claim 1, wherein obtaining an industrial data processing relationship graph comprises:

3. The method for processing industrial data for a low-code development platform according to claim 1, wherein obtaining the initial set of association relations comprises:

4. The method for processing industrial data for a low-code development platform according to claim 3, wherein determining the weight coefficient value of each graph vertex in the industrial data processing relationship graph according to the initial set of association relationships comprises:

5. The industrial data processing method for the low-code development platform according to claim 4, wherein performing iterative computation on the graph vertices according to a preset ordering rule to obtain weight coefficient values corresponding to each graph vertex comprises:

by the formulaCalculating to obtain a weight coefficient value corresponding to each graph vertex;

6. The method for processing industrial data for a low-code development platform according to claim 4, wherein constructing a complete set of association relationships based on the industrial data processing relationship graph and the weight coefficient values comprises:

7. The method for processing industrial data for a low-code development platform according to claim 3, wherein generating a data recommendation scheme according to the complete set of association relationships comprises:

8. An industrial data processing device for a low-code development platform, comprising:

9. A computing device, comprising: a processor, a memory storing a computer program which, when executed by the processor, performs the method of any one of claims 1 to 7.

10. A computer readable storage medium storing instructions which, when run on a computer, cause the computer to perform the method of any one of claims 1 to 7.