CN115935292A - Method for constructing full-life-cycle multi-source heterogeneous data fusion mode of complex equipment - Google Patents
Method for constructing full-life-cycle multi-source heterogeneous data fusion mode of complex equipment Download PDFInfo
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Abstract
The invention relates to a method for constructing a full-life-cycle multi-source heterogeneous data fusion mode of complex equipment. The method comprises the following steps: establishing an identification method of multi-source heterogeneous data redundancy attributes of complex equipment; establishing a reusable knowledge data association mapping, a design structure tree generation and design stage data association mapping, a manufacturing structure tree generation and manufacturing stage data association mapping and an operation and maintenance stage data association mapping algorithm based on a product family element structure tree; and constructing a complex equipment full-life-cycle data mode integrating the multi-source heterogeneous data based on the function dependence and the foreign key constraint of the relation model. The invention solves the problem of island caused by the difference of physical information data structures, the redundancy of characteristics and the loss of association relation among data caused by independent application of each business system in the stages of complex equipment design, manufacture and operation and maintenance by establishing a complex equipment design, manufacture and operation and maintenance stage data association mapping and an inter-stage data mapping method.
Description
Technical Field
The invention relates to a physical information data fusion mode construction method, in particular to a complex equipment full life cycle multi-source heterogeneous data fusion mode construction method.
Background
The design, manufacture and operation and maintenance processes of complex equipment such as high-speed trains, shield machines, nuclear power equipment and the like span a plurality of specialties and enterprises, relate to a plurality of physical information systems, and due to the fact that problems and providers of each physical information system are different, multi-source heterogeneity of data of the whole life cycle of the complex equipment is caused, and multidimensional heterogeneous information of different subjects and different stages in the life cycle of a product cannot be uniformly expressed on a system level. Currently, researchers mostly focus on research on a universal multi-source heterogeneous data conversion and fusion algorithm and a multi-source multi-dimensional heterogeneous multi-modal complex data classification and modeling method, and research on data association mapping of each stage of complex equipment design, manufacture, operation and maintenance, a data association mapping method among stages and an integrated data mode construction method is not seen.
The acquisition, the associated mapping and the closed-loop feedback of the complete life cycle data of the complex equipment are to construct a design optimization iteration and a digital twin foundation stone, parameters of each stage can be effectively improved through data mining and intelligent calculation, and important value is provided for continuous optimization iteration of products. However, due to the multi-source heterogeneity of a large amount of physical information data accumulated in each current stage, the data is lack of integrity and consistency, the reliability and the availability of the data are influenced to a great extent, and the value of the data is difficult to effectively reflect.
Disclosure of Invention
In order to achieve the above purpose, the invention provides the following technical scheme:
full life cycle multisource of complex equipment A method for constructing a heterogeneous data fusion mode, the method comprises the following steps:
step S1: identifying and reducing redundant attributes of multi-source heterogeneous data;
step S2: establishing an association mapping model between data entities;
and step S3: the product family element structure tree and the knowledge data are associated and mapped;
and step S4: designing phase data association mapping;
step (ii) of S5: manufacturing stage data association mapping;
<xnotran> S6: </xnotran> <xnotran> ; </xnotran>
Step (ii) of S7: a multi-source heterogeneous data conversion fusion warehousing method;
wherein, the step S1 comprises a step S11 to a step S12;
step S11: marking the main attribute of the multi-source heterogeneous data;
two data sources D for the same data object X x1 And D x2 Respectively proposing different redundant attribute identification and reduction methods aiming at the nominal attribute and the numerical value attribute; first, a database dictionary is read to obtain respective primary attributes PK (D) x1 ) And PK (D) x2 ) Judgment of PK (D) x1 ) And PK (D) x2 ) Whether the global unique identifier or the self-increment type is generated by the algorithm is treated in the following 3 conditions:
(1) If one of the two is a global unique identifier or a self-increment type, the attribute is marked as a deletion mark, and the other attribute is marked as a main attribute;
(2) If both are globally unique identifiers or self-increment types, the administrator is reminded to select from D x1 And D x2 Selecting main attributes from the residual attribute set, and making deletion marks on the two main attributes;
(3) If the two are not the global unique identification or the self-increment type, judging whether the name and the data type of the two are the same, if so, keeping one mark with more data samples as a main attribute, and if not, reminding an administrator of the slave PK (D) x1 ) And PK (D) x2 ) One of the attributes is selected to be marked as a main attribute;
step S12: dividing the attributes into nominal attributes and numerical attributes according to the attribute value range, and reducing the attributes belonging to the same data type; step S12 includes steps S121 to S122;
step S121: reduction of nominal attributes;
the attribute A and the attribute B are from different systems D x1 And D x2 Has c values, A (a) 1 ,a 2 ,…,a c ) Attribute B has r values B (B) 1 ,b 2 ,…,b r ) If the attribute A and the attribute B describe the same feature, the calculation is performed according to the formula (1):
wherein: x is the number of 2 As a chi-square detection value, o ij To observe the frequency, e ij To the desired frequency, e ij The calculation formula (2) is as follows:
count(A＝a i ) Indicates to take a i The number of data samples of the value, count (B = B) j ) To take a value of b j N is the total number of data samples; for calculated x 2 Comparing through a chi-square table, if the two attributes are related, comparing the data type definition lengths of the attributes A and B, keeping the attribute with the large data type definition length, and marking the other attribute as redundancy;
step S122: reduction of numerical attributes;
the attribute A and the attribute B are two attributes from different systems, and the correlation of the attribute A and the attribute B is measured by calculating covariance as shown in formula (3):
in the formula, a i ∈{a 1 ,a 2 ,…,a n ) Is the value of n samples of the attribute A, b i ∈{b 1 ,b 2 ,…,b n ) Is composed ofThe values of the n samples of the attribute B,
and &>
Respectively are the mean values of the attributes A and B, and n is the number of samples;
the correlation coefficients of the attributes a and B are further calculated from the covariance as in equation (4):
in the formula: sigma A And σ B Respectively representing the standard deviation of the data corresponding to the attributes A and B;
calculated result r A,B Comparing with a set threshold, if the threshold is exceeded, then:
comparing the data type definition lengths of the attributes A and B, reserving the attribute with the large data type definition length, and marking the other attribute as redundancy;
wherein, the step S2: establishing an association mapping model between data entities;
let D x And D y And (3) representing data entities of two types of different objects, and establishing a reference integrity association mapping between the two types of data entities by adopting a relational data model as shown in a formula (5):
in the formula: f (D) x ,D y ) Represents D x And D y Correlation mapping function between D x ×D y Represents D x And D y The two data sets are subjected to Cartesian product operation,
is shown at D x A Attribute and D of data schema y The condition selection operation under the condition that the A' attribute values of the data patterns are equal is performed on the mapping relationIn series, D x Referred to as reference data relationship entities, D x A is D x Reference attribute of (D) y Referred to as referenced relational entities, D y A' is D y <xnotran> ; </xnotran>
Wherein, the step S7 comprises a step S71-a step S74;
step S71: for data object X, the data schema of business system J is
Is based on n attributes->
Formed data pattern>
Its corresponding full lifecycle data warehouse schema S x (A x1 ,A x2 ,…A xm ) Is composed of m attributes A x1 ,A x2 ,…A xm The data pattern of the composition, judgment A x1 ,A x2 ,…A xm Whether the reference attribute established by the associated mapping exists in the database, and the following processing is carried out:
if a reference attribute exists: checking whether the data object data to which it is referred has been imported; if not introduced: reminding the leader to enter reference entity the data object is ended; if so: judging whether the reference attribute data set exists in the attribute of the referred entity, and reminding a user that the reference data is abnormal if the reference attribute data set does not exist in the attribute of the referred entity;
step S72: calculating a mark according to the similarity in the step S1, removing the attribute marked for deletion, and keeping the k attributes
Match by name or redundant tag, match and S x <xnotran> k </xnotran>
Step S3 includes steps S31 to S32;
step S31: construction of product family element structure tree sections self-correlation mapping between point data;
step S32: building product family element structure tree nodes and family models mapping between rule knowledge data.
<xnotran> S4 S41- S42; </xnotran>
Step S41: constructing node mapping of a product design structure tree and a meta structure tree;
step S41 includes step S411 to step S412;
step S411: establishing self-association mapping of node data of a design structure tree;
step S412: establishing association mapping between the nodes of the design structure tree and the nodes of the meta structure tree;
step S42: and establishing the mapping between the nodes of the design structure tree and the design model, the design parameters, the simulation analysis and the intensity analysis of the design stage data.
Step S5 includes steps S51 to S52;
<xnotran> S51: </xnotran> Constructing node mapping of a product manufacturing structure tree and a design structure tree;
wherein, step S51 comprises steps S511-S512;
step S511: establishing a self-correlation mapping of the node data of the manufacturing structure tree;
step S512: establishing association mapping between the nodes of the manufacturing structure tree and the nodes of the design structure tree;
step S52: and establishing the mapping between the nodes of the manufacturing structure tree and the process route, the working procedure, the production task, the task scheduling, the production execution work reporting and the quality detection data in the manufacturing stage.
Step S6 comprises steps S61-S62;
step S61: constructing node mapping of a product operation and maintenance structure tree and a design structure tree or a manufacturing structure tree;
step S611: establishing self-correlation mapping of node data of the operation and maintenance structure tree;
step S612: establishing an association mapping between the operation and maintenance structure tree nodes and the manufacturing structure tree nodes or the design structure tree nodes;
step S62: and establishing mapping between the nodes of the product operation and maintenance structure tree and the installed archives, operation and maintenance records, perception data, fault data and maintenance and piece-changing data in the operation and maintenance stage.
Step S73: for each attribute pair
Check->
And A xq And if the data type and the dimension are consistent, converting if the data type and the dimension are inconsistent.
Step S73 includes step S731 to step S733;
step S731: if the data types are not consistent but are numerical, the unit conversion function of the formula (6) is called:
in the formula:
is->
Data type of A xq DataType is A xq Based on the data type->
Data type of (A) xq Data type, calling corresponding function in type conversion library to implement conversion;
step S732: if the dimensions are not consistent, calling a dimension conversion function of the formula (7):
in the formula:
is->
Data dimension of (A) xq Dimension is A xq According to the data dimension of (4), the function is based on>
Data dimension of (A) xq And (5) data dimension, calling a corresponding function of the type conversion library to realize conversion.
Step S733: writing data to a data warehouse S x 。
Step S74: the process of S71-S73 is repeated for the business system data of other sources of data object X, and only for S, the data with the same main attribute value x And updating the hollow value attribute without repeated writing.
Compared with the prior art, the invention has the beneficial effects that:
(1) The inventor finds in practice that: in the data integration method in the prior art, modeling personnel need to manually analyze and extract characteristics of multi-source heterogeneous data sources, a set of characteristic attribute method is constructed for each type of data, the process is time-consuming and depends too much on experience of the modeling personnel.
(2) The inventor finds out in practice that part of business systems form reusable knowledge data such as professional family base, design rule, maintenance method and the like in application, such data is very important for establishing the traceability of the association relationship between products, the invention establishes the association mapping between the knowledge data and the element structure tree nodes through the product family element structure tree, and establishes the connection dependency relationship between the knowledge data through establishing the data reference integrity constraint.
(3) The inventor finds in practice that data generated by a complex equipment design process corresponds to a certain node of a product design structure tree, and the node of each design structure tree can be obtained from meta-structure tree node instantiation mapping. In contrast, the invention provides a data association mapping method of the meta structure tree and the design structure tree, and an association mapping method of data such as a design stage part model, a two-dimensional atlas, simulation analysis, strength analysis and the like and a design structure tree node and family model, and establishes a connection dependency relationship of the data in the design stage through data reference integrity mapping.
(4) The inventor finds in practice that the data generated by the complex equipment manufacturing process all correspond to a certain node of the product manufacturing structure tree, and the manufacturing structure tree can be generated by designing the structure tree mapping. In contrast, the invention provides a method for mapping between nodes of a manufacturing structure tree and nodes of a design structure tree, and associated mapping between data of a manufacturing process, a procedure, a production task, task scheduling, production reporting, quality inspection and the like and the manufacturing structure tree in a manufacturing stage, and establishes a connection dependency relationship of the data by referring to integrity mapping of the data, thereby associating the data in the manufacturing stage and the data in the design stage.
(5) In practice, the inventor finds that data generated in the operation and maintenance process of complex equipment corresponds to a certain node of a product operation and maintenance structure tree, and the node of the operation and maintenance structure tree can be obtained by mapping nodes of a manufacturing structure tree or a design structure tree. In contrast, the invention provides a mapping relation between the nodes of the operation and maintenance structure tree and the nodes of the design and manufacture structure tree, establishes an association mapping between the nodes of the operation and maintenance structure tree and installed files, operation and maintenance records, sensing data, fault alarming, maintenance and replacement and other data, and establishes a data connection dependency relation by referring to the integrity mapping, thereby associating the data of each stage of operation, maintenance and design and forming a full life cycle data system.
(6) The inventor finds that the integration of various heterogeneous data to a uniform data mode in multi-source heterogeneous data fusion has the problems of data type conversion and data protocol in practice. In contrast, the method abstracts the data conversion type, establishes a multi-source heterogeneous data conversion and data protocol fusion algorithm, and solves the problem of multi-source heterogeneous data extraction and conversion.
Description of the drawings:
FIG. 1 is a schematic diagram of an overall solution;
FIG. 2 is a diagram illustrating a product family meta-structure tree and knowledge data schema association mapping;
FIG. 3 is a schematic diagram of a product design structure tree and a design phase data pattern association mapping;
FIG. 4 is a schematic diagram of a product manufacturing structure tree and associated mapping of manufacturing stage data patterns;
FIG. 5 is a diagram illustrating a product operation and maintenance structure tree and an operation and maintenance stage data pattern association mapping.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments.
Thus, the following detailed description of the embodiments of the invention is not intended to limit the scope of the invention as claimed, but is merely representative of some embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the embodiments of the present invention and the features and technical solutions thereof may be combined with each other without conflict.
A method for constructing a full-life-cycle multi-source heterogeneous data fusion mode of complex equipment comprises the following steps:
step S1: and identifying and reducing redundant attributes of the multi-source heterogeneous data.
Preferably, the data is characterized by attributes, different source systems have the same characteristics for the same type of data, the attribute naming is different, and the problem of attribute explosion and data inconsistency is caused by directly combining the attributes. Aiming at the method, the invention provides a redundant attribute elimination method based on multi-source data, which aims at two data sources D of the same data object X x1 And D x2 Different redundancy attribute identification and reduction methods are proposed for the nominal attribute and the numerical attribute, respectively.
Preferably, step S1 includes steps S11-S12.
Preferably, step S11: and marking the main attribute of the multi-source heterogeneous data.
Reading the database dictionary to obtain respective primary attributes PK (D) x1 ) And PK (D) x2 ) Judgment of PK (D) x1 ) And PK (D) x2 ) Whether the global unique identifier or the self-increment type is generated by the algorithm is processed by the following conditions:
(1) If one of the two is a global unique identifier or a self-increment type, the attribute is marked as a deletion mark, and the other attribute is marked as a main attribute.
(2) If both are globally unique identifiers or self-increment types, the administrator is reminded to select from D x1 And D x2 And selecting the main attributes in the residual attribute set, and marking the deletion of the two main attributes.
(3) If the two are neither the global unique identification nor the self-increment type, judging whether the names and the data types of the two are the same, if the data samples are the same, one mark with more data samples is kept as a main attribute, and if the data samples are different, the manager is reminded of the slave PK (D) x1 ) And PK (D) x2 ) One of which is selected to be marked as the primary attribute.
Preferably, step S12: a redundant attribute identification method.
Dividing the attributes into nominal attributes and numerical attributes according to the attribute value range, and reducing the attributes belonging to the same data type, wherein the reduction is as follows:
preferably, step S121: nominal attribute reduction.
The attribute A and the attribute B are from different systems D x1 And D x2 Has c values, A (a) 1 ,a 2 ,…,a c ) Attribute B has r values B (B) 1 ,b 2 ,…,b r ) If the attribute A and the attribute B describe the same feature, the following calculation is performed:
wherein: x is the number of 2 As a chi-square detection value, o ij To observe the frequency, e ij In order for the frequency to be as high as desired,e ij the calculation formula of (a) is as follows:
count(A＝a i ) Indicates to take a i Number of data samples of value, count (B = B) j ) To take a value of b j N is the total number of data samples. For calculated x 2 And comparing through a chi-square table, if the attribute A is related to the attribute B, comparing the data type definition lengths of the attribute A and the attribute B, keeping the attribute with the large data type definition length, and marking the other attribute as redundancy.
And testing the nominal attributes from different systems pairwise according to the method to finish marking the redundancy attributes of all nominal types.
Preferably, step S122: reduction of numerical attributes.
The attribute A and the attribute B are two attributes from different systems, and the correlation of the attribute A and the attribute B is measured by calculating covariance, namely:
in the formula, a i ∈{a 1 ,a 2 ,…,a n ) Is the value of n samples of the attribute A, b i ∈{b 1 ,b 2 ,…,b n ) For the values of n samples of the attribute B,
and &>
The mean values of the properties a and B, respectively, and n is the number of samples.
The correlation coefficients for attributes a and B are further calculated from the covariance:
in the formula sigma A And σ B Which represent the standard deviation of the data for attributes a and B, respectively.
Calculated result r A,B Comparing with a set threshold, if the threshold is exceeded, then:
and comparing the data type definition lengths of the attributes A and B, reserving the attribute with the large data type definition length, and marking the other attribute as redundancy.
And testing the numerical attributes from different systems pairwise according to the method to finish the marking of the redundancy attributes of all numerical types.
In view of the fact that in the data integration method in the prior art, modeling personnel need to manually analyze and extract characteristics of multi-source heterogeneous data sources, a set of characteristic attribute method is built for each type of data, the process is time-consuming and depends too much on experience of the modeling personnel, the method for distinguishing and eliminating the main attributes is designed for each type of data from multiple sources, the similarity of the attributes is calculated through data mining, redundant attributes are marked, and a characteristic attribute set of each type of data entity is automatically built and formed. And marking and identifying a plurality of data sources one by one through the method of S1 to complete the construction of the data mode of the same data entity.
Preferably, step S2: and establishing an association mapping model between the data entities.
Let D x And D y Representing two types of data entities of different objects, and establishing reference integrity association mapping between the two types of data entities by adopting a relational data model:
in the formula: f (D) x ,D y ) Represents D x And D y Correlation mapping function between D x ×D y Represents D x And D y The two data sets are subjected to Cartesian product operation,
is shown at D x A Attribute and D of data schema y A' attribute value of data schemaConditional selection operation under equality, in the above mapping relation, D x Referred to as reference data relationship entities, D x A is D x Reference attribute of (D) y Referred to as referenced relational entities, D y A' is D y The main set of attributes.
Equation (5) can be implemented by the foreign key constraint of the relational model, and converted into the following by the relational database definition language: CONSTRAINT CONSTRAINT name FOREIGN KEYD x (A)REFERNECESD y (A'), i.e. at D x Adds a reference attribute to disassociate D y <xnotran>, . </xnotran>
Preferably, step S3: and the product family element structure tree and the knowledge data are associated and mapped.
Preferably, step S31: and constructing self-association mapping among node data of the product family meta-structure tree.
The parent-child integrity constraint association mapping for assembly among the nodes of the product family meta-structure tree is as follows by adopting a formula (5):
f (node of meta-structure tree ) = σ Node of meta-structure tree, parent node class code = node of meta-structure tree, node class code (Meta-structure tree node X Meta-structure tree node).
Step S32: and establishing mapping between the nodes of the product family meta-structure tree and knowledge data such as family models, rules and the like.
The integrity constraint association mapping for each family model belonging to a node of the meta-structure tree using equation (5) is as follows:
f (family model, meta-structure tree node) = σ Node class coding of family model = node class coding of element structure tree node (family model x meta-structure tree nodes).
The integrity constraint association mapping for each parameter belonging to a family model using equation (5) is as follows:
f (parameter, family model) = σ Family model coding = family model coding (parameter x family model).
The integrity constraint association mapping for each rule for a node of the meta-structure tree using equation (5) is as follows:
f (rule, meta-structure tree node) = σ Node class encoding = element structure tree node class encoding (regular x meta-structure tree nodes).
The integrity constraint association mapping for each rule for a lifecycle stage using equation (5) is as follows:
f (rule, lifecycle phase) = σ Rule phase encoding = lifecycle phase encoding (rule x lifecycle stage).
The integrity constraint association mapping for each criterion for one or more of the nodes of the meta-structure tree using equation (5) is as follows:
f (standard, meta-structure tree nodes) = σ Node class coding = element structure tree node (Standard x Meta Tree nodes).
The integrity constraint association map for each criterion for one or more lifecycle phases using equation (5) is as follows:
f (standard, life cycle phase) = σ Standard.phase encoding = lifecycle phase.phase encoding (standard × lifecycle stage).
The integrity constraint association map using equation (5) for each standard term that is a requirement to describe some aspect of a parameter is as follows:
f (standard terms, parameters) = σ Standard term parameter coding = parameter coding (standard term × parameter).
The integrity constraint association map from one criterion for each criterion term using equation (5) is as follows:
f (standard term, standard) = σ Standard term standard code = standard code (standard item × standard).
The integrity constraint association map for each data-driven analytical processing model that serves one node of the meta-structure tree using equation (5) is as follows:
f (data-driven analytical process model, element structure tree node) =
σ Node class coding = element structure tree node (data-driven analytical processing model x element Structure TreeA node).
The integrity constraint association map for each data-driven analytical processing model for a lifecycle phase using equation (5) is as follows:
f (data-driven analytical processing model, lifecycle phase) = g
σ Data-driven analytical processing model.phase encoding = lifecycle phase (data-driven analytical processing model × lifecycle stages).
Adopting formula (5) to map the integrity constraint association of each domain dictionary belonging to a meta-structure tree node as follows:
f (Domain dictionary, meta-Structure Tree node) = σ Node class coding of domain dictionary, node class coding = element structure tree node, node class coding (domain dictionary x meta structure tree node).
The integrity constraint association mapping for each external knowledge belonging to a node of the meta-structure tree using equation (5) is as follows:
f (external knowledge, meta-structure tree nodes) = σ Node class encoding = meta-structure tree node class encoding (external knowledge x meta structure tree nodes).
The integrity constraint association mapping for each external knowledge belonging to a lifecycle stage using equation (5) is as follows:
f (external knowledge, lifecycle stage) = σ Outer knowledge phase encoding = lifecycle phase encoding (external knowledge x lifecycle stage).
In view of the fact that reusable knowledge data such as a professional family base, a design rule, a maintenance method and the like are formed in application of part of business systems, and the data are very important for establishing correlation relationship traceability between products, the invention establishes correlation mapping between the knowledge data and nodes of a meta-structure tree through a product family meta-structure tree and establishes a connection dependency relationship between the knowledge data by establishing data reference integrity constraint.
And step S4: and designing a phase data association mapping.
Step S41: and constructing node mapping of the product design structure tree and the element structure tree.
Step S411: and establishing self-association mapping of the node data of the design structure tree.
The integrity constraint association mapping for the product design structure tree node assembly parent-child relationship using formula (5) is as follows:
f (design structure tree node ) = σ Design structure tree node parent node coding = design structure tree node coding (design Structure Tree node X design Structure Tree node).
Step S412: and establishing the association mapping between the nodes of the design structure tree and the nodes of the meta structure tree.
Adopting formula (5) to inherit the integrity constraint association mapping of the self-element structure tree node to the product design structure tree node as follows:
f (design structure tree node, meta structure tree node) = n
σ Node class coding = element structure tree node and node class coding (design structure tree node x element structure tree node).
Step S42: the method comprises the following steps of establishing a mapping between nodes of a design structure tree and design stage design model, design parameters, simulation analysis, strength analysis and other design stage data as follows:
and (3) adopting a formula (5) to map the integrity constraint incidence relation between the nodes of each design model belonging to a product design structure tree as follows:
f (design model, design structure tree node) = σ Node coding = design structure tree node (design model × design structure tree nodes).
The instantiation referential integrity constraint between the design model and the family model is mapped using equation (5) as follows:
f (design model, family model) = σ Design model family model coding = family model coding (design model x family model).
The integrity constraint association between each design parameter belonging to a design model using equation (5) is mapped as follows:
f (design parameters, design model) = σ Model coding = design modelModel coding (design parameters x design model).
The instantiation integrity constraint association mapping of the corresponding meta-parameter of each design parameter using formula (5) is as follows:
f (design parameter, parameter) = σ Design parameter class encoding = parameter encoding (design parameter x parameter).
Adopting formula (5) to map the integrity constraint association of one or more design structure tree nodes corresponding to each item of requirement data as follows:
f (requirement data, design structure tree node) = σ Node coding = design structure tree node (requirement data x design Structure Tree nodes).
The integrity constraint association mapping of each simulation analysis data corresponding to a design model by adopting the formula (5) is as follows:
f (simulation analysis, design model) = σ Simulation analysis model coding = design model coding (simulation analysis x design model).
The integrity constraint association mapping of each item of strength analysis data corresponding to one design model by adopting the formula (5) is as follows:
f (intensity analysis, design model) = σ Intensity analysis model coding = design model coding (intensity analysis x design model).
The integrity constraint association mapping of each two-dimensional atlas corresponding to a design model using equation (5) is as follows:
f (two-dimensional atlas, design model) = σ Two-dimensional atlas model coding = design model coding (two-dimensional atlas x design model).
And (3) performing association mapping on the constraint relation of each two-dimensional atlas belonging to a node of a design structure tree by adopting a formula (5) as follows:
f (two-dimensional atlas, design structure tree node) = sigma Two-dimensional atlas node code = design structure tree node code (two-dimensional atlas x design Structure Tree node).
The integrity constraint association map performed by a designer for each two-dimensional atlas using equation (5) is as follows:
f (two-dimensional atlas, employee) = sigma = Two-dimensional atlas work number = designer work number (two-dimensional album x employee).
The integrity constraint association mapping for each design review record corresponding to a design model using equation (5) is as follows:
f (design review, design model) = σ Model coding = design model coding (design review x design model).
The integrity constraint association map performed by one designer for each design model using equation (5) is as follows:
f (design model, employee) = σ Design model job number = employee job number (design model x employee).
Since the data generated by the complex equipment design process all correspond to a certain node of the product design structure tree, and the nodes of each design structure tree may be obtained from a meta structure tree node instantiation map. In contrast, the invention provides a data association mapping method of the meta structure tree and the design structure tree, and an association mapping method of data such as a design stage part model, a two-dimensional atlas, simulation analysis, strength analysis and the like and a design structure tree node and family model, and establishes a connection dependency relationship of the data in the design stage through data reference integrity mapping.
Preferably, step S5: manufacturing stage data association mapping.
Preferably, step S51: and constructing node mapping of the product manufacturing structure tree and the design structure tree.
Preferably, step S511: and establishing a self-correlation mapping of the node data of the manufacturing structure tree.
The integrity constraint association mapping for the product manufacturing structure tree node assembly parent-child relationship using equation (5) is as follows:
f (the nodes of the manufacturing structure tree, fabricated structure tree node) =
σ Manufactured structure tree node parent node code = manufactured structure tree node code (fabricated structure tree node x fabricated structure tree node).
Preferably, step S512: and establishing an association mapping between the nodes of the manufacturing structure tree and the nodes of the design structure tree.
The integrity constraint association mapping for the product manufacturing structure tree node inheriting the self-design structure tree node by adopting the formula (5) is as follows:
f (manufacturing structure tree node, design structure tree node) = σ Design node coding = design structure tree node (fabricated structure tree node x design structure tree node).
Step S52: and establishing the mapping between the nodes of the manufacturing structure tree and the data of the process route, the working procedure, the production task, the task scheduling, the production execution reporting, the quality detection and the like in the manufacturing stage.
One for each process route using equation (5) the integrity constraint association of the nodes of the structure tree maps as follows:
f (process route, design structure tree node) = σ Process route node code = design structure tree node code (Process route) design of x a structure tree node).
The integrity constraint association map for each process belonging to a process route using equation (5) is as follows:
f (procedure, process route) = σ Process code = process route and process code (process × process route).
The integrity constraint association mapping for each production task corresponding to a node of the manufacturing structure tree using equation (5) is as follows:
f (production task, manufacturing structure tree node) = σ Production task intelligent code making = manufacturing structure tree node manufacturing code (production task X manufacturing the nodes of the structural tree).
The integrity constraint association map for each production task corresponding to a process route using equation (5) is as follows:
f (production task, process route) = sigma Production task, process code = process route, process code (production task x process route).
The integrity constraint association map for each production job belonging to a production task using equation (5) is as follows:
f (production job, production task)＝σ Production job task code = production task code (production job × production task).
The integrity constraint association mapping of each quality inspection operation corresponding to one production operation by adopting the formula (5) is as follows:
f (quality control) the operation is carried out by the following steps, production operation) = σ Job code = production job code for quality inspection job (quality inspection operation. Production operation).
The integrity constraint association mapping for each quality inspection record corresponding to a quality inspection operation by adopting the formula (5) is as follows:
f (quality inspection record, quality inspection operation) = sigma Job code = quality inspection job code (quality control record. Times. Quality control operation).
The integrity constraint association map completed by one production service unit for each production job using equation (5) is as follows:
f (production job, production service unit) = sigma Production job.service unit code = production service unit.service unit code (production job × production service unit).
The integrity constraint association mapping of the tool information which is required to belong to the tool used for each production operation by adopting the formula (5) is as follows:
f (production operation, tool information) = sigma Tool code = tool information and tool code in production operation (production job × tooling information).
The assistive device used for each production operation by adopting the formula (5) must be an integrity constraint associated mapping belonging to assistive device information as follows:
f (production operation, auxiliary information) = sigma Production operation auxiliary tool code = auxiliary tool information auxiliary tool code (production job × information on auxiliary tools).
Adopting formula (5) to alarm each abnormal production service unit to belong to the integrity constraint association mapping of one production service unit as follows:
f (production service unit abnormity alarm, production service unit) = sigma Production service unit code = production service unit code (production service unit abnormity alarm x production clothesA business unit).
The integrity constraint association mapping for each execution report record corresponding to a production operation by adopting the formula (5) is as follows:
f (execution of labor reporting, production operation) = sigma Executing the newspaper work code = the production work code (executive newspaper and production book) production operations).
And (3) carrying out association mapping on the integrity constraint relation of each acquired processing data corresponding to one production operation by adopting a formula (5) as follows:
f (processing data acquisition, production operation) = sigma Processing data acquisition, operation code = production operation and operation code (processing data acquisition. Times. Production work).
The integrity constraint association mapping for each collected data belonging to a node of the manufacturing structure tree using equation (5) is as follows:
f (processing data acquisition, manufacturing structure tree node) = sigma Manufacturing code = manufacturing structure tree node manufacturing code for processing data acquisition (process data collection × fabrication tree nodes).
Adopting formula (5) to map the integrity constraint association between the collected data corresponding to each abnormal alarm record as follows:
f (abnormal alarm recording, processing data acquisition) = sigma Abnormal alarm recording, recording code = processing data acquisition and recording code (abnormal alarm recording x process data acquisition).
The integrity constraint association mapping for each anomaly alarm record belonging to a node of the manufacturing structure tree using equation (5) is as follows:
f (abnormal alarm record, manufacturing structure tree node) = sigma Abnormal alarm record manufacturing code = manufacturing structure tree node manufacturing code (anomaly alarm records x manufacturing structure tree nodes).
The integrity constraint association mapping for each quality inspection record belonging to a node of the manufacturing structure tree using formula (5) is as follows:
f (quality control record, manufacturing structure tree node) = σ Quality inspection record, manufacturing code = manufacturing structure tree node, manufacturing code (quality check record x manufacturing structure tree node).
The integrity constraint correlation mapping for each quality issue report belonging to a corresponding quality check record using equation (5) is as follows:
f (quality problem report, quality inspection record) = σ Quality problem report, record code = quality check record, record code (quality problem report. Times. Quality control record).
The integrity constraint association map for each quality issue report belonging to a node of the manufacturing structure tree using equation (5) is as follows:
f (quality problem report, manufacturing Structure Tree node) = σ Manufacturing code = manufacturing structure tree node manufacturing code (quality issue report × fabricated structure tree node).
The data generated in view of the complex equipment manufacturing process all correspond to a certain node of the product manufacturing structure tree, and the manufacturing structure tree can be generated through the design structure tree mapping. In contrast, the invention provides a method for mapping between nodes of a manufacturing structure tree and nodes of a design structure tree, and associated mapping between data of a manufacturing process, a procedure, a production task, task scheduling, production reporting, quality inspection and the like and the manufacturing structure tree in a manufacturing stage, and establishes a connection dependency relationship of the data by referring to integrity mapping of the data, thereby associating the data in the manufacturing stage and the data in the design stage.
Preferably, step S6: and (5) associating and mapping the operation and maintenance stage data.
Preferably, step S6 includes steps S61 to S62.
Preferably, step S61: and constructing node mapping of the product operation and maintenance structure tree and the design structure tree or the manufacturing structure tree.
Preferably, step S611: and establishing self-correlation mapping of the node data of the operation and maintenance structure tree.
The integrity constraint association mapping for assembling the parent-child relationship to the nodes of the product operation and maintenance structure tree by adopting the formula (5) is as follows:
f (operation and maintenance structure tree node) = sigma Operation and maintenance structure tree node parent node code = operation and maintenance structure tree node code (operation and maintenance structure tree node x operation and maintenance structure tree node).
Preferably, step S612: and establishing the association mapping between the operation and maintenance structure tree nodes and the manufacturing structure tree nodes or the design structure tree nodes.
Adopting formula (5) to inherit the integrity constraint association mapping of the node of the product operation and maintenance structure tree from the node of the design structure tree or the node of the manufacturing structure tree as follows:
f (operation and maintenance structure) the nodes of the tree are connected with each other, (fabricated structure tree node $ design structure tree node)) =
σ Operation and maintenance structure tree node, reference node code = design structure tree node, node code V operation and maintenance structure tree node, reference node code = manufacturing structure tree node, manufacturing code (fabricated structure tree node × (designed structure tree node × (fabricated structure tree node ×), wherein: U represents a data set union operation, and V represents a condition or operation.
Preferably, step S62: and establishing mapping between the nodes of the product operation and maintenance structure tree and the installed archives, operation and maintenance records, perception data, fault data and maintenance and piece-changing data in the operation and maintenance stage.
And (3) adopting a formula (5) to record the integrity constraint association mapping of each equipment node archive belonging to one operation and maintenance structure tree node as follows:
f (equipment node archive, operation and maintenance structure tree node) = sigma Node coding = node of operation and maintenance structure tree and node coding (equipment node archive x operation and maintenance structure tree node).
The integrity constraint association mapping for each operation record belonging to an equipment node archive using equation (5) is as follows:
f (operation record, equipment node archive) = σ Record of operation document coding = equipment node document coding (operation record x equipment node archive).
The integrity constraint association mapping for each monitored attribute belonging to an equipment node archive using equation (5) is as follows:
f (monitoring attribute, operation and maintenance structure tree node) = sigma Monitoring attribute node code = operation and maintenance structure tree node code (monitor attribute x operation and maintenance structure tree node).
Adopting formula (5) to map the integrity constraint association of each acquired sensing data corresponding to one monitoring attribute as follows:
f (perceptual data, monitoring attribute) = σ Perceptual data attribute coding = monitoring attribute coding (perception data x monitoring attributes).
An integrity constraint association mapping that reflects a monitored attribute status for each piece of collected anomaly data using equation (5) is as follows:
f (abnormal data, monitoring attribute) = σ Abnormal data attribute coding = monitoring attribute coding (abnormal data × monitoring attribute).
Adopting formula (5) to map the integrity constraint association of each piece of acquired abnormal data corresponding to one piece of alarm information as follows:
f (abnormal data, alarm information) = σ Abnormal data alarm code = alarm information alarm code (abnormal data × warning information).
And (3) adopting a formula (5) to correspond one operation and maintenance structure tree node integrity constraint association mapping to each overhaul record as follows:
f (maintenance record, operation and maintenance structure tree node) = sigma Node coding = operation and maintenance structure tree node and node coding (maintenance records x operation and maintenance structure tree nodes).
And (3) adopting a formula (5) to correspond to each alarm message to one overhaul record integrity constraint association mapping as follows:
f (alarm information, maintenance record) = sigma Alarm information maintenance task code = maintenance record and maintenance task code (alarm information x service record).
And (3) adopting a formula (5) to map the integrity constraint association of each fault record corresponding to one operation and maintenance structure tree node as follows:
f (fault record, operation and maintenance structure tree node) = sigma Node code = operation and maintenance structure tree node and node code of fault record (failure record x operation and maintenance structure tree node).
The integrity constraint association mapping for each fault record corresponding to a service record using equation (5) is as follows:
f (fault record, maintenance record) = sigma Fault record, overhaul task code = overhaul record, overhaulTask coding (failure record-service records).
And (3) adopting a formula (5) to correspond one operation and maintenance structure tree node integrity constraint association mapping to each maintenance and change record as follows:
f (maintenance and replacement record, operation and maintenance structure tree node) = sigma Node code = node of operation and maintenance structure tree and node code of maintenance and replacement record (repair change record x operation and maintenance tree node).
The integrity constraint association mapping for each repair change record corresponding to a service record using equation (5) is as follows:
f (maintenance and replacement record, maintenance record) = sigma Repair change record, repair task code = repair record, repair task code (repair change record x service record).
In view of the fact that the data generated in the operation and maintenance process of the complex equipment corresponds to a certain node of the operation and maintenance structure tree of the product, the node of the operation and maintenance structure tree can be obtained from the node mapping of the manufacturing structure tree or the design structure tree. In contrast, the invention provides a mapping relation between the nodes of the operation and maintenance structure tree and the nodes of the design and manufacture structure tree, establishes an association mapping between the nodes of the operation and maintenance structure tree and installed files, operation and maintenance records, sensing data, fault alarming, maintenance and replacement and other data, and establishes a data connection dependency relation by referring to the integrity mapping, thereby associating the data of each stage of operation, maintenance and design and forming a full life cycle data system.
Step S7: a multi-source heterogeneous data conversion fusion storage method.
And (5) establishing a complex full-life-cycle data warehouse mode of the complex equipment full-life-cycle data fusion according to the steps from the step S3 to the step S6, and mapping and warehousing the multi-source heterogeneous data based on the data mode.
Preferably, step S7 includes steps S71 to S74.
Step S71: for data object X, the data schema of business system J is
Is based on n attributes->
Is composed of data mode->
Its corresponding full lifecycle data warehouse schema S x (A x1 ,A x2 ,…A xm ) Is composed of m attributes A x1 ,A x2 ,…A xm The data pattern of the composition, judgment A x1 ,A x2 ,…A xm Whether the reference attribute established by the association mapping exists in (1):
if a reference attribute exists: it is checked whether the data object data it is referencing has been imported. If not introduced: reminding the leader to enter the reference entity data object and ending; if so: judging whether the reference attribute data set exists in the attribute of the referred entity, and reminding a user that the reference data is abnormal if the reference attribute data set does not exist in the attribute of the referred entity;
step S72: calculating a mark according to the similarity in the step S1, removing the attribute marked for deletion, and keeping the k attributes
Matching by name or a redundant tag, matching with S x Mapping pairs of k attributes
/>
Step S73: for each attribute pair
Check->
And A xq And if the data type and the dimension are consistent, converting if the data type and the dimension are inconsistent.
Preferably, step S73 includes steps S731 to S733.
Preferably, step S731: if the data types are not consistent but are numerical (such as int and float, datetime and string, etc.), the unit conversion function of equation (6) is called:
in the formula:
is->
Data type of A xq DataType is A xq Based on the data type->
Data type of (A) xq And (4) calling a corresponding function in the type conversion library to realize conversion according to the data type.
Preferably, step S732: if the dimensions are not consistent (e.g., km and m, or Fahrenheit and Centigrade, etc.), then the dimension transfer function of equation (7) is called:
in the formula:
is->
Data dimension of (A) xq Dimension is A xq According to the data dimension of (4), the function is based on>
Data dimension of (A) xq And (5) data dimension, calling a corresponding function of the type conversion library to realize conversion.
Preferably, step S733: writing data to a data warehouse S x 。
Preferably, step S74: business system to other sources of data object XRepeating the processes S71-S73 on the data, and only performing S on the data with the same main attribute value x And updating the hollow value attribute without repeated writing.
The data type conversion and data protocol problems exist in the integration of various heterogeneous data into a unified data mode in multi-source heterogeneous data fusion. In contrast, the method abstracts the data conversion type, establishes a multi-source heterogeneous data conversion and data protocol fusion algorithm, and solves the problem of multi-source heterogeneous data extraction and conversion.
The above embodiments are only used for illustrating the invention and not for limiting the technical solutions described in the invention, and although the present invention has been described in detail in the present specification with reference to the above embodiments, the present invention is not limited to the above embodiments, and therefore, any modification or equivalent replacement of the present invention is made; all such modifications and variations are intended to be included herein within the scope of this disclosure and the appended claims.
Claims (9)
1. A method for constructing a full-life-cycle multi-source heterogeneous data fusion mode of complex equipment is characterized by comprising the following steps: the method comprises the following steps:
step S1: identifying and reducing redundant attributes of multi-source heterogeneous data;
step S2: establishing an association mapping model between data entities;
and step S3: the product family element structure tree and the knowledge data are associated and mapped;
and step S4: designing phase data association mapping;
step S5: manufacturing stage data association mapping;
step S6: performing operation and maintenance stage data association mapping;
step S7: a multi-source heterogeneous data conversion fusion warehousing method;
wherein the content of the first and second substances, step S1 comprises steps S11-S12;
step S11: marking the main attribute of the multi-source heterogeneous data;
two data sources D for the same data object X x1 And D x2 Different redundancy attributes are proposed for the nominal attribute and the numerical attribute respectivelySex identification and reduction methods; first, a database dictionary is read to obtain respective primary attributes PK (D) x1 ) And PK (D) x2 ) Judgment of PK (D) x1 ) And PK (D) x2 ) Whether the global unique identifier or the self-increment type is generated by the algorithm is treated in the following 3 conditions:
(1) If one of the two is a global unique identifier or a self-increment type, the attribute is marked as a deletion mark, and the other attribute is marked as a main attribute;
(2) If both are globally unique identifiers or self-increment types, the administrator is reminded to select from D x1 And D x2 Selecting main attributes from the residual attribute set, and making deletion marks on the two main attributes;
(3) If the two are not the global unique identification or the self-increment type, judging whether the name and the data type of the two are the same, if so, keeping one mark with more data samples as a main attribute, and if not, reminding an administrator of the slave PK (D) x1 ) And PK (D) x2 ) One of the attributes is selected to be marked as a main attribute;
step S12: dividing the attributes into nominal attributes and numerical attributes according to the attribute value range, and reducing the attributes belonging to the same data type; step S12 includes steps S121 to S122;
step S121: reduction of nominal attributes;
the attribute A and the attribute B are from different systems D x1 And D x2 Has c values, A (a) 1 ,a 2 ,…,a c ) Attribute B has r values B (B) 1 ,b 2 ,…,b r ) If the attribute A and the attribute B describe the same feature, the calculation is performed according to the formula (1):
wherein: x is the number of 2 As a chi-square detection value, o ij In order to observe the frequency of the frequency, e.g. of the type ij To the desired frequency, e ij The calculation formula (2) is as follows:
count(A＝a i ) Indicates to take a i Number of data samples of value, count (B = B) j ) To take a value of b j The number of samples of (a) to be tested, n is the total number of data samples; for calculated x 2 Comparing through a chi-square table, if the two attributes are related, comparing the data type definition lengths of the attributes A and B, keeping the attribute with the large data type definition length, and marking the other attribute as redundancy;
step S122: reduction of numerical attributes;
the attribute A and the attribute B are two attributes from different systems, and the correlation of the attribute A and the attribute B is measured by calculating covariance as shown in formula (3):
in the formula, a i ∈{a 1 ,a 2 ,…,a n ) Is the value of n samples of the attribute A, b i ∈{b 1 ,b 2 ,…,b n ) For the values of n samples of the attribute B,
and &>
Respectively are the mean values of the attributes A and B, and n is the number of samples;
the correlation coefficients of the attributes a and B are further calculated from the covariance as in equation (4):
in the formula: sigma A And σ B Respectively representing the standard deviation of the data corresponding to the attributes A and B;
calculated result r A,B Comparing with a set threshold, if the threshold is exceeded, then:
comparing the data type definition lengths of the attributes A and B, reserving the attribute with the large data type definition length, and marking the other attribute as redundancy;
wherein, the step S2: establishing an association mapping model between data entities;
let D x And D y And (3) representing data entities of two types of different objects, and establishing a reference integrity association mapping between the two types of data entities by adopting a relational data model as shown in a formula (5):
in the formula: f (D) x ,D y ) Represents D x And D y Correlation mapping function between D x ×D y Represents D x And D y The two data sets are subjected to Cartesian product operation,
is shown at D x A Attribute and D of data schema y Conditional selection operation when the A' attribute values of the data patterns are equal, and D is the mapping relation x Referred to as reference data relationship entities, D x A is D x Reference attribute of (D) y Referred to as referenced relational entities, D y A' is D y A set of primary attributes;
wherein, the step S7 comprises a step S71-a step S74;
step S71: for data object X, the data schema of business system J is
Is composed of n attributes
Formed data pattern>
It is toFull lifecycle data warehouse schema for response S x (A x1 ,A x2 ,…A xm ) Is composed of m attributes A x1 ,A x2 ,…A xm The data pattern of the composition, judgment A x1 ,A x2 ,…A xm Whether the reference attribute established by the associated mapping exists in the database, and the following processing is carried out:
if a reference attribute exists: checking whether the data object data to which it is referred has been imported; if not introduced: reminding the leader to enter the reference entity data object and ending; if so: judging whether the reference attribute data set exists in the attribute of the referred entity, and reminding a user that the reference data is abnormal if the reference attribute data set does not exist in the attribute of the referred entity;
step S72: calculating a mark according to the similarity in the step S1, removing the attribute marked for deletion, and keeping the k attributes
Match by name or redundant tag, match and S x Mapping pairs of k attributes
2. The complex equipment full-life-cycle multi-source heterogeneous data fusion mode construction method of claim 1, characterized by comprising the following steps: step S3 includes steps S31 to S32;
step S31: building self-association mapping between node data of the product family meta-structure tree;
step S32: and establishing mapping between the nodes of the product family meta-structure tree and the family model and rule knowledge data.
3. The complex equipment full-life-cycle multi-source heterogeneous data fusion mode construction method of claim 2, characterized in that: step S4 comprises steps S41-S42;
step S41: constructing node mapping of a product design structure tree and a meta structure tree;
step S412: establishing association mapping between the nodes of the design structure tree and the nodes of the meta structure tree;
step S42: and establishing the mapping between the nodes of the design structure tree and the design model, the design parameters, the simulation analysis and the intensity analysis of the design stage data.
4. The complex equipment full-life-cycle multi-source heterogeneous data fusion mode construction method of claim 3, characterized in that: step S5 includes steps S51 to S52;
step S51: constructing node mapping of a product manufacturing structure tree and a design structure tree;
wherein, step S51 comprises steps S511-S512;
step S52: and establishing the mapping between the nodes of the manufacturing structure tree and the process route, the working procedure, the production task, the task scheduling, the production execution work reporting and the quality detection data in the manufacturing stage.
5. The complex equipment full-life-cycle multi-source heterogeneous data fusion mode construction method of claim 3, characterized in that: step S6 comprises steps S61-S62;
step S61: constructing node mapping of a product operation and maintenance structure tree and a design structure tree or a manufacturing structure tree;
step S62: and establishing mapping between the nodes of the product operation and maintenance structure tree and the installed archives, operation and maintenance records, perception data, fault data and maintenance and piece-changing data in the operation and maintenance stage.
6. The complex equipment full-life-cycle multi-source heterogeneous data fusion mode construction method of claim 5, wherein: step S73: for each attribute pair
Check->
And A xq And if the data type and the dimension are consistent, converting if the data type and the dimension are inconsistent.
7. The complex equipment full-life-cycle multi-source heterogeneous data fusion mode construction method of claim 6, wherein: step S73 includes step S731 to step S733;
step S731: if the data types are not consistent but are numerical, the unit conversion function of the formula (6) is called:
in the formula:
is->
Data type of A xq DataType is A xq According to the function
Data type of (A) xq Data type, calling corresponding function in type conversion library to implement conversion;
step S732: if the dimensions are not consistent, calling a dimension conversion function of the formula (7):
in the formula:
is->
Data dimension of (A) xq Dimension is A xq According to the data dimension of (4), the function is based on>
Data dimension of (A) xq And (5) data dimension, calling a corresponding function of the type conversion library to realize conversion.
8. The complex equipment full-life-cycle multi-source heterogeneous data fusion mode construction method of claim 7, wherein: step S733: writing data to a data warehouse S x 。
9. The complex equipment full-life-cycle multi-source heterogeneous data fusion mode construction method of claim 8, wherein: step S74: the process of S71-S73 is repeated for the business system data of other sources of data object X, and only for S, the data with the same main attribute value x And updating the hollow value attribute without repeated writing.
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