CN115481182A - Data fusion processing method and system, storage medium and electronic equipment - Google Patents
Data fusion processing method and system, storage medium and electronic equipment Download PDFInfo
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Abstract
The application discloses a data fusion processing method, a data fusion processing system, a storage medium and electronic equipment, wherein multi-party data are obtained, the multi-party data are subjected to structured analysis processing through a structured data model to obtain structured data, the structured data are subjected to matching operation through a rule engine configuration model, and the structured data subjected to matching operation are fused through a multiple data source data fusion model. Through the method, data integration is not needed through manual operation, structured analysis processing is only needed to be automatically carried out on the multi-party data through the multi-data-source data fusion model, transmission of structured data of a plurality of systems and a plurality of nodes is achieved, and data from different systems and users are fused, so that management risks and error operation probability are reduced, and data fusion efficiency is improved. In addition, the rule engine configuration model is used for carrying out operations such as updating and adjusting, newly adding new fusion rules and the like on the data fusion method in real time, and the rule maintenance efficiency of data fusion is improved.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a data fusion processing method and system, a storage medium, and an electronic device.
Background
With the rapid increase of bank digital business and the rapid improvement of digital capability, various demands are put forward on various aspects such as resource types, resource quantity, resource implementation modes and the like by various application systems.
The resource management and control party and the resource implementing party have stricter requirements on the examination and the distribution of different resource requirements. From the proposal of resource demand to the formal delivery of resources, the method relates to the fusion of various resource data of a plurality of systems, a plurality of departments and a plurality of users.
The existing data fusion method carries out data integration through manual operation, and the mode has the disadvantages of low efficiency, high manual processing cost, high management risk and error operation probability, and high redundancy in the data integration process, so that the whole data fusion efficiency is low.
Disclosure of Invention
In view of this, the present application discloses a data fusion processing method, system, storage medium and electronic device, which aim to reduce management risk and error operation probability, and improve data fusion efficiency and rule maintenance efficiency of data fusion.
In order to achieve the purpose, the technical scheme disclosed by the method is as follows:
the first aspect of the present application discloses a data fusion processing method, where the method includes:
acquiring multi-party data; the multi-party data at least comprises a resource application data source, an existing data source and a user newly added data source; the existing data source is used for representing the resource data in the in-use state; the user newly-added data source is used for representing newly-added data at the front end of the user;
carrying out structured analysis processing on the multi-party data through a structured data model to obtain structured data; the structured data characterizes data parameters, data types and data structure data;
matching operation is carried out on the structured data through a rule engine configuration model, and the structured data after matching operation is fused through a multiple data source data fusion model; the rule engine configuration model is a model for completing filling of structured data information according to rules and specific definitions in the rules.
Preferably, the process of acquiring multiparty data includes:
acquiring approved resource application formatted data from a resource auditing system; the resource application formatted data is defined by a pre-designed structural model;
acquiring an existing data source in use from a resource system;
and acquiring a newly added user data source from the user front-end system.
Preferably, the performing structured analysis processing on the multi-party data through a structured data model to obtain structured data includes:
designing a structured data model;
confirming a data parameter table, a data type and a data structure in the structured data model;
and carrying out structured analysis processing on the multi-party data through the data parameter table, the data type and the data structure to obtain structured data.
Preferably, the method further comprises the following steps:
judging whether the formatted resource application data source of the structured data is matched with the existing data source;
if the formatted resource application data source of the structured data is consistent with the existing data source, determining that the formatted resource application data source of the structured data is matched with the existing data source;
and if the formatted resource application data source of the structured data is not consistent with the existing data source, determining that the formatted resource application data source of the structured data is not matched with the existing data source.
Preferably, the configuring a model through a rule engine, performing matching operation on the structured data, and fusing the structured data after the matching operation through a multiple data source data fusion model includes:
if the formatted resource application data source of the structured data is matched with the existing data source, matching the formatted resource application data source of the structured data with the existing data source through parameter identification of a rule engine configuration model;
and fusing the matched formatted resource application data source, the existing data source and the user newly-added data source through a multiple data source data fusion model.
Preferably, the method further comprises the following steps:
carrying out rule definition operation on the rule through the rule engine configuration model; the rule definition operation at least comprises a definition rule name, a definition parameter rule, a definition rule tag and a definition operation; the definition parameter rule comprises a parameter name and a regular rule; the defining operation comprises an operation rule and an operation matching.
Preferably, the method further comprises the following steps:
if the formatted resource application data source of the structured data is not matched with the existing data source, initializing and assigning system parameters through server resource data of the formatted resource application data source.
A second aspect of the present application discloses a data fusion processing system, which includes:
an acquisition unit configured to acquire multiparty data; the multi-party data at least comprises a resource application data source, an existing data source and a user newly added data source; the existing data source is used for representing the resource data in the in-use state; the user newly-added data source is used for representing the newly-added data of the user front end;
the processing unit is used for carrying out structured analysis processing on the multi-party data through a structured data model to obtain structured data; the structured data represents data of data parameters, data types and data structures;
the matching unit is used for matching the structured data through a rule engine configuration model and fusing the structured data after matching operation through a multiple data source data fusion model; the rule engine configuration model is a model for completing filling of structured data information according to rules and specific definitions in the rules.
A third aspect of the present application discloses a storage medium, where the storage medium includes stored instructions, and when the instructions are executed, a device where the storage medium is located is controlled to execute the data fusion processing method according to any one of the first aspect.
A fourth aspect of the present application discloses an electronic device, comprising a memory, and one or more instructions, wherein the one or more instructions are stored in the memory and configured to be executed by the one or more processors to perform the data fusion processing method according to any one of the first aspect.
According to the technical scheme, the application discloses a data fusion processing method, a system, a storage medium and electronic equipment, wherein multi-party data is obtained, the multi-party data at least comprises a resource application data source, an existing data source and a newly added user data source, the existing data source is used for representing resource data in a using state, the newly added user data source is used for representing newly added data at the front end of a user, the multi-party data is subjected to structural analysis processing through a structural data model to obtain structural data, the structural data represents data parameters, data types and data structures, the structural data is subjected to matching operation through a rule engine configuration model, and the structural data after the matching operation is fused through a multiple data source data fusion model; the rule engine configuration model is a model for completing the filling of the structured data information according to the rules and the specific definitions in the rules. Through the method, data integration is not needed through manual operation, structured analysis processing is only needed to be automatically carried out on the multi-party data through the multi-data-source data fusion model, transmission of structured data of a plurality of systems and a plurality of nodes is achieved, and data from different systems and users are fused, so that management risks and error operation probability are reduced, and data fusion efficiency is improved. In addition, the rule engine configuration model is used for carrying out operations such as updating adjustment and newly-added fusion rules on the data fusion method in real time, and the rule maintenance efficiency of data fusion is improved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only the embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flowchart of a data fusion processing method disclosed in an embodiment of the present application;
fig. 2 is a schematic structural diagram of a data fusion processing system disclosed in an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
In this application, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
As known from the background art, the existing data fusion method integrates data through manual operation, and the mode has the disadvantages of low efficiency, high manual processing cost, high management risk, high error operation probability and high process redundancy, so that the whole data fusion efficiency is low.
In order to solve the above problems, the present application discloses a data fusion processing method, system, storage medium, and electronic device, which do not need to perform data integration through manual operation, and only need to perform structured analysis processing on multi-party data automatically through a multiple data source data fusion model, so as to realize the transmission of structured data of multiple systems and multiple nodes, and through fusing data from different systems and users, thereby reducing management risk and error operation probability, and improving the efficiency of data fusion. In addition, the rule engine configuration model is used for carrying out operations such as updating and adjusting, newly adding new fusion rules and the like on the data fusion method in real time, and the rule maintenance efficiency of data fusion is improved. In addition, the rule engine configuration model is used for carrying out operations such as updating and adjusting, newly adding new fusion rules and the like on the data fusion method in real time, and the rule maintenance efficiency of data fusion is improved. The specific implementation is illustrated by the following examples.
Referring to fig. 1, a schematic flow chart of a data fusion processing method disclosed in an embodiment of the present application is shown, where the data fusion processing method mainly includes the following steps:
s101: acquiring multi-party data; the multi-party data at least comprises a resource application data source, an existing data source and a user new data source; the existing data source is used for representing the resource data in the in-use state; and the user newly-added data source is used for representing the newly-added data of the user front end.
Wherein, the resource application data source mainly comprises: server resource data (such as central processing unit CPU, memory, specification, etc.), storage resource data (capacity, storage type, etc.), software installation data (middleware weblogic, database Oracle client, etc.), network resource data (access relationship, load balancing, etc.), and the like.
An existing data source refers to data that has been supplied for use (in use). The existing data sources include: server resource data in an in-use state (such as a Central Processing Unit (CPU), a memory, a specification and the like), storage resource data in an in-use state (capacity, a storage type and the like), software installation data in an in-use state (middleware weblogic, a database Oracle client and the like), network resource data in an in-use state (access relationship, load balancing and the like) and the like.
Each data source (resource application data source, existing data source and user newly-added data source) in the multi-party data relates to data information of multiple dimensions, and mainly comprises system data, deployment unit data, server data, storage data, software data, network data and the like, so that a relatively complex source data network is formed.
The process of specifically acquiring multi-party data is shown as A1-A3.
A1: acquiring approved resource application formatted data from a resource auditing system; the resource application formatting data is defined by a pre-designed structural model.
The resource application formatting data comprises server resource data, storage resource data, software installation data, network resource data and the like.
The resource application formatted data defines a resource application data source through a structured model to obtain the resource application formatted data.
The resource application formatting data comprises server resource data (such as a Central Processing Unit (CPU), an internal memory, specifications and the like) defined by a structural model, storage resource data (capacity, storage type and the like) defined by the structural model, software installation data (middleware weblogic, a database Oracle client and the like) defined by the structural model, network resource data (access relationship, load balance and the like) defined by the structural model and the like.
A2: an existing data source in use is obtained from a resource system.
The existing data sources mainly include server resource data in a use state, storage resource data in the use state, software installation data in the use state, network resource data in the use state and the like.
An existing data source in use is obtained and parsed from the resource system.
A3: and acquiring a newly added user data source from the user front-end system.
The method comprises the steps of providing a user front end data adding function in a user front end system and obtaining user added data.
A1, A2 and A3 are in parallel relation.
S102: carrying out structured analysis processing on the multi-party data through a structured data model to obtain structured data; the structured data characterizes data parameters, data types, and data structures.
Specifically, the process of obtaining structured data by performing structured analysis processing on multi-party data through a structured data model is shown as B1-B3.
B1: and designing a structured data model.
B2: data parameter tables, data types, and data structures in the structured data model are validated.
To facilitate understanding of the data parameter table, data types, and data structures, an example is illustrated here:
for example, the main parameters of the computing resources are: the number of resources, the type of the resources, a CPU, a memory and the like, which are data parameter tables; the data type of the resource quantity is 'number', the resource type is 'single selection text box', and the like; the data structure is a fixed parameter of the computing resource + a special parameter of various computing resource types.
B3: and performing structured analysis processing on the multi-party data through the data parameter table, the data type and the data structure to obtain structured data.
The structured data comprising the data parameter table, the data types and the data structures are obtained by carrying out structured analysis processing on the multi-party data through the data parameter table, the data types and the data structures.
And analyzing the resource application formatted data, and judging whether the formatted resource application data source of the structured data is matched with the existing data source.
The basis for judging whether the formatted resource application data source of the structured data is matched with the existing data source is various, such as: when the server resource expands horizontally, the resource type, CPU, memory and the like need to be consistent with the existing data, and the longitudinal expansion machine type needs to be consistent and the like.
And if the formatted resource application data source of the structured data is consistent with the existing data source, determining that the formatted resource application data source of the structured data is matched with the existing data source.
When it is determined that the formatted resource application data source of the structured data matches the existing data source, S103 is performed.
And if the formatted resource application data source of the structured data is not consistent with the existing data source, determining that the formatted resource application data source of the structured data is not matched with the existing data source.
If the formatted resource application data source of the structured data is not matched with the existing data source, the system parameters are initialized and assigned through the server resource data of the formatted resource application data source.
The system parameters are initialized and assigned, namely the data parameters and the data types defined in the structural model are obtained by analyzing the structured data, and the system parameters are initialized and assigned according to the data parameters and the data types defined in the structural model.
S103: matching operation is carried out on the structured data through a rule engine configuration model, and the structured data after matching operation is fused through a multiple data source data fusion model; the rule engine configuration model is a model for completing filling of structured data information according to rules and specific definitions in the rules.
If the formatted resource application data source of the structured data is matched with the existing data source, the formatted resource application data source of the structured data is matched with the existing data source through the parameter identification of the rule engine configuration model, and the system parameter is initialized and assigned to complete the fusion of the multi-party data.
The parameter identification is a data parameter, a data type, etc. of the rule engine configuration model.
Carrying out rule definition operation on the rule through a rule engine configuration model; the rule definition operation at least comprises a definition rule name, a definition parameter rule, a definition rule tag and a definition operation; defining parameter rules including parameter names and regular rules; defining operations includes operation rules and operation matching.
The rule engine configuration model is mainly used for rule definition operation, and after the rule definition operation, data information matching can be carried out according to the corresponding rule.
To facilitate understanding of the rule definition operations, the following is illustrated here by way of example:
for example, the matching of the two fields of the operating system type and the operating system version in the resource application data corresponds to the operating system image in the rule.
And acquiring and analyzing the structured data, acquiring rules in a rule base, matching the data parameters with the mark parameters defined by the rules, and completing filling of data information according to the matched rules and specific definitions in the rules.
The rule definition mainly comprises four parts of definition rule name, definition parameter rule, definition rule label and definition rule operation.
The parameter defining rule is mainly used for data identification matching, and the parameter defining operation is used for defining actions after parameter matching is successful, such as generating configuration data. Defining parameter rules includes defining parameter names, defining regular rules, etc.
Defining rule operations includes defining operation rules, defining operation matches, and the like.
And providing the fused data to a downstream system or process for use.
The scheme performs structured transmission, disassembly and fusion on different data sources, matches the data of multiple parties and applies the data to subsequent processes. Through structured data and automatic matching action, the effective fusion and use of the data are completed, and the data transmission accuracy is improved while the manual processing of the data is reduced.
The scheme provides a method for realizing a data fusion mechanism configured through a rule engine configuration model, the configuration method is suitable for the current situation of multi-system data interaction of a bank, and functions of rule name, parameter matching, successful matching action processing and the like are realized through providing a front-end configuration page. The configurability, the usability and the reusability of the data fusion rule are improved.
Aiming at the characteristics of banking business, the method reduces the amount of manual data processing and improves the data fusion efficiency and the resource delivery rate while meeting the accuracy of resource data supply. According to the scheme, the multi-party data is subjected to structured analysis processing, and then the resource application information and the component information are matched through the rule engine configuration model, so that the data is efficiently and accurately transmitted. In addition, the rule engine configuration model can update and adjust the data fusion method in real time or add new fusion rules, so that the rule maintenance efficiency of data fusion is greatly improved.
In the embodiment of the application, data integration is not needed through manual operation, structured analysis processing is only needed to be automatically carried out on multi-party data through a multi-data source data fusion model, the transmission of structured data of multiple systems and multiple nodes is achieved, and data from different systems and users are fused, so that management risk and misoperation probability are reduced, and data fusion efficiency is improved. In addition, the rule engine configuration model is used for carrying out operations such as updating adjustment and newly-added fusion rules on the data fusion method in real time, and the rule maintenance efficiency of data fusion is improved.
Based on the data fusion processing method disclosed in fig. 1 in the foregoing embodiment, an embodiment of the present application further discloses a data fusion processing system, which includes an obtaining unit 201, a processing unit 202, and a matching unit 203, as shown in fig. 2.
An acquisition unit 201 configured to acquire multi-party data; the multi-party data at least comprises a resource application data source, an existing data source and a user new data source; the existing data source is used for representing the resource data in the in-use state; the user newly-added data source is used for representing the newly-added data of the user front end.
The processing unit 202 is configured to perform structured analysis processing on the multi-party data through a structured data model to obtain structured data; the structured data characterizes data parameters, data types, and data structures.
The matching unit 203 is used for performing matching operation on the structured data through a rule engine configuration model and fusing the structured data after the matching operation through a multiple data source data fusion model; the rule engine configuration model is a model for completing filling of structured data information according to rules and specific definitions in the rules.
Further, the obtaining unit 201 includes a first obtaining module, a second obtaining module, and a third obtaining module.
The first acquisition module is used for acquiring approved resource application formatted data from the resource auditing system; the resource application formatting data is defined by a pre-designed structural model.
And the second acquisition module is used for acquiring the existing data source in use from the resource system.
And the third acquisition module is used for acquiring the user newly added data source from the user front-end system.
Further, the processing unit 202 includes a design module, a validation module, and a processing module.
A module is involved for designing a structured data model.
And the confirmation module is used for confirming the data parameter table, the data type and the data structure in the structured data model.
And the processing module is used for performing structured analysis processing on the multi-party data through the data parameter table, the data types and the data structure to obtain structured data.
Further, the data fusion processing system further comprises a judging unit, a first determining unit and a second determining unit.
And the judging unit is used for judging whether the formatted resource application data source of the structured data is matched with the existing data source.
The first determining unit is used for determining that the formatted resource application data source of the structured data is matched with the existing data source if the formatted resource application data source of the structured data is consistent with the existing data source.
And the second determining unit is used for determining that the formatted resource application data source of the structured data is not matched with the existing data source if the formatted resource application data source of the structured data is not consistent with the existing data source.
Further, the matching unit 203 includes a matching module and a fusion module.
And the matching module is used for matching the formatted resource application data source of the structured data with the existing data source through the parameter identification of the rule engine configuration model if the formatted resource application data source of the structured data is matched with the existing data source.
And the fusion module is used for fusing the matched formatted resource application data source, the existing data source and the user newly-added data source through the multiple data source data fusion model.
Further, the data fusion processing system also comprises an operation unit.
The operation unit is used for carrying out rule definition operation on the rule through a rule engine configuration model; the rule definition operation at least comprises a definition rule name, a definition parameter rule, a definition rule tag and a definition operation; defining parameter rules including parameter names and regular rules; defining operations includes operation rules and operation matching.
Further, the data fusion processing system also comprises an assignment unit.
And the assignment unit is used for carrying out initialization assignment on the system parameters through the server resource data of the formatted resource application data source if the formatted resource application data source of the structured data is not matched with the existing data source.
In the embodiment of the application, data integration is not needed through manual operation, and only structured analysis processing is needed to be automatically performed on multi-party data through a multi-data-source data fusion model, so that the transmission of structured data of multiple systems and multiple nodes is realized, and data from different systems and users are fused, so that the management risk and the error operation probability are reduced, and the data fusion efficiency is improved. In addition, the rule engine configuration model is used for carrying out operations such as updating adjustment and newly-added fusion rules on the data fusion method in real time, and the rule maintenance efficiency of data fusion is improved.
The embodiment of the present application further provides a storage medium, where the storage medium includes stored instructions, and when the instructions are executed, the device where the storage medium is located is controlled to execute the data fusion processing method.
An electronic device is provided in an embodiment of the present application, and a schematic structural diagram of the electronic device is shown in fig. 3, and specifically includes a memory 301 and one or more instructions 302, where the one or more instructions 302 are stored in the memory 301, and are configured to be executed by one or more processors 303 to execute the one or more instructions 302 to perform the data fusion processing method.
The specific implementation procedures and derivatives thereof of the above embodiments are within the scope of the present application.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the system or system embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described system and system embodiments are only illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed 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 modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the components and steps of the various examples have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical 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 application.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing is only a preferred embodiment of the present application and it should be noted that, as will be apparent to those skilled in the art, numerous modifications and adaptations can be made without departing from the principles of the present application and such modifications and adaptations are intended to be considered within the scope of the present application.
Claims (10)
1. A data fusion processing method is characterized by comprising the following steps:
acquiring multi-party data; the multi-party data at least comprises a resource application data source, an existing data source and a user newly added data source; the existing data source is used for representing the resource data in the in-use state; the user newly-added data source is used for representing the newly-added data of the user front end;
carrying out structured analysis processing on the multi-party data through a structured data model to obtain structured data; the structured data represents data of data parameters, data types and data structures;
matching operation is carried out on the structured data through a rule engine configuration model, and the structured data after matching operation is fused through a multiple data source data fusion model; the rule engine configuration model is a model for completing filling of structured data information according to rules and specific definitions in the rules.
2. The method of claim 1, wherein the act of obtaining multi-party data comprises:
acquiring approved resource application formatted data from a resource auditing system; the resource application formatted data is defined by a pre-designed structural model;
acquiring an existing data source in use from a resource system;
and acquiring a newly added user data source from the user front-end system.
3. The method of claim 1, wherein said performing structured analysis processing on said multi-party data through a structured data model to obtain structured data comprises:
designing a structured data model;
confirming a data parameter table, a data type and a data structure in the structured data model;
and carrying out structured analysis processing on the multi-party data through the data parameter table, the data type and the data structure to obtain structured data.
4. The method of claim 1, further comprising:
judging whether the formatted resource application data source of the structured data is matched with the existing data source;
if the formatted resource application data source of the structured data is consistent with the existing data source, determining that the formatted resource application data source of the structured data is matched with the existing data source;
and if the formatted resource application data source of the structured data is not consistent with the existing data source, determining that the formatted resource application data source of the structured data is not matched with the existing data source.
5. The method according to claim 4, wherein the configuring a model through a rule engine, performing a matching operation on the structured data, and fusing the structured data after the matching operation through a multiple data source data fusion model, comprises:
if the formatted resource application data source of the structured data is matched with the existing data source, matching the formatted resource application data source of the structured data with the existing data source through a parameter identifier of a rule engine configuration model;
and fusing the matched formatted resource application data source, the existing data source and the user newly-added data source through a multiple data source data fusion model.
6. The method of claim 5, further comprising:
carrying out rule definition operation on the rule through the rule engine configuration model; the rule definition operation at least comprises a definition rule name, a definition parameter rule, a definition rule tag and a definition operation; the definition parameter rule comprises a parameter name and a regular rule; the defining operation comprises an operation rule and an operation matching.
7. The method of claim 4, further comprising:
and if the formatted resource application data source of the structured data is not matched with the existing data source, performing initialization assignment on system parameters through server resource data of the formatted resource application data source.
8. A data fusion processing system, the system comprising:
an acquisition unit configured to acquire multiparty data; the multi-party data at least comprises a resource application data source, an existing data source and a user newly added data source; the existing data source is used for representing the resource data in the in-use state; the user newly-added data source is used for representing the newly-added data of the user front end;
the processing unit is used for carrying out structured analysis processing on the multi-party data through a structured data model to obtain structured data; the structured data represents data of data parameters, data types and data structures;
the matching unit is used for matching the structured data through a rule engine configuration model and fusing the structured data after matching operation through a multiple data source data fusion model; the rule engine configuration model is a model for completing filling of structured data information according to rules and specific definitions in the rules.
9. A storage medium, characterized in that the storage medium comprises stored instructions, wherein when the instructions are executed, the device on which the storage medium is located is controlled to execute the data fusion processing method according to any one of claims 1 to 7.
10. An electronic device comprising a memory and one or more instructions, wherein the one or more instructions are stored in the memory and configured to be executed by the one or more processors to perform the data fusion processing method of any one of claims 1-7.
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