A service scenario data processing method, system and electronic device

Abstract

The specification discloses a business scenario data processing method, system and electronic equipment, which is more convenient, fast and efficient for management, query and application of business scenario record data. The method comprises: acquiring business source data and business demand information under a current business scenario; extracting a plurality of demand items and determining the association relationship between the plurality of demand items according to the business demand information; constructing a graph data model according to the plurality of demand items and the association relationship between the plurality of demand items; and processing the business source data based on the graph data model to generate business graph data with a data structure conforming to the graph data model. The system comprises a data information acquisition module, a demand analysis module, a graph data model creation module and a source data processing module. The electronic equipment comprises a memory, a processor and a computer program stored in the memory and executable on the processor to execute the business scenario data processing method.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a business scenario data processing method and system and electronic equipment. BACKGROUND

[0002] With the continuous development of science and technology, more and more record data in production and life of enterprises and industries need to be managed and stored, which will involve more and more data statistics and statistical utilization research work, so it is necessary to introduce a data management system to scientifically and effectively manage the record data of different roles, different links, different types, different times and different definitions in different business scenarios. In some related technical solutions, the record data of the business scenario is stored in a table data structure, which is inconvenient in management, query and application, and the query efficiency is low and the operation cost is high. SUMMARY

[0003] Therefore, the embodiments of the present application provide a business scenario data processing method and system and electronic equipment, which are more convenient, fast and efficient in managing, querying and applying business scenario record data.

[0004] In a first aspect, the embodiments of the present application provide a business scenario data processing method, which comprises:

[0005] Obtaining business source data and business requirement information in a current business scenario;

[0006] According to the business requirement information, extracting a plurality of requirement items and determining the association relationship between the plurality of requirement items;

[0007] According to the plurality of requirement items and the association relationship between the plurality of requirement items, constructing a graph data model;

[0008] Processing the business source data based on the graph data model to generate business graph data with a data structure conforming to the graph data model.

[0009] Optionally, processing the business source data based on the graph data model comprises:

[0010] Determining the attribute label of a plurality of data items in the business source data;

[0011] Determining the model node corresponding to the attribute label in the graph data model;

[0012] Respectively storing a plurality of data items in the model node corresponding to the attribute label.

[0013] Optionally, the method further comprises:

[0014] extracting a target node name and an indirect node name from the data query instruction;

[0015] determining a target model node and an indirect model node corresponding to the target node name and the indirect node name;

[0016] determining a correlation degree between the target model node and the indirect model node in the graph data model;

[0017] querying data corresponding to the target model node as target data from the business graph data;

[0018] querying a shortest path between the target data and the indirect data, wherein a correlation degree between the target data and the indirect data is equal to the correlation degree between the target model node and the indirect model node, and the data corresponding to the indirect model node is indirect data.

[0019] Optionally, the method further comprises:

[0020] filtering a data subgraph related to a specified node from the business graph data;

[0021] dividing the data subgraph into a plurality of communities and determining whether there is corresponding specified node data in each community;

[0022] in response to the absence of corresponding specified node data in a certain community, defining new specified node data in the community.

[0023] Optionally, filtering a data subgraph related to a specified node from the graph data comprises:

[0024] querying data corresponding to the specified node and the associated nodes of the specified node as subgraph basic data from the business graph data;

[0025] querying a shortest path between the subgraph basic data and the specified node within a limit correlation degree value corresponding to the specified node;

[0026] generating the data subgraph based on the subgraph basic data and the shortest path.

[0027] Optionally, the model nodes in the graph data model comprise data domain nodes, business nodes, dimension nodes, indicator nodes, modifier nodes, theme nodes, and application nodes.

[0028] The connection relationships between the plurality of model nodes comprise sub-theme relationships, application-to-relationship, belonging-to-relationship, and combination relationship.

[0029] In the graph data model, the sub-theme relationship exists between two theme nodes, and is used to indicate a dependency relationship between the two nodes.

[0030] The application is related to the existence between the index node and the business node, and is used to indicate the application scenario corresponding to the index node.

[0031] The belonging is related to the existence between the index node and the theme node, the index node and the business node, the data domain node and the theme node, the dimension node and the data domain node, the business node and the theme node, the business node and the data domain node, and the application node and the theme node, and is used to indicate the membership between two nodes.

[0032] The combination is related to the existence between the modifier node and the index node, and between two index nodes, and is used to indicate the derivative relationship between two nodes.

[0033] Optionally, the data corresponding to the index node in the business graph data includes an atomic index and a derivative index.

[0034] The atomic index is an index that cannot be further divided, and the derivative index is an index defined by configuring a modifier and a time period based on the atomic index.

[0035] The method further includes the following steps when a derivative index is added:

[0036] An atomic index field, a modifier field, and a time period field are extracted from a derivative index new instruction.

[0037] An atomic index corresponding to the atomic index field in the business graph data is queried as a basic atomic index of the added derivative index.

[0038] All modifiers and all time periods associated with the basic atomic index are queried.

[0039] In response to the existence of a modifier and a time period that are the same as the modifier field and the time period field in all modifiers and all time periods associated with the basic atomic index, it is determined that the configuration information of the added derivative index is verified.

[0040] Optionally, the method further includes the following steps when a derivative index is added:

[0041] A plurality of derivative indexes corresponding to the basic atomic index are queried, and themes associated with the plurality of derivative indexes are recommended as recommended themes of the added derivative index.

[0042] In a second aspect, the embodiments of the present specification also provide a business scenario data processing system, the system comprising:

[0043] a data information acquisition module, configured to acquire service source data and service demand information in a current service scenario;

[0044] a demand analysis module, configured to extract a plurality of demand items and determine an association relationship between the plurality of demand items according to the service demand information;

[0045] a graph data model construction module, configured to construct a graph data model according to the plurality of demand items and the association relationship between the plurality of demand items; and

[0046] a source data processing module, configured to process the service source data according to the graph data model, to generate service graph data with a data structure conforming to the graph data model.

[0047] In a third aspect, the embodiments of the present specification also provide a service scenario data processing electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the service scenario data processing method as described in the first aspect when executing the program.

[0048] As can be seen from the above, the service scenario data processing method, system and electronic device provided by the embodiments of the present specification have the following beneficial technical effects:

[0049] The service scenario data processing method, system and electronic device acquire service source data and service demand information in a current service scenario, extract a plurality of demand items from the service demand information and determine an association relationship between the plurality of demand items, and construct a graph data model based thereon, and process the service source data to generate service graph data with a data structure conforming to the graph data model. In this way, the graph data model is constructed from the actual demand for managing and counting service data, the service source data is processed into service graph data with a data structure conforming to the graph data model, and management, query and application based on the service graph data are more convenient, fast and efficient, and the experience is better. BRIEF DESCRIPTION OF DRAWINGS

[0050] The features and advantages of the present application will be more clearly understood through reference to the following drawings, which are illustrative only and should not be construed as limiting the application, in which:

[0051] Figure 1 a service scenario data processing method provided by one or more optional embodiments of the present specification is shown;

[0052] Figure 2 a graph data model structure in a service scenario data processing method provided by one or more optional embodiments of the present specification is shown;

[0053] Figure 3 Fig. 1 shows a method schematic diagram of processing the business source data based on the graph data model in a business scenario data processing method provided by one or more optional embodiments of the present specification;

[0054] Figure 4 Fig. 2 shows a method schematic diagram of performing an associated query on the business graph data in a business scenario data processing method provided by one or more optional embodiments of the present specification;

[0055] Figure 5 Fig. 3 shows a method schematic diagram of performing a maintenance update on the business graph data in a business scenario data processing method provided by one or more optional embodiments of the present specification;

[0056] Figure 6 Fig. 4 shows a method schematic diagram of screening a data subgraph from the business graph data in a business scenario data processing method provided by one or more optional embodiments of the present specification;

[0057] Figure 7 Fig. 5 shows a method schematic diagram of performing configuration information verification on a newly added derived indicator in a business scenario data processing method provided by one or more optional embodiments of the present specification;

[0058] Figure 8 Fig. 6 shows a business scenario data processing system structure schematic diagram provided by one or more optional embodiments of the present specification;

[0059] Figure 9 Fig. 7 shows a business scenario data processing electronic device structure schematic diagram provided by one or more optional embodiments of the present specification. DETAILED DESCRIPTION

[0060] In order to make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0061] With the continuous development of science and technology, more and more recorded data in various enterprises and industries in production and life need to be managed and stored, which will involve more and more data statistics and statistical theory research work, and therefore it is necessary to introduce a data management system to scientifically and effectively manage the recorded data of different roles, different links, different types, different times and different definitions in different business scenarios of production and life.

[0062] In some related technical solutions, the record data of a business scenario is stored in a table data structure, and a large amount of business scenario data is stored in different data tables, and the data between the data tables is poorly connected. Different user roles in an enterprise, industry or platform have different data focus points, and different dimensions are used for data query application. The data storage method in the related technical solutions also causes a large amount of I / O operation and memory consumption when different users query data, and the query efficiency is low and the user experience is poor.

[0063] To solve the above problems, the technical solutions of the embodiments of the present specification aim to provide a business scenario data processing method, construct a graph data model from business demand information associated with business scenario data, and process the business scenario data into business graph data based on the graph data model, so that data management, query and application are more efficient and convenient.

[0064] Based on the above purpose, in one aspect, the embodiments of the present specification provide a business scenario data processing method.

[0065] As shown in Figure 1 The business scenario data processing method provided by one or more optional embodiments of the present specification comprises:

[0066] S1: Obtain business source data and business demand information under a current business scenario.

[0067] The business scenarios involved by different enterprises, industries and platforms are different, and the business scenarios can be, for example, an application scenario of webpage shopping payment, an application scenario of scientific research experiment process record, an application scenario of regional power consumption data statistical research, and the like. The business source data refers to record data obtained under the corresponding business scenario. For example, in the business scenario of webpage shopping payment, the corresponding business source data can include user information data, merchant information data, commodity information data, amount data, payment method data, time data, profit data and the like.

[0068] The business demand information refers to the demand for data information to meet the needs of data management, statistics and research under the corresponding business scenario. For example, in the business scenario of webpage shopping payment, the user information data and the merchant information data in the corresponding business source data need to be extracted according to the business source data to determine the corresponding indicators, businesses, themes and the like, so as to facilitate statistical management.

[0069] The business demand information can be determined according to the data meaning of the specific business source data and the specific demand for data management and statistics.

[0070] S2: Extract multiple requirement items based on the business requirement information and determine the relationship between the multiple requirement items.

[0071] Multiple requirement items can be extracted from the business requirement information. For example, for the business requirement information corresponding to the web shopping payment business scenario, multiple requirement items such as "indicators" and "businesses" can be extracted. That is to say, when conducting data management, statistics, and research, it is necessary to determine the corresponding indicators and the business to which the business source data belongs. In the web shopping payment business scenario, the indicators may specifically be indicators such as order payment amount, number of user registrations, user activity time, number of product types, and user rating scores, while the businesses may specifically be businesses such as payment, store operation, and search.

[0072] The relationships between the required items can be determined based on the specific meaning of the business source data and the specific needs of data management and statistics. For example, based on the specific data meaning, a "belonging" relationship can be determined between the indicator required item and the business required item, that is, a certain indicator belongs to a certain business. Specifically, in the scenario of web shopping payment business, it can be determined that the order payment amount indicator belongs to the payment business, the product category quantity indicator belongs to the search business, and the user rating score belongs to the store operation business.

[0073] S3: Construct a graph data model based on the multiple required items and the relationships between the multiple required items.

[0074] When constructing a graph data model, multiple model nodes can be created for multiple required items, and the connection relationships between the multiple model nodes can be determined according to the association relationships between the multiple required items. The graph data model is constructed based on the multiple model nodes and the connection relationships between the multiple model nodes. In the graph data model, the connection relationships between model nodes can be represented by directed edges.

[0075] like Figure 2 The diagram shown is a schematic representation of the graph data model described in some optional embodiments of this specification. In some optional embodiments, the model nodes in the graph data model include data domain nodes, business nodes, dimension nodes, indicator nodes, modifier nodes, topic nodes, and application nodes.

[0076] The connection relationships between multiple model nodes include subtopic relationships, applied relationships, belonging relationships, and composition relationships.

[0077] In the graph data model, the subtopic relationship exists between two topic nodes and is used to indicate the dependency relationship between the two related nodes.

[0078] The application relationship exists between the indicator node and the business node, and is used to indicate the application scenario corresponding to the indicator node.

[0079] The membership relationship exists between the indicator node and the topic node, between the indicator node and the business node, between the data domain node and the topic node, between the dimension node and the data domain node, between the business node and the topic node, between the business node and the data domain node, and between the application node and the topic node, and is used to indicate the membership relationship between the two related nodes.

[0080] The combination relationship exists between the modifier node and the indicator node, as well as between the two indicator nodes, and is used to indicate the derivation relationship between the two associated nodes.

[0081] S4: Process the business source data based on the graph data model to generate business graph data with a data structure that conforms to the graph data model.

[0082] Each piece of data in the business source data corresponds to a number of the requirements in the business requirement information. Based on the correspondence between each piece of data in the business source data and the number of the requirements, each piece of data is stored in the model node corresponding to the number of requirements. In this way, the original data structure of the business source data is adjusted, thereby processing the business source data into business graph data that conforms to the graph data model data structure.

[0083] The described business scenario data processing method acquires business source data and business requirement information under the current business scenario, extracts multiple requirement items from the business requirement information and determines the relationships between these items, and constructs a graph data model based on this. The business source data is then processed to generate business graph data with a data structure conforming to the graph data model. This approach, starting from the actual needs of managing and statistically analyzing business data, constructs a graph data model and processes the business source data into business graph data with a data structure conforming to the model. Management, querying, and application based on this business graph data are more convenient, faster, more efficient, and provide a better user experience.

[0084] like Figure 3 As shown, in one or more optional embodiments of this specification, a business scenario data processing method is provided, in which the business source data is processed based on the graph data model, including:

[0085] S301: Determine the attribute labels of multiple data items in the business source data.

[0086] The business source data can be composed of a plurality of data records, each data record can include a plurality of data items, each data item has a corresponding attribute label, and the attribute label is used to indicate the specific meaning, source and other information of the data item.

[0087] In the process of obtaining the business source data, the plurality of data items in each data record can be obtained through different data interfaces. The attribute label can be added to the data item in real time when obtaining the corresponding data item through the data interface.

[0088] S302: Determine the model node corresponding to the attribute label in the graph data model.

[0089] The model node corresponding to the attribute label can be determined according to the specific meaning and source of the corresponding data item indicated by the attribute label.

[0090] S303: Store a plurality of data items in the model node corresponding to the corresponding attribute label respectively.

[0091] For example, in the payment business scenario of web shopping, the business source data includes the transaction record of a user purchasing goods, the data related to payment behavior in the transaction record is obtained through the web payment data interface, the "payment" label can be added to the related data, and when processing these data, the corresponding data domain can be determined as "transaction domain", which corresponds to the data domain node in the graph data model, and the corresponding business is "payment", which corresponds to the business node in the graph data model.

[0092] According to the corresponding relationship between the attribute label and the model node in the graph data model, the data item is stored in the model node corresponding to the corresponding attribute label, so that the plurality of data records in the business source data can be processed into business graph data conforming to the data structure of the graph data model, which is convenient for query, management and research.

[0093] As Figure 4 The business scene data processing method provided by one or more optional embodiments of the present specification further includes performing associated query on the business graph data.

[0094] The associated query on the business graph data includes:

[0095] S401: Extract the target node name and indirect node name from the data query instruction.

[0096] S402: Determine the target model node and indirect model node corresponding to the target node name and indirect node name.

[0097] S403: Determine the association degree between the target model node and the indirect model node in the graph data model.

[0098] The association degree refers to the number of edges included in the shortest path between model nodes in the graph data model. Referring to Figure 2 As shown, it can be determined that the association degree between the modifier node and the application node is 2, the association degree between the dimension node and the subject node is 2, and the association degree between the dimension node and the indicator node is 3.

[0099] S404: Query the data corresponding to the target model node in the business graph data as target data.

[0100] S405: Query the data corresponding to the indirect model node as indirect data, whose association degree is equal to the association degree between the target model node and the indirect model node.

[0101] Taking the target node name and the indirect node name extracted from the data query instruction as "business" and "indicator" respectively, the data query instruction can directly query the business data and the indicator data associated with the business data from the business graph data.

[0102] The target model node and the indirect model node corresponding to the target node name and the indirect node name are the business node and the indicator node in the graph data model. Referring to Figure 2 As shown, the association degree between the business node and the indicator node is 1.

[0103] Query the data corresponding to the business node in the business graph data, i.e. the business data to be queried. Taking the business scenario of web shopping payment as an example, the business data directly queried in the corresponding business graph data can include search business, commodity business, after-sales business, and shop operation business, etc.

[0104] Further query the data corresponding to the indicator model and associated with the business data, i.e. the indicator data associated with the business data. Taking the business scenario of web shopping payment as an example, the indicator data associated with the business data can be queried, the indicator data associated with the search business includes search result indicator, the indicator data associated with the commodity business includes commodity indicator, the indicator data associated with the after-sales business includes wireless support indicator, the indicator data associated with the payment business includes order amount indicator and payment amount change rate indicator, etc.

[0105] For the business graph data, a correlation query can also be performed for other data. For example, the target node name and the indirect node name extracted in the data query instruction can be "dimension" and "indicator" respectively, as shown in FIG. 9, the correlation degree between the corresponding dimension node and the indicator node is 3, and the dimension data and the indicator data associated with the dimension data can be directly queried for the query instruction. The target node name and the indirect node name in the data query instruction can also be "indicator" and "modifier", that is, the indicator data in the business graph data and the modifier data associated with the indicator data are queried. Figure 2

[0106] As shown in FIG. 10, the business scenario data processing method provided by one or more optional embodiments of the present specification further includes maintaining and updating the data corresponding to the specified node in the business graph data. Figure 5

[0107] The maintaining and updating of the data corresponding to the specified node in the business graph data includes:

[0108] S501: filtering a data subgraph related to a specified node from the business graph data.

[0109] The purpose of maintaining and updating the data corresponding to the specified node is to analyze the existing graph data, thereby mining hidden data information corresponding to the specified node, and updating and maintaining the data corresponding to the specified node in the business graph data. The data subgraph related to the specified node can be filtered from the business graph data as an analysis basis for mining hidden information.

[0110] S502: dividing the data subgraph into multiple communities and determining whether there is corresponding specified node data in each community.

[0111] The data subgraph extracted from the business graph data naturally has certain community attributes, and the data subgraph can be divided into multiple communities corresponding to multiple specified node data. Specifically, a clustering algorithm or a community discovery algorithm can be used to process the data subgraph, and the data subgraph can be divided into multiple communities.

[0112] In the business graph data, if the data corresponding to the specified node is complete, each community determined by the division has corresponding specified node data, and if there is a community without corresponding specified node data, it means that there is hidden data information for the community.

[0113] S503: in response to the existence of a community without corresponding specified node data, defining new specified node data for the community.

[0114] ​​The specified node can be the data domain node, and first, data subgraphs related to the data domain node are filtered from the business graph data, the data subgraphs are divided into multiple communities, and it is determined whether each community has corresponding data domain data. If there is a community without corresponding data domain data, a new data domain can be defined for the community.

[0115] Taking the business scenario of webpage shopping payment as an example, the data subgraphs extracted from the business graph data can be divided into three communities by using a clustering algorithm. Currently, there are two data domains of transaction domain and commodity domain, and a new community formed by indicators such as user registration, after-sales, CTR, and UV. In combination with the business scenario, a new data domain can be defined for the indicators in the community.

[0116] It should be noted that the specified node can also be other model nodes in the graph data model, and the maintenance and update of the specified node can also mine hidden data information of data corresponding to other model nodes from the business graph data.

[0117] The business scenario data processing method uses business graph data for data storage, and by extracting data subgraphs related to specified nodes and performing community division, hidden data information associated with the specified nodes can be intuitively discovered and mined, and better interpretability is achieved.

[0118] As shown in Figure 6 In a business scenario data processing method provided by one or more optional embodiments of the present specification, filtering data subgraphs related to a specified node from the business graph data can include the following steps:

[0119] S601: Query data corresponding to the specified node and nodes associated with the specified node in the business graph data as subgraph basic data.

[0120] Taking the specified node as a data domain node, in the graph data model as shown in Figure 2 The nodes associated with the data domain node include indicator nodes, business nodes, and dimension nodes. Therefore, the data corresponding to the data domain node and the indicator nodes, the business nodes, and the dimension nodes in the business graph data can be queried as the subgraph basic data.

[0121] S602: Query the shortest path with an association degree within a limit association degree value corresponding to the specified node.

[0122] The limit association degree value refers to the maximum value in the minimum association degree between the specified node and any other model node in the graph data model. Taking Figure 2As shown in the graph data model, the maximum value of the correlation between the data domain node and any other node is 3, and the shortest path within 3 of the correlation between the subgraph basic data and the subgraph basic data can be queried.

[0123] S603: generating the data subgraph based on the subgraph basic data and the shortest path.

[0124] After determining the subgraph basic data and the corresponding shortest path, the data subgraph related to the specified node is formed by the subgraph basic data and the shortest path.

[0125] In one or more optional embodiments of the business scenario data processing method provided in the present specification, the data corresponding to the index node in the business graph data includes atomic indexes and derived indexes. The atomic index is an index that cannot be further divided, and the derived index is an index defined by configuring a modifier and a time period based on the atomic index. The time period is an associated attribute corresponding to the index node and needs to be configured when created.

[0126] When a new derived index is added, the method also performs configuration information verification on the new derived index.

[0127] As shown in the figure, Figure 7 In some optional embodiments of the business scenario data processing method provided in the present specification, the configuration information verification on the new derived index includes:

[0128] S701: extracting an atomic index field, a modifier field, and a time period field from the derived index new instruction.

[0129] S702: querying the atomic index corresponding to the atomic index field in the business graph data as the basic atomic index of the new derived index.

[0130] S703: querying all modifiers and all time periods associated with the basic atomic index.

[0131] S704: in response to the existence of the same modifier and time period as the modifier field and the time period field in all modifiers and all time periods associated with the basic atomic index, determining that the configuration information verification of the new derived index passes.

[0132] In the case of using a graph database as a data storage, a large number of derivative indicators are configured and recorded after the system has been running for a period of time, at this time, the atomic indicators have rich data to indicate which adjectives and time periods are used for each atomic indicator. In the business scenario data processing method, when a derivative indicator is added, the related historical data is mined, and whether the adjectives and time periods corresponding to the atomic indicators are abnormal is searched to warn that the parameters may have risks, and the risk is reduced before the indicator is applied.

[0133] The business scenario data processing method provided by one or more optional embodiments of the present specification further comprises querying a plurality of derivative indicators corresponding to the basic atomic indicators, and recommending the themes associated with the plurality of derivative indicators as recommended themes of the added derivative indicators.

[0134] The business scenario data processing method, when a derivative indicator is newly created, can provide recommended themes for the newly created derivative indicator according to the existing relationship data in the business graph data, i.e., the relationships between atomic indicators and businesses, atomic indicators and applications, atomic indicators and derivative indicators, derivative indicators and businesses, and derivative indicators and applications, so as to facilitate the improvement of the overall business graph data when a derivative indicator is added.

[0135] In the business scenario data processing method provided by one or more optional embodiments of the present specification, the model nodes in the graph data model include data domain nodes, business nodes, dimension nodes, indicator nodes, adjective nodes, theme nodes, and application nodes, which correspond to data domain management, business scenarios of indicators, dimensions corresponding to indicators, management of indicators themselves, adjectives corresponding to derivative indicators, theme label management of indicators, and application scenario management of indicators in the data indicator management. These model nodes all take data ID as the primary key and have different association attributes. The specific model node information is shown in Table 1 below

[0136] Table 1

[0137] Serial number Node Primary key Associated attribute 1 Data field id "id","name","business board" 2 Business id "id","name","business board" 3 Dimension id "id","name","dimension attribute" 4 Indicator id "id","name","time period","type" 5 Modifier id "id","name","modification type" 6 Theme id "id","name" 7 Application id "id","name"

[0138] The connection relationship between a plurality of the model nodes includes a sub-theme relationship, an application relationship, a belonging relationship, and a combination relationship. The specific connection relationship between the model nodes is shown in Table 2 below:

[0139] Table 2

[0140]

[0141]

[0142] Taking the business scenario of web shopping payment as an example, based on the graph data model as shown in FIG. 1, the data domain nodes include a user node, a product node, a payment node, and a transaction node, the business nodes include a shopping node, a payment node, and a transaction node, the dimension nodes include a user dimension node, a product dimension node, a payment dimension node, and a transaction dimension node, the indicator nodes include a user indicator node, a product indicator node, a payment indicator node, and a transaction indicator node, the adjective nodes include a user adjective node, a product adjective node, a payment adjective node, and a transaction adjective node, the theme nodes include a shopping theme node, a payment theme node, and a transaction theme node, and the application nodes include a shopping application node, a payment application node, and a transaction application node. Figure 2The illustrated graph data model processes the business source data under the business scenario to generate the business graph data.

[0143] For the business graph data, data domain node data and associated business node data and index node data can be queried, and the query results are recorded in the form of a table as shown in Table 3.

[0144] Table 3

[0145] Data field: name Data field: business board Business: name Indicator: name Indicator: type Transaction domain XX shopping website Payment Order payment amount Atomic indicator Transaction domain XX shopping website Payment Payment amount change rate Atomic indicator Transaction domain XX shopping website After-sales Wireless support Atomic indicator Transaction domain XX shopping website After-sales After-sales Atomic indicator Transaction domain XX shopping website Evaluation Evaluation rating Atomic indicator Commodity domain XX shopping website Search Search result Atomic indicator …… …… …… …… ……

[0146] For the business graph data, business node data and associated index node data can be queried, and the query results are recorded in the form of a table as shown in Table 4.

[0147] Table 4

[0148]

[0149]

[0150] For the business graph data, dimension node data and associated index node data can be queried, and the query results are recorded in the form of a table as shown in Table 5.

[0151] Table 5

[0152]

[0153] For the business graph data, index node data and associated modifier node data can be queried, and the query results are recorded in the form of a table as shown in Table 6.

[0154] Table 6

[0155]

[0156]

[0157] It should be noted that the method of one or more embodiments of the present specification can be executed by a single device, such as a computer or a server, etc. The method of the present embodiment can also be applied to a distributed scenario, and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only execute one or more steps in the method of one or more embodiments of the present specification, and the multiple devices will interact with each other to complete the method.

[0158] It is noted that the above describes particular embodiments of the present specification. Other embodiments are within the scope of the claims appended hereto. In some cases, the acts or steps recited in the claims can be performed in a different order than those in the embodiments and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily require the particular order shown or sequential order in order to achieve the desired results. In some implementations, multitasking and parallel processing can be advantageous or necessary.

[0159] Based on the same inventive concept, the embodiments of the present specification also provide a business scenario data processing system corresponding to the method of any of the above embodiments.

[0160] Reference Figure 8 The business scenario data processing system comprises:

[0161] A data information acquisition module is configured to acquire business source data and business demand information under a current business scenario.

[0162] A demand analysis module is configured to extract a plurality of demand items and determine an association relationship between the plurality of demand items according to the business demand information.

[0163] A graph data model creation module is configured to construct a graph data model according to the plurality of demand items and the association relationship between the plurality of demand items.

[0164] A source data processing module is configured to process the business source data according to the graph data model to generate business graph data with a data structure conforming to the graph data model.

[0165] In a business scenario data processing system provided by one or more optional embodiments of the present specification, the source data processing module is further configured to determine attribute labels of a plurality of data items in the business source data, determine the model nodes corresponding to the attribute labels in the graph data model, and store the plurality of data items in the model nodes corresponding to the attribute labels, respectively.

[0166] The business scenario data processing system provided by one or more optional embodiments of the present specification further comprises an association query module, which is configured to extract a target node name and an indirect node name from a data query instruction; determine a target model node and an indirect model node corresponding to the target node name and the indirect node name; determine an association degree between the target model node and the indirect model node in the graph data model; query data corresponding to the target model node in the business graph data as target data; and query data corresponding to the indirect model node as indirect data, which has an association degree equal to the association degree between the target model node and the indirect model node.

[0167] The business scenario data processing system provided by one or more optional embodiments of the present specification further comprises a data maintenance module, which is configured to filter out a data subgraph related to a specified node from the business graph data; divide the data subgraph into a plurality of communities, and determine whether there is corresponding specified node data in each community; and in response to the absence of corresponding specified node data in a certain community, define new specified node data in the community.

[0168] In the business scenario data processing system provided by one or more optional embodiments of the present specification, the data maintenance module is further configured to query data corresponding to the specified node and the associated node of the specified node in the business graph data as subgraph basic data; query a shortest path having an association degree within a limit association degree value corresponding to the specified node from the subgraph basic data; and generate the data subgraph based on the subgraph basic data and the shortest path.

[0169] In a business scenario data processing system provided by one or more optional embodiments of the present specification, the model nodes in the graph data model include data domain nodes, business nodes, dimension nodes, indicator nodes, modifier nodes, theme nodes, and application nodes; the connection relationships between the plurality of model nodes include sub-theme relationships, application-to-relationship, belonging relationships, and combination relationships; in the graph data model, the sub-theme relationship exists between two theme nodes, used to indicate the dependency relationship between the two nodes; the application-to-relationship exists between the indicator node and the business node, used to indicate the application scenario corresponding to the indicator node; the belonging relationship exists between the indicator node and the theme node, between the indicator node and the business node, between the data domain node and the theme node, between the dimension node and the data domain node, between the business node and the theme node, between the business node and the data domain node, and between the application node and the theme node, used to indicate the membership relationship between the two nodes; the combination relationship exists between the modifier node and the indicator node and between two indicator nodes, used to indicate the derivation relationship between the two nodes.

[0170] In a business scenario data processing system provided by one or more optional embodiments of the present specification, the data corresponding to the indicator node in the business graph data includes atomic indicators and derived indicators; the atomic indicator is an indicator that cannot be further divided, and the derived indicator is an indicator defined by configuring a modifier and a time period based on an atomic indicator. The business scenario data processing system further includes an indicator verification module, which is configured to extract an atomic indicator field, a modifier field, and a time period field from a derived indicator creation instruction; query an atomic indicator corresponding to the atomic indicator field in the business graph data as a basic atomic indicator of the newly added derived indicator; query all modifiers and all time periods associated with the basic atomic indicator; in response to the existence of a modifier and a time period that are the same as the modifier field and the time period field in all modifiers and all time periods associated with the basic atomic indicator, it is determined that the configuration information of the newly added derived indicator is verified.

[0171] A business scenario data processing system provided by one or more optional embodiments of the present specification further includes a theme recommendation module, which is configured to query a plurality of derived indicators corresponding to the basic atomic indicator, and recommend the themes associated with the plurality of derived indicators as recommended themes of the newly added derived indicator.

[0172] For ease of description, the above apparatus is described in various modules with functions respectively. Of course, the functions of each module can be implemented in one or more software and / or hardware when implementing one or more embodiments of the present specification.

[0173] The apparatus of the above embodiments is used to implement the corresponding method in the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described here again.

[0174] Figure 9 A more specific hardware structure schematic diagram of an electronic device provided by the present embodiment is shown, which can include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040 and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030 and the communication interface 1040 are connected to each other in the device through the bus 1050.

[0175] The processor 1010 can be implemented in the form of a general-purpose CPU (Central Processing Unit, central processor), a microprocessor, an application specific integrated circuit (ASIC), or one or more integrated circuits, etc., for executing related programs to implement the technical solutions provided by the present embodiment.

[0176] The memory 1020 can be implemented in the form of a ROM (Read Only Memory, read-only memory), a RAM (Random Access Memory, random access memory), a static storage device, a dynamic storage device, etc. The memory 1020 can store an operating system and other application programs, and when the technical solutions provided by the present embodiment are implemented by software or firmware, the related program codes are saved in the memory 1020 and called and executed by the processor 1010.

[0177] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured as a component in the device (not shown in the figure), or can be externally connected to the device to provide corresponding functions. The input device can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device can include a display, a speaker, a vibrator, an indicator light, etc.

[0178] The communication interface 1040 is configured to connect a communication module (not shown in the figure) to realize the communication interaction between the device and other devices. The communication module can realize communication through wired mode (such as USB, network cable, etc.), or can realize communication through wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0179] The bus 1050 includes a path for transmitting information between various components (such as the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040) of the device.

[0180] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in the specific implementation process, the device can also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device can also only contain the components necessary for the implementation of the embodiments of the present specification, and does not have to contain all the components shown in the figure.

[0181] The electronic device of the above embodiment is used to realize the corresponding method in the above embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0182] Based on the same inventive concept, the disclosure also provides a non-transitory computer readable storage medium storing computer instructions for causing the computer to execute the business scenario data processing method according to any one of the above embodiments.

[0183] The computer readable medium of the present embodiment includes permanent and non-permanent, removable and non-removable media, which can be realized by any method or technology to store information. The information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0184] The storage medium of the above-mentioned embodiments stores computer instructions for causing the computer to execute the business scenario data processing method according to any one of the above-mentioned embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described herein again.

[0185] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program. The program can be stored in a computer readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc. The storage medium can also include a combination of the above-mentioned types of memories.

[0186] The system, device, module or unit described in the above-mentioned embodiments can be implemented by a computer chip or entity, or by a product with certain functions. A typical implementation device is a computer. Specifically, the computer can be a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0187] For the convenience of description, the above-mentioned device is described as various units respectively according to functions. Of course, the functions of each unit can be implemented in the same or multiple software and / or hardware when implementing the present application.

[0188] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0189] It is also important to note that the term "comprising" or "including" or any other variation thereof is 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 can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without further constraints, exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0190] The application can be described in the general context of computer- executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types. The application can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote computer storage media including memory storage devices.

[0191] Each of the embodiments in the present specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the difference from other embodiments. In particular, for the system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiments.

[0192] Those skilled in the art should understand that the above discussion of any embodiment is only exemplary, and is not intended to limit the scope of the present disclosure (including claims) to these examples; under the idea of the present disclosure, the above embodiments or technical features between different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes of different aspects of one or more embodiments of the present specification as described above, which are not provided in details for the sake of brevity.

[0193] Although the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications and variations will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) can use the embodiments discussed.

[0194] One or more embodiments of the present specification are intended to cover all such alternatives, modifications and variations that fall within the broad scope of the appended claims. Accordingly, any omission, modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of the present specification shall be included in the protection scope of the present disclosure.

Claims

1. A data processing method for a business scenario, characterized in that, The method includes: Obtain business source data and business requirement information in the current business scenario; Extract multiple requirement items based on the business requirement information and determine the relationship between the multiple requirement items; Construct a graph data model based on the multiple requirements and the relationships between them; The business source data is processed based on the graph data model to generate business graph data whose data structure conforms to the graph data model; Processing the business source data based on the graph data model includes: determining attribute labels for multiple data items in the business source data; determining model nodes corresponding to the attribute labels in the graph data model; and storing the multiple data items into the model nodes corresponding to the respective attribute labels. Extract the target node name and indirect node name from the data query command; Determine the target model node and the indirect model node corresponding to the target node name and the indirect node name; Determine the degree of correlation between the target model node and the indirect model node in the graph data model; Query the data corresponding to the target model node in the business graph data and use it as the target data; The correlation between the query and the target data is equal to the correlation between the target model node and the indirect model node, and the data corresponding to the indirect model node is considered as indirect data; Filter out the data sub-graphs related to the specified nodes from the business graph data; The data subgraph is divided into multiple communities to determine whether each community has corresponding specified node data; In response to the existence of a community that does not have corresponding designated node data, new designated node data is defined for that community.

2. The method according to claim 1, characterized in that, Filtering out data subgraphs related to specified nodes from the graph data includes: Query the data corresponding to the specified node and its associated nodes in the business graph data, and use it as the basic data for the subgraph; Find the shortest path with a correlation degree between the subgraph and the basic data of the subgraph that is within the limit correlation degree value corresponding to the specified node; The data subgraph is generated based on the subgraph base data and the shortest path.

3. The method according to claim 1, characterized in that, The model nodes in the graph data model include data domain nodes, business nodes, dimension nodes, indicator nodes, modifier nodes, theme nodes, and application nodes; The connection relationships between multiple model nodes include subtopic relationships, applied relationships, belonging relationships, and composition relationships; In the graph data model, the subtopic relationship exists between two topic nodes and is used to indicate the dependency relationship between the two related nodes; The application relationship exists between the indicator node and the business node, and is used to indicate the application scenario corresponding to the indicator node; The membership relationship exists between the indicator node and the topic node, between the indicator node and the business node, between the data domain node and the topic node, between the dimension node and the data domain node, between the business node and the topic node, between the business node and the data domain node, and between the application node and the topic node, and is used to indicate the membership relationship between the two related nodes; The combination relationship exists between the modifier node and the indicator node, as well as between the two indicator nodes, and is used to indicate the derivation relationship between the two associated nodes.

4. The method according to claim 3, characterized in that, The data corresponding to the indicator nodes in the business graph data includes atomic indicators and derived indicators; The atomic index is an indivisible index, and the derived index is an index that is limited by configuring modifiers and time periods based on the atomic index. The method also includes the following when adding new derived indicators: Extract the atomic indicator field, modifier field, and time period field from the derived indicator creation instruction; In the business graph data, query the atomic indicator corresponding to the atomic indicator field as the base atomic indicator for the newly added derived indicator; Query all modifiers and time periods associated with the underlying atomic index; If, in response to the existence of modifiers and time periods that are identical to the modifier field and time period field in all modifiers and time periods associated with the basic atomic indicator, the configuration information of the newly added derived indicator is verified as passed.

5. The method according to claim 4, characterized in that, The method also includes the following when adding new derived indicators: Query multiple derived indicators corresponding to the basic atomic indicator, and recommend topics associated with the multiple derived indicators as recommended topics for the newly added derived indicator.

6. A business scenario data processing system, characterized in that, The system includes: The data acquisition module is used to acquire business source data and business requirement information in the current business scenario. The requirements analysis module is used to extract multiple requirements items based on the business requirements information and determine the relationships between the multiple requirements items; A graph data model creation module is used to construct a graph data model based on multiple said requirement items and the relationships between the multiple said requirement items; and The source data processing module is used to process the business source data according to the graph data model to generate business graph data whose data structure conforms to the graph data model; The source data processing module is further configured to determine attribute labels for multiple data items in the business source data; determine model nodes corresponding to the attribute labels in the graph data model; and store the multiple data items into the model nodes corresponding to the respective attribute labels. The association query module is used to extract the target node name and indirect node name from the data query instruction; determine the target model node and indirect model node corresponding to the target node name and the indirect node name; determine the association degree between the target model node and the indirect model node in the graph data model; query the data corresponding to the target model node in the business graph data as target data; and query the data corresponding to the indirect model node whose association degree with the target data is equal to the association degree between the target model node and the indirect model node as indirect data. The data maintenance module is used to filter out data subgraphs related to specified nodes from the business graph data; divide the data subgraphs into multiple communities and determine whether each community has corresponding specified node data; in response to the existence of a community without corresponding specified node data, define new specified node data for that community.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 5.

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