A method and system for data automation expansion and relationship mining

Abstract

The present invention provides a method for data automated expansion and relationship mining, including: obtaining various information of nodes connected to resources; dynamically obtaining resource table information based on the database of the resource nodes, and further importing a list of field information corresponding to the resource tables; further creating and setting a resource table mapping model; calling a scenario analysis module to obtain specific object data information and storing it; rendering the resource nodes according to the obtained object data information and displaying them on an analysis dashboard. By combining the analysis scenario and the resource table mapping model, the practicability of the technology is stronger, the applicable scenarios are wider, and the analysis is more flexible; according to the node graphic operation function provided by the analysis dashboard, the relationship graph is made more intuitive and concise; through the present invention, different resource tables can be analyzed flexibly, and the obtained data can be encapsulated into corresponding entity object information and relationship information of entity objects.

Description

Technical Field

[0001] The present invention belongs to the technical field of big data mining, and particularly relates to a method and system for automatic data expansion and relationship mining. Background Art

[0002] In the context of the information age, the value of data lies in the process of its generation, mining, and collection. Among them, the most crucial and meaningful part is data mining.

[0003] To meet the new requirements for data analysis under big data conditions, satisfy the needs of real-time big data analysis applications, and solve the problem that the current decentralized, single, and offline analysis mode is not suitable for the research and analysis work in the big data environment, a complete, flexible, and practical data visualization analysis is built uniformly to meet the work requirements of big data visualization research and judgment of each unit.

[0004] Based on the concept of brand-new visual data analysis, a large amount of scattered data of various types is presented in the form of graphics. Through graphics, the associations, aggregations, features, etc. between data are described and presented. Furthermore, a large number of graphic analysis methods (association analysis, network analysis, path analysis, etc.) are used to discover and reveal the common elements and associations hidden in the data. This helps users transform a large amount of information with unknown quality, low relevance, and low value into a small amount of information that is easy to understand, highly relevant, highly valuable, and operable, thus providing assistance for the analysis work.

[0005] In view of this, it is very meaningful to propose a method and system for automatic data expansion and relationship mining. Summary of the Invention

[0006] To solve the problems of the existing data relationship analysis system, where in analyzing different scenarios, resource data needs to be sorted into a fixed information table defined by the scenario, and then associated data is obtained by analyzing this fixed information table. This traditional analysis makes the fixed information table too bloated and it is difficult to trace the relationship data. The present invention provides a method and system for automatic data expansion and relationship mining to solve the above-mentioned technical defect problems.

[0007] In a first aspect, the present invention proposes a method for automatic data expansion and relationship mining, and the method includes the following steps:

[0008] Obtain various information of the nodes connected to the resource;

[0009] Dynamically obtain resource table information based on the database of the resource node, and further import the field information list corresponding to the resource table;

[0010] Further create and set up a resource table mapping model;

[0011] Call the scenario analysis module to obtain specific object data information and store it; and

[0012] Render the resource nodes according to the obtained object data information and display them on the analysis dashboard.

[0013] Preferably, the databases of the resource nodes include mysql, Elasticsearch, mongodb, hbase; the information of each node for obtaining resource connections includes name, type, IP address, port, database name, username, password; the obtained resource table information includes resource table name, Chinese name of the resource table, resource node name, database name; the imported list of field information corresponding to the resource table includes field name, type, field title.

[0014] More preferably, creating and setting up the resource table mapping model specifically includes:

[0015] First, create the information of the resource table mapping model, including model name, model creation time, model creator;

[0016] Create the execution steps of the model. On the model design dashboard, pull out and set the input parameter values, pull out the resource table, and set the analysis conditions and fields to be analyzed for the resource table. The analysis conditions include conditions such as equal to, not equal to, less than, less than or equal to, greater than, greater than or equal to, contains, is empty, is not empty;

[0017] Select a certain field or a combination of multiple fields as the input parameter values according to the obtained data, as the input parameter values of the current step, and run the step according to the configured different analysis conditions and analysis fields;

[0018] If there are subsequent steps, repeat the previous step for configuration;

[0019] Configure the mapping information and object relationship information of the output object, and map the output data fields to the corresponding fields of the object;

[0020] Save the resource table mapping model configuration information to the model resource table;

[0021] Configure the associated information of the resource table mapping model to which the scenario belongs.

[0022] More preferably, the scenario analysis module obtaining specific object data information specifically includes:

[0023] The analysis dashboard calls the scenario analysis operation program of the system, and the input conditions include the value of the input parameter address, scenario information, and the group of resource table mapping models to be analyzed;

[0024] If the condition for analysis does not specifically select a resource table mapping model group, all resource table mapping models associated with the scenario information will be used for calculation. If there is a selected group, the incoming resource table mapping model group will be used for calculation;

[0025] The system program will put the obtained resource table mapping model information into the model data analysis pool through redis, and the data analysis thread pool of the system program will allocate corresponding threads for data analysis according to the data in the model data analysis pool;

[0026] The data analysis thread obtains corresponding data based on the input parameter data, input parameter types, execution step sequence of the model, and resource tables involved in the resource table mapping model, and extracts corresponding data output entity object data and entity relationship data according to the output entity object mapping information and output entity relationship information;

[0027] Finally, entity object data and entity relationship data are returned.

[0028] Further preferably, rendering the resource nodes according to the obtained object data information and displaying them on the analysis dashboard specifically includes:

[0029] Obtain the entity object data and entity relationship data returned by the scenario analysis;

[0030] Render the icons of the corresponding entity object nodes on the analysis dashboard according to the entity object data, and assign the corresponding entity object data to the corresponding node icons;

[0031] According to the entity relationship data, find two relative entity nodes on the analysis dashboard, draw a connection line between the two nodes, and assign the corresponding entity relationship data to the connection line;

[0032] According to the filtering tools provided by the analysis dashboard, input corresponding conditions to filter nodes and lines, and remove nodes and lines that do not meet the conditions;

[0033] According to the layout tool of the analysis dashboard, select the corresponding layout to typeset the nodes, and the nodes and connection lines are typeset according to certain calculation rules to make the node relationship graph more orderly and intuitive;

[0034] Save the node relationship graph to the Janusgraph graph database.

[0035] In a second aspect, the present invention also proposes a system for data automatic expansion and relationship mining, including:

[0036] An acquisition module: used to acquire various information of the nodes connected to the resources, dynamically acquire resource table information based on the database of the resource nodes, and call the scenario analysis module to obtain specific object data information;

[0037] Creation module: used to create and set the resource table mapping model;

[0038] Scenario analysis module: used to call the scenario analysis module to obtain specific object data information and store it;

[0039] Analysis dashboard module: used to render the resource nodes according to the obtained object data information;

[0040] Display module: used to render the resource nodes according to the obtained object data information and display them on the analysis dashboard.

[0041] In a third aspect, an embodiment of the present invention provides an electronic device, including: one or more processors; a storage device for storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the method described in any implementation manner of the first aspect.

[0042] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the method described in any implementation manner of the first aspect.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0044] (1) By combining the analysis scenario and the resource table mapping model, the technology has stronger practicability, can be applied to a wider range of scenarios, and the analysis is more flexible; according to the node graph operation function provided by the analysis dashboard, the relationship graph is more intuitive and concise; through the present invention, different resource tables can be analyzed flexibly, and the obtained data can be encapsulated into corresponding entity object information and entity object relationship information.

[0045] (2) With this data relationship mining visualization technology, through rich graphical display methods, information can be analyzed from different angles; enable the machine to acquire business knowledge, automatically complete a large amount of mental work, liberate human resources, and reduce time consumption; at the same time, provide visualization analysis means such as association analysis and flow analysis based on the graph, making difficult analysis and judgment work simpler. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings are included to provide a further understanding of the embodiments and are incorporated into and constitute a part of this specification. The drawings illustrate the embodiments and are used together with the description to explain the principles of the present invention. Other embodiments and many of the intended advantages of the embodiments will be readily appreciated as they become better understood by reference to the following detailed description. The elements of the drawings are not necessarily to scale with each other. Like reference numerals refer to corresponding like parts.

[0047] Figure 1It is an exemplary device architecture diagram to which an embodiment of the present invention can be applied;

[0048] Figure 2 It is a schematic flowchart of the method for data automation expansion and relationship mining according to an embodiment of the present invention;

[0049] Figure 3 It is a schematic flowchart of step S13 in the method for data automation expansion and relationship mining according to an embodiment of the present invention;

[0050] Figure 4 It is a schematic flowchart of step S14 in the method for data automation expansion and relationship mining according to an embodiment of the present invention;

[0051] Figure 5 It is a schematic flowchart of step S15 in the method for data automation expansion and relationship mining according to an embodiment of the present invention;

[0052] Figure 6 It is a schematic flowchart of the system for data automation expansion and relationship mining according to an embodiment of the present invention;

[0053] Figure 7 It is a schematic diagram of the structure of a computer device of an electronic device suitable for implementing the embodiments of the present invention. Detailed implementation manners

[0054] In the following detailed description, reference is made to the accompanying drawings which form a part of the detailed description and in which are shown illustrative specific embodiments by which the invention may be practiced. In this regard, directional terms such as "top", "bottom", "left", "right", "upper", "lower", etc. are used with reference to the orientation of the depicted figures. Since the components of the embodiments may be positioned in several different orientations, the directional terms are used for purposes of illustration and are in no way limiting. It should be understood that other embodiments may be utilized or logical changes may be made without departing from the scope of the present invention. Accordingly, the following detailed description should not be taken in a limiting sense, and the scope of the present invention is defined by the appended claims.

[0055] It should be understood that Figure 1 the number of terminal devices, networks and servers in

[0056] Figure 1 shows an exemplary system architecture 100 of a method for processing information or a device for processing information to which embodiments of the present invention can be applied.

[0057] As Figure 1As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0058] Users can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications may be installed on the terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0059] The terminal devices 101, 102, 103 may be various electronic devices with communication functions, including but not limited to smart phones, tablet computers, laptop portable computers, and desktop computers, etc.

[0060] The server 105 may be a server that provides various services, such as a background information processing server that processes the verification request information sent by the terminal devices 101, 102, 103. The background information processing server may analyze and process the received verification request information, and obtain a processing result (such as verification success information used to indicate that the verification request is a legitimate request).

[0061] It should be noted that the method for processing information provided by the embodiments of the present invention is generally executed by the server 105. Correspondingly, the device for processing information is generally set in the server 105. In addition, the method for sending information provided by the embodiments of the present invention is generally executed by the terminal devices 101, 102, 103. Correspondingly, the device for sending information is generally set in the terminal devices 101, 102, 103.

[0062] It should be noted that the server may be hardware or software. When the server is hardware, it can be implemented as a distributed server cluster composed of multiple servers, or as a single server. When the server is software, it can be implemented as multiple software or software modules (such as used to provide distributed services), or as a single software or multiple software modules, which is not specifically limited herein.

[0063] Different resource tables have different table fields, and different table fields have different actual meanings. If traditional analysis of different scenarios requires governing all resource data into a fixed information table defined by the scenario, and then obtaining associated data by analyzing this fixed information table, this traditional analysis will make the fixed information table too bloated, and it is relatively difficult to trace the source of relational data.

[0064] The present invention provides a method for data automatic expansion and relationship mining to mine and display data relationships. It mainly includes scenario configuration of data analysis, data mining, and data relationship display. The scenario configuration of data analysis makes the technology more adaptable; data mining is to perform calculations and analyses on corresponding resource data based on the information with scenario configuration of data analysis; the display of data relationships is to generate entity object nodes and entity relationship lines in a graphical manner according to the associated data mined from the data for display.

[0065] Figure 2 An embodiment of the present invention discloses a method for data automatic expansion and relationship mining, as Figure 2 shown, the method includes the following steps:

[0066] S11. Obtain various information of the nodes connected to the resources;

[0067] S12. Dynamically obtain resource table information based on the database of the resource nodes, and further import the field information list corresponding to the resource table;

[0068] Specifically, databases of resource node types include mysql, Elasticsearch, mongodb, hbase, etc. The content obtained for the nodes connected to the resources includes information such as name, type, IP address, port, database name, username, and password. Dynamically obtaining resource table information through the database of the resource nodes includes: resource table name, Chinese name of the resource table, resource node name, and database name; it is also necessary to import the field information list corresponding to the resource table, including field name, type, and field title.

[0069] S13. Further create and set a resource table mapping model;

[0070] In this embodiment, as Figure 3 shown, creating and setting a resource table mapping model specifically includes:

[0071] S131. First, create the information of the resource table mapping model, including model name, model creation time, and model creator. For example, create a resource table mapping model for transaction funds, named transaction model.

[0072] S132. Create the execution steps of the model. On the model design dashboard, pull out the setting code address node (input parameter value), pull out the resource table, and set the analysis conditions and fields to be analyzed for the resource table. The analysis conditions include equal to, not equal to, less than, less than or equal to, greater than, greater than or equal to, contains, is empty, is not empty, etc. For example, for the transaction model, the code address is the card number, pull out the transaction resource table, and the analysis condition is that when the card number is compared with the transaction account field, they are equal.

[0073] In S133, select a certain field or a combination of multiple fields from the data obtained in the second step as the input parameter for the current step. According to different configured analysis conditions and analysis fields, run the step.

[0074] S134. If there are subsequent steps, repeat the configuration in step 133.

[0075] S135. Configure the mapping information of the output object and the object relationship information. The data fields of the output are mapped to the fields of the corresponding object. For example, the transaction model is mapped to an object, and the card number object includes card number value, card number information, cardholder information, etc.; for the extraction of the object relationship of the transaction model, the object relationship includes information such as transfer card number, receiving card number, transfer amount, transfer time, transfer location, etc.

[0076] S136. Save the mapping model configuration information of the resource table to the model resource table.

[0077] S137. Configure the associated information of the resource table mapping model to which the scenario belongs.

[0078] S14. Call the scenario analysis module to obtain specific object data information and store it;

[0079] In this embodiment, as Figure 4 shown, the scenario analysis obtains specific object data, and the specific steps are as follows:

[0080] S141. The analysis dashboard calls the scenario analysis operation program of the system. The input conditions include the value of the input parameter code address, scenario information, and the resource table mapping model group to be analyzed.

[0081] S142. If the analysis condition does not specifically select a resource table mapping model group, calculate using all the resource table mapping models associated with the scenario information. If there is a selected group, calculate using the passed-in resource table mapping model group.

[0082] S143. The system program puts the obtained resource table mapping model information into the model data analysis pool through redis.

[0083] S144. The data analysis thread pool of the system program will allocate corresponding threads for data analysis according to the data in the model data analysis pool.

[0084] S145. The data analysis thread obtains corresponding data according to the input parameter data, input parameter type, execution step sequence of the model, and the resource table involved in the resource table mapping model, and extracts the corresponding data output entity object data and entity relationship data according to the output entity object mapping information and output entity relationship information.

[0085] S146. Return the entity object data and entity relationship data.

[0086] S15. Render the resource node according to the obtained object data information and display it on the analysis dashboard.

[0087] The analysis dashboard performs operations such as node rendering and relationship line drawing according to the return value. The process is as Figure 5 shown, and the specific steps are as follows:

[0088] S151. Obtain the entity object data and entity relationship data returned by the scenario analysis.

[0089] S152. Render the icons of the corresponding entity object nodes on the analysis dashboard according to the entity object data, and assign the corresponding entity object data to the corresponding node icons.

[0090] S153. According to the entity relationship data, find the two relative entity nodes on the analysis dashboard, draw the connection line between the two nodes, and assign the corresponding entity relationship data to the connection line.

[0091] S154. According to the filtering tool provided by the analysis dashboard, input the corresponding conditions to filter the nodes and lines, and remove the nodes and lines that do not meet the conditions.

[0092] S155. According to the layout tool of the analysis dashboard, select the corresponding layout to typeset the nodes. The nodes and connection lines are typeset according to certain calculation rules to make the node relationship graph more orderly and intuitive.

[0093] S156. Save the node relationship graph to the Janusgraph graph database.

[0094] Furthermore, in this embodiment, the specific solution of a specific embodiment of the present invention is as follows:

[0095] S1. Use the information of the entity and the information of the entity relationship, and add them to the resource table mapping configuration set;

[0096] S2. Use the resource table information and add it to the resource data set of the resource table dashboard;

[0097] S3. Set the corresponding resource conditions for the input parameter values of the resource table in the resource data set, and combine the entity information and entity relationship information in the mapping configuration set to form a complete resource table mapping model information;

[0098] S4. Use the scenario information and store it in the scenario mapping configuration set;

[0099] S5. Use the resource table mapping model information and enter it into the scenario mapping configuration set;

[0100] S6. Correlate the data in the scenario mapping configuration set accordingly, map the model information according to the data association resource table of the scenario information, and combine them into a group of scenario mapping information;

[0101] S7. Use the scenario mapping information and store it in the analysis dashboard data analysis set;

[0102] S8. Obtain the input parameter values to be analyzed. According to the data analysis set of the analysis dashboard, analyze the resource table with the scenario mapping information, and obtain the entity object information data and entity relationship information data. Then store the data in the data set of the analysis dashboard.

[0103] S9. In the analysis dashboard, render the nodes and connections according to the entity object data and entity relationship data in the data set to generate a relationship diagram.

[0104] S10. The analysis dashboard can trace the relationship information of the entity objects using the data in the data set, and can obtain the node data of the entity objects and the data of the entity relationships;

[0105] S11. The analysis dashboard uses the relationship diagram and saves it to the JanusGraph graph database.

[0106] Further, step S3 specifically includes:

[0107] S31. Use the code address information and store it in the resource table mapping configuration set;

[0108] S32. Use the entity object information and entity relationship information and store them in the resource table mapping configuration set;

[0109] S33. Use the resource table information and store it in the resource table mapping configuration set;

[0110] S34. Use the scenario information and store it in the resource table mapping configuration combination;

[0111] S35. Obtain the code address information and resource table information from the configuration set. Use the code address information as the input parameter condition to associate the corresponding fields of the resource table, select the condition for comparing the input parameter and the field, filter out the corresponding fields after analyzing the resource table as the next output value, and store the configuration in the resource table mapping configuration step set;

[0112] S36. Obtain the input field value from the configuration step set as the input parameter condition, obtain the resource table information of the configuration set, associate the input parameter value and the corresponding field of the resource table, select the comparison condition, filter out the corresponding fields after analyzing the resource table as the input value, and store the configuration in the resource table configuration step set;

[0113] S37. If there are multiple-step operations, repeat the steps of S33;

[0114] S38. Obtain the analyzed data fields from the configuration step set, obtain the entity object information from the configuration set, map the fields of the data fields and the entity object information, and store the mapping information in the resource table mapping set;

[0115] S39. Obtain the analyzed data fields from the configuration step set, obtain the entity relationship information from the configuration set, map the fields of the data fields and the entity relationship information, and store the mapped information in the resource table configuration step set;

[0116] S310. Obtain the resource table mapping model information from the configuration step set, obtain the scenario information from the configuration set, associate the resource table mapping model information and the scenario information, and store the associated information in the scenario information table.

[0117] Further, step S8 specifically includes:

[0118] S81. The system first obtains the code address value, the analyzed scenario information, and the resource table mapping model group information passed in by the analysis dashboard;

[0119] S82. According to the scenario information and the resource table mapping model group information, obtain the configured resource table mapping model information;

[0120] S83. According to the obtained resource table mapping model information group, execute the resource table mapping model through different threads, analyze the data of the resource table, and obtain the entity object information and the entity object relationship information data;

[0121] S84. Obtain the entity object information and the entity object relationship information data and return them to the analysis dashboard;

[0122] Further, step S9 specifically includes:

[0123] S91. The analysis dashboard obtains the entity object information and the entity relationship information data calculated by the system;

[0124] S92. According to the entity object information, render the entity object nodes onto the analysis dashboard and assign corresponding attribute values to the entity object nodes;

[0125] S93. According to the entity relationship information, render the lines of the relationship connections between the nodes and assign the corresponding relationship data, the relationship data of the original resource table, to the lines of the relationship connections;

[0126] S94. Filter the nodes on the dashboard according to the filtering conditions provided by the analysis dashboard. For example, in the financial transaction scenario, filter and delete the nodes with a transaction amount less than one hundred thousand.

[0127] By combining the analysis scenario and the resource table mapping model, the present invention has stronger practicality, can be applied to a wider range of scenarios, and the analysis is more flexible; according to the node graph operation function provided by the analysis dashboard, the relationship graph is more intuitive and concise.

[0128] In a second aspect, an embodiment of the present invention further provides a system for data automation expansion and relationship mining, as Figure 6 shown, including an acquisition module 61, a creation module 62, a scenario analysis module 63, an analysis dashboard module 64, and a display module 65.

[0129] Among them, the acquisition module 61: is used to acquire various information of the nodes connected by resources, dynamically acquire resource table information based on the database of the resource nodes, and call the scenario analysis module to acquire specific object data information; the creation module 62: is used to create and set the resource table mapping model; the scenario analysis module 63: is used to call the scenario analysis module to acquire specific object data information and store it; the analysis dashboard module 64: is used to render the resource nodes according to the acquired object data information; the display module 65: is used to render the resource nodes according to the acquired object data information and display them on the analysis dashboard.

[0130] Next, refer to Figure 7 , which shows a schematic structural diagram of a computer device 600 suitable for implementing an embodiment of the present invention (such as Figure 1 the server or terminal device shown). Figure 7 The electronic device shown is only an example and should not bring any restrictions to the functions and usage ranges of the embodiments of the present invention.

[0131] As Figure 7 shown, the computer device 600 includes a central processing unit (CPU) 601 and a graphics processing unit (GPU) 602, which can perform various appropriate actions and processes according to the programs stored in the read-only memory (ROM) 603 or the programs loaded from the storage section 609 into the random access memory (RAM) 606. In the RAM 604, various programs and data required for the operation of the device 600 are also stored. The CPU 601, GPU 602, ROM 603, and RAM 604 are connected to each other through a bus 605. The input / output (I / O) interface 606 is also connected to the bus 605.

[0132] The following components are connected to the I / O interface 606: an input part 607 including a keyboard, a mouse, etc.; an output part 608 including, for example, a liquid crystal display (LCD), etc. and a speaker, etc.; a storage part 609 including a hard disk, etc.; and a communication part 610 including a network interface card such as a LAN card, a modem, etc. The communication part 610 performs communication processing via a network such as the Internet. A drive 611 may also be connected to the I / O interface 606 as needed. A removable medium 612, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 611 as needed so that a computer program read therefrom is installed into the storage part 609 as needed.

[0133] Specifically, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present disclosure includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes program codes for performing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network via the communication part 610, and / or installed from the removable medium 612. When the computer program is executed by a central processing unit (CPU) 601 and a graphics processing unit (GPU) 602, the above-described functions defined in the method of the present invention are executed.

[0134] It should be noted that the computer-readable medium described in the present invention can be a computer-readable signal medium, a computer-readable medium, or any combination of the two. The computer-readable medium can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor devices, apparatuses, or components, or any combination of the above. More specific examples of the computer-readable medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable medium can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution device, apparatus, or component. In the present invention, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries the computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution device, apparatus, or component. The program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination of the above.

[0135] The computer program code for performing the operations of the present invention can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages - such as Java, Smalltalk, C++, and also include conventional procedural programming languages - such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network - including a local area network (LAN) or a wide area network (WAN) - or can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0136] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of apparatuses, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in an order different from that noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based device that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0137] The modules described in the embodiments of the present invention can be implemented in software or in hardware. The described modules can also be provided in a processor.

[0138] As another aspect, the present invention also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device is caused to perform the method steps described in the first aspect of the present invention.

[0139] The above description is only a preferred embodiment of the present invention and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the present invention is not limited to the technical solution formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the (but not limited to) technical features having similar functions disclosed in the present invention.

Claims

1. A method for data automated expansion and relationship mining, characterized in that, The method includes the following steps: Obtain various information of the nodes connected to the resources; Dynamically obtain resource table information based on the database of the resource nodes, and further import the list of field information corresponding to the resource table; Further create and set up a resource table mapping model; Call the scenario analysis module to obtain specific object data information and store it; and Render the resource nodes according to the obtained object data information and display them on the analysis dashboard; The scenario analysis module obtaining specific object data information specifically includes: the analysis dashboard calls the scenario analysis operation program of the system, and the input conditions include the value of the input parameter code address, scenario information, and the resource table mapping model group to be analyzed; if the resource table mapping model group is not specifically selected for the analysis conditions, the calculation will be performed using all the resource table mapping models associated with the scenario information, and if there is one, the calculation will be performed using the input resource table mapping model group; the system program will put the obtained resource table mapping model information into the model data analysis pool through redis, and the data analysis thread pool of the system program will allocate corresponding threads for data analysis according to the data in the model data analysis pool; the data analysis thread obtains corresponding data based on the input parameter data, input parameter type, execution step sequence of the model, and the resource table involved in the analysis of the resource table mapping model, and extracts the corresponding data output entity object data and entity relationship data according to the output entity object mapping information and output entity relationship information; finally, return the entity object data and entity relationship data; Rendering the resource nodes according to the obtained object data information and displaying them on the analysis dashboard specifically includes: obtaining the entity object data and entity relationship data returned by the scenario analysis; rendering the icons of the corresponding entity object nodes on the analysis dashboard according to the entity object data, and assigning the corresponding entity object data to the corresponding node icons; according to the entity relationship data, find two opposite entity nodes on the analysis dashboard, draw a connection line between the two nodes, and assign the corresponding entity relationship data to the connection line; according to the filtering tool provided by the analysis dashboard, input the corresponding conditions to filter the nodes and lines, and remove the nodes and lines that do not meet the conditions; according to the layout tool of the analysis dashboard, select the corresponding layout to typeset the nodes, and the nodes and connection lines are typeset according to a certain calculation rule to make the node relationship graph more orderly and intuitive; save the node relationship graph to the Janusgraph graph database.

2. The method for data automated expansion and relationship mining according to claim 1, characterized in that, The database of the resource nodes includes mysql, Elasticsearch, mongodb, and hbase; obtaining various information of the nodes connected to the resources includes name, type, IP address, port, database name, username, and password; the obtained resource table information includes resource table name, Chinese name of the resource table, resource node name, and database name; importing the list of field information corresponding to the resource table includes field name, type, and field name.

3. The method for data automated expansion and relationship mining according to claim 2, characterized in that, Creating and setting up a resource table mapping model specifically includes: First, create the information of the resource table mapping model, including model name, model creation time, and model creator; Execution steps for creating a model. On the model design dashboard, pull out the input parameter values, pull out the resource table, and set the analysis conditions and fields required for the resource table. The analysis conditions include equal to, not equal to, less than, less than or equal to, greater than, greater than or equal to, contains, is empty, and is not empty conditions; Select one or more fields or a combination of fields as the input parameter values based on the obtained data, and use them as the input parameter values for the current step. Run the step according to the configured different analysis conditions and analysis fields; If there are subsequent steps, repeat the previous step for configuration; Configure the mapping information and object relationship information of the output object, and map the data fields of the output to the fields of the corresponding object; Save the resource table mapping model configuration information to the model resource table; Configure the associated information of the resource table mapping model to which the scenario belongs.

4. A system for data automated expansion and relationship mining, characterized in that, Including the method for data automation extension and relationship mining according to any one of claims 1-3, further comprising: Acquisition module: used to acquire various information of the nodes connected to the resources, dynamically acquire the resource table information based on the database of the resource nodes, and call the scenario analysis module to acquire specific object data information; Creation module: used to create and set the resource table mapping model; Scenario analysis module: used to call the scenario analysis module to acquire specific object data information and store it; Analysis dashboard module: used to render the resource nodes according to the acquired object data information; Display module: used to render and display the resource nodes on the analysis dashboard according to the acquired object data information.

5. An electronic device, comprising: One or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 3.

6. A computer-readable storage medium, on which a computer program is stored, characterized in that, When the program is executed by the processor, it implements the method according to any one of claims 1 to 3.

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