Method and system for integrating multivariate heterogeneous knowledge data

Abstract

The invention discloses a multivariate heterogeneous knowledge data integration method and system. The method comprises the following steps: determining a to-be-processed knowledge data set, and performing structural analysis on each piece of knowledge data in the knowledge data set; wherein the knowledge data set comprises a plurality of pieces of multivariate and heterogeneous knowledge data; constructing metadata and a data relationship for each piece of knowledge data based on an analysis result, and integrating to obtain a knowledge graph; wherein the data relationship is an association relationship between each piece of knowledge data and other one or more pieces of knowledge data; judging whether data update exists in the knowledge data set in which the metadata and the data relationship are constructed; and under the condition that the judgment result is yes, performing integrated updating on the knowledge graph according to an updating type. Therefore, the overhead caused by format conversion between heterogeneous data can be avoided, and the new knowledge convergence and fusion cost is reduced; meanwhile, the problem of content inconsistency caused by modification of multiple data copies is avoided, and global consistency of knowledge data in the using process is conveniently maintained.

Description

Technical Field

[0001] The present application relates to the field of computer technology, specifically to the technical fields of databases and graph databases, and in particular to a method and system for integrating multi-dimensional heterogeneous knowledge data. Background Art

[0002] With the continuous development and progress of computer technology, the importance and necessity of the integration and fusion of heterogeneous knowledge in database business are self-evident. In the decision-making process of an enterprise or organization, various heterogeneous knowledge data need to be integrated and analyzed; data from different sources and in different formats have differences in storage, query and use.

[0003] Existing heterogeneous knowledge data integration methods mainly achieve data fusion between different data sources through steps such as data cleaning, data conversion, and data integration. Data cleaning is used to remove noise and outliers in the data, data conversion is used to convert data into a unified format and structure, and data integration is used to integrate data from different data sources. Data fusion algorithms are the core part of achieving data fusion. Commonly used data fusion algorithms include weighted average method, decision tree method, Bayesian network, etc. In the data fusion process, data from different data sources need to be matched and aligned to ensure data consistency and accuracy.

[0004] However, considering that data conversion is required every time new knowledge is added or updated, the integration cost is high. In addition, the formats and structures of different data vary greatly, and not all formats can be or are conveniently converted to each other. For example, some data is time series data and is stored in the time series database format, while some data is attribute data and is stored in the relational database format. Therefore, conversion is difficult. In addition, data conversion results in multiple copies of a data (original data and converted data). During the use of data, any modification of a copy may cause data inconsistency. Summary of the invention

[0005] The present application provides a method and system for integrating multi-dimensional heterogeneous knowledge data to solve the problems of high fusion cost, great conversion difficulty and inconsistent data after fusion of existing heterogeneous knowledge data.

[0006] The technical solution is as follows: In a first aspect, a method for integrating multivariate heterogeneous knowledge data is provided, comprising: Determine a knowledge data set to be processed, and perform structural analysis on each knowledge data in the knowledge data set; wherein the knowledge data set includes a plurality of multivariate heterogeneous knowledge data; Based on the analysis results, metadata and data relationships are constructed for each knowledge data, and integrated to obtain a knowledge graph; wherein the data relationship is an association relationship between each knowledge data and one or more other knowledge data; Determine whether there is data update in the knowledge data set with established metadata and data relationships; When the judgment result is yes, the knowledge graph is integrated and updated according to the update type.

[0007] In a possible implementation, determining a knowledge data set to be processed and performing structural analysis on each piece of knowledge data in the knowledge data set specifically includes: Acquire a number of multivariate and heterogeneous knowledge data and determine them as the knowledge data set to be processed; A structural analysis is performed on each knowledge data in the knowledge data set, and each knowledge data is stored in a corresponding database according to its own structural characteristics; wherein different databases are configured for knowledge data with different structural characteristics.

[0008] In a possible implementation, the plurality of multi-dimensional and heterogeneous knowledge data at least includes knowledge types defined according to the following three types of structural characteristics: Simulation model attribute knowledge, simulation model control knowledge, and adversarial process result knowledge.

[0009] In a possible implementation, metadata and data relationships are constructed for each knowledge data based on the analysis results, and a knowledge graph is obtained by integration, which specifically includes: Modeling is performed based on historical knowledge data to determine metadata attributes used to describe basic data information, as well as data relationship models used to describe associations between knowledge data; Acquire attribute content corresponding to the metadata attribute based on the analysis result, and use the attribute content to construct corresponding metadata for each knowledge data; According to the metadata constructed for each knowledge data and the data relationship model, the association relationship between the metadata of different knowledge data is found, and the knowledge graph is created based on the integration of the found association relationships.

[0010] In a possible implementation, determining whether there is data update in the knowledge data set for which metadata and data relationships have been constructed specifically includes: Determine whether there is new data in the knowledge data set that needs to be merged; or, Determine whether any integrated knowledge data in the knowledge data set has been deleted; or, Determine whether any integrated knowledge data in the knowledge data set has been modified; or, It is determined whether there is any integrated knowledge data to be queried in the knowledge data set.

[0011] In a possible implementation, the knowledge graph is integrated and updated according to the update type, specifically including: If the update type is new, then the corresponding metadata is constructed for the new knowledge data, and the data relationship between the metadata and the metadata of other knowledge data is determined; the knowledge graph is updated based on the data relationship, and the metadata is added to the corresponding database for storage; If the update type is deletion, the data relationship of the deleted knowledge data is deleted from the knowledge graph, and the metadata of the deleted knowledge data is deleted from the corresponding database; If the update type is modification, then the corresponding new metadata is rebuilt based on the modified knowledge data, and the new data relationship is re-determined based on the modified knowledge data; the knowledge graph is updated based on the new data relationship, and the new metadata replaces the original metadata and is saved in the corresponding database; If the update type is query, the original metadata is updated based on the query operation, and the new data relationship is determined based on the updated metadata; the knowledge graph is updated based on the new data relationship, and the updated metadata replaces the original metadata and is saved in the corresponding database.

[0012] In a possible implementation, the different databases include at least: a file database, a relational database, a non-relational database, and a time series database.

[0013] In a possible implementation, the metadata attributes include at least: data name, data ID, and storage address of the data in the corresponding database; As the attribute knowledge of the simulation model, the metadata of the simulation model includes: the name of the simulation model, the simulation model ID, and the storage address of the simulation model in the SQLite database; As the control knowledge of the simulation model, the metadata of the control rule includes: the control rule name, the control rule ID, and the storage address of the control rule in the MangoDB database; As the result knowledge of the adversarial process, the metadata of the deduction process includes: the deduction process name, the deduction process ID, and the storage address of the deduction process in the InfluxDB database.

[0014] In a possible implementation, the data relationship between the metadata of the simulation model and the metadata of the control rule is an applicable rule, and the applicable rule represents the control rule applicable to the simulation model; The data relationship between the metadata of the deduction process and the metadata of the simulation model is a model composition, and the model composition represents the simulation model used in the deduction process.

[0015] In the second aspect, a system for integrating multi-dimensional heterogeneous knowledge data is provided, including: An analysis module, used to determine a knowledge data set to be processed, and perform structural analysis on each knowledge data in the knowledge data set; wherein the knowledge data set includes a plurality of multivariate heterogeneous knowledge data; An integration module is used to construct metadata and data relationships for each knowledge data based on the analysis results, and integrate to obtain a knowledge graph; wherein the data relationship is an association relationship between each knowledge data and one or more other knowledge data; A judgment module, used to judge whether there is data update in the knowledge data set with constructed metadata and data relations; The update module is used to integrate and update the knowledge graph according to the update type when the judgment result is yes.

[0016] In a third aspect, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement the method of the aspect as described above and any possible implementation method.

[0017] In a fourth aspect, a computing device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method of the aspects described above and any possible implementation method.

[0018] The beneficial effects of the technical solution provided by this application include at least: It can be seen from the above technical solution that the knowledge data set to be processed is determined, and the structure analysis of each knowledge data in the knowledge data set is performed; wherein, the knowledge data set contains a number of multivariate heterogeneous knowledge data; metadata and data relationships are constructed for each knowledge data based on the analysis results, and a knowledge graph is obtained by integration; wherein, the data relationship is the association relationship between each knowledge data and one or more other knowledge data; it is determined whether there is data update in the knowledge data set for which metadata and data relationships have been constructed; if the judgment result is yes, the knowledge graph is integrated and updated according to the update type. In this way, the overhead caused by format conversion between heterogeneous data can be avoided, and the cost of gathering and integrating new knowledge can be reduced; at the same time, the content inconsistency problem caused by the modification of multiple data copies can be avoided, so as to facilitate the maintenance of the global consistency of knowledge data during use.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 It is a schematic diagram of the steps of a method for integrating multi-heterogeneous knowledge data provided in an embodiment of the present application.

[0022] Figure 2 It is a schematic diagram of the metadata design structure provided in the embodiment of the present application.

[0023] Figure 3 It is a flowchart of performing different integrated updates on the knowledge graph based on different update types provided by an embodiment of the present application.

[0024] Figure 4 It is a framework diagram of the knowledge graph formed based on different update types and the underlying database stored in the embodiment of the present application.

[0025] Figure 5 It is a schematic diagram of metadata design in a competitive gaming scenario provided in an embodiment of the present application.

[0026] Figure 6 It is a schematic diagram of the data relationship design in the game confrontation game scenario provided in the embodiment of the present application.

[0027] Figure 7 It is a schematic diagram of the knowledge graph constructed in the game confrontation scenario provided in the embodiment of the present application.

[0028] Figure 8 It is a schematic diagram of the knowledge spectrum after the aerial unit C is added to the game scenario provided in the embodiment of the present application.

[0029] Fig. 9 It is a structural block diagram of the integrated system of multi-heterogeneous knowledge data provided in the embodiment of the present application.

[0030] Fig.10 It is a block diagram of a computing device according to an embodiment of the present application. DETAILED DESCRIPTION

[0031] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0032] Obviously, the described embodiments are only part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0033] As can be seen from the background technology, if knowledge data, especially multi-heterogeneous knowledge data, is not managed in a unified way, users need to spend a lot of time and energy to find and integrate this knowledge. To this end, it can be considered that by unified management of heterogeneous knowledge data, a complete and orderly knowledge system can be formed to improve the efficiency and value of knowledge utilization; in addition, knowledge barriers can be eliminated to promote the sharing and transfer of knowledge; in addition, a common knowledge interface and standard can be established to enhance the interoperability between different systems, thereby achieving seamless connection and efficient circulation of information; at the same time, a comprehensive and accurate knowledge view can be provided to support decision makers to conduct in-depth data mining and correlation analysis, so as to make more informed decisions.

[0034] To this end, this application proposes an integration solution for multi-heterogeneous knowledge data with centralized relationship management and independent data storage. The inventive concept is that, unlike the existing fusion methods, all raw data do not need to be stored in a unified format, but are stored in the most suitable way according to the characteristics of the data themselves, thus forming a heterogeneous and independent data storage bottom layer; then on this basis, based on the knowledge graph technology, two parts of data are managed in a unified manner: one is the data that describes these underlying data, namely metadata; the other is the relationship between data, namely data relationship. In this way, when new data is added or updated, only metadata and relationship data need to be added or updated, avoiding the cleaning of the underlying data and the format conversion between each other; and there is always only one copy of the data globally, thus avoiding the consistency problem caused by the modification of multiple copies of data.

[0035] In this application, metadata is information that describes the characteristics of data, including descriptive, structural, administrative, and technical metadata. Metadata mainly ranges from basic attributes such as title, description, subject, keywords, to detailed technical information such as data type, file format, field, data size, number of records, creation date, modification date, etc.

[0036] The technical solution claimed in this application is described in detail below through specific embodiments.

[0037] Reference Figure 1 FIG. 1 is a schematic diagram of the steps of a method for integrating multi-heterogeneous knowledge data provided by an embodiment of the present application. The execution subject of the integration method can be a software module with computing and data processing capabilities, or a computing device integrated with similar software modules.

[0038] The method for integrating multivariate heterogeneous knowledge data may include the following steps: Step 102: Determine a knowledge data set to be processed, and perform structural analysis on each knowledge data in the knowledge data set; wherein the knowledge data set includes a plurality of multivariate and heterogeneous knowledge data.

[0039] In the present application, the knowledge data set to be processed may be a data set consisting of a plurality of multivariate heterogeneous knowledge data. The plurality of multivariate heterogeneous knowledge data in the knowledge data set may be stored based on a simple storage method, and each knowledge data contains basic data information, such as data type, structure, data size, data content, etc.

[0040] Optionally, when determining the knowledge data set to be processed and performing structural analysis on each knowledge data in the knowledge data set, a number of multivariate heterogeneous knowledge data can be obtained and determined as the knowledge data set to be processed; structural analysis is performed on each knowledge data in the knowledge data set, and each knowledge data is stored in a corresponding database according to its own structural characteristics; wherein knowledge data with different structural characteristics are configured with different databases.

[0041] In specific implementation, the data characteristics of knowledge data can be analyzed. The structural characteristics of knowledge data that need to be integrated and fused are analyzed, and based on the analysis, the data is classified and stored in different databases, such as relational databases, time series databases, file databases, structural databases, etc. For example, if knowledge data A belongs to relational data, then knowledge data A is stored in a relational database; if knowledge data B belongs to time series data, then knowledge data B is stored in a time series database.

[0042] Optionally, in a simulated game of game confrontation, the plurality of multi-dimensional heterogeneous knowledge data includes at least three types of knowledge defined according to the following structural characteristics: simulation model attribute knowledge, simulation model control knowledge, and confrontation process result knowledge. Among them, the simulation model attribute knowledge describes the attributes of the simulation model required for confrontation, such as the maximum speed and maximum fuel volume of the attacking unit, which belongs to structured data; the simulation model control knowledge describes the logic of the control model when conducting confrontation, such as attacking when the opponent unit is found, returning when the energy is insufficient, etc., which is in the form of program code and belongs to file data; the confrontation process result knowledge describes the process of game confrontation according to the simulation time sequence, which belongs to time series data. These three types of knowledge data need to be integrated and fused together to facilitate query and modification, and support game confrontation based on simulation.

[0043] Step 104: construct metadata and data relationships for each knowledge data based on the analysis results, and integrate them to obtain a knowledge graph; wherein the data relationship is the association relationship between each knowledge data and one or more other knowledge data.

[0044] Optionally, in step 104, when constructing metadata and data relationships for each knowledge data based on the analysis results and integrating to obtain a knowledge graph, modeling can be performed based on historical knowledge data to determine metadata attributes used to describe basic data information, as well as a data relationship model used to describe the association relationship between knowledge data; attribute content corresponding to the metadata attributes is obtained based on the analysis results, and the attribute content is used to construct corresponding metadata for each knowledge data; based on the metadata constructed for each knowledge data and the data relationship model, the association relationship between the metadata of different knowledge data is searched, and a knowledge graph is created based on the integration of the found association relationships.

[0045] Specifically, we can first design knowledge data modeling and data relationships, and then build metadata and data relationships. We can model different knowledge data and design data that describes the data, i.e. metadata, such as Figure 2 As shown. And design the association relationship between data. Among them, the metadata should contain the location index of the data in the database, so as to find the corresponding data location through the metadata. Based on the designed knowledge data modeling and data relationship, the corresponding metadata and data relationship are constructed in the knowledge graph to form a knowledge graph between metadata.

[0046] Step 106: Determine whether there is any data update in the knowledge data set for which metadata and data relationships have been constructed.

[0047] In the present application, data update may include: data addition, data deletion, data modification, data query and other update operations. Then, when determining whether there is data update in the knowledge data set with constructed metadata and data relationships, it can be determined whether there is new data in the knowledge data set that needs to be merged; or, whether the integrated knowledge data in the knowledge data set is deleted; or, whether the integrated knowledge data in the knowledge data set is modified; or, whether the integrated knowledge data in the knowledge data set is queried.

[0048] Step 108: When the judgment result is yes, the knowledge graph is integrated and updated according to the update type.

[0049] Optionally, refer to Figure 3 As shown, when the knowledge graph is integrated and updated according to the update type, it may specifically include: If the update type is new, then the corresponding metadata is constructed for the new knowledge data, and the data relationship between the metadata and the metadata of other knowledge data is determined; the knowledge graph is updated based on the data relationship, and the metadata is added to the corresponding database for storage; If the update type is deletion, the data relationship of the deleted knowledge data is deleted from the knowledge graph, and the metadata of the deleted knowledge data is deleted from the corresponding database; If the update type is modification, then the corresponding new metadata is rebuilt based on the modified knowledge data, and the new data relationship is re-determined based on the modified knowledge data; the knowledge graph is updated based on the new data relationship, and the new metadata replaces the original metadata and is saved in the corresponding database; If the update type is query, the original metadata is updated based on the query operation, and the new data relationship is determined based on the updated metadata; the knowledge graph is updated based on the new data relationship, and the updated metadata replaces the original metadata and is saved in the corresponding database.

[0050] In addition, when the knowledge graph is integrated and updated according to the update type, the knowledge graph can also be integrated and updated according to other update types, and can be specifically implemented according to the content of other update types, which is not limited here.

[0051] Furthermore, the data characteristics of the data to be added can be analyzed and stored in the corresponding database. When knowledge data needs to be deleted, modified, or queried, operations are performed separately according to the metadata layer and the actual data layer. The final technical architecture is as follows: Figure 4 shown.

[0052] In the present application, the different databases include at least: a file database, a relational database, a non-relational database, and a time series database.

[0053] In the simulated game confrontation scenario, the metadata attributes include at least: data name, data ID, and the storage address of the data in the corresponding database; as simulation model attribute knowledge, the metadata of the simulation model includes: simulation model name, simulation model ID, and the storage address of the simulation model in the SQLite database; as simulation model control knowledge, the metadata of the control rule includes: control rule name, control rule ID, and the storage address of the control rule in the MangoDB database; as confrontation process result knowledge, the metadata of the deduction process includes: deduction process name, deduction process ID, and the storage address of the deduction process in the InfluxDB database.

[0054] Furthermore, the data relationship between the metadata of the simulation model and the metadata of the control rules is the applicable rules, which represent the control rules applicable to the simulation model; the data relationship between the metadata of the deduction process and the metadata of the simulation model is the model composition, which represents the simulation model used in the deduction process.

[0055] It should be noted that the specific implementation process provided in this implementation can be combined with the multiple specific implementation processes provided in the aforementioned implementation to implement the multi-heterogeneous knowledge data integration method of this embodiment. For a detailed description, please refer to the relevant content in the aforementioned implementation, which will not be repeated here.

[0056] The integration method of the present application is described in detail below based on the multi-dimensional heterogeneous knowledge data required in the simulated game confrontation game.

[0057] Step 1: Data feature analysis. The data features of three types of knowledge, namely, simulation model attribute knowledge, simulation model control knowledge, and confrontation process result knowledge, are analyzed. Simulation model attribute knowledge belongs to structured data and is suitable for structured database storage, such as SQLite; simulation model control knowledge belongs to file data and is suitable for file database storage, such as MangoDB; confrontation process result knowledge belongs to time series data and is suitable for time series database storage, such as InfluxDB.

[0058] Step 2: Knowledge data modeling and data relationship design. Model different knowledge data and design data describing the data, i.e. metadata, such as Figure 5 As shown in the figure, the relationship between the design data is a directed edge, pointing from the starting point data to the end point data, such as Figure 6 shown.

[0059] Step 3: Metadata and data relationship construction. Based on the design in step 2, corresponding metadata and data relationships are constructed in the knowledge graph to form a knowledge graph between metadata, such as Figure 7 shown.

[0060] Step 4: Determine whether new data is added. Determine whether there is new knowledge data that needs to be integrated into the existing data. For example, an air unit (i.e., an adversarial unit capable of attacking in the air, such as a drone) C is added, and it jointly attacks the water unit (i.e., an adversarial unit capable of defending or attacking on the water) B with the air unit (i.e., a drone) A.

[0061] Step 5: Add metadata and data relationships. Based on steps 2 and 3, construct metadata and data relationships for air unit C. After updating, Figure 8 shown.

[0062] Step 6: Add underlying data. According to step 1, add the model data of air unit C to SQLite, and the added address should be consistent with data_reference in step 5.

[0063] From the above operations, it can be seen that when new knowledge data needs to be integrated, the data does not need to undergo format conversion. It only needs to be added to the appropriate database and the corresponding metadata updated according to its format characteristics. Moreover, since no format conversion is required, there is only one copy of the data globally, avoiding the consistency problems caused by multiple copies of the data.

[0064] It should be understood that in the above embodiments, the aerial unit is mainly described by taking a drone as an example.

[0065] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0066] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0067] Fig. 9 FIG. 1 shows a structural block diagram of a device for integrating multi-heterogeneous knowledge data provided by an embodiment of the present application. Fig. 9As shown. The integration device 900 of multivariate heterogeneous knowledge data of this embodiment may include an analysis module 901, an integration module 902, a judgment module 903 and an update module 904. The analysis module is used to determine the knowledge data set to be processed, and perform a structural analysis on each knowledge data in the knowledge data set; wherein the knowledge data set contains a number of multivariate heterogeneous knowledge data. The integration module is used to construct metadata and data relationships for each knowledge data based on the analysis results, and integrate to obtain a knowledge graph; wherein the data relationship is an association relationship between each knowledge data and one or more other knowledge data. The judgment module is used to determine whether there is a data update in the knowledge data set for which metadata and data relationships have been constructed. The update module is used to integrate and update the knowledge graph according to the update type when the judgment result is yes.

[0068] It should be noted that part or all of the integration device for multi-heterogeneous knowledge data of this embodiment may be an application located in a local terminal, or may also be a functional unit such as a plug-in or software development kit (SDK) set in an application located in a local terminal, or may also be a processing engine located in a network-side server, or may also be a distributed system located on the network side.

[0069] It is understandable that the application may be a local program (nativeApp) installed on the local terminal, or may also be a webpage program (webApp) of a browser on the local terminal, which is not limited in this embodiment.

[0070] Optionally, in a possible implementation of the present embodiment, when the analysis module 901 determines the knowledge data set to be processed and performs structural analysis on each knowledge data in the knowledge data set, it is specifically used to obtain a number of multivariate heterogeneous knowledge data and determine them as the knowledge data set to be processed; structural analysis is performed on each knowledge data in the knowledge data set respectively, and each knowledge data is stored in a corresponding database according to its respective structural characteristics; wherein different databases are configured for knowledge data with different structural characteristics.

[0071] In a possible implementation, the plurality of multi-dimensional and heterogeneous knowledge data at least include knowledge types defined according to the following three structural characteristics: simulation model attribute knowledge, simulation model control knowledge, and adversarial process result knowledge.

[0072] In one possible implementation, when the integration module 902 constructs metadata and data relationships for each knowledge data based on the analysis results and integrates to obtain a knowledge graph, it is specifically used to perform modeling based on historical knowledge data, determine metadata attributes used to describe basic data information, and determine a data relationship model used to describe the association relationship between knowledge data; obtain attribute content corresponding to the metadata attributes based on the analysis results, and use the attribute content to construct corresponding metadata for each knowledge data; based on the metadata constructed for each knowledge data and the data relationship model, find the association relationship between the metadata of different knowledge data, and create a knowledge graph based on the integration of the found association relationships.

[0073] In one possible implementation, when judging whether there is any data update in the knowledge data set with constructed metadata and data relationships, the judgment module 903 is specifically used to judge whether there is new data in the knowledge data set that needs to be merged; or, to judge whether any integrated knowledge data in the knowledge data set has been deleted; or, to judge whether any integrated knowledge data in the knowledge data set has been modified; or, to judge whether any integrated knowledge data in the knowledge data set has been queried.

[0074] In one possible implementation, when the update module 904 performs an integrated update on the knowledge graph according to the update type, it is specifically used to, if the update type is addition, construct corresponding metadata for the newly added knowledge data, and determine the data relationship between the metadata and the metadata of other knowledge data; update the knowledge graph based on the data relationship, and add the metadata to the corresponding database for storage; if the update type is deletion, delete the data relationship of the deleted knowledge data from the knowledge graph, and delete the metadata of the deleted knowledge data from the corresponding database; if the update type is modification, reconstruct the corresponding new metadata based on the modified knowledge data, and redetermine the new data relationship based on the modified knowledge data; update the knowledge graph based on the new data relationship, replace the original metadata with the new metadata, and save it to the corresponding database; if the update type is query, update the original metadata based on the query operation, and determine the new data relationship based on the updated metadata; update the knowledge graph based on the new data relationship, replace the original metadata with the updated metadata, and save it to the corresponding database.

[0075] In a possible implementation, the different databases include at least: a file database, a relational database, a non-relational database, and a time series database.

[0076] In a possible implementation, the metadata attributes include at least: data name, data ID, and storage address of the data in the corresponding database; as simulation model attribute knowledge, the metadata of the simulation model includes: simulation model name, simulation model ID, and storage address of the simulation model in the SQLite database; as simulation model control knowledge, the metadata of the control rule includes: control rule name, control rule ID, and storage address of the control rule in the MangoDB database; as adversarial process result knowledge, the metadata of the deduction process includes: deduction process name, deduction process ID, and storage address of the deduction process in the InfluxDB database.

[0077] In a possible implementation, the data relationship between the metadata of the simulation model and the metadata of the control rule is the applicable rule, which represents the control rule applicable to the simulation model; the data relationship between the metadata of the deduction process and the metadata of the simulation model is the model composition, which represents the simulation model used in the deduction process.

[0078] In the present application scheme, a knowledge data set to be processed is determined, and a structural analysis is performed on each knowledge data in the knowledge data set; wherein, the knowledge data set contains a number of multivariate heterogeneous knowledge data; metadata and data relationships are constructed for each knowledge data based on the analysis results, and a knowledge graph is obtained by integration; wherein, the data relationship is the association relationship between each knowledge data and one or more other knowledge data; it is determined whether there is data update in the knowledge data set for which metadata and data relationships have been constructed; if the judgment result is yes, the knowledge graph is integrated and updated according to the update type. In this way, the overhead caused by format conversion between heterogeneous data can be avoided, and the cost of gathering and integrating new knowledge can be reduced; at the same time, the problem of content inconsistency caused by the modification of multiple data copies can be avoided, so as to facilitate the maintenance of the global consistency of knowledge data during use.

[0079] An embodiment of the present application provides a computer-readable storage medium, wherein the storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement the above-mentioned method for integrating multi-dimensional heterogeneous knowledge data.

[0080] An embodiment of the present application provides a computing device, which includes a processor and a memory, wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the integration method of multi-heterogeneous knowledge data as described above.

[0081] In the technical solution of this application, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the relevant laws and regulations and do not violate public order and good morals.

[0082] Fig.10 A schematic block diagram of an example computing device 1000 that can be used to implement an embodiment of the present application is shown. The computing device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The computing device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0083] like Fig.10 As shown, the computing device 1000 includes a computing unit 1001, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1002 or a computer program loaded from a storage unit 1008 into a random access memory (RAM) 1003. In the RAM 1003, various programs and data required for the operation of the electronic device 1000 can also be stored. The computing unit 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0084] A number of components in the computing device 1000 are connected to the I / O interface 1005, including: an input unit 1006, such as a keyboard, a mouse, etc.; an output unit 1007, such as various types of displays, speakers, etc.; a storage unit 1008, such as a disk, an optical disk, etc.; and a communication unit 1009, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1009 allows the computing device 1000 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0085] The computing unit 1001 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1001 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1001 performs the various methods and processes described above, such as the integration method of multivariate heterogeneous knowledge data. For example, in some embodiments, the integration method of multivariate heterogeneous knowledge data may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 1008. In some embodiments, part or all of the computer program may be loaded and / or installed on the computing device 1000 via the ROM 1002 and / or the communication unit 1009. When the computer program is loaded into the RAM 1003 and executed by the computing unit 1001, one or more steps of the integration method of multivariate heterogeneous knowledge data described above may be performed. Alternatively, in other embodiments, the computing unit 1001 may be configured to execute the integration method of multivariate heterogeneous knowledge data in any other appropriate manner (for example, by means of firmware).

[0086] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0087] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, implements the functions / operations specified in the flow chart and / or block diagram. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0088] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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 foregoing.

[0089] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0090] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0091] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0092] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in this application can be achieved, and this document is not limited here.

[0093] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for integrating multivariate heterogeneous knowledge data, characterized in that: include: Determine a knowledge data set to be processed, and perform structural analysis on each knowledge data in the knowledge data set; wherein the knowledge data set includes a plurality of multivariate heterogeneous knowledge data; Based on the analysis results, metadata and data relationships are constructed for each knowledge data, and integrated to obtain a knowledge graph; wherein the data relationship is an association relationship between each knowledge data and one or more other knowledge data; Determine whether there is data update in the knowledge data set with established metadata and data relationships; When the judgment result is yes, the knowledge graph is integrated and updated according to the update type.

2. The method according to claim 1, characterized in that Determine the knowledge data set to be processed, and perform structural analysis on each knowledge data in the knowledge data set, specifically including: Acquire a number of multivariate and heterogeneous knowledge data and determine them as the knowledge data set to be processed; A structural analysis is performed on each knowledge data in the knowledge data set, and each knowledge data is stored in a corresponding database according to its own structural characteristics; wherein different databases are configured for knowledge data with different structural characteristics.

3. The method according to claim 2, characterized in that The plurality of multi-dimensional and heterogeneous knowledge data at least include knowledge types defined according to the following three structural characteristics: Simulation model attribute knowledge, simulation model control knowledge, and adversarial process result knowledge.

4. The method according to any one of claims 1 to 3, characterized in that: Based on the analysis results, metadata and data relationships are constructed for each knowledge data, and the knowledge graph is integrated, including: Modeling is performed based on historical knowledge data to determine metadata attributes used to describe basic data information, as well as data relationship models used to describe associations between knowledge data; Acquire attribute content corresponding to the metadata attribute based on the analysis result, and use the attribute content to construct corresponding metadata for each knowledge data; According to the metadata constructed for each knowledge data and the data relationship model, the association relationship between the metadata of different knowledge data is found, and the knowledge graph is created based on the integration of the found association relationships.

5. The method according to claim 1, characterized in that Determine whether there is data update in the knowledge data set with established metadata and data relationships, including: Determine whether there is new data in the knowledge data set that needs to be merged; or, Determine whether any integrated knowledge data in the knowledge data set has been deleted; or, Determine whether any integrated knowledge data in the knowledge data set has been modified; or, It is determined whether there is any integrated knowledge data to be queried in the knowledge data set.

6. The method according to claim 5, characterized in that The knowledge graph is integrated and updated according to the update type, specifically including: If the update type is new, then the corresponding metadata is constructed for the new knowledge data, and the data relationship between the metadata and the metadata of other knowledge data is determined; the knowledge graph is updated based on the data relationship, and the metadata is added to the corresponding database for storage; If the update type is deletion, the data relationship of the deleted knowledge data is deleted from the knowledge graph, and the metadata of the deleted knowledge data is deleted from the corresponding database; If the update type is modification, then the corresponding new metadata is rebuilt based on the modified knowledge data, and the new data relationship is re-determined based on the modified knowledge data; the knowledge graph is updated based on the new data relationship, and the new metadata replaces the original metadata and is saved in the corresponding database; If the update type is query, the original metadata is updated based on the query operation, and the new data relationship is determined based on the updated metadata; the knowledge graph is updated based on the new data relationship, and the updated metadata replaces the original metadata and is saved in the corresponding database.

7. The method according to claim 6, characterized in that The different databases include at least: a file database, a relational database, a non-relational database, and a time series database.

8. The method according to claim 4, characterized in that The metadata attributes include at least: data name, data ID, and storage address of the data in the corresponding database; As the attribute knowledge of the simulation model, the metadata of the simulation model includes: the name of the simulation model, the simulation model ID, and the storage address of the simulation model in the SQLite database; As the control knowledge of the simulation model, the metadata of the control rule includes: the control rule name, the control rule ID, and the storage address of the control rule in the MangoDB database; As the result knowledge of the adversarial process, the metadata of the deduction process includes: the deduction process name, the deduction process ID, and the storage address of the deduction process in the InfluxDB database.

9. The method according to claim 8, characterized in that The data relationship between the metadata of the simulation model and the metadata of the control rule is an applicable rule, and the applicable rule represents the control rule applicable to the simulation model; The data relationship between the metadata of the deduction process and the metadata of the simulation model is a model composition, and the model composition represents the simulation model used in the deduction process.

10. An integrated system for multi-dimensional heterogeneous knowledge data, characterized in that: include: An analysis module, used to determine a knowledge data set to be processed, and perform structural analysis on each knowledge data in the knowledge data set; wherein the knowledge data set includes a plurality of multivariate heterogeneous knowledge data; An integration module is used to construct metadata and data relationships for each knowledge data based on the analysis results, and integrate to obtain a knowledge graph; wherein the data relationship is an association relationship between each knowledge data and one or more other knowledge data; A judgment module, used to judge whether there is data update in the knowledge data set with constructed metadata and data relations; The update module is used to integrate and update the knowledge graph according to the update type when the judgment result is yes.

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