CN115129897B - Method, device, equipment and medium for analyzing perception data by utilizing knowledge graph - Google Patents
Method, device, equipment and medium for analyzing perception data by utilizing knowledge graph Download PDFInfo
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Abstract
The invention provides a method, a device, equipment and a medium for analyzing perception data by utilizing a knowledge graph, wherein the method comprises the following steps: obtaining perception data, and analyzing the perception data to obtain clue information and/or event information; converting the clue information and/or the event information according to a preset conversion rule to obtain triplet data, and storing the triplet data into a knowledge graph; and respectively creating a mapping according to the triplet data obtained after the conversion of each cue information and/or event information, and carrying out association analysis in the knowledge graph by combining each mapping, the cue information and/or event information to obtain an analysis result. The invention creates mapping for the triples of the perception data, so that a map is further formed on the basis of the mapping relation in the knowledge map, analysis of the perception data can be displayed in a visual data mode, correct decision analysis is facilitated, and clue events with great value can be obtained from analysis results.
Description
Technical Field
The present invention relates to the field of computer information processing technologies, and in particular, to a method, an apparatus, a device, and a medium for analyzing perceptual data by using a knowledge graph.
Background
With the generation of mass data, knowledge extraction is performed from mass data, a large-scale domain knowledge graph is constructed, and the requirements of various scenes such as intelligent question-answering, intelligent recommendation, graph relation analysis, knowledge construction management, knowledge semantic retrieval, intelligent text extraction, geospatial analysis and the like are realized, so that unified knowledge graph capability is required to be provided. The concept of Knowledge Graph (knowledgegraph) was formally proposed by google 2012, aims to realize a more intelligent search engine, and starts to be popularized in academia and industry after 2013, and plays an important role in applications such as intelligent question-answering, information analysis and anti-fraud.
Knowledge graph is essentially a knowledge base called semantic network (semanteme network), i.e. a knowledge base with a directed graph structure, wherein nodes of the graph represent entities (entities) or concepts (concepts) and edges of the graph represent various semantic relationships between entities/concepts, such as similarity relationships between two entities.
At present, digital government affairs are in a development stage, but the digital government affairs are related to various data, and although a method for classifying and grading the digital government affairs is available at present, in some special scenes, such as coastline prevention and control data, port monitoring data and the like, how to analyze perceived data of the digital government affairs to obtain useful clues and further analyze the perceived data, and no corresponding analysis method or device exists in the industry.
With the increasing awareness of basic users of informationized systems, new interpretations and understandings are also being made to big data retrieval applications. The traditional clue search is only the search of data resources, the search results are only the display of resource lists, the search intention understanding capability is poor, the problems of fragmentation, weak relevance and the like of the search results are increasingly prominent, the requirements of users at the lower level cannot be met, and the analysis requirements of the artificial intelligence era on big data cannot be supported.
Therefore, if knowledge graph technology is introduced or combined in the development process of digital government affairs to promote analysis of clue data, the problem of data analysis fragmentation in a specific scene may be solved.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
In view of the above, the present invention aims to provide a method, apparatus, device and medium for analyzing perception data by using a knowledge graph.
Based on the above object, the present invention provides a method for analyzing perception data by using a knowledge-graph, the method comprising:
obtaining perception data, and analyzing the perception data to obtain clue information and/or event information;
converting the clue information and/or the event information according to a preset conversion rule to obtain triplet data, and storing the triplet data into a knowledge graph;
and respectively creating a mapping according to the triplet data obtained after the conversion of each cue information and/or event information, and carrying out association analysis in the knowledge graph by combining each mapping, the cue information and/or event information to obtain an analysis result.
In combination with the foregoing description, in another possible implementation manner of the embodiment of the present invention, the converting, according to a preset conversion rule, the thread information and/or the event information to obtain triple data, and storing the triple data into a knowledge graph includes:
analyzing the clue information and the event information to obtain relationship information, wherein the relationship information comprises: relationship information between thread information and thread, relationship information between event information and event information, and relationship information between thread information and event information;
And converting the relation information, the clue information and the event information into triplet data according to a preset conversion rule and storing the triplet data into a knowledge graph.
In combination with the foregoing description, in another possible implementation manner of the embodiment of the present invention, the creating a mapping according to the triplet data obtained after conversion of each piece of thread information and/or event information, and performing association analysis in the knowledge graph by combining each piece of mapping, the piece of thread information and/or the piece of event information, so as to obtain an analysis result, further includes:
and adding the relation information to the association analysis process of the knowledge graph, and carrying out mining analysis by combining the attribute values of the relation information, the clue information and the event information so as to obtain mining analysis results.
In combination with the above description, in another possible implementation manner of the embodiment of the present invention, the method further includes:
calculating the weight of entity information in each triplet data by an information entropy weighting method, and determining a clue label of the triplet data according to the priority of the weight value sequencing;
and taking the clue label as a mapping name created by the triple data so as to call the corresponding mapping according to the clue label.
In combination with the above description, in another possible implementation manner of the embodiment of the present invention, the method further includes:
acquiring a plurality of attribute information of an entity related to the perception data;
adding each piece of attribute information to the entity representation related to the perception data;
and carrying out association analysis on the entity portraits and the mappings in the knowledge graph to obtain analysis results.
In combination with the above description, in another possible implementation manner of the embodiment of the present invention, the method further includes:
constructing a correlation diagram of the perception data according to each mapping;
judging whether abnormal clue information and/or event information exist in the association diagram;
when the limit coefficient quantity formed by the clue information and/or the event information is greater than or equal to an abnormal threshold value, marking the corresponding clue information and/or event information as an abnormal clue event;
further mining and analyzing the abnormal clue event to obtain a mining and analyzing result of the abnormal clue event;
and when the quantity of the side relation coefficient formed by the clue information and/or the event information is smaller than an abnormal threshold value, combing the development context of the clue information and/or the event information to obtain a combing analysis result.
In combination with the above description, in another possible implementation manner of the present embodiment, the performing, in association with each of the mappings, a correlation analysis in the knowledge-graph includes:
any one or two or more of association search, association graph analysis and addition map analysis.
In a second aspect, the present invention also provides an apparatus for analyzing perception data using a knowledge-graph, the apparatus comprising:
the analysis module is used for acquiring the perception data and analyzing the perception data to obtain clue information and/or event information;
the conversion module is used for converting the clue information and/or the event information according to a preset conversion rule to obtain triple data and storing the triple data into a knowledge graph;
and the analysis module is used for respectively creating mapping according to the triplet data obtained after the conversion of each cue information and/or event information, and carrying out association analysis in the knowledge graph by combining each mapping, the cue information and/or event information so as to obtain an analysis result.
In a third aspect, the present invention further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the method for analyzing sensory data using a knowledge graph as described above when executing the program.
In a fourth aspect, the present invention also provides a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the above-described method of analyzing perceptual data using a knowledge-graph.
From the above, it can be seen that the method, apparatus, device and medium for analyzing perception data by using a knowledge graph provided by the present invention analyze the perception data into clue information and/or event information that can be identified by the knowledge graph, and further convert the perception data into triplet data that is easy to store, and further analyze the mapping relationship in the knowledge graph architecture by creating a mapping, so that the analysis of the perception data can be displayed in the form of intuitive data, which is favorable for making a correct decision analysis, and clue events with significant value can be obtained from the analysis result.
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In order to more clearly illustrate the technical solutions of the present invention or related art, the drawings that are required to be used in the description of the embodiments or related art will be briefly described below, and it is apparent that the drawings in the following description are only embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive effort to those of ordinary skill in the art.
FIG. 1 is a basic flow diagram of a method for analyzing perception data using a knowledge-graph according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating selection of properties of a triplet data field according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the basic building blocks of a mapping created by triple data according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating the composition of a map into which a portion of perceptual data is transformed according to an embodiment of the present invention;
FIG. 5 is a schematic diagram showing association analysis according to an embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating the changes made during a case-related analysis according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a basic flow of determining an abnormal clue event according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of the basic structure of an apparatus for analyzing perception data using knowledge-graph according to an embodiment of the present invention;
fig. 9 is a schematic diagram of an electronic device implementing a method for analyzing perception data using a knowledge-graph according to an embodiment of the present invention.
Detailed Description
The present invention will be further described in detail below with reference to specific embodiments and with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present invention more apparent.
It should be noted that unless otherwise defined, technical or scientific terms used in the embodiments of the present invention should be given the ordinary meaning as understood by one of ordinary skill in the art to which the present invention belongs. The terms "first," "second," and the like, as used in embodiments of the present invention, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
In the current mode, aiming at pictures or videos and the like perceived by a perception device, a graph database management system is generally adopted, a data model generally adopts an attribute graph form, but the storage of the structural form is realized, global operation on the graph becomes lower and lower along with the upgrading of the perception device, more and more perception events or picture contents and larger volume, the query language adopted by the system is smaller, the query efficiency of a developer facing the perception event query of the form is lower after the query is not started, and the defect is particularly obvious when the system is applied to a social management informatization platform.
The invention relates to a knowledge graph analysis sensing data, a device, equipment and a medium, which are mainly applied to a scene for sensing and analyzing related information obtained by an island, a port and a shoreline control loop, and the basic idea is as follows: suspicious information is obtained from sensing equipment related to island, port and shoreline prevention and control circles, pictures or videos are stored, the stored pictures or videos are converted based on preset rules to obtain triplet data, and correlation analysis, thermodynamic diagram analysis or map analysis are carried out on the obtained triplet data through a knowledge graph to obtain analysis results, so that analysis on sensing events is more accurate, efficient and visual to display.
The invention uses knowledge graph analysis to perceive data, device, equipment and medium, can be applied to a social management informatization platform or used as a subsystem of the social information management platform, and can set information intercommunication authority among all subsystems of the social information management platform to realize information transmission and sharing, wherein the social management informatization platform is a comprehensive platform for digital government affairs, can connect various business systems for assisting the social management implementation of government affairs, such as port office and the like, and sets certain basic hardware on each business system and the like according to requirements to obtain corresponding information, and the information is collected or uploaded and processed according to corresponding processing strategies to obtain expected results.
The embodiment of the invention is applicable to the condition of carrying out clue analysis by a knowledge graph analysis device, the method can be executed by the knowledge graph analysis device, wherein the device can be realized by software and/or hardware, and can be generally integrated in a server or controlled by a central control module of a terminal, as shown in fig. 1, and the method for analyzing clue events by utilizing the knowledge graph of the invention specifically comprises the following steps:
In step 110, obtaining perception data, and analyzing the perception data to obtain clue information and/or event information;
in one implementation of the exemplary embodiment of the present invention, the perceptual data may be obtained through:
monitoring equipment arranged on one or a plurality of shoreline control circles;
monitoring equipment installed on the signal tower;
shooting equipment arranged at a port;
satellite remote sensing imaging;
……
in a possible implementation manner of the exemplary embodiment of the present invention, the sensing data may also be obtained from other scenes, for example, when analyzing a clue event of road information, the sensing data may be obtained from a photographing device disposed on a road, and when controlling and judging epidemic personnel in a commercial facility related to an epidemic situation, the corresponding sensing data may be obtained through a monitoring device in the commercial facility, or a code scanning device disposed at an entrance, etc.
The sensing data can be electronic information in various formats such as photographed pictures, videos and the like, and also can be structured data information obtained directly through other devices, for example, basic information of user personnel stored in a document or table structure.
Parsing the perceived event includes:
Analyzing information such as photographed pictures, videos and the like, for example, performing image recognition and/or combining text recognition;
in one implementation manner of the exemplary embodiment of the present invention, the sensing data may be obtained by taking pictures, for example, when a monitoring/sensing device installed in a certain port senses that a corresponding event occurs, corresponding information may be obtained and stored according to analysis of the sensing data, and generally, corresponding preprocessing needs to be performed to enable the sensing data to meet a requirement of being able to be used, for example, when the sensing data is downloaded clue document data, keyword, author, mechanism, date, title, etc. need to be extracted from the clue document data, and stop words, repeat items, etc. need to be removed.
In one implementation of the exemplary embodiments of the present invention, specifically:
when a certain sensing device monitors that a certain ship leaves a port, the ship leaves a port, so that a sensing event is called, other sensing devices in the port, which can furthest shoot pictures of ship identification numbers or IMO numbers of the sensing devices, can be called according to a shooting coordination mechanism of the sensing devices to shoot so as to obtain pictures which can be used for later pre-judging or early warning, the pictures can be used for obtaining a large amount of information related to the ship after picture identification, for example, the shot pictures can be obtained with ship identification numbers, IMO numbers or calling codes and the like, the ship identification numbers (unique codes for permanently identifying the ship and usually consist of English letters and several digits) or the unique numbers of IMOs (international maritime organization) (including IMO identification and seven-digit Arabic numbers), or the calling codes (unique wireless communication codes of the IMOs on ship calling), the identification numbers are unique identification numbers of the ship, and when the shot object is a person, the identity of the person can be identified through face matching, and the like.
Then parsing the perceptual data at this time includes:
analyzing the perception picture, and obtaining the departure time, the airline flight number, the airline flight date, the affiliated ship company, the passenger entry and exit records, the departure station, the ship name and the like of the ship from the related subsystem of the upper-level social management informatization platform through analyzing the obtained ship identification number.
Analyzing the obtained information, such as ship identification number, ship name, affiliated ship company and the like, as clue information; event information is generally triggered according to behavior of cue information, such as departure time, airline flight number, airline flight date, etc., for example, when a ship of IMO1234567 leaves a port, event information such as "departure time" and "airline flight number" is triggered.
It can be appreciated that the above steps of the present invention can obtain and parse multiple pieces of sensing data at the same time, and perform parallel processing according to the computing power of the corresponding computer device or server.
In step 120, according to a preset conversion rule, converting the clue information and/or the event information to obtain triplet data, and storing the triplet data into a knowledge graph;
The preset conversion rule is used for converting the clue information and/or the event information obtained through analysis into the triplet data which can be stored in the knowledge graph.
Specifically, the preset conversion rule is used for defining entity types and information of triple data, and the information of the triple data comprises association relations among all entity types and attributes and attribute values corresponding to all entity types. Wherein, the association relation among the entity types corresponds to the form of triple data (entity 1-relation-entity 2) and is used for respectively defining the relation among the entity 1 and the entity 2 and the relation among the entity 1 and the entity 2; the attributes and attribute values corresponding to each entity type correspond to the form of the triplet data (entity-attribute value) for defining the entity, attribute, and corresponding attribute value.
More specifically, when the IMO number of the ship is taken as the clue information of the tag type, the IMO number is taken as an entity corresponding to the acquired perception data, other clue information and/or event information obtained by parsing the IMO number can be stored in a field and attribute mode, the stored triplet data, combined with some triplet data converted from the perception data shown in fig. 2, shows some attributes of the obtained clue information, the attribute modifiable through the clue information is stored in the field so as to modify the corresponding attributes of the clue information or event information under different scenes of different types, for example, the field "qfsj" represents "departure time", "hkgs" represents "the ship company", the field "ddsj" represents "departure time", the field "hbh" represents "airline flight number", the field "qfs" represents "departure port name" and the like, special symbols in the figure are respectively indicated as "setting clue tags", "harbor", "deleting" and "separating" in the order from left to right, and each symbol can respectively represent operations for modifying some attributes of the entity or relationship in the triplet data.
In an optional implementation manner of the exemplary embodiment of the present invention, the preset conversion rule may be further implemented by a custom rule generating template, and if the clue information and/or event information obtained by parsing the same type of sensing data and the relationships between entity names, attributes and attribute values possibly related to the clue information and/or event information are input into the custom rule generating template, automatic parsing may be completed and corresponding conversion rules may be obtained.
In another alternative embodiment, the obtained clue information and/or event information may be written into a preset conversion rule by a professional according to the requirement, so as to further convert and analyze the same type of perception data.
In the embodiment of the present application, the generation manner of the conversion rule related to the triplet data is not specifically limited.
In the implementation manner of the exemplary embodiment of the present invention, a general interface is used when the knowledge graph is connected to the social management informatization platform, so that the triplet data obtained after the conversion of the perception data can be stored in the knowledge graph based on various types of databases, and the interfaces are not required to be respectively set for each type of databases, so that the efficiency of obtaining the analysis result is greatly improved when the sub-systems connected to upload different types of perception data for analysis.
In step 130, mapping is created according to the triplet data obtained after the conversion of each cue information and/or event information, and association analysis is performed in the knowledge graph in combination with each mapping to obtain an analysis result.
In a possible implementation manner of the exemplary embodiment of the present invention, when analysis is required, since the original data source is the perception data, it is required to query corresponding cue information and/or event information according to the perception data, and further create mappings for corresponding triplet data, where the mappings may be various relationships between entities, for example:
in connection with fig. 3, when the analyzed object is a person, the relationship attribute between the person and the person needs to be configured into a mapping relationship, the relationship attribute in the mapping may be a relationship primary key, a relationship type, a relationship field and the like in the form of data representation, and the relationship fields may be multiplexed, when one relationship field in one data table stores various relationship attribute values, the relationship field may be directly configured into a link field, and in other mapping relationships, the link field is directly adopted, and then the relationship between the person and the person may be represented through the relationship field, and a special symbol in the figure, i.e., a section of the relationship field, indicates that the content is selectable.
When analyzing one or several groups of sensing data, the mapping to be performed may include many groups, for example, the mapping diagram shown in fig. 4, which is obtained by parsing and converting a piece of sensing data, may be a mapping relationship obtained by sensing data, and on the basis of the clear explanation of the basic mapping, the mapping relationship in fig. 4 is not repeated here.
Performing association analysis in the knowledge graph by combining each mapping to obtain an analysis result, specifically:
the correlation analysis diagram shown in fig. 5 shows an analysis result of correlation analysis by mapping, and of course, the result in the diagram can be further re-analyzed on the basis of the analysis result, and in the process, the following steps are needed:
firstly, the acquired perception data are text data transmitted in a summarizing way, such as: after entering port a, the ship a goes to and receives the relevant personnel on the ship, the vehicle B goes to port a to transport the goods of the ship a, after inquiry, the ship a stays at island a and takes place to dock with the ship B, the ship B goes to port B, the vehicle C goes to port B to receive the relevant personnel, the vehicle C goes to and receives the relevant personnel, and the vehicle C goes to and from port C and goes to and from warehouse a, and the owner of the vehicle B is personnel D.
Through analysis, the ships, vehicles, personnel and the like can form clue information, the round trip of the vehicles and ports or the ships or warehouses can form event information, and the event information is converted into triplet data according to a preset conversion rule, which can be as follows:
(vessel A, in, port A), (vehicle A, pickup, vessel A), (vehicle B, pickup, vessel A), (vessel A, berth, island A) … …
The mapping created from the triplet data formed from the above set of perceptual data may be combined with that shown in fig. 4, and each adjacent arrow and entity may form a mapping, not shown in any exploded form.
Each mapping can be used for subsequent analysis of the data in the knowledge-graph.
Each connected body in the mapping may be used to add or delete a new mapping, for example, when there is a correlation between the owner Y company of one of the vessels B and the owner D, and the Y company is the owner of the warehouse a at the same time, the correlation analysis is added in the correlation diagram shown in fig. 5, and the added correlation analysis diagram is shown in fig. 6.
In some possible embodiments of the exemplary embodiments of the present invention, when preprocessing the perception data, different types of rule templates may be preset to match, so as to facilitate rapid use, for example: and (3) automatically generating ship files, berthing and berthing records, household population, ship basic information and ship files.
In a possible implementation manner of the exemplary embodiment of the present invention, the converting the thread information and/or the event information according to a preset conversion rule to obtain triplet data, storing the triplet data into a knowledge graph, and converting the relation information formed by the thread information and the event information into triplet data includes:
analyzing the clue information and the event information to obtain relationship information, wherein the relationship information comprises: relationship information between thread information and thread, relationship information between event information and event information, and relationship information between thread information and event information;
in connection with the associated diagrams shown in fig. 5 and 6, when the ship a stays at the island a and the ship B is connected with the island a, the information of the company to which the ship a/B belongs is analyzed to obtain that the ship a and the ship B belong to the same company X for control, and then the obtained relationship information is converted into the triplet data (ship a, company, ship B).
And converting the relation information, the clue information and the event information into triplet data according to a preset conversion rule and storing the triplet data into a knowledge graph.
And respectively creating a mapping according to the triplet data obtained after the conversion of each cue information and/or event information, and carrying out association analysis in the knowledge graph by combining each mapping, the cue information and/or event information to obtain an analysis result.
The map created by the method can be the map shown in fig. 5, and the mapping relationship can be used for the query case, and the displayed effect is shown in fig. 6.
The knowledge base of the knowledge graph with larger scale in the traditional mode cannot contain all information, some entities, categories, attributes or relations among the entities, the categories, the attributes or the relations among the entities, the categories, the attributes are not captured, but the method creates the relation information between the clue information and the event information obtained through analysis into the mapping, and adds the mapping into the analysis process of the knowledge graph, so that the problem of inaccurate analysis caused by incomplete knowledge base of the traditional knowledge graph is solved, and the obtained result is more beneficial to auxiliary analysis and decision.
In a possible implementation manner of the exemplary embodiment of the present invention, the creating a mapping according to the triplet data obtained after the conversion of each cue information and/or event information, and performing association analysis in the knowledge graph by combining each mapping, the cue information and/or event information, so as to obtain an analysis result, further includes:
And adding the relation information to the association analysis process of the knowledge graph, and carrying out mining analysis by combining the attribute values of the relation information, the clue information and the event information so as to obtain mining analysis results.
When a plurality of mapping relations are included among a plurality of entities in the mapping relation created in fig. 5 and 6, the mapping relation is added to the analysis process of the original knowledge graph for association analysis, and then the generated relation information is further associated with the previous analysis result, so that the association diagram is changed, and the analysis result after the change is the mining analysis result.
According to the method, through the addition of the relation information, the result obtained when the knowledge graph is used for carrying out association analysis on the perception data is further mined, so that the analysis result which can be displayed by the knowledge graph is deeper and more similar to the essence.
In a possible implementation manner of the exemplary embodiment of the present invention, the method further includes:
calculating the weight of entity information in each triplet data by an information entropy weighting method, and determining a clue label of the triplet data according to the priority of the weight value sequencing;
and taking the clue label as a mapping name created by the triple data so as to call the corresponding mapping according to the clue label.
Calculating the weight of each triplet data and the keywords thereof by using an information entropy weighting method, storing the weights in a knowledge graph database, respectively calculating similar triplet data of each triplet data by using a triplet similarity formula, and carrying out priority sorting according to the similarity, and determining the keywords corresponding to the triplet data with the highest priority as clue labels of the triplet data, for example: after the calculation, the determined clue label can be "ship A", and the mapping name can be determined as "ship A", and in the subsequent analysis process, the corresponding triplet data and the mapping created according to the clue label can be called at any time so as to simplify the analysis decision process of the knowledge graph.
In a possible implementation manner of the exemplary embodiment of the present invention, the method further includes:
acquiring a plurality of attribute information of an entity related to the perception data;
adding each piece of attribute information to the entity representation related to the perception data;
and carrying out association analysis on the entity portraits and the mappings in the knowledge graph to obtain analysis results.
In combination with the above cases, the relevant entity for analyzing the sensing data may include "ship a", "ship B", "vehicle a", "vehicle B", "person C", etc., and the attribute information related to "ship a" is the attribute of the analyzed clue information, such as the control company, the registered place, the ship owner, the sharing person, etc. to which the ship a belongs.
The attribute information is added into the portrait of the ship A, the entity portrait refers to a user model formed by at least one clue information and/or event information, the user model can be continuously updated and enriched along with the continuous change or accumulation of clue events, the process of adding the attribute information enriches the entity portrait, and the portrait can be combined and mapped to be used for carrying out association analysis in a knowledge graph in the subsequent association analysis process, so that the analysis result is further mined, and the obtained analysis result is more accurate.
In a possible implementation manner of the exemplary embodiment of the present invention, in conjunction with the schematic diagram of the determination flow of the abnormal clues shown in fig. 7, the method further includes a process of determining and further analyzing the abnormal clues:
in step 710, constructing a correlation map of the perceptual data based on each of the mappings;
The mapping obtained according to the same perception data can be directly used for constructing a correlation diagram;
in step 720, it is determined whether abnormal clue information and/or event information exists in the association diagram;
performing abnormality judgment and early warning by the fact that the limit relation coefficient quantity formed by the clue information and/or the event information is greater than or equal to an abnormality threshold value;
in step 730, when the edge relation coefficient quantity formed by the clue information and/or the event information is greater than or equal to the abnormal threshold value, marking the corresponding clue information and/or event information as an abnormal clue event;
in the step, further mining and analyzing the abnormal clue event to obtain a mining and analyzing result of the abnormal clue event;
referring to fig. 6, the side relationship data between the vehicle C and the port C is 3, and the anomaly threshold is 2, and the vehicle C and the port C can be determined as an anomaly clue event.
In step 740, after the abnormal clue event is determined, further mining analysis is performed on the abnormal clue event, for example, whether all people of the mining warehouse a and the vehicle C are the same person, so as to mine whether the mining warehouse a and the vehicle C have internal association, so that the mining process and the mining result further assist in analysis and decision;
In step 750, when the amount of the side relationship coefficient formed by the clue information and/or the event information is smaller than the abnormal threshold, the development context of the clue information and/or the event information is combed, so as to obtain a combing analysis result.
The boundary coefficient data formed between the vehicle C and the warehouse A is 1, and the abnormal threshold value is 2, so that the vehicle C and the warehouse A are normal clue events.
At this time, the clue events formed by the vehicle C and the warehouse a can be normally combed, for example, the edge relation generated by the normal guest receiving and delivering actions of the user is added into the relation formed by the knowledge graph to perform the context combing at this time, and the combing analysis result can be also used for auxiliary analysis and decision.
According to the method of the exemplary embodiment of the invention, through judgment and determination of the abnormal clue event, the abnormal clue event can be focused in the analysis process, so that rule determination and depth exploration of the abnormal clue event are realized.
In a possible implementation manner of the exemplary embodiment of the present invention, the performing, in the knowledge-graph, association analysis in combination with each of the mappings includes:
any one or two or more of association search, association graph analysis and addition map analysis.
The association search is to perform association search analysis based on the triplet data, the association graph analysis is search analysis based on the created map, and the joining map is association analysis based on the geographic position after the triplet data and the created map are all joined into the map.
The method of the exemplary embodiment of the invention provides a plurality of ways suitable for analyzing the perception data, so that the suitable ways can be selected under different application scenes, or the ways are mutually combined for common reasoning analysis, thereby greatly enriching the application scenes of the method of the invention.
The method, the device, the equipment and the medium for analyzing the perception data by utilizing the knowledge graph, which are provided by the exemplary embodiment of the invention, expand the data model of the knowledge graph by fusing the mapping data and the knowledge graph, correspondingly provide the data query and analysis modes of the triples, are greatly different from the traditional query model, realize more effective query on the expanded knowledge graph, and further use the materialized mapping for re-analysis of operations such as adding and deleting the primary analysis result aiming at the analysis result, so that the accuracy of the analysis result is greatly improved.
In the correlation analysis stage, the method can be manually processed by special professionals, and the channel of information transmission and distribution, as well as the analysis, conversion and mapping creation of the perception data are also commonly completed by the cooperation of multiple subsystems of the social management informatization platform in the process.
It should be noted that, the method of the embodiment of the present invention may be performed by a single device, for example, a computer or a server. The method of the embodiment can also be applied to a distributed scene, and is completed by mutually matching a plurality of devices. In the case of such a distributed scenario, one of the devices may perform only one or more steps of the method according to the embodiments of the present invention, and the devices interact with each other to complete the method for analyzing sensory data using a knowledge graph.
It should be noted that the foregoing describes some embodiments of the present invention. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments described above and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
Based on the same inventive concept, corresponding to the method for analyzing the perception data by using the knowledge-graph in any of the above embodiments, the present invention further provides an apparatus for analyzing the perception data by using the knowledge-graph, and in combination with the apparatus schematic diagram shown in fig. 8, the apparatus includes:
the parsing module 810 is configured to obtain sensing data, parse the sensing data to obtain clue information and/or event information;
the conversion module 820 is configured to convert the cue information and/or the event information according to a preset conversion rule to obtain triplet data and store the triplet data in a knowledge graph;
and the analysis module 830 is configured to create a mapping according to the triplet data obtained after the conversion of each cue information and/or event information, and perform association analysis in the knowledge graph by combining each mapping, the cue information and/or event information, so as to obtain an analysis result.
For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, the functions of each module may be implemented in the same piece or pieces of software and/or hardware when implementing the present invention.
The device of the foregoing embodiment is used for implementing the method for analyzing the perceived data by using the knowledge-graph in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein.
Based on the same inventive concept, the invention also provides an electronic device corresponding to the method of any embodiment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the method for analyzing the perception data by utilizing the knowledge graph according to any embodiment when executing the program.
Fig. 9 shows a more specific hardware architecture of an electronic device according to this embodiment, where the device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 implement communication connections therebetween within the device via a bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit ), microprocessor, application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc. for executing relevant programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory ), static storage device, dynamic storage device, or the like. Memory 1020 may store an operating system and other application programs, and when the embodiments of the present specification are implemented in software or firmware, the associated program code is stored in memory 1020 and executed by processor 1010.
The input/output interface 1030 is used to connect with an input/output module for inputting and outputting information. The input/output module may be configured as a component in a device (not shown) or may be external to the device to provide corresponding functionality. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various types of sensors, etc., and the output devices may include a display, speaker, vibrator, indicator lights, etc.
It should be noted that although the above-described device only shows processor 1010, memory 1020, input/output interface 1030, communication interface 1040, and bus 1050, in an implementation, the device may include other components necessary to achieve proper operation. Furthermore, it will be understood by those skilled in the art that the above-described apparatus may include only the components necessary to implement the embodiments of the present description, and not all the components shown in the drawings.
The electronic device of the foregoing embodiment is configured to implement the method for analyzing the perceived data by using the knowledge-graph according to any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which is not described herein.
Based on the same inventive concept, the present invention also provides a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method for analyzing perception data using a knowledge-graph according to any of the embodiments above, corresponding to the method of any of the embodiments above.
The computer readable media of the present embodiments, including both permanent and non-permanent, removable and non-removable media, may be used to implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device.
The computer instructions stored in the storage medium of the foregoing embodiments are configured to cause the computer to perform the method for analyzing the perceptual data using the knowledge-graph according to any one of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which are not described herein.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the invention (including the claims) is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the invention, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the invention as described above, which are not provided in detail for the sake of brevity.
Additionally, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures, in order to simplify the illustration and discussion, and so as not to obscure the embodiments of the present invention. Furthermore, the devices may be shown in block diagram form in order to avoid obscuring the embodiments of the present invention, and also in view of the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the embodiments of the present invention are to be implemented (i.e., such specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the invention, it should be apparent to one skilled in the art that embodiments of the invention can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative in nature and not as restrictive.
While the invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of those embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.
The present embodiments are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, and the like, which are within the spirit and principles of the embodiments of the invention, are intended to be included within the scope of the invention.
Claims (6)
1. A method for analyzing perceptual data using a knowledge-graph, the method comprising:
obtaining perception data, and analyzing the perception data to obtain clue information and/or event information; the perception data is picture, video or structured data information;
converting the clue information and/or the event information according to a preset conversion rule to obtain triplet data, and storing the triplet data into a knowledge graph; comprising the following steps:
Analyzing the clue information and the event information to obtain relationship information, wherein the relationship information comprises: relationship information between thread information and thread, relationship information between event information and event information, and relationship information between thread information and event information;
converting the relation information, clue information and event information into triplet data according to a preset conversion rule and storing the triplet data into a knowledge graph;
creating a mapping according to the triplet data obtained after the conversion of each cue information and/or event information, wherein the mapping corresponding to one perception data comprises a plurality of groups, the mapping is of various relations between entities, and the mapping, the cue information and/or the event information are combined to perform association analysis in the knowledge graph so as to obtain an analysis result of association analysis by using the mapping; adding the relation information to the association analysis process of the knowledge graph, and carrying out mining analysis by combining the attribute values of the relation information, the clue information and the event information to obtain mining analysis results; the mapping is in the form of data representation as a relation primary key, a relation type and a relation field, the relation information is in the form of representation of a graph as a relation attribute among analyzed objects, and each connected main body of the mapping can add and delete new mapping;
The method further comprises the steps of:
constructing a correlation diagram of the perception data according to each mapping;
judging whether abnormal clue information and/or event information exist in the association diagram;
when the limit coefficient quantity formed by the clue information and/or the event information is greater than or equal to an abnormal threshold value, marking the corresponding clue information and/or event information as an abnormal clue event;
further mining and analyzing the abnormal clue event to obtain a mining and analyzing result of the abnormal clue event;
when the side relationship coefficient quantity formed by the clue information and/or the event information is smaller than an abnormal threshold value, combing the development context of the clue information and/or the event information to obtain a combing analysis result;
the method further comprises the steps of:
calculating the weight of entity information in each triplet data by an information entropy weighting method, and determining a clue label of the triplet data according to the priority of the weight value sequencing;
and taking the clue label as a mapping name created by the triple data so as to call the corresponding mapping according to the clue label.
2. The method for analyzing sensory data using a knowledge-graph according to claim 1, further comprising:
Acquiring a plurality of attribute information of an entity related to the perception data;
adding each piece of attribute information to the entity representation related to the perception data;
and carrying out association analysis on the entity portraits and the mappings in the knowledge graph to obtain analysis results.
3. The method of claim 1, wherein said performing a correlation analysis in said knowledge-graph in combination with each of said mappings comprises:
any one or two or more of association search, association graph analysis and addition map analysis.
4. An apparatus for analyzing perceptual data using a knowledge-graph, the apparatus comprising:
the analysis module is used for acquiring the perception data and analyzing the perception data to obtain clue information and/or event information; the perception data is picture, video or structured data information;
the conversion module is used for converting the clue information and/or the event information according to a preset conversion rule to obtain triple data and storing the triple data into a knowledge graph;
the analysis module is used for respectively creating a mapping according to the triplet data obtained after the conversion of each cue information and/or event information, and carrying out association analysis in the knowledge graph by combining each mapping, the cue information and/or event information to obtain an analysis result, wherein the mapping is a relation primary key, a relation type and a relation field, and each connected main body of the mapping can add and delete a new mapping;
The analysis module is also configured to: creating a mapping according to the triplet data obtained after the conversion of each cue information and/or event information, wherein the mapping corresponding to one perception data comprises a plurality of groups, the mapping is of various relations between entities, and the mapping, the cue information and/or the event information are combined to perform association analysis in the knowledge graph so as to obtain an analysis result of association analysis by using the mapping; adding the relation information to the association analysis process of the knowledge graph, and carrying out mining analysis by combining the attribute values of the relation information, the clue information and the event information to obtain mining analysis results; the mapping is in the form of data representation as a relation primary key, a relation type and a relation field, the relation information is in the form of representation of a graph as a relation attribute among analyzed objects, and each connected main body of the mapping can add and delete new mapping;
the device is also for:
constructing a correlation diagram of the perception data according to each mapping;
judging whether abnormal clue information and/or event information exist in the association diagram;
when the limit coefficient quantity formed by the clue information and/or the event information is greater than or equal to an abnormal threshold value, marking the corresponding clue information and/or event information as an abnormal clue event;
Further mining and analyzing the abnormal clue event to obtain a mining and analyzing result of the abnormal clue event;
when the side relationship coefficient quantity formed by the clue information and/or the event information is smaller than an abnormal threshold value, combing the development context of the clue information and/or the event information to obtain a combing analysis result;
the device is also for:
calculating the weight of entity information in each triplet data by an information entropy weighting method, and determining a clue label of the triplet data according to the priority of the weight value sequencing;
and taking the clue label as a mapping name created by the triple data so as to call the corresponding mapping according to the clue label.
5. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a method of analysing sensory data using a knowledge-graph as claimed in any one of claims 1 to 3 when the program is executed.
6. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of analyzing perception data using a knowledge-graph according to any one of claims 1 to 3.
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