CN111582488A - Event deduction method and device - Google Patents
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- CN111582488A CN111582488A CN202010327255.4A CN202010327255A CN111582488A CN 111582488 A CN111582488 A CN 111582488A CN 202010327255 A CN202010327255 A CN 202010327255A CN 111582488 A CN111582488 A CN 111582488A
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Abstract
The invention discloses an event deduction method and an event deduction device, wherein the event deduction method comprises the following steps: acquiring service data of an event to be analyzed; obtaining a characteristic vector according to a preset knowledge graph and the service data; inputting the characteristic vector into a preset simulation analysis model to obtain a simulation analysis result of an event; and performing deduction prediction on the event according to the simulation analysis result. By implementing the method, the structural characteristics of the knowledge graph and the dynamic characteristics of machine learning can be fully combined, and a plurality of influence factors are brought into the simulation analysis model for multi-source simulation analysis, so that the analysis result is more accurate, the complexity of simulation analysis is reduced, and the accuracy of event deduction prediction is improved.
Description
Technical Field
The invention relates to the technical field of simulation analysis, in particular to an event deduction method and device.
Background
The simulation analysis is the analysis and evaluation of decision simulation process and result. The basis of the decision simulation is the similarity and correlation between things, i.e. when a set of conditions is similar, it is possible to generate the result related to the set of conditions. The purpose of the decision simulation is to serve the overall implementation of the decision, so that scientific simulation analysis is performed on the decision simulation.
The simulation analysis in the related art is generally established on a system model, and the model for describing a large-scale system is often too complicated, so that the complexity of a simulation analysis algorithm is exponentially increased.
Disclosure of Invention
Therefore, the technical problem to be solved by the present invention is to overcome the defects of too complicated system model and too complicated simulation analysis algorithm for describing a large-scale system in the prior art, thereby providing an event deduction method and apparatus.
According to a first aspect, an embodiment of the present invention discloses an event deduction method, including the following steps: acquiring service data of an event to be analyzed; obtaining a characteristic vector according to a preset knowledge graph and the service data; inputting the characteristic vector into a preset simulation analysis model to obtain a simulation analysis result of an event; and performing deduction prediction on the event according to the simulation analysis result.
With reference to the first aspect, in a first embodiment of the first aspect, the preset knowledge-graph is constructed by: acquiring service training data; the business training data comprises: service system training data, service peripheral training data and service simulation analysis data; the business simulation analysis data is event prediction analysis data; carrying out structuring processing on the service system data and the service peripheral data to obtain structured data, and extracting service entities of the structured data and entity relations among the service entities; and establishing a knowledge graph according to the business entity and the entity relation.
With reference to the first implementation manner of the first aspect, in a second implementation manner of the first aspect, the establishing a knowledge graph according to the business entity and the entity relationship includes: and taking the service entity as a node, and taking the entity relation as an edge to construct the knowledge graph.
With reference to the first embodiment of the first aspect, in a third embodiment of the first aspect, the preset simulation analysis model is constructed by the following steps: extracting features according to the knowledge graph to obtain a training feature vector; and training a preset machine learning model according to the business simulation analysis data and the training characteristic vector to obtain a simulation analysis model.
With reference to the third embodiment of the first aspect, in a fourth embodiment of the first aspect, the method further includes: acquiring service test data; the service test data comprises: service system test data, service peripheral test data and service simulation analysis test data; carrying out structuralization processing on the service system test data and the service peripheral test data to obtain structuralization data to be tested; inputting the structured data to be tested into the simulation analysis model to obtain a test result; and when the test result meets a preset condition, determining the simulation analysis model as a usable simulation analysis model.
With reference to the fourth embodiment of the first aspect, in the fifth embodiment of the first aspect, the method further includes: and training and updating the simulation analysis model by using the service data to be analyzed and the corresponding simulation analysis result.
According to a second aspect, an embodiment of the present invention further discloses an event deduction apparatus, including: the first acquisition module is used for acquiring the service data of the event to be analyzed; the characteristic vector obtaining module is used for obtaining a characteristic vector according to a preset knowledge graph and the service data; the first input module is used for inputting the characteristic vector into a preset simulation analysis model to obtain a simulation analysis result of an event; and the deduction module is used for deducting and predicting the event according to the simulation analysis result.
According to a third aspect, an embodiment of the present invention further discloses a computer device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the one processor to cause the at least one processor to perform the steps of the event deduction method as described in the first aspect or any of the embodiments of the first aspect.
According to a fourth aspect, an embodiment of the present invention further discloses a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the event deduction method according to the first aspect or any of the embodiments of the first aspect.
The technical scheme of the invention has the following advantages:
according to the event deduction method and device, the service data of the event to be analyzed are obtained, the feature vector is obtained according to the preset knowledge graph and the service data, the feature vector is input into the preset simulation analysis model, the simulation analysis result of the event is obtained, the event is deduced and predicted according to the simulation analysis result, the structural features and the machine learning dynamic features of the knowledge graph can be fully combined, the multiple influence factors are brought into the simulation analysis model to conduct multi-source simulation analysis, the analysis result is more accurate, the complexity of simulation analysis is reduced, and the accuracy of event deduction and prediction is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a specific example of an event deduction method in embodiment 1 of the present invention;
fig. 2 is a schematic block diagram of a specific example of an event deduction apparatus in embodiment 2 of the present invention;
fig. 3 is a schematic block diagram of a specific example of a computer device in embodiment 3 of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Example 1
In the embodiment of the invention, because the event deduction and prediction process involves many influencing factors, complex data sources and different event evolution laws, when an event needs to be analyzed and deduced, the related service data of the current event to be analyzed is firstly analyzed, the obtained service data is input into the simulation analysis model to obtain a simulation analysis result, and the deduction and prediction can be carried out on the service data to be analyzed through the statistical arrangement of the simulation analysis result.
The present embodiment provides an event deduction method, as shown in fig. 1, including:
step S11: and acquiring the service data of the event to be analyzed.
For example, the business data of the event to be analyzed may include internal and external data related to the event, specifically, the internal data may be business System data, and the business System data may include data in information systems such as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and Warehouse Management System (WMS); the external data can be business peripheral data, the business peripheral data can comprise information of markets, clients and suppliers and the like, the business data can be directly called from an enterprise database and can also be provided by an event responsible person in a sorting mode.
Step S12: obtaining a characteristic vector according to a preset knowledge graph and service data;
for example, in the embodiment of the present invention, the feature vector may be an event influence factor, such as an event field, an event schedule, an event influence range, an event related person, and the like, and the preset knowledge graph is constructed by combining a business system of an enterprise with historical data.
Step S13: and inputting the characteristic vector into a preset simulation analysis model to obtain a simulation analysis result of the event.
The event simulation analysis result may be an event duration, an event duration range, an event evolution rule, an event attribution department, an event correlation department, and the like, and the analysis result may respectively correspond to different specific evaluation indexes, where the specific evaluation index may be a specific numerical value or a trend curve that changes with time. The specific evaluation index is not limited in the embodiment of the invention, and can be determined according to a specific event.
Step S14: and performing deduction prediction on the event according to the simulation analysis result.
Illustratively, in the simulation analysis, a development result is predicted according to the similarity and correlation between events (that is, when a set of similar conditions is provided, a result similar to the set of conditions may be generated), a simulation analysis model trained in advance is used to perform simulation analysis on multidimensional input service data to obtain an event simulation analysis result, further, an external reaction of an event to be analyzed is predicted, trend judgment is performed to obtain a deduction result, and specifically, weighting may be set for different specific evaluation indexes to obtain the deduction result of the event.
Taking an insurance company as an example, when the insurance business of a certain client is about to expire, the client-related data, the insurance data of the company, the historical transaction data of the client and the like can be input into the simulation analysis model as input data, whether the client will continue to select the insurance business of the company can be predicted according to the selection of each client in the historical data, and an insurance salesman can take corresponding measures according to the prediction result to maintain the client, so that the client can be kept in cooperation for a longer time.
The event deduction method provided by the invention obtains the service data of the event to be analyzed, obtains the characteristic vector according to the preset knowledge graph and the service data, inputs the characteristic vector into the preset simulation analysis model to obtain the simulation analysis result of the event, deduces and predicts the event according to the simulation analysis result, and brings a plurality of influence factors into the simulation analysis model to carry out multi-source simulation analysis by fully combining the structural characteristics of the knowledge graph and the dynamic characteristics of machine learning, so that the analysis result is more accurate, the complexity of simulation analysis is reduced, and the accuracy of event deduction and prediction is improved.
As an alternative embodiment of the present application, the knowledge-graph preset in step S12 is obtained by training as follows:
firstly, acquiring service training data; the business training data includes: service system training data, service peripheral training data and service simulation analysis data; the business simulation analysis data is event prediction analysis data.
Illustratively, the business training data may be data related to historical events of a certain Enterprise, and the business system training data may include data in information systems such as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and Warehouse Management System (WMS); business peripheral data may include market, customer, and supplier feedback information; the service simulation analysis data can be the same as the simulation analysis result; the business data can be directly called from an enterprise database or can be obtained by searching through a search engine. The invention comprehensively analyzes the internal and external data of the enterprise, and can perform independent analysis and correlation analysis on a plurality of data sources when considering the influence of events, thereby obtaining more comprehensive prediction results.
And then, carrying out structuring processing on the service system training data and the service peripheral training data to obtain structured data, and extracting service entities of the structured data and entity relations among the service entities.
Illustratively, the structured data is generally clear in data structure, for an enterprise knowledge graph, the business data and the relation between the business data are relatively clear, and the business system data and the business peripheral data are directly structured according to the relation between the business data of the enterprise to obtain the structured data with clear data structures, so that the knowledge graph can be conveniently constructed and a simulation analysis model can be conveniently trained subsequently.
Illustratively, for the structured data, field information can be directly extracted, entities in the extracted field information are identified, business entities are extracted by using a named entity identification technology, and relationships between the business entities are summarized and extracted by adopting an analysis method with business logic, so that an association relationship, namely an entity relationship, between the business entities is obtained.
Then, establishing a knowledge graph according to the business entity and the entity relation;
illustratively, the knowledge graph can be divided into a data model and specific data as a whole, the data model is a data organization framework of the knowledge graph, and different data models can be adopted by different knowledge graphs. The knowledge graph can be built in a top-down mode, namely, a data model of the knowledge graph is determined firstly, and then data are supplemented according to a frame agreed by the data model to complete the construction of the knowledge graph. The construction of the data model generally determines a basic reference model, and the reference model can integrate the requirements for data in the standard by referring to the relevant data standard of the industry, then forms a basic data model, and perfects the data model according to the condition of actually collected data. Or the data model related to the industry can be extracted from the public knowledge map data model and then is perfected by combining the industry knowledge. The embodiment of the invention perfects the data model according to the extracted service entity and the relation of the service entity to obtain the knowledge graph, and particularly, the knowledge graph can be constructed by taking the service entity as a node and taking the entity relation as a side.
As an alternative embodiment of the present application, the simulation analysis model preset in step S12 is obtained by training the following steps:
firstly, feature extraction is carried out according to a knowledge graph to obtain a training feature vector.
Illustratively, in the embodiment of the present invention, the training feature vector may represent service data in a form of a knowledge graph data for an event impact factor, for example, an event field, an event schedule, an event impact range, an event related person, and the like, and in the embodiment of the present invention, the knowledge graph data may be further extracted by a broad-First Search (BFS) and a Label Propagation Algorithm (LPA), so as to extract training feature vectors suitable for model training at different knowledge levels.
And then, training a preset machine learning model according to the business simulation analysis data and the training characteristic vector to obtain a simulation analysis model.
Illustratively, complete supervision and weak supervision training are carried out on a preset machine learning model according to training feature vectors extracted from a knowledge graph, the training method is not limited, the training method can be selected according to an actual training scene, the weights are continuously adjusted according to a specific scene in the training process to obtain a simulation analysis model, and in simulation calculation, multidimensional data are input into the simulation analysis model for simulation analysis to predict the reaction of an event to be analyzed.
The training method can fully combine the structural characteristics of the knowledge graph and the dynamic characteristics of machine learning, brings a plurality of influence factors into the knowledge graph for multi-source simulation analysis, directly inputs data into a simulation analysis model to obtain a simulation analysis result, reduces the complexity of simulation analysis, and achieves a more accurate analysis result.
As an optional embodiment of the present application, after the simulation analysis model is obtained by training, the model needs to be tested and verified, and when a certain condition is satisfied, the simulation analysis model is used as the simulation analysis model, specifically:
firstly, acquiring service test data; the service test data comprises: the service system test data, the service peripheral test data and the service simulation analysis test data can be obtained in the same way as the service training data in the steps, in the embodiment of the invention, the obtained service training data can be divided according to the proportion of 7:3, the service training data with larger proportion is used as the service training data, and the service testing data with smaller proportion is used as the service test data.
Then, carrying out structuralization processing on the service system test data and the service peripheral test data to obtain structuralization data to be tested; the specific implementation manner is described in the above related description of the corresponding steps, and is not described in detail herein.
And then, inputting the structured data to be tested into the simulation analysis model to obtain a test result, and determining the simulation analysis model as an available simulation analysis model when the test result meets a preset condition.
For example, the preset condition may be that the accuracy of the test result is greater than a preset value, and the preset value may be 98%, the size of the preset threshold is not limited in the embodiment of the present invention, and the preset condition may be set as required, and when the probability that the test result is consistent with the service analysis result is greater than 98%, the simulation analysis model is determined to be an available simulation analysis model.
As an optional implementation manner of the present application, after the simulation analysis model is used to analyze the multidimensional to-be-analyzed service data in real time and obtain a simulation analysis result, the simulation analysis model may be directly updated and optimized, and specifically, the simulation analysis model may be trained and updated by using the to-be-analyzed service data and a corresponding simulation analysis result. And updating and optimizing the established simulation analysis model through the data of the operating environment, so that the simulation analysis model can dynamically adapt to the service scene.
Example 2
The present embodiment provides an event deduction device, as shown in fig. 2, including:
the first obtaining module 21 is configured to obtain service data of an event to be analyzed. The specific implementation manner is described in relation to step S11 in embodiment 1, and is not described herein again.
A feature vector obtaining module 22, configured to obtain a feature vector according to a preset knowledge graph and the service data; the specific implementation manner is described in relation to step S12 in embodiment 1, and is not described herein again.
The first input module 23 is configured to input the service data to be analyzed into a preset simulation analysis model, so as to obtain a simulation analysis result of an event. The specific implementation manner is described in relation to step S13 in embodiment 1, and is not described herein again.
And the deduction module 24 is configured to perform deduction prediction on the event according to the simulation analysis result. The specific implementation manner is described in relation to step S14 in embodiment 1, and is not described herein again.
The event deduction device provided by the invention obtains the service data of the event to be analyzed, obtains the characteristic vector according to the preset knowledge graph and the service data, inputs the characteristic vector into the preset simulation analysis model to obtain the simulation analysis result of the event, deduces and predicts the event according to the simulation analysis result, and brings a plurality of influence factors into the simulation analysis model to perform multi-source simulation analysis by fully combining the structural characteristics and the machine learning dynamic characteristics of the knowledge graph, so that the analysis result is more accurate, the complexity of simulation analysis is reduced, and the accuracy of event deduction and prediction is improved.
As an optional embodiment of the present application, the event deduction apparatus further comprises:
the second acquisition module is used for acquiring service training data; the business training data includes: service system training data, service peripheral training data and service simulation analysis data; the business simulation analysis data is event prediction analysis data. The specific implementation manner is described in association with corresponding steps in embodiment 1, and is not described herein again.
The first processing module is used for carrying out structuralization processing on the service system training data and the service peripheral training data to obtain structuralization data, and extracting service entities of the structuralization data and entity relations among the service entities. The specific implementation manner is described in association with corresponding steps in embodiment 1, and is not described herein again.
The establishing module is used for establishing a knowledge graph according to the business entity and the entity relation; the specific implementation manner is described in association with corresponding steps in embodiment 1, and is not described herein again.
As an optional embodiment of the present application, the event deduction apparatus further comprises:
and the extraction module is used for extracting features according to the knowledge graph to obtain a training feature vector. The specific implementation manner is described in association with corresponding steps in embodiment 1, and is not described herein again.
And the training module is used for training a preset machine learning model according to the business simulation analysis data and the training characteristic vector to obtain a simulation analysis model. The specific implementation manner is described in association with corresponding steps in embodiment 1, and is not described herein again.
As an optional embodiment of the present application, the event deduction apparatus further comprises:
the third acquisition module is used for acquiring the service test data; the service test data comprises: service system test data, service peripheral test data and service simulation analysis test data. The specific implementation manner is described in association with corresponding steps in embodiment 1, and is not described herein again.
The second processing module is used for carrying out structuralization processing on the service system test data and the service peripheral test data to obtain structuralization data to be tested; the specific implementation manner is described in association with corresponding steps in embodiment 1, and is not described herein again.
The second input module is used for inputting the structured data to be tested into the simulation analysis model to obtain a test result; the specific implementation manner is described in association with corresponding steps in embodiment 1, and is not described herein again.
And the determining module is used for determining the simulation analysis model as an available simulation analysis model when the test result meets the preset condition. The specific implementation manner is described in association with corresponding steps in embodiment 1, and is not described herein again.
As an optional embodiment of the present application, the event deduction apparatus further comprises:
and the construction submodule is used for constructing the knowledge graph by taking the service entity as a node and taking the entity relationship as an edge. The specific implementation manner is described in association with corresponding steps in embodiment 1, and is not described herein again.
As an optional embodiment of the present application, the event deduction apparatus further comprises:
and the updating module is used for training and updating the simulation analysis model by utilizing the service data to be analyzed and the corresponding simulation analysis result. The specific implementation manner is described in association with corresponding steps in embodiment 1, and is not described herein again.
Example 3
An embodiment of the present invention further provides a computer device, as shown in fig. 3, the computer device may include a processor 31 and a memory 32, where the processor 31 and the memory 32 may be connected by a bus or in another manner, and fig. 3 takes the example of being connected by a bus as an example.
The processor 31 may be a Central Processing Unit (CPU). The Processor 31 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof.
The memory 32, which is a non-transitory computer-readable storage medium, can be used for storing non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the event deduction method in the embodiment of the present invention (for example, the first obtaining module 21, the feature vector obtaining module 22, the first input module 23, and the deduction module 24 shown in fig. 2). The processor 31 executes various functional applications and data processing of the processor by executing non-transitory software programs, instructions and modules stored in the memory 32, that is, implements the event deduction method in the above method embodiment.
The memory 32 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 31, and the like. Further, the memory 32 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 32 may optionally include memory located remotely from the processor 31, and these remote memories may be connected to the processor 31 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 32 and, when executed by the processor 31, perform the event deduction method as in the embodiment shown in fig. 1.
The details of the computer device can be understood with reference to the corresponding related descriptions and effects in the embodiment shown in fig. 1, and are not described herein again.
Example 4
The embodiment of the invention also provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions can execute the event deduction method in any method embodiment. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (9)
1. An event deduction method, comprising the steps of:
acquiring service data of an event to be analyzed;
obtaining a characteristic vector according to a preset knowledge graph and the service data;
inputting the characteristic vector into a preset simulation analysis model to obtain a simulation analysis result of an event;
and performing deduction prediction on the event according to the simulation analysis result.
2. The method of claim 1, wherein the predetermined knowledge-graph is constructed by:
acquiring service training data; the business training data comprises: service system training data, service peripheral training data and service simulation analysis data; the business simulation analysis data is event prediction analysis data;
carrying out structuring processing on the service system data and the service peripheral data to obtain structured data, and extracting service entities of the structured data and entity relations among the service entities;
and establishing a knowledge graph according to the business entity and the entity relation.
3. The method of claim 2, wherein the building a knowledge graph from the business entities and the entity relationships comprises:
and taking the service entity as a node, and taking the entity relation as an edge to construct the knowledge graph.
4. The method of claim 2, wherein the predetermined simulation analysis model is constructed by:
extracting features according to the knowledge graph to obtain a training feature vector;
and training a preset machine learning model according to the business simulation analysis data and the training characteristic vector to obtain a simulation analysis model.
5. The method of claim 4, further comprising:
acquiring service test data; the service test data comprises: service system test data, service peripheral test data and service simulation analysis test data;
carrying out structuralization processing on the service system test data and the service peripheral test data to obtain structuralization data to be tested;
inputting the structured data to be tested into the simulation analysis model to obtain a test result;
and when the test result meets a preset condition, determining the simulation analysis model as a usable simulation analysis model.
6. The method of claim 5, further comprising:
and training and updating the simulation analysis model by using the service data to be analyzed and the corresponding simulation analysis result.
7. An event deduction apparatus, comprising:
the first acquisition module is used for acquiring the service data of the event to be analyzed;
the characteristic vector obtaining module is used for obtaining a characteristic vector according to a preset knowledge graph and the service data;
the first input module is used for inputting the characteristic vector into a preset simulation analysis model to obtain a simulation analysis result of an event;
and the deduction module is used for deducting and predicting the event according to the simulation analysis result.
8. A computer device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the one processor to cause the at least one processor to perform the steps of the event deduction method as claimed in any one of claims 1 to 6.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the event deduction method as claimed in any one of claims 1 to 6.
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