CN113434627A - Work order processing method and device and computer readable storage medium - Google Patents
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Abstract
The disclosure relates to a work order processing method and device and a computer readable storage medium, and relates to the technical field of computers. The method of the present disclosure comprises: extracting entities in the work order to be processed and attributes of each entity; searching related entities in the work order knowledge graph by using the entities in the work order to be processed and the attributes of the entities; the work order knowledge graph is a knowledge graph generated by a work order finished by historical processing; and generating and displaying the work order processing instruction information according to the searched related entities.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for processing a work order, and a computer-readable storage medium.
Background
The work order system is an important means for the network of the telecom operator to monitor operation and maintenance. The system has the characteristics of multiple professional work orders, multiple types of work orders, complex description failure reasons and different solution methods, and therefore the system brings difficulty to work order analysis of network operation and maintenance personnel.
At present, work orders are processed mainly by depending on the experience of network operation and maintenance personnel.
Disclosure of Invention
The inventor finds that: although a large amount of historical work orders processed by professional network operation and maintenance personnel are retained in the work order system, a large amount of valuable information is contained in the work orders and can be used for new operation and maintenance personnel to learn and refer, but the work orders are not effectively utilized at present, so that the processing efficiency of the new work orders is low.
One technical problem to be solved by the present disclosure is: how to improve the processing efficiency of the work order.
According to some embodiments of the present disclosure, a method for processing a work order is provided, including: extracting entities in the work order to be processed and attributes of each entity; searching related entities in the work order knowledge graph by using the entities in the work order to be processed and the attributes of the entities; the work order knowledge graph is a knowledge graph generated by a work order finished by historical processing; and generating and displaying the work order processing instruction information according to the searched related entities.
In some embodiments, extracting the entities and attributes of each entity in the work order to be processed includes: taking a field value corresponding to structured data in a work order to be processed as an entity, and taking a corresponding field name as an attribute of the entity; and carrying out named entity identification on the unstructured data in the work order to be processed to obtain entities and attributes of the entities in the unstructured data.
In some embodiments, each node in the work order knowledge graph includes entities and entity attributes extracted from the historically processed work orders, edges between the nodes include strength of relationships between the entities, and edges exist between nodes corresponding to entities appearing on the same historically processed work order.
In some embodiments, searching the work order knowledge graph for the associated entity using the entity and the attribute of the entity in the pending work order comprises: generating corresponding feature vectors according to the entities in the work order to be processed and the attributes of the entities, and using the corresponding feature vectors as feature vectors to be searched; determining the similarity between each feature vector to be searched and the feature vector corresponding to each node in the work order knowledge graph as the similarity between the entity in the work order to be processed and the entity in the work order knowledge graph; and searching the entity in the work order knowledge graph with the similarity to the entity in the work order to be processed larger than the threshold value under each attribute as the associated entity.
In some embodiments, generating and displaying work order processing instruction information from the searched associated entities comprises: and aiming at each attribute, arranging the associated entities corresponding to the attribute from large to small according to the similarity of the entities in the work order to be processed, and displaying the entities and the attribute together.
In some embodiments, the method further comprises: extracting entities, attributes of the entities and relations among the entities in the work order which is processed in history; and constructing a work order knowledge graph according to the entities in the work order finished by the historical processing, the attributes of the entities and the relationship among the entities.
In some embodiments, extracting relationships between entities comprises: determining that the entities in the same work order processed in history have a relationship; determining similarity among entities with relation; and determining the relationship strength between the entities according to the similarity between the entities.
In some embodiments, extracting the entity and the entity attribute in the work order after the historical processing comprises: extracting entities and entity attributes respectively aiming at structured data and unstructured data in a work order which is finished by historical processing; the structured data includes: at least one field of the single number, the processing type, the located link, the arrival time, the completion time, the processing department and the processing personnel, and the field value corresponding to each field; unstructured data includes: at least one group of data in the order filling item and the corresponding text and the receipt filling item and the corresponding text; at least one of the fill-out items of the order form and the fill-back item of the return order form comprises: at least one of the steps of processing the opinions and processing the opinions.
According to other embodiments of the present disclosure, there is provided a processing apparatus of a work order, including: the extraction module is used for extracting the entities in the work order to be processed and the attributes of each entity; the searching module is used for searching the related entities in the work order knowledge graph by using the entities in the work order to be processed and the attributes of the entities; the work order knowledge graph is a knowledge graph generated by a work order finished by historical processing; and the display module is used for generating and displaying the work order processing instruction information according to the searched related entities.
In some embodiments, the extraction module is configured to use a field value corresponding to the structured data in the work order to be processed as an entity, and use a corresponding field name as an attribute of the entity; and carrying out named entity identification on the unstructured data in the work order to be processed to obtain entities and attributes of the entities in the unstructured data.
In some embodiments, each node in the work order knowledge graph includes entities and entity attributes extracted from the historically processed work orders, edges between the nodes include strength of relationships between the entities, and edges exist between nodes corresponding to entities appearing on the same historically processed work order.
In some embodiments, the search module is configured to generate a corresponding feature vector according to an entity and an attribute of the entity in the work order to be processed, and use the feature vector as the feature vector to be searched; determining the similarity between each feature vector to be searched and the feature vector corresponding to each node in the work order knowledge graph as the similarity between the entity in the work order to be processed and the entity in the work order knowledge graph; and searching the entity in the work order knowledge graph with the similarity to the entity in the work order to be processed larger than the threshold value under each attribute as the associated entity.
In some embodiments, the display module is configured to, for each attribute, arrange the associated entities corresponding to the attribute from large to small according to the similarity between the associated entities and the entities in the work order to be processed, and display the associated entities and the attribute together.
In some embodiments, the apparatus further comprises: the construction module is used for extracting entities, entity attributes and relations among the entities in the work order which is processed in the history; and constructing a work order knowledge graph according to the entities in the work order finished by the historical processing, the attributes of the entities and the relationship among the entities.
In some embodiments, the build module is configured to determine that there is a relationship between entities appearing in the same historically processed work order; determining similarity among entities with relation; and determining the relationship strength between the entities according to the similarity between the entities.
In some embodiments, the construction module is configured to extract entities and entity attributes for structured data and unstructured data, respectively, in the historically processed work order; the structured data includes: at least one field of the single number, the processing type, the located link, the arrival time, the completion time, the processing department and the processing personnel, and the field value corresponding to each field; unstructured data includes: at least one group of data in the order filling item and the corresponding text and the receipt filling item and the corresponding text; at least one of the fill-out items of the order form and the fill-back item of the return order form comprises: at least one of the steps of processing the opinions and processing the opinions.
According to still other embodiments of the present disclosure, a processing apparatus of a work order is provided, including: a processor; and a memory coupled to the processor for storing instructions that, when executed by the processor, cause the processor to perform a method of processing a work order as in any of the preceding embodiments.
According to still other embodiments of the present disclosure, there is provided a processing apparatus of a work order, including: a non-transitory computer readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the steps of the method of processing a work order of any of the preceding embodiments.
In the method, a work order knowledge graph is generated by using a work order finished by historical processing, and after a new work order to be processed arrives, entities in the work order to be processed and attributes of each entity are extracted; and searching in the work order knowledge graph by utilizing the entities and the attributes of the entities in the work order to be processed to obtain the associated entities. The related entities contain information in the work order subjected to historical processing, work order processing indicating information is generated and displayed according to the related entities, and network operation and maintenance personnel can process the work order processing indicating information by referring to the work order processing indicating information, so that comprehensive analysis support can be provided for scenes such as work order fault analysis and the like, and the work order processing efficiency is improved.
Other features of the present disclosure and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure 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, it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 illustrates a flow diagram of a method of processing a work order of some embodiments of the present disclosure.
FIG. 2 illustrates a flow diagram of a method of processing a work order according to further embodiments of the disclosure.
FIG. 3 illustrates a schematic block diagram of a work order processing device according to some embodiments of the present disclosure.
Fig. 4 shows a schematic configuration of a work order processing apparatus according to further embodiments of the present disclosure.
Fig. 5 shows a schematic structural diagram of a processing device of a work order according to further embodiments of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
Some embodiments of the disclosed work order processing method are described below in conjunction with FIG. 1.
FIG. 1 is a flow diagram of some embodiments of a method of processing a work order of the present disclosure. As shown in fig. 1, the method of this embodiment includes: steps S102 to S106.
In step S102, entities and attributes of the respective entities in the work order to be processed are extracted.
The work order may include: the work orders of the types of fault, complaint, consultation, advice, or service opening are not limited to the examples given. Different types of work orders may be processed separately.
In some embodiments, structured data and unstructured data may be included in the work order to be processed. Structured data includes, for example: at least one field of a single number, a processing type, a link, arrival time, completion time, a processing department and processing personnel, and a field value corresponding to each field. For work orders to be processed, the unstructured data may be null as it has not yet been processed. Alternatively, unstructured data includes, for example: at least one group of data in the order filling item and the corresponding text and the receipt filling item and the corresponding text; at least one of the fill-out items of the order form and the fill-back item of the return order form comprises: at least one of the steps of processing the opinions and processing the opinions. The content of the unstructured data is manually filled, and the format and the content are flexible and changeable, for example, the unstructured data may further include information such as description of a fault type, a processing state, a processing result, and the like, and may also include content similar to or repeated in the structured data, which is not limited to the above-mentioned examples.
The work order may be stored in a database and the structured data may be read directly from the database. For example, a field value corresponding to the structured data in the work order to be processed is used as an entity, and a corresponding field name is used as an attribute of the entity. For example, for structured data, the single number (field): 00001 (field value), 00001 may be taken as an entity and a single number as an attribute of the entity. For unstructured data, the text needs to be processed to extract entities and attributes of the entities. For example, a natural language processing method may be adopted to perform semantic recognition or named entity recognition on the text corresponding to the fill-in item for the assignment or the fill-in item for the receipt, to obtain the entity and the attribute of the entity in the unstructured data, and to convert the unstructured data into structured data. For example, the text corresponding to the receipt filling item is "fiber is broken and repaired". After natural language processing, an obtained entity is optical fiber breakage, and the attributes of the entity are as follows: the fault type, another entity is: the repaired entity has the following attributes: and (6) processing the result. The entity and entity attribute correspondence may be predefined, for example: the fault type (entity attribute) corresponds to entities such as optical fiber breakage, power failure, machine room power failure and the like. The non-structured data can be subjected to semantic recognition and semantic similarity matching with a predefined entity, so that the entity and the attribute of the entity in the non-structured data can be extracted. Reference is made to the prior art for specific methods.
In step S104, the entity and the attribute of the entity in the work order to be processed are utilized to search the associated entity in the work order knowledge graph.
The work order knowledge graph is a knowledge graph generated by using a work order finished by historical processing. In some embodiments, each node in the work order knowledge graph includes entities and entity attributes extracted from the historically processed work orders, there are edges between nodes corresponding to entities appearing on the same historically processed work order, and the edges between nodes include the strength of relationships between the entities. The process of establishing the work order knowledge graph will be described in the following embodiments.
The method for searching the associated entities in the work order knowledge graph by using the entities and the attributes of the entities in the work order to be processed can adopt the scheme of the prior art. In some embodiments, the similarity between the entities in the to-be-processed work order and the entities in the work order knowledge graph may be calculated, and the entities in the work order knowledge graph with the similarity to the entities in the to-be-processed work order greater than a threshold value may be used as the associated entities. Traversal searches can be performed in the work order knowledge graph for the entities in the work order to be processed, or other search methods can be adopted, and the method is not limited to the illustrated examples.
In some embodiments, corresponding feature vectors are generated according to the entities in the work order to be processed and the attributes of the entities, the feature vectors are used as feature vectors to be searched, the similarity between each feature vector to be searched and the feature vector corresponding to each node in the work order knowledge graph is determined, and the similarity between the entities in the work order to be processed and the entities in the work order knowledge graph is used as the similarity between the entities in the work order to be processed and the entities in the work order knowledge graph; and searching the entity in the work order knowledge graph with the similarity to the entity in the work order to be processed larger than the threshold value under each attribute as the associated entity. The similarity between the two feature vectors can be calculated by using the existing methods such as cosine similarity, euclidean distance, etc., and is not limited to the illustrated examples. The method for calculating the similarity between the entity in the work order to be processed and the entity in the work order knowledge graph can refer to the prior art, and is not limited to the illustrated example.
The machine learning model may be trained in advance for generating feature vectors corresponding to the respective entities. For example, the entity and the corresponding attribute in the work order to be processed are input into a machine learning model trained in advance, and the feature vector corresponding to the entity is obtained and used as the feature vector to be searched. The machine learning model may employ an existing model. The machine learning model may be trained, for example, in the following manner. And extracting entities and attributes of the entities from the work orders which are processed in the history, regarding one entity, taking all the entities which appear in the work orders which are processed in the same history with the entity as the characteristics of the entity, taking the attributes of the entity as the characteristics, and generating a characteristic sequence according to a preset sequence to be used as a training sample. The relationships between the entities are labeled, for example, similarity or dissimilarity between the entities, or similarity value between the entities, etc. Inputting the training samples into a machine learning model for training, calculating a loss function according to the labeling and the output, and adjusting the parameters of the machine learning model according to the loss function to finally obtain the trained machine learning model. And using the generated part of the feature vector in the trained machine learning model for subsequently generating the feature vector to be searched, and generating the feature vector aiming at the entity in the work order knowledge graph. The similarity between any two entities can also be determined using an overall machine learning model. The machine learning model is, for example, a neural network. In the method, all entities related to one entity are used as the characteristics of the entity for training, and the relation of the entity aiming at various attributes such as fault types, processing methods and other entities can be learned, so that the generated characteristic vector can reflect the relation, the characteristics of the entity can be reflected more accurately, and the accuracy of the searching process is also improved.
In step S106, work order processing instruction information is generated from the searched related entities and displayed.
In some embodiments, for each attribute, the associated entities corresponding to the attribute are arranged from large to small according to the similarity of the entities in the work order to be processed, and are displayed together with the attribute. For each attribute, the associated entity corresponding to the attribute may be displayed together with the attribute according to the entity with the greatest similarity to the entity in the work order to be processed.
For example, the work order processing instruction information may include: and the entity and the attribute of the entity can be correspondingly displayed. For example, the work order processing instruction information includes: the type of failure: a line fault; and (4) a processing department: a zone maintenance center; the treating staff: zhang III; belongs to the specialty: transmission speciality; the contact way is as follows: 1234567; processing the opinions: and (6) transferring. The work order processing instruction information includes, for example: at least one of processing steps, processing opinions, processing personnel and contact information.
Furthermore, the work order to be processed and the work order processing indication information can be combined into a work order file to be displayed. And taking the content of the work order to be processed as a work order file title and taking the work order processing instruction information as the content of the work order file. For example, the work order file is entitled: no light is received at the 27-slot 2 port of the network element 11-1241-welcome extension 0SN7500 device, a tail fiber fault occurs, and the end position: and 6, opening 1 of 28 grooves of the south China of 14-403-Nanchang pond (C net access point), and turning to a corresponding fragment area for treatment. The work order file content is as follows: the type of failure: line fault/pigtail fault; the treating staff: zhang III; belongs to the specialty: transmission speciality; the contact way is as follows: 1234567; processing the opinions: and (6) transferring.
In the embodiment, the work order knowledge graph is generated by using the work order finished by the historical processing, and the entities and the attributes of the entities in the work order to be processed are extracted after a new work order to be processed arrives; and searching in the work order knowledge graph by utilizing the entities and the attributes of the entities in the work order to be processed to obtain the associated entities. The related entities contain information in the work order subjected to historical processing, work order processing indicating information is generated and displayed according to the related entities, and network operation and maintenance personnel can process the work order processing indicating information by referring to the work order processing indicating information, so that comprehensive analysis support can be provided for scenes such as work order fault analysis and the like, and the work order processing efficiency is improved.
Further embodiments of the method of processing a work order of the present disclosure are described below in conjunction with FIG. 2.
FIG. 2 is a flow diagram of additional embodiments of a method for processing a work order according to the present disclosure. As shown in fig. 2, the method of this embodiment includes: steps S202 to S204.
In step S202, entities, attributes of the entities, and relationships between the respective entities in the work order in which the history processing is completed are extracted.
The historical processed work order also includes structured data and unstructured data. Entities and entity attributes can be extracted respectively for structured data and unstructured data in a work order which is finished by historical processing, and the extraction method can refer to the foregoing embodiment, and is not described herein again.
In some embodiments, the relationships between the entities include a strength of the relationship between the entities. The strength of the relationship between the various entities can be determined using the following method. Determining that the entities in the same work order processed in history have a relationship; determining similarity among entities with relation; and determining the relationship strength between the entities according to the similarity between the entities. The foregoing embodiments describe that the similarity between the entities can be determined by using a trained machine learning model, and details are not repeated here.
In step S204, a work order knowledge graph is constructed based on the entities in the work order in which the history processing is completed, the attributes of the entities, and the relationships between the respective entities.
And taking the entities in the work order subjected to the historical processing and the attributes of the entities as nodes, taking the relationship among the entities as edges among the nodes, and taking the value corresponding to the edges as the relationship strength. The process of constructing a work order knowledge graph may be referred to in the art, including, for example: the steps of knowledge fusion, etc. are not described herein again.
According to the method, the machine automatically learns the relation between the knowledge and various kinds of knowledge in the work order which is finished through the historical processing, and the work order knowledge map is constructed according to the work order which is finished through the historical processing, so that the method can be used for reference in the subsequent work order processing, and the processing efficiency is improved.
The present disclosure also provides a processing apparatus for a work order, described below with reference to fig. 3.
FIG. 3 is a block diagram of some embodiments of a processing device for a work order of the present disclosure. As shown in fig. 3, the apparatus 30 of this embodiment includes: an extraction module 310, a search module 320, and a display module 330.
The extraction module 310 is used for extracting the entities and the attributes of the entities in the work order to be processed.
In some embodiments, the extraction module 310 is configured to use a field value corresponding to the structured data in the work order to be processed as an entity, and use a corresponding field name as an attribute of the entity; and carrying out named entity identification on the unstructured data in the work order to be processed to obtain entities and attributes of the entities in the unstructured data.
The searching module 320 is used for searching the associated entities in the work order knowledge graph by using the entities in the work order to be processed and the attributes of the entities; the work order knowledge graph is a knowledge graph generated by using a work order which is processed by history.
In some embodiments, each node in the work order knowledge graph includes entities and entity attributes extracted from the historically processed work orders, edges between the nodes include strength of relationships between the entities, and edges exist between nodes corresponding to entities appearing on the same historically processed work order.
In some embodiments, the searching module 320 is configured to generate a corresponding feature vector according to the entity and the attribute of the entity in the work order to be processed, as the feature vector to be searched; determining the similarity between each feature vector to be searched and the feature vector corresponding to each node in the work order knowledge graph as the similarity between the entity in the work order to be processed and the entity in the work order knowledge graph; and searching the entity in the work order knowledge graph with the similarity to the entity in the work order to be processed larger than the threshold value under each attribute as the associated entity.
The display module 330 is configured to generate and display work order processing instruction information according to the searched associated entities.
In some embodiments, the display module 330 is configured to, for each attribute, arrange the associated entities corresponding to the attribute from large to small according to the similarity between the associated entities and the entities in the work order to be processed, and display the associated entities and the attribute together.
In some embodiments, the apparatus 30 further comprises: the construction module 340 is configured to extract entities, attributes of the entities, and relationships among the entities in the work order in which the history processing is completed; and constructing a work order knowledge graph according to the entities in the work order finished by the historical processing, the attributes of the entities and the relationship among the entities.
In some embodiments, the build module 340 is configured to determine that there is a relationship between entities appearing in the same historically processed work order; determining similarity among entities with relation; and determining the relationship strength between the entities according to the similarity between the entities.
In some embodiments, the construction module 340 is configured to extract entities and entity attributes for structured data and unstructured data, respectively, in the historically processed work order; the structured data includes: at least one field of the single number, the processing type, the located link, the arrival time, the completion time, the processing department and the processing personnel, and the field value corresponding to each field; unstructured data includes: at least one group of data in the order filling item and the corresponding text and the receipt filling item and the corresponding text; at least one of the fill-out items of the order form and the fill-back item of the return order form comprises: at least one of the steps of processing the opinions and processing the opinions.
The processing devices of the work orders in the embodiments of the present disclosure may each be implemented by various computing devices or computer systems, which are described below in conjunction with fig. 4 and 5.
FIG. 4 is a block diagram of some embodiments of a processing device for a work order of the present disclosure. As shown in fig. 4, the apparatus 40 of this embodiment includes: a memory 410 and a processor 420 coupled to the memory 410, the processor 420 configured to execute a method of processing a work order in any of the embodiments of the present disclosure based on instructions stored in the memory 410.
FIG. 5 is a block diagram of another embodiment of a processing device for a work order of the present disclosure. As shown in fig. 5, the apparatus 50 of this embodiment includes: memory 510 and processor 520 are similar to memory 410 and processor 420, respectively. An input output interface 530, a network interface 540, a storage interface 550, and the like may also be included. These interfaces 530, 540, 550 and the connections between the memory 510 and the processor 520 may be, for example, via a bus 560. The input/output interface 530 provides a connection interface for input/output devices such as a display, a mouse, a keyboard, and a touch screen. The network interface 540 provides a connection interface for various networking devices, such as a database server or a cloud storage server. The storage interface 550 provides a connection interface for external storage devices such as an SD card and a usb disk.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only exemplary of the present disclosure and is not intended to limit the present disclosure, so that any modification, equivalent replacement, or improvement made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.
Claims (11)
1. A method of processing a work order, comprising:
extracting entities in the work order to be processed and attributes of each entity;
searching the associated entities in the work order knowledge graph by using the entities in the work order to be processed and the attributes of the entities; the work order knowledge graph is a knowledge graph generated by a work order which is processed by using history;
and generating and displaying the work order processing instruction information according to the searched related entities.
2. The method of processing a work order according to claim 1,
the extracting of the entities in the work order to be processed and the attributes of each entity comprises:
taking a field value corresponding to structured data in a work order to be processed as an entity, and taking a corresponding field name as an attribute of the entity;
and carrying out named entity identification on the unstructured data in the work order to be processed to obtain the entities and the attributes of the entities in the unstructured data.
3. The method of processing a work order according to claim 1,
each node in the work order knowledge graph comprises entities and entity attributes extracted from the work orders finished through the historical processing, edges among the nodes comprise the relation strength among the entities, and edges exist among the nodes corresponding to the entities appearing in the work orders finished through the historical processing.
4. The method of processing a work order according to claim 3,
the searching the associated entities in the work order knowledge graph by using the entities in the work order to be processed and the attributes of the entities comprises the following steps:
generating corresponding feature vectors according to the entities in the work order to be processed and the attributes of the entities, and using the corresponding feature vectors as feature vectors to be searched;
determining the similarity between each feature vector to be searched and the feature vector corresponding to each node in the work order knowledge graph as the similarity between the entity in the work order to be processed and the entity in the work order knowledge graph;
and searching the entity in the work order knowledge graph with the similarity to the entity in the work order to be processed larger than a threshold value under each attribute as the associated entity.
5. The method of processing a work order according to claim 4,
the generating and displaying work order processing instruction information according to the searched associated entities comprises:
and aiming at each attribute, arranging the associated entities corresponding to the attribute from large to small according to the similarity of the entities in the work order to be processed, and displaying the entities and the attribute together.
6. The method of processing a work order of claim 1, further comprising:
extracting entities, attributes of the entities and relations among the entities in the work order which is processed in the history;
and constructing the work order knowledge graph according to the entities in the work order finished by the historical processing, the attributes of the entities and the relationship among the entities.
7. The method of processing a work order according to claim 6,
the extracting the relationship among the entities comprises:
determining that the entities in the same work order processed in history have a relationship;
determining similarity among entities with relation;
and determining the relationship strength between the entities according to the similarity between the entities.
8. The method of processing a work order according to claim 6,
the extracting of the entity and the entity attribute in the work order finished by the historical processing comprises:
extracting entities and entity attributes respectively aiming at the structured data and the unstructured data in the work order which is finished by the historical processing;
the structured data includes: at least one field of the single number, the processing type, the located link, the arrival time, the completion time, the processing department and the processing personnel, and the field value corresponding to each field;
the unstructured data comprises: at least one group of data in the order filling item and the corresponding text and the receipt filling item and the corresponding text;
the at least one of the fill-out items of the order and the fill-back items of the order comprise: at least one of the steps of processing the opinions and processing the opinions.
9. A work order handling apparatus comprising:
the extraction module is used for extracting the entities in the work order to be processed and the attributes of each entity;
the searching module is used for searching the related entities in the work order knowledge graph by using the entities in the work order to be processed and the attributes of the entities; the work order knowledge graph is a knowledge graph generated by a work order which is processed by using history;
and the display module is used for generating and displaying the work order processing instruction information according to the searched related entities.
10. A work order handling apparatus comprising:
a processor; and
a memory coupled to the processor for storing instructions that, when executed by the processor, cause the processor to perform the method of processing a work order of any of claims 1-8.
11. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the steps of the method of processing a work order of any of claims 1-8.
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