CN111861250A - Scheduling decision generation method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention discloses a scheduling decision generation method, a scheduling decision generation device, electronic equipment and a storage medium. The invention comprises the following steps: acquiring a plurality of scheduling decision concepts according to a preset scheduling rule; deconstructing the preset scheduling procedure, and determining the relationship among the multiple scheduling decision concepts; generating an entity extraction rule according to the preset scheduling rule; constructing a mode layer according to the multiple scheduling decision concepts and the relationship; performing entity learning on a preset scheduling accident instance according to the entity extraction rule, extracting entities in the preset scheduling accident instance, and constructing a data layer by adopting the entities; constructing a scheduling decision knowledge graph by adopting the mode layer and the data layer; and when a scheduling accident occurs, generating a scheduling decision according to the scheduling decision knowledge graph. In the embodiment of the invention, when a scheduling accident occurs, the scheduling decision aiming at the scheduling accident can be automatically generated according to the knowledge graph, the decision efficiency is high, and the limitation of manual decision is avoided.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a scheduling decision generating method and apparatus, an electronic device, and a storage medium.
Background
The electric power system is an electric energy production and consumption system which consists of links such as a power plant, a power transmission and transformation line, a power supply and distribution station, power utilization and the like. The function of the device is to convert the primary energy of the nature into electric energy through a power generation device, and then supply the electric energy to each user through power transmission, power transformation and power distribution. In order to realize the function, the power system is also provided with corresponding information and control systems at each link and different levels, and the production process of the electric energy is measured, regulated, controlled, protected, communicated and scheduled so as to ensure that users obtain safe and high-quality electric energy.
At present, the scale of a power grid is gradually enlarged, new equipment is gradually accessed, and new services are continuously expanded, which all generate new knowledge in a power system. The traditional knowledge management and organization modes in the power system are not suitable any more due to a large amount of new knowledge which is continuously input, and the power system urgently needs a mode for effectively organizing, managing and utilizing massive knowledge. Particularly, in scheduling control, the current mode depends more on information processing and decision of a dispatcher, and an automatic system can only realize a simple and fixed decision-making assisting function. Therefore, the method of utilizing manual decision-making has limitations in the face of massive multi-dimensional heterogeneous information.
Disclosure of Invention
The invention provides a scheduling decision method, a scheduling decision device, electronic equipment and a storage medium, which are used for solving the technical problem that manual decision is limited in the existing scheduling control.
The invention provides a scheduling decision knowledge graph generation method, which comprises the following steps:
acquiring a plurality of scheduling decision concepts according to a preset scheduling rule;
deconstructing the preset scheduling procedure, and determining the relationship among the multiple scheduling decision concepts;
learning the preset scheduling rules to generate entity extraction rules;
constructing a mode layer according to the multiple scheduling decision concepts and the relationship;
performing entity learning on a preset scheduling accident instance according to the entity extraction rule, extracting entities in the preset scheduling accident instance, and constructing a data layer by adopting the entities;
constructing a scheduling decision knowledge graph by adopting the mode layer and the data layer;
and when a scheduling accident occurs, generating a scheduling decision according to the scheduling decision knowledge graph.
Optionally, the step of obtaining multiple scheduling decision concepts according to a preset scheduling procedure includes:
acquiring a power scheduling decision term;
and extracting concept terms from the power scheduling decision terms based on a preset scheduling procedure to form a plurality of scheduling decision concepts.
Optionally, the deconstructing the preset scheduling procedure to determine the relationship between the plurality of scheduling decision concepts comprises:
deconstructing the preset scheduling procedure to determine semantic relationships among the multiple scheduling decision concepts;
and extracting the relation among the multiple scheduling decision concepts according to the semantic relation.
Optionally, the step of learning the preset scheduling procedure and generating an entity extraction rule includes:
extracting general sentence patterns from the preset scheduling rules;
and learning the general sentence pattern to generate an entity extraction rule.
The invention provides a scheduling decision generating device, which comprises:
the scheduling decision concept acquisition module is used for acquiring a plurality of scheduling decision concepts according to a preset scheduling procedure;
a relation determining module, configured to deconstruct the preset scheduling procedure, and determine a relation between the multiple scheduling decision concepts;
the entity extraction rule generation module is used for learning the preset scheduling rules to generate entity extraction rules;
the mode layer construction module is used for constructing a mode layer according to the multiple scheduling decision concepts and the relation;
the data layer construction module is used for performing entity learning on a preset scheduling accident instance according to the entity extraction rule, extracting entities in the preset scheduling accident instance and constructing a data layer by adopting the entities;
the knowledge graph construction module is used for constructing a scheduling decision knowledge graph by adopting the mode layer and the data layer;
and the scheduling decision generation module is used for generating a scheduling decision according to the scheduling decision knowledge graph when a scheduling accident occurs.
Optionally, the scheduling decision concept obtaining module includes:
the term obtaining submodule is used for obtaining a power scheduling decision term;
and the scheduling decision concept extraction submodule is used for extracting concept terms from the power scheduling decision terms based on a preset scheduling procedure to form a plurality of scheduling decision concepts.
Optionally, the relationship determining module includes:
a semantic relation determining submodule for deconstructing the preset scheduling procedure to determine semantic relations among the multiple scheduling decision concepts;
and the relation extraction submodule is used for extracting the relation among the multiple scheduling decision concepts according to the semantic relation.
Optionally, the entity extraction rule generating module includes:
a general sentence pattern extraction submodule for extracting a general sentence pattern from the preset scheduling rule;
and the entity extraction rule generation submodule is used for learning the general sentence pattern to generate an entity extraction rule.
The electronic device provided by the present invention includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the scheduling decision generating method according to any one of the above embodiments.
The invention provides a computer readable storage medium having stored thereon a computer program which, when executed by the processor, implements a scheduling decision generating method as defined in any of the above.
According to the technical scheme, the invention has the following advantages: determining a plurality of scheduling decision concepts and a relation between the scheduling decision concepts according to a preset scheduling procedure, and generating an entity extraction rule based on the preset scheduling procedure so as to construct a mode layer; and extracting entities in the preset scheduling accident instance through an entity extraction rule to construct a data layer, so that a scheduling decision knowledge graph is generated according to the mode layer and the data layer. When a scheduling accident occurs, a scheduling decision aiming at the scheduling accident can be automatically generated according to the knowledge graph, the decision efficiency is high, and the limitation of manual decision is avoided.
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 only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a flowchart illustrating steps of a scheduling decision generating method according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating steps of a scheduling decision concept obtaining method according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating steps for determining relationships between scheduling decision concepts according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a process for constructing a scheduling decision knowledge graph according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a schema layer of a knowledge-graph according to an embodiment of the invention;
FIG. 6 is a data layer of a scheduling decision knowledge graph after a real line trip event has been extracted;
fig. 7 is a block diagram of a scheduling decision generating apparatus according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a scheduling decision generation method, a scheduling decision generation device, electronic equipment and a storage medium, which are used for solving the technical problem that manual decision utilization has limitation in the existing scheduling control.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
Referring to fig. 1, fig. 1 is a flowchart illustrating a scheduling decision generating method according to an embodiment of the present invention.
The invention provides a scheduling decision generation method, which comprises the following steps:
the power system is a very complex system consisting of a plurality of power plants, substations, transmission and distribution lines and electrical equipment of users, and the change of the operation condition of any equipment in the system can affect other equipment and even the whole system, so the coordination work of the system operation is regulated according to the regulation of a scheduling rule. In the scheduling procedure, a handling procedure for different scheduling incidents is specified. For example, the scheduling procedure "after occurrence of a, make B", specifies the processing flow after occurrence of a.
The scheduling decision concept refers to a concept representing a relatively abstract class of objects in the power system, and may include a thing class concept, an event class concept and an operation class concept without specific objects. For example, three-winding transformers, autotransformers and box-type transformers all belong to transformers, and the transformers can be collectively referred to as the three transformers. The transformer may be any type of transformer, but does not refer to any kind of transformer in detail, so the transformer is only a relatively abstract concept of things in the power system. It may be referred to herein as the body of a three-winding transformer, autotransformer, box-type transformer, which is a solid body. In the embodiment of the invention, a plurality of scheduling decision concepts in the power system can be obtained according to the preset scheduling rules and the actual scheduling requirements.
Referring to fig. 2, fig. 2 is a flowchart illustrating a method for obtaining a scheduling decision concept according to an embodiment of the present invention. As shown in fig. 2, step 101 may comprise the following sub-steps:
s11, acquiring a power scheduling decision term;
and S12, extracting concept terms from the power scheduling decision terms based on a preset scheduling procedure to form a plurality of scheduling decision concepts.
In a specific implementation, a power scheduling decision term in the aspect of a power system can be obtained by extracting public encyclopedia data on the internet, and a manual supplementing mode is adopted for supplementing partial missing terms. In the embodiment of the invention, the power scheduling decision term is a professional mark set of concepts, entities or attributes in the power scheduling decision field. Specifically, entities in the power scheduling decision field may include lines, switches, and the like; the attributes are associated with the entities, and are different from entity to entity, such as the length of the line, the restriction value, and the like.
After the power scheduling decision terms are extracted, representative concept terms can be obtained from the power scheduling decision terms, so that a plurality of scheduling decision concepts can be obtained. Representative conceptual terms refer to terms that represent abstractions of a class of terms. As mentioned above, transformers can represent many different types of transformers, but are not specifically referred to as a class of transformers. Therefore, the transformer is a representative concept term, which can be used as a scheduling decision concept of the embodiment of the present invention.
102, deconstructing the preset scheduling procedure, and determining the relationship among the multiple scheduling decision concepts;
in the embodiment of the invention, when different concepts appear in the same scheduling procedure, a certain association relationship exists between the concepts to connect the two concepts. For example, "after a occurs, B is done," it can be understood that the occurrence of a triggers a behavior for B. By deconstructing the preset scheduling procedures, the relationship between multiple scheduling decision concepts can be obtained. The relationship between the scheduling decision concepts may provide a basis for the operation of the power system.
Taking a power system fault as an example, when a certain module of the power system has a fault, the operation aiming at the fault can be determined according to the relation between scheduling decision concepts, so that the fault is eliminated or the influence of the fault on the operation of the power system is reduced.
Referring to fig. 3, fig. 3 is a flowchart illustrating a step of determining a relationship between scheduling decision concepts according to an embodiment of the present invention; as shown in fig. 3, step 102 may include the following sub-steps:
s21, deconstructing the preset scheduling rules to determine semantic relations among the multiple scheduling decision concepts;
s22, extracting the relation among the plurality of scheduling decision concepts according to the semantic relation.
In the embodiment of the present invention, the relation between scheduling decision concepts can be determined by analyzing the semantics of sentences in the scheduling procedure, for example, after the scheduling procedure "a occurs, B is made, where a and B are two concepts, and the semantic relation between a and B is: a- (decision relationship) -B. The occurrence of A determines the operation for B, i.e. the relationship between A and B is the decision relationship.
In the power system operation scene, the relationship types of the scheduling decision concepts are different according to the difference of the head-end concept and the tail-end concept.
As table 1, the kind of relationship between scheduling decision concepts is shown:
TABLE 1
The concept of object class mainly refers to a specific object class, such as an equipment, a system, an organization name, a place name, and the like.
The event class concept mainly represents objective events occurring in the power grid, such as line tripping, protection actions, unit tripping and other events.
The operation concept mainly represents the regulation and control operation of a dispatching personnel on the power grid, such as the operations of on-off switch, generator processing adjustment, load transfer and the like.
in the embodiment of the invention, the preset scheduling procedure comprises a general sentence pattern or a template of concepts and relations, the general sentence pattern or the template is subjected to rule learning, and the rule can be extracted according to the fixed relation learning among the concepts. The extraction rules are used for extracting entities from the scheduling incidents and determining relationships between the entities, thereby generating corresponding scheduling decisions for the scheduling incidents.
104, constructing a mode layer according to the multiple scheduling decision concepts and the relation;
in the embodiment of the invention, a mode layer can be constructed according to the scheduling decision concept and the relationship between the scheduling decision concepts, and data is stored in the mode layer in a triple form of concept-relationship-concept. In another alternative, it may also be stored in the form of "attribute-value" pairs.
In the embodiment of the present invention, the process of constructing the mode layer in step 101-104 may be referred to as ontology learning, and the object thereof is a concept with abstract meaning, so that it stores the relationship between the concept and the concept in the power system.
The purpose of constructing the mode layer in the embodiment of the invention is to provide a template for a scheduling accident which has occurred so as to construct a data layer based on a specific scheduling accident, and compared with the mode layer, the data layer can embody the relationship between certain power system entities, thereby determining a scheduling decision for the specific entity. In the embodiment of the present invention, the process of constructing the data layer is shown in step 105.
the entity learning is to learn the existing scheduling accident instance based on the entity extraction rule, so as to extract the entity from the scheduling accident instance according to the object represented by the concept, and form a triple in the form of entity-relation-entity according to the relation stored in the entity extraction rule, thereby constructing a data layer. That is, the ontology and its attributes in the schema layer all have specific entities and their attributes corresponding to them in the data layer, the schema layer gives the frame structure of the data, and the data layer is the specific implementation of the structure.
106, constructing a scheduling decision knowledge graph by adopting the mode layer and the data layer;
the scheduling decision knowledge graph of the embodiment of the invention comprises a mode layer and a data layer, so that the scheduling decision knowledge graph can be constructed after the mode layer and the data layer are constructed.
Referring to fig. 4, fig. 4 is a schematic diagram of a process for constructing a scheduling decision knowledge graph according to an embodiment of the present invention.
As shown in fig. 4, the process of constructing the scheduling decision graph includes a process of constructing a knowledge graph pattern layer through ontology learning and a process of constructing a knowledge graph data layer through entity learning. The ontology learning process includes four processes, namely term extraction, concept extraction, relationship extraction and rule learning, the specific process is shown in step 101-.
And 107, when a scheduling accident occurs, generating a scheduling decision according to the scheduling decision knowledge graph.
In the embodiment of the invention, when a scheduling accident occurs, a series of subsequent scheduling treatment processes can be determined from the scheduling decision knowledge graph, so that manual decision making through experience is not needed, and the scheduling accident can be rapidly processed.
Determining a plurality of scheduling decision concepts and a relation between the scheduling decision concepts according to a preset scheduling procedure, and generating an entity extraction rule based on the preset scheduling procedure so as to construct a mode layer; and extracting entities in the preset scheduling accident instance through an entity extraction rule to construct a data layer, so that a scheduling decision knowledge graph is generated according to the mode layer and the data layer. When a scheduling accident occurs, a scheduling decision aiming at the scheduling accident can be automatically generated according to the knowledge graph, scheduling personnel are guided to process the scheduling accident, the decision efficiency is high, and the limitation of manual decision is avoided.
To facilitate understanding of embodiments of the present invention by those skilled in the art, the following description is given by way of specific examples.
Taking the knowledge graph corresponding to the line trip handling in the scheduling decision knowledge graph as an example, fig. 5 is a schematic diagram of a mode layer of the knowledge graph provided by the embodiment of the present invention.
As shown in fig. 5, the mode layer includes various scheduling decision concepts and relationships, wherein:
the concept of things includes: transformer substation, circuit.
The event class concept comprises: failure of forced delivery, success of forced delivery, failure of multiple times of forced delivery, failure of reclosing, line tripping and success of reclosing.
The operation class concept comprises: the method comprises the steps of line forced delivery, forced delivery condition checking, repeated forced delivery, line patrol notification, information collection, temporary maintenance, line power transmission, log recording and information reporting.
The connection line between the scheduling decision concepts is a relationship between each other, and may specifically include: occurrence, decision, support, connection, etc.
In the mode level shown in fig. 5, if a trip accident occurs, starting from the body "line", two consequences may occur: "coincidence successful" or "coincidence failed". For these two different cases, there are different treatment principles: for a line successfully superposed, trip information needs to be collected, and the line body information needs to be supported; meanwhile, the maintenance power supply bureau of the line needs to be informed to patrol the line; after the information collection is completed, logs need to be recorded according to the collected information, and then the information reporting is completed. For a line which fails to be superposed, after trip information is collected, the forced transmission condition of the line is checked according to specific information, and then forced transmission is carried out; after the line is forcibly sent, different treatment modes exist subsequently according to whether the forced sending is successful or not.
Referring to fig. 6, fig. 6 is a data layer of a scheduling decision knowledge map after a certain real line trip accident is extracted. The A station and the B station are two entities of the transformer substation in the figure 5, and the AB A line is one entity of the line in the figure 5.
As can be seen from fig. 6, after the AB-deck line is tripped, the forced delivery is successful, and the actual scheduling and handling process is just developed according to a part of branches in the mode layer shown in fig. 5, and then the corresponding handling process is performed: after the line is tripped, the reclosing fails, then tripping information is collected, forced transmission conditions are checked according to the collected information, and the line is forced to be transmitted. And (4) successfully sending the line, subsequently informing a power supply bureau to carry out line patrol, and recording the log and reporting the information.
As can be seen from fig. 5 and 6, in the case of a scheduling accident, a scheduling decision for the scheduling accident can be determined according to the knowledge graph, so that a scheduler is guided to handle the scheduling accident, decision efficiency is high, and limitation of manual decision is avoided.
The body and the attribute in the knowledge graph mode layer have specific entities and the attribute thereof corresponding to the body and the attribute thereof in the data layer, the mode layer gives a frame structure of the data, and the data layer realizes the structure specifically.
Table 2 shows the ontology and entity attributes of "line" in the knowledge-graph referred to in fig. 5 and 6:
referring to fig. 7, fig. 7 is a block diagram illustrating a scheduling decision generating apparatus according to an embodiment of the present invention.
The invention provides a scheduling decision generating device, which comprises:
a scheduling decision concept obtaining module 701, configured to obtain multiple scheduling decision concepts according to a preset scheduling procedure;
a relation determining module 702, configured to deconstruct the preset scheduling procedure, and determine a relation between the multiple scheduling decision concepts;
an entity extraction rule generating module 703, configured to learn the preset scheduling procedure to generate an entity extraction rule;
a mode layer construction module 704, configured to construct a mode layer according to the multiple scheduling decision concepts and the relationship;
a data layer construction module 705, configured to perform entity learning on a preset scheduling accident instance according to the entity extraction rule, extract an entity in the preset scheduling accident instance, and construct a data layer by using the entity;
a knowledge graph construction module 706 configured to construct a scheduling decision knowledge graph using the mode layer and the data layer;
and a scheduling decision generating module 707, configured to generate a scheduling decision according to the scheduling decision knowledge graph when a scheduling accident occurs.
In this embodiment of the present invention, the scheduling decision concept obtaining module 701 includes:
the term obtaining submodule is used for obtaining a power scheduling decision term;
and the scheduling decision concept extraction submodule is used for extracting concept terms from the power scheduling decision terms based on a preset scheduling procedure to form a plurality of scheduling decision concepts.
In this embodiment of the present invention, the relationship determining module 702 includes:
a semantic relation determining submodule for deconstructing the preset scheduling procedure to determine semantic relations among the multiple scheduling decision concepts;
and the relation extraction submodule is used for extracting the relation among the multiple scheduling decision concepts according to the semantic relation.
In this embodiment of the present invention, the entity extraction rule generating module 703 includes:
a general sentence pattern extraction submodule for extracting a general sentence pattern from the preset scheduling rule;
and the entity extraction rule generation submodule is used for learning the general sentence pattern to generate an entity extraction rule.
The electronic device provided by the present invention includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the scheduling decision generating method according to the embodiment of the present invention.
The invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by the processor, implements the scheduling decision generating method according to the embodiment of the invention.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A scheduling decision generation method, comprising:
acquiring a plurality of scheduling decision concepts according to a preset scheduling rule;
deconstructing the preset scheduling procedure, and determining the relationship among the multiple scheduling decision concepts;
learning the preset scheduling rules to generate entity extraction rules;
constructing a mode layer according to the multiple scheduling decision concepts and the relationship;
performing entity learning on a preset scheduling accident instance according to the entity extraction rule, extracting entities in the preset scheduling accident instance, and constructing a data layer by adopting the entities;
constructing a scheduling decision knowledge graph by adopting the mode layer and the data layer;
and when a scheduling accident occurs, generating a scheduling decision according to the scheduling decision knowledge graph.
2. The method of claim 1, wherein the step of obtaining a plurality of scheduling decision concepts according to a predetermined scheduling procedure comprises:
acquiring a power scheduling decision term;
and extracting concept terms from the power scheduling decision terms based on a preset scheduling procedure to form a plurality of scheduling decision concepts.
3. The method of claim 2, wherein the step of deconstructing the pre-set scheduling procedure, determining the relationship between the plurality of scheduling decision concepts, comprises:
deconstructing the preset scheduling rules, and determining semantic relations among the multiple scheduling decision concepts;
and extracting the relation among the multiple scheduling decision concepts according to the semantic relation.
4. The method of claim 1, wherein the step of learning the preset scheduling procedure to generate entity extraction rules comprises:
extracting general sentence patterns from the preset scheduling rules;
and learning the general sentence pattern to generate an entity extraction rule.
5. A scheduling decision generating apparatus, comprising:
the scheduling decision concept acquisition module is used for acquiring a plurality of scheduling decision concepts according to a preset scheduling procedure;
a relation determining module, configured to deconstruct the preset scheduling procedure, and determine a relation between the multiple scheduling decision concepts;
the entity extraction rule generation module is used for learning the preset scheduling rules to generate entity extraction rules;
the mode layer construction module is used for constructing a mode layer according to the multiple scheduling decision concepts and the relation;
the data layer construction module is used for performing entity learning on a preset scheduling accident instance according to the entity extraction rule, extracting entities in the preset scheduling accident instance and constructing a data layer by adopting the entities;
the knowledge graph construction module is used for constructing a scheduling decision knowledge graph by adopting the mode layer and the data layer;
and the scheduling decision generation module is used for generating a scheduling decision according to the scheduling decision knowledge graph when a scheduling accident occurs.
6. The apparatus of claim 5, wherein the scheduling decision concept obtaining module comprises:
the term obtaining submodule is used for obtaining a power scheduling decision term;
and the scheduling decision concept extraction submodule is used for extracting concept terms from the power scheduling decision terms based on a preset scheduling procedure to form a plurality of scheduling decision concepts.
7. The apparatus of claim 6, wherein the relationship determination module comprises:
a semantic relation determining submodule for deconstructing the preset scheduling procedure to determine semantic relations among the multiple scheduling decision concepts;
and the relation extraction submodule is used for extracting the relation among the multiple scheduling decision concepts according to the semantic relation.
8. The apparatus of claim 5, wherein the entity extraction rule generating module comprises:
a general sentence pattern extraction submodule for extracting a general sentence pattern from the preset scheduling rule;
and the entity extraction rule generation submodule is used for learning the general sentence pattern to generate an entity extraction rule.
9. An electronic device, comprising a memory and a processor, the memory having stored therein a computer program that, when executed by the processor, causes the processor to perform the steps of the scheduling decision generating method according to any of claims 1-4.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the scheduling decision generating method according to any one of claims 1-4.
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