CN111552820A - Water engineering scheduling data processing method and device - Google Patents
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Abstract
The invention discloses a method and a device for processing scheduling data of a water project, wherein the method comprises the following steps: acquiring water engineering scheduling data, wherein the water engineering scheduling data comprises: one or any combination of structured data, semi-structured data and unstructured data; extracting information from the water project scheduling data, and performing knowledge fusion processing on the extracted information; and constructing a knowledge graph corresponding to the water engineering scheduling data according to a knowledge fusion processing result. The method and the system have the advantages of efficiently processing the water engineering scheduling data, improving the data processing efficiency, saving manpower and material resources, ensuring the data completeness and being beneficial to assisting the scheduling business decision.
Description
Technical Field
The invention relates to the technical field of water engineering scheduling, in particular to a water engineering scheduling data processing method and device.
Background
In the field of water engineering scheduling, data related to scheduling needs to be organized and applied. In the prior art, a relational database mode is usually adopted to process water engineering scheduling data. The storage structure comprises three modes of stream data storage, unstructured data storage and structured data storage. The stream database comprises a real-time monitoring database and a real-time video database, the structured database comprises a basic database, a monitoring database, a business database and a space database (vector), and the unstructured database comprises a document database, a multimedia database, a space database (grid), a model parameter database and the like.
However, the existing water engineering scheduling data processing needs a lot of manual analysis and experience, has low processing efficiency, consumes manpower and material resources, lacks completeness of data, and is difficult to effectively accumulate to assist future scheduling business decision.
Disclosure of Invention
The embodiment of the invention provides a method for processing water engineering scheduling data, which is used for efficiently processing the water engineering scheduling data, improving the data processing efficiency, saving manpower and material resources, ensuring the data completeness and being beneficial to assisting the decision of scheduling service, and comprises the following steps:
acquiring water engineering scheduling data, wherein the water engineering scheduling data comprises: one or any combination of structured data, semi-structured data and unstructured data;
extracting information from the water project scheduling data, and performing knowledge fusion processing on the extracted information;
and constructing a knowledge graph corresponding to the water engineering scheduling data according to a knowledge fusion processing result.
The embodiment of the invention provides a water engineering scheduling data processing device, which is used for efficiently processing water engineering scheduling data, improving the data processing efficiency, saving manpower and material resources, ensuring the data completeness and being beneficial to assisting scheduling service decision, and the device comprises:
the data acquisition module is used for acquiring water engineering scheduling data, and the water engineering scheduling data comprises: one or any combination of structured data, semi-structured data and unstructured data;
the extraction and fusion module is used for extracting information from the water engineering scheduling data and performing knowledge fusion processing on the extracted information;
and the map construction module is used for constructing a knowledge map corresponding to the water engineering scheduling data according to the knowledge fusion processing result.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can be run on the processor, wherein the processor realizes the water engineering scheduling data processing method when executing the computer program.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program for executing the above water engineering scheduling data processing method is stored in the computer-readable storage medium.
The embodiment of the invention obtains the water engineering scheduling data, and the water engineering scheduling data comprises the following components: one or any combination of structured data, semi-structured data and unstructured data; extracting information from the water project scheduling data, and performing knowledge fusion processing on the extracted information; and constructing a knowledge graph corresponding to the water engineering scheduling data according to a knowledge fusion processing result. According to the embodiment of the invention, a large amount of manual analysis and experience are not required to be relied on, the information of the water engineering scheduling data is extracted, the extracted information is subjected to knowledge fusion processing, and then the knowledge map corresponding to the water engineering scheduling data is constructed according to the result of the knowledge fusion processing, so that the water engineering scheduling data is efficiently processed, the data processing efficiency is improved, manpower and material resources are saved, the data completeness is ensured, and the auxiliary scheduling service decision is facilitated.
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, 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 the drawings without creative efforts. In the drawings:
FIG. 1 is a schematic diagram of a method for processing scheduling data of water engineering according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a method for processing scheduling data of water engineering according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of knowledge fusion of a scheduling data processing method of water engineering according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a system architecture of a scheduling data processing method for water engineering according to an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating entity relationships of a scheduling data processing method for water engineering according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of data fusion of a scheduling data processing method of water engineering according to an embodiment of the present invention;
FIG. 7 is a schematic view of a scheduling event in a scheduling data processing method for water engineering according to an embodiment of the present invention;
FIG. 8 is a schematic view of a scheduling scheme of a method for processing scheduling data of a water project according to an embodiment of the present invention;
FIG. 9 is a schematic view of an intelligent question-answering perspective of a method for processing water engineering dispatching data according to an embodiment of the present invention;
fig. 10 is a structural diagram of a water engineering dispatching data processing device according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
First, terms referred to in the embodiments of the present application are described:
knowledge graph: knowledge Graph (Knowledge Graph) describes concepts, entities and relations among the concepts and the entities in an objective world in a structured form, expresses information of the internet into a form closer to the human cognitive world, and provides the capability of better organizing, managing and understanding mass information of the internet. The knowledge graph brings vitality to the internet semantic search, shows strong power in intelligent question answering, and becomes an infrastructure of internet knowledge-driven intelligent application.
Domain knowledge maps: the domain knowledge graph is also called as an industry knowledge graph or a vertical knowledge graph, generally faces a specific domain, and can be regarded as an industry knowledge base based on semantic technology.
Information extraction: information extraction is a technique for discovering entities (entries) and relationships (relationships) between entities from text.
And (3) knowledge fusion: the current internet big data has the characteristic of heterogeneous distribution, semantic annotation and linkage can be carried out on the data resources through a knowledge graph, and resource semantic integration service with knowledge as the center is established;
a body: ontologies are defined from the schema perspective (top-down) and are formalized expressions of domain-specific concepts and relationships between concepts, ontologies are abstractions of knowledge, and may be represented in the literature by Classes or Concept. Generally, the data management level is called "metadata", and the data management level is called "class" (abstract class) in image-plane object programming.
Entity: an entity refers to something that is distinguishable and exists independently. Such as a person, a city, a plant, a commodity, etc. Everything in the world is composed of specific things, which are referred to as entities. The entity is the most basic element in the knowledge graph, and different relationships exist among different entities.
The concept is as follows: concepts are a collection of entities with the same characteristics, such as countries, nationalities, books, computers, etc.
The attributes are as follows: attributes are features that are used to distinguish concepts, with different concepts having different attributes. Different attribute value types correspond to edges of different types of attributes. If the attribute value corresponds to a concept or an entity, the attribute describes the relationship between the two entities and is called an object attribute; if the attribute value is a specific numerical value, it is referred to as a data attribute.
As mentioned above, the existing water engineering scheduling data processing needs a lot of human analysis and experience, has low processing efficiency, consumes manpower and material resources, and is difficult to effectively accumulate due to lack of completeness of data to assist future scheduling business decision. The existing water engineering scheduling data processing method has the following problems:
1. fragmenting scheduling information of water engineering: aiming at one-time scheduling service, needed rain condition information, work condition dangerous condition information and forecast information need to be inquired through different systems, forecast scheme calculation, fax inquiry, engineering dangerous condition understanding and other processes, and are organized and summarized by workers, and support basin scheduling decision service information can be formed through artificial analysis processing and summary.
2. The water engineering scheduling decision is highly dependent on experience: the processes of occurrence, development, consultation, treatment, effect evaluation and the like of water engineering scheduling do not realize organized record, most of the processes exist in the brain of experts in an empirical mode or record main processes in a text description mode, data information lacks completeness, and the data information cannot be effectively accumulated to assist future scheduling business decision.
3. The query efficiency is low: the existing system provides a data query window for a user, and for the same scheduling service, a worker needs to open different query pages respectively to input keywords such as corresponding stations of rain conditions, water conditions and work conditions to query corresponding data respectively, and the data are summarized and sorted.
4. The information display mode is limited: the traditional information is mainly displayed in the form of maps and diagrams, the detailed information display of a certain specific data content is emphasized, the display of the association relation among data is lacked, the relation among various data needs to be established in the mind of a worker, and the experience dependence degree is high.
In recent years, the knowledge-graph technology is widely concerned by people as a new method for describing concepts, examples and relationships in an objective world, and the breadth of search results can be effectively expanded by using the knowledge-graph. The water conservancy object data-oriented knowledge graph construction method and the inference rule-based knowledge inference method realize intelligent data retrieval by using knowledge hidden in the water conservancy information knowledge graph, effectively utilize the relation between water conservancy objects and fully exert the value of water conservancy information resources. The knowledge graph changes the traditional mode of data storage by virtue of the basin, essentially changes the form of data organization, and completes the logical preservation of water industry data by utilizing the organization form of knowledge. The construction of the knowledge graph is based on an ontology, and a set of complete data ontology can carry out series conversion on different data forms and fuse the data forms into a comprehensive data system for use. The dynamic knowledge graph based on the ontology unifies data expression, flexible modeling of the same concept in various modes can be realized, object types, attributes and relationships are dynamically updated, and the relationship reasoning analysis can be dynamically calculated along with scenes. And finally, the quick fusion of the related multi-source heterogeneous data of the water engineering scheduling is realized, the normalization, the correctness, the integrity and the consistency of the data are improved, the data assets are optimized, the data are deeply processed, and the deeper data relation is excavated.
In order to efficiently process water engineering scheduling data, improve data processing efficiency, save manpower and material resources, ensure data completeness, and facilitate auxiliary scheduling service decision, an embodiment of the present invention provides a water engineering scheduling data processing method, which, as shown in fig. 1, may include:
102, extracting information of the water project scheduling data, and performing knowledge fusion processing on the extracted information;
and 103, constructing a knowledge graph corresponding to the water engineering scheduling data according to a knowledge fusion processing result.
As shown in fig. 1, in the embodiment of the present invention, by obtaining the water engineering scheduling data, the water engineering scheduling data includes: one or any combination of structured data, semi-structured data and unstructured data; extracting information from the water project scheduling data, and performing knowledge fusion processing on the extracted information; and constructing a knowledge graph corresponding to the water engineering scheduling data according to a knowledge fusion processing result. According to the embodiment of the invention, a large amount of manual analysis and experience are not required to be relied on, the information of the water engineering scheduling data is extracted, the extracted information is subjected to knowledge fusion processing, and then the knowledge map corresponding to the water engineering scheduling data is constructed according to the result of the knowledge fusion processing, so that the water engineering scheduling data is efficiently processed, the data processing efficiency is improved, manpower and material resources are saved, the data completeness is ensured, and the auxiliary scheduling service decision is facilitated.
In specific implementation, water engineering scheduling data is obtained, and the water engineering scheduling data comprises: structured data, semi-structured data, unstructured data, or any combination thereof.
In an embodiment, the semi-structured data comprises: GIS space data and/or business data. The method comprises the steps of preprocessing structured basic data, GIS spatial data, business data, unstructured data and other data with various sources and different structures, partitioning the data, performing entity linkage in parallel through load balancing, evaluating entities with high similarity according to similarity analysis of the entity data and other technical means, and fusing the entities.
And during specific implementation, extracting information from the water project scheduling data, and performing knowledge fusion processing on the extracted information.
In an embodiment, the extracting information of the water engineering scheduling data includes: and extracting entity information, relation information and attribute information of the water engineering scheduling data. In the embodiment, an industry semantic library and content analysis service are combined, entities (or ontology concepts), attributes and interrelations among the entities are extracted from various data sources such as the existing database, the flood prevention and drought control bulletin, the scheduling scheme and the emergency plan, and knowledge expression based on a semantic network is formed on the basis.
In an embodiment, the performing knowledge fusion processing on the extracted information includes: preprocessing the extracted information, the preprocessing comprising: data normalization processing and syntax normalization processing; partitioning the preprocessed information, and carrying out balanced load processing on each piece of information; carrying out entity linking and result evaluation on the information subjected to load balancing processing; and determining a knowledge fusion processing result according to the evaluation result.
In the embodiment, coreference resolution and entity disambiguation are carried out to eliminate contradiction and ambiguity and complete entity linking work; and selecting a proper third-party knowledge base product and the existing structured data to acquire and schedule related knowledge input, and enriching the knowledge base.
In this embodiment, assuming that the values of the records x and y of the two entities, x and y on the ith attribute are xi, yi, the record linkage is performed by the following two steps: 1. attribute similarity: and (5) integrating the similarity of the single attributes to obtain an attribute similarity vector [ sim (x1, y1), sim (x2, y2), … and sim (xN, yN) ]. The calculation of attribute similarity applies edit distance (character-based), set similarity calculation, or vector-based similarity calculation. 2. Entity similarity: and obtaining the similarity of an entity according to the attribute similarity vector, and carrying out knowledge shallow entry by adopting a knowledge representation learning-based method. Mapping the entities and the relations in the knowledge graph to low-dimensional space vectors, and directly calculating the similarity between the entities by using a mathematical expression, wherein a TransE model is adopted.
In this embodiment, the result evaluation is performed by measuring the accuracy, the recall rate, the F value, and the running time of the entire algorithm.
And during specific implementation, constructing a knowledge graph corresponding to the water engineering scheduling data according to a knowledge fusion processing result.
In the embodiment, aiming at the particularity of a water engineering scheduling service, the structural design of a knowledge graph reflects the protection relationship between a determined scheduling object (a reservoir, a diversion project, an important pump station, an important culvert, a sub-accumulation flood area and the like) and a related scheduling target node, the hydrologic element relationship between the scheduling object and the scheduling target node under a specific incoming water and scheduling scheme, the influence relationship between the upstream and downstream of a combined scheduling object and the like, a specific scheduling scheme, a scheduling effect relationship and the like. Besides the upper and lower relations of various concepts and subclasses thereof, a plurality of non-upper and lower ontology incidence relations can be sorted out in the ontology structure design, such as (station) - [ Located in ] - (administrative division) relation, (natural disaster) - [ Triggered ] - (human activity) triggering (trigger), and the like. The incidence relation design is combed from the objective physical existence of space, time and the like, the relation existing among various entities in the event development process and the relation of human activities influencing the physical world, and the relation (edge) in the knowledge graph is continuously expanded and enriched along with the extraction of the relation along with the expansion and the update of a business application scene. And combing a two-dimensional matrix of the relationship between the entities by the modes of relationship extraction, characteristic engineering labeling and the like, and finally obtaining the definition of the relationship between the entities. The data of different structures of multiple sources such as structured basic data, GIS spatial data, business data and unstructured data are partitioned after being preprocessed through normalized data, entity linkage is carried out in parallel through load balancing, and entities with higher similarity are fused after result evaluation according to technical means such as similarity analysis of the entity data, as shown in FIG. 2, the method specifically comprises the following steps:
1. information extraction: and (3) extracting entities (or ontology concepts), attributes and interrelations among the entities from various data sources such as the conventional database, a flood prevention and drought control briefing, a scheduling scheme, an emergency plan and the like by combining an industry semantic library and content analysis service, and forming knowledge expression based on a semantic network on the basis.
2. And (3) knowledge fusion: and cleaning and integrating the extracted information. Performing coreference resolution and entity disambiguation to eliminate contradiction and ambiguity and complete entity linking work; and selecting a proper third-party knowledge base product and the existing structured data to acquire and schedule related knowledge input, and enriching the knowledge base, as shown in fig. 3.
3. Knowledge processing: and finishing the scheduling construction of the ontology. Proceeding from the existing entity relation data in the knowledge base, carrying out computer reasoning and establishing new association among entities to expand and enrich the knowledge network; and performing quality evaluation on the formed knowledge and forming a quality evaluation report so as to ensure the quality of the knowledge base.
4. Knowledge graph analysis service: the method provides knowledge calculation algorithm service for meeting the deduction requirement of the scheduling case, and can provide more insights by aggregation on the graph in combination with other graph classical algorithms. Taking a scheduling event as a core, and associating the scenes of all time nodes related to the event; taking a scheduling scheme as a core, and associating various entities related to the scheduling scheme; and (4) understanding the requirements input by the user and returning and recommending the optimal information content by combining the natural language searching capability.
A specific embodiment is given below to illustrate a specific application of the method for processing water engineering scheduling data in the embodiment of the present invention. In this embodiment, the overall architecture of the system includes different levels of multi-source data sources, knowledge-graph services, business application demonstrations, users, and the like. The overall architecture of the system is shown in fig. 4. The water engineering scheduling data comprises structural data such as water conservancy space data, hydrological time sequence data and the like; the text data comprises unstructured data such as water engineering scheduling related emergency plans, scheduling schemes and the like; the domain knowledge comprises bulletin, report, water conservancy common knowledge and the like related to flood prevention. The knowledge graph service comprises four levels of knowledge acquisition, knowledge fusion, knowledge storage and analysis service. The knowledge acquisition part extracts entities and relations in structured data and unstructured data by means of NLP, space-time data fusion and the like. The entity relationships are shown in FIG. 5. The knowledge fusion part realizes fusion of spatial data, time sequence data and management information data through entity linkage, knowledge combination and the like, and realizes the goal of finally realizing insights from data to information to knowledge, as shown in fig. 6. The knowledge storage part comprises a knowledge representation part, a graph data part, a case knowledge base part and the like, and case knowledge related to water engineering scheduling, such as typhoon events, rainstorm events, flood events, scheduling events and the like, is stored in a graph database in the form of descriptive knowledge, procedural knowledge and the like by combining knowledge representation modes such as ontology design, seed dictionaries and the like. And the analysis service part is used for mining the common indexes of the scheduling cases through technologies such as graph mining, graph calculation and the like and recommending historical similar scheduling cases through similar measurement, a decision tree and the like. The service application demonstration mainly aims at the reality of water engineering scheduling services, provides functional services such as entity relationship visualization, a full-period scene type 360 view, consultation auxiliary decision, measure suggestion recommendation and the like, and assists the scheduling decision. The system provides application services for different management main users such as flood control scheduling, water quantity scheduling, water quality scheduling, water ecology scheduling, sediment scheduling, shipping scheduling and the like in water engineering scheduling by combining different scheduling objects such as single reservoir scheduling, reservoir group scheduling and the like.
The following description is made from a scheduling event perspective, a scheduling scheme perspective and an intelligent question and answer perspective respectively.
1. Scheduling an event view: associating event causes including the basis of scheduling objects, scheduling targets and forecasting nodes, monitoring and forecasting data related to the event causes; and the relevant scheduling working process reflects relevant data such as a scheduling behavior subject and an object, a scheduling consultation process, a scheduling scheme comparison and selection result, a scheduling effect evaluation, a scheduling order issuing process, a scheduling execution result and the like. Taking 2016 No. 01 flood scheduling event as an example, a flood scene node of 6 months and 25 days starts to search information such as working deployment conditions, monitoring and forecasting data, related scheduling schemes (including scheduling objects, targets, behaviors and the like), scheduling consultation processes, scheduling order issuing processes and the like at the current time through graph query. After inquiring about flood scenes of 25 days in 6 months, rainfall is concentrated in Han river, Qing river and Henhe at the middle and lower reaches, the cut water level of the reservoir at the upper reaches of Jinsha river, the inflow water of branch is increased, the Pengshui reservoir at Wujiang is close to the flood limit, the rainfall still exists in the forecast period, the water level of the Sanxia reservoir at 8 hours is 146.5m, and the water level of the Sanxia reservoir is 31000m when the Sanxia reservoir enters and leaves the reservoir respectively3/s，24200m3And/s, considering forecast rainfall, deciding to increase discharge capacity of the three gorges reservoir to 31000m3And/s, and issuing a dispatching order to the three gorges group. As shown in fig. 7.
2. From the perspective of the scheduling scheme: the data is constructed through a knowledge graph engine, rules in a scheduling scheme including scheduling targets, scheduling objects, scheduling conditions, scheduling behaviors, scheduling mechanisms and the like in the scheme are subjected to knowledge storage and association in a parameterized mode, and then the rules are used for supporting knowledge graph analysis services. In the analysis service application, retrieval and statistical analysis can be performed through a related graph algorithm and graph query, for example, monitoring data of the scheduling node is provided to match with a specific scheduling rule, including information such as a condition for triggering scheduling, a scheduling target, scheduling behavior and the like. Taking the compensation scheduling of los angeles as an example, after a query object is input, the system gives a compensation scheduling rule of los angeles: the method is characterized in that water is not large at the upstream of the Yangtze river, the three gorges reservoir does not need to store a large amount of flood control water for the river section of the Jingjiang river, the flood control situation near the Chengling rock is severe, and the water level of the three gorges reservoir is not higher than 155.0 m, the three gorges reservoir gives consideration to the flood control compensation scheduling for the river section of the Chengling rock, namely the flood control compensation scheduling is carried out according to the control that the water level of the Sanchi city is not higher than 44.50 m and the water level of the Chengling rock is not higher than 34.40 m. When the reservoir water level of the three gorges is higher than 155.0 meters, the river section of the Jingjiang river is switched to flood control compensation scheduling, and the river section of the Gong's rock is not subjected to flood control compensation scheduling any more. As shown in fig. 8.
3. Intelligent question-answering view angle: the system can combine the natural language and the services provided by the natural language search and the machine learning model to realize semantic analysis and match or recommend rules (including but not limited to scheduling targets, scheduling objects, scheduling conditions, scheduling behaviors, scheduling mechanisms and the like) in the corresponding scheduling scheme. For example: taking the three gorges reservoir as an example, Q: how to schedule the current water level of the three gorges reservoir of 168.3 meters, A: and (5) performing flood control compensation scheduling on the river reach of the Jingjiang river. When the water level of the three gorges reservoir is lower than 171.0 m, the water level of the sand city is controlled not to be higher than 44.50 m, as shown in figure 9.
The embodiment of the invention has the following beneficial effects:
1. the expression capacity of the relationship is strong: the traditional database is usually read in the modes of tables, fields and the like, the hierarchy and expression modes of the relationship are various, and based on graph theory and a probabilistic graph model, the complex and various association analysis can be processed, so that the analysis and management requirements of various role relationships of enterprises are met.
2. Analysis was done like human thinking: the interactive exploration type analysis based on the knowledge graph can simulate the thinking process of a person to discover, ask for evidence and reason, and even business personnel can finish the whole process without the assistance of professional personnel.
3. Knowledge learning: by utilizing the interactive machine learning technology, the learning function of interactive actions such as reasoning, error correction, marking and the like is supported, knowledge logic and models are continuously precipitated, the intelligence of the system is improved, the knowledge is precipitated, and the dependence on experience is reduced.
4. High-speed feedback: compared with the traditional storage mode, the data storage mode of the schema has the advantages that the data retrieval speed is higher, the graph database can calculate the attribute distribution of over millions of potential entities, the second-level return result can be realized, the real-time response of man-machine interaction is really realized, and the user can make a real-time decision.
Based on the same inventive concept, the embodiment of the present invention further provides a device for processing water engineering scheduling data, as described in the following embodiments. Because the principles for solving the problems are similar to the water engineering scheduling data processing method, the implementation of the device can refer to the implementation of the method, and repeated details are not repeated.
Fig. 10 is a structural diagram of a water engineering scheduling data processing apparatus in an embodiment of the present invention, and as shown in fig. 10, the apparatus includes:
a data obtaining module 1001, configured to obtain hydraulic engineering scheduling data, where the hydraulic engineering scheduling data includes: one or any combination of structured data, semi-structured data and unstructured data;
an extraction and fusion module 1002, configured to extract information from the water engineering scheduling data, and perform knowledge fusion processing on the extracted information;
and the map building module 1003 is configured to build a knowledge map corresponding to the water engineering scheduling data according to a knowledge fusion processing result.
In one embodiment, the semi-structured data comprises: GIS space data and/or business data.
In one embodiment, the extraction and fusion module 1002 is further configured to: and extracting entity information, relation information and attribute information of the water engineering scheduling data.
In one embodiment, the extraction and fusion module 1002 is further configured to:
preprocessing the extracted information, the preprocessing comprising: data normalization processing and syntax normalization processing;
partitioning the preprocessed information, and carrying out balanced load processing on each piece of information;
carrying out entity linking and result evaluation on the information subjected to load balancing processing;
and determining a knowledge fusion processing result according to the evaluation result.
In summary, in the embodiments of the present invention, by obtaining the water engineering scheduling data, the water engineering scheduling data includes: one or any combination of structured data, semi-structured data and unstructured data; extracting information from the water project scheduling data, and performing knowledge fusion processing on the extracted information; and constructing a knowledge graph corresponding to the water engineering scheduling data according to a knowledge fusion processing result. According to the embodiment of the invention, a large amount of manual analysis and experience are not required to be relied on, the information of the water engineering scheduling data is extracted, the extracted information is subjected to knowledge fusion processing, and then the knowledge map corresponding to the water engineering scheduling data is constructed according to the result of the knowledge fusion processing, so that the water engineering scheduling data is efficiently processed, the data processing efficiency is improved, manpower and material resources are saved, the data completeness is ensured, and the auxiliary scheduling service decision is facilitated.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A water engineering scheduling data processing method is characterized by comprising the following steps:
acquiring water engineering scheduling data, wherein the water engineering scheduling data comprises: one or any combination of structured data, semi-structured data and unstructured data;
extracting information from the water project scheduling data, and performing knowledge fusion processing on the extracted information;
and constructing a knowledge graph corresponding to the water engineering scheduling data according to a knowledge fusion processing result.
2. The method of claim 1, wherein the semi-structured data comprises: GIS space data and/or business data.
3. The method for processing water engineering dispatching data of claim 1, wherein extracting information from the water engineering dispatching data comprises: and extracting entity information, relation information and attribute information of the water engineering scheduling data.
4. The method of claim 1, wherein the knowledge fusion processing of the extracted information comprises:
preprocessing the extracted information, the preprocessing comprising: data normalization processing and syntax normalization processing;
partitioning the preprocessed information, and carrying out balanced load processing on each piece of information;
carrying out entity linking and result evaluation on the information subjected to load balancing processing;
and determining a knowledge fusion processing result according to the evaluation result.
5. A water engineering scheduling data processing device is characterized by comprising:
the data acquisition module is used for acquiring water engineering scheduling data, and the water engineering scheduling data comprises: one or any combination of structured data, semi-structured data and unstructured data;
the extraction and fusion module is used for extracting information from the water engineering scheduling data and performing knowledge fusion processing on the extracted information;
and the map construction module is used for constructing a knowledge map corresponding to the water engineering scheduling data according to the knowledge fusion processing result.
6. The water engineering dispatch data processing apparatus of claim 5, wherein the semi-structured data comprises: GIS space data and/or business data.
7. The water engineering scheduling data processing apparatus of claim 5 wherein the extraction and fusion module is further configured to: and extracting entity information, relation information and attribute information of the water engineering scheduling data.
8. The water engineering scheduling data processing apparatus of claim 5 wherein the extraction and fusion module is further configured to:
preprocessing the extracted information, the preprocessing comprising: data normalization processing and syntax normalization processing;
partitioning the preprocessed information, and carrying out balanced load processing on each piece of information;
carrying out entity linking and result evaluation on the information subjected to load balancing processing;
and determining a knowledge fusion processing result according to the evaluation result.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 4.
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