CN113112790A - Urban road operation situation monitoring method combined with knowledge graph - Google Patents
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Abstract
The invention provides an urban road operation situation monitoring method combined with a knowledge graph, wherein the method comprises an urban road knowledge base construction method and an urban road operation situation monitoring method; the method comprises the steps of building a knowledge base of the urban road, storing data based on a graph mode, respectively collecting prior information and real-time operation data from text data, roadside sensing equipment and a vehicle terminal, extracting effective information from the data, converting the information into knowledge, building a knowledge entity, determining an attribute value of the entity, building an association relation for different entities through an algorithm, and building a knowledge graph based on the operation of the urban road, so that various evaluation indexes of road traffic operation are monitored. The method for monitoring the running situation of the urban road provided by the embodiment of the invention is used for calculating the condition change of real-time data based on the established urban road knowledge map so as to realize monitoring of the running situation of the road.
Description
Technical Field
The invention relates to the field of IT application and smart city construction, in particular to the field of business, data collection and situation monitoring of urban public infrastructure Internet of things.
Background
With the high-speed development of smart city construction, the intelligent capability is enabled to urban public management activities, so that the smart city management system has a good development prospect, a plurality of application requirements are generated on the basis, and roads, bridges and tunnels of cities are important scenes for smart city construction. The method mainly comprises the steps of monitoring the situation of the urban public key infrastructure, preventing potential dangerous situations of the infrastructure, converting real-time operation data of the public infrastructure and possible situations into knowledge rules in advance, monitoring the operation situation of the infrastructure based on a knowledge base, and calculating potential risks when the situations change. The method has the advantages that the urban public facility operation situation is monitored based on the knowledge graph, urban multi-source heterogeneous data can be better integrated and analyzed, deep association among events is completely described, and therefore the method is selected for use in our patent, and the decision-making capability of intelligent operation and maintenance is improved through mutual conversion between data, information and knowledge.
In the aspect of knowledge rule storage, a graph database Neo4j, an open source NoSQL graph database, is adopted, and unlike the data tables of a relational database, structured data is not stored in the data tables, but is stored in the form of nodes, attributes and relations. The graph database can rapidly calculate the offset of the nodes, edges and attributes when reading records, search query results efficiently and timely, and meanwhile, by using the graph database, the relationship between events can be better mined, the maximum effectiveness of data is fully exerted, and the graph database plays an extremely important role in situation monitoring.
In the prior art, the CN105023089B GIS platform based city management data monitoring system and method thereof designs a city management data monitoring system and method including city system service, Windows communication interface, and map service. The invention adopts distributed calculation, designs five service applications of a region statistical module, a classification statistical module, a trend analysis module, a trend statistical module and a data drilling module, requests a GIS platform and a service layer to acquire city management data in a database, performs data statistics and analysis, receives an instruction requested by the application layer at the service layer, and provides a communication interface and service required by the operation of a monitoring system. The GIS platform collects geographic information data of city management, stores the geographic information data to a corresponding database and a corresponding spatial data engine, provides a service interface for GIS data query, marking and positioning for related services in a service layer, analyzes and extracts valuable real-time data more intelligently, and shows system analysis results in various modes.
This patent is through collecting the real-time data at roadside equipment and vehicle terminal, handles the analysis to urban road traffic operation data, demonstrates monitoring result, broadcasting trend early warning. The implementation steps of the sensor equipment for fusing and sensing road data information are not the research focus of the patent, so the invention content mainly includes that data output by the sensor equipment are directly used, a knowledge base is built for the data, and the road running state and the road running trend are monitored by combining the basis.
Disclosure of Invention
The invention provides a road traffic operation situation monitoring method combined with a knowledge graph, which comprises the steps of extracting entities and attribute values by analyzing priori knowledge and real-time data, getting through the association between the data through an algorithm, showing the operation state of road facilities in the modes of graphs and the like, finding risks according to the real-time data, and determining an alarm range according to the association relation.
The invention provides a knowledge graph-combined urban road operation situation monitoring method, which comprises the steps of constructing a knowledge base of an urban road and monitoring the operation situation of the urban road;
the method for constructing the urban road knowledge base stores basic information of road facilities through prior knowledge such as literature data and the like, collects real-time data reported by road side equipment and real-time data reported by vehicle terminals, generates knowledge rules for storage, and constructs a knowledge map;
the text data knowledge conversion method is characterized in that the knowledge rules are extracted from text data by turning over document data such as encyclopedia introduction, national standard documents, professional papers and the like related to road traffic operation and applying word segmentation algorithm, matching rules and the like to obtain basic prior knowledge of road traffic facilities.
The method is used for collecting operation data of road side traffic facilities, acquiring information such as signal lamp time phase, road congestion statistics, identification signs and road abnormal conditions from sensing fusion equipment such as signal lamps, cameras and laser radars, converting the data information into road traffic knowledge, verifying and storing.
The vehicle terminal reports the real-time data analysis method, which is used for collecting the vehicle running real-time data, obtaining the information of the coordinates, running direction, speed and the like of the vehicle from the vehicle-mounted unit, forming the knowledge rule of the vehicle end, verifying and storing.
The module comprises algorithm model incidence relation mining, operation trend prejudgment and risk early warning;
and (3) digging an incidence relation of the algorithm model, establishing the incidence relation between road entities, acquiring real-time operation data from the road side sensing equipment and the vehicle-mounted unit, and taking the collected information as a knowledge rule to be updated, so that a user can more intuitively observe the incidence relation between discrete knowledge and monitor the road operation state in real time.
The method comprises the steps of pre-judging the operation trend and early warning the risk, finding the state change of the road public facilities in real-time data on the basis of data acquisition, knowledge conversion, algorithm model matching and incidence relation establishment, judging the risk hazard in the state change, finding the road entity associated with the risk from the incidence relation, generating early warning and informing the early warning, and providing support for analysis decision making of a user.
The urban road operation situation monitoring method combined with the knowledge graph provided by the embodiment of the invention can comprehensively show the road traffic operation and the operation states of all traffic participants, and realize the monitoring and prediction of urban road public facilities.
Drawings
FIG. 1 is a schematic structural diagram of a method for monitoring urban road operation situation by combining a knowledge graph according to an embodiment of the invention;
FIG. 2 is a data illustration of an embodiment of the present invention;
FIG. 3 is a schematic diagram of the overall framework of the urban road knowledge-graph of the invention.
Detailed Description
The technical method of the scheme of the invention is further elaborated by combining the drawings and the embodiment.
The embodiment of the invention provides a method for monitoring urban road running situation by combining a knowledge graph, which comprises the following steps: a knowledge base construction method of urban roads and an operation situation monitoring method of urban roads; the method for constructing the knowledge base of the urban road extracts prior knowledge from literature data, collects road operation conditions and operation data of road-side traffic participants from road-side sensors, reports vehicle-mounted units to related real-time data of vehicles, and establishes the incidence relation between road-side public facilities and road-side public facilities, between vehicles and road-side facilities and between vehicles through algorithm models such as a position matching algorithm, so that the road operation conditions are completely monitored, and risk pre-judgment and warning are performed on the road traffic facilities and the participants according to implementation data information.
Fig. 1 is a schematic structural diagram of a method for monitoring urban road operation situation in combination with a knowledge graph according to an embodiment of the present invention, and as shown in fig. 1, the method for monitoring urban road operation situation in combination with a knowledge graph according to an embodiment of the present invention specifically includes: a knowledge base construction method of urban roads and an operation situation monitoring method of urban roads. The method for constructing the knowledge base of the urban road comprises the steps of collecting priori knowledge, collecting roadside real-time data and collecting vehicle real-time data; the method for monitoring the running situation of the urban road comprises the steps of algorithm model incidence relation mining, running trend prejudgment and risk early warning.
In one embodiment of the method, the whole framework of the road knowledge graph is constructed on the basis of the geographical position of facilities, the prior knowledge of urban roads is identified and extracted from text data, and is directly extracted and used from structured data such as a map database interface and the like, and the mapping rule of a design field is directly stored; constructing labels according to similar semi-structured data introduced by open web pages and encyclopedia, establishing nodes one by one from the labels, assigning values for the attributes of the nodes, such as classification of roads, connection relation, basic construction, speed limit requirements, bridge navigation, earthquake-proof grade, platform-proof coefficient and the like, acquiring data through page analysis, and designing a data matching model for knowledge conversion; in the face of unstructured text data such as industrial standards, research documents and the like, words are mined and entities are identified by a natural language understanding mode through the steps of sentence breaking, word segmentation, part of speech tagging, syntactic analysis, specific entity identification and semantic analysis, and the basic representation model of the knowledge graph is formed by extracting the relationship between the entities through supervised learning.
In one embodiment of the method, the roadside sensing module collects road pavement traffic operating conditions. FIG. 2 is a schematic diagram of data and description of various sources in the present invention. As shown in fig. 2, the road conditions include information such as signal lights, road surface conditions, traffic flow, identification signs, road traffic participants, etc., and the information is counted and analyzed and converted into knowledge rules.
The signal lamp real-time condition monitoring comprises the step of monitoring data information such as phase value change, control range, phase start-stop time, residual time and the like of the signal lamp.
And monitoring the condition of the identification plate, namely monitoring the traffic digital plate on the road surface, and converting the traffic digital plate into a knowledge rule for storage.
And monitoring the road surface condition, including monitoring data information such as road traffic flow, congestion degree, accident detection, construction detection and the like, and occurrence position, severity degree, influence range, duration and the like.
Monitoring road traffic participants comprises monitoring data information of sensed vehicles, pedestrians, foreign bodies, coordinate positions, sizes, orientations and the like on a road.
In one embodiment of the method, the vehicle-mounted terminal module collects the real-time running condition of the registered vehicle, collects heartbeat data of the vehicle through the vehicle equipment unit, simultaneously comprises information such as the real-time coordinate position, the running direction, the running speed and the vehicle type of the vehicle, and converts the information into the knowledge rule of the vehicle end.
In one embodiment of the method, knowledge rules obtained by converting data acquired by the vehicle-mounted terminal and the road side equipment are respectively used as independent entities, the association relation between different entities is established through the calculation of a position matching algorithm model, and the association relation is stored in a graph mode to support the monitoring and control of real-time running condition data of road facilities and traffic participants. FIG. 3 is an overall framework of the urban road knowledge map in the present invention. As shown in fig. 3, in the process of establishing the association relationship by using the position matching algorithm model, all the road traffic entities form an association with each other through the intersection center.
In one embodiment of the method, the implementation of the operation trend prejudging and risk early warning module is based on data acquisition, entity identification, knowledge conversion and relationship establishment, data of each module are monitored in real time, data are associated and fused from a knowledge rule to be updated, potential risks during the operation condition change of the road facilities are calculated, entities associated with the risks are found from a knowledge graph according to the association relationship generated by an algorithm module, the operation trend is judged, and warning information is generated and informed; the method can broadcast the alarm information according to different angles such as entity dimension, regional dimension and the like, comprehensively shows the specific operation trend of the whole road and each facility, realizes quantitative statistics and evaluation of each monitoring index of urban road operation, and provides a basis for more effective decision making for the operation and maintenance of smart cities.
Claims (9)
1. A method for monitoring urban road operation situation by combining a knowledge graph is characterized by comprising the following steps:
constructing a knowledge base of the urban road and monitoring the running situation of the urban road;
constructing a knowledge base of the urban road, wherein the main content of the knowledge base comprises road basic information, roadside perception data and vehicle terminal data;
basic road information, namely, selecting points of roads in a research range, finding position coordinates of the selected points from a map interface, inquiring additional information of the roads, such as speed limit requirements, lane width, road length, road types and the like, from encyclopedic webpages according to road names, and establishing entity nodes according to the selected points to be researched;
a roadside device reporting real-time data analysis method is characterized in that roadside state information in road traffic operation is collected through roadside devices such as a signal machine, a camera and a laser radar, a roadside data base is provided for algorithm model incidence relation mining, and the main attributes of the roadside information comprise description of an event, longitude and latitude of the event, priority of the event, types of road traffic participants, operation speed of the road traffic participants, operation direction of the road traffic participants, phase values of signal lamps, time of current phase of the signal lamps and the like;
a real-time data analysis method for reporting of a vehicle terminal collects real-time data of vehicle operation through vehicle-mounted terminal equipment, the real-time data are used as an important participant of road traffic operation, a vehicle-side data basis is provided for algorithm model association relation mining, the data of a vehicle side comprise the operation position, the orientation, the driving speed, the brake condition, the abnormal condition and the like of the vehicle, an entity node is established according to each vehicle-mounted unit, and the vehicle-side data are used as additional attributes;
the method is characterized in that the running situation monitoring of the urban road is mainly characterized by algorithm model incidence relation mining, running trend prejudgment and risk early warning;
in the algorithm model incidence relation mining, collected information is used as a knowledge rule to be updated in the road traffic operation process, and roadside data and vehicle-end data are associated together according to a GeoHash algorithm, so that the incidence relation between discrete knowledge can be observed, and the real-time monitoring of the road operation state is realized;
pre-judging the running trend and early warning the risk, calculating the potential risk from the running state in the road public facilities, and carrying out alarm broadcasting in the action area;
the knowledge base construction of the urban road extracts knowledge rules from text data, determines a complete knowledge base framework of road facilities, collects road side operation data and vehicle operation data information from road side equipment and a vehicle terminal respectively, acquires an algorithm of an association relation from an algorithm model, associates all traffic participants of road operation together in order, comprehensively implements monitoring of road operation conditions, finds potential risk hazards from real-time data, finds a risk influence range through the association relation, broadcasts risk early warning to the range, and implements situation monitoring of the knowledge map on the road operation.
2. The method as claimed in claim 1, wherein all intersection nodes are found in the area of the range to be researched, each intersection node is taken as a selected point to be researched, the point to be researched looks up position coordinates from a coordinate picking interface, different road nodes are connected according to road names, terms of roads are found from encyclopedic, and encyclopedic web pages are analyzed to obtain road attribute values.
3. The method according to claim 1, wherein the roadside equipment mainly comprises roadside equipment such as a signal machine, a camera, a laser radar and the like, collects and uploads real-time state change information of a signal lamp, traffic flow detection, a sign board, road conditions and real-time information of traffic participants, and establishes nodes of all the roadside information in the knowledge graph.
4. The signal opportunity of claim 3 pushing signal light data for the roadway including attributes of a current phase value of the signal light, a remaining time of the signal light, a location of the signal light, a next phase of the signal light, and the like.
5. As described in claim 3, the video camera senses the road side data, and obtains the road traffic event data from the output of the video camera, including information such as description of the road traffic event, event type, occurrence location and influence range.
6. As described in claim 3, the lidar senses the road side data and obtains road traffic participant data from the output of the lidar, including attributes such as traffic participant type, participant position coordinates, heading, and speed of travel.
7. The method according to claim 1, wherein the vehicle-mounted terminal device installed on the vehicle collects real-time status data of road vehicle operation, including position, direction, speed and the like information, establishes nodes of all vehicle information in the knowledge graph, and takes the attribute values in the data as the attribute values of the entity nodes.
8. The method as claimed in claim 1, wherein after the system receives real-time data uploaded by a vehicle terminal and a roadside device, an entity node is constructed according to each piece of real-time data, an attribute value in the data is used as an attribute value of a node, a GeoHash code is calculated according to position coordinates in the attribute value, the longitude and latitude of the node are alternately divided, a region to be researched is determined to be located in which the coordinate falls, the region with a smaller longitude and latitude value is coded into 0, the region with a larger longitude and latitude value is coded into 1, the division is divided to the precision specified by us, Base32 coding is carried out on the divided binary data to obtain the GeoHash code, intersection nodes closest to the GeoHash code are found in nine regions including northwest, north, northeast, west, east, southwest and southeast of the region located by the GeoHash code, an associated edge between the two nodes is used as an association relationship, and according to a GeoHash association, the road traffic entities with discrete traffic are organically combined together, the relationship of the entities in the knowledge graph is established, and the road traffic operation condition is monitored.
9. The method of claim 1, wherein after receiving real-time data uploaded by the vehicle terminal and the roadside device, the system changes data of road conditions of each time into potential risks in road traffic operation, finds entities associated with the data through all association relations in the knowledge graph, generates early warning and broadcasts for vehicles and the roadside devices in an action area, and achieves prediction of road traffic operation trends.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113537647A (en) * | 2021-09-15 | 2021-10-22 | 深圳市光明顶照明科技有限公司 | Data processing method and system based on knowledge graph and readable storage medium |
| CN113837028A (en) * | 2021-09-03 | 2021-12-24 | 广州大学 | Road flow analysis method and device based on space-time knowledge graph |
| CN116403432A (en) * | 2022-12-05 | 2023-07-07 | 山东睿振建筑工程有限公司 | Smart city roadside parking space supervision system and method |
| CN117252449A (en) * | 2023-11-20 | 2023-12-19 | 水润天府新材料有限公司 | Full-penetration drainage low-noise pavement construction process and system |
| CN117455121A (en) * | 2023-12-19 | 2024-01-26 | 广东申创光电科技有限公司 | Information management method and system for intelligent road |
| CN117671979A (en) * | 2023-12-25 | 2024-03-08 | 中傲智能科技（苏州）有限公司 | Smart city data management system and method based on knowledge graph |
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