Supervision-oriented urban traffic situation semantic description method

Abstract

The invention discloses a supervision-oriented urban traffic situation semantic description method, which is used for carrying out situation semantic description on urban traffic and supporting automatic supervision and timely finding abnormal information. The method specifically comprises the following steps: (1) defining a supervision-oriented urban traffic situation as a six-tuple including a situation identifier, situation description, a situation environment, a supervision entity, a supervision relationship and a supervision rule; (2) defining a situation environment; (3) defining a supervision entity; (4) defining a supervision relation; (5) defining a supervision rule; (6) during application, extracting a situation environment and a supervision entity from the urban traffic scene, and performing situation supervision according to the supervision relation and the supervision rule; and (7) storing the constructed urban traffic description method by using a database, and performing data integration management. According to the invention, the complex scene can be defined through various urban traffic situations, the computable definition of the complex situation supervision scene is realized, and the urban traffic situation supervision cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of situation supervision, and in particular to a supervision-oriented urban traffic situation semantic description method. Background Art

[0002] The rapid development of information technologies such as cloud computing, artificial intelligence, and big data has brought new opportunities for urban traffic situation monitoring. However, as cities become more information-based, the complexity and uncertainty of the entire urban traffic system increase, posing significant challenges to situation monitoring. Currently, there is no unified standard for defining and representing urban traffic situation in China, which can lead to unexpected problems. There is an urgent need to develop key technologies for situation semantic modeling based on urban traffic data.

[0003] In urban traffic, situational monitoring refers to the use of advanced technologies to monitor, analyze, and manage traffic conditions on urban roads in real time to ensure efficient and safe traffic flow. This monitoring involves the collection, processing, and analysis of large amounts of data, including vehicle location, speed, traffic density, and information on various emergencies. By deploying sensors, cameras, and other monitoring equipment, traffic management centers can obtain real-time data and use data analysis techniques to predict and control traffic flow. The purpose of situational monitoring is to proactively identify potential traffic congestion and accident risks, allowing for timely adjustments to traffic signal operation or the issuance of traffic directives to optimize vehicle routing and improve traffic conditions. Furthermore, situational monitoring can assist in emergency response, such as rapid response to traffic accidents or natural disasters, ensuring the rapid passage of rescue vehicles, thereby maximizing public safety and smooth urban traffic flow.

[0004] Patent publication number CN115472003A discloses a multi-source information-based urban traffic monitoring system and method for analyzing and predicting traffic conditions on different sections of a city's roads. The system integrates road, weather, terrain, and drainage system data to construct a comprehensive urban road model. This method utilizes sensor-collected road and drainage pipe data to obtain real-time weather intelligence. Combined with historical traffic flow data, it predicts potentially affected sections of the road and assesses traffic pressure on alternative sections. By predicting traffic pressure during different time periods and implementing personnel deployment, the system can effectively evacuate congested vehicles, ensure smooth traffic flow, and improve the efficiency and responsiveness of urban traffic management.

[0005] The patent with publication number CN116052315A provides a smart city traffic supervision system, which combines a monitoring device, a terminal control device and a background server. The monitoring device includes an infrared temperature measurement component for detecting the body temperature of the person and activating the audio module through the internal control module to issue an alarm, and at the same time sends the abnormal body temperature information to the terminal control device and the background server so that the service personnel can respond in time. The system also includes a display screen to facilitate special hearing-impaired people to view body temperature information. In addition, the monitoring device is also equipped with a dilution box and a mobile box, as well as supporting facilities such as a sprayer, which is connected to the sprayer through a water pump system, thereby enhancing the functionality and operability of the system, aiming to improve the efficiency and responsiveness of urban traffic supervision while reducing the spread of health risks.

[0006] Establishing a city's traffic situation is crucial for modern urban management. By monitoring and analyzing urban traffic flows in real time, managers can obtain critical traffic data, enabling more effective resource allocation and decision-making. Furthermore, effective traffic situation monitoring enhances a city's sustainable development capabilities. Effective urban traffic management improves the quality of life of city residents and the overall attractiveness of the city. Therefore, establishing and maintaining an effective urban traffic situation system is crucial for achieving smart cities and improving urban management efficiency.

[0007] Therefore, developing a supervision-oriented semantic description method for urban traffic situation has important practical significance and application value. Summary of the Invention

[0008] In response to the shortcomings in this field such as complex and diverse situations and high supervision costs, the present invention provides a supervision-oriented urban traffic situation semantic description method, which can define complex scenarios through various urban traffic situations, realize the computable definition of complex situation supervision scenarios, reduce the cost of urban traffic situation supervision, and expand new directions for service scientific research.

[0009] A supervision-oriented urban traffic situation semantic description method is used to describe the situation semantics of urban traffic and support automatic supervision to detect abnormal information in a timely manner. Specifically, it includes:

[0010] (1) Define the urban traffic situation for supervision as a six-tuple where id f is the situation identifier, des is the situation description, env is the situation environment, is a collection of regulatory entities, is the set of regulatory relations, Φ is the set of regulatory rules;

[0011] (2) Define the situational environment env = (ES, ori, gra, EG, EW, EE), where ES represents the urban traffic area range matrix, ori = (lon, lat) represents the longitude and latitude coordinates of the origin of the urban traffic area, gra is the granularity of the longitude and latitude division, EG represents the topographic environment of the location, EW represents the weather environment of the location, and EE represents the signal shielding status of the location;

[0012] (3) Definition of regulatory entity Among them, id a ∈ID a Identifier representing the entity, ID a is the set of all entity identifiers, loc∈LOC represents the location of the entity in the situation matrix and must be within the city, aff∈ID a Link to another entity to indicate its affiliation. Represents the entity type, represents the vehicle type, π v Indicates the sensing range, the radiation radius with the entity as the center, in meters, π a Indicates the service range, the service capability radius with the entity as the center, in meters, v = (vx, vy) indicates the entity's moving speed in the east and north directions (in km / h), Indicates the service resource reserve. represents the state, i∈LOC represents the service intention;

[0013] (4) Define the supervisory relationship r = (id s ,id o ,T r ), where id s ,id o ∈ID a Represents the subject entity identifier and object entity identifier, T r For the relationship type, r represents id s For the subject, id o For objects, the relationship between the two is T r ;

[0014] (5) Define the supervision rule φ∈Φ={φ l ,φ e ,φ c ,φ m ,φ a ,φ s}, where φ l ,φ e ,φ c ,φ m ,φ a ,φ sThey represent location supervision rules, regional supervision rules, communication supervision rules, consistency supervision rules, behavior supervision rules, and attribute supervision rules respectively;

[0015] (6) When applied, the situation environment and regulatory entities are extracted from urban traffic scenarios, and situation supervision is performed based on regulatory relationships and regulatory rules;

[0016] (7) Use the database to store the constructed urban traffic description method and conduct data integrated management.

[0017] In step (2) where es nm Indicates whether the coordinate (n, m) position belongs to the city range and regional division. In particular, when es nm = -1, it means the location is not within the city; when es nm = 0, it means that the location is within the city, but has no clear ownership; for ease of understanding, it is agreed that DOM(X) represents the value range of the variable X. When es nm ∈DOM(a.aff), it means that the location is within the city and belongs to the regional range a.aff of entity a; LOC={(n,m)|es nm ≠-1} represents the location matrix coordinate set within the city.

[0018] Among them, e.g. nm =(egh,egg,egp),eg nm .egh represents the height information of the coordinate (n,m) position, the data type is integer, and the unit is meter (m). nm .egg represents the landform type at the coordinate (n,m). nm .egp represents the terrain pass rate at the coordinate (n,m) position. The data type is a floating point number and the value range is 0 to 1.

[0019] Furthermore, nm .egg represents the type of landform at the coordinate (n, m). It is a floating point number with a unitless range of 0 to 1. Different values represent different urban landforms. Specifically, coastal landforms have a range of [0, 0.2], valley landforms have a range of [0.2, 0.4], plain landforms have a range of [0.4, 0.6], hilly landforms have a range of [0.6, 0.8], and mountainous landforms have a range of [0.8, 1].

[0020] where ew nm =(ewt,eww,ewp),ew nm.ewt represents the temperature information at the coordinate (n,m), the data type is a floating point number, and the unit is degrees Celsius (℃). nm .eww indicates the weather type at the coordinate (n,m).ew nm .ewp represents the rate discount at the coordinate (n,m) position. The data type is a floating point number and the value range is 0 to 1.

[0021] Furthermore, ew nm .eww represents the weather type at the coordinate (n, m). The data type is a floating point number with a value range of 0 to 1 and no unit. Different values represent different city weather conditions. The value range for sunny is [0, 0.1], the value range for cloudy is [0.1, 0.2], the value range for overcast is [0.2, 0.3], the value range for rain is [0.3, 0.4], the value range for snow is [0.4, 0.5], the value range for fog is [0.5, 0.6], the value range for hail is [0.6, 0.7], the value range for sandstorm is [0.7, 0.8], the value range for thunderstorm is [0.8, 0.9], and the value range for tornado is [0.9, 1].

[0022] EE=(ee nm ) M*N ∈{0,1} M*N , where ee nm Indicates the signal shielding status of the coordinate position (n, m). The data type is an integer with a value range of 0 and 1. It has no unit. Different values represent different signal shielding states. 0 indicates that the coordinate has no signal shielding, and 1 indicates that the coordinate has signal shielding.

[0023] In step (3), Indicates the entity type, the data type is an integer, the value range is [0-7], no unit, in particular, 0 can be used to represent non-motor vehicles, 1 to represent private cars, 2 to represent buses, 3 to represent taxis, 4 to represent trucks, 5 to represent special vehicles, 6 to represent school buses, and 7 to represent others. Indicates the vehicle type. The data type is an integer with a value range of 0, 1, 2, or 3. It has no unit. In particular, 0 can be used to represent small, 1 to represent medium, 2 to represent large, and 3 to represent others. Indicates the status. The data type is an integer. The value range is 0, 1, and 2. It has no unit. In particular, 0 can be used to indicate driving, 1 to indicate waiting, and 2 to indicate others.

[0024] In step (4), the relationship type Different values represent different relationship types. The data type is an integer, and the value range is 0, 1, 2, and 3. It has no unit. In particular, 0 can represent formation, 1 represents competition, 2 represents cooperation, and 3 represents substitution.

[0025] In step (5), the location supervision rule φ l =(loc1,loc2,rad,reg b ), where loc1,loc2∈LOC∪{l|l=a.loc}, represents the city coordinates or entity coordinates, is the alarm threshold, reg b ∈{<,>,=,≠,≤,≥}, represents the supervision operator, when the formula dist(loc a ,loc b )reg b When rad is true, it means that the supervision has detected an exception, otherwise the exception does not exist, where dist(loc a ,loc b ) means loc a ,loc b Euclidean distance in a matrix.

[0026] Regional regulatory rulesφ e =(loc1,attr e ,θ e ,reg b ), where loc1∈LOC corresponds to a coordinate in the above situation environment, attr e ∈{egh,egg,egp,ewt,eww,ewp} represents the environmental attributes of supervision, θ e Indicates the regulatory threshold, reg b Represents a supervisory operator, when the formula env[loc:loc1].attr e reg b θ e If the result is true, it means that the exception is detected, otherwise the exception does not exist, where env[loc:loc1].attr e Represents the environmental attributes of the supervisory unit with coordinate loc1 in the situation environment.

[0027] Communications Regulatory Rules c =(loc1,reg e ), where loc1∈LOC corresponds to a coordinate in the above situation environment, reg e ∈{0,1} indicates whether the signal is shielded. The data type is an integer with a value range of 0 and 1. It has no unit. 0 indicates no signal shielding and 1 indicates signal shielding. When env[loc:loc1].ee=reg e When true, it indicates that an abnormality is detected; otherwise, no abnormality exists. env[loc:loc1].ee indicates the signal shielding status of the coordinate loc1 in the situation environment.

[0028] Consistency regulatory rulesφ m =(loc1,loc2,attr e ,reg b ,θ), where loc1,loc2∈LOC correspond to two coordinates in the above situation environment, attr e ∈{egh,egg,egp,ewt,eww,ewp,ee} represents the environmental attributes of supervision, reg b represents the supervision operator, and θ represents the supervision threshold. e -env[loc:loc2].attr e reg b θ e If the result is true, it means that the exception is detected, otherwise the exception does not exist, where env[loc:loc1].attr e Represents the environment attributes of the supervisor with coordinate loc1 in the situation environment, env[loc:loc2].attr e Represents the environmental properties of the supervisory unit with coordinate loc2 in the situational environment.

[0029] Conduct Regulation Rules a =(idr a ,len,reg a ), where idr a ∈ID a ∪DOM(T a ) represents the entity identifier or entity type of the regulated object, Indicates the regulatory threshold, the unit is time slice, reg a Indicates regulatory behavior, if the regulated entity or such entity behavior is reg a If the duration exceeds the regulatory threshold len, it means that there is behavioral abnormality, otherwise there is no behavioral abnormality; reg a Different values represent different regulatory behaviors. The data type is an integer, the value range is 0, 1, 2, 3, and it has no unit. 0 means stationary, 1 means speeding, 2 means driving against the flow, 3 means circular navigation, and 4 means deviating from the route.

[0030] Attribute supervision rule φ s =(attr s ,reg b ,θ s ), where attr s Indicates custom attribute values or statistical values that can be calculated in the model, including entity attributes, regional statistics, relationship statistics, entity statistics, etc. b represents the supervision operator, θ s Indicates the alarm value, when the type attrs reg b θ s If true, it indicates that the exception is detected, otherwise it does not exist.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. This invention overcomes the incompleteness of traditional semantic scene descriptions of urban traffic situations. By defining complex scenarios through a variety of urban traffic situations, it enables the computational definition of complex situation supervision scenarios, reduces the cost of urban traffic situation supervision, and opens up new directions for service scientific research. The system describes the situation environment, supervision entities, supervision relationships, and supervision rules, and combines them with case studies for application.

[0033] 2. This invention establishes comprehensive regulatory rules and supports customization of regulatory rules, describing various attribute anomalies. These rules will restrict and regulate various elements in the situation according to regulatory purposes and goals to ensure the stability and security of the situation;

[0034] 3. Compared to other methods, this semantic description of urban traffic situation transforms complex urban traffic data and information into easily understandable semantic descriptions, helping managers quickly and accurately grasp the current status and potential problems of urban traffic operations. This method effectively integrates data from various sources, providing real-time, meaningful insights and promoting the development of decision support systems. This description enables urban traffic managers to more effectively allocate resources, respond to emergencies, and plan long-term, thereby improving the efficiency and responsiveness of urban traffic management and ensuring smooth and safe urban traffic operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The figure is a flow chart of a method for describing urban traffic for situation supervision according to the present invention.

[0036] Figure 2 This paper provides a detailed definition of an urban traffic description method for situation monitoring in the present invention. DETAILED DESCRIPTION

[0037] The present invention will be described in further detail below with reference to the accompanying drawings and examples. It should be noted that the following examples are intended to facilitate understanding of the present invention and do not have any limiting effect on the present invention.

[0038] like Figure 1 and Figure 2 As shown in FIG, a method for describing urban traffic for situational supervision includes:

[0039] (1) Sort out the logic of the situation model, determine and describe the overall framework of the model, including situation identifier, situation description, situation environment, regulatory entity, regulatory relationship, and regulatory rules.

[0040] The urban traffic situation for regulation is defined as a six-tuple where id f is the situation identifier, des is the situation description, env is the situation environment, is a collection of regulatory entities, is the set of regulatory relations, and Φ is the set of regulatory rules.

[0041] (2) Define the main contents of the situation environment

[0042] env = (ES, ori, gra, EG, EW, EE), where ES represents the urban traffic area range matrix, ori = (lon, lat) represents the longitude and latitude coordinates of the origin of the urban traffic area, gra is the granularity of the longitude and latitude division, EG represents the geomorphic environment of the location, EW represents the weather environment of the location, and EE represents the signal shielding status of the location;

[0043] (3) Define the main contents of the regulatory entity

[0044] Among them, id a ∈ID a Identifier representing the entity, ID a is the set of all entity identifiers, loc∈LOC represents the location of the entity in the situation matrix and must be within the city, aff∈ID a A link to another entity indicates its affiliation; Represents the entity type, Indicates the vehicle type; π v Indicates the perception range, the radiation radius with the entity as the center; π a Indicates the service range, which is the radius of the service capability with the entity as the center; v = (vx, vy) represents the entity's movement speed in the east and north directions; Indicates the service resource reserve. represents the state, i∈LOC represents the service intention;

[0045] (4) Define the main contents of the supervisory relationship

[0046] r=(id s ,id o ,T r ), where id s ,id o ∈ID aRepresents the subject entity identifier and object entity identifier respectively; T r For the relationship type, r represents id s For the subject, id o For objects, the relationship between the two is T r ;

[0047] (5) Define the main contents of regulatory rules

[0048] φ∈Φ={φ l ,φ e ,φ c ,φ m ,φ a ,φ s}, where φ l represents the location supervision rule, φ e represents regional regulatory rules, φ c represents the communication supervision rules, φ m represents the consistency supervision rule, φ a represents the behavior supervision rules, φ s Represents attribute supervision rules;

[0049] (6) When applied, the situation environment and regulatory entities are extracted from urban traffic scenarios, and situation supervision is performed based on regulatory relationships and regulatory rules;

[0050] (7) Use the database to store the constructed urban traffic description method and conduct data integrated management.

[0051] Urban traffic situation supervision monitors and manages urban traffic in real time through analysis and prediction of urban traffic situation models, obtains situation factors such as the location, number, service capacity, and status of relevant vehicles, formulates corresponding supervision rules and supervision language for the description language of different situations such as the status and changes of urban traffic, and takes corresponding early warning, monitoring, dispatching and other measures according to the urban traffic situation, timely discovers emergencies, and formulates response strategies, so as to effectively and quickly respond to these emergencies and ensure the orderliness of urban traffic.

[0052] The following uses a virtual urban traffic scene as an example to specifically illustrate the technical solution of the present invention.

[0053] Here is a text description of a hypothetical urban traffic scene:

[0054] The city center is located at 39.991°N, 116.345°E, with an average altitude of 43.5 meters. The city is located in a plain area and is dotted with various radio transmission facilities, including radio and television transmission towers, mobile communication base stations, and satellite communication facilities.

[0055] The city's surface transportation system is a complex and dynamic network encompassing multiple modes of transport that interact on the city's streets, creating a dense pattern of traffic flows.

[0056] On a busy weekend in a busy city, Xiao Li was rushing to an important meeting in the city center, but found that all legal parking spaces were full. In desperation, he parked illegally in a no-parking zone, hoping to quickly conclude the meeting and leave. However, his behavior caused a localized congestion on the road, affecting nearby traffic flow. His car usually travels within 20 kilometers, mainly for commuting, but if he takes a break, he will park within 100 kilometers.

[0057] At that moment, Xiao Wang was giving a driving lesson at a nearby roundabout, repeatedly instructing his students in circling the road. Due to the congestion caused by Xiao Li's illegal parking, Xiao Wang and his students were forced to circle the intersection slowly, at a speed of approximately 15 km / h, increasing traffic pressure on the roundabout. Xiao Wang's car was an ordinary small car affiliated with the driving school, with a typical range of 100 kilometers.

[0058] Meanwhile, Xiao Zhao's bus had to reroute due to road maintenance on the main road ahead. His original route was 35 km long, a route he usually traveled. Today, he was supposed to take a 38 km detour, but that route had become even more congested due to Xiao Li's illegal parking and Xiao Wang's driving training. Xiao Zhao was forced to choose an alternate route through a narrower street, which further reduced his bus's speed from the normal 50 km / h on city roads to just 20 km / h.

[0059] It can be seen that there are many vague areas in the urban traffic scene described in this text. The information description is incomplete, and the core elements of the situation are not obvious, making it difficult to supervise and not conducive to urban managers to quickly discover traffic conditions.

[0060] The following is a description of the technical solution of the present invention.

[0061]

[0062]

[0063] (A) Situational Environment:

[0064]

[0065]

[0066] (B) Supervisory Entity 1:

[0067]

[0068]

[0069] (C) Supervisory Entity 2:

[0070]

[0071]

[0072] (D) Supervisory Entity 3:

[0073]

[0074] (E) Supervisory Relationship:

[0075]

[0076]

[0077] (F) Regulatory Rules:

[0078]

[0079]

[0080] Compared with other methods, the urban traffic description method for situational supervision involved in the present invention can help urban traffic managers in the area quickly understand the ground traffic conditions in the area and make reasonable management and control arrangements under the condition of mastering these core conditions. It is also beneficial for traffic managers in different areas to quickly grasp the traffic conditions in each area and thus cooperate with each other.

[0081] The embodiments described above provide a detailed description of the technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, supplements and equivalent substitutions made within the scope of the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A supervision-oriented urban traffic situation semantic description method, characterized by: Used to describe the semantics of urban traffic situations and support automatic supervision to detect abnormal information in a timely manner; specifically including: (1) Define the urban traffic situation for supervision as a six-tuple Among them, id f is the situation identifier, des is the situation description, env is the situation environment, is a collection of regulatory entities, is the set of regulatory relations, Φ is the set of regulatory rules; (2) Define the situational environment env = (ES, ori, gra, EG, EW, EE), where ES represents the urban traffic area range matrix, ori = (lon, lat) represents the longitude and latitude coordinates of the origin of the urban traffic area, gra is the granularity of the longitude and latitude division, EG represents the topographic environment of the location, EW represents the weather environment of the location, and EE represents the signal shielding status of the location; (3) Definition of regulatory entity Among them, id a ∈ID a Identifier representing the entity, ID a is the set of all entity identifiers, loc∈LOC represents the location of the entity in the situation matrix and must be within the city, aff∈ID a A link to another entity indicates its affiliation; Represents the entity type, Indicates the vehicle type; π v Indicates the perception range, the radiation radius with the entity as the center; π a Indicates the service range, which is the radius of the service capability with the entity as the center; v = (vx, vy) represents the entity's movement speed in the east and north directions; Indicates the service resource reserve. represents the state, i∈LOC represents the service intention; (4) Define the supervisory relationship r = (id s ,id o ,T r ), where id s ,id o ∈ID a Represents the subject entity identifier and object entity identifier respectively; T r For the relationship type, r represents id s For the subject, id o For objects, the relationship between the two is T r ; (5) Define the supervision rule φ∈Φ={φ l ,φ e ,φ c ,φ m ,φ a ,φ s }, where φ l represents the location supervision rule, φ e represents regional regulatory rules, φ c represents the communication supervision rules, φ m represents the consistency supervision rule, φ a represents the behavior supervision rules, φ s Represents attribute supervision rules; (6) When applied, the situation environment and regulatory entities are extracted from urban traffic scenarios, and situation supervision is performed based on regulatory relationships and regulatory rules; (7) Use the database to store the constructed urban traffic description method and conduct data integrated management.

2. The method for semantic description of urban traffic situation for supervision according to claim 1 is characterized in that: In step (2), where es nm Indicates whether the coordinate (n, m) position belongs to the city range and regional division; when es nm = -1, it means the location is not within the city; when es nm =0, it means that the location is within the city, but has no clear ownership; DOM(X) is the value range of the variable X. When es nm ∈DOM(a.aff), it means that the location is within the city and belongs to the regional range a.aff of entity a; LOC={(n,m)|es nm ≠-1} represents the set of position matrix coordinates within the city; Among them, e.g. nm =(egh,egg,egp),eg nm .egh represents the height information of the coordinate (n,m) position, eg nm .egg represents the landform type at the coordinate (n,m); nm .egp represents the terrain passing rate at the coordinate (n,m) position. The data type is a floating point number with a value range of 0 to 1. where ew nm =(ewt,eww,ewp),ew nm .ewt represents the temperature information at the coordinate (n, m) position. The data type is a floating point number and the unit is degrees Celsius. nm .eww indicates the weather type at the coordinate (n,m); ew nm .ewp represents the rate discount at the coordinate (n,m) position. The data type is a floating point number and the value range is 0 to 1. EE=(ee nm ) M*N ∈{0,1} M*N , where ee nm Indicates the signal shielding status of the coordinate position (n, m). The data type is an integer with a value range of 0 and 1. It has no unit. Different values represent different signal shielding states. 0 indicates that the coordinate has no signal shielding, and 1 indicates that the coordinate has signal shielding.

3. The method for semantic description of urban traffic situation for supervision according to claim 1 is characterized in that: In step (3), Indicates the entity type. The data type is an integer with a value range of [0-7] and no unit. 0 represents non-motor vehicles, 1 represents private cars, 2 represents buses, 3 represents taxis, 4 represents trucks, 5 represents special vehicles, 6 represents school buses, and 7 represents others. Indicates the vehicle type. The data type is an integer with a value range of 0, 1, 2, or 3. It has no unit and uses 0 for small, 1 for medium, 2 for large, and 3 for other. Indicates the status, has no unit, and the value range is 0, 1, and 2, including 0 for driving, 1 for waiting, and 2 for others.

4. The method for describing the semantics of urban traffic situation for supervision according to claim 1 is characterized in that: In step (4), the relationship type Different values represent different relationship types. The data type is an integer, and the value range is 0, 1, 2, and 3. It has no unit and uses 0 to represent formation, 1 to represent competition, 2 to represent cooperation, and 3 to represent substitution.

5. The method for describing the semantics of urban traffic situation for supervision according to claim 1 is characterized in that: In step (5), the location supervision rule φ l =(loc1,loc2,rad,reg b ), where loc1,loc2∈LOC∪{l|l=a.loc}, represents the city coordinates or entity coordinates; is the alarm threshold; reg b ∈{<,>,=,≠,≤,≥}, represents the supervision operator, when the formula dist(loc a ,loc b )reg b When rad is true, it means that the supervision has detected an exception, otherwise the exception does not exist, where dist(loc a ,loc b ) means loc a ,loc b Euclidean distance in a matrix.

6. The method for semantic description of urban traffic situation for supervision according to claim 1 is characterized in that: In step (5), the regional regulatory rule φ e =(loc1,attr e ,θ e ,reg b ), where loc1∈LOC corresponds to a coordinate in the above situation environment; attr e ∈{egh,egg,egp,ewt,eww,ewp} represents the environmental attributes of supervision, θ e Indicates the regulatory threshold, reg b Represents a supervisory operator, when the formula env[loc:loc1].attr e reg b θ e If the result is true, it means that the exception is detected, otherwise the exception does not exist, where env[loc:loc1].attr e Represents the environmental attributes of the supervisory unit with coordinate loc1 in the situation environment.

7. The method for semantic description of urban traffic situation for supervision according to claim 1 is characterized in that: In step (5), the communication supervision rule φ c =(loc1,reg e ), where loc1∈LOC corresponds to a coordinate in the above situation environment; reg e ∈{0,1} indicates whether the signal is shielded. The data type is an integer with a value range of 0 and 1. It has no unit, where 0 indicates no signal shielding and 1 indicates signal shielding. When env[loc:loc1].ee=reg e When true, it indicates that an abnormality is detected; otherwise, no abnormality exists. env[loc:loc1].ee indicates the signal shielding status of the coordinate loc1 in the situation environment.

8. The method for semantic description of urban traffic situation for supervision according to claim 1 is characterized in that: In step (5), the consistency supervision rule φ m =(loc1,loc2,attr e ,reg b ,θ), where loc1,loc2∈LOC correspond to two coordinates in the above situation environment, attr e ∈{egh,egg,egp,ewt,eww,ewp,ee} represents the environmental attributes of supervision, reg b represents the supervision operator, θ represents the supervision threshold; when env[loc:loc1].attr e -env[loc:loc2].attr e reg b θ e If the result is true, it means that the exception is detected, otherwise the exception does not exist, where env[loc:loc1].attr e Represents the environment attributes of the supervisor with coordinate loc1 in the situation environment, env[loc:loc2].attr e Represents the environmental properties of the supervisory unit with coordinate loc2 in the situational environment.

9. The method for semantic description of urban traffic situation for supervision according to claim 1 is characterized in that: In step (5), the behavior supervision rule φ a =(idr a ,len,reg a ), where idr a ∈ID a ∪DOM(T a ), representing the entity identifier or entity type of the regulated object; Indicates the regulatory threshold, the unit is time slice; reg a Indicates regulatory behavior, if the regulated entity or such entity behavior is reg a If the duration exceeds the regulatory threshold len, it means that there is behavioral abnormality, otherwise there is no behavioral abnormality; reg a Different values represent different regulatory behaviors. The data type is an integer, the value range is 0, 1, 2, 3, and it has no unit. 0 means stationary, 1 means speeding, 2 means driving against the flow, 3 means circular navigation, and 4 means deviating from the route.

10. The supervision-oriented urban traffic situation semantic description method according to claim 1 is characterized in that: In step (5), the attribute supervision rule φ s =(attr s ,reg b ,θ s ), where attr s Represents custom attribute values or statistical values that can be calculated in the model, including entity attributes, region statistics, relationship statistics, and entity statistics; reg b represents the supervision operator, θ s Indicates the alarm value, when the type attr s reg b θ s If true, it indicates that the exception is detected, otherwise it does not exist.

Images