WO2011116673A1 - 逻辑拓扑网络模型、人工智能控制方法及人工智能系统 - Google Patents

逻辑拓扑网络模型、人工智能控制方法及人工智能系统 Download PDF

Info

Publication number
WO2011116673A1
WO2011116673A1 PCT/CN2011/071995 CN2011071995W WO2011116673A1 WO 2011116673 A1 WO2011116673 A1 WO 2011116673A1 CN 2011071995 W CN2011071995 W CN 2011071995W WO 2011116673 A1 WO2011116673 A1 WO 2011116673A1
Authority
WO
WIPO (PCT)
Prior art keywords
logical node
logical
node
starting
artificial intelligence
Prior art date
Application number
PCT/CN2011/071995
Other languages
English (en)
French (fr)
Inventor
林定伟
Original Assignee
Lin Dingwei
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from CN201010133556A external-priority patent/CN101827024A/zh
Priority claimed from CN201010170345A external-priority patent/CN101833282A/zh
Application filed by Lin Dingwei filed Critical Lin Dingwei
Priority to GB1219013.8A priority Critical patent/GB2493303A/en
Publication of WO2011116673A1 publication Critical patent/WO2011116673A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/12—Discovery or management of network topologies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/33—Director till display
    • G05B2219/33002—Artificial intelligence AI, expert, knowledge, rule based system KBS
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/026—Route selection considering the moving speed of individual devices

Definitions

  • the invention relates to a logical topology network model, an artificial intelligence control method and an artificial intelligence system.
  • the invention provides a logical topology network model, an artificial intelligence control method and an artificial intelligence system, which can simulate a person to solve daily problems.
  • a logical topology network model including: The logical subject with things, things, actions, etc. is the node of the network, and the causal link between the logical subjects is the arc.
  • An artificial intelligence control method includes: Step 1: acquire each logical node, connect a logical node having a causal relationship, and establish a logical topology network model; Step 2: Receive input text information, and scan a starting logical node and an ending logical node from the logical topology network model, where the starting logical node is a logical node including the text information, and the endpoint logical node is the starting point A logical node with a causal relationship; Step 3: Display the connection path between the scanned end point logical node and the starting logical node.
  • An artificial intelligence system comprising: A logical topology network model building module is configured to acquire each logical node, connect a logical node having a causal relationship, and establish a logical topology network model; a receiving module, configured to receive input text information; An execution module, configured to scan, according to the data instruction received by the receiving module, a starting point logical node and a destination logical node, where the starting logical node is a logical node including the text information, and the ending logical node has a logical node with the starting point The logical node of the causal relationship; and shows the connection path between the scanned end node logical node and the starting logical node.
  • the artificial intelligence control method and the artificial intelligence control system of the invention can establish a logical topology network model, scan the starting logical node and the ending logical node according to the text information input by the user, and display the scanned end point logical node and the starting logical node
  • the connection path, the connection path between the destination logical node and the starting logical node is a method for solving daily problems, and the user can find a solution to the daily problem according to the connection path between the displayed end point logical node and the starting logical node, and does not need Users think about how to solve problems themselves, and play a role in simulating people to solve everyday problems.
  • the entry of logical subjects and their connections mimics human “learning”.
  • FIG. 1 is a flowchart of an artificial intelligence control method of the present invention
  • 2 is a structural block diagram of an artificial intelligence system of the present invention.
  • the logic topology network model, the artificial intelligence control method and the artificial intelligence system of the invention adopt a logical subject such as a thing, a thing and an action as a node of the network, and a causal connection between the logical subjects is an arc; thereby simulating a logical thinking or actual state of the human being Model.
  • the starting logical node and the ending logical node are scanned according to the text information input by the user, and the connection path between the scanned end logical node and the starting logical node is displayed, and the ending logical node and the starting logical node are
  • the connection path is a method for solving daily problems. The user can find a solution to the daily problem according to the connection path between the displayed end point logical node and the starting logical node, and does not require the user to think about how to solve the problem, and plays a simulation solution. The role of everyday problems.
  • the artificial intelligence control method of the present invention includes: S101: Acquire each logical node, connect a logical node having a causal relationship, and establish a logical topology network model; the logical node may be a solution to the problem, or may be a logical node existing in an actual situation, and the logical nodes may be preset Constructing a logical topology network model by connecting logical nodes having a causal relationship (that is, from a derivable fruit or a fruit derivation) by means of a connection; S102.
  • the step S101 may further include: assigning a weight to the connection path between the logical nodes according to the difficulty achievement degree or the priority level between the logical nodes; that is, respectively assigning weights to the connection between the logical nodes, To show the degree of difficulty or priority between logical nodes;
  • step S103 the connection path between the scanned end point logical node and the starting point logical node is displayed, and the connection path with the smallest weight is displayed, or the scanning is performed in the order of the weights.
  • the connection path between the destination logical node and the starting logical node which makes it easy for the user to find the connection path with the smallest weight.
  • the step S102 may further include: collecting actual logical node information, and determining whether the scanned end node logical node actually exists;
  • the step S103 further includes: when it is determined that the scanned end point logical node does not actually exist, the connection path between the actually existing non-existing end point logical node and the starting logical node is cleared. In this way, the scanned end point logical node can be updated according to the actual situation, so as to avoid that the scanned end point logical node does not exist under actual conditions.
  • the step S102 may further include: collecting actual logical node information, and scanning, from the actual logical node, an intermediate logical node that has a NAND relationship with the scanned end point logical node;
  • Step 3 further includes: displaying a connection path between the intermediate logical node and the destination logical node. This way you can find out more about the connection path to solve the problem in more detail.
  • the present invention also discloses an artificial intelligence system, as shown in FIG. 2, including:
  • a logical topology network model building module is configured to acquire each logical node, connect a logical node having a causal relationship, and establish a logical topology network model; the logical node may be a solution to a problem, or may be a logical node existing in an actual situation.
  • a logical topology network model is formed by connecting logical nodes having a causal relationship (that is, from a derivable fruit or a fruit derivation) by means of a connection; a receiving module, configured to receive input text information; An execution module, configured to scan, according to the text information received by the receiving module, a starting logical node and a destination logical node, where the starting logical node is a logical node including the text information, and the endpoint logical node is configured with the starting logical node The logical node of the causal relationship; and shows the connection path between the scanned end node logical node and the starting logical node.
  • connection paths between the scanned end node logical node and the starting logical node.
  • the artificial intelligence control system of the present invention may further include an acquisition and determination module, and is connected to the execution module, and is configured to collect actual logical node information, and determine whether the scanned logical node of the destination actually exists, and if it does not exist,
  • the notification execution module clears the displayed connection path of the actual non-existing end point logical node and the starting logical node.
  • the collection and judgment module may include a device such as a sensor for collecting actual logical node information. In this way, the scanned end point logical node can be updated according to the actual situation, so as to avoid that the scanned end point logical node does not exist under actual conditions.
  • the collection and determination module may be further configured to collect actual logical node information, scan an intermediate logical node that has a NAND relationship with the scanned end point logical node from an actual logical node, and notify the execution module to display
  • the connection path between the intermediate logical node and the destination logical node is three kinds of relationships, namely, AND, OR, and NOT. One of the three relationships can be satisfied. Of course, two or three can be satisfied at the same time. This can further find the connection path to solve the problem in further detail. .
  • the execution module drinks water from the logical topology network model to find the starting logic including the text information drinking water according to the text information.
  • the node that is, the drinking water logical node, and continues to scan out the logical node of the end point with causal relationship with the drinking water logical node, namely, two logical nodes of boiled water and mineral water; and the logical nodes of the boiled water and mineral water scanned separately
  • the connection path between the water logical nodes is displayed; if the weight of the connection path between the plain water logic node and the drinking water logical node is 2, the weight of the connection path between the mineral water logical node and the drinking water logical node is 3, then Displayed according to the weight of the weight, that is, the connection path between the boiled water logic node and the drinking water logic node is displayed first, and then the connection path between the mineral water logical node and the drinking water logical node is displayed, and of
  • the execution module no longer displays the connection path between the boiled water logical node and the drinking water logical node, but only displays the mineral water logical node and drinks.
  • the connection path between water logical nodes When the actual logical node information is collected by the acquisition judging module and it is found that the boiled water logical node does not actually exist, the execution module no longer displays the connection path between the boiled water logical node and the drinking water logical node, but only displays the mineral water logical node and drinks. The connection path between water logical nodes.
  • an intermediate logical node having a NAND relationship with the scanned mineral water or the boiled water end point logical node is scanned from the actual logical node. If the logical node is in a relationship with the mineral water or the boiled water logical node, the connection path between the logical node of the cup and the mineral water or the boiling water endpoint logical node is displayed, so that the user can be told in detail how to drink water, that is, through the cup. Mineral water or boiled water.
  • the road section between two adjacent intersections is an arc (edge) of the electronic network diagram; the mileage of the arc, the appropriate speed of the interval, and the driving can also be defined.
  • Trackless vehicles can be provided with position tracking by a GPS receiving system. Since the rail vehicle runs in the line mode, the mileage meter or the driven wheel can be used to detect the mileage, and the mileage of the electronic network map is obtained, and the instantaneous location of the train is obtained, thereby achieving the purpose of tracking the train position.
  • the path finding algorithm calculates the most suitable path from the start point to the end point.
  • the automatic command (navigation) subsystem can take smaller values and interval mileage according to the appropriate speed of the interval and the rated running speed of the vehicle, and can calculate the traffic passage time period and the suitable time period of the intersection of the vehicle.
  • the execution unit here is the scheduling system, automatically runs the control system through the logical network to automatically select the most suitable path, that is, the shortest path or the most continuous path.
  • the scheduling system can control various units on the transportation network, such as vehicles and traffic facilities, and complete the function of automatic scheduling.
  • geographical location region, latitude and longitude
  • network wireless transmission network, GSM network, satellite communication network
  • network scheduling control can also be used to schedule information between various networks to achieve different traffic on land, sea and air. Network scheduling control.
  • passenger network carriers, freight network carriers and garbage sorting carriers have different priorities.
  • the order in which the passenger carriers are focused on is safety, timeliness, and economy.
  • the order in which the freight carriers are focused is safety, economy, and timeliness.
  • the order in which the garbage classification carriers are focused is safety and economy. Therefore, when selecting a path for a large-scale network, classifying and marking different types of carriers, and focusing on selecting paths, is undoubtedly a more appropriate solution to the problem.
  • the speed calculating unit in the operating system can calculate the running speed curve of the vehicle according to the speed limit requirements of each section, and submit it to the speed control unit, such as the frequency converter, the oil and gas gate control, the transmission, etc.
  • Directional control units such as gyroscopes, positioning systems, and altitude control units, such as altimeters, control the speed, direction, and altitude of the vehicle.
  • the system can accurately control the running speed, direction and height of the vehicle through the speed detecting unit, the direction detecting unit, the height detecting unit and the distance detecting unit, such as a range finder, via the signals fed back by the bus.
  • the existing automatic operation systems include: elevators and EMUs that are regulated by frequency modulation and voltage regulation; vehicles that control the speed of vehicles through throttles, valves and automatic transmissions.
  • Communication subsystem Since there are many operating units in the traffic network, the information transmission generally enters the information transmission network from the sending unit via the network adapter, transmits through the channels of various data transmission protocols, or switches between the networks through the gateway, and then arrives and receives through the network adapter. Unit, complete the communication process.
  • the wireless local area network is used as a transmission tunnel. Assume that the core message is not interpreted by the inter-network transmission protocol, but only the destination address and the source address are interpreted, and the link is composed of multiple transmission protocols.
  • the transmitting unit processes the information into packets of multiple frame headers according to the transmission protocol, and the innermost layer is a fieldbus message.
  • the wireless gateway enters the wireless local area network, and the original network frame data is used as a new network frame, and the destination address of the new destination gateway is interpreted according to the transmission protocol used by the new network. After transmission, and then arrive at the corresponding wireless gateway, the data packet that is restored to the same transmission protocol as the transmitting unit is transmitted to the receiving unit via the bus transmission, and then verified and operated.
  • the changing road conditions such as the traffic speed of the trackless traffic network
  • the system needs to constantly update the data in order to track the dynamic network.
  • the central control method monitored by the positioning system. After the vehicle obtains the location according to the positioning system, it feeds back to the control center through the data link. Through the data feedback from the vehicle, the control center can obtain information on the position, running speed, time consumption, destination and other road conditions of each road segment.
  • the traffic condition data is processed by the central control system of the dispatching system. For example, if the traffic time of the vehicles on a certain road is time-consuming, the system can adjust the corresponding amount on the corresponding arc to enable each vehicle to obtain a new optimal route and return the grooming information to avoid traffic. Blocked.
  • Data detection devices for vehicles are set up at the entrances or exits of the road sections, and the number of vehicles, the running speed, and the time-consuming data are transmitted to the data center via the data link.
  • the data center transmits the road condition information to the navigation or dispatching system of each vehicle, and is processed by the navigation or dispatching system. If the number of passing vehicles on a certain road will be saturated, the system can increase the corresponding amount on the corresponding arc, so that the system will increase the corresponding amount on the corresponding arc. Vehicles on this section get a new optimal route to divert the vehicle and avoid traffic jams.
  • the path finding algorithm can obtain the least.
  • the automatic identification system including feedback on weight measurement, volume and currency payment information, can form a public transport (linkage) automatic ticketing system and (linkage) logistics automatic pricing system, providing fully automatic Logistics solutions.
  • Each receiving, transmitting, and repeating device can be used as a node of the logical topology network model, and the channel between the ports is an arc (edge); the router obtains network connection, bandwidth, load, delay, packet loss rate, and packet type by each node.
  • the network model is updated, and the routing algorithm can be used to automatically run the control system through the logical network to obtain the optimal dynamic network transmission, that is, the link with the fastest transmission process and the lowest packet loss rate, and the data can be obtained.
  • Different types of transmission messages are handled in the information transmission network with different optimal transmission strategies to ensure network QoS. For example, the sampling data and control commands of the self-controlled network system need to be balanced in transmission speed and reliability to achieve the best control accuracy. Multimedia packets are more time-sensitive, while text packets are more reliable.
  • the above network operation system can be configured and controlled by central control mode, decentralized control mode or hybrid control mode.
  • the above describes the scheduling of the power operation network and the information transmission network.
  • the pressure transmission network detects different quantities according to different networks through network detection instruments.
  • the transmission network detection instruments are voltmeter, ammeter, electric meter, and gas network detection instrument.
  • the transmission network detection instruments are voltmeter, ammeter, electric meter, and gas network detection instrument.
  • infusion network testing instruments are pressure gauges, flow meters, thermometers, fed back to the control system via the data bus, automatically run the control system with a logic network, select the route to be transported, and control the current limiting components
  • the transmission network is a disconnect switch
  • the gas transmission and infusion network is a throttle valve to limit the transmission line.
  • the intelligent pressure network transmission of the quantitative and billing can be realized.
  • this system can also be used to achieve intelligent power supply for electrified track or pipeline traffic supply networks.
  • the power network As an example, as with the traffic grooming system, when the local power of the power network reaches the load limit of the transmission line, it is necessary to integrate more transmission lines or reduce the load.
  • the line between the active devices on the power transmission network such as the distribution transformer and the disconnect switch is the edge of the logical topology network and the transmission power on the line is used.
  • the power dispatch on the network can be realized.
  • the active device also includes a power generation system and establishes a linkage mechanism between the line network load and the power generation system, power supply on demand can be realized.
  • Minimally invasive surgery is a node with a logical topology network of cutting or suturing points.
  • the main organs, the areas between tissues, or the channels that are self-forming channels in the body are the arcs (edges) of the topological network, with the stroke and error allowed.
  • the range, the degree of damage, etc. are the quantities on the arc (edge).
  • the vascular dredge is a branch of the pipe network, and the pipe between the branches of the pipe network is an arc (edge), and the stroke or the degree of damage is the amount on the arc (edge).
  • the optimal path can be planned.
  • This logical network also has a good application for working components composed of a unit component such as a pixel constituting an image, a Hall element, and a MIC.
  • each component is used as a node of the logical network topology map, and adjacent nodes are connected by arcs to form a logical network lattice, and the components that define the front frame signal are used as the starting point, and the subsequent frame functions.
  • the component is the end point, and the logic network automatic operation control system according to claim 2 can directly obtain the change track of the action component; if various weight variables are assigned to the arc, the components can exhibit a gradual process, and The overall change of the active component within a certain range, the intelligent system can also be used to identify whether it is a reflection of a moving object.
  • more than one sensor or generator is used in combination to distinguish or synthesize the distance, direction, spatial position and speed of the signal source according to the exchange of time parameters and distance parameters between the received or transmitted signals between the devices. , running track and other effects.
  • each pixel can be used as a node of a logical network, and adjacent nodes are connected by edges to form a logical network dot matrix.
  • the pixels of the previous frame are defined as the starting point, and the pixels of the subsequent frame are the end points.
  • the path algorithm can directly obtain the change track of the pixel. If you assign a color scale variable on the edge, the process of gradation can appear between pixels. While the overall movement of the pixels within a certain range, the intelligent system can also be used to identify whether it is an image of a moving object. In the aspect of 3D image processing, such as shading adjustment, it is more necessary to simulate the causal reflection of the situation by the intelligent system.
  • This logical network system can also simulate various real-world situations, such as pharmacology, physics, chemistry, etc., to arrive at results and generate cross-domain solutions to problems.

Landscapes

  • Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Signal Processing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Software Systems (AREA)
  • Medical Informatics (AREA)
  • Evolutionary Computation (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Databases & Information Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)

Abstract

本发明公开了一种逻辑拓扑网络模型、人工智能控制方法及人工智能系统，所述方法包括：获取各个逻辑节点，连接具有因果关系的逻辑节点，建立逻辑拓扑网络模型；接收输入的文字信息，从所述逻辑拓扑网络模型中扫描出起点逻辑节点和终点逻辑节点，该起点逻辑节点是包括所述文字信息的逻辑节点，该终点逻辑节点是与所述起点逻辑节点具有因果关系的逻辑节点；显示扫描出的终点逻辑节点和起点逻辑节点间的连接路径。本发明可以建立逻辑拓扑网络模型，根据用户输入的文字信息扫描出起点逻辑节点和终点逻辑节点，该终点逻辑节点和起点逻辑节点间的连接路径即为解决问题的办法，起到了模拟人解决日常问题的作用。

Description

逻辑拓扑网络模型、人工智能控制方法及人工智能系统
技术领域
本发明涉及一种逻辑拓扑网络模型、人工智能控制方法及人工智能系统。
背景技术
在人的日常生活中，还未出现一种能模拟人解决日常问题的人工智能控制系统，一般都是需要用户自己去解决问题，碰到简单容易的问题还好解决，如果需要计算或选择的话就比较难，这种情况下就不容易找出解决方案。例如现有计算机都需要编程人员编写运行程序才能运作。这实质是编程人员经思考后产生的算法的固化，而非机器本身的“思考”所得。所以，现有的计算机还不是智能的机器，而只是人类智慧的执行器。为制造一种智能的机器帮助人类解决问题，就需要建立一种逻辑拓扑网络模型。
发明内容
本发明提供了一种逻辑拓扑网络模型、人工智能控制方法及人工智能系统，其能模拟人解决日常问题。
本发明的技术方案是：
一种逻辑拓扑网络模型，包括：
以事、物、动作等逻辑主体为网络的节点，逻辑主体间的因果联系为弧。
一种人工智能控制方法，包括：
步骤一、获取各个逻辑节点，连接具有因果关系的逻辑节点，建立逻辑拓扑网络模型；
步骤二、接收输入的文字信息，从所述逻辑拓扑网络模型中扫描出起点逻辑节点和终点逻辑节点，该起点逻辑节点是包括所述文字信息的逻辑节点，该终点逻辑节点是与所述起点逻辑节点具有因果关系的逻辑节点；
步骤三、显示扫描出的终点逻辑节点和起点逻辑节点间的连接路径。
一种人工智能系统，包括：
逻辑拓扑网络模型建立模块，用于获取各个逻辑节点，连接具有因果关系的逻辑节点，建立逻辑拓扑网络模型；
接收模块，用于接收输入的文字信息；
执行模块，用于根据所述接收模块接收的数据指令扫描出起点逻辑节点和终点逻辑节点，该起点逻辑节点是包括所述文字信息的逻辑节点，该终点逻辑节点是与所述起点逻辑节点具有因果关系的逻辑节点；并显示扫描出的终点逻辑节点和起点逻辑节点间的连接路径。
本发明的人工智能控制方法及人工智能控制系统，其可以建立逻辑拓扑网络模型，根据用户输入的文字信息扫描出起点逻辑节点和终点逻辑节点，并显示扫描出的终点逻辑节点和起点逻辑节点间的连接路径，该终点逻辑节点和起点逻辑节点间的连接路径即为解决日常问题的方法，用户即可根据显示的终点逻辑节点和起点逻辑节点间的连接路径找到解决日常问题的办法，不需要用户自己去思考如何解决问题，起到了模拟人解决日常问题的作用。对逻辑主体及其联系的录入就模拟了人类的“学习”。而对解决问题的思路的记录，就形成了人们常说的“经验”。在模型的弧（边）上赋上相应的值（如成本、优先级等），就形成了人类的“价值观”，进而更好地模拟人类的思想。而对思路的借鉴与逻辑推导就相当于人类的理论研究了。在文化、艺术方面对这种“经验”的借鉴与运用就能使受众产生逻辑共鸣，从而产生认同感。
附图说明
图1是本发明人工智能控制方法的流程图；
图2是本发明人工智能系统的结构框图。
具体实施方式
本发明的逻辑拓扑网络模型、人工智能控制方法及人工智能系统，其以事、物、动作等逻辑主体为网络的节点，逻辑主体间的因果联系为弧；从而模拟人类的逻辑思维或实际状态的模型。在建立逻辑拓扑网络模型后，根据用户输入的文字信息扫描出起点逻辑节点和终点逻辑节点，并显示扫描出的终点逻辑节点和起点逻辑节点间的连接路径，该终点逻辑节点和起点逻辑节点间的连接路径即为解决日常问题的方法，用户即可根据显示的终点逻辑节点和起点逻辑节点间的连接路径找到解决日常问题的办法，不需要用户自己去思考如何解决问题，起到了模拟人解决日常问题的作用。
下面结合附图对本发明的具体实施例做一详细的阐述。
本发明的人工智能控制方法，如图1，包括：
S101、获取各个逻辑节点，连接具有因果关系的逻辑节点，建立逻辑拓扑网络模型；该逻辑节点可以是解决问题的办法，也可以是实际情况下就存在的逻辑节点，可以预先设置好这些逻辑节点；通过连线的方式将具有因果关系（即从因可以推导果，或者从果推导因）的逻辑节点连接起来，从而组成逻辑拓扑网络模型；
S102、接收输入的文字信息，从所述逻辑拓扑网络模型中扫描出起点逻辑节点和终点逻辑节点，该起点逻辑节点是包括所述文字信息的逻辑节点，该终点逻辑节点是与所述起点逻辑节点具有因果关系的逻辑节点；
S103、显示扫描出的终点逻辑节点和起点逻辑节点间的连接路径。该扫描出的终点逻辑节点可以有多个，即显示扫描出的终点逻辑节点和起点逻辑节点间的连接路径可以有多个。通过该显示的连接路径即可找到解决问题的方法，从而可以模拟人解决日常问题。
在具体实施时，步骤S101，进一步可以包括：根据逻辑节点间的难易达成度或优先级别为逻辑节点间的连接路径赋上权值；即分别为逻辑节点间的连线赋上权值，以示逻辑节点间的难易达成度或优先级别；
此时步骤S103，具体可以为：显示扫描出的终点逻辑节点与所述起点逻辑节点的权值最小的连接路径，即将权值最小的连接路径显示出来，或者按权值的大小顺序显示扫描出的终点逻辑节点和起点逻辑节点间的连接路径，这样可以方便用户查找权值最小的连接路径。
在具体实施时，步骤S102，进一步可以包括：采集实际的逻辑节点信息，判断扫描出的终点逻辑节点是否实际存在；
此时步骤S103，进一步包括：在判断扫描出的终点逻辑节点实际不存在时，则清除显示的该实际不存在的终点逻辑节点与起点逻辑节点间的连接路径。这样可以根据实际情况来更新扫描出的终点逻辑节点，避免扫描出的终点逻辑节点在实际情况下不存在。
在具体实施时，步骤S102，进一步可以包括：采集实际的逻辑节点信息，从实际的逻辑节点中扫描出与所述扫描出的终点逻辑节点具有与或非关系的中间逻辑节点；与或非关系是包括三种关系即与、或、非，该三种关系有一个满足即可，当然也可以有两个或三个同时满足；
步骤三进一步包括：显示该中间逻辑节点和终点逻辑节点间的连接路径。这样可以进一步详细的找出解决问题的连接路径。
与上述本发明的人工智能控制方法对应，本发明还公开了一种人工智能系统，如图2，包括：
逻辑拓扑网络模型建立模块，用于获取各个逻辑节点，连接具有因果关系的逻辑节点，建立逻辑拓扑网络模型；该逻辑节点可以是解决问题的办法，也可以是实际情况下就存在的逻辑节点，通过连线的方式将具有因果关系（即从因可以推导果，或者从果推导因）的逻辑节点连接起来，从而组成逻辑拓扑网络模型；
接收模块，用于接收输入的文字信息；
执行模块，用于根据所述接收模块接收的文字信息扫描出起点逻辑节点和终点逻辑节点，该起点逻辑节点是包括所述文字信息的逻辑节点，该终点逻辑节点是与所述起点逻辑节点具有因果关系的逻辑节点；并显示扫描出的终点逻辑节点和起点逻辑节点间的连接路径。该扫描出的终点逻辑节点可以有多个，即显示扫描出的终点逻辑节点和起点逻辑节点间的连接路径可以有多个。通过该显示的连接路径即可找到解决问题的方法，从而可以模拟人解决日常问题。
在具体实施时，本发明的人工智能控制系统还可以包括采集判断模块，与执行模块连接，用于采集实际的逻辑节点信息，判断扫描出的终点逻辑节点是否实际存在，如果实际不存在，则通知执行模块清除显示的该实际不存在的终点逻辑节点与起点逻辑节点的连接路径。具体应用中，该采集判断模块可以包括传感器等器件，用于采集实际的逻辑节点信息。这样可以根据实际情况来更新扫描出的终点逻辑节点，避免扫描出的终点逻辑节点在实际情况下不存在。
另外，所述采集判断模块还可以用于采集实际的逻辑节点信息，从实际的逻辑节点中扫描出与所述扫描出的终点逻辑节点具有与或非关系的中间逻辑节点；并通知执行模块显示该中间逻辑节点和终点逻辑节点间的连接路径。与或非关系是包括三种关系即与、或、非，该三种关系有一个满足即可，当然也可以有两个或三个同时满足；这样可以进一步详细的找出解决问题的连接路径。
具体应用实施例：
在该实施例中，当用户向本发明的人工智能控制系统的接收模块输入文字信息喝水时；执行模块根据该文字信息喝水从逻辑拓扑网络模型中找出包括文字信息喝水的起点逻辑节点，即喝水逻辑节点，并继续扫描出与该喝水逻辑节点具有因果关系的终点逻辑节点，即白开水、矿泉水两个逻辑节点；并将扫描出的白开水、矿泉水逻辑节点分别与喝水逻辑节点间的连接路径显示出来；如果白开水逻辑节点和喝水逻辑节点间的连接路径的权值为2，矿泉水逻辑节点和喝水逻辑节点间的连接路径的权值为3，则可以按权值的大小依次显示，即先显示白开水逻辑节点与喝水逻辑节点间的连接路径，再显示矿泉水逻辑节点与喝水逻辑节点间的连接路径，当然也可以只显示白开水逻辑节点和喝水逻辑节点间的连接路径；
当通过采集判断模块采集实际的逻辑节点信息，发现白开水逻辑节点实际并不存在，则执行模块不再显示该白开水逻辑节点与喝水逻辑节点间的连接路径，而只显示矿泉水逻辑节点和喝水逻辑节点间的连接路径。
当通过采集判断模块采集实际的逻辑节点信息，从实际的逻辑节点中扫描出与所述扫描出的矿泉水或白开水终点逻辑节点具有与或非关系的中间逻辑节点，比如杯子逻辑节点，该杯子逻辑节点与矿泉水或白开水逻辑节点是与的关系，则将该杯子逻辑节点与矿泉水或白开水终点逻辑节点间的连接路径显示出来，这样可详细告诉用户如何去喝水，即通过杯子去取矿泉水或白开水。
显而易见，在陆路交通网络方面也能解决相关问题，其通过以下子系统实现：
1.自动指挥（导航）子系统。
a.在逻辑拓扑网络模型内扫描出陆路交通网络模型。此模型是以交通网络中道路的交点为此网络的节点，两相邻交点间的一条道路区间为电子网络图的一条弧（边）；还可定义弧的里程、区间适行速度、可行驶方向，拥堵级别等。
b.初始设置。把交通工具的所在位置标示在电子网络图的相对位置上，监控系统就获得交通工具的所在位置。
c.位置跟踪。无轨交通工具可由GPS接收系统提供位置跟踪。轨道交通工具由于以线路方式运行，还可用里程计提供里程数或从动轮检测里程数，对应电子网络图的里程数，获得列车的即时所在位置，从而达到列车位置跟踪的目的。
d.智能选径。以寻径算法算出由起点到终点的最适合路径。
自动指挥（导航）子系统可根据区间的适行速度及车辆的额定运行速度取较小值、区间里程，可算出车辆的路口通行占用时间段和区间适行时间段。在这些数据的基础上，执行单元，这里即为调度系统，通过逻辑网络自动运行控制系统就可自动选出最适合的路径了，即为最短的路径或最连续通行的路径。根据以上参数，调度系统可控制交通网络上的各单元，如车辆、交通通行设施，完成自动调度的功能。而在系统内以地理位置（地域、经纬度）或网络（有线传输网络、GSM网络、卫星通信网络）等划分还可对各种网络间的信息进行调度，以达成对陆、海、空不同交通网络的调度控制。
在交通网络中，也有与信息网络相同的最优运行策略问题。如客运网络载体、货运网络载体与垃圾分类载体的运行就有各自不同的侧重点。客运载体侧重的顺序是安全性、时效性、经济性，而货运载体侧重的顺序是安全性、经济性与时效性，垃圾分类载体侧重的顺序是安全性、经济性。所以在大型网络选择路径时，对不同类型的载体加以分类及标识，有侧重地进行选径，无疑是更贴切的解决问题的办法。
2.自动运行子系统。在接到调度系统运行指令后，运行系统内的速度计算单元就能根据各区间的速度限制要求计算出车辆的运行速度曲线，交由速度控制单元，如变频器、油气门控制、变速器等、方向控制单元，如陀螺仪、定位系统及高度控制单元，如测高仪，控制交通工具的运行速度、方向和高度。运行中，系统可通过速度检测单元、方向检测单元、高度检测单元及距离检测单元，如测距仪，经总线反馈的信号，精确控制交通工具的运行速度、方向和高度。现有的自动运行系统有：以调频调压调速的电梯、动车组；通过油门、气门及自动变速器控制车速的车辆。
3.通信子系统。由于交通网络中的运行单元很多，所以其信息传输一般从发送单元经网络适配器进入信息传输网络，经各种数据传输协议的信道传输，或经网关进行网络间的切换，再经网络适配器到达接收单元，完成通信过程。
在一般的长距离移动单元控制上，必须有无线传输信道才能组成有效的信息链路。但现在无线网络与有线“互联网”基本是分立的，手机、网络电视等上互联网只能通过指定的网关翻译才能上网，所以多网融合的互联网有更大的发展空间。但如果在多网融合的背景下，翻译传输方式就显得笨拙了，且保密性不好。
现以无线局域网为传输隧道为例：假设核心报文不经网间传输协议解释，而只解释目的地址和源地址，且链路由多个传输协议组成。发送单元按传输协议把信息加工成多重帧头的数据包，最内层为现场总线报文。经无线网关进入无线局域网，原网帧数据作为新网帧，根据新网络所使用的传输协议解释新目的网关的目的地址。经传输，再到达相应的无线网关后，还原成与发送单元相同的传输协议的数据包，经总线传输到达接收单元，再进行校验并运行。
但上面只针对路程一种情况作出选择。而随时变化的路况，如无轨交通网络的车流速度，就是动态网络。这样，系统就需要不断更新数据，才能跟踪动态网络。模式有二：定位系统监测及路况直接监测。
1、通过定位系统监测的中央控制方式。车辆根据定位系统获得所在位置后，通过数据链路，反馈给控制中心。控制中心通过车辆反馈的数据，就可获得各路段车辆的位置、运行速度、耗时、目的地等路况信息了。路况数据经过调度系统中央控制处理，如某段道路上车辆的通行耗时情况，系统就可调节相应弧上对应的量，使各车辆获得新的最优路线，返回疏导信息，就可避免交通阻塞。
2、通过路况监测的分散控制方式。在各路段出入口或路面设立交通工具的数据检测装置，交通工具的数量、运行速度、耗时等数据经数据链路传输至数据中心。数据中心将路况信息传输到各交通工具的导航或调度系统，经导航或调度系统处理，如某段道路上通行车辆的数量将达到饱和，系统就可增大相应弧上对应的量，使将要途径该路段的车辆获得新的最优路线，从而分流车辆，就可避免交通阻塞了。
而对于交通（物流）公司，只要将调度系统逻辑网络上路径，即一次无转运的运输或配送过程的量被定义为相应的配送费用或配送时段或时间，通过寻径算法，则可获得最少费用的配送路线及总费用或最快的配送路线及总时间。这样运输公司就可满足客户的不同需要；结合公司基本配送成本，就可统计出盈亏临界点。
在这基础上，只要结合自动识别系统，包括测重、体积及货币支付信息的反馈，就能构成公共交通的（联程）自动售票系统及（联程）物流自动计价系统，提供全自动的物流解决方案。
在通信领域：
各收、发、中继器均可作为逻辑拓扑网络模型的节点，端口间的信道为弧（边）；路由器由各节点获得网络连接、带宽、负载、时延、丢包率、数据包类型等信息，而对此网络模型进行更新，运用寻径算法，就可通过逻辑网络自动运行控制系统，获得动态网络传输最优，即传输过程最快、丢包率最低的链路，数据就可在信息传输网络内以不同的最优传输策略，应对不同类型的传输报文，从而保证网络QoS。如自控运行网络系统的采样数据及控制指令就需要在传输速度与可靠性上获得平衡才可达到最好的控制精度。多媒体数据包则较注重时效性，而文本数据包则较注重可靠性。
以上的网络运作系统可以中央控制方式、分散控制方式或混合控制方式进行组态控制。
以上叙述了动力运行网络及信息传输网络的调度，压力输送网络则根据不同网络通过网络检测仪器检测不同的量，这里输电网络检测仪器为电压计、电流计、电度计，输气网络检测仪器为压强计、流量计、温度计、湿度计，输液网络检测仪器为压强计、流量计、温度计，经数据总线反馈到控制系统，用逻辑网络自动运行控制系统，选择输送的路线，控制限流元件，其中输电网络为断路开关，输气输液网络为节流阀，以限制输送线路。在这个基础上，如在控制器设定检测仪器的限定触发值与节流阀或增压设备的连动，就可以实现定量、计费的智能压力网络输送。显然，对于电气化的轨道或管道交通供电网络亦可运用此系统达到智能供电的目的。
以电力网络为例，也如交通疏导系统一样，当电力网络的局部用电达到输送线路的负载上限时，就需要整合更多的输电线路或减少负载。以配电变压器、断路开关等电力输送网络上的作用器件间的线路为逻辑拓扑网络的边、线路上的输送功率为量，根据逻辑网络自动运行控制系统就可实现网络上的电力调度。如作用器件还包括发电系统，并建立线网负载与发电系统的联动机制，则可实现按需供电。
而在医用领域，也有很多应用，如微创手术、血管栓塞疏通等。
1）微创手术是以切割或缝合点为逻辑拓扑网络的节点，各主要脏器、组织间的区域，或在体内自成通道的管道为拓扑网络的弧（边），以行程、误差允许范围、伤害程度等为弧（边）上的量。
2）血管疏通是以管网的分支为节点，管网分支间的管道为弧（边），以行程或伤害程度为弧（边）上的量。
在以上的基础上，使用逻辑网络自动运行控制系统，就能规划出最优的路径。如结合超声波、X光、工作部信号源定位这些检测装置，及可控工作部这样的执行装置，就构成一种微创手术系统或血管疏通系统了。
对于以单位元件，如组成图像的像素、霍尔元件、MIC等集合组成的工作部件，此逻辑网络也有很好的应用。
对于各种集合元件组成的工作部件，把每个元件作为逻辑网络拓扑结构图的节点，相邻节点间由弧相连而形成逻辑网络点阵，定义前帧信号作用的元件为起点，后帧作用的元件为终点，通过权利要求2所述的逻辑网络自动运行控制系统能直接获得作用元件的变化轨迹；如果弧上赋上各种权值变量，则各元件间可呈现出渐变的过程，而一定范围内作用元件的整体变化，智能系统还可用于识别其是否为运动物体的反映。而在空间技术方面，一个以上的感应器或发生器组合使用，根据各器件间接收或发送信号的时间参数与距离参数的互换，可辨别或合成信号源的距离、方向、空间定位、速度、运行轨迹等效果。
以图形显示、处理为例：可把每个像素作为逻辑网络的节点，相邻节点间由边相连而形成逻辑网络点阵，定义前帧的像素为起点，后帧的像素为终点，通过寻径算法就可直接获得像素的变化轨迹。如果边上赋上色阶变量，则像素间可呈现出渐变的过程。而一定范围内像素的整体移动，智能系统还可用于识别其是否为运动物体的图像。在3D图像加工方面，如明暗度调节等，就更需智能系统对境况的因果反映模拟了。
这个逻辑网络系统还可对各种的实态情况进行模拟，如药理、物理、化学等，从而得出结果，对问题生成跨领域的解决方法。
以上所述的本发明实施方式，并不构成对本发明保护范围的限定。任何在本发明的精神和原则之内所作的修改、等同替换和改进等，均应包含在本发明的权利要求保护范围之内。

Claims (10)

  1. 逻辑拓扑网络模型的特征在于：以事、物、动作等逻辑主体为网络的节点，逻辑主体间的因果联系为弧。
  2. 一种人工智能控制方法，其特征在于，包括：
    步骤一、获取各个逻辑节点，连接具有因果关系的逻辑节点，建立逻辑拓扑网络模型；
    步骤二、接收输入的文字信息，从所述逻辑拓扑网络模型中扫描出起点逻辑节点和终点逻辑节点，该起点逻辑节点是包括所述文字信息的逻辑节点，该终点逻辑节点是与所述起点逻辑节点具有因果关系的逻辑节点；
    步骤三、显示扫描出的终点逻辑节点和起点逻辑节点间的连接路径。
  3. 根据权利要求2所述的人工智能控制方法，其特征在于：步骤二，按预定时间间隔接收输入信息，根据输入的信息更新扫描出的终点逻辑节点和起点逻辑节点间的连接路径。
  4. 根据权利要求2所述的人工智能控制方法，其特征在于：步骤一，进一步包括：根据具有因果关系的逻辑节点间的难易达成度或优先级别的各种考察量为逻辑节点间的连接路径赋上权值；
    步骤三，具体为：利用两点间寻径算法算出并显示终点逻辑节点和起点逻辑节点间的权值最小的连接路径，或者权值的大小顺序显示扫描出的终点逻辑节点与起点逻辑节点间的连接路径，还可根据不同组合的考察量选择对应的最短路径或后备路径作为最优路径。
  5. 根据权利要求2所述的人工智能控制方法，其特征在于：步骤二，进一步包括：采集实际的逻辑节点信息，从实际的逻辑节点中扫描出与所述扫描出的终点逻辑节点具有与或非关系的中间逻辑节点；
    步骤三进一步包括：显示该中间逻辑节点和终点逻辑节点间的连接路径。
  6. 根据权利要求2所述的人工智能控制方法，其特征在于：步骤三，对终点逻辑节点和起点逻辑节点间连接路径的记录。
  7. 一种人工智能系统，其特征在于，包括：
    逻辑拓扑网络模型建立模块，用于获取各个逻辑节点，连接具有因果关系的逻辑节点，建立逻辑拓扑网络模型；
    接收模块，用于接收输入的文字信息；
    执行模块，用于根据所述接收模块接收的数据指令扫描出起点逻辑节点和终点逻辑节点，该起点逻辑节点是包括所述文字信息的逻辑节点，该终点逻辑节点是与所述起点逻辑节点具有因果关系的逻辑节点；并显示扫描出的终点逻辑节点和起点逻辑节点间的连接路径。
  8. 根据权利要求7所述的人工智能系统，其特征在于，还包括采集判断模块，与执行模块连接，用于采集实际的逻辑节点及相连逻辑节点间的信息，判断扫描出的终点逻辑节点是否实际存在及逻辑节点间相连的成本，如果实际不存在，则通知执行模块清除显示的该实际不存在的终点逻辑节点与起点逻辑节点间的连接路径及显示终点逻辑节点与起点逻辑节点间的连接成本。
  9. 根据权利要求7所述的人工智能系统，其特征在于，还包括采集判断模块，与执行模块连接，用于采集实际的逻辑节点信息，从实际的逻辑节点中扫描出与所述扫描出的终点逻辑节点具有与或非关系的中间逻辑节点；并通知执行模块显示该中间逻辑节点和终点逻辑节点间的连接路径。
  10. 根据权利要求7所述的人工智能系统，其特征在于，上述各模块间的信息传输链路，由一个或多个传输协议组成，发送单元按传输协议把信息加工成多重协议格式的数据包，最内层为接收单元所用格式报文，经网关进行跨网传输，原网帧数据域作为新网帧，根据新网络所使用的传输协议解释新目的网关的目的地址，经传输，到达终端网关后，成为接收单元所用格式的报文传输到接收单元。
PCT/CN2011/071995 2010-03-24 2011-03-21 逻辑拓扑网络模型、人工智能控制方法及人工智能系统 WO2011116673A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
GB1219013.8A GB2493303A (en) 2010-03-24 2011-03-21 Logical topology network model, artificial intelligence control method and artificial intelligence system

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
CN201010133556.X 2010-03-24
CN201010133556A CN101827024A (zh) 2010-03-24 2010-03-24 一种网络路径查找方法、最优路径选择方法及其系统
CN201010170345.3 2010-04-30
CN201010170345A CN101833282A (zh) 2010-04-30 2010-04-30 一种人工智能控制系统及人工智能控制方法

Publications (1)

Publication Number Publication Date
WO2011116673A1 true WO2011116673A1 (zh) 2011-09-29

Family

ID=44672466

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2011/071995 WO2011116673A1 (zh) 2010-03-24 2011-03-21 逻辑拓扑网络模型、人工智能控制方法及人工智能系统

Country Status (2)

Country Link
GB (1) GB2493303A (zh)
WO (1) WO2011116673A1 (zh)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9258195B1 (en) 2012-08-08 2016-02-09 Shoretel, Inc. Logical topology visualization

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1684074A (zh) * 2004-04-14 2005-10-19 上海晖洋信息技术有限公司 基于城市路网结构的任意建筑物间最优路径选择方法
CN1837755A (zh) * 2005-03-22 2006-09-27 株式会社日立制作所 导航装置、导航方法、服务器装置以及导航信息发送系统
CN101102462A (zh) * 2007-01-04 2008-01-09 深圳清华大学研究院 基于手机电视安全模块的无线数字电视加密通信系统和工作方法
CN101827024A (zh) * 2010-03-24 2010-09-08 林定伟 一种网络路径查找方法、最优路径选择方法及其系统
CN101833282A (zh) * 2010-04-30 2010-09-15 林定伟 一种人工智能控制系统及人工智能控制方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1684074A (zh) * 2004-04-14 2005-10-19 上海晖洋信息技术有限公司 基于城市路网结构的任意建筑物间最优路径选择方法
CN1837755A (zh) * 2005-03-22 2006-09-27 株式会社日立制作所 导航装置、导航方法、服务器装置以及导航信息发送系统
CN101102462A (zh) * 2007-01-04 2008-01-09 深圳清华大学研究院 基于手机电视安全模块的无线数字电视加密通信系统和工作方法
CN101827024A (zh) * 2010-03-24 2010-09-08 林定伟 一种网络路径查找方法、最优路径选择方法及其系统
CN101833282A (zh) * 2010-04-30 2010-09-15 林定伟 一种人工智能控制系统及人工智能控制方法

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9258195B1 (en) 2012-08-08 2016-02-09 Shoretel, Inc. Logical topology visualization

Also Published As

Publication number Publication date
GB2493303A (en) 2013-01-30
GB201219013D0 (en) 2012-12-05

Similar Documents

Publication Publication Date Title
Rahmani et al. Path inference from sparse floating car data for urban networks
WO2015051718A1 (zh) 一种动态轨迹导航方法及云平台
CN103134498B (zh) 道路状态确定方法及装置、路径规划方法、导航系统
WO2015166876A1 (ja) 交通信号制御装置、交通信号制御方法、及びコンピュータプログラム
JP7207670B2 (ja) コネクテッド自動運転車のハイウエイシステムとそれを用いた方法
Buchholz et al. Handling occlusions in automated driving using a multiaccess edge computing server-based environment model from infrastructure sensors
CN107564310A (zh) 一种基于道路交通信息云处理的车路互联系统及方法
US20190172344A1 (en) System for forecasting traffic condition pattern by analysis of traffic data and forecasting method thereof
CN104319893A (zh) 基于agps与4g的输电线路gis系统及实现方法
WO2011076081A1 (zh) 逻辑网络自动运行控制系统及自动化控制系统及应用方法
Hu et al. Simulation-assignment-based travel time prediction model for traffic corridors
Sotelo et al. Introduction to the special issue on emergent cooperative technologies in intelligent transportation systems
Costanzo et al. Implementing Cyber Physical social Systems for smart cities: A semantic web perspective
CN112163701A (zh) 一种车站枢纽换乘管理方法及装置
CN111309845B (zh) 一种轨道交通路网宏观态势数据显示方法及系统
Knake-Langhorst et al. Test site AIM–toolbox and enabler for applied research and development in traffic and mobility
WO2011116673A1 (zh) 逻辑拓扑网络模型、人工智能控制方法及人工智能系统
CN109141422A (zh) 一种基于路边单元机器学习的车辆定位方法及系统
CN101840229A (zh) 逻辑网络自动运行控制系统及自动化控制系统及应用方法
US20170328946A1 (en) Method for operating a fault indicator
KR101112191B1 (ko) 에이전트 기반 실시간 통행시간 산정장치 및 그 방법
Ciuffo et al. Guest editorial introduction of the special issue on management of future motorway and urban traffic systems
Malena et al. Validation of an online state estimation concept for microscopic traffic simulations
Bolle et al. Early level 4/5 automation by restriction of the use-case
Agrawal et al. Intelligent Roadside Infrastructure for Connected Mobility

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 11758783

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 1219013

Country of ref document: GB

Kind code of ref document: A

Free format text: PCT FILING DATE = 20110321

WWE Wipo information: entry into national phase

Ref document number: 1219013.8

Country of ref document: GB

122 Ep: pct application non-entry in european phase

Ref document number: 11758783

Country of ref document: EP

Kind code of ref document: A1