GB2493303A - Logical topology network model, artificial intelligence control method and artificial intelligence system - Google Patents
Logical topology network model, artificial intelligence control method and artificial intelligence system Download PDFInfo
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Abstract
A logical topology network model, artificial intelligence control method and artificial intelligence system are disclosed by the present invention. The method includes: obtaining each logical node, connecting the logical nodes which are causally related to each other, and establishing the logical topology network model; receiving inputted text information, scanning a starting logical node and an ending logical node from the logical topology network model, wherein the starting logical node is the node including the text information, and the ending logical node is the node which is causally related to the starting logical node; displaying the scanned connection path between the ending logical node and the starting logical node. The present invention can establish a logical topology network model, can scan a starting logical node and an ending logical node according to text information inputted by users, wherein the connection path between the ending logical node and the starting logical node is the solution of a problem, having the effect of simulating human for solving daily problems.
Description
LOGICAL TOPOLOGICAL NETWORK MODEL, ARTIFICIAL INTELLIGENCE
CONTROL METHOD AND ARTIFICIAL INTELLIGENCE SYSTEM
FIELD OF THE INVENTION
The present invention relates to a logical topological network model, artificial intelligence control method and artificial intelligence system.
BACKGROUND OF THE INVENTION
In people's daily life, it has not yet appeared an artificial intelligence control system which is capable of simulating human to solve daily problems, instead the users usually have to solve the problems by themselves; it is OK for the machine to solve the simple and easy problems, but \vhen it comes to calculating and selecting, things would become difficult, \vhen it is not easy to find a solution. For example, it requires the programmers to write programs for the existing computers for operation. A program is indeed the solidification of algorithm achieved by the programmers' thinking other than the results of "thinking" of the machine. Therefore, the existing computers are not intelligent machines, but only actuators of the human wisdom. In order to manufacture an intelligent machine to help people solve problems, it is necessary to establish a logical topological network model.
SUMMARY OF THE INVENTION
The present invention provides a logical topological network model, artificial intelligence control method and artificial intelligence system which can solve daily problems by simulating human.
The technical solutions of the present invention are as following: A logical topological network model comprises: nodes of the network which are logical subjects such as events, objects and actions, and arcs indicating the causal relation between the logical subjects.
An artificial intelligence control method comprises: step 1: obtaining each logical node, connecting the logical nodes which are causally relatcd to each other, and cstablishing the logical topological network model; step 2: receiving inputted text information, scanning a starting logical node and an S ending logical nodc from the logical topological network niodcl dcscribcd above, wherein thc starting logical node is the node including text information, and the ending logical node is the node which is causally related to the starting logical node; step 3: displaying the scanned connection path between the ending logical node and the starting logical node.
An artificial intelligence system comprises: an establishing module for the logical topological network model, configured to obtain each logical node, to connect the logical nodes which are causally related to each other, and to establish the logical topological network model; a receiving module, configured to receive the inputted text information; an executing module, configured to scan the starting logical node and the ending logical end according to the data instruction received by the receiving module, wherein the starting logical node is a node including text information, and the ending logical node is a node which is causally related to the starting logical node; and to display the scanned path of the connection between the ending logical node and the starting logical node.
The artificial intelligence control method and artificial intelligence system of the present invention can establish the logical topological network model, scan the starting logical node and the ending logical node according to the text information inputted by users, and display the scanned connection path between the ending logical node and the starting logical nod, which is also the method for solving daily problems. Users can then fmd out the method for solving daily problems according to the displayed connection path between the ending logical node and the starting logical node; they don't need to think about how to solve problems by themselves. Therefore, the present invention can solve daily problems by simulating human.
Inputting the logical subjects and their relations simulates "learning" of human. while recording the thoughts for solving problems forms the commonly known "experience".
Assigning corresponding values (e.g. cost, priority) on the arcs (or edges) of the model forms "sense of values" of human, so as to simulate human thought better. And learning from the thoughts and the logical deduction are equivalent to human thcoiy research. Learning from such "experience" and the related application in the field of culture and art can make audiences produce logical resonation and result in a sense of identity.
BRIEF DESCRIPTION OF THE DRAWINGS
Fig. 1 is a flow chart of the artificial intelligence control method of the present invention.
Fig. 2 is a structural diagram of the artificial intelligence system of the present invention.
DETAILED DESCRIPTION OF TilE PREFERRED EMBODIMENTS
In the logical topological network model, artificial intelligence control method and artificial intelligence system of the present invention, logical subjects such as events, objects and actions are the nodes of the network, and causal relation of the logical subjects is the arc, the two of which form the model that simulates human logical thinking or actual state. After establishing the logical topological network model, the system scans the starting logical node and the ending logical node according to the text information inputted by users, and dispinys the scanned connection path between the ending logical node and the starting logical node, which is also the method for solving daily problems. Users can find out a method for solving daily problems according to the displayed connection path between the ending logical node and the starting logical node, and they don't need to think about how to solve problems by themselves; thereforc, the prcscnt invention plays a role in solving daily problems by simulating human.
The embodiments of the present invention will be explained in detail with reference to accompany drawings.
The artificial intelligence control method of the present invention as shown in Fig 1 includes: SiOl: obtaining each logical node, connecting the logical nodes which are causally related, and establishing the logical topological network model; the logical nodes can be either solutions to the problems, or the actually existed logical nodes which can be set up in advance; connecting the logical nodes which are causally related to each other (i.e. deriving a result from the reason, or deriving a reason from the result) by linking, so as to form the logical topological network model; SI 02: receiving inputted text information, scanning a starting logical node and an ending logical node from the logical topological network model described above, wherein the starting logical node is node including the text infonnation, and the ending logical node is node which is causally related to the starting logical node; S103: displaying the scanned connection path between the ending logical node and the starting logical node. There may be a multiple of the scanned ending logical nodes, i.e. a multiple of the scanned connection paths between the ending logical node and the starting logical node. To find out a method for solving problems according to the displayed 1 5 connection path can thus solve daily problems by simulating human.
In a specific embodiment, step 5101 may further comprise: assigning the weighted values to the connection paths among the logical nodes according to the difficulty degree of realization or priority level among the logical nodes, i.e. assigning the weighted values to the connecting lines between the nodes respectively, in order to display the difficulty degree of realization or priority level between the logical nodes; Under the circumstance, step S103 can be described as following: displaying the scanned connection path with a minimal weighted value between the ending logical node and the starting logical node, i.e. displaying the connection path which has the minimal weighted value, or displaying the scanned connection paths between the ending logical node and the starting logical node and listing them according to the order the weighted values, in order to facilitate the users to find out a connection path which has minimal weighted value.
In a specific embodiment, step S102 may further comprise: collecting information about the actual logical nodes, and determining whether a scanned ending logical nodes actually exists; Under the circumstance, step S103 further comprises: when a scanned ending logical node is determined as not existing, then deleting the displayed connection path between the 11011-existing ending logical node and the starting logical node. In that way the scanned ending logical nodes are updated according to the actual situation, so as to avoid the cases when a scanned ending logic node does not actually exist.
In an embodiment, step S102 thither comprises: collecting information about the actual logical nodes, and scanning from the actual logical nodes the intermediate logical nodes which have the logical AND, OR or NOT relations with the scarmed ending logical node; the logical AND, OR or NOT relations include three types of relations, i.e. "AND", "OR", and "NOT", among which it may be of one of the relations, surely it may also include two or three relations; Step SI 03 thither comprises: displaying the connection path between the intennediate logical node and the ending logical node, by which a connection path for solving problems can be found out in detail.
Corresponding to the artificial intelligence control method of the present invention as above described, the present invention also discloses an artificial intelligence system, as shown in Fig. 2, comprises: an establishing module for the logical topological network model, configured to obtain each logical node, to connect the logical nodes which are causally related, and to establish the logical topological network model; the logical nodes can be either solutions to the problems, or the actually existed logical nodes which can be set up in advance; connecting the logical nodes which are causally related to each other (i.e. deriving a result from the reason, or deriving a reason from the result) by linking, so as to form the logical topological network model; a receiving module, configured to receive the inputted text information; an executing module, configured to scan the starting logical node and the ending logical end according to the data instruction received by the receiving module, wherein the starting logical node is the node including text information, and the ending logical node is the node which is causally related to the starting logical node; and to display the scanned connection path between the ending logical node and the starting logical node. There may be a multiple of the scanned ending logical nodes, i.e. a multiple of the scanned connection path between the ending logical node and the starting logical node. To find out a method for solving problems according to the displayed connection path can thus solve daily problems by
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simulating human.
Tn a specific embodiment, the artificial intelligence system of the present invention may further include a collection and detenthnation module which is connected with the executing module to collect the information about the actual logical nodes and to determine whether the scanned ending logical nodes actually exist; if they do not actually exist, then the collection and determination module informs the executing module to delete the displayed connection path between the non-existing ending logical node and the starting logical node. In specific application, the collection and determination module may comprise devices like sensors to collect the information about the actual logical nodes. In that way the scanned ending logical node is updatcd according to the actual situation, so as to avoid the cases when a scanned ending logic node does not actually exist.
In addition, the collection and determination module described above can also be used to collect the information about the actual logical nodes, to scan from the actual logical nodes the intermediate logical nodes which have the logical AND, OR or NOT relations with the 1 5 scanned ending logical node, and to inform the executing module to display the connection path bctwccn the intermediate logical node and the ending logical nodc. The logical AND, OR or NOT relations include three types of relations, i.e. "AND", "OR", and "NOT", among which it may be of one relation, of course it may include two or three relations, by which a connection path for solving problems can be found out in detail.
Tn this embodiment, when the user inputs the text information " to drink water" into the artificial intelligence system of the present invention, the executing module will identify from the logical topological network model the starting logical node including the text information "to drink water", i.e. the logical node of "drinking water", and continue to scan the ending logical node which is causally related to the logical node of "to drink water", i.e. the two logical nodes of "plain boiled water" and "mineral waterS'; then display respectively each scanned connection path between the logical node of"to drink water" and the logical node of "plain boiled water" or "mineral water"; if the weighted value of the connection path between the logical node of "plain boiled water" and the logical node of"to drink water" is 2, whereas the weighted value of the connection path between the logical node of "mineral water" and the logical node of"to drink water" is 3, then display the connection paths according to the order of weighted values, i.e. firstly the connection path between the logical node of "plain boiled water" and the logical node of "to drink water" and secondly the connection path between the logical node of "mineral water" and the logical node of "to drink water"; optionally it can oniy display the connection path between the logical node of "plain boiled water" and the logical node of"to drink water".
If, by collecting the information about the actual logical nodes, the collection and determination module finds that the connection path between the logical node of "plain boiled water" does not exist, then the executing module will not display the connection path between the logical node of "plain boiled \vatcr" and the logical node of "to drink water" anymore, but only display the connection path between the logical node of "mineral water" and the logical node of"to drink water".
IL by collecting the information about the actual logical nodes, the collection and determination module scans out among the actual logical nodes the intermediate logical nodes which have the logical AND, OR or NOT relations with the scanned ending logical node of "plain boiled water" or "mineral water", e.g. the logical node of "cup", which has the relation of "AND" with the ending logical node of "plain boiled water" or "mineral water", 1 5 then display the connection path between the logical node of "cup" and the ending logical node of "plain boiled water" or "mineral water", which can inform the user how to drink water in detail, i.e. to get mineral water or plain boiled water by a cup.
Obviously, relevant problems in the land transportation network can also be solved by the following subsystem; 1. Automatic command (navigation) subsystem.
a; Scan the land transportution network model in the logical topological network model.
By the road intersections in the transportation network as nodes and road interval between two adjacent intersections as an arc (or edge) of the electronic network diagram of the network, said model is formed. The mileage of the arc, appropriate speed during intervals, available travel direction, and congestion level can also be defined.
b; Initial setting. By marking the location of the transportation vehicle on relative position of the electronic network map, the monitoring system obtains the location of the transportation \Tehieles c; Location tracking. The location tracking of railless transportation vehicles is provided by the GPS receiving system. Because the rail transportation vehicles run on line roads, the real-time location of the train can thus be obtained using mileage counted by odometer or monitored by driven wheels which corresponds to the mileage of the electronic network map, so as to achieve thc goal of location tracking of the train position.
d: Intelligence route selection. Select the optimal route from the starting position to the final position using route algorithm.
According to the appropriate speed of the inteival and the vehicle's rated speed, the automatic command (navigation) subsystem can select the smaller values and interval mileage to calculate crossroad occupation time and appropriate speed during interval s. On the basis of these data, the executing unit, i. e. the scheduling system, can automatically select by the automatic operating control system of the logical network the most suitable route, i.e. the shortest route or the most continuous passing route. Based on the above parameters, the scheduling system can control each unit of the transportation network, such as vehicles and traffic passing facilities, so as to complete the thnction of automatic scheduling. By dividing different transportation networks according to geographical locations 1 5 (region, latitude and longitude) or telecommunication networks (wired transmission network, OSM network, satellite communication network), information across different networks can also be scheduled in order to control the scheduling of different transportation networks including land, sea and air.
In the transportation network, there is also such problem of optimal operating strategy as in the information network. For example, the focusing points are different among operation of the passenger transportation network carrier, freight network carrier and garbage classification carrier. The passenger transportation network carrier emphasizes on the order of safety, timeliness and economy, while the freight can-icr emphasizes on the order of safety, economy and timeliness, and the gathage classification carrier emphasizes on the order of safety and economy. Therefore, when it comes to selecting paths in large-scale networks, classification and identification of different types of carriers helps to choose with an emphasis, which is definitely a more practical solution.
2. Automatic operating subsystem. After receiving the operating instructions from the scheduling system, the speed calculation unit within the operating system can calculate vehicle's running speed curve according to the speed limit requirement of each interval, and send it to the speed control unit such as the frequency converter, oil valve control and
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transmission, and send it to the direction control unit such as gyroscopes and positioning system, and also send it to height control units such as the altimeter, which are used to control running speed, dircetion and height of the vehicle. When running, the system can precisely control thc running speed, direction and height of the transportation vehicle by the speed detecting unit, the direction detecting unit, height detecting unit and the distance detecting unit such as a range finder, through the bus feedback signal. The existing automatic operating system includes elevators and bullet trains with FM, voltage and speed variation controls, and vehicles with speed controlled by the throttle, engine valve and automatic transmission.
3. Communication subsystem. Because there are many operating units in the transportation nctwork, the information is generally transmitted from the sending unit to the information transmission network via the network adapter. which travels through channels of various datum transport protocols, or switched between networks by gateway, then transmitted to the receiving unit via the network adapter to complete the communication process.
1 5 Tn the control of common long-distance mobile unit, in order to form an effective information link, there must be a wireless transmission channel. But now the wireless network and wired "Internet" are generally separated, and mobile phone and network TV can access to the Internet only when translated by the specified gateway therefore, there is a broad prospect for the development of the Internet of multi-network integration. However, in the context of multi-network integration, the transmission using the method of translation is clumsy with low confidentiality.
Now take the wireless local area network (LAN) as the transmission tunnel as an example: assuming that the core message is not explained by network transport protocol, instead only the destination and origin addresses are explained, and the link is composed of a plurality of transport protocols. The sending unit processes the information into multiple-framed header packet according to the transport protocol, of which the innermost layer is the field bus message. After getting into the wireless LAN through the wireless gateway, the original network frame data becomes a new network frame, and the destination address of the new destination gateway is explained according to the transportation protocol used by the new network. Upon transmission, the data packet arrives at corresponding wireless gateway, and is restored to the data packet which has the same transport protocol with the sending unit, and arrives at the receiving unit through bus transmission, and then is checked and operated.
However, the choice described above is only made for the single situation of route. As for the changing road conditions such as the traffic speed in the railless transportation network, it comes to dynamic network. Thus, in order to track the dynamic network, the system needs to update its data constantly. There are two types of models: monitoring by positioning system and direct monitoring of traffic condition.
I. Central control mode monitored by positioning system. Thc vehicle obtains its location by the positioning system, and feedbacks the data to the control center via the data link. Based on tile data received from the vehiclc, the control center can obtain traffic infonnation including the location, speed, time consumption and destination of vehicles in each interval. Traffic data are processed by the central control of the scheduling system, e.g. time consumption by vehicles when passing through a certain road, and the system can adjust the corresponding vector of the respective arc, so that each vehicle will obtain a ncw optimal route, while traffic obstruction can be avoided by feedback of the information of traffic dispersion.
2. Decentralized control mode by monitoring road condition. The traffic data detecting devices are provided in the exit and entrance of each interval or on each road, which transmits data including the number of vehicles, the running speed and time consumption to the datum center via data link. The traffic information is then transmitted by the datum center to the navigating or scheduling system in each vehicle, and is processed by the navigating or scheduling system. If the number of vehicles on a certain road is to be maximum, then the system can increase the corresponding vector in the corresponding arc, which provides vehicles that are going to pass through the road updated optimal route, so as to avoid traffic jam by allowing traffic dispersion.
For transportation (or logistics) companies, as long as the scheduling system is combined with the logical network path, i.e. a non-secondary haulage or distribution process is defined with a vector of corresponding distribution cost or distribution period or time, the cheapest distribution line and or the fastest distribution time and its total time can be obtained via routing algorithm. Thus, the transportation company can meet different needs of customers, and calculate the break-even point combined with the basic distribution cost.
On this basis, as long as the automatic identification system is combined, together with the information feedback of weight, volume and monetary payment, then the (linkage) automatic ticketing system and (linkage) logistics automatic valuation system of public transportation can be formed, which provides an automated logistics solution plan.
In the field of communications:
S The receiver, the sender and the repeater can be used as the nodes of the logical topological network model, and the information channel between ports can be the arcs(edge); the router obtains information including network connectivity, bandwidth, load, delay, packet loss rate, types of data packets, etc via each node, updating the network model and seeking for routing algorithm, by which the automatic operating control system of the logical network can achieve optimal transmission of dynamic network, i.e. links with fastest transmission process and lowest rate of packet loss. Therefore, data can he transmitted in the information transmission network with different optimal transmission strategy so as to respond to different types of transmission messages, which ensures QoS of network. For example, it is necessary for the sampling data and control instruction of automatic control network system 1 5 to achieve equilibrium between the transmission speed and reliability in order to obtain the best control precision. Multimedia datum packet lays emphasis on timeliness, whereas text datum packet lays emphasis on reliability The above network operating system carries out the configuration control via central control mode, decentralized control mode or mixed control mode.
The above describes scheduling of the power operation network and information transmission network, whereas pressure transportation network detects different values via the network testing instruments according to the different network, wherein the detecting instruments for electric power transmission network include voltage meter, galvanometer and watt-hour meter, and the detecting instruments for gas transmission network include manometer, hygrometer, flowmeter, thermometer and hygrometer, whereas the detecting instruments for power transmission network include hygrometer, flowmcter and thermometer.
After the data feedback to the control system via data bus, the automatic operation control system of the logic network chooses the transmission line and control the flow-limiting element, wherein the electric power transmission network is the circuit breaker, and gas and liquid transmission network is the throttle valve, so as to limit the transmission line. On this basis, if the detecting instruments on the controllers are provided with limited triggering value and linkage associated with the throttle valve or pressurization equipments, then an intelligence pressure network transmission can be achieved with fUnctions of quantitative and charging. Obviously, this system can also be used in transportation and electric power supply network of the rail or pipeline of electrification to achieve the goal of intelligence electric power supply.
Take the electric power network as an example, which is similar to the traffic dispersion system. When local electricity usage of the electric power network reaches the load limit of the transportation lines, it is required to integrate more power transmission lines or to reduce the load. Taking the lines between the function devices including the distribution transformer, circuit breaker on the electric power transmission network as the edge of the logical topological network, and transmission power of the line as the vector, then the electric power dispatching on the network can be realized based on the automatic operating control system of logical network, if the fUnction device also includes an electric power generation system, and the linkage mechanism between network load and electric power generation system is established, then distributing electric power as required can be realized.
In the medical field, there are also many applications, such as minimally invasive surgery and vascular embolism dredge.
1) For minimally invasive surgery, a cutting or stitching point is a node of the logical topological network, and the area between main organs and tissues, ora natural channel of the body is an arc (edge) of the topological network, whereas the route, the allowable error range and the injury degree are vectors of the arc (edge).
2) For vascular embolism dredge, a branch of the vascular network is a node, and a channel among the branches of the vascular network is an arc (edge) of the topological network, whereas the route or the injury degree is vectors of the arc (edge).
On the basis mentioned above, the optimal path can be designed by using automatic operating control system of the logical network. For example, the combination of the detection devices like ultrasound, X-ray, working department signal source localization, and executive devices like controllable work department, constitutes a minimally invasive surgery system or blood vessels to clear the system.
For working pans which are composed of the assembling of the unit elements, like the pixels making up an image, 1-lolzer element and MIC element, this logical network also has a good application.
For working parts which are composed of the assembling unit elements, each unit element is a node of the logical topological network, and adjacent nodes are connected by the arc to form a logical network lattice. The unit clement with the function of early frame signal definition is the starting point, whereas the element with the function of later frame definition is the ending point, and the automatic operating control system of the logical network as described in claim 2 can directly obtain the changing trajectories of the function unit elements; If the arc is given with various weight variables, then each unit element can exhibit a gradual process, and the whole change of the function unit elements within a certain range can also bc used by the intelligence system to identi' whcthcr it is the reflection of the moving object. While in space technology, if more than one sensors or generators are combined to use, then the effects of the signal source distance, direction, spatial orientation, speed and running track, etc can be identified and synthesized according to the interchange of time parameter and distance parameter of receiving or sending signals between each device.
Taking graphical display and processing as an example: take each pixel as an node of the logical nctwork, and adjacent nodcs arc connected by the edgc forming the lattice of the logical network; the pixel in the early frame pixel is defined as the starting point and the pixel in the later frame pixel is defined as the ending point; changing trajectory of the pixel can be obtained directly via routing algorithm. If the edge is assigned with color variable, then gradual changing process will be displayed among the pixels. The whole movement of the pixels within a certain range also can be used by the intelligence system to identi' whether it is the reflection of a moving object. In processing 3D image, like brightness adjustment, it is more nccdcd to simulate the reficction of causation on cnvironmcnt via the intelligence system.
The present logical network system can also be used to simulate various virtual circumstances of pharmacology, physics, chemistry etc., so as to obtain the results, and to generate cross-domain solutions to problems.
The above embodiments of the present invention are not limited within the application scope of the present invention. Any other equivalent modification, replacement and improvcmcnt within the spirit and principle disclosed in the present invention should be included in the protection scope of the present invcntion.
Claims (1)
- <claim-text>CLAIMSI. A logical topological network model, wherein logical subjects such as events, objects and actions are nodes of thc network, and causal relations of the logical subjects arc arcs.</claim-text> <claim-text>2. An artificial intelligence control method, comprising: step 1: obtaining each logical node, eomiecting the logical nodes which are causally related to each other, and establishing the logical topological network model; stcp 2: rccciving inputted text information and scanning a starting logical node and an ending logical node from the logical topological nctwork model described above, wherein the starting logical node is a node including text inforniation, and the ending logical node is a node which is causally related to the starting logical node; step 3: displaying a scanned connection path between the ending logical node and the starting logical node.</claim-text> <claim-text>3. The artificial intelligence control method according to claim 2, wherein the step 2 further comprises receiving imputed information at predefined time intervals, and updating the scanned connection path between the ending logical node and the starting logical node according to the imputed information.</claim-text> <claim-text>4. The artificial intelligence control method according to claim 2, wherein the step I further comprises: assigning weighted values to the connection paths among the logical nodes according to observation parameters including difficulty degree for realization or priority level among the causal logical nodes; and the step 3 comprises: calculating via routing algorithm between two points and displaying the connection path between the ending logical node and the starting logical node which has the minimal weighted value, or displaying the scanned connection paths between the ending logical node and the starting logical node according to the order of weighted values, or choosing the corresponding shortest path or back-up path as the optimal path according to different combination of the observation parameters.</claim-text> <claim-text>5. The artificial intelligence control method according to claim 2, wherein the step 2 further comprises: collecting information about the actual logical nodes, and scanning from the actual logical nodes the intermediate logical nodes which have logical AND, OR or NOT relations with the scanned ending logical node; and the step 3 fUrther comprises: displaying the connection path between the intermediate logical node and the ending logical node.</claim-text> <claim-text>6. The artificial intelligence control method according to claim 2, wherein the step 3 furthcr comprises: recording the connection path between the ending logical node and the starting logical node.</claim-text> <claim-text>S 7.An artificial intelligence system comprises: an establishing module of the logical topological network model, configured to obtain each logical node, to connect the logical nodes which are causally related to each other, and to establish the logical topological network model; a receiving module, configured to receive the inputted text infonnation; and an executing module, configured to scan the starting logical node and the ending logical end according to the data instruction received by the receiving module, wherein the starting logical node is a node including text information, and the ending logical node is a node which is causally related to the starting logical node, and to display the scanned connection path between the ending logical node and the starting logical node.</claim-text> <claim-text>S. The artificial intelligence system according to claim 7, wherein the system further comprises a collection and detemiination module configured to be connected to the executing module and to collect information about the actual logical nodcs and information betwecn connected logical nodes, so as to determine whether a scanned ending logical node actually exists and the cost of connection between logical nodes; if said scanned ending logical node does not actually exist, then the executing module is informed to delete the displayed connection path between the non-existing ending logical node and the starting logical node and to display the cost of connection between the ending logical node and the starting logical node.</claim-text> <claim-text>9. Thc artificial intelligence system according to claim 7, wherein the system further comprises a collection and determination module configured to be connected to the executing module and to collect the information about the actual logical nodes, and to scan from the actual logical nodes the intermediate logical nodes which have logical AND, OR or NOT relations with the scanned ending logical node, and to inform the executing module to display the connection path between the intermediate logical node and the ending logical node.</claim-text> <claim-text>10. The artificial intelligence system according to claim 7, wherein information transmission links among each above module are composed of one or more transport protocols; a sending unit processes information into datum packets with multiple protocol formats according to the transport protocols, of which an innermost layer is the format message used by a receiving unit; after cross-network transmission via gateway, original network frame data becomes a new network frame, and the destination address of the new destination gateway is explained according to the transport protocol used by the new network; upon transmission, and after arriving at the corresponding ending gateway, the original network frame data is transmitted to the receiving unit as the format message used in the receiving unit.</claim-text>
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
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| CN201010133556A CN101827024A (en) | 2010-03-24 | 2010-03-24 | Network path finding method, optimal path selecting method and system thereof |
| CN201010170345A CN101833282A (en) | 2010-04-30 | 2010-04-30 | Artificial intelligent control system and method |
| PCT/CN2011/071995 WO2011116673A1 (en) | 2010-03-24 | 2011-03-21 | Logical topology network model, artificial intelligence control method and artificial intelligence system |
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| GB201219013D0 GB201219013D0 (en) | 2012-12-05 |
| GB2493303A true GB2493303A (en) | 2013-01-30 |
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| GB1219013.8A Withdrawn GB2493303A (en) | 2010-03-24 | 2011-03-21 | Logical topology network model, artificial intelligence control method and artificial intelligence system |
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| GB (1) | GB2493303A (en) |
| WO (1) | WO2011116673A1 (en) |
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| US9258195B1 (en) | 2012-08-08 | 2016-02-09 | Shoretel, Inc. | Logical topology visualization |
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| CN1684074A (en) * | 2004-04-14 | 2005-10-19 | 上海晖洋信息技术有限公司 | Optimum path selecting method between arbitrary buildings based on city road net structure |
| CN1837755A (en) * | 2005-03-22 | 2006-09-27 | 株式会社日立制作所 | Navigation device, navigation method, server device, and navigation information transmission system |
| CN101102462A (en) * | 2007-01-04 | 2008-01-09 | 深圳清华大学研究院 | Wireless digital TV encryption communication system based on mobile phone TV safety module and its working method |
| CN101827024A (en) * | 2010-03-24 | 2010-09-08 | 林定伟 | Network path finding method, optimal path selecting method and system thereof |
| CN101833282A (en) * | 2010-04-30 | 2010-09-15 | 林定伟 | Artificial intelligent control system and method |
-
2011
- 2011-03-21 GB GB1219013.8A patent/GB2493303A/en not_active Withdrawn
- 2011-03-21 WO PCT/CN2011/071995 patent/WO2011116673A1/en active Application Filing
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN1684074A (en) * | 2004-04-14 | 2005-10-19 | 上海晖洋信息技术有限公司 | Optimum path selecting method between arbitrary buildings based on city road net structure |
| CN1837755A (en) * | 2005-03-22 | 2006-09-27 | 株式会社日立制作所 | Navigation device, navigation method, server device, and navigation information transmission system |
| CN101102462A (en) * | 2007-01-04 | 2008-01-09 | 深圳清华大学研究院 | Wireless digital TV encryption communication system based on mobile phone TV safety module and its working method |
| CN101827024A (en) * | 2010-03-24 | 2010-09-08 | 林定伟 | Network path finding method, optimal path selecting method and system thereof |
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| WO2011116673A1 (en) | 2011-09-29 |
| GB201219013D0 (en) | 2012-12-05 |
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