CN116094818B

CN116094818B - Network access method of artificial intelligent device

Info

Publication number: CN116094818B
Application number: CN202310084517.2A
Authority: CN
Inventors: 凌晓华; 肖开品
Original assignee: Suzhou Libote Information Technology Co ltd
Current assignee: Suzhou Libote Information Technology Co ltd
Priority date: 2023-02-08
Filing date: 2023-02-08
Publication date: 2023-11-03
Anticipated expiration: 2043-02-08
Also published as: CN116094818A

Abstract

The invention relates to the technical field of network access, in particular to a network access method of artificial intelligent equipment. The method comprises the following steps: the central controller receives network access request information sent by artificial intelligent equipment and obtains equipment networking request information; responding to the equipment networking request information to carry out network connection on the artificial intelligent equipment to obtain the networking state information of the artificial intelligent equipment; network state characteristic processing is carried out on the networking state information of the artificial intelligent equipment, and network access preliminary information is obtained; constructing a current environment model through artificial intelligent equipment to obtain network environment modeling information; judging network influence by utilizing the network access preliminary information and the network environment modeling information to obtain the optimal access mode information; and performing network access on the artificial intelligent equipment by utilizing the information of the optimal access mode to finish the network access of the artificial intelligent equipment. The network access method has higher reliability.

Description

Network access method of artificial intelligent device

Technical Field

The invention relates to the technical field of network access, in particular to a network access method of artificial intelligent equipment.

Background

Due to the geometric growth of the Internet of things and the artificial intelligent devices, with the popularization of the Internet and the continuous deep network application, the range of the network is greatly expanded from the aspects of breadth and depth, the aspects of the network are covered, and the network boundary is also clearly developed from the past to the present blurring, even to no boundary. The openness, internationality and freedom of the internet are increasing the convenience of use, so that social and economic activities are increasingly supported on the network, and people are used to participate in various network activities such as e-government affairs, e-commerce and the like by using various services provided by the network. But the security becomes an important problem affecting the network performance, the popularization and the increase of the application of the network, the interleaving of different networks and the weakness of the security consciousness of application personnel, so that illegal personnel can more and more easily use the personal privacy unconsciously revealed on the network, and the situation of network security threat is more and more serious. However, the reliability of the conventional network access method is low, and new fault information of the equipment cannot be obtained quickly, so that the equipment cannot be identified quickly and accurately when the equipment fails in the same way.

Disclosure of Invention

In view of the above, the present invention provides a network access method for an artificial intelligent device to solve at least one of the above-mentioned technical problems.

In order to achieve the above object, the network access method of the artificial intelligent device of the present invention comprises the following steps:

step S1: the central controller receives network access request information sent by the artificial intelligent device and analyzes the network access request information to obtain device networking request information;

step S2: responding to the equipment networking request information to carry out network connection on the artificial intelligent equipment, generating network connection mode information, and sharing the network connection mode information to obtain the networking state information of the artificial intelligent equipment;

step S3: network state characteristic processing is carried out on the networking state information of the artificial intelligent equipment, so that network access preliminary information is obtained;

step S4: constructing a current environment model through artificial intelligent equipment to obtain network environment modeling information;

step S5: judging network influence by utilizing the network access preliminary information and the network environment modeling information, so as to obtain the optimal access mode information;

step S6: and performing network access on the artificial intelligent equipment by utilizing the information of the optimal access mode, thereby completing the network access of the artificial intelligent equipment.

When network access request information is sent by artificial intelligent equipment, a central controller receives and analyzes the network access request information, analyzes the details of the network request information, and then utilizes equipment networking information to carry out network connection on the artificial intelligent equipment, so that the artificial intelligent equipment is subjected to primary network connection, network connection mode information is generated, the network connection mode information is shared, thus obtaining artificial intelligent equipment networking state information, analyzing the artificial intelligent equipment networking state information, judging whether equipment connection is not up or the whole network is problematic after networking, sharing the network connection mode information to each artificial intelligent equipment, carrying out network diagnosis on the obtained network access preliminary information by utilizing the artificial intelligent equipment networking state information, judging network influence of the obtained network access preliminary information, which is obtained by constructing an environment model of the artificial intelligent equipment, and carrying out network influence judgment on the network environment modeling information, so that the optimal access mode information is obtained, carrying out correct modification on the access mode of the artificial intelligent equipment, thus completing network access of the artificial intelligent equipment, and enabling the network access fault to be fast and accurately identified when the network access method is fast and the fault information is high in reliability.

In one embodiment of the present specification, step S1 includes the steps of:

step S11: the central controller starts a network information transmitter which only realizes local area network information data transmission, and generates an access request network;

step S12: receiving a network access request sent by artificial intelligent equipment through an access request network to obtain network access request information;

step S13: historical connection information analysis is carried out on the artificial intelligent equipment according to the network access request information, and equipment connection historical information is obtained;

step S14: dividing the artificial intelligent equipment into primary connection equipment and non-primary connection equipment according to the equipment connection history information, and marking the primary connection equipment and the non-primary connection equipment as network application connection equipment;

step S15: for the primary connection equipment, transmitting the connection mode information of the adjacent connected artificial intelligent equipment stored in the central controller to the primary connection equipment through an access request network;

step S16: for the non-primary connection equipment, the historical connection mode information stored in the central controller is transmitted to the non-primary connection equipment through an access request network;

step S17: the network application connection equipment applies for access to the central controller through the connection mode information to obtain equipment networking request information.

The method comprises the steps of obtaining networking request information of equipment, starting a network information transmitter for local area network information data transmission by a central controller, utilizing the network transmitter to realize more efficient data transmission between artificial intelligent equipment and network access, thereby generating an access request network, receiving network access request of the artificial intelligent equipment through the access request network to generate network access request information, analyzing historical connection information of the artificial intelligent equipment by the network access request information to obtain equipment connection historical information, judging whether the equipment is primary connection equipment or not by utilizing the connection historical information, transmitting connection mode information to carry out connection application through the connected artificial intelligent equipment stored in the central controller if the equipment is primary connection equipment, transmitting historical connection information stored in the central controller to carry out connection application if the equipment is not primary connection equipment, transmitting an access application to a central controller network by the network application connection equipment through the connection mode information, judging whether the equipment is the artificial intelligent equipment to be connected through the central controller, and reducing network security hazard, thereby obtaining the networking request information of the equipment.

In one embodiment of the present specification, step S15 includes the steps of:

step S151: predicting the behavior of the equipment for the first time to obtain behavior prediction result information;

step S152: carrying out potential risk prediction analysis by utilizing the behavior prediction result information to obtain potential risk prediction information;

step S153: and calculating the potential risk level of the first-time connected equipment through a predicted risk level formula based on the potential risk prediction information to obtain the potential risk level, wherein the predicted risk level formula is as follows:

wherein f is the potential risk level, t is the duration of prediction in the potential risk prediction information, s is the number of predicted effects on other devices, l is the economic loss caused by predicting the access of the device, t ₀ For the duration of time that other devices are affected, g is the predicted post-connection network throughput change value, k is the predicted post-connection network delay value change, p is the predicted post-connection network transmission speed value, p ₀ For the network transmission speed value before connection, epsilon is expressed as an adjustment item of an abnormality index;

step S154: threshold judgment is carried out according to the potential risk level, and primary connection equipment is classified, so that primary connection safety equipment within a threshold range and primary connection dangerous equipment not within the threshold range are obtained;

Step S155: rejecting a network access request of the equipment aiming at the first connection dangerous equipment, and disconnecting the network connection of an access request network for data with the equipment;

step S156: aiming at the primary connection safety equipment, the connection mode information of the connected artificial intelligent equipment adjacent to the equipment is judged and obtained by utilizing the access request network, and the connection mode information is sent to the primary connection safety equipment through the access request network.

The embodiment is in the artificial intelligent deviceThe method comprises the steps of judging whether the primary connection equipment is connected with a network through the connection information safety problem, firstly predicting the behavior of the primary connection equipment, distinguishing whether the primary connection equipment has a connection function, obtaining behavior prediction result information, then performing potential risk prediction analysis by using the behavior prediction result information, detecting and observing whether the primary connection equipment has Trojan horse virus to cause the whole network to be short-circuited, obtaining potential risk prediction information, then calculating the potential risk grade of the primary connection equipment through a risk prediction information and a prediction risk grade formula, and calculating the potential risk grade of the primary connection equipment through a negative correlation relation between the magnitude of a network throughput change value g and the risk grade after the prediction connection, wherein when the network throughput change value is larger, the network is more stable, the risk grade is lower, and the duration t predicted in the potential risk prediction information is longer than the duration t of the influence of other equipment ₀ Will affect the risk level, t and t ₀ The network is more stable within a certain range of values, the risk level is lower, whether the risk level exceeds a threshold value or not is judged, the safety equipment which does not exceed the threshold value is the safety equipment which is connected for the first time, for the safety equipment which is connected for the first time, the network connection of an access request network for carrying out data with the equipment is disconnected, the network access request information of the equipment is refused, for the safety equipment which is connected for the first time, the access request network is used for judging and obtaining the connection mode information of the connected artificial intelligent equipment adjacent to the equipment, and the information is sent to the safety equipment which is connected for the first time through the access request network, so that the safety of the network access method by the artificial intelligent equipment is greatly improved when the risk evaluation is carried out.

In one embodiment of the present specification, step S2 includes the steps of:

step S21: responding to the equipment networking request information to perform network connection on the artificial intelligent equipment, and performing connection analysis on the network connection state to obtain abnormal equipment and network connection equipment;

step S22: the connection mode information corresponding to the abnormal connection equipment is cleared from the historical connection mode information, the connection mode information is acquired again, and updated equipment networking request information is obtained;

Step S23: responding to the updated equipment networking request information, carrying out network connection again, modifying equipment which is successfully connected into network connection equipment, and generating network connection mode information;

step S24: establishing a connection mode information sharing network by using a network information transmitter;

step S25: and sharing the network connection mode information by using the connection mode information sharing network, and updating the connection mode information stored in the central controller, so as to obtain the networking state information of the artificial intelligent device.

In this embodiment, network connection is performed through device networking request information, when the obtained device networking information is problematic, or connection may occur due to occurrence of poor connection and connection failure in network connection, the device is marked as an abnormal device, and the abnormal device is disconnected, and because the device networking information is problematic, historical connection mode information needs to be eliminated, connection mode information is reacquired and marked as updated device networking request information, network connection is performed again in response to the updated device networking information, and because more artificial intelligent devices need to be connected subsequently, a connection mode information sharing network is established through a network information transmitter, and network connection mode information of network connection devices in the artificial intelligent devices is shared, and when the network connection mode information stored in a central controller is changed, the network connection mode information in the network is updated and shared, so that the connection mode information of the whole artificial intelligent device is networking state information of the artificial intelligent device.

In one embodiment of the present specification, step S3 includes the steps of:

step S31: performing wireless network detection creation according to the networking state information of the artificial intelligent equipment so as to obtain wireless network creation information;

step S32: analyzing the networking state of the current artificial intelligent device according to the wireless network creation information, so as to obtain wireless network state data;

step S33: detecting abnormal state of equipment networking for the wireless network state data to obtain abnormal artificial intelligent equipment information of the network and normal artificial intelligent equipment information of the network, and repairing the abnormal artificial intelligent equipment information of the network to restore the abnormal artificial intelligent equipment information of the network to the normal artificial intelligent equipment information of the network;

step S34: performing network throughput threshold analysis on the network normal artificial intelligent device information to acquire wireless network throughput information of the current artificial intelligent device network access;

step S35: performing network signal intensity threshold analysis on the normal artificial intelligent equipment information of the network to acquire the wireless network receiving signal intensity information of the current artificial intelligent equipment network access;

step S36: and carrying out network state characteristic processing on the wireless network throughput information and the wireless network signal strength information to obtain network access preliminary information.

According to the embodiment, wireless network detection creation of networking information of the artificial intelligent equipment is built, the networking information of the artificial intelligent equipment is distributed in a wireless network mode, so that wireless network creation information is obtained, the networking state of the current artificial intelligent equipment is analyzed according to the wireless network creation information, whether the networking state of the wireless network creation information is good or not is analyzed, wireless network state data is obtained, equipment networking abnormal state detection is conducted on the wireless network state data, the detected wireless network state data is marked as network abnormal artificial intelligent equipment information which is not good in standard, the wireless network state data is marked as network normal artificial intelligent equipment information, the network abnormal artificial intelligent equipment information is subjected to wireless network state data restoration, the network normal artificial intelligent equipment information is recovered, the throughput and the signal strength of a wireless network of the artificial intelligent equipment can be known through the wireless network of the wireless network through-put information and the wireless network receiving signal strength information, and network state feature processing is conducted on the wireless network state information, so that network access preliminary information can be obtained.

In one embodiment of the present specification, step S33 includes the steps of:

Step S331: and carrying out data anomaly analysis on the wireless network state data to obtain wireless network state analysis data.

Step S332: detecting abnormal state of equipment networking for the wireless network state analysis data, and marking abnormal information of the wireless network state analysis data as abnormal artificial intelligent equipment information of the network, and marking abnormal information not to occur as normal artificial intelligent equipment information of the network;

step S333: and carrying out data restoration on the wireless network state data of the network abnormal artificial intelligent equipment information, and returning the wireless network state data to a normal value range to restore the wireless network state data to the network normal artificial intelligent equipment information.

According to the implementation, data anomaly analysis is carried out on wireless network state data to obtain wireless network state analysis data, wherein the analysis data comprise analysis data which reach the standard and analysis data which do not reach the standard, equipment networking anomaly state detection is carried out on the wireless network state analysis data, network anomaly artificial intelligent equipment information marked as good standard is not reached in the wireless network state data, the wireless network state data are good network normal artificial intelligent equipment information marked, data restoration is carried out on the wireless network state data in the network anomaly artificial intelligent equipment information, and the wireless network state data are returned well, so that the network normal artificial intelligent equipment is restored.

In one embodiment of the present specification, step S4 includes the steps of:

step S41: an environment modeling signal is sent to the artificial intelligent device and the central controller through the access request network so as to start the environment detection modules of the artificial intelligent device and the central controller, thereby establishing an environment detection communication network;

step S42: the environment detection modules are used for receiving the detection results of the environments from the artificial intelligent equipment and the central controller and marking the detection results as primary environment information;

step S43: performing environment detection fusion by using the primary environment information to obtain final environment information;

step S44: and 3D modeling of the environment is carried out by utilizing the ultimate environment information and the environment detection communication network, so as to obtain network environment modeling information.

According to the embodiment, the environment modeling signal is sent to the artificial intelligent device and the central controller through the access request network, the access request network is subjected to position modeling by the environment modeling signal, the geographic position is set in a safer and hidden simulation environment, the problems of invasion and the like caused by hackers are prevented, so that an environment detection communication network is established, the environment detection module of the artificial intelligent device and the central controller is started again, the environment detection module detects the positions of the artificial intelligent device and other central controllers, the environment safety of the artificial intelligent device and other central controllers is improved, the detection result is marked as primary environment information, the primary environment information is subjected to environment detection fusion, the primary environment information is added with a multi-temperature environment, a humidity environment and the like, the position is subjected to modeling, final environment information is obtained, the environment is subjected to 3D modeling of the environment by the final environment information and the environment detection communication network, the simulated network environment is obtained, and the network environment modeling signal is obtained.

In one embodiment of the present specification, step S5 includes the steps of:

step S51: extracting network information data from the network access preliminary information so as to obtain artificial intelligent equipment information and access mode information corresponding to the artificial intelligent equipment;

step S52: acquiring the distance between the central controller and the artificial intelligent device through an environment detection module, and marking the distance as device network distance information;

step S53: judging network influence of the artificial intelligent device by utilizing the network distance information and the network environment modeling information, so as to obtain network detection information;

step S54: and analyzing the optimal access mode according to the network detection information to obtain the optimal access mode information.

According to the embodiment, network information data extraction is carried out on network access preliminary information, artificial intelligent equipment information and access mode information of the artificial intelligent equipment in the network access preliminary information are extracted, the distance between the central controller and the artificial controller is obtained through the environment detection module obtained through the steps, the distance between the central controller and the artificial controller is the distance between simulation positions and is marked as equipment network distance information, network influence judgment is carried out on the artificial intelligent equipment through the network distance information and network environment modeling information, a network access mode can be found through the network distance information and the network environment modeling information, network detection information is obtained, and then optimal access mode analysis is carried out through the network detection information, so that optimal access mode information is obtained.

In one embodiment of the present specification, step S55 includes the steps of:

step S541: performing network connectivity detection analysis on the network detection information to obtain network connectivity information;

step S542: judging the network signal intensity according to the network connectivity information to obtain the network intensity information of the equipment;

step S543: and analyzing the optimal access mode according to the equipment network strength information to obtain the optimal access mode information.

In this embodiment, because the relationship between the location and the environment of the device affects the network detection information, the network detection information is firstly subjected to network connectivity detection analysis to obtain network connectivity information, and then is subjected to network signal strength judgment to obtain the network strength of the device, and the poor location and the environment are the reasons for the strong information and the weak information of the network connectivity information and the strong network signal, and the best access mode analysis is performed according to the network strength information, so that the network strength after the device is connected can be greatly increased through the best access mode analysis, and the best access mode information is obtained.

In one embodiment of the present specification, step S6 includes the steps of:

step S61: transmitting optimal access mode information to the artificial intelligent device through an access request network;

Step S62: receiving optimal networking request information sent by equipment of the artificial intelligent equipment, and verifying the optimal networking request information so as to obtain optimal request verification information, wherein the optimal networking request information is obtained by the artificial intelligent equipment according to the optimal access mode information;

step S63: and enabling the artificial intelligent device to perform network access according to the optimal request verification information, thereby completing the network access of the artificial intelligent device.

According to the embodiment, the optimal access mode information is sent to the artificial intelligent device through the access request network in the steps, the optimal access mode information is processed in the artificial intelligent device, a detailed method of how to access the artificial intelligent device to the network is obtained through the optimal access mode information, so that optimal networking request information is generated, and because the security of the network is considered, the dangerous problems that a hacker invades the network or a script robot obtains network information and the like are prevented, verification information is added to the optimal networking request, so that optimal request verification information is obtained, and the artificial intelligent device is enabled to access the network according to the optimal request verification information, so that the network access of the artificial intelligent device is completed.

The network access method of the artificial intelligent device comprises the steps of carrying out networking request on a network through the artificial intelligent device to obtain a network access method, sharing the network access method to each artificial intelligent device, conveniently accessing other artificial intelligent devices to the network, and carrying out safe network connection by utilizing networking state information of the artificial intelligent device and environment model construction collocation of the artificial intelligent device and a central controller so as to improve the security of network connection and provide the stability of network connection of the artificial intelligent device, thereby better ensuring the network access load balance of the artificial intelligent device.

Drawings

FIG. 1 is a flow chart illustrating steps of a network access method for an artificial intelligent device according to the present application;

FIG. 2 is a detailed flowchart illustrating the implementation of step S1 in FIG. 1;

FIG. 3 is a detailed flowchart illustrating the implementation of step S2 in FIG. 1;

FIG. 4 is a schematic diagram of a network server sharing network connection method and an artificial intelligent device access method in a network access method of an artificial intelligent device according to the present application;

FIG. 5 is a flowchart illustrating the detailed implementation of step S3 in FIG. 1;

FIG. 6 is a flowchart illustrating the detailed implementation of step S4 in FIG. 1;

FIG. 7 is a flowchart illustrating the detailed implementation of step S5 in FIG. 1;

FIG. 8 is a flowchart illustrating the detailed implementation of step S6 in FIG. 1;

the achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

The embodiment of the application provides a network access method of artificial intelligent equipment. The network access method execution body of the artificial intelligent device comprises, but is not limited to, a sea system: mechanical devices, network transmission devices, network server platforms, etc. may be considered general purpose computing nodes of the present application. The mechanical devices include, but are not limited to: at least one of intelligent refrigerator, intelligent household equipment, intelligent television, etc.

The invention discloses a network access method of artificial intelligent equipment, which comprises the following steps:

When the network access request information sent by the artificial intelligent device is passed through, the central controller receives and analyzes the network access request information, analyzes the details of the network request information, and then utilizes the device networking information to connect the artificial intelligent device with the network, so that the artificial intelligent device is connected with the primary network, and generates network connection mode information, the network connection mode information is shared, so as to obtain the networking state information of the artificial intelligent device, and analyze the networking state information of the artificial intelligent device, and see whether the network connection is not on the device connection or the whole network is problematic, and the like, and share the network connection mode information with each artificial intelligent device, and the network access primary information obtained by carrying out network diagnosis by utilizing the networking state information of the artificial intelligent device is network environment modeling information obtained by constructing an environment model for subsequent patch network access, and the network access primary information is judged by the network environment modeling information obtained by the artificial intelligent device, so that the optimal access mode information is obtained, and the network access mode information of the artificial intelligent device is correctly modified by the optimal access mode information, so that the network access of the artificial intelligent device is correctly completed.

In the embodiment of the present invention, as described with reference to fig. 1, a flow chart of steps of a network access method of an artificial intelligent device of the present invention is shown, and in this example, the steps of the network access method applied to the artificial intelligent device include:

in the embodiment of the invention, the central controller of the network server platform receives the network access request information sent by the intelligent home equipment, tries network connection, and analyzes the network access request information so as to obtain the networking request information.

in the embodiment of the invention, the network server platform responds to the equipment networking request information, and performs network connection on the intelligent home equipment through the equipment networking request information to generate network connection mode information, wherein the network connection mode information is how the artificial intelligent equipment performs network connection, and generates detailed information of the network connection, and the network server platform shares the network connection mode information to obtain the networking state information of the artificial intelligent equipment.

in the embodiment of the invention, network state characteristic processing is performed on the networking state information of the artificial intelligent equipment, for example, after the intelligent home equipment is connected to the network, the network server platform needs to process whether the network connection of the connected artificial intelligent equipment is abnormal, the signal intensity between the network connections and the like, so as to obtain network access preliminary information.

in the embodiment of the invention, the network position, the network environment and other information of the equipment are simulated through the construction of the environment model of the intelligent household equipment, so as to obtain the network environment modeling information.

in the embodiment of the invention, network influence judgment is carried out by utilizing the network access preliminary information and the network environment modeling information, various proper network access mode information is obtained by combining the information between the network access preliminary information and the network environment modeling information, and the network access mode information is optimized to obtain the optimal access mode information.

In the embodiment of the invention, the intelligent home equipment is subjected to network access by utilizing the information of the optimal access mode, so that the intelligent home equipment is truly and completely connected with the network server, and the network access of the artificial intelligent equipment is completed.

In one embodiment of the present specification, step S1 includes the steps of:

As an example of the present invention, referring to fig. 2, a detailed implementation step flow diagram of step S1 in fig. 1 is shown, where the content in this example includes:

in the embodiment of the invention, the central controller starts the network information transmitter, and the network transmitter realizes the transmission of local area network information data, such as the transmission of local area network information by each node between network servers. Thereby generating an access request network.

in the embodiment of the invention, the access request network transmits local area network information among nodes of the network server, receives the network access request sent by the artificial intelligent device, and obtains the network access request information.

in the embodiment of the invention, the historical connection information of the intelligent home equipment is analyzed according to the network access request information, and whether the network connection information exists before the intelligent home equipment is observed to obtain the equipment connection historical information.

in the embodiment of the invention, whether the intelligent home equipment has network connection information or not is analyzed according to the equipment connection history information, if the intelligent home equipment does not have the network connection information, the intelligent home equipment is the primary connection equipment, and if the intelligent home equipment does not have the network connection information, the intelligent home equipment is the non-primary connection equipment, and the intelligent home equipment are marked as network application connection equipment.

in the embodiment of the invention, the network connection processing method for the primary connection equipment can transmit the connection mode information of the connected artificial intelligent equipment stored in the current central controller to the primary connection equipment through the access request network of the steps.

In the method for processing the non-primary connection device according to the embodiment of the present invention, since the non-primary connection device has experience of connection, only the history connection information stored in the current central controller needs to be sent to the non-primary connection device through the access request network.

In the embodiment of the invention, the network application connection equipment applies for access to the central controller through the connection mode information, and the central controller generates corresponding equipment networking information by identifying the connection mode information of different network application connection equipment.

In one embodiment of the present specification, step S15 includes the steps of:

The embodiment is that the connection information safety problem of the primary connection equipment in the artificial intelligent equipment is solved, whether the primary connection equipment is connected with a network is judged through the connection information safety problem, firstly, the primary connection equipment is subjected to behavior prediction, whether the primary connection equipment has a connection function is judged, behavior prediction result information is obtained, potential risk prediction analysis is carried out by using the behavior prediction result information, the primary connection equipment is detected and observed whether the Trojan virus causes the whole network to be short-circuited or not, potential risk prediction information is obtained, the potential risk grade of the primary connection equipment is calculated by using the risk prediction information and a prediction risk grade formula, the magnitude of a network throughput change value g after the connection is predicted and the risk grade are in a negative correlation, when the network throughput change value is larger, the network is more stable, the risk grade is lower, and the duration t predicted in the potential risk prediction information is influenced by the duration t of other equipment ₀ Will affect the risk level, t and t ₀ The network is more stable within a certain range of values, the risk level is lower, whether the risk level exceeds a threshold value or not is judged, the risk level which does not exceed the threshold value is the primary connection safety equipment, the primary connection safety equipment which exceeds the threshold value is the primary connection safety equipment, for which, because the equipment is afraid of causing the problems of safety and the like of the whole network, the network connection of an access request network for carrying out data with the equipment is disconnected, the network access request information of the equipment is refused, for the primary connection safety equipment, the access request network is utilized to judge and obtain the connection mode information of the connected artificial intelligent equipment adjacent to the equipment, and the information is sent to the primary connection safety equipment through the access request network, so that risk evaluation is carried out by people The safety of the intelligent equipment to the network access method is greatly improved.

According to the embodiment of the invention, the connection mode information is transmitted to the primary connection equipment through the access request network, equipment behavior prediction is firstly carried out on the primary connection equipment, the result information which appears after the equipment connection is predicted, so that behavior prediction result information is obtained, potential risk prediction analysis is carried out by utilizing the behavior prediction result information, the potential risk prediction is whether the primary connection equipment is normal intelligent computer equipment or not or whether the intelligent computer equipment carries Trojan virus and further irradiates as network safety hazard and the like, the potential risk prediction information is obtained, then the potential risk prediction information of the primary connection equipment is subjected to risk level calculation by utilizing a potential prediction risk level formula, the potential risk level of the primary connection equipment is obtained, if the network server is not influenced by the calculation of the potential risk level, the potential risk level is marked as dangerous, the potential risk level which is not influenced by the network server is marked as normal, threshold value judgment is carried out on the potential risk level, the primary connection dangerous equipment is classified as the primary connection dangerous equipment, the primary connection dangerous equipment is rejected due to the safety of the primary connection dangerous equipment, the network access request of the primary connection dangerous equipment is disconnected, and the network access request is manually connected to the network safety information through the network access request is controlled by the network controller, and the current network access request is transmitted to the central connection equipment.

In one embodiment of the present specification, step S2 includes the steps of:

As an example of the present invention, referring to fig. 3, a detailed implementation step flow diagram of step S2 in fig. 1 is shown, where the content in this example includes:

in the embodiment of the invention, the network server platform responds to the equipment networking request information to try to carry out network connection on the artificial intelligent equipment, analyzes the network connection state, marks the abnormal connection state of the network connection as abnormal connection equipment, and can carry out normal network connection as network connection equipment.

in the embodiment of the invention, the step is to process the abnormal connection equipment, clear the connection mode information of the abnormal connection equipment from the history connection mode information, re-acquire the connection mode information through the access request network, and update the equipment networking request information to acquire new equipment networking request information through the new connection mode information.

in the embodiment of the invention, network connection is carried out again on the equipment with abnormal connection in response to the updated equipment networking request information, the equipment with successful connection is modified into the network connection equipment, the steps are repeated without success, and the information in the network connection equipment is utilized to generate network connection mode information.

in the embodiment of the invention, the connection mode information sharing network is established in the network information transmitter in the step, and the information is shared by the connection mode information sharing network.

In the embodiment of the invention, the network connection mode information generated by the network server platform because of the network connection equipment is shared by using the connection mode information sharing network, for example, the network connection mode information generated by the network server platform because of the successfully connected intelligent home equipment is shared to other artificial intelligent equipment by using the connection mode information, and the connection mode information stored in the central controller is updated, so that the networking state information of the artificial intelligent equipment is obtained.

In the embodiment of the present invention, as described with reference to fig. 4, a network server in a network access method of an artificial intelligent device shares a network connection method and makes the network access method of the artificial intelligent device schematically shown in the embodiment, the method includes:

the central controller in the network server platform responds to the networking request information to enable the network connection module of the artificial intelligent device to conduct network connection, the network information transmitter is started through the central controller, the network information transmitter updates the connection mode information in the central controller, and the network connection mode information in the connection information storage module of the artificial intelligent device is shared, so that the network server platform primarily obtains the networking state information of the artificial intelligent device.

In one embodiment of the present specification, step S3 includes the steps of:

As an example of the present invention, referring to fig. 5, a flowchart illustrating a detailed implementation step of step S3 in fig. 1 is shown, where the content in this example includes:

in the embodiment of the invention, the wireless network detection creation is performed in the network server platform according to the networking state information of the artificial intelligent equipment, which is obtained through the steps, so that the wireless network creation information is obtained.

in the embodiment of the invention, the networking state of the wireless network creation information, the current network server platform and the artificial intelligent device is analyzed according to the wireless network creation information, and whether the networking state is abnormal or not is observed, so that wireless network state data is obtained.

In the embodiment of the invention, the device networking abnormality detection is carried out on the wireless network state data just detected, the device with abnormal detection result is marked as network abnormal artificial intelligent device information, the device with normal detection result is marked as network normal artificial intelligent device information, and the wireless network state data restoration is carried out on the network artificial intelligent device information, so as to obtain the wireless network state data.

in the embodiment of the invention, the network throughput threshold analysis is carried out on the normal artificial intelligent equipment of the network, and the network access method of the artificial intelligent equipment and the network throughput when the artificial intelligent equipment is accessed show a positive correlation relationship, so that the wireless network throughput information of the network access of the current artificial intelligent equipment is obtained, and the strength of network connection is observed.

in the embodiment of the invention, the network signal intensity threshold analysis is carried out on the normal artificial intelligent equipment of the network, and the network access method of the artificial intelligent equipment and the network signal intensity when the artificial intelligent equipment is accessed show positive correlation, so that the wireless network signal intensity information of the network access of the current artificial intelligent equipment is obtained to observe the intensity of network connection.

In the embodiment of the invention, the wireless network throughput information and the wireless network signal intensity information obtained in the steps are subjected to network state feature processing, wherein the network state feature processing is to extract the effective network access information of the wireless network throughput information and the wireless network signal intensity information, so that a special network access method is obtained, and the special network access method is marked as network access preliminary information.

In one embodiment of the present specification, step S33 includes the steps of:

In the embodiment of the invention, the device networking abnormal state detection and the network abnormal device repair are carried out in the step S33, firstly, the wireless network state is subjected to data abnormal analysis, the obtained analysis data has standard-reaching analysis data and non-standard-reaching analysis data, the non-standard-reaching analysis data is the abnormal value of the network state data, the wireless network state analysis data is obtained according to the analyzed standard-reaching and non-standard-reaching analysis data, the device networking abnormal state detection is carried out on the wireless network state analysis data, the abnormal data, namely the non-standard-reaching data in the just-analyzed data, are marked as network abnormal artificial intelligent device information, the abnormal data is not marked as network normal artificial intelligent device information, the network service platform is utilized to repair the wireless network state data of the network abnormal artificial intelligent device information, the abnormal value in the data is replaced, and the normal value range of the wireless network state data is returned, so that the network abnormal artificial intelligent device information is restored to the network normal artificial intelligent device information.

In one embodiment of the present specification, step S4 includes the steps of:

As an example of the present invention, referring to fig. 6, a flowchart of a detailed implementation step of step S4 in fig. 1 is shown, where the content in this example includes:

in the embodiment of the invention, the access request network obtained through the steps sends environment modeling signals to the artificial intelligent device and other central controllers, the access request network is subjected to position simulation through the environment modeling signals, the geographic position is set in a safer and hidden simulation environment, and the problems of invasion and the like caused by hackers are prevented, so that the environment detection module of the central controllers in the artificial intelligent device and the network server platform is started, and the environment detection communication network is established.

in the embodiment of the invention, the environment detection module of the artificial intelligent device and the central controller is started, and the environment detection module detects the positions of the artificial intelligent device and other central controllers, so that the environment safety of the artificial intelligent device and other central controllers is improved, and the detection result is marked as primary environment information.

in the embodiment of the invention, the primary environment information is subjected to environment detection fusion, and then the primary environment information is added with a multi-temperature environment, a humidity environment and the like, so that the position is simulated, and the final environment information is obtained.

In the embodiment of the invention, the terminal environment information and the environment detection communication network are subjected to 3D modeling of the environment, and the terminal environment information and the environment detection communication network are combined, so that a simulated network environment is obtained, and a network environment modeling signal is obtained.

In one embodiment of the present specification, step S5 includes the steps of:

As an example of the present invention, referring to fig. 7, a flowchart of a detailed implementation step of step S5 in fig. 1 is shown, where the content in this example includes:

In the embodiment of the invention, the network information data extraction is carried out on the network access preliminary information by utilizing the data extraction module of the network server platform, and the artificial intelligent equipment information in the network access preliminary information and the access mode information of the artificial intelligent equipment are extracted, so that the artificial intelligent equipment information and the access mode information corresponding to the artificial intelligent equipment are obtained.

in the embodiment of the invention, the distance between the central controller and the artificial intelligent device is obtained through the environment detection module in the steps, the distance between the central controller and the artificial intelligent device is the distance between the set simulation positions, and the distance is marked as the device network distance information.

in the embodiment of the invention, network influence judgment is carried out on the artificial intelligent equipment by utilizing the network distance information and the network environment modeling information, and a network access mode can be found by the network distance information and the network environment modeling information, so that network detection information is obtained.

In the embodiment of the invention, the best access mode is analyzed according to the network detection information, the access mode with the easiest connection and the best comprehensive stability and safety is found out from a plurality of access modes, and the best access mode is marked as the best access mode information.

In one embodiment of the present specification, step S55 includes the steps of:

In the embodiment of the invention, because the relation between the position of the equipment and the environment influences the network detection information, the network connectivity detection analysis is firstly carried out on the network detection information to obtain the network connectivity information, the strong influence of the network signals on the access mode of the equipment possibly influences the network signal strength of the equipment on different connection modes, the network signal strength judgment is carried out again to obtain the network strength of the equipment, the bad position and the environment are the reasons for the weak information causing the strong information of the network connectivity information and the strong information of the network signals, the optimal access mode analysis is carried out according to the network strength information, and the network strength after the equipment is communicated can be greatly increased through the optimal access mode analysis, so that the optimal access mode information is obtained.

In one embodiment of the present specification, step S6 includes the steps of:

As an example of the present invention, referring to fig. 8, a flowchart of a detailed implementation step of step S6 in fig. 1 is shown, where the content in this example includes:

in the embodiment of the invention, the access request network obtained through the steps transmits the optimal access mode information to the artificial intelligent equipment, and the artificial intelligent equipment can access the network by receiving the optimal access mode information.

in the embodiment of the invention, the detailed method of how to access the artificial intelligent device to the network is obtained by utilizing the optimal access mode information, so that the optimal networking request information is generated, and the optimal networking request is added with verification information because the security of the network is considered to prevent hackers from invading the network or script robots from obtaining the network information and other dangerous problems, so that the optimal request verification information is obtained.

In the embodiment of the invention, the artificial intelligent device is enabled to carry out network access according to the optimal request verification information, thereby completing the network access method of the artificial intelligent device and the network server platform.

According to the method, network access request information sent by the artificial intelligent device is received through the central controller, the network access request information is analyzed, device networking request information is obtained, network connection is conducted on the artificial intelligent device in response to the device networking request information, network connection mode information is generated, network connection mode information is shared, network state information of the artificial intelligent device is obtained, network state feature processing is conducted on the network state information of the artificial intelligent device, initial network access information is obtained, a current environment model is built through the artificial intelligent device, network environment modeling information is obtained, network influence judgment is conducted by the initial network access information and the network environment modeling information, optimal access mode information is utilized for network access to the artificial intelligent device, network access of the artificial intelligent device is achieved, safety of network connection is improved, stability of network connection of the artificial intelligent device is provided, and network access load balance of the artificial intelligent device is guaranteed better.

The method comprises the steps of collecting unstructured data of an Internet of things platform, wherein the collected unstructured data comprises audio data, image data and video data, cleaning the collected data into a processable data set, obtaining a cleaning data set, conducting block processing on the data set, enabling the data to be fast in speed by means of data block processing, conducting noise reduction on the data set subjected to block processing, removing data noise points existing in the data set, conducting data processing on the data to obtain a block file of a standard data set capable of conducting data analysis, conducting feasible display on the data in the block file, and designing a data visual interface comprehensive page.

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.

It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A network access method for an artificial intelligence device, the method comprising the steps of:

step S1, including:

Step S15, including:

；

wherein ,for the potential risk class->For predicting the duration of the time in the risk potential prediction information, < +.>To predict the amount of influence on other devices +.>To predict the economic loss caused by the access of the device, < >>The duration of time that the other device is affected, +.>To predict post-connection network throughput variation value, < >>For predicting the change of the network delay value after connection, < >>For predicting the network transmission speed value after connection +.>For the network transmission speed value before connection, +.>An adjustment term expressed as an abnormality index;

step S156: aiming at the primary connection safety equipment, judging and obtaining the connection mode information of the connected artificial intelligent equipment adjacent to the equipment by utilizing an access request network, and sending the connection mode information to the primary connection safety equipment through the access request network;

step S17: the network application connection equipment applies for access to the central controller through the connection mode information to obtain equipment networking request information;

step S2, including:

step S25: sharing the network connection mode information by using a connection mode information sharing network, and updating the connection mode information stored in the central controller so as to obtain the networking state information of the artificial intelligent device;

step S3, including:

step S33, including:

step S331: performing data anomaly analysis on the wireless network state data to obtain wireless network state analysis data;

Step S333: performing data restoration on wireless network state data of network abnormal artificial intelligent equipment information, and returning the wireless network state data to a normal value range to restore the wireless network state data to the network normal artificial intelligent equipment information;

step S36: performing network state characteristic processing on the wireless network throughput information and the wireless network signal strength information to obtain network access preliminary information;

step S4, including:

step S44: 3D modeling of the environment is carried out by utilizing the ultimate environment information and the environment detection communication network, and network environment modeling information is obtained;

step S5, including:

step S54, including:

step S543: analyzing the optimal access mode according to the equipment network strength information to obtain the optimal access mode information;

2. The network access method of an artificial intelligence device according to claim 1, wherein step S6 comprises the steps of:

step S63: and modifying the network access mode of the artificial intelligent equipment according to the optimal request verification information, thereby completing the network access of the artificial intelligent equipment.