CN114727215A - Network quality optimization method and device - Google Patents

Network quality optimization method and device Download PDF

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Publication number
CN114727215A
CN114727215A CN202210179837.1A CN202210179837A CN114727215A CN 114727215 A CN114727215 A CN 114727215A CN 202210179837 A CN202210179837 A CN 202210179837A CN 114727215 A CN114727215 A CN 114727215A
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intelligent robot
network
communication network
network quality
switching
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袁国勇
王伟健
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Cloudminds Shanghai Robotics Co Ltd
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Cloudminds Shanghai Robotics Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/14—Reselecting a network or an air interface
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/24—Reselection being triggered by specific parameters
    • H04W36/30—Reselection being triggered by specific parameters by measured or perceived connection quality data
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The present disclosure provides a method and a device for optimizing network quality, wherein the method comprises the following steps: and acquiring a network detection result of the intelligent robot in the current environment, and determining the communication network quality of the intelligent robot in the current environment according to the network detection result. If the communication network quality of the intelligent robot in the current environment does not meet the network quality requirement of the current service scene, the communication network quality of the switched intelligent robot can meet the network quality requirement by switching the position of the intelligent robot and also by switching the type of the communication network connected with the intelligent robot. The method switches the position of the intelligent robot and/or the type of the connected communication network based on the quality of the communication network of the intelligent robot in the current environment, so that the quality of the switched communication network of the intelligent robot meets the requirement of the network quality, the quality of the communication network of the intelligent robot is greatly improved, and the stability of the connection between the intelligent robot and the communication network is ensured.

Description

Network quality optimization method and device
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a method and an apparatus for optimizing network quality.
Background
With the development of artificial intelligence technology, the application scenarios of intelligent robots are more and more extensive. Such as industrial parks, warehouses, ports, logistics centers, coal mines, hospitals, shopping malls, banks, etc.
In the operation process of the intelligent robot, the intelligent robot needs to be connected with a server through a communication network or connected with other intelligent robots through the communication network so as to execute corresponding tasks. In different application scenarios, blind spots or areas with weak signal strength exist in signal coverage of the communication network, and therefore, the intelligent robot in the areas loses packets or even cannot communicate, and the operation of the intelligent robot is affected.
In summary, how to ensure the connection stability of the intelligent robot and the communication network becomes a technical problem to be solved by the present disclosure.
Disclosure of Invention
The disclosure provides a network quality optimization method and device, which are used for improving the communication network quality of an intelligent robot and ensuring the connection stability of the intelligent robot and a communication network.
According to a first aspect of an embodiment of the present disclosure, the present disclosure provides a network quality optimization method, including:
acquiring a network detection result of the intelligent robot in the current environment;
determining the communication network quality of the intelligent robot in the current environment according to the network detection result;
and if the communication network quality does not meet the network quality requirement of the current service scene, switching the position of the intelligent robot and/or the type of the connected communication network so that the communication network quality of the intelligent robot after switching meets the network quality requirement.
According to a second aspect of the embodiments of the present disclosure, the present disclosure provides a network quality optimization apparatus, including:
the acquisition module is configured to acquire a network detection result of the intelligent robot in the current environment;
a determining module configured to determine the communication network quality of the intelligent robot in the current environment according to the network detection result;
and the switching module is configured to switch the position of the intelligent robot and/or the type of the connected communication network if the communication network quality does not meet the network quality requirement of the current service scene, so that the communication network quality of the intelligent robot after switching meets the network quality requirement.
According to a third aspect of the embodiments of the present disclosure, there is provided an electronic device, which includes a processor and a memory, where the memory stores executable code thereon, and when the executable code is executed by the processor, the processor is enabled to implement at least the network quality optimization method in the first aspect.
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium, in which instructions, when executed by an electronic device, enable the electronic device to perform a method that at least may implement the network quality optimization method of the first aspect.
According to a fifth aspect of embodiments of the present disclosure, there is provided a computer program product comprising computer programs/instructions which, when executed by a processor, implement the network quality optimization method in the first aspect.
The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects:
according to the method and the device, the network detection result of the intelligent robot in the current environment is obtained, and the communication network quality of the intelligent robot in the current environment is determined according to the network detection result. If the communication network quality of the intelligent robot in the current environment does not meet the network quality requirement of the current service scene, the communication network signal strength of the current connection of the intelligent robot is relatively weak, and the communication requirement of the current service scene cannot be met. The intelligent robot can also be switched to a communication network with higher communication network quality by switching the type of the communication network connected with the intelligent robot, so that the communication network quality of the switched intelligent robot meets the network quality requirement. According to the method and the device, the position of the intelligent robot and/or the type of the connected communication network are/is switched based on the communication network quality of the intelligent robot in the current environment, so that the communication network quality of the switched intelligent robot meets the network quality requirement, the communication network quality of the intelligent robot is greatly improved, and the connection stability of the intelligent robot and the communication network is ensured.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure and are not to be construed as limiting the disclosure.
FIG. 1 is a flow diagram illustrating a method for network quality optimization in accordance with an exemplary embodiment;
FIG. 2 is a schematic diagram illustrating a network quality optimization method in accordance with an exemplary embodiment;
fig. 3 is a schematic diagram illustrating a network quality optimization apparatus according to an exemplary embodiment;
fig. 4 is a schematic structural diagram of an electronic device according to an exemplary embodiment.
Detailed Description
In order to make the technical solutions of the present disclosure better understood by those of ordinary skill in the art, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the disclosure, as detailed in the appended claims.
As described above, during the operation of the intelligent robot, the intelligent robot needs to be connected to a server through a communication network, or to other intelligent robots through the communication network, so as to perform corresponding tasks. In different application scenarios, blind spots or areas with weak signal strength exist in signal coverage of the communication network, and therefore, the intelligent robot in the areas loses packets, has poor communication efficiency, and even cannot communicate, thereby affecting the operation of the intelligent robot. For example, in an application scenario in which a server is used to schedule an intelligent robot, if the intelligent robot cannot receive a scheduling instruction issued by the server, the intelligent robot does not move according to a set route. For example, in a scenario in which two intelligent robots are docked with each other, if the two intelligent robots cannot communicate with each other, it is difficult to transmit image information and position information in the current environment, so that the docking task of the intelligent robots fails to be executed. For example, in a human-computer interaction scene, the signal quality of a communication network also affects the communication efficiency, so that the intelligent robot cannot respond in time, the instant response requirement of a human-computer interaction service is difficult to meet, and the user experience is affected.
Therefore, how to ensure the connection stability of the intelligent robot and the communication network becomes a technical problem to be solved by the present disclosure.
In order to solve at least one technical problem in the related art, the present disclosure provides a method and an apparatus for optimizing network quality.
The core idea of the technical scheme is as follows: firstly, a network detection result of the intelligent robot in the current environment is obtained, and then the communication network quality of the intelligent robot in the current environment is evaluated according to the network detection result. If the communication network quality of the intelligent robot in the current environment does not meet the network quality requirement of the current service scene, the communication network signal strength of the current connection of the intelligent robot is relatively weak, and the communication requirement of the current service scene cannot be met. On the other hand, the intelligent robot can be switched to a communication network with higher communication network quality by switching the type of the communication network connected with the intelligent robot, so that the communication network quality of the switched intelligent robot meets the network quality requirement.
According to the method and the device, the position of the intelligent robot and/or the type of the connected communication network are/is switched based on the communication network quality of the intelligent robot in the current environment, so that the communication network quality of the switched intelligent robot meets the network quality requirement, the communication network quality of the intelligent robot is greatly improved, and the connection stability of the intelligent robot and the communication network is ensured.
In the present disclosure, the above-described scheme may be implemented by one electronic device, which may be, for example, a robot. For example, a warehouse robot, a floor sweeping robot, and a service robot (e.g., a robot applied to service scenarios such as a dish delivery scenario and an indoor navigation scenario). Specifically, the method can be implemented by calling a special application program loaded in the robot, can also be implemented by calling other application programs set in the robot, and can also be implemented by calling a cloud server through the robot. Or the above scheme can also be implemented by a server. The scheme can be realized by the two-two negotiation of the robots in the same area. The scheme can also be realized by matching a plurality of electronic devices. For example, the server may send the execution result to the terminal device to schedule the terminal device to implement the execution result. The server may be a physical server including an independent host, or may also be a virtual server borne by a host cluster, or may also be a cloud server, which is not limited in the present disclosure.
Based on the core ideas introduced in the foregoing, an embodiment of the present disclosure provides a method for optimizing network quality, and fig. 1 is a flowchart illustrating the method for optimizing network quality according to an exemplary embodiment of the present disclosure. As shown in fig. 1, the method includes:
101. acquiring a network detection result of the intelligent robot in the current environment;
102. determining the communication network quality of the intelligent robot in the current environment according to the network detection result;
103. and if the communication network quality does not meet the network quality requirement of the current service scene, switching the position of the intelligent robot and/or the type of the connected communication network so that the communication network quality of the switched intelligent robot meets the network quality requirement.
According to the method, the position of the intelligent robot and/or the type of the connected communication network are/is switched based on the communication network quality of the intelligent robot in the current environment, so that the switched communication network quality of the intelligent robot meets the network quality requirement, the communication network quality of the intelligent robot is greatly improved, and the connection stability of the intelligent robot and the communication network is ensured.
Each step in the network quality optimization method is described below with reference to specific embodiments.
101, obtaining the network detection result of the intelligent robot in the current environment.
And 102, determining the communication network quality of the intelligent robot in the current environment according to the network detection result.
In the present disclosure, the communication Network type in the environment of the intelligent robot includes, but is not limited to, one or more of wireless communication technology (Wi-Fi), Mobile Network (Mobile Network), Ethernet (Ethernet), and BT-pan. Mobile terminal networks such as 4G networks, 5G networks. Taking an Android system as an example, if a plurality of usable communication networks exist, Wi-Fi is selected preferentially, and then a 4G network or a 5G network is selected. In practical application, the intelligent robot is in different application scenes and different communication network types under different environments.
The communication network quality (also referred to as network performance indicators) to be evaluated in the present disclosure includes, but is not limited to, packet loss rate, communication efficiency, data transmission rate, delay, jitter. Specifically, the packet loss rate is subject to the uplink bandwidth and the signal coverage condition of the communication network (such as the signal strength of the communication network). In particular, it is assumed that the communication network quality to be probed includes a packet loss rate. Based on this, the Network performance index detected by a Robot Network management module (RNM) arranged in the intelligent Robot is used to determine the packet loss rate of the intelligent Robot in the current environment according to the Network performance index, so as to judge the current Network state of the intelligent Robot. The communication efficiency may be obtained from the amount of communication signals and the total amount of communication. The communication efficiency, the data transmission rate and the time delay of the communication network all affect the instant response rate of the networking service in the communication network, for example, the interaction function of the intelligent robot in the human-computer interaction scene (for example, the cloud server is required to provide a response answer, or a remote function is implemented for a user) is affected by the network performance index.
In order to improve compatibility and avoid affecting the current online running intelligent robot, a manual switching mode and an automatic switching mode are optionally provided in the disclosure. The manual switching mode may be implemented by responding to a selection instruction of a user for an available communication network type, and switching the communication network type connected to the intelligent robot, or performing mobile switching on the position where the intelligent robot is located. And the automatic switching mode is described in detail with reference to 103 in the following embodiments.
And 103, if the communication network quality does not meet the network quality requirement of the current service scene, switching the position of the intelligent robot and/or the type of the connected communication network so that the communication network quality of the switched intelligent robot meets the network quality requirement.
In the present disclosure, the application scenarios where the intelligent robot is located may be divided into two types, a fixed scenario and a mobile scenario, according to the motion state where the intelligent robot is located. Of course, in some examples, the intelligent robot may be classified into a warehousing scene, a shopping mall navigation scene, a home scene, and the like according to the function of the scene where the intelligent robot is located. The present disclosure does not limit the classification manner of the scene where the intelligent robot is located.
Based on the first scene classification method, for a fixed scene, the following switching mechanism can be adopted to adjust the quality of the intelligent robot communication network, that is:
assume that the communication network quality includes a packet loss rate. Based on this, in an optional embodiment, in 103, if the communication network quality does not meet the network quality requirement of the current service scenario, the position where the intelligent robot is located is switched, which may be implemented as:
and if the packet loss rate of the intelligent robot in the current position is greater than the packet loss rate threshold value required by the current service scene, triggering the intelligent robot to move the position, and detecting the packet loss rate of the intelligent robot in the moving process. And if the packet loss rate of the intelligent robot in the moving process is smaller than the packet loss rate threshold value, indicating the intelligent robot to stop moving.
For example, assume that the current business scenario is a conversation scenario. Assuming that the packet loss rate threshold required in the dialog scene is 30%, based on the above assumptions, if the packet loss rate of the intelligent robot in the current location is greater than 30%, it indicates that the communication network signal strength of the intelligent robot in the current location is relatively weak, and since the communication network signal strength is non-uniformly distributed in the same space, the intelligent robot may be triggered to switch to the navigation state to perform location movement, so that the intelligent robot can find a location with better signal strength. Furthermore, if it is detected that the packet loss rate of the intelligent robot in the moving process is less than 30%, the communication network signal strength at the moving position of the intelligent robot is considered to be relatively strong, and under the condition, the intelligent robot can be instructed to switch from the navigation state to the non-navigation state so as to stop the position moving, and a network quality optimization process is completed. It should be noted that, besides the navigation and network searching manner in the above example, the position movement may also be implemented in other manners, for example, the position movement may be implemented by randomly moving any distance, or the position of the intelligent robot may be adjusted by adjusting the orientation of the intelligent robot.
In practical application, in the moving process, the packet loss rate of the intelligent robot can be detected in real time, and the packet loss rate of the intelligent robot can also be detected periodically. For example, the packet loss rate of the intelligent robot in the current mobile position is detected once every 3 seconds, and three times of detection is one period. If the average packet loss rate of the intelligent robot in one period is less than 30%, the intelligent robot is considered to have moved to a position where the signal intensity of the communication network is relatively strong in the current period, and under the condition, the intelligent robot can be instructed to switch from a navigation state to a non-navigation state, and the position movement is stopped.
Through the steps, the switching of the position of the intelligent robot can be realized, so that the intelligent robot is moved to the position with relatively strong communication network signal intensity, and the communication network quality of the intelligent robot after the position switching meets the network quality requirement.
In another optional embodiment, in 103, if the communication network quality does not meet the network quality requirement of the current service scenario, switching the type of the communication network connected to the intelligent robot may be implemented as:
if the packet loss rate of the intelligent robot in the current position is greater than the threshold value of the packet loss rate required by the current service scene, determining the current running state of the intelligent robot; and if the current running state of the intelligent robot is a non-navigation state, switching the intelligent robot from the first network connected currently to a second network of another type.
In the present disclosure, the first network and the second network belong to different communication network types. For example, the first network is a wifi network, and the second network is a 4G network or a 5G network. Or the first network is a 4G network or a 5G network, and the second network is a wifi network.
For example, assume that the current business scenario is a conversation scenario. Assuming that the packet loss rate threshold required in the dialog scenario is 30%, based on the above assumptions, if the packet loss rate of the intelligent robot in the current location is greater than 30%, it also indicates that the signal strength of the communication network to which the intelligent robot is currently connected is relatively weak, and since the signal coverage conditions of different types of communication networks in the same space are different, that is, the signal of the network a in the current location is strong, and the signal of the network b in the current location is possibly weak, the current operating state of the intelligent robot is determined. Wherein the operational state comprises a navigational state or a non-navigational state. And further, if the current running state of the intelligent robot is a non-navigation state, switching the intelligent robot from the first network connected currently to a second network of another type, and completing a network quality optimization process.
Through the steps, the switching of the communication network type connected with the intelligent robot can be realized, so that the intelligent robot is switched into the communication network with higher communication network quality, and the communication network quality of the switched intelligent robot meets the network quality requirement.
In another optional embodiment, in 103, if the communication network quality does not meet the network quality requirement of the current service scenario, switching the communication network type connected to the intelligent robot may be implemented as: and if the communication efficiency of the intelligent robot in the current position is lower than the communication efficiency threshold value, switching the intelligent robot from the first network which is connected currently to the second network of which the communication efficiency is higher than the communication efficiency threshold value.
Optionally, before 103, a communication efficiency threshold required by the communication task of the current service scenario is determined according to the demand for immediate response in the current service scenario. For example, assume that the current service scenario is a human-computer interaction scenario. The demand of the instant response in the scene is high, for example, the intelligent robot needs to answer the user within 1 second, and based on the demand, a communication efficiency threshold required by the communication task in the human-computer interaction scene is calculated according to an acceptable range (such as not less than 1 second) of the instant response rate.
Continuing with the above example, assuming that the communication efficiency threshold required in the human-computer interaction scenario is m, based on the above assumption, if the communication efficiency of the intelligent robot in the current location is less than m, it also indicates that the data transmission rate currently connected to the intelligent robot is relatively slow, and the instant response requirement of the human-computer interaction scenario cannot be met, for example, the instant response rate of the intelligent robot is slower than 1 second. Because the signal coverage conditions of different types of communication networks in the same space are different, namely the data transmission rate of the network c at the current position is higher, and the data transmission rate of the network d at the current position is possibly lower, in order to improve the data transmission rate and meet the instant response requirement of a man-machine interaction scene, the intelligent robot can be switched from a first network connected currently to a second network of another type, and a network quality optimization process is completed.
Through the steps, the switching of the communication network type connected with the intelligent robot can be realized, so that the intelligent robot is switched into a communication network with higher communication network quality (such as higher data transmission rate), and the communication network quality of the switched intelligent robot meets the network quality requirement.
Optionally, in 103, the network switching times of the intelligent robot in a preset time period is also obtained. And if the network switching times exceed a set time threshold, triggering the intelligent robot to switch to a navigation state for position movement, and stopping the position movement until the packet loss rate of the intelligent robot is less than a packet loss rate threshold. Therefore, the problem that the quality of the communication network cannot be improved due to frequent switching of the communication network type in an area with poor signal intensity of the communication network by the intelligent robot is avoided, and the quality of the communication network is greatly improved. The method can reduce repeated switching of communication network types, reduce loss of system performance caused by frequent switching of communication network types, and improve task execution efficiency of the intelligent robot.
Optionally, in the present disclosure, a communication network type and a network signal coverage condition that the intelligent robot can connect in a mobile scene are obtained. Specifically, the network signal coverage condition may be determined according to the network signal strength received by the intelligent robot at each location. In an optional embodiment, the intelligent robots in the same scene may report their respective positions and the currently detected network signal strength to the cloud server, so as to form a network signal coverage condition of the current scene in the cloud server. Of course, in another alternative embodiment, the network signal coverage in the current scene may be maintained by mutually transmitting the respective detected network signal strengths through a plurality of intelligent robots in the same scene. In order to save the calculation power, in this case, each intelligent robot can maintain the network signal coverage condition of the local area in the current scene.
Furthermore, in 103, switching the location of the intelligent robot and/or the type of the connected communication network may be implemented as: and switching the intelligent robot from the first network connected currently to a second network of another type which can be connected according to the network signal coverage condition. In practical applications, the first network may be a wifi network, and the second network may be a 4G network or a 5G network; alternatively, the first network may be a 4G network or a 5G network and the second network may be a wifi network.
The following describes a specific implementation manner of each step in conjunction with the network quality optimization flow shown in fig. 2.
In fig. 2, a Robot Network Client (RNC) module initiates monitoring of a packet loss rate every 3 seconds, and 3 monitoring operations are a period. Therefore, the network detection result is determined according to the packet loss rate in each period, and the network detection result is sent to other service function modules at least once in each period. And then, the other service function modules judge whether the quality of the currently connected communication network meets the network quality requirement of the current service scene according to the network quality requirement of the current module and the received network detection result, and optimize the network quality based on the judgment result.
Specifically, if the packet loss rate currently received is less than 30%, it may be considered that the communication network currently connected to the intelligent robot is normally available. And further, judging whether the intelligent robot is in a navigation state or not. If the intelligent robot is in the navigation state and the navigation type is network finding navigation, the intelligent robot can be considered to have moved to a position with relatively strong communication network signal strength in the current period, and under the condition, the intelligent robot can be instructed to switch from the navigation state to the non-navigation state and stop moving the position. For example, the instant positioning And Mapping (SLAM) module is notified to stop navigation. If the intelligent robot is in a non-navigation state, no network quality optimization operation needs to be performed. Other models besides the SLAM module may be used to implement navigation functions, and the disclosure is not limited thereto.
If the packet loss rate currently received is greater than 30%, it is also necessary to further determine whether the intelligent robot is in the navigation state. If the intelligent robot is in the navigation state, the SLAM module can work off line, and can be considered not to reach a position with relatively strong communication network signal intensity, and in this case, any network quality optimization operation is not required to be executed, and the navigation state is continuously maintained. If the intelligent robot is in a non-navigation state, the signal intensity of a communication network connected with the intelligent robot is considered to be relatively weak, and the communication network type connected with the intelligent robot needs to be switched, so that the intelligent robot is switched into a communication network with high communication network quality, and the communication network quality of the switched intelligent robot meets the network quality requirement. Furthermore, if the Network switching times of the intelligent robot exceed 3 times and the Network detection result still indicates that the communication Network quality is not good after each switching, it can be considered that each communication Network quality in the position is not good (such as Wi-Fi and Mobile Network), at this time, the intelligent robot can be triggered to switch to a navigation state for position movement, and the position movement is stopped until the packet loss rate of the intelligent robot is smaller than the packet loss rate threshold.
In the network quality optimization method shown in fig. 1, the position of the intelligent robot and/or the type of the connected communication network are/is switched based on the communication network quality of the intelligent robot in the current environment, so that the communication network quality of the switched intelligent robot meets the network quality requirement, the communication network quality of the intelligent robot is greatly improved, and the connection stability between the intelligent robot and the communication network is ensured.
Fig. 3 is a network quality optimization apparatus provided in an embodiment of the present disclosure. As shown in fig. 3, the network quality optimization apparatus includes:
an obtaining module 301 configured to obtain a network detection result of the intelligent robot in a current environment;
a determining module 302 configured to determine the communication network quality of the intelligent robot in the current environment according to the network detection result;
the switching module 303 is configured to switch the location of the intelligent robot and/or the type of the connected communication network if the communication network quality does not meet the network quality requirement of the current service scenario, so that the communication network quality of the intelligent robot after switching meets the network quality requirement.
Optionally, the communication network quality comprises a packet loss rate. Based on this, if the communication network quality does not meet the network quality requirement of the current service scenario, the switching module 303 is configured to, in the process of switching the position of the intelligent robot:
if the packet loss rate of the intelligent robot in the current position is larger than the threshold value of the packet loss rate required by the current service scene, determining that the current service scene is the current service scene
Triggering the intelligent robot to move the position, and detecting the packet loss rate of the intelligent robot in the moving process;
and if the packet loss rate of the intelligent robot in the moving process is smaller than the packet loss rate threshold value, indicating the intelligent robot to stop moving.
Optionally, the communication network quality comprises a packet loss rate. Based on this, if the communication network quality does not meet the network quality requirement of the current service scenario, the switching module 303 is configured to, in the process of switching the communication network type connected to the intelligent robot:
if the packet loss rate of the intelligent robot in the current position is larger than the threshold value of the packet loss rate required by the current service scene, determining the current running state of the intelligent robot;
and if the current running state of the intelligent robot is a non-navigation state, switching the intelligent robot from the first network connected currently to a second network of another type.
Optionally, the navigation state switching module is further configured to acquire the network switching times of the intelligent robot in a preset time period. And if the network switching times exceed a set time threshold, triggering the intelligent robot to carry out position movement, and stopping the position movement until the packet loss rate of the intelligent robot is less than the packet loss rate threshold.
Optionally, the system further comprises a determining module configured to determine a communication efficiency threshold required by the communication task of the current service scenario according to the demand for immediate response in the current service scenario.
If the communication network quality does not meet the network quality requirement of the current service scenario, the switching module 303 is configured to, in the process of switching the communication network type connected to the intelligent robot:
and if the communication efficiency of the intelligent robot in the current position is lower than the communication efficiency threshold, switching the intelligent robot from a first network which is connected currently to a second network of which the communication efficiency is higher than the communication efficiency threshold.
Optionally, the obtaining module 301 is further configured to obtain a communication network type and a network signal coverage that the intelligent robot can connect to in a mobile scenario.
The switching module 303, in switching the location of the intelligent robot and/or the type of the connected communication network, is configured to: and switching the intelligent robot from the first network which is connected currently to another type of second network which is connected to the intelligent robot according to the network signal coverage condition.
Optionally, if the first network is a wifi network, the second network is a 4G network or a 5G network; or if the first network is a 4G network or a 5G network, the second network is a wifi network.
The network quality optimization apparatus may execute the systems or methods provided in the foregoing embodiments, and for parts not described in detail in this embodiment, reference may be made to relevant descriptions of the foregoing embodiments, and details are not repeated here.
In one possible design, the structure of the network quality optimization device may be implemented as an electronic device. As shown in fig. 4, the electronic device may include: a processor 21 and a memory 22. Wherein the memory 22 has stored thereon executable code which, when executed by the processor 21, at least makes the processor 21 capable of implementing the network quality optimization method as provided in the previous embodiments.
The electronic device may further include a communication interface 23 for communicating with other devices or a communication network.
In addition, the present disclosure also provides a computer-readable storage medium comprising instructions, which stores executable code thereon, and when the executable code is executed by a processor of a wireless router, the processor is caused to execute the neural network-based feature data processing method provided in the foregoing embodiments. Alternatively, the computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
In an exemplary embodiment, there is also provided a computer program product comprising computer programs/instructions which, when executed by a processor, implement the neural network-based feature data processing methods provided in the foregoing embodiments. The computer program/instructions are implemented by a program running on a terminal or a server.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims (11)

1. A method for optimizing network quality, comprising:
acquiring a network detection result of the intelligent robot in the current environment;
determining the communication network quality of the intelligent robot in the current environment according to the network detection result;
and if the communication network quality does not meet the network quality requirement of the current service scene, switching the position of the intelligent robot and/or the type of the connected communication network so that the communication network quality of the intelligent robot after switching meets the network quality requirement.
2. The method of claim 1, wherein the communication network quality comprises a packet loss rate;
if the communication network quality does not meet the network quality requirement of the current service scene, switching the position of the intelligent robot, including:
if the packet loss rate of the intelligent robot in the current position is larger than the threshold value of the packet loss rate required by the current service scene, determining that the current service scene is the current service scene
Triggering the intelligent robot to move the position, and detecting the packet loss rate of the intelligent robot in the moving process;
and if the packet loss rate of the intelligent robot in the moving process is smaller than the packet loss rate threshold value, indicating the intelligent robot to stop moving.
3. The method of claim 1, wherein the communication network quality comprises a packet loss rate;
if the communication network quality does not meet the network quality requirement of the current service scene, switching the communication network type connected with the intelligent robot, including:
if the packet loss rate of the intelligent robot in the current position is greater than the threshold value of the packet loss rate required by the current service scene, determining the current running state of the intelligent robot;
and if the current running state of the intelligent robot is a non-navigation state, switching the intelligent robot from the first network connected currently to a second network of another type.
4. The method of claim 3, further comprising:
acquiring the network switching times of the intelligent robot in a preset time period;
and if the network switching times exceed a set time threshold, triggering the intelligent robot to carry out position movement, and stopping the position movement until the packet loss rate of the intelligent robot is less than the packet loss rate threshold.
5. The method of claim 1, further comprising:
determining a communication efficiency threshold value required by a communication task of the current service scene according to the instant response requirement in the current service scene;
if the communication network quality does not meet the network quality requirement of the current service scene, switching the communication network type connected with the intelligent robot, including:
and if the communication efficiency of the intelligent robot in the current position is lower than the communication efficiency threshold, switching the intelligent robot from a first network which is connected currently to a second network of which the communication efficiency is higher than the communication efficiency threshold.
6. The method of claim 1, further comprising:
the method comprises the steps of obtaining the connectable communication network type and the network signal coverage condition of the intelligent robot in a mobile scene;
the switching the position of the intelligent robot and/or the type of the connected communication network comprises:
and switching the intelligent robot from the first network connected currently to a second network of another type which can be connected according to the network signal coverage condition.
7. The method according to any one of claims 3 to 6, wherein if the first network is a wifi network, the second network is a 4G network or a 5G network; or
And if the first network is a 4G network or a 5G network, the second network is a wifi network.
8. A network quality optimization apparatus, comprising:
the acquisition module is configured to acquire a network detection result of the intelligent robot in the current environment;
a determining module configured to determine the communication network quality of the intelligent robot in the current environment according to the network detection result;
and the switching module is configured to switch the position of the intelligent robot and/or the type of the connected communication network if the communication network quality does not meet the network quality requirement of the current service scene, so that the communication network quality of the intelligent robot after switching meets the network quality requirement.
9. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the network quality optimization method of any of claims 1 to 7.
10. A computer-readable storage medium whose instructions, when executed by an electronic device, enable the electronic device to perform the network quality optimization method of any of claims 1 to 7.
11. A computer program product comprising computer programs/instructions, characterized in that the computer programs/instructions, when executed by a processor, implement the network quality optimization method of any of claims 1 to 7.
CN202210179837.1A 2022-02-25 2022-02-25 Network quality optimization method and device Pending CN114727215A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210179837.1A CN114727215A (en) 2022-02-25 2022-02-25 Network quality optimization method and device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210179837.1A CN114727215A (en) 2022-02-25 2022-02-25 Network quality optimization method and device

Publications (1)

Publication Number Publication Date
CN114727215A true CN114727215A (en) 2022-07-08

Family

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Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210179837.1A Pending CN114727215A (en) 2022-02-25 2022-02-25 Network quality optimization method and device

Country Status (1)

Country Link
CN (1) CN114727215A (en)

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