CN115550968A - A Self-learning Mesh Network Work Control Method - Google Patents

Abstract

The invention relates to a self-learning Mesh network work control method, wherein a Mesh main node MPP and each sub-node MP perform autonomous environment scanning to learn and acquire data information in the environment; MPP and each MP learn to record data information of each user connection; the MPP and each MP screen and store the learned data; each MP reports the screened data to the MPP, and the MPP selects and stores the optimal working configuration; the MPP selects the optimal working configuration to work. The Mesh network is fully optimized, the network configuration is dynamically adjusted and optimized, and the use experience of a user is greatly improved.

Description

A Self-learning Mesh Network Work Control Method

technical field

The invention relates to a self-learning Mesh network work control method.

Background technique

At present, with the development of wireless communication technology, the current wireless WiFi network has been applied to thousands of households, which greatly facilitates the user's surfing experience on the Internet. As a new type of wireless network technology, wireless Mesh network has the advantages of self-organizing network, self-repair, self-optimization, and convenient expansion, which greatly facilitates the use of wireless networks and is increasingly used in homes and small and medium-sized enterprises. The Mesh network includes a master node (MPP) and multiple sub-nodes (MP). The master and sub-nodes can be connected through Ethernet cables or wirelessly, and each sub-node must access the network through the master node. Because general users are not familiar with wireless technology configuration, or do not change the configuration after a deployment, and the external environment is constantly changing, the Mesh network is getting worse and worse, and some blind spots even appear, seriously affecting user experience.

Therefore, if a Mesh network can continuously optimize its own network configuration according to changes in the external use environment, it will greatly improve the user experience.

Contents of the invention

The purpose of the present invention is to overcome the deficiencies in the prior art, and provide a self-learning Mesh network work control method, through which each Mesh node independently learns the external environment and user usage conditions, dynamically optimizes the Mesh network, and improves user experience.

The purpose of the present invention is achieved through the following technical solutions:

Compared with the prior art, the present invention has significant advantages and beneficial effects, which are embodied in the following aspects:

The present invention uses Mesh nodes to collect and study the use environment and users, and after quantitative or time-based learning analysis, automatically adjusts the working channel, power, bandwidth and speed of each node, so that the entire Mesh network presents an optimal experience effect ;

Fully optimize the Mesh network, dynamically adjust and optimize the network configuration, and greatly improve the user experience.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and appended drawings.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present invention more clearly, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and thus It should be regarded as a limitation on the scope, and those skilled in the art can also obtain other related drawings based on these drawings without creative work.

Figure 1: Schematic flow chart of the present invention.

detailed description

The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. The components of the embodiments of the invention generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations. Accordingly, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts belong to the protection scope of the present invention.

It should be noted that like numerals and letters denote similar items in the following figures, therefore, once an item is defined in one figure, it does not require further definition and explanation in subsequent figures. Meanwhile, in the description of the present invention, orientation terms, order terms, etc. are only used to distinguish descriptions, and cannot be understood as indicating or implying relative importance.

As shown in Figure 1, a self-learning Mesh network work management method specifically includes the following steps:

1) The Mesh master node MPP and each child node MP conduct autonomous environment scanning to learn and acquire data information in the environment.

2), MPP and each MP study and record the data information of each user connection.

3) The MPP and each MP calculate, screen and store the learned data.

4) Each MP reports the screened data to the MPP, and the MPP screens out the best working configuration and stores it.

5). The MPP selects the best working configuration to work.

Step 1) specifically includes: the MPP and the MP detect their own work and operation conditions, and obtain the information of their own connected devices, working channels, bandwidth, CPU and memory usage.

The MP reports the information and bandwidth of its own downlink users to the MPP, and the MPP judges that the current user is in use according to the situation reported by the MP and whether there are downlink users or the user traffic is greater than a certain threshold (such as 100kbps). Scan to learn. And if it is in an idle state, an instruction is issued to the MP for scanning and learning. The same determination is also made for the MPP in the same manner.

MPP and MP need to obtain other AP channels, RSSI, Signal, SSID and other information in the environment through scanning. These information are used to judge the signal strength between MPP and MP, to decide whether to adjust power, reduce mutual interference and Speed up roaming handoffs.

Wherein step 2) specifically includes: MPP and MP acquire the RSSI, MAC of the currently connected user, and simultaneously acquire the RSSI of users connected to other nodes. These information are used to judge the signal strength between the user and the MPP or MP for Determine whether there is a blind spot in signal use. For some points with poor signal, it is necessary to use a low rate to optimize the stable use of the connection.

Wherein, step 3) specifically includes: the MPP and each MP firstly eliminate signals with a signal of 0, then add all signals of the same channel, and select 3 channels with the smallest Signal values as target channels for storage. Because the larger the Signal, the stronger the interference signal, so the channel with the minimum value is taken as the target channel.

The MPP and each MP update and store the maximum and minimum values of RSSI between it and other nodes MP and MPP. Storing this value can be used to judge the stability of the connection between nodes, and combined with the learning situation after the configuration change, to judge which best configuration.

The MPP and each MP update and store the maximum and minimum RSSI values of connected users. This value is stored to determine whether there is a blind spot in use. If there is an unstable connection blind spot, the connection stability is improved by fixing a low rate.

MPP and each MP clears user records that have not been updated for more than 3 days. It can reduce some useless learning references and reduce storage space.

Step 4) specifically includes: the MPP compares the target channel screened by itself with the data reported by each MP, and selects the target channel with the smallest Signal for storage. The screening method is to compare the signal on which channel is the smallest according to the product of different node weights and Signal, so as to select the target channel. The weight can be determined according to the node that the user is most frequently connected to or the node with the largest number of users connected to it.

The MPP determines whether to perform transmission power adjustment in combination with the RSSI between nodes reported by each MP. If there are no learning records under other powers, and the current RSSI value is greater than -50dBm, the MPP can issue instructions to the MP for dynamic power adjustment, obtain the RSSI between nodes under other powers again, and report again. The MPP then decides which level of power to use as the target power according to the roaming threshold.

The MPP decides whether to fix the user rate on a certain node device in combination with the RSSI of the user reported by each MP. If the RSSI of a certain user is often less than -80dBm, it means that there is a blind spot that makes the user's signal poor. Then adjust when the RSSI is less than -80dBm, and fix the connection rate at CCK b mode to improve connection stability.

Step 5) specifically includes: after the MPP repeatedly learns for 3 days, finally select the configuration decision with the highest matching degree and apply the corresponding decision. When applying, it is necessary to choose free time to configure and apply. Secondly, for decision-making configurations that do not have a high degree of matching, multiple learning is required. The degree of matching is determined by the goal of each decision. If the goal is consistent for more than three times, the degree of matching is considered high.

MPP only keeps the last three final decision configurations, and clears the rest of the records.

To sum up, the present invention uses each Mesh node to collect and learn the usage environment and users, and after quantitative or time-based learning and analysis, then automatically adjusts the working channel, power, bandwidth and speed of each node, so that the entire Mesh network presents a The best experience effect.

In the traditional Mesh network environment, because users seldom adjust the Mesh network configuration, but the environment is constantly changing, this may lead to worse and worse Mesh network experience, and users do not know how to adjust and optimize, but the present invention fully optimizes Mesh network dynamically adjusts and optimizes network configuration, greatly improving user experience.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention. It should be noted that like numerals and letters denote similar items in the following figures, therefore, once an item is defined in one figure, it does not require further definition and explanation in subsequent figures.

The above is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present invention, which should be included in the scope of the present invention. within the protection scope of the present invention.

It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is a relationship between these entities or operations. There is no such actual relationship or order between them. Furthermore, the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also includes elements not expressly listed. other elements of or also include elements inherent in such a process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.

Claims (6)

1. A self-learning Mesh network work control method is characterized in that: the method comprises the following steps:

1) Carrying out autonomous environment scanning on the Mesh master node MPP and each child node MP, and learning to obtain data information in the environment;

2) MPP and each MP learn to record data information of each user connection;

3) The MPP and each MP screen and store the learned data;

4) Each MP reports the screened data to MPP, and the MPP selects and stores the optimal working configuration;

5) The MPP selects the optimal working configuration to work.

2. The self-learning Mesh network work control method according to claim 1, characterized in that: step 1), MPP and MP scan the environment autonomously, learn to acquire environmental data information, and further include:

MPP and MP detect the working operation condition of the MPP and MP and acquire the information of equipment hung under the MPP and the working channel, the bandwidth, the CPU and the memory occupation condition;

the MPP judges whether the MP can be scanned and learned or not according to the condition reported by the MP and sends an instruction to the MP for scanning and learning;

MPP and MP scan and acquire channels, RSSI, signal and SSID of other APs in the environment.

3. The work control method of the self-learning Mesh network as claimed in claim 1, wherein: said step 2), MPP and each MP learning record makes data information by user connection each time, further comprising:

MPP and MP acquire RSSI and MAC conditions of a current connected user;

MPP and MP obtain RSSI of users under other nodes.

4. The work control method of the self-learning Mesh network as claimed in claim 1, wherein: step 3), the MPP and each MP filter and store the learned data, and the MPP and each MP further comprise:

MPP and each MP firstly eliminate 0 Signal, then add all the signals of the same channel, and select 3 channels with the minimum Signal value as target channels for storage;

MPP and each MP update the maximum and minimum values of RSSI between the MPP and other nodes MP and MPP and store the maximum and minimum values;

MPPs and each MP update and store the maximum and minimum values of the RSSI of the connected users;

the MPP and each MP clear the user records that have not been updated for more than 3 days.

5. The work control method of the self-learning Mesh network as claimed in claim 1, wherein: step 4), each MP reports the screened data to the MPP, which selects and stores the optimal working configuration, and further includes:

MPP compares the target channels screened by the MPP with the data reported by each MP, and selects the target channel with the minimum Signal for storage;

MPP determines whether transmission power adjustment is needed or not by combining with RSSI among nodes reported by each MP, and if so, sends an instruction to the MP to perform dynamic power adjustment and repeats step 1);

the MPP determines whether the user rate needs to be fixed on certain node equipment or not by combining the RSSI of the users reported by each MP, and records and stores the decision result.

6. The self-learning Mesh network work control method according to claim 1, characterized in that: step 5), the MPP selects the optimal working configuration for working, and the method further comprises the following steps:

after MPP learns repeatedly for 3 days, final decision configuration is applied, wherein the final decision configuration comprises channel adjustment, power adjustment and fixed rate adjustment;

the MPP only keeps the final decision configuration of the last 3 times and clears the rest records.

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