Multi-user wireless resource collaborative optimization allocation method, apparatus, device and program product

Abstract

The invention discloses a multi-user wireless resource collaborative optimization distribution method, device, equipment and program product, and the method comprises the steps: obtaining multi-source network environment data of a plurality of target access equipment in a target region, and uploading the multi-source network environment data to a cloud; determining a signal interference matrix of the target area according to the peripheral AP list and the equipment position code, and generating a resource conflict thermodynamic diagram of the target area according to the signal interference matrix; predicting a network load of each target access device in a preset future time period according to the traffic time sequence data; and determining a target channel and target signal power of each target access device in a preset future time period according to the resource conflict thermodynamic diagram and the network load, obtaining a wireless resource allocation strategy, and issuing the wireless resource allocation strategy to each target access device. According to the invention, co-channel interference between adjacent users is avoided, the efficiency of wireless resource allocation and the accuracy of power control are improved, and the method can be widely applied to the technical field of wireless communication.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a method, apparatus, device, and program product for multi-user wireless resource collaborative optimization allocation. Background Technology

[0002] With the increasing prevalence of smart home devices and rising residential density, traditional wireless channel allocation mechanisms for home gateways or routers (such as static configuration or single-gateway adaptive tuning) are struggling to cope with dynamic interference. For example, the 2.4GHz band only has three non-overlapping channels (1, 6, and 11), while in densely populated areas (such as apartment buildings), up to 80% of gateways or routers default to using channel 6, leading to increased co-channel interference.

[0003] Currently, home gateways or routers mainly employ two types of channel optimization schemes: single-device adaptive optimization and manual adjustment. The single-device adaptive optimization scheme automatically switches channels based on local traffic monitoring (such as channels 1 / 6 / 11 in the 2.4GHz band), but it can only perceive its own environment and cannot obtain the status of devices in neighboring homes; the manual adjustment scheme relies on users to select channels using Wi-Fi analysis tools, which is complex to operate and easily reset by interference from newly connected devices.

[0004] In summary, existing wireless channel allocation schemes have the following drawbacks: 1) Lack of global coordination: a single gateway or router cannot achieve coordinated allocation of channel resources across users, forming "information islands". Isolated decision-making leads to the same channel and serious co-channel interference; 2) The use of fixed power control and resource allocation strategies lacks the ability to dynamically respond to real-time channel conditions, changes in user demand, and fluctuations in network load, resulting in low resource allocation efficiency and easy channel conflicts and resource waste. 3) The 5GHz band is underutilized, and traditional strategies tend to ignore the dynamic frequency selection (DFS) mechanism, making it impossible to avoid radar channel interference.

[0005] 4) It is impossible to allocate and reserve channel resources in advance based on historical data.

[0006] The above problems urgently need to be addressed.

[0007] Terminology Explanation: AP (Access Point): A wireless access point, also known as a wireless local area network (WLAN) access point, plays the role of a "bridge" between wireless and wired networks.

[0008] DFS (Dynamic Frequency Selection) is an intelligent frequency allocation technology used in wireless communication systems. It is widely used in scenarios such as Wi-Fi and 5G when radar avoidance or spectrum sharing is involved. Summary of the Invention

[0009] The purpose of this invention is to at least partially solve one of the technical problems existing in the prior art.

[0010] Therefore, one objective of this invention is to provide a multi-user wireless resource collaborative optimization allocation method that avoids co-channel interference between adjacent users and improves the efficiency of wireless resource allocation and the accuracy of power control.

[0011] Another objective of this invention is to provide a multi-user wireless resource collaborative optimization allocation device.

[0012] To achieve the above-mentioned technical objectives, the technical solutions adopted in the embodiments of the present invention include: On one hand, embodiments of the present invention provide a multi-user wireless resource collaborative optimization allocation method, including the following steps: Acquire multi-source network environment data of multiple target access devices within the target area, and upload the multi-source network environment data to the cloud. The multi-source network environment data includes a list of surrounding APs, device location codes, and traffic time series data. The signal interference matrix of the target area is determined based on the surrounding AP list and the device location code, and a resource conflict heatmap of the target area is generated based on the signal interference matrix. Predict the network load of each target access device in a preset future time period based on the traffic time series data; Based on the resource conflict heatmap and the network load, the target channel and target signal power of each target access device in the preset future time period are determined, a wireless resource allocation strategy is obtained, and the wireless resource allocation strategy is sent to each target access device.

[0013] Furthermore, in one embodiment of the present invention, the step of acquiring multi-source network environment data of multiple target access devices within a target area and uploading the multi-source network environment data to the cloud specifically includes: The target access device collects channel information, received signal strength indicators, and occupied frequency band information of surrounding APs to obtain a list of surrounding APs; The device location code of the target access device is obtained by querying the IP address and broadband registration information of the target access device. The traffic time-series data is obtained by statistically analyzing the traffic volume of multiple data types of the target access device at different times. The list of surrounding APs, the device location codes, and the traffic time-series data are uploaded to the cloud.

[0014] Furthermore, in one embodiment of the present invention, determining the signal interference matrix of the target area based on the surrounding AP list and the device location code specifically includes: The access device distribution topology of the target area is determined based on the surrounding AP list and the device location code; The signal interference intensity between the two target access devices is determined based on the received signal strength index. The signal interference matrix is ​​constructed based on the distribution topology of the access devices and the signal interference intensity.

[0015] Furthermore, in one embodiment of the present invention, the step of generating a resource conflict heatmap of the target area based on the signal interference matrix specifically includes: Based on the channel information and the occupied frequency band information, multiple target access devices with the same frequency band and the same channel are identified as co-frequency access devices; The signal interference intensity between each pair of the same-frequency access devices is determined based on the signal interference matrix, and the resource conflict intensity of the corresponding frequency band and corresponding channel is determined based on the signal interference intensity. The resource conflict heatmap is generated based on the resource conflict intensity, with the channel number as the horizontal axis and the frequency band number as the vertical axis.

[0016] Furthermore, in one embodiment of the present invention, the step of predicting the network load of each of the target access devices in a preset future time period based on the traffic time-series data specifically includes: The traffic time series data is input into a pre-trained network load prediction model to obtain the network load of each target access device in the preset future time period; The network load prediction model is a model obtained by training a long short-term memory network using historical traffic sequences.

[0017] Furthermore, in one embodiment of the present invention, the step of determining the target channel and target signal power of each target access device in the preset future time period based on the resource conflict heatmap and the network load, obtaining a wireless resource allocation strategy, and distributing the wireless resource allocation strategy to each target access device specifically includes: Based on the resource conflict heatmap, multiple idle channels with resource conflict levels below a preset threshold are identified, and the target channels of each target access device in the preset future time period are determined based on the idle channels and the triangulation rule. The target signal power of each target access device in the preset future time period is determined based on the network load; The target channel, the target signal power, and the policy effective period are encapsulated into a wireless resource allocation policy corresponding to each of the target access devices, and the wireless resource allocation policy is sent to the corresponding target access device.

[0018] Furthermore, in one embodiment of the present invention, the multi-user radio resource cooperative optimization allocation method further includes the following steps: The target access device parses the wireless resource allocation policy to obtain the target channel, the target signal power, and the effective period of the policy. When the time reaches the effective period of the strategy, the target access device is switched according to the target channel, and the signal transmission power of the target access device is adjusted according to the target signal power.

[0019] On the other hand, embodiments of the present invention provide a multi-user wireless resource collaborative optimization allocation apparatus, comprising: The data acquisition module is used to acquire multi-source network environment data of multiple target access devices within the target area and upload the multi-source network environment data to the cloud. The multi-source network environment data includes a list of surrounding APs, device location codes, and traffic time series data. The resource conflict analysis module is used to determine the signal interference matrix of the target area based on the surrounding AP list and the device location code, and to generate a resource conflict heat map of the target area based on the signal interference matrix. The network load prediction module is used to predict the network load of each target access device in a preset future time period based on the traffic time series data. The resource allocation module is used to determine the target channel and target signal power of each target access device in the preset future time period based on the resource conflict heat map and the network load, obtain a wireless resource allocation strategy, and send the wireless resource allocation strategy to each target access device.

[0020] On the other hand, embodiments of the present invention provide an electronic device, the electronic device including a memory, a processor, a computer program stored in the memory and executable on the processor, and a data bus for implementing communication between the processor and the memory, wherein the computer program, when executed by the processor, implements the multi-user wireless resource cooperative optimization allocation method as described above.

[0021] On the other hand, embodiments of the present invention also provide a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more computer programs, which can be executed by one or more processors to implement the multi-user wireless resource collaborative optimization allocation method as described above.

[0022] On the other hand, embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the multi-user wireless resource collaborative optimization allocation method as described above.

[0023] The advantages and beneficial effects of the present invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention: This invention acquires multi-source network environment data of multiple target access devices within a target area and uploads this data to the cloud. The multi-source network environment data includes a list of surrounding APs, device location codes, and traffic time-series data. Based on the surrounding AP list and device location codes, a signal interference matrix for the target area is determined. A resource conflict heatmap for the target area is generated based on the signal interference matrix. The network load of each target access device in a preset future time period is predicted based on the traffic time-series data. The target channel and target signal power of each target access device in the preset future time period are determined based on the resource conflict heatmap and network load, resulting in a wireless resource allocation strategy. This strategy is then distributed to each target access device. By collecting and analyzing multi-source network environment data from multiple target access devices, this invention can accurately identify the wireless resource conflict situation in a target area and predict the network load of each target access device. This enables channel allocation and power control for each target access device, avoiding co-channel interference between adjacent users and improving the efficiency of wireless resource allocation and the accuracy of power control. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the embodiments of the present invention are described below. It should be understood that the drawings described below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating the steps of a multi-user wireless resource collaborative optimization allocation method provided in an embodiment of the present invention; Figure 2 A flowchart of step S101 provided in an embodiment of the present invention; Figure 3 A flowchart of step S102 provided in an embodiment of the present invention; Figure 4 Another flowchart of step S102 provided in an embodiment of the present invention; Figure 5 A flowchart of step S103 provided in an embodiment of the present invention; Figure 6 A flowchart of step S104 provided in an embodiment of the present invention; Figure 7 Another flowchart of the multi-user wireless resource collaborative optimization allocation method provided in the embodiments of the present invention; Figure 8 This is a schematic diagram of the structure of a multi-user wireless resource collaborative optimization allocation device provided in an embodiment of the present invention; Figure 9 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention; Figure 10 This is a schematic diagram of the structure of the storage medium provided in an embodiment of the present invention. Detailed Implementation

[0026] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application. It should be noted that although functional modules are divided in the system schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system schematic diagram or the order in the flowchart. The step numbers in the following embodiments are only set for ease of explanation and do not limit the order between steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0028] The multi-user wireless resource collaborative optimization allocation method provided in this application can be applied to terminals, servers, or software running on terminals or servers. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing a driver status monitoring method based on machine vision, but is not limited to the above forms.

[0029] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0030] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards of the relevant countries and regions. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirects to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data for the proper functioning of the embodiments of this application obtained.

[0031] like Figure 1 The diagram shown is a flowchart of a multi-user wireless resource cooperative optimization allocation method provided in an embodiment of the present invention. (Refer to...) Figure 1 This invention provides a multi-user wireless resource collaborative optimization allocation method, which specifically includes the following steps: S101. Obtain multi-source network environment data of multiple target access devices in the target area, and upload the multi-source network environment data to the cloud. The multi-source network environment data includes a list of surrounding APs, device location codes, and traffic time series data. S102. Determine the signal interference matrix of the target area based on the list of surrounding APs and the device location codes, and generate a resource conflict heat map of the target area based on the signal interference matrix; S103. Predict the network load of each target access device in a preset future time period based on traffic time series data; S104. Based on the resource conflict heatmap and network load, determine the target channel and target signal power of each target access device in a preset future time period, obtain the wireless resource allocation strategy, and send the wireless resource allocation strategy to each target access device.

[0032] This invention aims to solve the problem of wireless network channel conflict between multiple different households. Specifically, this invention mainly includes the following aspects: 1) Unified configuration of Wi-Fi channels and power for multiple home users: By collecting and analyzing data from gateways or routers for multiple users, and dynamically monitoring and analyzing global wireless resources, the unified allocation and management of Wi-Fi channels and power for multiple users is realized. This overcomes the isolated allocation strategy of traditional gateways or routers, effectively solves the problem of co-channel interference between adjacent users, and improves the overall system performance. 2) Provides dynamic response capability: By dynamically monitoring and analyzing the wireless resources of multiple users and generating strategies, it overcomes the problem of traditional AP devices' fixed power control and resource allocation strategies lacking dynamic response capability, and significantly improves the allocation efficiency of Wi-Fi channels and power; 3) Avoid co-channel interference degradation in high-density scenarios: Analyze the Wi-Fi channel occupancy of geographically adjacent user network devices through an intensive platform to generate non-overlapping channel allocation strategies; 4) AI-driven predictive optimization: The cloud platform uses machine learning algorithms to analyze long-term historical data, thereby predicting network load trends in different time periods and regions. Based on these predictions, the system can pre-allocate and reserve Wi-Fi channel resources, and even adjust the network structure before peak traffic arrives, achieving a leap from reactive to proactive.

[0033] It can be recognized that, by collecting and analyzing multi-source network environment data of multiple target access devices, the embodiments of the present invention can accurately identify the wireless resource conflict status of the target area and predict the network load of each target access device, thereby realizing channel allocation and power control for each target access device, avoiding co-channel interference between adjacent users, and improving the efficiency of wireless resource allocation and the accuracy of power control.

[0034] like Figure 2 The diagram shown is a flowchart of step S101 provided in an embodiment of the present invention. (Refer to...) Figure 2 As an optional implementation, multi-source network environment data of multiple target access devices within the target area is acquired and uploaded to the cloud, specifically including: S1011. Collect channel information, received signal strength indicators and occupied frequency band information of surrounding APs through the target access device to obtain a list of surrounding APs; S1012. Obtain the device location code of the target access device based on the IP address and broadband registration information of the target access device; S1013. Statistically analyze the traffic volume of multiple data types of the target access device at different times to obtain traffic time-series data; S1014. Upload the list of surrounding APs, device location codes, and traffic timing data to the cloud.

[0035] The scanning function collects a list of surrounding access points (APs), including channel information, Received Signal Strength Index (RSSI), and occupied frequency band information for each AP. Specifically, gateway or router devices scan surrounding Wi-Fi signals in real time, recording the frequency band information, signal strength, channel number, and network name of each Wi-Fi hotspot.

[0036] Based on the WAN IP address and reported broadband account information of the gateway or router, the approximate geographical location of the device can be inferred. Specifically, the gateway or router reports both the WAN IP address and broadband information; the user's location information can then be retrieved using the IP address and broadband registration information.

[0037] Real-time traffic data is collected, including the size and proportion of video streams, game data, and download data. Specifically, traffic analysis software is used to monitor network transmission protocol types in real time, count the number of data packets for each type of traffic, and calculate the traffic proportion based on data packet size and transmission timestamps.

[0038] A lightweight MQTT protocol is used to establish a communication connection between the cloud and the gateway. Collected network environment information is uploaded to the cloud via MQTT, and the cloud parses and stores the received information. Specifically, an MQTT proxy server is configured on the cloud server, with the default port set to 1883. TLS encryption is used to ensure data transmission security. Clients connect to the server using the standard MQTT 5.0 protocol and are configured for persistent connection. The collected list of surrounding access points, gateway IPs, broadband accounts, and traffic statistics are encapsulated into JSON messages and sent to a cloud-based topic queue using the MQTT publish / subscribe pattern. An EMQ service is set up on the cloud server to store and queue environmental information from each gateway. The paho-mqtt library in Python is used to parse MQTT messages, and the parsed data is stored in a database.

[0039] like Figure 3 The diagram shown is a flowchart of step S102 provided in an embodiment of the present invention. (Refer to...) Figure 3 As an optional implementation, the signal interference matrix of the target area is determined based on the surrounding AP list and device location codes, specifically including: S1021. Determine the access device distribution topology of the target area based on the surrounding AP list and device location codes; S1022. Determine the signal interference intensity between two target access devices based on the received signal strength index; S1023. Construct a signal interference matrix based on the topology of the access devices and the signal interference intensity.

[0040] By performing correlation analysis on geographic location information and reported surrounding AP information, an interference matrix is ​​constructed to describe the interference relationships between each AP. Specifically, based on the collected AP signal strength information, the interference intensity between each target access device is calculated, generating an N*N interference matrix, where N is the total number of target access devices in the area.

[0041] like Figure 4 The diagram shown is another flowchart of step S102 provided in an embodiment of the present invention. (Refer to...) Figure 4 As a further optional implementation, a resource conflict heatmap of the target area is generated based on the signal interference matrix, which specifically includes: S1024. Based on channel information and occupied frequency band information, determine multiple target access devices with the same frequency band and the same channel as co-frequency access devices; S1025. Determine the signal interference intensity between each pair of devices accessing the same frequency based on the signal interference matrix, and determine the resource conflict intensity of the corresponding frequency band and corresponding channel based on the signal interference intensity. S1026. Generate a resource conflict heatmap based on the channel number as the horizontal axis and the frequency band number as the vertical axis, according to the intensity of the resource conflict.

[0042] Generate a conflict heatmap to show the severity of resource conflicts in the network. Specifically, use the matplotlib library to draw the heatmap, with the X-axis representing the channel number, the Y-axis representing the frequency band number, and the color intensity of the heatmap indicating the degree of conflict.

[0043] like Figure 5 The diagram shown is a flowchart of step S103 provided in an embodiment of the present invention. (Refer to...) Figure 5 As an optional implementation, the network load of each target access device in a preset future time period is predicted based on traffic time-series data, specifically as follows: S1031. Input the traffic time series data into the pre-trained network load prediction model to obtain the network load of each target access device in a preset future time period. The network load prediction model is a model obtained by training a long short-term memory network using historical traffic sequences.

[0044] A load forecasting model is built based on the LSTM time series model to predict future network load trends. Specifically, an LSTM network is constructed using the Keras library, with historical traffic time series as input data and load forecasts for the next hour as output.

[0045] like Figure 6 The diagram shown is another flowchart of step S104 provided in an embodiment of the present invention. (Refer to...) Figure 6 As an optional implementation, the target channel and target signal power of each target access device in a preset future time period are determined based on the resource conflict heatmap and network load, a wireless resource allocation strategy is obtained, and the wireless resource allocation strategy is distributed to each target access device, specifically including: S1041. Based on the resource conflict heatmap, determine multiple idle channels with resource conflict levels below a preset threshold, and based on the triangulation rule, determine the target channel for each target access device in a preset future time period. S1042. Determine the target signal power of each target access device in a preset future time period based on network load; S1043. Encapsulate the target channel, target signal power, and policy effective period into a wireless resource allocation policy for each target access device, and send the wireless resource allocation policy to the corresponding target access device.

[0046] Based on load forecasting results, a triangular rule is used to adjust channel and power allocation strategies. Specifically, multiple idle channels with resource conflict levels below a preset threshold are identified using a resource conflict heatmap. Based on the predicted peak equipment load periods, idle channels and higher bandwidths are prioritized for allocation. For channels already used by multiple APs, the remaining bandwidth is allocated using the triangular rule.

[0047] The optimization policy is converted into a lightweight instruction set in JSON format, which includes the new channel allocation, power allocation, and policy effective time window for each AP. The instruction set is then sent to the corresponding gateway via the MQTT protocol and published as a message to the MQTT topic queue, which is set as a persistent queue to ensure that each gateway can successfully receive it. The policy execution time window parameter is set to 30 minutes to monitor the policy execution effect and allow the gateway to switch over before the policy expires.

[0048] like Figure 7 The diagram shown is another step flowchart of the multi-user wireless resource cooperative optimization allocation method provided in an embodiment of the present invention. (Refer to...) Figure 7 As an optional implementation, the multi-user radio resource collaborative optimization allocation method further includes the following steps: S105. The target channel, target signal power, and policy effective period are obtained by parsing the wireless resource allocation policy through the target access device; S106. When the time reaches the policy effective period, the target access device is switched according to the target channel, and the signal transmission power of the target access device is adjusted according to the target signal power.

[0049] Specifically, after receiving the optimization strategy, the gateway parses it, using Python's JSON parsing library to parse the instruction set and extract channel allocation and power allocation information. Based on the parsing results, it performs channel switching operations, such as switching from a congested channel to an idle channel, releasing the current connection and reconnecting to a new channel using the wireless network connection management module. It adjusts the transmit power and sub-channel allocation, using the software-defined RF device control interface to adjust the transmit power based on the power allocation information, and updates the sub-channel configuration of the baseband processor based on the sub-channel allocation information. It monitors changes in the network environment; if the environmental changes exceed a preset threshold, it returns to step S101 to re-collect data. The environmental change monitoring period can be set to 5 minutes, and data collection is triggered if a channel quality degradation of more than 20% is detected.

[0050] In some alternative embodiments, artificial intelligence (AI) and more refined control strategies are introduced for ultra-high-density residential areas, high-end smart communities, or scenarios with extremely stringent network quality requirements.

[0051] 1. AI-driven predictive optimization: The cloud platform uses machine learning algorithms to analyze long-term historical data, thereby predicting network load trends in different time periods and regions. For example, the system may learn that the network load in Building A is low during the daytime on weekdays, while gaming traffic in Building B surges at night.

[0052] Based on prediction, the system can allocate and reserve channel resources in advance, and even complete the adjustment of the network structure before the traffic peak arrives, realizing the leap from "reactive" to "proactive".

[0053] 2. Refined spectrum management: Cross-band coordination: The system not only manages channels, but also coordinates the management of both the 2.4GHz and 5GHz frequency bands. It can intelligently guide terminal devices to prioritize connecting to the better frequency band (for example, guiding high-speed devices to 5GHz, and allocating better channels on 2.4GHz for devices that need to penetrate walls), thus achieving dual-band load balancing.

[0054] Micro-power adjustment: In addition to the channel, the transmit power of each gateway and router can be dynamically adjusted. For routers that are close together, the power can be appropriately reduced to reduce interference; for users located in corners, the power can be appropriately increased or surrounding nodes can be coordinated to provide better coverage, achieving fine-grained management.

[0055] 3. Mesh network and cloud-based collaborative self-healing: In this solution, it is recommended to deploy a Wi-Fi Mesh-enabled system (such as multiple access points or sub-nodes) within the home. The cloud platform can not only allocate channels but also optimize the Mesh wireless backhaul channels, ensuring high reliability of the backbone link.

[0056] When the cloud detects that a home node is abnormally offline or its performance is severely degraded (e.g., due to hardware failure), it can automatically trigger the network self-healing process: coordinate with its neighboring nodes, temporarily adjust the channel and power, expand the coverage to "take over" users in the faulty area, and send alarms to maintenance personnel to minimize the impact on users.

[0057] This extended solution elevates wireless network management from "optimization" to "intelligent operation," enabling it to handle extremely complex wireless environments, providing users with a near-enterprise-level network experience, while offering operators powerful operation and maintenance tools to reduce network operating costs.

[0058] The method flow of the embodiments of the present invention has been described above. It can be understood that, by collecting and analyzing multi-source network environment data of multiple target access devices, the embodiments of the present invention can accurately identify the wireless resource conflict status of the target area and predict the network load of each target access device, thereby realizing channel allocation and power control for each target access device, avoiding co-channel interference between adjacent users, and improving the efficiency of wireless resource allocation and the accuracy of power control.

[0059] Compared with the prior art, the embodiments of the present invention have the following advantages: 1) This invention collects and analyzes data from multi-user gateways or routers, dynamically monitors and analyzes global wireless resources, and realizes the overall management of multi-user wireless Wi-Fi channels and power. It overcomes the island-style allocation strategy of traditional gateways or routers, effectively solves the problem of co-channel interference between adjacent users, and improves the overall system performance. 2) By dynamically monitoring and analyzing global wireless resources, this invention overcomes the problem of lack of dynamic response capability in the fixed power control and resource allocation strategies of traditional wireless communication systems, and significantly improves resource allocation efficiency. 3) This invention introduces a load prediction module based on the LSTM time series model, which can predict future network load changes, providing basic data support for the generation of wireless channel and power strategies, and realizing intelligent allocation of network resources. 4) This invention combines load prediction data and adjusts the wireless channel and power according to the triangular law for the overlapping parts of wireless AP signals. For the non-overlapping parts of the signals, the wireless channel is adjusted to make them different, thus achieving efficient allocation of wireless resources and avoiding a lot of co-channel interference and resource waste. 5) The technical solution of this invention can be implemented by developing plugins on existing home gateways or routers, without requiring large-scale hardware upgrades or modifications to existing devices, thus possessing strong scalability and practicality. Furthermore, since the technical solution of this invention can be centrally managed and analyzed on the platform side, it also facilitates future functional expansion and system maintenance.

[0060] like Figure 8 The diagram shown is a structural schematic of a multi-user wireless resource collaborative optimization allocation device provided in an embodiment of the present invention. (Refer to...) Figure 8 This invention provides a multi-user wireless resource collaborative optimization allocation device, comprising: The data acquisition module is used to acquire multi-source network environment data of multiple target access devices within the target area and upload the multi-source network environment data to the cloud. The multi-source network environment data includes a list of surrounding APs, device location codes, and traffic time series data. The resource conflict analysis module is used to determine the signal interference matrix of the target area based on the list of surrounding APs and the device location codes, and to generate a resource conflict heat map of the target area based on the signal interference matrix. The network load prediction module is used to predict the network load of each target access device in a preset future time period based on traffic time series data; The resource allocation module is used to determine the target channel and target signal power of each target access device in a preset future time period based on the resource conflict heat map and network load, obtain the wireless resource allocation strategy, and distribute the wireless resource allocation strategy to each target access device.

[0061] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0062] This invention also provides an electronic device, comprising: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for communication between the processor and the memory. When the program is executed by the processor, it implements the aforementioned multi-user wireless resource collaborative optimization allocation method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0063] like Figure 9 The diagram shown is a hardware structure schematic of an electronic device provided in an embodiment of the present invention. (Refer to...) Figure 9 This invention provides an electronic device, comprising: The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention. The memory 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 to execute the multi-user wireless resource cooperative optimization allocation method of the embodiments of this invention. The input / output interface 903 is used to implement information input and output; The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904); The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0064] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0065] like Figure 10 The diagram shown is a structural schematic of the storage medium provided in an embodiment of the present invention. (Refer to...) Figure 10 The present invention also provides a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs 1001, which can be executed by one or more processors to implement the above-described multi-user wireless resource collaborative optimization allocation method.

[0066] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0067] This invention also discloses a computer program product, including a computer program that, when executed by a processor, implements the above-described multi-user wireless resource collaborative optimization allocation method.

[0068] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0069] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0070] The embodiments described in this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0071] The terms "first," "second," "third," "fourth," etc. (if present) in the specification and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0072] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the aforementioned blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0073] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the aforementioned functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0074] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0075] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0076] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the aforementioned program can be printed, because the aforementioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0077] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0078] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0079] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0080] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A multi-user wireless resource collaborative optimization allocation method, characterized in that, Includes the following steps: Acquire multi-source network environment data of multiple target access devices within the target area, and upload the multi-source network environment data to the cloud. The multi-source network environment data includes a list of surrounding APs, device location codes, and traffic time series data. The signal interference matrix of the target area is determined based on the surrounding AP list and the device location code, and a resource conflict heatmap of the target area is generated based on the signal interference matrix. Predict the network load of each target access device in a preset future time period based on the traffic time series data; Based on the resource conflict heatmap and the network load, the target channel and target signal power of each target access device in the preset future time period are determined, a wireless resource allocation strategy is obtained, and the wireless resource allocation strategy is sent to each target access device.

2. The multi-user wireless resource collaborative optimization allocation method according to claim 1, characterized in that, The step of acquiring multi-source network environment data of multiple target access devices within the target area and uploading the multi-source network environment data to the cloud specifically includes: The target access device collects channel information, received signal strength indicators, and occupied frequency band information of surrounding APs to obtain a list of surrounding APs; The device location code of the target access device is obtained by querying the IP address and broadband registration information of the target access device. The traffic time-series data is obtained by statistically analyzing the traffic volume of multiple data types of the target access device at different times. The list of surrounding APs, the device location codes, and the traffic time-series data are uploaded to the cloud.

3. The multi-user wireless resource collaborative optimization allocation method according to claim 2, characterized in that, The step of determining the signal interference matrix of the target area based on the surrounding AP list and the device location code specifically includes: The access device distribution topology of the target area is determined based on the surrounding AP list and the device location code; The signal interference intensity between the two target access devices is determined based on the received signal strength index. The signal interference matrix is ​​constructed based on the distribution topology of the access devices and the signal interference intensity.

4. The multi-user wireless resource collaborative optimization allocation method according to claim 2, characterized in that, The step of generating a resource conflict heatmap of the target area based on the signal interference matrix specifically includes: Based on the channel information and the occupied frequency band information, multiple target access devices with the same frequency band and the same channel are identified as co-frequency access devices; The signal interference intensity between each pair of the same-frequency access devices is determined based on the signal interference matrix, and the resource conflict intensity of the corresponding frequency band and corresponding channel is determined based on the signal interference intensity. The resource conflict heatmap is generated based on the resource conflict intensity, with the channel number as the horizontal axis and the frequency band number as the vertical axis.

5. The multi-user wireless resource collaborative optimization allocation method according to claim 1, characterized in that, The specific steps of predicting the network load of each target access device in a preset future time period based on the traffic time-series data are as follows: The traffic time series data is input into a pre-trained network load prediction model to obtain the network load of each target access device in the preset future time period; The network load prediction model is a model obtained by training a long short-term memory network using historical traffic sequences.

6. The multi-user wireless resource collaborative optimization allocation method according to claim 1, characterized in that, The step of determining the target channel and target signal power of each target access device in the preset future time period based on the resource conflict heatmap and the network load, obtaining a wireless resource allocation strategy, and distributing the wireless resource allocation strategy to each target access device specifically includes: Based on the resource conflict heatmap, multiple idle channels with resource conflict levels below a preset threshold are identified, and the target channels of each target access device in the preset future time period are determined based on the idle channels and the triangulation rule. The target signal power of each target access device in the preset future time period is determined based on the network load; The target channel, the target signal power, and the policy effective period are encapsulated into a wireless resource allocation policy corresponding to each of the target access devices, and the wireless resource allocation policy is sent to the corresponding target access device.

7. The multi-user wireless resource collaborative optimization allocation method according to claim 6, characterized in that, The multi-user wireless resource collaborative optimization allocation method further includes the following steps: The target access device parses the wireless resource allocation policy to obtain the target channel, the target signal power, and the effective period of the policy. When the time reaches the effective period of the strategy, the target access device is switched according to the target channel, and the signal transmission power of the target access device is adjusted according to the target signal power.

8. A multi-user wireless resource collaborative optimization allocation device, characterized in that, include: The data acquisition module is used to acquire multi-source network environment data of multiple target access devices within the target area and upload the multi-source network environment data to the cloud. The multi-source network environment data includes a list of surrounding APs, device location codes, and traffic time series data. The resource conflict analysis module is used to determine the signal interference matrix of the target area based on the surrounding AP list and the device location code, and to generate a resource conflict heat map of the target area based on the signal interference matrix. The network load prediction module is used to predict the network load of each target access device in a preset future time period based on the traffic time series data. The resource allocation module is used to determine the target channel and target signal power of each target access device in the preset future time period based on the resource conflict heat map and the network load, obtain a wireless resource allocation strategy, and send the wireless resource allocation strategy to each target access device.

9. An electronic device, characterized in that, The electronic device includes a memory, a processor, a computer program stored in the memory and executable on the processor, and a data bus for enabling communication between the processor and the memory. When the computer program is executed by the processor, it implements the multi-user wireless resource cooperative optimization allocation method as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the multi-user wireless resource cooperative optimization allocation method as described in any one of claims 1 to 7.

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