Near field discovery and configuration optimization method, system and device and storage medium

Abstract

The invention discloses a near field discovery and configuration optimization method, system and device and a storage medium. The method comprises the following steps: acquiring environment information and state information of a plurality of devices, and grouping the devices according to the environment information and the state information to form a plurality of device groups; adjusting the broadcast frequency of each device in the device group based on an intelligent algorithm according to the environment information and the state information, so that the devices transmit and receive signals according to the broadcast frequency; according to a collaborative search and load balancing mechanism, a discovery and pairing task of each device is scheduled; and optimizing the discovery and pairing tasks according to a prediction result of the pairing prediction model. According to the invention, signal interference can be effectively reduced, and the efficiency of device discovery and pairing can be improved. The method can be widely applied to the technical field of near field communication.

Description

Technical Field

[0001] The present invention relates to the technical field of near - field communication, and in particular to an optimization method, system, device and storage medium for near - field discovery and configuration. Background Art

[0002] With the rapid development of the Internet of Things and intelligent devices, near - field communication technology has been widely used in multiple fields, especially in application scenarios such as smart home, Internet of Things devices and industrial automation. Near - field communication technology uses radio waves for short - distance data exchange, with advantages such as low power consumption and fast transmission rate, and has become one of the core technologies for realizing fast discovery and connection between devices. In near - field communication applications, the automatic discovery and configuration mechanism between devices is a key link to ensure that devices can smoothly access the network and exchange data. Currently, many devices use wireless communication protocols such as Bluetooth and NFC to achieve near - field discovery and pairing between devices. However, there is generally a problem with the existing near - field discovery and configuration mechanism, that is, in a complex environment, the communication between devices may be interfered by various factors, resulting in low discovery and pairing efficiency, and even device connection failure.

[0003] In related technologies, the near - field discovery and configuration mechanism usually adopts a method based on broadcasting and active search. Devices discover by broadcasting signals and pair after receiving the signals. However, in the case of a large number of devices, complex network environment and strong interference factors, problems such as slow device discovery process, configuration delay and pairing failure are likely to occur, which greatly affects the user experience. For example, in a large - scale Internet of Things environment, factors such as mutual interference between devices, limitation of signal coverage range and environmental changes will lead to delay and failure in device discovery. This makes the intelligent level of the fast discovery and automatic configuration mechanism of devices difficult to meet the needs of modern intelligent devices and systems, and seriously affects the interconnection efficiency of devices and the expansion of application scenarios. Therefore, how to optimize the existing near - field discovery and configuration mechanism, improve the discovery efficiency and pairing success rate between devices, especially the adaptability in a complex environment, has become a technical problem to be solved urgently. Summary of the Invention

[0004] An object of the present invention is to solve at least to some extent one of the technical problems existing in the prior art.

[0005] To this end, an object of the present invention is to provide a fast and practical optimization method, system, device and storage medium for near - field discovery and configuration.

[0006] To achieve the above technical objectives, on the one hand, an embodiment of the present invention provides an optimization method for near-field discovery and configuration, including the following steps: obtaining the environmental information and status information of several devices, and grouping the devices according to the environmental information and the status information to form several device groups; adjusting the broadcast frequencies of each device in the device group based on an intelligent algorithm according to the environmental information and the status information, so that the devices transmit and receive signals according to the broadcast frequencies; scheduling the discovery and pairing tasks of each device according to a collaborative search and load balancing mechanism; and optimizing the discovery and pairing tasks according to the prediction results of a pairing prediction model. This application realizes the intelligent grouping of devices and the dynamic adjustment of broadcast frequencies through the environmental information and status information of the devices, and then optimizes the discovery and pairing tasks; it can effectively reduce signal interference and is beneficial to improving the efficiency of device discovery and pairing.

[0007] In some embodiments, for the optimization method of near-field discovery and configuration in an embodiment of the present invention, the step of adjusting the broadcast frequencies of each device in the device group based on an intelligent algorithm according to the environmental information and the status information, so that the devices transmit and receive signals according to the broadcast frequencies, includes:

[0008] If the signal strength in the environment where the device is located is strong, increase the broadcast frequency; the environmental information includes the signal strength;

[0009] If the battery power of the device is less than or equal to the power threshold, decrease the broadcast frequency; the status information includes the battery power.

[0010] In some embodiments, the step of scheduling the discovery and pairing tasks of each device according to a collaborative search and load balancing mechanism includes:

[0011] Selecting the broadcast time slots and frequency bands of the devices based on collaborative search according to the environmental information;

[0012] Adjusting the discovery and pairing tasks based on load balancing according to the network resource situation and the requirements of the devices.

[0013] In some embodiments, the step of optimizing the discovery and pairing tasks according to the prediction results of a pairing prediction model includes:

[0014] Obtaining historical data, and training the pairing prediction model based on the historical data to obtain a trained pairing prediction model;

[0015] Inputting the environmental information and the status information into the pairing prediction model to obtain prediction results, and optimizing the discovery and pairing tasks based on the prediction results.

[0016] In some embodiments, optimizing the discovery and pairing task according to the prediction result of the pairing prediction model includes:

[0017] Optimizing the collaborative search and load balancing mechanism according to the prediction result;

[0018] Scheduling to obtain an optimized discovery and pairing task according to the optimized collaborative search and load balancing mechanism.

[0019] In some embodiments, optimizing the discovery and pairing task according to the prediction result of the pairing prediction model includes:

[0020] Adjusting the priority of the device according to the prediction result;

[0021] Allocating the discovery and pairing task according to the adjusted priority.

[0022] In some embodiments, the method further includes:

[0023] If it is determined according to the status information that the first device has an abnormality, adjusting the discovery and pairing task of the first device; the abnormality includes pairing failure and abnormal fluctuations during the discovery process.

[0024] On the other hand, an embodiment of the present invention provides an optimization system for near-field discovery and configuration, including:

[0025] A first module, configured to obtain the environment information and status information of a plurality of devices, and group the devices according to the environment information and the status information to form a plurality of device groups;

[0026] A second module, configured to adjust the broadcast frequencies of the devices in each device group based on an intelligent algorithm according to the environment information and the status information, so that the devices transmit and receive signals according to the broadcast frequencies;

[0027] A third module, configured to schedule the discovery and pairing tasks of each device according to the collaborative search and load balancing mechanism;

[0028] A fourth module, configured to optimize the discovery and pairing task according to the prediction result of the pairing prediction model.

[0029] On the other hand, an embodiment of the present invention provides an optimization device for near-field discovery and configuration, including:

[0030] At least one processor;

[0031] At least one memory, configured to store at least one program;

[0032] When the at least one program is executed by the at least one processor, the at least one processor is caused to implement the above-mentioned optimization method for near-field discovery and configuration.

[0033] On the other hand, an embodiment of the present invention provides a storage medium, in which a program executable by a processor is stored, and the program executable by the processor is used to implement the above-mentioned optimization method for near-field discovery and configuration when executed by the processor.

[0034] The embodiments of the present application at least include the following beneficial effects: The method provided by the embodiments of the present invention includes: obtaining the environmental information and status information of a plurality of devices, and grouping the devices according to the environmental information and the status information to form a plurality of device groups; based on the environmental information and the status information, adjusting the broadcast frequencies of the devices in each device group based on an intelligent algorithm, so that the devices transmit and receive signals according to the broadcast frequencies; scheduling the discovery and pairing tasks of each device according to a collaborative search and load balancing mechanism; optimizing the discovery and pairing tasks according to the prediction results of a pairing prediction model. The present application realizes the intelligent grouping of devices and the dynamic adjustment of broadcast frequencies through the environmental information and status information of the devices, and then optimizes the discovery and pairing tasks; it can effectively reduce signal interference and is beneficial to improving the efficiency of device discovery and pairing. Description of the Drawings

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following introduces the drawings related to the technical solutions in the embodiments of the present invention or the prior art. It should be understood that the drawings introduced below are only for conveniently and clearly expressing some embodiments of the technical solutions in the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative efforts.

[0036] Figure 1 It is a schematic flowchart of an embodiment of the optimization method for near-field discovery and configuration provided by the present invention;

[0037] Figure 2 It is a schematic flowchart of another embodiment of the optimization method for near-field discovery and configuration provided by the present invention;

[0038] Figure 3 It is a schematic structural diagram of an embodiment of the optimization system for near-field discovery and configuration provided by the present invention;

[0039] Figure 4 It is a schematic structural diagram of an embodiment of the optimization device for near-field discovery and configuration provided by the present invention. Detailed Embodiments

[0040] Embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where like or similar reference numerals denote like or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary only for explaining the present invention and should not be construed as limiting the present invention. For the step numbers in the following embodiments, they are only set for the convenience of elaboration and explanation, and no limitation is imposed on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0041] With the rapid development of the Internet of Things and smart devices, near-field communication technology has been widely used in multiple fields, especially in application scenarios such as smart home, Internet of Things devices, and industrial automation. Near-field communication technology uses radio waves for short-distance data exchange and has advantages such as low power consumption and fast transmission rate, and has become one of the core technologies for realizing rapid discovery and connection between devices. In near-field communication applications, the automatic discovery and configuration mechanism between devices is a key link to ensure that devices can successfully access the network and exchange data. Currently, many devices use wireless communication protocols such as Bluetooth and NFC to achieve near-field discovery and pairing between devices. However, there is generally a problem with the existing near-field discovery and configuration mechanism, that is, in a complex environment, the communication between devices may be interfered by various factors, resulting in low discovery and pairing efficiency, and even device connection failure.

[0042] In related technologies, the near-field discovery and configuration mechanism usually adopts a method based on broadcasting and active searching. Devices discover by broadcasting signals and pair after receiving the signals. However, this method is prone to problems such as slow device discovery process, configuration delay, and pairing failure in the case of a large number of devices, complex network environment, and strong interference factors, which greatly affects the user experience. For example, in a large-scale Internet of Things environment, factors such as mutual interference between devices, limitations of signal coverage, and environmental changes will cause delays and failures in device discovery. This makes the intelligent level of the rapid discovery and automated configuration mechanism of devices difficult to meet the needs of modern smart devices and systems, seriously affecting the interconnection efficiency of devices and the expansion of application scenarios. Therefore, how to optimize the existing near-field discovery and configuration mechanism, improve the discovery efficiency and pairing success rate between devices, especially the adaptability in a complex environment, has become an urgent technical problem to be solved.

[0043] The optimization method and system for near-field discovery and configuration proposed according to the embodiments of the present invention will be described in detail below with reference to the accompanying drawings. First, the optimization method for near-field discovery and configuration proposed according to the embodiments of the present invention will be described with reference to the accompanying drawings.

[0044] Refer to Figure 1, in an embodiment of the present invention, an optimization method for near-field discovery and configuration is provided. The optimization method for near-field discovery and configuration in the embodiment of the present invention can be applied to a terminal, can also be applied to a server, or can also be software running on a terminal or a server, etc. The terminal can be a tablet computer, a notebook computer, a desktop computer, etc., but is not limited thereto. The server can be an independent physical server, can also be a server cluster or a distributed system composed of multiple physical servers, or can also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The optimization method for near-field discovery and configuration in the embodiment of the present invention mainly includes the following steps:

[0045] S100: Obtain the environmental information and status information of several devices, and group the devices according to the environmental information and the status information to form several device groups;

[0046] S200: Based on the environmental information and the status information, adjust the broadcast frequencies of the devices in each device group based on an intelligent algorithm, so that the devices transmit and receive signals according to the broadcast frequencies;

[0047] S300: Schedule the discovery and pairing tasks of each device according to a collaborative search and load balancing mechanism;

[0048] S400: Optimize the discovery and pairing tasks according to the prediction results of the pairing prediction model.

[0049] In some possible implementation manners, the intelligent algorithm is a strategy for adjusting the broadcast frequency according to the environmental information and the status information. The collaborative search and load balancing mechanism is used to adjust the number of devices that perform broadcasting and pairing at the same moment.

[0050] Optionally, in an embodiment of the present invention, the adjusting the broadcast frequencies of the devices in each device group based on an intelligent algorithm according to the environmental information and the status information, so that the devices transmit and receive signals according to the broadcast frequencies includes:

[0051] If the signal strength of the environment where the device is located is strong, increase the broadcast frequency; the environmental information includes the signal strength;

[0052] If the battery power of the device is less than or equal to the power threshold, decrease the broadcast frequency; the status information includes the battery power.

[0053] In some possible implementations, the present application dynamically groups devices and optimizes broadcast frequency based on environmental perception and device status detection. In this implementation, in order to improve the discovery efficiency and pairing success rate of devices in complex environments, the present invention uses environmental perception technology and device status detection technology to intelligently manage devices. The core of this implementation is to dynamically adjust the broadcast frequency and search strategy of the device, thereby achieving more intelligent device discovery and configuration.

[0054] The device's environmental perception module evaluates the device's working environment by detecting environmental parameters such as signal strength, signal-to-noise ratio, and network bandwidth around the device. The environmental perception module can monitor the external signal strength and the communication quality of the device in real time, and then select the appropriate search and broadcast time for the device. For example, when the device is in an area with a strong signal, the device will increase the broadcast frequency to ensure that the discovery between devices is completed quickly. In an environment with a weak signal, the device's broadcast frequency will be reduced to avoid unnecessary battery consumption and signal interference. The environmental perception module will take into account the number and strength of surrounding interference sources, intelligently select the best broadcast frequency, and reduce excessive load on the device.

[0055] On the other hand, the device status detection module detects the device's internal status parameters such as battery power, processing power, network status, etc. in real time. This module can monitor the health status of the device and dynamically adjust the device's workload. Specifically, when the device's battery power is lower than the preset threshold, the device will automatically reduce its broadcast and scanning frequency to extend the battery life, and maintain stable operation of the device by adjusting its power consumption mode. In addition, device status detection also includes device load conditions. If the device load is high (such as running multiple background tasks at the same time), the system will adjust its broadcast and discovery tasks based on the device's computing power to avoid discovery failures or delays due to device overload.

[0056] Based on the device's environmental perception and status detection data, the system groups the devices and specifies different broadcast strategies for each group of devices. The device grouping is based on the device's signal strength, network bandwidth, device battery status, etc. Each device automatically adjusts its broadcast frequency based on the environmental conditions and status conditions of the group it belongs to. For example, when some devices are in a low-battery state, the system will prioritize other devices with sufficient power for pairing, ensuring that low-battery devices can complete the update without causing interference.

[0057] This grouping and broadcast frequency optimization strategy based on environmental perception and status detection can not only improve the efficiency of device discovery in complex environments, but also greatly reduce signal interference between devices and effectively extend the battery life of devices. This method is particularly suitable for scenarios that require frequent device discovery and configuration, such as smart homes, the Internet of Things, and large-scale device management.

[0058] In summary, through the combination of intelligent grouping, environment perception and status detection, the present application realizes the dynamic optimization of the device broadcast frequency. The device intelligently adjusts its broadcast and scanning frequencies according to its location environment, network conditions and internal status, thereby reducing interference and improving the device discovery efficiency, ensuring that the device can quickly and accurately complete the pairing task in a changing environment.

[0059] Optionally, in an embodiment of the present invention, scheduling the discovery and pairing tasks of each of the devices according to the collaborative search and load balancing mechanism includes:

[0060] Based on the environment information, select the broadcast time slot and frequency band of the device based on collaborative search;

[0061] Based on the network resource situation and the requirements of the device, adjust the discovery and pairing tasks based on load balancing.

[0062] In some possible implementation manners, the present application provides an intelligent scheduling mechanism for collaborative search and load balancing. In this implementation manner, to solve the problems of signal interference and network congestion during multi-device concurrent connection, the present invention introduces a collaborative search and load balancing mechanism. This mechanism optimizes the search and broadcast processes of devices in the same network by intelligently scheduling the pairing tasks of devices, thereby reducing signal competition between devices and ensuring that devices can still efficiently complete pairing in a high-load environment.

[0063] Before pairing, the system will evaluate the devices based on parameters such as the geographical location, signal strength, and network bandwidth of the devices and assign tasks according to the evaluation results. First, the collaborative search mechanism will allocate the devices to different time slots or frequency bands for broadcasting according to the geographical distribution and network status of the devices, avoiding multiple devices occupying the same frequency for broadcasting at the same time period and reducing signal interference. The system intelligently selects the best broadcast time period for each device to optimize the device discovery efficiency and reduce the transmission delay. For example, when a device is in an area with strong signals, the system will allow the device to use a larger time slot for broadcasting, while in an area with poor signals, the system will limit the broadcast frequency of the device to avoid excessive interference between devices.

[0064] The key to the collaborative search mechanism is that before device pairing, the system will evaluate devices based on parameters such as the device's geographical location, signal strength, and network bandwidth, and allocate devices to different time slots or frequency bands for broadcasting. The purpose of this is to prevent multiple devices in the same group from occupying the same frequency for broadcasting at the same time, thereby reducing signal interference and frequency conflicts. The system will intelligently select the optimal broadcasting time period and frequency band according to the specific situation of each device, enabling each device to transmit signals at an appropriate time, optimizing the efficiency in the device discovery process, and at the same time reducing signal transmission latency. In this way, the collaborative search mechanism can effectively improve the success rate and stability of device pairing, especially in multi-device and complex environments, ensuring that devices can efficiently complete the discovery and pairing tasks.

[0065] In addition, the load balancing mechanism solves the network congestion problem caused by frequent device access and disconnection. When the number of connected devices is large, the load balancing mechanism will automatically adjust the device pairing order and task allocation according to the device's load status and bandwidth requirements. For example, in the case of tight bandwidth, the system preferentially schedules devices with low bandwidth requirements for pairing to avoid excessive bandwidth occupation and ensure that all devices can successfully complete the pairing task under limited bandwidth conditions. In addition, the system will also dynamically adjust the task priorities and pairing times of devices according to their operating states, reducing unnecessary pairing delays.

[0066] This intelligent scheduling mechanism also takes into account the real-time state changes between devices. During the device pairing process, the mutual cooperation of the collaborative search mechanism and the load balancing mechanism enables devices to adjust task allocation in a dynamic environment, avoiding the situation where multiple devices compete for resources simultaneously, and significantly improving the efficiency of the pairing process. The system not only optimizes the signal transmission between devices but also reduces interference between devices through reasonable resource allocation, significantly increasing the success rate of pairing.

[0067] It should be noted that "cooperating with each other" means that the collaborative search mechanism and the load balancing mechanism cooperate during the device pairing process to jointly optimize task allocation and resource scheduling. Specifically, the collaborative search mechanism dynamically selects the broadcast time slots and frequency bands of devices according to parameters such as the geographical location, signal strength, and network bandwidth of the devices, so as to avoid multiple devices simultaneously occupying the same frequency band within the same period, and reduce signal interference. The load balancing mechanism, on the other hand, intelligently adjusts task allocation according to the actual needs of devices when the device load is heavy or the network bandwidth is limited, ensuring the reasonable utilization of resources and avoiding excessive competition for network bandwidth among devices. An example of their cooperation is as follows: when the system detects that the bandwidth requirements of some devices are large, the load balancing mechanism will adjust the pairing priorities of these devices and postpone their tasks, while the collaborative search mechanism will allocate these devices to idle network periods to reduce interference. In this way, the collaborative search mechanism and the load balancing mechanism combine to optimize the signal transmission and resource allocation of devices, thereby improving the pairing efficiency and success rate.

[0068] This application solves the problems of signal interference and network congestion in a multi-device environment through intelligent collaborative search and load balancing mechanisms, optimizes the device pairing process, and effectively improves the pairing efficiency and stability in large-scale device management.

[0069] Optionally, in an embodiment of the present invention, optimizing the discovery and pairing tasks according to the prediction result of the pairing prediction model includes:

[0070] Obtain historical data, and train the pairing prediction model based on the historical data to obtain a trained pairing prediction model;

[0071] Input the environmental information and the status information into the pairing prediction model to obtain a prediction result, and optimize the discovery and pairing tasks based on the prediction result.

[0072] In some possible implementation manners, this application provides an adaptive pairing strategy for machine learning and the central control system. This implementation manner combines machine learning and the central control system, and proposes an adaptive device pairing strategy through continuous learning of the historical data of devices and changes in the network environment. Through the combination of the central control system and machine learning algorithms, the system can continuously optimize the pairing strategy between devices according to information such as the status of devices, historical pairing data, and device response time, improving the pairing efficiency and success rate.

[0073] The central control system first collects the operating data of the devices, including the response time, pairing history, signal strength, device health status, etc. of each device. These data will be transmitted to the machine learning module for analysis. Through analyzing these historical data, the machine learning algorithm can predict the optimal pairing timing and strategy between devices. By continuously collecting the pairing history data of the devices, the system can, when a new pairing task arrives, make intelligent adjustments based on the historical behavior and current status of the devices.

[0074] The machine learning module predicts the optimal pairing timing by analyzing data such as the response time, pairing history, and signal strength of the devices, and makes intelligent adjustments according to the historical behavior and current status of the devices. This process complements the pairing time adjustment of the collaborative search and load balancing mechanisms. Specifically, the prediction results of the machine learning algorithm can provide data support for the collaborative search mechanism and the load balancing mechanism, helping them to more accurately select and adjust the pairing timing. For example, the machine learning module may predict that the pairing success rate of certain devices is relatively high during a specific period. At this time, the central control system can notify the collaborative search mechanism to allocate more devices for pairing during this period. At the same time, the load balancing mechanism can reasonably allocate task priorities according to the status and bandwidth of the devices. The integration of the two means that machine learning provides in-depth insights into the behavior of the devices, thereby optimizing the real-time scheduling of collaborative search and load balancing, ensuring the avoidance of pairing timing conflicts under different network conditions and device states, and improving the pairing success rate and efficiency.

[0075] For example, the machine learning algorithm can predict the pairing probability of the devices based on the response time and pairing success rate of the devices, and adjust the priority of the devices accordingly. When a device has a long response time, the system will adjust its priority to a lower level to avoid it occupying too many resources during the pairing process; while for a device with a fast response speed and a high historical pairing success rate, the system will automatically increase its priority to accelerate the pairing process. In this way, the system can dynamically adapt to the changes in the network and device status and continuously optimize the pairing strategy.

[0076] At the same time, the central control system will automatically adjust the pairing order and task allocation according to the real-time status of the devices. The system can intelligently adjust the pairing timing of the devices according to factors such as the signal strength, device load, and bandwidth requirements of the devices. For example, when a device is in a low power or high load state, the system will postpone its pairing task and give priority to processing devices in a better state, so as to ensure the smooth progress of the pairing task. In addition, the machine learning algorithm can also predict the optimal pairing window of the devices and schedule in advance, thereby reducing the risk of pairing failure.

[0077] This application realizes the intelligent optimization and adaptive adjustment of the device pairing strategy by combining machine learning and a central control system. This method can not only significantly improve the pairing success rate between devices, but also continuously optimize the pairing process according to the changes in the dynamic environment, ensuring efficient and stable connection of devices in a complex Internet of Things environment.

[0078] Optionally, in an embodiment of the present invention, optimizing the discovery and pairing task according to the prediction result of the pairing prediction model includes:

[0079] Optimizing the collaborative search and load balancing mechanism according to the prediction result;

[0080] Scheduling to obtain an optimized discovery and pairing task according to the optimized collaborative search and load balancing mechanism.

[0081] Optionally, in an embodiment of the present invention, optimizing the discovery and pairing task according to the prediction result of the pairing prediction model includes:

[0082] Adjusting the priority of the device according to the prediction result;

[0083] Allocating the discovery and pairing task according to the adjusted priority.

[0084] In some possible implementation manners, the priority is the priority for the device to execute the pairing task.

[0085] Optionally, in an embodiment of the present invention, the method further includes:

[0086] If it is determined according to the status information that the first device has an abnormality, adjusting the discovery and pairing task of the first device; the abnormality includes pairing failure and abnormal fluctuations during the discovery process.

[0087] The following details the optimization method for near-field discovery and configuration proposed in this application with a specific embodiment:

[0088] The object of the present invention is to provide an intelligent optimization method for a near-field discovery and configuration mechanism, which realizes intelligent grouping of devices and dynamic adjustment of broadcast frequencies through environmental perception and status detection technologies, effectively reduces signal interference, and improves the efficiency of device discovery and pairing. In a multi-device environment, devices can intelligently adjust their broadcast frequencies according to their physical locations, battery states, and network bandwidths, avoid resource competition, and improve the pairing success rate. Through a collaborative search and load balancing mechanism, the system dynamically allocates pairing tasks for devices, reduces frequency conflicts and communication congestion, and ensures stable operation of devices in a complex network environment. In addition, by combining machine learning with a central control system, the system analyzes historical data and device status, adaptively optimizes the pairing strategy, and ensures that it can still maintain high-efficient device discovery and connection capabilities when the number of devices increases and the environment changes, improving the flexibility and scalability of the system to solve the problems in the above background technology.

[0089] Referring Figure 2 As shown, to achieve the above object, the present invention provides the following technical solutions: An intelligent optimization method for a near-field discovery and configuration mechanism, including the following steps:

[0090] Step 1: Based on the environmental perception and device status detection of devices, perform intelligent grouping on devices, group devices in a similar physical environment or network state into one group to form different device groups, and perform optimized near-field discovery among devices.

[0091] Step 2: According to the environment or location where each device is located, dynamically adjust the broadcast and scan frequencies of the device through intelligent algorithms, so that each device emits and receives signals at an appropriate time, thereby optimizing the signal transmission efficiency in the discovery process.

[0092] Step 3: Adopt a collaborative search and load balancing mechanism, and avoid multiple devices broadcasting and pairing at the same time by intelligently scheduling the discovery and pairing tasks of different devices, thereby reducing the mutual interference between devices and improving the pairing success rate.

[0093] Step 4: Use machine learning algorithms to perform data analysis and learning on the discovery history of devices, the response time and success rate of devices, etc., establish a prediction model for discovery and pairing between devices, and provide an optimization strategy according to the model in future pairing processes.

[0094] Step 5: Dynamically adjust the connection strategy of devices according to the operating status, remaining power, and environmental changes of devices, and ensure that a high device discovery rate and pairing success rate can still be maintained in a complex environment with a large number of devices and strong signal interference.

[0095] Optionally, in Step 1, the device environmental perception and device status detection include:

[0096] Environmental perception dynamically evaluates the changes in the environment where the device is located by sensing parameters such as signal strength, signal-to-noise ratio, and network bandwidth around the sensing device, and automatically adjusts the search and discovery strategies of the device;

[0097] Device status detection monitors the battery power, connection status, and processing capacity of the device, and preferentially selects devices with better status and sufficient power to participate in the access process, thereby ensuring the stability and reliability of the device connection. This step can ensure that in a multi-device environment, the device can preferentially select a suitable device for effective pairing and optimization update according to the current environment and status, avoid unnecessary interference, and improve the device discovery efficiency.

[0098] Optionally, in step 2, the dynamic adjustment of the broadcast and scan frequencies of the device is optimized in the following way:

[0099] Based on the environmental perception module, analyze the signal transmission status of each device, and adjust the broadcast frequency in real time according to the transmission distance, signal strength, and interference status in the surrounding environment of the device;

[0100] For devices with weak signal strength, adjust their scan frequencies to avoid signal transmission during the period when radio wave interference between devices is the most severe, and ensure that the discovery process is not interfered;

[0101] For devices with low battery power, optimize energy consumption by reducing their broadcast frequencies, and ensure that the devices can participate in the pairing process for a longer time through the low-power mode. This step can greatly improve the efficiency of the device discovery process, especially in a high-interference environment, ensure the smooth transmission of device signals, and reduce unnecessary energy waste.

[0102] Optionally, in step 3, the collaborative search and load balancing mechanism is specifically implemented in the following way:

[0103] Before the device performs pairing, the system dynamically allocates search and pairing tasks according to the real-time changes in the location, signal strength, and device status of the device, and avoids multiple devices occupying network resources for broadcasting at the same time;

[0104] Adopt a load balancing strategy to allocate the devices to be paired to different frequency bands or time slots, so that the devices can independently complete the discovery process within their respective frequency ranges, thereby reducing interference between devices;

[0105] For some areas where certain frequency bands or channels are busy, intelligently schedule and control the broadcast time period of the device, so that the network load can be effectively allocated, and avoid broadcast competition of too many devices in the same time period. This step effectively improves the pairing success rate between devices by optimizing the search tasks and pairing time periods of the devices, solves the problems of signal congestion and interference, and ensures the effective discovery and connection of devices under high load.

[0106] Optionally, the machine learning algorithm in step 4 further optimizes the pairing efficiency between devices in the following ways:

[0107] Based on parameters such as the response time and successful pairing rate of the devices, train an adaptive learning model that can analyze the historical data of the devices and predict the optimal pairing time between the devices;

[0108] The model generates personalized pairing strategies according to the types, functional requirements, and historical pairing records of the devices, and dynamically selects and schedules the devices according to the current operating conditions of the devices;

[0109] The learning algorithm can quickly adapt to and adjust the discovery priority of the devices when the device status changes and the network environment fluctuates, reducing the probability of pairing failures. This method improves the intelligence level of device discovery and pairing through machine learning, reduces manual intervention, and can continuously optimize the pairing strategy in a changing environment, further improving the success rate and efficiency of device discovery.

[0110] Optionally, in step 5, the adjustment of the device connection strategy is further based on the following strategies:

[0111] Dynamically evaluate the battery power, processing capacity of the device, and the current network bandwidth, and adjust the connection strategy according to the evaluation results to ensure that each device can connect at the most appropriate time, avoiding connection tasks when the battery power is insufficient or the device resources are overloaded;

[0112] When the device is in a low battery state, the system will automatically lower the task priority of the device and postpone its connection time to protect the battery life and avoid unnecessary connection conflicts;

[0113] When the network bandwidth is sufficient, optimize the synchronization ability and transmission rate of the device, reduce network latency, and ensure that the device can efficiently complete the pairing process. This step further optimizes the connection strategy of the device in a dynamic environment, ensuring the stability and efficiency during the device connection process. Especially in a multi-device environment, it effectively avoids connection failures caused by insufficient battery power or bandwidth limitations.

[0114] Optionally, the device grouping, broadcast frequency adjustment, load balancing, and optimization of machine learning in steps 1 to 5 can all be centrally scheduled through the central control system according to the real-time status of the devices, specifically including:

[0115] The central control system collects the status information of all devices in real time, and dynamically adjusts the task priorities and task assignments of each device according to the environmental changes, load status, and pairing task requirements of the devices;

[0116] The system, through centralized scheduling, intelligently selects paired devices and broadcast frequencies according to the load conditions of each device and network bandwidth conditions, optimizing the discovery efficiency of the entire device group;

[0117] In a large-scale device environment, the system can flexibly adjust the device pairing strategy according to the performance and environmental changes of each device, ensuring that all devices can still be effectively paired when network resources are scarce. This step realizes the unified management of dynamic scheduling between devices through the central control system, improving the overall efficiency of the system. Especially in an environment with a large number of devices, the intelligent scheduling mechanism ensures that each device can participate in the pairing process at the appropriate time, thereby reducing interference between devices and increasing the pairing success rate.

[0118] Optionally, the central control system also includes an anomaly monitoring and feedback module, which enhances the robustness of the device pairing process through the following steps:

[0119] The anomaly monitoring module continuously monitors parameters such as the pairing status of devices, device response time, and signal strength. If it detects a pairing failure of a certain device or abnormal fluctuations during the discovery process, it immediately feeds back this information to the central control system;

[0120] Based on the feedback anomaly information, the central control system quickly adjusts the task allocation of devices, re-evaluates and selects suitable alternative devices or adjusts the task execution order of devices to avoid the pairing failure from affecting the overall process;

[0121] The anomaly feedback module can also automatically adjust the pairing strategy or perform priority processing on certain devices according to the historical data of the devices, ensuring the stability and connection quality of the devices in a high-load environment. This step further enhances the robustness of the entire system through the real-time anomaly monitoring and feedback mechanism, ensuring that the pairing process can proceed smoothly even in a complex device network and unstable environment.

[0122] The present invention realizes intelligent grouping and dynamic adjustment of the broadcast frequency of devices in different environments and network states by introducing environment perception and status detection technologies. This mechanism can automatically optimize the broadcast and scanning strategies of devices, reduce signal interference between devices, and perform dynamic adjustment according to the battery power, load status, and network bandwidth of the devices. Especially in a multi-device environment, devices can intelligently adjust the broadcast frequency according to their physical location and battery status, effectively avoiding the phenomenon of excessive competition for wireless resources by devices within the same time period. Through this intelligent grouping and frequency optimization, devices can complete discovery and pairing more quickly in complex environments, improving the pairing success rate while reducing unnecessary resource waste. This enables devices to perform remote updates and communications more efficiently in large-scale Internet of Things, smart home, etc. applications. Especially when the number of devices is large and the network environment is complex, it can still maintain a high connection efficiency.

[0123] The collaborative search and load balancing mechanism of the present invention can effectively reduce the mutual interference of devices when broadcasting at the same time, significantly improving the stability of the pairing process. Through intelligent scheduling, the pairing tasks of devices can be dynamically allocated according to signal strength, device load, and bandwidth conditions, avoiding frequency conflicts and communication congestion caused by multiple devices broadcasting signals simultaneously. The system can intelligently adjust the pairing order of devices according to the real-time load and signal quality of the devices, and reasonably allocate wireless resources when devices are connected, thereby reducing signal interference and optimizing the pairing timing. This optimized load balancing strategy not only improves the pairing success rate between devices but also ensures that devices can stably complete the update task under different network conditions, especially suitable for environments with dense devices, such as scenarios that require high reliability in smart cities, industrial automation, etc.

[0124] The present invention realizes the adaptive optimization of the device pairing strategy by combining machine learning and the central control system. The machine learning algorithm analyzes the pairing historical data, response time, and device status of the devices, continuously optimizing the pairing strategy to ensure a high pairing efficiency and success rate even when the number of devices increases and the environment changes complexly. The central control system dynamically adjusts the pairing priority according to the real-time status of the devices, ensuring that the system can flexibly adjust the pairing tasks of the devices when the bandwidth is limited or the device power is low. In addition, the machine learning model automatically learns according to the behavior patterns of the devices and optimizes the pairing timing, enabling the system to adapt to future changes and have better adaptability. This enables the present invention to be widely applied under different scales and environmental conditions, and can continue to maintain an efficient and intelligent device discovery and pairing ability as the system expands continuously, improving the flexibility and scalability of the system.

[0125] Based on the environmental perception and device status detection of the device, this application intelligently groups the devices, classifies the devices in a similar physical environment or network state into one group to form different device groups, and performs optimized near-field discovery among the devices. The present invention realizes intelligent device grouping and dynamic broadcast frequency adjustment through environmental perception and status detection, reduces interference and improves pairing efficiency. In a multi-device environment, the devices adjust the broadcast frequency according to their positions, batteries, and bandwidths to avoid resource competition. Through the collaborative search and load balancing mechanism, the task allocation is optimized, signal conflicts are reduced, and stable connections are ensured. Combining machine learning with the central control system, the system adaptively optimizes the pairing strategy by analyzing historical data, maintains efficient device discovery, and improves the flexibility and scalability of the system.

[0126] In summary, the method provided by the embodiment of this application includes: obtaining the environmental information and status information of a number of devices, and grouping the devices according to the environmental information and the status information to form a number of device groups; based on the environmental information and the status information, adjusting the broadcast frequencies of each device in the device group based on an intelligent algorithm, so that the devices transmit and receive signals according to the broadcast frequencies; scheduling the discovery and pairing tasks of each device according to the collaborative search and load balancing mechanism; and optimizing the discovery and pairing tasks according to the prediction results of the pairing prediction model. This application realizes intelligent device grouping and dynamic broadcast frequency adjustment through the environmental information and status information of the devices, and then optimizes the discovery and pairing tasks; it can effectively reduce signal interference and is beneficial to improving the efficiency of device discovery and pairing.

[0127] Secondly, refer to the attached Figure 3 Describe an optimized system for near-field discovery and configuration proposed according to an embodiment of the present invention.

[0128] Figure 3 It is a schematic structural diagram of an optimized system for near-field discovery and configuration according to an embodiment of the present invention. The system specifically includes:

[0129] A first module 310, configured to obtain the environmental information and status information of a number of devices, and group the devices according to the environmental information and the status information to form a number of device groups;

[0130] A second module 320, configured to adjust the broadcast frequencies of each device in the device group based on an intelligent algorithm according to the environmental information and the status information, so that the devices transmit and receive signals according to the broadcast frequencies;

[0131] A third module 330, configured to schedule the discovery and pairing tasks of each device according to the collaborative search and load balancing mechanism;

[0132] The fourth module 340 is configured to optimize the discovery and pairing task according to the prediction result of the pairing prediction model.

[0133] It can be seen that the content in the above method embodiments is applicable to the system embodiments. The functions specifically implemented in the system embodiments are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0134] Referring to Figure 4 , an optimization device for near-field discovery and configuration according to an embodiment of the present invention includes:

[0135] At least one processor 410;

[0136] At least one memory 420 for storing at least one program;

[0137] When the at least one program is executed by the at least one processor 410, the at least one processor 410 implements the optimization method for near-field discovery and configuration.

[0138] Similarly, the content in the above method embodiments is applicable to the device embodiments. The functions specifically implemented in the device embodiments are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0139] An embodiment of the present invention further provides a computer-readable storage medium, in which a program executable by a processor is stored, and the program executable by the processor is used to execute the optimization method for near-field discovery and configuration described above when executed by the processor.

[0140] Similarly, the content in the above method embodiments is applicable to the storage medium embodiments. The functions specifically implemented in the storage medium embodiments are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0141] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order mentioned in the operation diagrams. For example, depending on the functions / operations involved, two consecutive blocks shown may actually be executed substantially simultaneously or the blocks can sometimes be executed in the reverse order. In addition, the embodiments presented and described in the flowcharts of the present invention are provided by way of example for a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logical flows presented herein. Alternative embodiments are contemplated, in which the order of various operations is changed and the sub-operations described as part of a larger operation are executed independently.

[0142] In addition, although the present invention has been described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and / or features may be integrated in a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It should also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. Rather, given the attributes, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the modules will be understood within the ordinary skills of an engineer. Thus, those skilled in the art can implement the present invention as set forth in the claims without undue experimentation. It should also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0143] If the described function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this 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 programs for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0144] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable programs for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by a program execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can retrieve and execute programs from the program execution system, apparatus, or device), or in conjunction with these program execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with a program execution system, apparatus, or device.

[0145] More specific examples (nonexhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0146] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable program execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0147] In the foregoing description of the present specification, the descriptions referring to the terms "one embodiment / example", "another embodiment / example", or "certain embodiments / examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0148] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the claims and their equivalents.

[0149] The above has specifically described the preferred embodiments of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art can also make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included within the scope defined by the claims of the present invention.

Claims

1. An optimization method for near-field discovery and configuration, characterized in that Including the following steps: Obtain the environmental information and status information of a number of devices, and group the devices according to the environmental information and the status information to form a number of device groups; Based on the environmental information and the status information, adjust the broadcast frequencies of the devices in each device group based on an intelligent algorithm, so that the devices transmit and receive signals according to the broadcast frequencies; Schedule the discovery and pairing tasks of each device according to the collaborative search and load balancing mechanism; Optimize the discovery and pairing tasks according to the prediction results of the pairing prediction model.

2. The optimization method for near-field discovery and configuration according to claim 1, characterized in that The adjusting the broadcast frequencies of the devices in each device group based on the environmental information and the status information, so that the devices transmit and receive signals according to the broadcast frequencies includes: If the signal strength of the environment where the device is located is strong, increase the broadcast frequency; the environmental information includes the signal strength; If the battery power of the device is less than or equal to the power threshold, decrease the broadcast frequency; the status information includes the battery power.

3. The optimization method for near-field discovery and configuration according to claim 1, characterized in that The scheduling the discovery and pairing tasks of each device according to the collaborative search and load balancing mechanism includes: Based on the environmental information, select the broadcast time slots and frequency bands of the devices based on collaborative search; Based on the network resource situation and the requirements of the devices, adjust the discovery and pairing tasks based on load balancing.

4. The optimization method for near-field discovery and configuration according to claim 1, wherein The optimizing the discovery and pairing tasks according to the prediction results of the pairing prediction model includes: Obtain historical data, and train the pairing prediction model based on the historical data to obtain a trained pairing prediction model; Input the environmental information and the status information into the pairing prediction model to obtain a prediction result, and optimize the discovery and pairing tasks based on the prediction result.

5. The optimization method for near-field discovery and configuration according to claim 1, wherein The optimizing the discovery and pairing tasks according to the prediction results of the pairing prediction model includes: Optimize the collaborative search and load balancing mechanism according to the prediction result; Schedule the optimized discovery and pairing tasks according to the optimized collaborative search and load balancing mechanism.

6. The optimization method for near-field discovery and configuration according to claim 1, wherein The optimizing the discovery and pairing tasks according to the prediction results of the pairing prediction model includes: Adjust the priority of the device according to the prediction result; Allocate the discovery and pairing tasks according to the adjusted priority.

7. The optimization method for near-field discovery and configuration according to claim 1, characterized in that The method further includes: If it is determined according to the status information that the first device has an abnormality, adjust the discovery and pairing tasks of the first device; the abnormality includes pairing failure and abnormal fluctuations during the discovery process.

8. An optimized system for near-field discovery and configuration, characterized in that, Including: The first module is used to obtain the environmental information and status information of a number of devices, and group the devices according to the environmental information and the status information to form a number of device groups; The second module is used to adjust the broadcast frequencies of the devices in each device group based on the environmental information and the status information, so that the devices transmit and receive signals according to the broadcast frequencies; The third module is used to schedule the discovery and pairing tasks of each device according to the collaborative search and load balancing mechanism; The fourth module is used to optimize the discovery and pairing task according to the prediction result of the pairing prediction model.

9. An optimization device for near-field discovery and configuration, characterized in that, It includes: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the optimization method for near-field discovery and configuration as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that, The program executable by the processor is used to implement the optimization method for near-field discovery and configuration as described in any one of claims 1 to 7 when executed by the processor.

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