Intelligent internet-of-things device remote management method and device, and storage medium

Abstract

The present application relates to the technical field of Internet of Things device management, and particularly relates to a kind of intelligent Internet of Things device remote management method, device and storage medium, the method comprises: the channel data of Internet of Things device is collected;Based on the channel signal-to-noise ratio, bandwidth and occupancy rate collected in monitoring period, the dynamic quantitative evaluation of channel quality index and priority coefficient is executed;Based on priority analysis result, using dynamic proportion distribution strategy, the bandwidth of the i channel is allocated;Channel idle feature analysis is executed in combination with the idle time and switching time in management period;Channel packet loss rate and channel data transmission delay collected in management period are coupled to determine channel abnormal characteristics;The analysis process of channel priority coefficient of next management period is adjusted, and the analysis process of channel transmission state is optimized according to the data transmission delay fluctuation state of channel.The present application effectively improves the remote management efficiency of Internet of Things device.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) device management technology, and in particular to a method, apparatus and storage medium for remote management of smart IoT devices. Background Technology

[0002] With the popularization and in-depth application of IoT technology, smart IoT devices face the severe challenge of dynamic adaptation of channel resources in complex and ever-changing wireless environments. Traditional remote management methods often rely on a single static parameter (such as signal-to-noise ratio or bandwidth) to locally evaluate channel quality, lacking collaborative analysis of multi-dimensional data such as channel occupancy, idle time, and handover latency. This results in coarse channel priority allocation and rigid bandwidth distribution. Especially in densely populated device scenarios, the contradiction between highly dynamic channel states and fixed weight strategies becomes even more prominent: high-occupancy channels may be over-utilized, and frequent inefficient handovers caused by the lack of a decision model for handover costs can exacerbate transmission delay and packet loss rate fluctuations. Existing technologies struggle to balance real-time and stability requirements and fail to couple and mine abnormal channel characteristics (such as sudden packet loss and latency spikes), causing management strategies to lag behind network state changes and restricting the overall performance of IoT systems.

[0003] To address the aforementioned issues, existing research has attempted to improve channel assessment accuracy through dynamic threshold optimization or simple multi-parameter weighting, but two core bottlenecks remain: First, the channel quality quantification model does not incorporate the nonlinear relationship between signal-to-noise ratio, bandwidth, and occupancy rate into its design, leading to assessment results deviating from actual usability in dynamic environments. Second, anomaly detection and transmission state optimization processes are disconnected, and the correlation between packet loss rate, delay, and idle characteristics is not fully coupled, making it difficult to support global adaptive decision-making. Fine-grained allocation of dynamic channel resources urgently requires an intelligent method that can integrate multi-dimensional real-time data, quantify handover costs, and coordinate abnormal states and transmission fluctuations to overcome the dual limitations of existing technologies in terms of flexibility and robustness. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus and storage medium for remote management of smart IoT devices, so as to solve at least one of the problems existing in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for remote management of smart IoT devices, comprising:

[0007] Perform channel idle characteristic analysis by combining idle time and handover duration within the management cycle;

[0008] The channel packet loss rate and channel data transmission delay collected during the management period are coupled to determine the channel anomaly characteristics;

[0009] The analysis process involves determining the channel's transmission status based on channel idle and abnormal characteristics within the management period, and adjusting the channel priority coefficient for the next management period according to the channel's transmission status. The analysis process also involves determining the channel's data transmission delay fluctuation status based on the channel data transmission delay collected within the management period, and optimizing the channel's transmission status according to the channel's data transmission delay fluctuation status.

[0010] Optionally, it also includes: collecting channel data from IoT devices;

[0011] Based on the channel signal-to-noise ratio, bandwidth, and occupancy rate collected during the monitoring period, a dynamic quantitative evaluation of the channel quality index and priority coefficient is performed.

[0012] Based on the priority analysis results, a dynamic proportional allocation strategy is adopted to allocate bandwidth to the i-th channel.

[0013] Optionally, the quality index of each channel is constructed based on the channel signal-to-noise ratio, channel bandwidth and channel occupancy collected during the monitoring period, and the quality index of the i-th channel is set as XZi.

[0014] The priority coefficients of each channel are assigned based on the normalized calculation results of XZi, and the priority coefficient of the i-th channel is set as Yi;

[0015] Bandwidth allocation is performed based on Yi, and the allocated bandwidth of the i-th channel is set as Di.

[0016] Optionally, the idle time index kx of the i-th channel is analyzed based on the idle time ti0 of the i-th channel collected within the management period, and kx is set to lg(ti0 / T+1), where T is the duration of the management period.

[0017] Optionally, the handover consumption index is analyzed based on the channel handover duration t1 and the handover duration threshold t2. If t1 is less than or equal to t2, the handover consumption index is set to qh1; if t1 is greater than t2, the handover consumption index is set to qh2.

[0018] Based on the analysis results of the handover consumption index and the idle time index of the i-th channel within the management cycle, the idle characteristics of the i-th channel are analyzed. If kx is less than or equal to the handover consumption index, the idle characteristics of the i-th channel are set to KT1, and KT1 = 0. If kx is greater than the handover consumption index, the idle characteristics of the i-th channel are set to KT2, and KT2 = exp[3 × (kx - handover consumption index) - 3].

[0019] Optionally, the anomaly of the packet loss rate of the i-th channel is analyzed based on the channel packet loss rate collected within the management period and the preset packet loss rate, and the first anomaly coefficient is determined based on the analysis results;

[0020] The anomaly of the delay of the i-th channel is analyzed based on the data transmission delay of the i-th channel collected within the management cycle and the preset delay, and the second anomaly coefficient is determined based on the analysis results.

[0021] The abnormal characteristics YZi of the i-th channel are determined based on the first and second abnormal coefficients of the i-th channel within the management period.

[0022] Optionally, when u1 is less than or equal to f1 × i-th channel idle feature - f2 × YZi is less than or equal to u2, the transmission status of the i-th channel in the current management period is determined to be normal and no adjustment is made; otherwise, the transmission status of the i-th channel in the current management period is determined to be abnormal. In this case, if f1 × i-th channel idle feature - f2 × YZi is less than u1, the preset ratio threshold of the i-th channel in the next management period is set to αi 1; if f1 × i-th channel idle feature - f2 × YZi is greater than u1, the preset ratio threshold of the i-th channel in the next management period is set to αi 2.

[0023] Where f1 is the idle weight, f2 is the abnormal weight, u1 is the first preset state threshold, and u2 is the second preset state threshold.

[0024] Optionally, the data transmission delay fluctuation coefficient SKI of the i-th channel is compared with a preset fluctuation threshold bd. If SKI is less than or equal to bd, the data transmission delay fluctuation state of the i-th channel is determined to be normal and no optimization is performed. If SKI is greater than bd, the data transmission delay fluctuation state of the i-th channel is determined to be abnormal, and the first preset state threshold is set to u1' and the second preset state threshold is set to u2'.

[0025] In another aspect of this application, a remote management device for smart IoT devices is provided, comprising:

[0026] The data acquisition module is used to collect channel data from IoT devices;

[0027] The priority analysis module is used to perform dynamic quantitative evaluation of the channel quality index and priority coefficient based on the channel signal-to-noise ratio, bandwidth and occupancy rate collected during the monitoring period.

[0028] The bandwidth allocation module is used to allocate bandwidth to the i-th channel based on the priority analysis results and a dynamic proportional allocation strategy.

[0029] The idle analysis module is used to perform channel idle characteristic analysis by combining the idle time and handover duration within the management cycle;

[0030] The anomaly monitoring module is used to couple the channel packet loss rate and channel data transmission delay collected during the management period in order to determine the channel anomaly characteristics.

[0031] The management module is used to determine the channel transmission status based on the channel idle and abnormal characteristics within the management period, and to adjust the channel priority coefficient for the next management period based on the channel transmission status. It also determines the channel data transmission delay fluctuation status based on the channel data transmission delay collected within the management period, and optimizes the channel transmission status based on the channel data transmission delay fluctuation status.

[0032] In another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, wherein the computer program is used to control the electronic device on which the computer-readable storage medium is located to perform the aforementioned smart IoT device remote management method during runtime.

[0033] The beneficial effects of this invention are as follows: By employing a dynamic channel quality assessment model, a channel quality index is constructed by integrating multi-dimensional parameters such as signal-to-noise ratio, bandwidth, and occupancy rate. Furthermore, by combining idle time and handover duration to analyze the potential availability of the channel, precise quantification of channel status is achieved. This mechanism can adapt to dynamic changes in the network environment, providing a scientific basis for priority decisions and ensuring that resource allocation strategies are both flexible and meet actual needs. Secondly, based on dynamic priority adjustment and bandwidth allocation strategies, high-demand channels are preferentially matched with critical data transmission tasks, significantly improving resource utilization efficiency. Simultaneously, by integrating indicators such as packet loss rate and transmission delay through an anomaly feature detection module, a multi-dimensional evaluation system for channel health status is established. This system can quickly identify abnormal channels and adjust strategies in a timely manner, while also reducing the risk of misjudgment and enhancing fault tolerance through adaptive threshold optimization technology. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a flowchart illustrating the remote management method for smart IoT devices in this embodiment.

[0036] Figure 2 This is a flowchart illustrating the quantitative evaluation method in this embodiment.

[0037] Figure 3 This is a flowchart illustrating the idle feature analysis method in this embodiment.

[0038] Figure 4 This is a flowchart illustrating the method for determining channel anomaly characteristics in this embodiment.

[0039] Figure 5This is a flowchart illustrating the management method of this embodiment.

[0040] Figure 6 This is a schematic diagram of the structure of the remote management device for smart IoT devices in this embodiment.

[0041] Figure 7 This is a schematic diagram of the electronic device in this embodiment. Detailed Implementation

[0042] To more clearly illustrate the present invention, the following description, in conjunction with preferred embodiments and accompanying drawings, further explains the invention. Similar components in the drawings are indicated by the same reference numerals. Those skilled in the art should understand that the specific description below is illustrative rather than restrictive and should not be construed as limiting the scope of protection of the present invention.

[0043] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application 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 for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover 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.

[0044] Specifically, the remote management method, device, and storage medium for smart IoT devices described in this embodiment are applied to the remote management of medical devices. By analyzing information such as the signal-to-noise ratio, bandwidth, switching consumption, and occupancy rate of the channel, the channel priority and bandwidth allocation are dynamically adjusted, thereby achieving intelligent management of the wireless channel, ensuring the real-time performance and reliability of data transmission, and optimizing the efficiency of resource utilization.

[0045] Please see Figure 1 As shown, it is a flowchart illustrating the remote management method for smart IoT devices in this embodiment, including:

[0046] Step S101: Collect channel data from the IoT device. The channel data includes channel signal-to-noise ratio (SNR), channel bandwidth, channel occupancy rate, channel idle time, channel switching duration, channel packet loss rate, and channel data transmission delay. The channel switching duration is the time required to switch from one channel to another, and the channel data transmission delay is the delay from data transmission to reception. In this embodiment, the method for collecting channel data from the IoT device is not specifically limited. Those skilled in the art can freely set it, as long as it meets the requirements for collecting channel data from the IoT device. The channel SNR and channel bandwidth can be collected by a signal analyzer, and the channel occupancy rate, channel idle time, channel switching duration, channel packet loss rate, and channel data transmission delay can be collected by a network monitoring device.

[0047] Please continue reading. Figure 1 As shown, the remote management method for smart IoT devices further includes:

[0048] Step S102: Based on the channel signal-to-noise ratio, bandwidth, and occupancy rate collected during the monitoring period, perform dynamic quantitative evaluation of the channel quality index and priority coefficient. In this embodiment, the setting of the monitoring period is not specifically limited. Those skilled in the art can set it freely, as long as the setting requirements of the monitoring period are met. The monitoring period can be set to 1 second.

[0049] Please see Figure 2 As shown, the quantitative evaluation method includes:

[0050] Step S201: Construct the quality index of each channel based on the channel signal-to-noise ratio, channel bandwidth, and channel occupancy collected during the monitoring period.

[0051] Specifically, in step S201, the quality index of the i-th channel is set as XZi, and the expression of XZi is: XZi=αi×{w1×[1+(si0-s1) / (si0+s1)]+w2×exp{-ki0 / k1×Li}};

[0052] In the formula, w1 is the first weight, w2 is the second weight, w1+w2=1, si0 is the channel signal-to-noise ratio of the i-th channel collected during the monitoring period, s1 is the preset signal-to-noise ratio threshold, ki0 is the channel bandwidth of the i-th channel collected during the monitoring period, k1 is the bandwidth of the IoT device, Li is the channel occupancy rate of the i-th channel collected during the monitoring period, and αi is the preset proportion threshold of the i-th channel.

[0053] Specifically, by calculating the quality index of each channel, the channel analysis unit can quantify the specific state of the channel and comprehensively evaluate and integrate the performance of each channel based on parameters such as signal-to-noise ratio, bandwidth, and occupancy rate, thereby improving the accuracy of channel priority coefficient analysis.

[0054] It is understood that this embodiment does not specifically limit the settings of the preset ratio threshold and preset signal-to-noise ratio threshold for each weight and the i-th channel. Those skilled in the art can set them freely, as long as the setting requirements of the preset ratio threshold and preset signal-to-noise ratio threshold for each weight and the i-th channel are met. Among them, the optimal value of w1 is 0.75, the optimal value of w2 is 0.25, the optimal value of s1 is 20dB, and the optimal value of αi is 1. The bandwidth of the IoT device described in this embodiment can be obtained through user interaction.

[0055] Please continue reading. Figure 2 As shown, the quantitative evaluation method includes:

[0056] Step S202: Construct the priority coefficient of each channel based on the analysis results of the quality index of each channel within the monitoring period.

[0057] Specifically, in step S202, priority coefficients for each channel are allocated based on the normalized calculation result of XZi, and the priority coefficient of the i-th channel is set as Yi, where Yi is expressed as: In the formula, n is the number of IoT device channels.

[0058] Specifically, based on the analysis results of the channel quality index, the priority analysis unit can reasonably allocate priorities to each channel, thereby improving the remote management efficiency of IoT devices.

[0059] Please continue reading. Figure 1 As shown, the remote management method for smart IoT devices further includes:

[0060] Step S103: Based on the priority analysis results, a dynamic proportional allocation strategy is adopted to allocate bandwidth to the i-th channel.

[0061] Specifically, in step S103, bandwidth is allocated according to Yi, and the allocated bandwidth of the i-th channel is set as Di, and the expression of Di is Di = Yi × k1.

[0062] Specifically, by allocating bandwidth according to priority, bandwidth resources can be utilized to the maximum extent, ensuring that more bandwidth is used for high-priority channels, thereby improving the efficiency of remote management of IoT devices.

[0063] Please continue reading. Figure 1 As shown, the remote management method for smart IoT devices further includes:

[0064] Step S104: Perform channel idle characteristic analysis by combining the idle time and handover duration within the management cycle.

[0065] Please see Figure 3 As shown, the idle feature analysis method includes:

[0066] Step S301: Perform a nonlinear transformation on the channel idle time within the management period to generate an idle time index.

[0067] Specifically, based on the idle time ti0 of the i-th channel collected within the management cycle, the idle time index kx of the i-th channel is analyzed, and kx is set to lg(ti0 / T+1), where T is the duration of the management cycle.

[0068] Specifically, by monitoring and analyzing channel idle time, the utilization rate of the channel in terms of time can be analyzed, providing a basis for the rational allocation of resources, thereby improving the smoothness and stability of data transmission.

[0069] Please continue reading. Figure 3 As shown, the idle feature analysis method includes:

[0070] Step S302: Introduce a handover cost quantification model, calculate the handover consumption index by combining the handover duration and the handover duration threshold, and determine the channel idle characteristics by comparing the idle time index and the handover consumption index.

[0071] Specifically, the handover consumption index is analyzed based on the channel handover duration t1 and the handover duration threshold t2. If t1 is less than or equal to t2, the handover consumption index is set to qh1, and qh1 = 0. If t1 is greater than t2, the handover consumption index is set to qh2, and qh2 = β × (t1 - t2) / (t1 + t2), where β is a preset adjustment ratio.

[0072] Based on the analysis results of the handover consumption index and the idle time index of the i-th channel within the management cycle, the idle characteristics of the i-th channel are analyzed. If kx is less than or equal to the handover consumption index, the idle characteristics of the i-th channel are set to KT1, and KT1 = 0. If kx is greater than the handover consumption index, the idle characteristics of the i-th channel are set to KT2, and KT2 = exp[3 × (kx - handover consumption index) - 3].

[0073] Specifically, the results of switching consumption indices can provide a reference for channel priority configuration, which is conducive to making more solid and long-term decisions.

[0074] It is understood that this embodiment does not impose specific limitations on the setting of the switching duration threshold and the preset adjustment ratio. Those skilled in the art can set them freely, as long as the setting requirements of the switching duration threshold and the preset adjustment ratio are met. The optimal value of t2 is 200ms, and the optimal value of β is 0.15.

[0075] Please continue reading. Figure 1 As shown, the remote management method for smart IoT devices further includes:

[0076] Step S105: Data coupling is performed on the channel packet loss rate and channel data transmission delay collected during the management period to determine the channel anomaly characteristics.

[0077] Please see Figure 4 As shown, the method for determining the channel anomaly characteristics includes:

[0078] Step S401: Analyze the anomaly of the packet loss rate of the i-th channel based on the channel packet loss rate collected within the management period and the preset packet loss rate, and determine the first anomaly coefficient based on the analysis results.

[0079] Specifically, the packet loss rate bi 0 of the i-th channel collected within the management period is compared with the preset packet loss rate b1. If bi 0 is less than or equal to b1, the packet loss rate of the i-th channel in the current management period is determined to be normal, and the first abnormality coefficient of the i-th channel is set to YD1, with YD1 = 0. Otherwise, the packet loss rate of the i-th channel in the current management period is determined to be abnormal, and the first abnormality coefficient of the i-th channel is set to YD2, with YD2 = ln[(bi 0-b1) / b1+1].

[0080] Specifically, by analyzing the first anomaly coefficient to quantify the degree of anomaly in the channel packet loss rate, the accuracy of anomaly feature analysis is improved, thereby enhancing the management efficiency of IoT devices.

[0081] It is understood that this embodiment does not specifically limit the value of the preset packet loss rate. Those skilled in the art can set it freely, as long as the preset packet loss rate requirement is met. The optimal value of b1 is 0.01.

[0082] Please see Figure 4 As shown, the method for determining the channel anomaly characteristics further includes:

[0083] Step S402: Analyze the anomaly of the delay of the i-th channel based on the data transmission delay of the i-th channel collected within the management period and the preset delay, and determine the second anomaly coefficient based on the analysis results.

[0084] Specifically, the data transmission delay ci0 of the i-th channel collected within the management period is compared with the preset delay c1. If ci0 is less than or equal to c1, the channel delay of the i-th channel in the current management period is determined to be normal, and the second abnormality coefficient of the i-th channel is set to YJ1, with YJ1 = 0. Otherwise, the channel delay of the i-th channel in the current management period is determined to be abnormal, and the second abnormality coefficient of the i-th channel is set to YJ2, with YJ2 = lg[4×(ci0-c1) / (c1+ci0)+1].

[0085] Specifically, by analyzing the second anomaly coefficient to quantify the degree of channel delay anomaly, the accuracy of anomaly feature analysis is improved, thereby enhancing the management efficiency of IoT devices.

[0086] It is understood that this embodiment does not specifically limit the value of the preset delay. Those skilled in the art can set it freely, as long as the value of the preset delay is met. The optimal value of c1 is 50ms.

[0087] Please see Figure 4 As shown, the method for determining the channel anomaly characteristics further includes:

[0088] Step S403: Determine the abnormal characteristics of the i-th channel based on the first and second abnormal coefficients of the i-th channel within the management period.

[0089] Specifically, the first and second abnormal coefficients of the i-th channel within the management period are fused to determine the abnormal feature YZi of the i-th channel. YZi is set as x1 × first abnormal coefficient + x2 × second abnormal coefficient; where x1 is the packet loss rate weight, x2 is the delay weight, and x1 + x2 = 1.

[0090] Specifically, by analyzing the channel packet loss rate and data transmission delay, abnormal channel conditions can be detected in real time. Using packet loss rate and delay as evaluation dimensions, the health status of the channel can be comprehensively and accurately assessed, and strategies can be adjusted in a timely manner to maintain network stability.

[0091] Please continue reading. Figure 1 As shown, the remote management method for smart IoT devices includes:

[0092] Step S106 involves determining the channel's transmission status based on the channel's idle and abnormal characteristics within the management period, and adjusting the channel priority coefficient for the next management period according to the channel's transmission status. It also involves determining the channel's data transmission delay fluctuation status based on the channel's data transmission delay collected within the management period, and optimizing the channel's transmission status according to the channel's data transmission delay fluctuation status.

[0093] It is understood that this embodiment does not impose specific limitations on the setting of the management cycle. Those skilled in the art can set it freely, as long as the setting requirements of the management cycle are met. The management cycle can be set to 20 minutes, 30 minutes, etc.

[0094] It is understood that this embodiment does not impose specific limitations on the setting of each weight. Those skilled in the art can set them freely, as long as the setting requirements of each weight are met. The optimal value of x1 is 0.6, and the optimal value of x2 is 0.4.

[0095] Please see Figure 5As shown, the management method includes:

[0096] Step S501 is an analysis process that determines the channel transmission status based on the channel idle characteristics and channel abnormal characteristics within the management period, and adjusts the channel priority coefficient for the next management period according to the channel transmission status.

[0097] Specifically, when u1 is less than or equal to f1 × i-th channel idle feature - f2 × YZi and less than or equal to u2, the transmission status of the i-th channel in the current management period is determined to be normal, and no adjustment is made; otherwise, the transmission status of the i-th channel in the current management period is determined to be abnormal. At this time, if f1 × i-th channel idle feature - f2 × YZi is less than u1, the preset ratio threshold of the i-th channel in the next management period is set to αi 1, and αi 1 = αi × (1 - u1 + f1 × i-th channel idle feature - f2 × YZi). If f1 × i-th channel idle feature - f2 × YZi is greater than u1, the preset ratio threshold of the i-th channel in the next management period is set to αi2, and αi2 = αi × (1 + f1 × i-th channel idle feature - f2 × YZi - u2).

[0098] Where f1 is the idle weight, f2 is the abnormal weight, f1+f2=1, u1 is the first preset state threshold, u2 is the second preset state threshold, and u1 is less than u2.

[0099] Specifically, by coordinating the interaction and information flow between each step, the priority of the channel and the allocation of resources are balanced, and the management strategy is adjusted according to the analysis results, providing more accurate channel management and improving the remote management efficiency of IoT devices.

[0100] It is understood that this embodiment does not impose specific limitations on the settings of each weight and each preset state threshold. Those skilled in the art can set them freely, as long as the setting requirements of each weight and each preset state threshold are met. Among them, the optimal value of f1 is 0.6, the optimal value of f2 is 0.4, the optimal value of u1 is 0.1, and the optimal value of u2 is 0.36.

[0101] Please continue reading. Figure 5 As shown, the management method further includes:

[0102] Step S502: Determine the data transmission delay fluctuation status of the channel based on the data transmission delay collected during the management period, and optimize the channel transmission status analysis process based on the data transmission delay fluctuation status of the channel.

[0103] Specifically, the data transmission delay fluctuation coefficient of the i-th channel is set as SKi, and then... In the formula, J represents the number of data transmitted by the i-th channel within the management period, s(i,j) represents the transmission delay of the j-th data transmitted by the i-th channel within the management period, and sip represents the average transmission delay of the data transmitted by the i-th channel within the management period.

[0104] The data transmission delay fluctuation coefficient SKI of the i-th channel is compared with the preset fluctuation threshold bd. If SKI is less than or equal to bd, the data transmission delay fluctuation state of the i-th channel is determined to be normal and no optimization is performed; if SKI is greater than bd, the data transmission delay fluctuation state of the i-th channel is determined to be abnormal, and the first preset state threshold is set to u1', where u1' = u1 × [1 + (SKi - bd)]. 2 Set the second preset state threshold to u2', and set u2' = u2 × [1 - (SKi - bd)]. 2 ].

[0105] Specifically, by analyzing the fluctuations in channel data transmission delay, when the transmission delay exceeds a preset threshold, the system automatically adjusts its priority and bandwidth allocation strategy to reduce potential risks, thereby improving the remote management efficiency of IoT devices.

[0106] It is understood that this embodiment does not specifically limit the setting of the preset fluctuation threshold. Those skilled in the art can set it freely, as long as the setting requirements of the preset fluctuation threshold are met. The optimal value of bd is 0.1.

[0107] This embodiment also provides a remote management device for smart IoT devices, such as... Figure 6 As shown, it includes:

[0108] The data acquisition module is used to collect channel data from IoT devices;

[0109] The priority analysis module is used to perform dynamic quantitative evaluation of the channel quality index and priority coefficient based on the channel signal-to-noise ratio, bandwidth and occupancy rate collected during the monitoring period.

[0110] The bandwidth allocation module is used to allocate bandwidth to the i-th channel based on the priority analysis results and a dynamic proportional allocation strategy.

[0111] The idle analysis module is used to perform channel idle characteristic analysis by combining the idle time and handover duration within the management cycle;

[0112] The anomaly monitoring module is used to couple the channel packet loss rate and channel data transmission delay collected during the management period in order to determine the channel anomaly characteristics.

[0113] The management module is used to determine the channel transmission status based on the channel idle and abnormal characteristics within the management period, and to adjust the channel priority coefficient for the next management period based on the channel transmission status. It also determines the channel data transmission delay fluctuation status based on the channel data transmission delay collected within the management period, and optimizes the channel transmission status based on the channel data transmission delay fluctuation status.

[0114] This application also provides an electronic device for executing the aforementioned remote management method for smart IoT devices, such as... Figure 7 As shown, the electronic device includes: a processor 601, a memory 602, a communication interface 603, and a system bus 604. The processor includes at least one of a central processing unit (CPU), a graphics processing unit (GPU), or a field-programmable gate array (FPGA), configured to call computer programs and data stored in the memory and generate control instructions; the memory includes random access memory (RAM) and / or non-volatile memory (NVM), the NVM including flash memory, solid-state drive (SSD), or a combination thereof, used to store computer programs, process intermediate data, and historical data sets; the communication interface includes a wired communication module and a wireless communication module, the wired communication module supporting Ethernet or RS-485 protocols for connecting to sensor networks; the wireless communication module supporting LoRa, 5G, or satellite communication protocols for transmitting processing results to a remote server; the system bus adopts a PCI Express or AXI bus architecture to achieve high-speed data interaction and clock synchronization between the processor, memory, and communication interface.

[0115] This application also provides a computer-readable storage medium storing computer program code. The program code is loaded onto a processor via an integrated circuit carrier board and written to memory via a system bus.

[0116] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. For those skilled in the art, other variations or modifications can be made based on the above description. It is impossible to exhaustively list all the implementation methods here. All obvious variations or modifications derived from the technical solutions of the present invention are still within the protection scope of the present invention.

Claims

1. A method for remote management of intelligent IoT devices, characterized in that, include: Collect channel data from IoT devices; Based on the channel signal-to-noise ratio, bandwidth, and occupancy rate collected during the monitoring period, a dynamic quantitative evaluation of the channel quality index and priority coefficient is performed. Based on the priority analysis results, a dynamic proportional allocation strategy is adopted to allocate bandwidth to the i-th channel; Perform channel idle characteristic analysis by combining idle time and handover duration within the management cycle; The channel packet loss rate and channel data transmission delay collected during the management period are coupled to determine the channel anomaly characteristics; The analysis process involves determining the channel's transmission status based on channel idle and abnormal characteristics within the management period, and adjusting the channel priority coefficient for the next management period according to the channel's transmission status. The analysis process also involves determining the channel's data transmission delay fluctuation status based on the channel data transmission delay collected within the management period, and optimizing the channel's transmission status according to the channel's data transmission delay fluctuation status.

2. The remote management method for intelligent IoT devices according to claim 1, characterized in that, The quality index of each channel is constructed based on the channel signal-to-noise ratio, channel bandwidth and channel occupancy collected during the monitoring period, and the quality index of the i-th channel is set as XZi. The priority coefficients of each channel are assigned based on the normalized calculation results of XZi, and the priority coefficient of the i-th channel is set as Yi; Bandwidth allocation is performed based on Yi, and the allocated bandwidth of the i-th channel is set as Di.

3. The remote management method for intelligent IoT devices according to claim 2, characterized in that, The idle time index kx of the i-th channel is analyzed based on the idle time ti0 of the i-th channel collected within the management cycle. kx is set as lg(ti0 / T+1), where T is the duration of the management cycle.

4. The remote management method for intelligent IoT devices according to claim 3, characterized in that, The handover consumption index is analyzed based on the channel handover duration t1 and the handover duration threshold t2. If t1 is less than or equal to t2, the handover consumption index is set to qh1; if t1 is greater than t2, the handover consumption index is set to qh2. Based on the analysis results of the handover consumption index and the idle time index of the i-th channel within the management cycle, the idle characteristics of the i-th channel are analyzed. If kx is less than or equal to the handover consumption index, the idle characteristics of the i-th channel are set to KT1, and KT1=0; if kx is greater than the handover consumption index, the idle characteristics of the i-th channel are set to KT2, and KT2=exp[3×(kx-handover consumption index)-3].

5. The remote management method for intelligent IoT devices according to claim 4, characterized in that, The abnormality of the packet loss rate of the i-th channel is analyzed based on the channel packet loss rate collected within the management period and the preset packet loss rate, and the first abnormality coefficient is determined based on the analysis results. The anomaly of the delay of the i-th channel is analyzed based on the data transmission delay of the i-th channel collected within the management cycle and the preset delay, and the second anomaly coefficient is determined based on the analysis results. The abnormal characteristics YZi of the i-th channel are determined based on the first and second abnormal coefficients of the i-th channel within the management period.

6. The remote management method for intelligent IoT devices according to claim 5, characterized in that, When u1 is less than or equal to f1 × i-th channel idle feature - f2 × YZi and less than or equal to u2, the transmission status of the i-th channel in the current management period is determined to be normal, and no adjustment is made; Conversely, if the transmission status of the i-th channel in the current management period is abnormal, then if f1 × i-th channel idle feature - f2 × YZi is less than u1, the preset ratio threshold of the i-th channel in the next management period is set to αi1; if f1 × i-th channel idle feature - f2 × YZi is greater than u1, the preset ratio threshold of the i-th channel in the next management period is set to αi2. Where f1 is the idle weight, f2 is the abnormal weight, u1 is the first preset state threshold, and u2 is the second preset state threshold.

7. The remote management method for intelligent IoT devices according to claim 6, characterized in that, The data transmission delay fluctuation coefficient SKI of the i-th channel is compared with the preset fluctuation threshold bd. If SKI is less than or equal to bd, the data transmission delay fluctuation state of the i-th channel is determined to be normal and no optimization is performed. If SKI is greater than bd, the data transmission delay fluctuation state of the i-th channel is determined to be abnormal, and the first preset state threshold is set to u1' and the second preset state threshold is set to u2'.

8. A remote management device for intelligent IoT devices, applied to the remote management method for intelligent IoT devices as described in any one of claims 1-7, characterized in that, include: The data acquisition module is used to collect channel data from IoT devices; The priority analysis module is used to perform dynamic quantitative evaluation of the channel quality index and priority coefficient based on the channel signal-to-noise ratio, bandwidth and occupancy rate collected during the monitoring period. The bandwidth allocation module is used to allocate bandwidth to the i-th channel based on the priority analysis results and a dynamic proportional allocation strategy. The idle analysis module is used to perform channel idle characteristic analysis by combining the idle time and handover duration within the management cycle; The anomaly monitoring module is used to couple the channel packet loss rate and channel data transmission delay collected during the management period in order to determine the channel anomaly characteristics. The management module is used to determine the channel transmission status based on the channel idle and abnormal characteristics within the management period, and to adjust the channel priority coefficient for the next management period based on the channel transmission status. It also determines the channel data transmission delay fluctuation status based on the channel data transmission delay collected within the management period, and optimizes the channel transmission status based on the channel data transmission delay fluctuation status.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is used to control the electronic device on which the computer-readable storage medium is located to perform the remote management method for smart IoT devices according to any one of claims 1-7 during runtime.

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