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

Abstract

The invention relates to the technical field of Internet of Things equipment management, in particular to an intelligent remote management method and device for Internet of Things equipment and a storage medium, and the method comprises the steps: collecting channel data of the Internet of Things equipment; based on the signal-to-noise ratio, the bandwidth and the occupancy rate of the channel collected in the monitoring period, dynamic quantitative evaluation of the channel quality index and the priority coefficient is executed; based on a priority analysis result, adopting a dynamic proportional distribution strategy to distribute bandwidth for the ith channel; performing channel idle feature analysis in combination with idle time and switching duration in a management period; performing data coupling on the channel packet loss rate and the channel data transmission delay collected in the management period to determine channel abnormal characteristics; and adjusting the analysis process of the channel priority coefficient of the next management period, and optimizing the analysis process of the channel transmission state according to the data transmission delay fluctuation state of the channel. According to the invention, the remote management efficiency of the Internet of Things equipment is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things device management, and particularly to a method, device and storage medium for remotely managing intelligent Internet of Things devices. Background Art

[0002] With the popularization and in-depth application of Internet of Things technology, intelligent Internet of Things devices face severe challenges in dynamically adapting channel resources in a complex and changeable wireless environment. 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 rate, idle time, and handover delay, resulting in rough channel priority division and rigid bandwidth allocation. Especially in scenarios with dense devices, the contradiction between high-dynamic channel states and fixed-weight strategies is further highlighted: high-occupancy channels may be over-occupied, and frequent inefficient handovers caused by the lack of handover consumption in the decision-making model will exacerbate transmission delays and packet loss rate fluctuations. Existing technologies are neither able to balance the requirements of real-time and stability, nor can they couple and mine abnormal channel characteristics (such as sudden packet loss and sharp increase in delay), resulting in management strategies lagging behind network state changes and restricting the overall efficiency of the Internet of Things system.

[0003] In response to the above problems, although existing research has tried to improve channel evaluation accuracy through dynamic threshold optimization or simple multi-parameter weighting, there are still two core bottlenecks: on the one hand, the channel quality quantization model does not incorporate the non-linear relationship between signal-to-noise ratio, bandwidth, and occupancy rate into the design, resulting in evaluation results deviating from true usability in a dynamic environment; on the other hand, the abnormal detection and transmission state optimization processes are mutually disjointed, and the correlation characteristics of packet loss rate, delay, and idle characteristics are not fully coupled, making it difficult to support global adaptive decision-making. The refined allocation of dynamic channel resources urgently requires an intelligent method that can integrate multi-dimensional real-time data, quantify handover costs, collaborate abnormal states, and transmission fluctuations to break through the dual limitations of existing technologies in flexibility and robustness. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, device and storage medium for remotely managing intelligent Internet of Things devices to solve at least one of the problems existing in the prior art.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] A method for remotely managing intelligent Internet of Things devices includes:

[0007] Performing channel idle feature analysis in combination with the idle time and handover duration within a management period;

[0008] Coupling the channel packet loss rate and channel data transmission delay collected within a management period to determine channel abnormal characteristics;

[0009] The analysis process of determining the transmission state of a channel based on the channel idle characteristics and channel anomaly characteristics within a management period, and adjusting the channel priority coefficient for the next management period according to the transmission state of the channel, and the analysis process of determining the data transmission delay fluctuation state of the channel based on the channel data transmission delay collected within the management period, and optimizing the channel transmission state according to the data transmission delay fluctuation state of the channel.

[0010] Optionally, it further includes: collecting channel data of the IoT device;

[0011] Based on the channel signal-to-noise ratio, bandwidth, and occupancy rate collected within a monitoring period, perform dynamic quantization evaluation of the channel quality index and priority coefficient;

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

[0013] Optionally, construct the quality index of each channel according to the channel signal-to-noise ratio, channel bandwidth, and channel occupancy rate collected within the monitoring period, and set the quality index of the i-th channel as XZi;

[0014] Allocate the priority coefficients of each channel according to the normalization calculation result of XZi, and set the priority coefficient of the i-th channel as Yi;

[0015] Allocate bandwidth according to Yi, and set the allocated bandwidth of the i-th channel as Di.

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

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

[0018] Analyze the idle characteristics of the i-th channel according to the analysis results of the switching consumption index and the idle time index of the i-th channel within the management period. If kx is less than or equal to the switching consumption index, set the idle characteristics of the i-th channel as KT1 and set KT1 = 0; if kx is greater than the switching consumption index, set the idle characteristics of the i-th channel as KT2 and set KT2 = exp[3×(kx - switching consumption index) - 3].

[0019] Optionally, analyze the abnormality of the packet loss rate of the i-th channel based on the packet loss rate of the i-th channel collected within the management period and the preset packet loss rate, and determine the first anomaly coefficient according to the analysis result;

[0020] Analyze the abnormality of the data transmission 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 abnormality coefficient according to the analysis result;

[0021] Determine the abnormal feature YZi of the i-th channel based on the first abnormality coefficient and the second abnormality coefficient of the i-th channel within the management period.

[0022] Optionally, when u1 ≤ f1 × the idle feature of the i-th channel - f2 × YZi ≤ u2, determine that the transmission state of the i-th channel in the current management period is normal and no adjustment is made; otherwise, determine that the transmission state of the i-th channel in the current management period is abnormal. At this time, if f1 × the idle feature of the i-th channel - f2 × YZi < u1, set the preset ratio threshold of the i-th channel in the next management period to αi1, and if f1 × the idle feature of the i-th channel - f2 × YZi > u1, set the preset ratio threshold of the i-th channel in the next management period to αi2;

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

[0024] Optionally, compare the data transmission delay fluctuation coefficient SKi of the i-th channel with the preset fluctuation threshold bd. If SKi ≤ bd, determine that the data transmission delay fluctuation state of the i-th channel is normal and no optimization is performed; if SKi > bd, determine that the data transmission delay fluctuation state of the i-th channel is abnormal, and set the first preset state threshold to u1' and the second preset state threshold to u2'.

[0025] In another aspect of the present application, there is provided an intelligent IoT device remote management device, including:

[0026] A data acquisition module for acquiring the channel data of the IoT device;

[0027] A priority analysis module for performing dynamic quantization evaluation of the channel quality index and the priority coefficient according to the channel signal-to-noise ratio, bandwidth, and occupancy rate collected within the monitoring period;

[0028] A bandwidth allocation module for allocating bandwidth to the i-th channel according to the priority analysis result by adopting a dynamic ratio allocation strategy;

[0029] An idle analysis module for performing channel idle feature analysis by combining the idle time and the switching duration within the management period;

[0030] An abnormality monitoring module for coupling the channel packet loss rate and the channel data transmission delay collected within the management period to determine the channel abnormal feature;

[0031] The management module is used to determine the transmission state of the channel according to the channel idle characteristics and channel anomaly characteristics within the management period, and adjust the channel priority coefficient of the next management period based on the transmission state of the channel. The analysis process is to determine the data transmission delay fluctuation state of the channel based on the channel data transmission delay collected within the management period, and optimize the channel transmission state based on the data transmission delay fluctuation state of the channel.

[0032] In another aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, wherein the computer program is used to control the electronic device where the computer-readable storage medium is located to execute the intelligent IoT device remote management method when running.

[0033] The beneficial effects of the present invention are as follows: Through the dynamic channel quality evaluation model, a channel quality index is constructed by integrating multi-dimensional parameters such as signal-to-noise ratio, bandwidth, and occupancy rate, and the potential availability of the channel is analyzed by combining the idle time and handover duration, realizing the precise quantification of the channel state. This mechanism can adapt to the dynamic changes of the network environment, provide a scientific basis for priority decision-making, and ensure that the resource allocation strategy is both flexible and meets the actual needs. Secondly, based on the dynamic priority adjustment and bandwidth allocation strategy, high-demand channels are preferentially matched with critical data transmission tasks, significantly improving the resource utilization efficiency. At the same time, through the anomaly feature detection module, indicators such as packet loss rate and transmission delay are fused to establish a multi-dimensional evaluation system for the channel health state, which can not only quickly identify abnormal channels and adjust the strategy in a timely manner, but also rely on the adaptive threshold optimization technology to reduce the misjudgment risk and enhance the fault tolerance ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0035] Figure 1 It is a schematic flow chart of the intelligent IoT device remote management method in this embodiment.

[0036] Figure 2 It is a schematic flow chart of the quantization evaluation method in this embodiment.

[0037] Figure 3 It is a schematic flow chart of the idle feature analysis method in this embodiment.

[0038] Figure 4 It is a schematic flow chart of the method for determining the channel anomaly characteristics in this embodiment.

[0039] Figure 5It is a schematic flowchart of the management method of this embodiment.

[0040] Figure 6 It is a schematic structural diagram of the remote management device for intelligent Internet of Things devices in this embodiment.

[0041] Figure 7 It is a schematic structural diagram of the electronic device in this embodiment. Detailed implementation manners

[0042] To describe the present invention more clearly, the present invention will be further described below in conjunction with preferred embodiments and the accompanying drawings. Similar components in the drawings are denoted by the same reference numerals. Those skilled in the art should understand that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the protection scope of the present invention.

[0043] It should be noted that the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so as to implement the embodiments of this application described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or devices.

[0044] Specifically, the remote management method, device and storage medium for intelligent Internet of Things devices in this embodiment are applied to the remote management of medical devices. By analyzing information such as the signal-to-noise ratio, bandwidth, handover consumption, occupancy rate, etc. of the channel, the channel priority and bandwidth allocation are dynamically adjusted, so as to realize the intelligent management of the wireless channel, ensure the real-time performance and reliability of data transmission, and optimize the resource utilization efficiency.

[0045] Please refer to Figure 1 as shown, which is a schematic flowchart of the remote management method for intelligent Internet of Things devices in this embodiment, including

[0046] Step S101: Collect the channel data of the IoT device. The channel data includes channel signal-to-noise ratio, 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 for the channel to switch to other channels, and the channel data transmission delay is the delay time from data sending to receiving. In this embodiment, the collection method of the channel data of the IoT device is not specifically limited, and those skilled in the art can freely set it as long as the collection requirements of the channel data of the IoT device are met. Among them, the channel signal-to-noise ratio 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 to refer to Figure 1 as shown, the intelligent IoT device remote management method further includes:

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

[0049] Please refer to Figure 2 as shown, the quantization evaluation method includes:

[0050] Step S201: Construct the quality index of each channel according to the channel signal-to-noise ratio, channel bandwidth, and channel occupancy rate collected within 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 within the monitoring period, s1 is the preset signal-to-noise ratio threshold, ki0 is the channel bandwidth of the i-th channel collected within the monitoring period, k1 is the bandwidth of the IoT device, Li is the channel occupancy rate of the i-th channel collected within the monitoring period, and αi is the preset ratio 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 synthesize the performance of each channel according to parameters such as signal-to-noise ratio, bandwidth, and occupancy rate, so as to improve the accuracy of channel priority coefficient analysis.

[0054] It can be understood that in this embodiment, no specific limitations are imposed on the setting of each weight, the preset proportional threshold and the preset signal-to-noise ratio threshold of the i-th channel. Those skilled in the art can freely set them as long as the setting requirements of each weight, the preset proportional threshold and the preset signal-to-noise ratio threshold of 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 20 dB, and the optimal value of αi is 1. The bandwidth of the IoT device in this embodiment can be obtained through user interaction.

[0055] Please continue to refer to Figure 2 As shown, the quantization evaluation method includes:

[0056] Step S202, construct the priority coefficient of each channel according to the analysis result of the quality index of each channel within the monitoring period.

[0057] Specifically, in step S202, the priority coefficient of each channel is allocated according to the normalized calculation result of XZi, and the priority coefficient of the i-th channel is set as Yi. The expression of Yi is: In the formula, n is the number of channels of the IoT device.

[0058] Specifically, according to the analysis result of the channel quality index, the priority analysis unit can reasonably allocate priorities for each channel, thereby improving the remote management efficiency of the IoT device.

[0059] Please continue to refer to Figure 1 As shown, the remote management method for the intelligent IoT device further includes:

[0060] Step S103, based on the priority analysis result, adopt a dynamic proportional allocation strategy to allocate bandwidth for the i-th channel.

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

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

[0063] Please continue to refer to Figure 1 As shown, the remote management method for the intelligent IoT device further includes:

[0064] Step S104, combine the idle time and the switching duration within the management period to perform channel idle feature analysis.

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

[0066] Step S301, perform a non-linear transformation on the channel idle time within the management period to generate an idle time exponent.

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

[0068] Specifically, by monitoring and analyzing the channel idle time, analyze the utilization rate of the channel over time, provide a basis for the reasonable allocation of resources, and thus improve the smoothness and stability of data transmission.

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

[0070] Step S302, introduce a handover cost quantification model, calculate the handover consumption exponent by combining the handover duration and the handover duration threshold, and determine the channel idle feature by comparing the idle time exponent with the handover consumption exponent.

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

[0072] Analyze the idle feature of the i-th channel according to the analysis results of the handover consumption exponent and the idle time exponent of the i-th channel within the management period. If kx is less than or equal to the handover consumption exponent, set the idle feature of the i-th channel as KT1 and set KT1 = 0; if kx is greater than the handover consumption exponent, set the idle feature of the i-th channel as KT2 and set KT2 = exp[3×(kx - handover consumption exponent) - 3].

[0073] Specifically, through the result of the handover consumption exponent, it can provide a reference for channel priority configuration, which is conducive to making more solid and long-term decisions.

[0074] It can be understood that in this embodiment, the settings of the handover duration threshold and the preset adjustment ratio are not specifically limited, and those skilled in the art can freely set them as long as they meet the setting requirements of the handover duration threshold and the preset adjustment ratio. Among them, the best value of t2 is 200ms, and the best value of β is 0.15.

[0075] Please continue to refer to Figure 1 As shown, the intelligent IoT device remote management method further includes:

[0076] Step S105: Couple the channel packet loss rate and the channel data transmission delay collected within the management period to determine the channel anomaly characteristics.

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

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

[0079] Specifically, compare the packet loss rate bi0 of the i-th channel collected within the management period with the preset packet loss rate b1. If bi0 is less than or equal to b1, it is determined that the packet loss rate of the i-th channel in the current management period is normal, and the first anomaly coefficient of the i-th channel is set to YD1, where YD1 = 0. Otherwise, it is determined that the packet loss rate of the i-th channel in the current management period is abnormal, and the first anomaly coefficient of the i-th channel is set to YD2, where YD2 = ln[(bi0 - b1) / b1 + 1].

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

[0081] It can be understood that in this embodiment, no specific limitation is imposed on the value of the preset packet loss rate, and those skilled in the art can freely set it as long as the value requirement of the preset packet loss rate is met. Among them, the optimal value of b1 is 0.01.

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

[0083] Step S402: Analyze the abnormality 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 according to the analysis result.

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

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

[0086] It can be understood that in this embodiment, no specific limitation is imposed on the value of the preset delay, and those skilled in the art can freely set it as long as the value requirement of the preset delay is satisfied. Among them, the optimal value of c1 is 50ms.

[0087] Please refer to Figure 4 As shown, the method for determining the channel anomaly feature further includes:

[0088] Step S403, determining the anomaly feature of the i-th channel based on the first anomaly coefficient and the second anomaly coefficient of the i-th channel within the management period.

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

[0090] Specifically, through the analysis of the channel packet loss rate and data transmission delay, the abnormal state of the channel can be detected in real time. Taking the packet loss rate and delay as evaluation dimensions, the channel health status can be comprehensively and accurately evaluated, and the strategy can be adjusted in time to maintain network stability.

[0091] Please continue to refer to Figure 1 As shown, the intelligent IoT device remote management method includes:

[0092] Step S106, determining the transmission state of the channel based on the channel idle feature and the channel anomaly feature within the management period, and adjusting the analysis process of the channel priority coefficient in the next management period according to the transmission state of the channel. Based on the data transmission delay of the channel collected within the management period, determining the data transmission delay fluctuation state of the channel, and optimizing the analysis process of the channel transmission state according to the data transmission delay fluctuation state of the channel.

[0093] It can be understood that in this embodiment, no specific limitation is imposed on the setting of the management period, and those skilled in the art can freely set it as long as the setting requirement of the management period is satisfied. Among them, the management period can be set to 20min, 30min, etc.

[0094] It can be understood that in this embodiment, no specific limitation is imposed on the setting of each weight, and those skilled in the art can freely set it as long as the setting requirement of each weight is satisfied. Among them, the optimal value of x1 is 0.6, and the optimal value of x2 is 0.4.

[0095] Please refer to Figure 5As shown in the figure, the management method includes:

[0096] Step S501, a process of analyzing to determine the transmission state of a channel based on the channel idle characteristics and channel anomaly characteristics within a management period, and adjusting the channel priority coefficient for the next management period according to the transmission state of the channel.

[0097] Specifically, when u1 ≤ f1 × the idle characteristic of the i-th channel - f2 × YZi ≤ u2, it is determined that the transmission state of the i-th channel in the current management period is normal and no adjustment is made; otherwise, it is determined that the transmission state of the i-th channel in the current management period is abnormal. At this time, if f1 × the idle characteristic of the i-th channel - f2 × YZi < u1, the preset ratio threshold of the i-th channel for the next management period is set to αi 1, and αi 1 = αi × (1 - u1 + f1 × the idle characteristic of the i-th channel - f2 × YZi) is set. If f1 × the idle characteristic of the i-th channel - f2 × YZi > u1, the preset ratio threshold of the i-th channel for the next management period is set to αi2, and αi2 = αi × (1 + f1 × the idle characteristic of the i-th channel - f2 × YZi - u2);

[0098] Among them, f1 is the idle weight, f2 is the anomaly weight, f1 + f2 = 1, u1 is the first preset state threshold, u2 is the second preset state threshold, and u1 < u2.

[0099] Specifically, by coordinating the interaction and information flow between each step, the priority of the channel and resource allocation are balanced, the management strategy is adjusted according to the analysis result, more accurate channel management is provided, and the remote management efficiency of the IoT device is improved.

[0100] It can be understood that in this embodiment, the settings of each weight and each preset state threshold are not specifically limited, and those skilled in the art can freely set them as long as the settings of each weight and each preset state threshold meet the requirements. 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 to refer to Figure 5 As shown in the figure, the management method further includes:

[0102] Step S502, a process of analyzing to determine the data transmission delay fluctuation state of a channel based on the data transmission delay of the channel collected within a management period, and optimizing the transmission state of the channel according to the data transmission delay fluctuation state of the channel.

[0103] Specifically, the data transmission delay fluctuation coefficient of the i-th channel is set to SKi, and Where J is the number of data transmitted by the i-th channel within the management period, s(i,j) is the transmission delay of the j-th data transmitted by the i-th channel within the management period, and sip is the average value of the transmission delays of the data transmitted by the i-th channel within the management period.

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

[0105] Specifically, by analyzing the fluctuation state of the channel data transmission delay, when it is found that the transmission delay exceeds the preset threshold, the priority and bandwidth allocation strategy are automatically adjusted to reduce potential risks, thereby improving the remote management efficiency of the IoT devices.

[0106] It can be understood that in this embodiment, no specific limitation is imposed on the setting of the preset fluctuation threshold, and those skilled in the art can freely set it as long as the setting requirements of the preset fluctuation threshold are met. Among them, the best value of bd is 0.1.

[0107] This embodiment also provides a remote management device for intelligent IoT devices, as Figure 6 shown, including:

[0108] A data acquisition module for acquiring the channel data of the IoT device;

[0109] A priority analysis module for performing dynamic quantization evaluation of the channel quality index and the priority coefficient according to the channel signal-to-noise ratio, bandwidth, and occupancy rate collected within the monitoring period;

[0110] A bandwidth allocation module for allocating bandwidth to the i-th channel according to the priority analysis result by adopting a dynamic proportional allocation strategy;

[0111] An idle analysis module for performing channel idle feature analysis in combination with the idle time and switching duration within the management period;

[0112] An anomaly monitoring module for coupling the channel packet loss rate and the channel data transmission delay collected within the management period to determine the channel anomaly feature;

[0113] The management module is used to determine the transmission status of a channel based on the channel idle characteristics and channel anomaly characteristics within a management period, and adjust the channel priority coefficient for the next management period according to the transmission status of the channel. The analysis process is based on determining the data transmission delay fluctuation status of the channel according to the channel data transmission delay collected within the management period, and optimizing the channel transmission status according to the data transmission delay fluctuation status of the channel.

[0114] An embodiment of this application also provides an electronic device, which is used to execute the intelligent IoT device remote management method, as Figure 7 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), and is configured to call computer programs and data stored in the memory and generate control instructions; the memory includes a random access memory (RAM) and / or a non-volatile memory (NVM), and the NVM includes a flash memory, a solid state drive (SSD), or a combination thereof, and is used to store computer programs, processing intermediate data, and historical data sets; the communication interface includes a wired communication module and a wireless communication module. The wired communication module supports Ethernet or RS-485 protocols and is used to connect to a sensor network; the wireless communication module supports LoRa, 5G, or satellite communication protocols and is used to transmit 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, the memory, and the communication interface.

[0115] An embodiment of this application also provides a computer-readable storage medium, on which computer program code is stored. When the program code is loaded onto the processor through an integrated circuit carrier board and written into the memory via the system bus.

[0116] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, rather than limitations on the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation manners here. Any obvious changes or modifications derived from the technical solutions of the present invention still fall within the protection scope of the present invention.

Claims

1. A method for remotely managing an intelligent Internet of Things device, characterized in that, Including: Performing channel idle feature analysis by combining the idle time and handover duration within the management cycle; Coupling the channel packet loss rate and channel data transmission delay collected within the management cycle to determine the channel anomaly feature; Analyzing the transmission state of the channel based on the channel idle feature and channel anomaly feature within the management cycle, and adjusting the channel priority coefficient of the next management cycle according to the transmission state of the channel. Analyzing the data transmission delay fluctuation state of the channel based on the channel data transmission delay collected within the management cycle, and optimizing the channel transmission state analysis process according to the data transmission delay fluctuation state of the channel.

2. The remote management method of the intelligent IoT device according to claim 1, wherein, Also including: Collecting the channel data of the IoT device; Performing dynamic quantization evaluation of the channel quality index and priority coefficient based on the channel signal-to-noise ratio, bandwidth, and occupancy rate collected within the monitoring cycle; Based on the priority analysis result, adopting a dynamic proportional allocation strategy to allocate bandwidth to the i-th channel.

3. The remote management method of the intelligent Internet of Things device according to claim 2, wherein Constructing the quality index of each channel according to the channel signal-to-noise ratio, channel bandwidth, and channel occupancy rate collected within the monitoring cycle, and setting the quality index of the i-th channel as XZi; Allocating the priority coefficient of each channel according to the normalization calculation result of XZi, and setting the priority coefficient of the i-th channel as Yi; Allocating bandwidth according to Yi, and setting the allocated bandwidth of the i-th channel as Di.

4. The remote management method for intelligent IoT devices according to claim 3, wherein Analyzing the idle time index kx of the i-th channel based on the channel idle time ti0 of the i-th channel collected within the management cycle, and setting kx = lg(ti0 / T + 1), where T is the duration of the management cycle.

5. The remote management method of the intelligent Internet of Things device according to claim 4, wherein Analyzing the handover consumption index based on the channel handover duration t1 and the handover duration threshold t2. If t1 is less than or equal to t2, setting the handover consumption index as qh1; if t1 is greater than t2, setting the handover consumption index as qh2; Analyzing the idle feature of the i-th channel according to the analysis results of the handover consumption index and the idle time index of the i-th channel within the management cycle. If kx is less than or equal to the handover consumption index, setting the idle feature of the i-th channel as KT1 and setting KT1 = 0; if kx is greater than the handover consumption index, setting the idle feature of the i-th channel as KT2 and setting KT2 = exp[3×(kx - handover consumption index) - 3].

6. The remote management method of the intelligent Internet of Things device according to claim 5, wherein Analyzing the abnormality of the packet loss rate of the i-th channel based on the channel packet loss rate of the i-th channel collected within the management cycle and the preset packet loss rate, and determining the first anomaly coefficient according to the analysis result; Analyzing the abnormality of the delay of the i-th channel based on the data transmission delay of the i-th channel collected within the management cycle and the preset delay, and determining the second anomaly coefficient according to the analysis result; Determining the anomaly feature YZi of the i-th channel based on the first anomaly coefficient and the second anomaly coefficient of the i-th channel within the management cycle.

7. The remote management method of the intelligent Internet of Things device according to claim 6, wherein, When u1 is less than or equal to f1×idle feature of the i-th channel - f2×YZi is less than or equal to u2, it is determined that the transmission state of the i-th channel in the current management cycle is normal and no adjustment is made; Conversely, it is determined that the transmission state of the i-th channel in the current management period is abnormal. At this time, if f1 × the idle feature of the i-th channel - 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 × the idle feature of the i-th channel - f2 × YZi is greater than u1, the preset ratio threshold of the i-th channel in the next management period is set to αi2; Wherein, 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.

8. The remote management method for the intelligent IoT device according to claim 7, wherein Compare the data transmission delay fluctuation coefficient SKi of the i-th channel with the preset fluctuation threshold bd. If SKi is less than or equal to bd, it is determined that the data transmission delay fluctuation state of the i-th channel is normal and no optimization is performed; if SKi is greater than bd, it is determined that the data transmission delay fluctuation state of the i-th channel is abnormal, and the first preset state threshold is set to u1', and the second preset state threshold is set to u2'.

9. A remote management device for intelligent Internet of Things devices, characterized in that, Including: A data acquisition module for acquiring channel data of Internet of Things devices; A priority analysis module for performing dynamic quantization evaluation of channel quality index and priority coefficient according to the channel signal-to-noise ratio, bandwidth, and occupancy rate collected during the monitoring period; A bandwidth allocation module for allocating bandwidth to the i-th channel using a dynamic ratio allocation strategy according to the priority analysis result; An idle analysis module for performing channel idle feature analysis by combining the idle time and switching duration within the management period; An anomaly monitoring module for coupling the channel packet loss rate and channel data transmission delay collected during the management period to determine the channel anomaly feature; A management module for determining the transmission state of the channel according to the channel idle feature and channel anomaly feature within the management period, and adjusting the analysis process of the channel priority coefficient in the next management period according to the transmission state of the channel, determining the data transmission delay fluctuation state of the channel based on the channel data transmission delay collected during the management period, and optimizing the analysis process of the channel transmission state according to the data transmission delay fluctuation state of the channel.

10. 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 where the computer-readable storage medium is located to execute the intelligent Internet of Things device remote management method according to any one of claims 1-8 when running.

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