An optimization method, system, device and medium for communication quality of an internet of things device

Abstract

The application discloses an optimization method, system, device and medium for communication quality of Internet of Things equipment, which comprises the following steps: acquiring a first channel parameter of a current operator channel, a second channel parameter of a candidate operator channel and service data of a plurality of accessed Internet of Things equipment in real time; determining a current bearable service amount corresponding to the first channel parameter, and a ratio of admission capacity corresponding to the second channel parameter and the service data; calculating a channel switch prediction value of the plurality of Internet of Things equipment in a preset time window; in the case that the channel switch prediction value reaches a preset switch threshold, determining an allowed switch duration and a switch sequence of each Internet of Things equipment according to the service data; and generating an operator channel switch strategy based on the preset time window, the allowed switch duration and the switch sequence to perform operator channel switching, so as to optimize the communication quality of the plurality of Internet of Things equipment, improve the timeliness and accuracy of channel switching, and optimize the communication quality of the Internet of Things equipment.

Description

Technical Field

[0001] This application belongs to the field of wireless communication technology, and specifically relates to a method, system, device and medium for optimizing the communication quality of Internet of Things (IoT) devices. Background Technology

[0002] With the rapid development of IoT technology, more and more IoT devices are accessing the Internet through portable gateways and carrier networks to achieve data interaction. Due to factors such as the mobility of portable gateways, the coverage and layout of carrier base stations, the real-time load of cells, and electromagnetic interference in wireless frequency bands, the communication quality of carrier channels accessed by IoT devices varies at different times. Therefore, how to optimize the communication quality of IoT devices has become an urgent technical problem to be solved in this field.

[0003] In existing technologies, the main method for optimizing the communication quality of IoT devices is to monitor the device's communication data in real time. When the packet loss rate reaches a preset threshold, the IoT device is switched to a candidate channel with a higher signal strength at that moment. However, the switching method in existing technologies is passively triggered, and the switching decision is only triggered after the current channel has experienced a significant failure. This results in untimely channel switching, and existing technologies cannot identify the carrying capacity of candidate channels for the service data to be transmitted by the IoT device, leading to inaccurate channel switching results and an inability to guarantee the communication quality of the device. Summary of the Invention

[0004] This application provides a method, system, device, and medium for optimizing the communication quality of IoT devices. It solves the problems of untimely channel switching, inaccurate channel switching results, and inability to guarantee device communication quality in the prior art. By determining the channel switchability prediction value of IoT devices within a preset time window based on the ratio of the current carrying capacity of the current operator channel to the acceptance capacity of candidate operators for IoT device service data, and when the channel switchability prediction value reaches a preset switching threshold, an operator channel switching strategy is generated based on the preset time window, the allowed switching duration of each IoT device, and the switching order to perform operator channel switching. This can achieve the purpose of triggering channel switching in advance by comprehensively considering the real-time channel transmission of the current channel and candidate channels, improving the timeliness and accuracy of channel switching, and optimizing the communication quality of IoT devices.

[0005] In a first aspect, embodiments of this application provide a method for optimizing the communication quality of Internet of Things (IoT) devices, the method comprising: Real-time acquisition of the first channel parameters of the current operator channel, the second channel parameters of the candidate operator channels, and the service data of multiple connected IoT devices; Determine the current service volume that can be carried corresponding to the first channel parameter, and the ratio of the acceptance capacity corresponding to the second channel parameter and service data. Calculate the normalized difference between the current service volume that can be carried and the ratio of the acceptance capacity to obtain the channel switchability prediction value for multiple IoT devices within a preset time window. When the channel switchability prediction value reaches the preset switchability threshold, the allowable switchability duration and switchability order of each IoT device are determined based on the service data. Based on a preset time window, allowed switching duration, and switching order, a carrier channel switching strategy is generated, and multiple IoT devices are switched to candidate carrier channels according to the carrier channel switching strategy to optimize the communication quality of multiple IoT devices.

[0006] Furthermore, the first channel parameters include the current operator's channel's maximum throughput, average transmission delay, and packet loss rate; Determine the current service capacity corresponding to the first channel parameter, and the ratio of the acceptance capacity corresponding to the second channel parameter and service data, including: The invalid transmission traffic of the current operator channel is determined based on the average transmission delay and packet loss rate, and the traffic difference between the maximum throughput and the invalid transmission traffic is calculated to obtain the service capacity that the current operator channel can carry. Calculate the amount of business to be transmitted by multiple IoT devices within a preset time window based on real-time business load parameters in business data. The ratio of the acceptance capacity of a candidate operator's channel for the traffic to be transmitted is calculated based on the bandwidth resource parameter and channel quality parameter in the second channel parameter.

[0007] Furthermore, the bandwidth resource parameters include total bandwidth resource parameters and occupied bandwidth resource parameters; The ratio of the acceptance capacity of candidate operator channels for the traffic to be transmitted is calculated based on the bandwidth resource parameters and channel quality parameters in the second channel parameters, including: The idle bandwidth resource ratio of candidate operators is calculated based on the total bandwidth resource parameters and the occupied bandwidth resource parameters, and the idle bandwidth resource ratio is used as the handover gain coefficient. Determine the effective transmission bandwidth corresponding to the channel quality parameters, and calculate the product of the effective transmission bandwidth and the switching gain coefficient to obtain the acceptable service capacity of the candidate operator's channel. The ratio of the acceptable service capacity to the amount of service to be transmitted in a candidate operator's channel is calculated based on the acceptable service capacity and the amount of service to be transmitted.

[0008] Furthermore, based on real-time service load parameters in the service data, the amount of service to be transmitted by multiple IoT devices within a preset time window is calculated, including: Identify the service load transmission time corresponding to the real-time service load parameters in the service data, and assign a service load prediction impact weight coefficient to each service load parameter based on the time interval between the service load transmission time and the current time. The weighted sum of the service load parameters is calculated based on the predicted impact weighting coefficient, and the amount of services to be transmitted by multiple IoT devices within the preset time window is determined according to the coverage duration of the preset time window of the weighted sum.

[0009] Furthermore, before determining the allowable handover duration and handover sequence for each IoT device based on business data, the method also includes: Obtain device communication reports from each IoT device. The device communication reports include communication signal strength and cumulative communication duration. Accordingly, the allowable switching duration and switching order for each IoT device are determined based on business data, including: Based on business data, determine the business type of each IoT device, determine the business security level that matches the business type, and determine the maximum allowable interruption duration corresponding to the business security level. Use the maximum allowable interruption duration as the allowable switching duration for each IoT device. The business security levels are sorted in descending order to obtain the initial switching order of multiple IoT devices; The weighted sum of communication signal strength and cumulative communication duration is calculated based on preset weights, and the initial switching order is adjusted based on the weighted sum to obtain the switching order of each IoT device.

[0010] Furthermore, an operator channel handover strategy is generated based on a preset time window, allowed handover duration, and handover order, including: Calculate the percentage of the allowed handover time for each IoT device in the sum of the allowed handover times for all IoT devices. Identify the start time of the preset time window, and allocate the switching start time and available switching time to each IoT device according to the switching order, start time, coverage duration and duration ratio of the preset time window; The amount of business to be transmitted by each IoT device within a preset time window is calculated based on the real-time business load parameters in the business data, and the traffic contraction step size of each IoT device during the switching process is determined according to the switching start time, available switching duration and the amount of business to be transmitted of each IoT device. Aggregate the handover sequence, handover start time, and traffic shrinkage step size to generate the operator's channel handover strategy.

[0011] Furthermore, based on the handover start time, available handover duration, and pending data transmission volume of each IoT device, the traffic contraction step size during the handover process is determined, including: Obtain the current service transmission rate of each IoT device, and determine the target service transmission rate based on the amount of service to be transmitted and the available switching time; Calculate the rate difference between the current service transmission rate and the target service transmission rate, and then calculate the ratio of the rate difference to the preset shrinkage step size to obtain the traffic shrinkage step size of each IoT device during the switching process.

[0012] Secondly, embodiments of this application provide a system for optimizing the communication quality of Internet of Things (IoT) devices, the system comprising: The data acquisition module is used to acquire in real time the first channel parameters of the current operator channel, the second channel parameters of the candidate operator channel, and the service data of multiple connected IoT devices; The switching prediction module is used to determine the current service volume that can be carried corresponding to the first channel parameter, and the ratio of the acceptance capacity corresponding to the second channel parameter and service data. It calculates the normalized difference between the current service volume that can be carried and the ratio of the acceptance capacity to obtain the channel switching prediction value of multiple IoT devices within a preset time window. The switching order determination module is used to determine the allowable switching duration and switching order of each IoT device based on service data when the channel switchability prediction value reaches the preset switching threshold. The communication quality optimization module is used to generate operator channel switching strategies based on preset time windows, allowed switching durations, and switching order, and to switch multiple IoT devices to candidate operator channels according to the operator channel switching strategies in order to optimize the communication quality of multiple IoT devices.

[0013] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0014] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0015] Fifthly, embodiments of this application also provide a computer program product comprising a computer program stored in a computer-readable storage medium, wherein at least one processor of the device reads from the computer-readable storage medium and executes the computer program, causing the device to perform the method described in the first aspect.

[0016] In this embodiment, the first channel parameters of the current operator channel, the second channel parameters of the candidate operator channel, and the service data of multiple accessing IoT devices are acquired in real time. The current service capacity corresponding to the first channel parameters and the ratio of the acceptance capacity corresponding to the second channel parameters and the service data are determined. The normalized difference between the current service capacity and the ratio of the acceptance capacity is calculated to obtain the channel switchability prediction value of multiple IoT devices within a preset time window. When the channel switchability prediction value reaches a preset switchability threshold, the allowable switchability duration and switchability order of each IoT device are determined based on the service data. An operator channel switchability strategy is generated based on the preset time window, the allowable switchability duration, and the switchability order. The multiple IoT devices are then switched to the candidate operator channel according to the operator channel switchability strategy to optimize the communication quality of the multiple IoT devices. The above-mentioned method for optimizing the communication quality of IoT devices solves the problems of untimely channel switching, inaccurate channel switching results, and inability to guarantee device communication quality in existing technologies. By determining the channel switching prediction value of IoT devices within a preset time window based on the ratio of the current carrying capacity of the operator's channel to the acceptance capacity of candidate operators for IoT device service data, and when the channel switching prediction value reaches the preset switching threshold, an operator channel switching strategy is generated based on the preset time window, the allowed switching duration of each IoT device, and the switching order to perform operator channel switching. This achieves the goal of triggering channel switching in advance by comprehensively considering the real-time channel transmission of the current channel and candidate channels, improving the timeliness and accuracy of channel switching, and optimizing the communication quality of IoT devices. Attached Figure Description

[0017] Figure 1 This is a flowchart of a method for optimizing the communication quality of an Internet of Things (IoT) device, as provided in an embodiment of this application. Figure 2 This is a flowchart of a method for determining channel bearer capability provided in an embodiment of this application; Figure 3 This is a flowchart of a method for generating operator channel switching strategies provided in an embodiment of this application; Figure 4 This is a structural block diagram of an IoT device communication quality optimization system provided in an embodiment of this application; Figure 5 This is a structural block diagram of the electronic device provided in the embodiments of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application are described in detail below with reference to the accompanying drawings. It is understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. Furthermore, it should be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0019] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0020] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0021] Firstly, this solution can be used to optimize the communication quality of IoT devices connected to the internet, especially those connected via portable gateways and carrier networks. By determining the predicted channel switchability value for IoT devices within a preset time window based on the ratio of the current capacity of the carrier channel to the acceptance capacity of candidate carriers for IoT device data, and when the predicted channel switchability value reaches a preset switch threshold, a carrier channel switchover strategy is generated based on the preset time window, the allowed switchover duration for each IoT device, and the switchover order. This achieves the goal of triggering channel switchover in advance by comprehensively considering the real-time channel transmission of the current and candidate channels, improving the timeliness and accuracy of channel switchover and optimizing the communication quality of IoT devices. Based on the above use case, it is understandable that the execution entity of each step in this solution can be a gateway with data reception, data processing, and strategy generation functions. For example, a portable gateway such as a personal Wi-Fi device.

[0022] The following description, in conjunction with the accompanying drawings, details a method, system, device, and medium for optimizing the communication quality of IoT devices provided in this application, through specific embodiments and application scenarios.

[0023] Figure 1 This is a flowchart illustrating a method for optimizing the communication quality of IoT devices according to an embodiment of this application. Figure 1 As shown, the specific steps include the following: S101, real-time acquisition of the first channel parameters of the current operator channel, the second channel parameters of the candidate operator channel, and the service data of multiple connected IoT devices.

[0024] The current operator channel can be an in-use operator network communication link currently carrying IoT devices to perform business data transmission and in a normal communication service state. The first channel parameter can be a channel characteristic parameter characterizing the real-time communication operation status of the current operator channel and used to calculate the channel's service carrying capacity. The candidate operator channel can be a backup operator network communication link, other than the currently in-use communication link, that can be used as a handover alternative and has communication access capabilities. The second channel parameter can be a link characteristic parameter characterizing the candidate operator channel's resource reserve conditions and channel transmission quality, used to evaluate service access adaptability. The IoT device can be an IoT terminal communication device that accesses the communication link and can autonomously generate and upload service transmission data. Service data can be various service-related characteristic data generated during the operation of the IoT device, reflecting the service operation status and traffic transmission requirements.

[0025] In one embodiment, the method for obtaining the first channel parameters of the current operator channel, the second channel parameters of the candidate operator channel, and the service data of multiple connected IoT devices in real time can be as follows: continuously monitor the status of the in-use communication links to collect the first channel parameters corresponding to the current operator channel, simultaneously monitor the status of the backup communication links to collect the second channel parameters corresponding to the candidate operator channels, and continuously collect data from various IoT devices in the network, summarizing and obtaining the service data corresponding to all IoT devices to complete the synchronous real-time collection of multiple types of communication-related data.

[0026] S102, determine the current service volume that can be carried corresponding to the first channel parameter, and the ratio of the acceptance capacity corresponding to the second channel parameter and service data, calculate the normalized difference between the current service volume that can be carried and the ratio of the acceptance capacity, and obtain the channel switchability prediction value of multiple IoT devices within a preset time window.

[0027] The current carrying capacity can be calculated based on the first channel parameter, representing the maximum effective volume of data transmission that the current operator channel can stably support for IoT devices. The acceptance capacity ratio can be calculated by combining the second channel parameter and device service data, representing a quantitative ratio of the adaptability and acceptance degree of candidate operator channels to the service requirements to be transmitted by the devices. The preset time window can be a pre-defined fixed continuous duration interval used to centrally evaluate the channel transmission status and predict service transmission requirements. The channel switchability prediction value can be obtained by performing a normalized difference operation on the current carrying capacity and acceptance capacity ratio, serving as a quantitative assessment value to measure the strength of channel switching requirements.

[0028] In one embodiment, the method for determining the current carryable traffic volume corresponding to the first channel parameter and the ratio of the acceptance capacity corresponding to the second channel parameter and the service data can be as follows: Figure 2 As shown. Figure 2 This is a flowchart of a method for determining channel bearer capability provided in an embodiment of this application. Figure 2 As shown, the first channel parameters include the current operator's channel's maximum throughput, average transmission delay, and packet loss rate, specifically including the following steps: S201, determine the invalid transmission traffic of the current operator channel based on the average transmission delay and packet loss rate, and calculate the traffic difference between the maximum throughput and the invalid transmission traffic to obtain the service capacity that the current operator channel can carry.

[0029] Here, average transmission latency can be the average time required for an IoT device to complete the transmission of a message from sending to receiving under the current operator's channel. Packet loss rate can be the proportion of data packets that are lost or fail to be delivered during transmission on the current operator's channel. Invalid transmission traffic can be invalid data traffic that cannot complete the effective service transmission due to transmission latency and data packet loss. Maximum throughput can be the maximum data transmission traffic that the current operator's channel can carry per unit of time.

[0030] In one embodiment, determining the invalid transmission traffic of the current operator channel based on average transmission delay and packet loss rate, and calculating the difference between the maximum throughput and the invalid transmission traffic to obtain the service capacity that the current operator channel can carry, can be achieved by multiplying the packet loss rate by the maximum throughput of the current operator channel to obtain the invalid transmission traffic wasted due to packet loss. For example, when the maximum throughput is 100Mbps and the packet loss rate is 5%, the invalid transmission traffic caused by packet loss is 5Mbps. The average transmission delay is compared with a preset delay tolerance threshold. If the average transmission delay exceeds the preset delay tolerance threshold, the excess percentage is calculated, and this excess percentage is multiplied by the maximum throughput of the current operator channel to obtain the invalid transmission traffic caused by excessive delay. The preset delay tolerance threshold can be a pre-set maximum delay that will not cause the current channel to generate invalid transmission traffic. The preset delay tolerance threshold can be set based on the maximum average transmission delay of IoT devices without generating invalid transmission traffic. The invalid transmission traffic caused by packet loss is added to the invalid transmission traffic caused by delay to obtain the total invalid transmission traffic of the current operator channel. The difference between the maximum throughput and the total invalid transmission traffic is calculated to obtain the current service capacity of the operator's channel.

[0031] S202, calculate the amount of business to be transmitted by multiple IoT devices within a preset time window based on the real-time business load parameters in the business data.

[0032] Among them, the real-time service load parameter can be a dynamic load characteristic parameter that characterizes the service traffic generated during the real-time operation of IoT devices. The amount of service data to be transmitted can be the total amount of service data that IoT devices are waiting to push and have not yet completed transmission within a preset time window.

[0033] In one embodiment, the method for calculating the amount of business to be transmitted by multiple IoT devices within a preset time window based on real-time business load parameters in business data can be as follows: identify the business load transmission time corresponding to the real-time business load parameters in business data, assign a business volume prediction impact weight coefficient to each business load parameter according to the time interval between the business load transmission time and the current time, calculate the weighted sum of the business load parameters based on the prediction impact weight coefficient, and determine the amount of business to be transmitted by multiple IoT devices within the preset time window according to the coverage duration of the preset time window of the weighted sum.

[0034] The service load transmission time can be the recorded moment when the service traffic corresponding to the real-time service load parameters is generated and reported. The service volume prediction impact weighting coefficient can be a weight value assigned to each service load parameter based on the time proximity of historical service load to the current moment, used to measure the degree of impact of traffic prediction. The coverage duration can be the continuous time length that a preset time window can cover for service volume statistical prediction.

[0035] In one embodiment, the method of identifying the service load transmission time corresponding to the real-time service load parameters in the service data and assigning a service volume prediction influence weight coefficient to each service load parameter based on the time interval between the service load transmission time and the current time can be as follows: Identify the service load transmission time corresponding to the real-time service load parameters in the service data, calculate the time interval between the service load transmission time corresponding to each real-time service load parameter and the current time. Since the larger the time interval, the weaker the timeliness of the group of service load data and the lower the reference value for future service volume prediction, and the smaller the time interval, the stronger the timeliness of the data and the higher the reference value for service volume prediction, calculate the reciprocal of each time interval, normalize the reciprocals of all time intervals, and use the normalized reciprocal result as the service volume prediction influence weight coefficient corresponding to each real-time service load parameter. The method for determining the amount of traffic to be transmitted by multiple IoT devices within a preset time window by calculating the weighted sum of the traffic load parameters based on the predicted impact weighting coefficients and determining the amount of traffic to be transmitted by multiple IoT devices within a preset time window can be as follows: Calculate the product of each real-time traffic load parameter and its corresponding traffic volume predicted impact weighting coefficient to obtain each weighted traffic load value; sum the weighted traffic load values ​​to obtain the weighted sum of the traffic load parameters; use this weighted sum as the average traffic load predicted value per unit time. Calculate the product of the weighted sum and the coverage duration of the preset time window to obtain the predicted traffic volume within the preset time window.

[0036] This solution assigns differentiated weighting coefficients based on the time of service load generation, performs weighted calculations on historical load data, and combines the time window duration to predict service scale. This approach can reduce the reference weight of recent service data, weaken interference from invalid data from the distant past, and accurately predict the overall service transmission volume of the equipment within a specified time period. This provides accurate and reliable service traffic data support for channel capacity assessment and switching scheme formulation.

[0037] S203, calculate the ratio of the acceptance capacity of the candidate operator's channel for the traffic to be transmitted based on the bandwidth resource parameter and channel quality parameter in the second channel parameter.

[0038] Among these, bandwidth resource parameters can be parameters used to reflect the channel bandwidth occupancy status and resource availability of candidate operators. Channel quality parameters can be performance parameters used to characterize the stability and efficiency of channel transmission for candidate operators.

[0039] In one embodiment, the method for calculating the ratio of the acceptance capacity of a candidate operator's channel for the traffic to be transmitted based on the bandwidth resource parameters and channel quality parameters in the second channel parameters can be as follows: calculate the proportion of idle bandwidth resources of the candidate operator based on the total bandwidth resource parameters and the occupied bandwidth resource parameters, and use the proportion of idle bandwidth resources as the handover gain coefficient; determine the effective transmission bandwidth corresponding to the channel quality parameters, and calculate the product of the effective transmission bandwidth and the handover gain coefficient to obtain the acceptable traffic capacity of the candidate operator's channel; calculate the ratio of the acceptance capacity of the candidate operator's channel for the traffic to be transmitted based on the acceptable traffic capacity and the traffic to be transmitted.

[0040] The total bandwidth resource parameter can be the total amount of bandwidth resources that the candidate operator's channel can provide. The occupied bandwidth resource parameter can be the bandwidth resource parameter that is currently occupied by service data on the candidate operator's channel. The idle bandwidth resource ratio can be the proportion of the candidate channel's unoccupied remaining bandwidth in the total bandwidth. The handover gain coefficient can be set based on the idle bandwidth resource ratio and is a correction coefficient used to characterize the channel's bandwidth resource surplus. The effective transmission bandwidth can be the available bandwidth value of the candidate operator's channel that can actually be used for service transmission after eliminating transmission losses. The acceptable service capacity can be the maximum amount of service data that the candidate channel can carry, calculated by combining the effective transmission bandwidth and the handover gain coefficient.

[0041] In one embodiment, the method for calculating the idle bandwidth resource ratio of a candidate operator based on the total bandwidth resource parameter and the occupied bandwidth resource parameter, and using the idle bandwidth resource ratio as the handover gain coefficient, can be as follows: calculate the difference between the total bandwidth resource parameter and the occupied bandwidth resource parameter, and calculate the ratio of the difference to the total bandwidth resource parameter to obtain the idle bandwidth resource ratio corresponding to the candidate operator, and use the idle bandwidth resource ratio as the handover gain coefficient. The method for determining the effective transmission bandwidth corresponding to the channel quality parameter and calculating the product of the effective transmission bandwidth and the handover gain coefficient to obtain the acceptable service capacity of the candidate operator's channel can be as follows: determine the effective transmission bandwidth corresponding to the channel quality parameter based on the preset mapping relationship between the channel quality parameter range and the effective transmission bandwidth, and multiply the effective transmission bandwidth by the handover gain coefficient to obtain the acceptable service capacity of the candidate operator's channel. The method for calculating the ratio of the acceptable service capacity to the amount of service to be transmitted for a candidate operator's channel can be as follows: calculate the ratio of the acceptable service capacity to the amount of service to be transmitted to obtain the ratio of the acceptable service capacity to the amount of service to be transmitted for the candidate operator's channel.

[0042] This solution combines bandwidth occupancy to determine the proportion of idle bandwidth and uses it as a handover gain coefficient. It also determines the effective transmission bandwidth in conjunction with channel quality, and then calculates the service capacity that candidate channels can accept. Finally, it combines the equipment transmission requirements to obtain the service acceptance capacity ratio. This solution can comprehensively integrate the resource margin of candidate channels with the actual transmission performance, accurately quantify the ability of candidate channels to carry and adapt to service traffic, avoid judgment bias caused by relying on a single parameter evaluation, and effectively improve the accuracy and matching degree of channel handover decisions.

[0043] This solution combines transmission latency and packet loss rate to eliminate invalid traffic, accurately calculates the current channel's effective service capacity, predicts the overall scale of services to be transmitted within a given time period, and comprehensively assesses the service acceptance level of candidate channels based on bandwidth and channel quality. It can comprehensively compare the carrying capacity of in-use channels with the adaptability of alternative channels, objectively assess the channel's operating load and handover adaptability conditions, and provide comprehensive and accurate channel and service data for subsequent handover decisions.

[0044] In one embodiment, the method for calculating the normalized difference between the current carrying capacity and the ratio of acceptance capacity to obtain the channel switchability prediction value for multiple IoT devices within a preset time window can be as follows: Normalize both the current carrying capacity and the ratio of acceptance capacity, mapping them to the same numerical range to obtain the normalized result of the current carrying capacity and the normalized result of the acceptance capacity ratio. For example, if the preset normalization range is [0, 1], the current carrying capacity is 45 Mbps, and the maximum possible value of this indicator is 100 Mbps, then the normalized result of the current carrying capacity is 45 ÷ 100 = 0.45; the acceptance capacity ratio is 1.2, and the maximum possible value of this indicator is 2.0, then the normalized result of the acceptance capacity ratio is 1.2 ÷ 2.0 = 0.6. Calculate the absolute value of the difference between the normalized result of the current carrying capacity and the normalized result of the acceptance capacity ratio, and use this absolute value as the channel switchability prediction value for multiple IoT devices within the preset time window.

[0045] S103: When the channel switchability prediction value reaches the preset switchability threshold, determine the allowable switchability duration and switchability sequence of each IoT device based on the service data.

[0046] The preset switching threshold can be a pre-defined critical quantization value used to determine whether the conditions for initiating channel switching are met. The allowed switching duration can be determined based on the device's service attributes, representing the maximum time an IoT device can occupy to complete the channel switching process. The switching order can be defined according to the device's service requirements and communication status, indicating the sequence in which multiple IoT devices sequentially perform channel switching.

[0047] In one embodiment, when the channel switchability prediction value reaches a preset switchability threshold, the method for determining the allowable switchability duration and switchability order of each IoT device based on service data can be as follows: When the calculated channel switchability prediction value reaches the preset switchability threshold, it indicates that the carrying capacity of the current operator's channel is significantly lower than the acceptance capacity of the candidate operator's channel, meeting the switchability triggering condition and requiring channel switchover. The service type identifier of each IoT device is extracted from the service data. Based on the preset mapping relationship between service type and service security level, the service security level corresponding to each IoT device is determined. Based on the preset mapping relationship between service security level and maximum allowable interruption duration, the maximum allowable interruption duration corresponding to each IoT device is determined, and this maximum allowable interruption duration is used as the allowable switchability duration for each IoT device. The IoT devices are sorted in descending order according to their service security level to obtain the switchability order, where devices with higher service security levels are switched earlier.

[0048] In one embodiment, before determining the allowable switching duration and switching order of each IoT device based on business data, the method further includes: obtaining device communication reports reported by each IoT device, the device communication reports including communication signal strength and cumulative communication duration; correspondingly, determining the allowable switching duration and switching order of each IoT device based on business data includes: determining the business type of each IoT device based on business data, and determining the business security level matching the business type, as well as the maximum allowable interruption duration corresponding to the business security level, and using the maximum allowable interruption duration as the allowable switching duration of each IoT device; sorting the business security levels in descending order to obtain the initial switching order of multiple IoT devices; calculating the weighted sum of communication signal strength and cumulative communication duration based on preset weights, and adjusting the initial switching order based on the weighted sum to obtain the switching order of each IoT device.

[0049] The device communication report can be an active upload from the IoT device, recording its own communication operation status information. Communication signal strength can be the strength of the wireless signal received by the IoT device on its current access link. Cumulative communication duration can be the total duration of normal communication transmission since the IoT device connected to the network. Service type can be the category of business data transmission carried and executed by the IoT device during daily operation. Service security level can be the service security priority level determined based on service transmission requirements and operational importance. Maximum allowable interruption duration can be the longest critical time that different security level services can withstand during communication transmission interruption. Allowable handover duration can be the maximum allowable interruption duration, the maximum time a device can take to complete a channel handover. Initial handover order can be the order of device handover based solely on the service security level. Preset weights can be fixed weight values ​​pre-set to allocate the proportion of communication signal strength and cumulative communication duration in the calculation. The handover order can be the final determined order of channel handover for each IoT device after adjusting and optimizing the initial order based on weighted calculation results.

[0050] In one embodiment, obtaining device communication reports reported by each IoT device can be achieved by: before determining that the switching conditions are met and determining the switching duration and order, obtaining all previously received device communication reports uploaded by IoT devices, and extracting the communication signal strength and cumulative communication duration information of the device from the device communication reports. Determining the service type of each IoT device based on service data, and determining the service security level matching the service type, as well as the maximum allowable interruption duration corresponding to the service security level, and using the maximum allowable interruption duration as the allowable switching duration for each IoT device can be achieved by: identifying the service type identifier in the service data to determine the service type corresponding to each IoT device; matching the service security level corresponding to each IoT device according to the correspondence between service type and service security level; and determining the maximum allowable interruption duration for each IoT device according to the correspondence between service security level and maximum allowable interruption duration, using this as the allowable switching duration for that device. Sort the service security levels in descending order to obtain the initial switching order for multiple IoT devices can be achieved by: first arranging the security levels of multiple IoT devices in descending order of service security level to generate the initial switching order. The method for obtaining the handover order of IoT devices by calculating a weighted sum of communication signal strength and cumulative communication duration based on preset weights, and then adjusting the initial handover sorting based on this weighted sum, can be as follows: Calculate the weighted sum of communication signal strength and cumulative communication duration according to preset weights, and then sort the handover orders of multiple IoT devices with the same handover order in the initial handover sorting according to the order of the weighted sum from largest to smallest. Since the initial handover sorting is based on security level, but multiple IoT devices may exist under the same security level, the handover order of multiple IoT devices with the same security level is sorted again according to the size of the weighted sum to obtain the adjusted result of the initial handover sorting. This adjusted result is then used as the handover order of each IoT device.

[0051] This solution categorizes service security levels based on service type and sets the allowable handover time for each device according to the corresponding maximum allowable interruption duration. It first determines the initial handover order based on security level, and then optimizes the sorting by weighting the communication signal strength and cumulative communication duration. This approach can determine reasonable handover time limits that fit the service operation tolerance characteristics of different devices, taking into account both service priority and actual communication conditions when arranging the handover order. It ensures that high-priority services are handed over first, reducing the risk of service interruption, while also making the overall handover arrangement more consistent with the actual communication status, effectively balancing the rationality of handover and the security of service operation.

[0052] S104 generates an operator channel switching strategy based on a preset time window, allowed switching duration, and switching order, and switches multiple IoT devices to candidate operator channels according to the operator channel switching strategy to optimize the communication quality of multiple IoT devices.

[0053] Among them, the operator's channel switching strategy can be formed by integrating the time window range, the allowed switching time of the equipment, and the switching order, and is used to guide multiple devices to complete the channel replacement in an orderly manner as an overall scheduling and execution scheme.

[0054] In one embodiment, the method for generating an operator channel handover strategy based on a preset time window, allowed handover duration, and handover order can be as follows: Figure 3 As shown. Figure 3 This is a flowchart illustrating a method for generating operator channel switching strategies, as provided in an embodiment of this application. Figure 3 As shown, the specific steps include the following: S301, calculate the percentage of the allowed switching time of each IoT device in the sum of the allowed switching times of all IoT devices.

[0055] In one embodiment, the proportion of the allowed switching time of each IoT device in the sum of the allowed switching times of all IoT devices can be calculated by: summing the total allowed switching times of all IoT devices and calculating the proportion of time for each individual device.

[0056] S302 identifies the start time of the preset time window and allocates the switching start time and available switching duration to each IoT device according to the switching order, start time, coverage duration and duration ratio of the preset time window.

[0057] The time start point can be the initial moment when the channel switching scheduling calculation and execution officially begin within a preset time window. The switching start time can be the specific execution moment allocated individually to each IoT device to initiate its own channel switching process. The available switching duration can be the actual switching execution duration allocated exclusively to a single IoT device within the preset time window.

[0058] In one embodiment, identifying the start time of a preset time window and allocating the switching start time and available switching time to each IoT device according to the switching order, start time, coverage duration of the preset time window, and duration percentage can be achieved by: calculating the product of the duration percentage and the coverage duration of the preset time window to obtain the available switching time for each IoT device within the preset time window. Alternatively, the coverage duration of the preset time window can be used as the total duration, and the coverage duration can be divided according to the switching order, start time, and available switching time. The start time corresponding to each segment of the divided duration can be used as the switching start time for the corresponding IoT device.

[0059] S303 calculates the amount of business to be transmitted by each IoT device within a preset time window based on the real-time business load parameters in the business data, and determines the traffic contraction step size of each IoT device during the switching process based on the switching start time, available switching duration and the amount of business to be transmitted of each IoT device.

[0060] Among them, the traffic contraction step size can be the unit change range of the service transmission rate of IoT devices gradually reduced during channel switching.

[0061] In one embodiment, the method for calculating the amount of traffic to be transmitted by each IoT device within a preset time window based on real-time traffic load parameters in the business data, and determining the traffic contraction step size of each IoT device during the handover process based on the handover start time, available handover duration, and amount of traffic to be transmitted by each IoT device, can be as follows: obtain the current service transmission rate of each IoT device, and determine the target service transmission rate based on the amount of traffic to be transmitted and the available handover duration; calculate the rate difference between the current service transmission rate and the target service transmission rate, and calculate the ratio of the rate difference to the preset contraction step size to obtain the traffic contraction step size of each IoT device during the handover process.

[0062] The current service transmission rate can be the real-time data transmission speed at which IoT devices are currently transmitting service data normally. The target service transmission rate can be calculated based on the amount of service to be transmitted and the available handover time, representing the standard service transmission speed that the device needs to achieve to complete the handover phase. The preset shrinkage step count can be the maximum number of adjustments to gradually reduce the traffic transmission scale during operator channel handover.

[0063] In one embodiment, obtaining the current service transmission rate of each IoT device and determining the target service transmission rate based on the amount of service to be transmitted and the available handover time can be achieved by: collecting the current service transmission rate of each IoT device in real-time operation, and calculating the target service transmission rate based on the amount of service to be transmitted corresponding to the device and the allocated available handover time. Calculating the rate difference between the current service transmission rate and the target service transmission rate, and then calculating the ratio of this rate difference to a preset shrinkage step number to obtain the traffic shrinkage step size for each IoT device during the handover process can be achieved by: calculating the rate difference between the current service transmission rate and the target service transmission rate, and then calculating the ratio of this rate difference to a preset shrinkage step number to finally calculate the corresponding traffic shrinkage step size for each IoT device during channel handover.

[0064] This solution collects the real-time service transmission rate of the acquisition device, sets a reasonable target transmission rate based on service transmission requirements and available handover time, and then calculates the precise traffic shrinkage step size through rate difference and shrinkage step number. This enables a smooth gradient reduction of the service transmission rate during the handover process, avoiding data packet loss and transmission interruptions caused by sudden rate changes, and ensuring continuous and stable service data transmission during the handover period.

[0065] S304 aggregates the handover sequence, handover start time, and traffic shrinkage step size to generate the operator's channel handover strategy.

[0066] In one embodiment, the way to generate an operator channel switching strategy by aggregating the switching order, switching start time, and traffic contraction step size can be as follows: the switching order is used as the execution order logic of each IoT device, the switching start time is used as the switching start time of the corresponding IoT device, and the traffic contraction step size is used as the magnitude of the reduction in service transmission traffic for each IoT device. The switching order, switching start time, and traffic contraction step size of all devices are integrated to form a complete operator channel switching strategy.

[0067] This solution, by statistically analyzing the percentage of allowed switching time for each device, and combining time windows and switching order to differentiate the allocation of switching start time and available switching time, and further combining business load to calculate the amount of business to be transmitted and matching and determining the traffic contraction step size, integrates multi-dimensional information to form a dedicated channel switching strategy. This enables multiple IoT devices to complete channel switching in an orderly manner during off-peak hours, reasonably divides the switching execution period, and, with the help of a gradient traffic rate control method, effectively avoids network congestion and data transmission fluctuations caused by concentrated switching of multiple devices, smoothly completes link replacement, and comprehensively improves the overall communication transmission stability and the smoothness of business data transmission.

[0068] The technical solution provided in this application provides real-time acquisition of the first channel parameters of the current operator channel, the second channel parameters of the candidate operator channel, and the service data of multiple accessed IoT devices; determination of the current bearable service volume corresponding to the first channel parameters, and the ratio of the acceptance capacity corresponding to the second channel parameters and the service data; calculation of the normalized difference between the current bearable service volume and the acceptance capacity ratio to obtain the channel switchability prediction value of multiple IoT devices within a preset time window; when the channel switchability prediction value reaches a preset switchability threshold, determination of the allowable switchability duration and switchability order of each IoT device based on the service data; generation of an operator channel switchability strategy based on the preset time window, allowable switchability duration, and switchability order; and switching of multiple IoT devices to the candidate operator channel according to the operator channel switchability strategy to optimize the communication quality of multiple IoT devices. The above-mentioned method for optimizing the communication quality of IoT devices solves the problems of untimely channel switching, inaccurate channel switching results, and inability to guarantee device communication quality in existing technologies. By determining the channel switching prediction value of IoT devices within a preset time window based on the ratio of the current carrying capacity of the operator's channel to the acceptance capacity of candidate operators for IoT device service data, and when the channel switching prediction value reaches the preset switching threshold, an operator channel switching strategy is generated based on the preset time window, the allowed switching duration of each IoT device, and the switching order to perform operator channel switching. This achieves the goal of triggering channel switching in advance by comprehensively considering the real-time channel transmission of the current channel and candidate channels, improving the timeliness and accuracy of channel switching, and optimizing the communication quality of IoT devices.

[0069] Figure 4 This is a structural block diagram of an IoT device communication quality optimization system provided in an embodiment of this application. For example... Figure 4 As shown, it specifically includes: The data acquisition module 401 is used to acquire in real time the first channel parameters of the current operator channel, the second channel parameters of the candidate operator channel, and the service data of multiple connected IoT devices; The switching prediction module 402 is used to determine the current service volume that can be carried corresponding to the first channel parameter, and the ratio of the acceptance capacity corresponding to the second channel parameter and service data, calculate the normalized difference between the current service volume that can be carried and the ratio of the acceptance capacity, and obtain the channel switching prediction value of multiple IoT devices within a preset time window. The switching order determination module 403 is used to determine the allowable switching duration and switching order of each IoT device based on service data when the channel switchability prediction value reaches the preset switching threshold. The communication quality optimization module 404 is used to generate an operator channel switching strategy based on a preset time window, allowed switching duration and switching order, and switch multiple IoT devices to candidate operator channels according to the operator channel switching strategy in order to optimize the communication quality of multiple IoT devices.

[0070] Furthermore, the first channel parameters include the current operator's channel's maximum throughput, average transmission delay, and packet loss rate; Switching prediction module 402 is specifically used for: The invalid transmission traffic of the current operator channel is determined based on the average transmission delay and packet loss rate, and the traffic difference between the maximum throughput and the invalid transmission traffic is calculated to obtain the service capacity that the current operator channel can carry. Calculate the amount of business to be transmitted by multiple IoT devices within a preset time window based on real-time business load parameters in business data. The ratio of the acceptance capacity of a candidate operator's channel for the traffic to be transmitted is calculated based on the bandwidth resource parameter and channel quality parameter in the second channel parameter.

[0071] Furthermore, the bandwidth resource parameters include total bandwidth resource parameters and occupied bandwidth resource parameters; Switching prediction module 402 is specifically used for: The idle bandwidth resource ratio of candidate operators is calculated based on the total bandwidth resource parameters and the occupied bandwidth resource parameters, and the idle bandwidth resource ratio is used as the handover gain coefficient. Determine the effective transmission bandwidth corresponding to the channel quality parameters, and calculate the product of the effective transmission bandwidth and the switching gain coefficient to obtain the acceptable service capacity of the candidate operator's channel. The ratio of the acceptable service capacity to the amount of service to be transmitted in a candidate operator's channel is calculated based on the acceptable service capacity and the amount of service to be transmitted.

[0072] Furthermore, the prediction module 402 is switched, specifically for: Identify the service load transmission time corresponding to the real-time service load parameters in the service data, and assign a service load prediction impact weight coefficient to each service load parameter based on the time interval between the service load transmission time and the current time. The weighted sum of the service load parameters is calculated based on the predicted impact weighting coefficient, and the amount of services to be transmitted by multiple IoT devices within the preset time window is determined according to the coverage duration of the preset time window of the weighted sum.

[0073] Furthermore, the system also includes: Device communication includes an acquisition module, which is used to acquire device communication reports reported by each IoT device. The device communication reports include communication signal strength and cumulative communication duration. Correspondingly, the switching order determination module 403 is specifically used for: Based on business data, determine the business type of each IoT device, determine the business security level that matches the business type, and determine the maximum allowable interruption duration corresponding to the business security level. Use the maximum allowable interruption duration as the allowable switching duration for each IoT device. The business security levels are sorted in descending order to obtain the initial switching order of multiple IoT devices; The weighted sum of communication signal strength and cumulative communication duration is calculated based on preset weights, and the initial switching order is adjusted based on the weighted sum to obtain the switching order of each IoT device.

[0074] Furthermore, the communication quality optimization module 404 is specifically used for: Calculate the percentage of the allowed handover time for each IoT device in the sum of the allowed handover times for all IoT devices. Identify the start time of the preset time window, and allocate the switching start time and available switching time to each IoT device according to the switching order, start time, coverage duration and duration ratio of the preset time window; The amount of business to be transmitted by each IoT device within a preset time window is calculated based on the real-time business load parameters in the business data, and the traffic contraction step size of each IoT device during the switching process is determined according to the switching start time, available switching duration and the amount of business to be transmitted of each IoT device. Aggregate the handover sequence, handover start time, and traffic shrinkage step size to generate the operator's channel handover strategy.

[0075] Furthermore, the communication quality optimization module 404 is specifically used for: Obtain the current service transmission rate of each IoT device, and determine the target service transmission rate based on the amount of service to be transmitted and the available switching time; Calculate the rate difference between the current service transmission rate and the target service transmission rate, and then calculate the ratio of the rate difference to the preset shrinkage step size to obtain the traffic shrinkage step size of each IoT device during the switching process.

[0076] The technical solution provided in this application includes a data acquisition module for real-time acquisition of the first channel parameters of the current operator channel, the second channel parameters of the candidate operator channel, and the service data of multiple IoT devices; a handover prediction module for determining the current bearable traffic volume corresponding to the first channel parameters and the ratio of the acceptance capacity corresponding to the second channel parameters and the service data, calculating the normalized difference between the current bearable traffic volume and the acceptance capacity ratio, and obtaining the channel handover prediction value of multiple IoT devices within a preset time window; a handover order determination module for determining the allowed handover duration and handover order of each IoT device based on the service data when the channel handover prediction value reaches a preset handover threshold; and a communication quality optimization module for generating an operator channel handover strategy based on the preset time window, allowed handover duration, and handover order, and handing over multiple IoT devices to the candidate operator channel according to the operator channel handover strategy to optimize the communication quality of multiple IoT devices. The aforementioned IoT device communication quality optimization system solves the problems of untimely channel switching, inaccurate channel switching results, and inability to guarantee device communication quality in existing technologies. By determining the channel switching prediction value of IoT devices within a preset time window based on the ratio of the current carrying capacity of the operator's channel to the acceptance capacity of candidate operators for IoT device service data, and when the channel switching prediction value reaches the preset switching threshold, an operator channel switching strategy is generated based on the preset time window, the allowed switching duration of each IoT device, and the switching order to perform operator channel switching. This achieves the goal of triggering channel switching in advance by comprehensively considering the real-time channel transmission of the current channel and candidate channels, improving the timeliness and accuracy of channel switching, and optimizing the communication quality of IoT devices.

[0077] The IoT device communication quality optimization system described in this application can be configured in a device, or in a component, integrated circuit, or chip within a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network-attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. This application does not impose specific limitations.

[0078] The communication quality optimization system for IoT devices in this application embodiment can be an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit the specific operating system used.

[0079] The IoT device communication quality optimization system provided in this application embodiment can realize the various processes implemented in the above method embodiments, and will not be repeated here to avoid repetition.

[0080] like Figure 5 As shown, this application embodiment also provides an electronic device 500, including a processor 501, a memory 502, and a program or instructions stored in the memory 502 and executable on the processor 501. When the program or instructions are executed by the processor 501, they implement the various processes of the above-described embodiment of the method for optimizing the communication quality of an Internet of Things device and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0081] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0082] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described method for optimizing the communication quality of an IoT device and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0083] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0084] This application also provides a program product including program code. When the program product is run on a computer device, the program code causes the computer device to perform the steps of the methods described above according to various exemplary embodiments of this application. For example, the computer device can execute a method for optimizing the communication quality of an Internet of Things (IoT) device as described in an embodiment of this application. The program product can be implemented using any combination of one or more readable media.

[0085] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element. Furthermore, it should be noted that the scope of the methods and systems in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0086] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0087] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0088] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the claims.

Claims

1. A method for optimizing the communication quality of Internet of Things (IoT) devices, characterized in that, The method includes: Real-time acquisition of the first channel parameters of the current operator channel, the second channel parameters of the candidate operator channels, and the service data of multiple connected IoT devices; Determine the current service capacity corresponding to the first channel parameter, and the ratio of the acceptance capacity corresponding to the second channel parameter and the service data. Calculate the normalized difference between the current service capacity and the ratio of the acceptance capacity to obtain the channel switchability prediction value of the multiple IoT devices within a preset time window. When the channel switchability prediction value reaches the preset switchability threshold, the allowable switchability duration and switchability order of each IoT device are determined based on the service data. Based on the preset time window, the allowed switching duration, and the switching order, a carrier channel switching strategy is generated, and the multiple IoT devices are switched to the candidate carrier channel according to the carrier channel switching strategy to optimize the communication quality of the multiple IoT devices.

2. The method for optimizing communication quality of IoT devices according to claim 1, characterized in that, The first channel parameters include the maximum throughput, average transmission delay, and packet loss rate of the current operator channel; The determination of the current service capacity corresponding to the first channel parameter, and the ratio of the acceptance capacity corresponding to the second channel parameter and the service data, includes: The invalid transmission traffic of the current operator channel is determined based on the average transmission delay and the packet loss rate, and the traffic difference between the maximum throughput and the invalid transmission traffic is calculated to obtain the service capacity that the current operator channel can carry. The amount of business to be transmitted by the multiple IoT devices within a preset time window is calculated based on the real-time business load parameters in the business data. The ratio of the acceptance capacity of the candidate operator's channel for the traffic to be transmitted is calculated based on the bandwidth resource parameter and channel quality parameter in the second channel parameter.

3. The method for optimizing communication quality of IoT devices according to claim 2, characterized in that, The bandwidth resource parameters include total bandwidth resource parameters and occupied bandwidth resource parameters; The step of calculating the acceptance ratio of the candidate operator channel for the traffic to be transmitted based on the bandwidth resource parameters and channel quality parameters in the second channel parameters includes: The idle bandwidth resource ratio of the candidate operator is calculated based on the total bandwidth resource parameters and the occupied bandwidth resource parameters, and the idle bandwidth resource ratio is used as the handover gain coefficient. Determine the effective transmission bandwidth corresponding to the channel quality parameters, and calculate the product of the effective transmission bandwidth and the switching gain coefficient to obtain the acceptable service capacity of the candidate operator channel; The ratio of the acceptance capacity of the candidate operator's channel to the traffic to be transmitted is calculated based on the acceptable service capacity and the traffic volume to be transmitted.

4. The method for optimizing communication quality of IoT devices according to claim 2, characterized in that, The calculation of the amount of services to be transmitted by the multiple IoT devices within a preset time window based on the real-time service load parameters in the service data includes: Identify the service load transmission time corresponding to the real-time service load parameters in the service data, and assign a service volume prediction impact weight coefficient to each service load parameter based on the time interval between the service load transmission time and the current time. The weighted sum of the service load parameters is calculated based on the predicted impact weighting coefficients, and the amount of services to be transmitted by the multiple IoT devices within the preset time window is determined according to the coverage duration of the preset time window based on the weighted sum.

5. The method for optimizing communication quality of IoT devices according to claim 1, characterized in that, Before determining the allowed switching duration and switching order of each IoT device based on the business data, the method further includes: Obtain device communication reports reported by each of the IoT devices, the device communication reports including communication signal strength and cumulative communication duration; Accordingly, determining the allowed switching duration and switching order of each IoT device based on the business data includes: Based on the business data, the business type of each IoT device is determined, and the business security level matching the business type and the maximum allowable interruption duration corresponding to the business security level are determined. The maximum allowable interruption duration is used as the allowable switching duration of each IoT device. The security levels of the services are sorted in descending order to obtain the initial switching order of the multiple IoT devices. The weighted sum of the communication signal strength and the cumulative communication duration is calculated based on preset weights, and the initial switching order is adjusted based on the weighted sum to obtain the switching order of each IoT device.

6. The method for optimizing communication quality of IoT devices according to claim 1, characterized in that, The process of generating an operator channel handover strategy based on the preset time window, the allowed handover duration, and the handover order includes: Calculate the percentage of the allowed handover time of each of the IoT devices in the sum of the allowed handover times of all the IoT devices; Identify the start time of the preset time window, and allocate a switching start time and available switching time to each IoT device according to the switching order, the start time, the coverage duration of the preset time window, and the duration ratio; The amount of business to be transmitted by each IoT device within a preset time window is calculated based on the real-time business load parameters in the business data, and the traffic contraction step size of each IoT device during the switching process is determined according to the switching start time of each IoT device, the available switching duration, and the amount of business to be transmitted. By aggregating the handover sequence, the handover start time, and the traffic contraction step size, an operator channel handover strategy is generated.

7. The method for optimizing communication quality of IoT devices according to claim 6, characterized in that, The step of determining the traffic contraction step size of each IoT device during the handover process based on the handover start time of each IoT device, the available handover duration, and the amount of traffic to be transmitted includes: Obtain the current service transmission rate of each of the IoT devices, and determine the target service transmission rate based on the amount of service to be transmitted and the available switching time; Calculate the rate difference between the current service transmission rate and the target service transmission rate, and then calculate the ratio of the rate difference to a preset number of contraction steps to obtain the traffic contraction step size for each IoT device during the switching process.

8. A system for optimizing the communication quality of Internet of Things (IoT) devices, characterized in that, The system includes: The data acquisition module is used to acquire in real time the first channel parameters of the current operator channel, the second channel parameters of the candidate operator channel, and the service data of multiple connected IoT devices; The switching prediction module is used to determine the current service volume that can be carried corresponding to the first channel parameter, and the ratio of the acceptance capacity corresponding to the second channel parameter and the service data, calculate the normalized difference between the current service volume that can be carried and the ratio of the acceptance capacity, and obtain the channel switching prediction value of the multiple IoT devices within a preset time window. The switching order determination module is used to determine the allowed switching duration and switching order of each IoT device based on the service data when the channel switchability prediction value reaches a preset switching threshold. The communication quality optimization module is used to generate a carrier channel switching strategy based on the preset time window, the allowed switching duration, and the switching order, and to switch the multiple IoT devices to the candidate carrier channel according to the carrier channel switching strategy, so as to optimize the communication quality of the multiple IoT devices.

9. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of a method for optimizing the communication quality of an Internet of Things device as described in any one of claims 1-7.

10. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of a method for optimizing the communication quality of an Internet of Things device as described in any one of claims 1-7.

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