A gateway power consumption adjustment method and system for internet of things devices

Abstract

The application discloses a gateway power consumption adjustment method and system for Internet of Things devices, and the method comprises the following steps: acquiring power data of each heat generating module in the gateway and a thermal coupling coefficient between a plurality of heat generating modules in real time, calculating a heat generating influence value of each heat generating module on the gateway; determining a frequency reduction adjustment priority corresponding to each heat generating influence value, screening a heat generating module to be frequency-reduced from the plurality of heat generating modules, and determining a first frequency reduction amplitude of the heat generating module to be frequency-reduced based on the plurality of heat generating influence values; determining a frequency reduction influence coefficient of each Internet of Things device on the heat generating module to be frequency-reduced according to service flow information and communication link information; adjusting the first frequency reduction amplitude based on the frequency reduction influence coefficient to obtain a second frequency reduction amplitude, and performing module frequency reduction on the heat generating module to be frequency-reduced according to the second frequency reduction amplitude, so as to complete the power consumption adjustment of the gateway, and the rationality of the gateway power consumption adjustment mode is improved, and the stability of the gateway in service data transmission is ensured.

Description

Technical Field

[0001] This application belongs to the field of Internet of Things (IoT) communication technology, specifically relating to a gateway power consumption adjustment method and system for IoT devices. Background Technology

[0002] With the rapid development of IoT technology, a large number of IoT devices are accessing the network through gateways. Portable gateways, as key devices in IoT scenarios, face higher requirements for power consumption management and thermal control due to their compact structure, limited internal heat dissipation space, and battery power supply. How to reasonably adjust the power consumption of each heat-generating module in the gateway and reduce the thermal coupling effect between heat-generating modules while ensuring the communication quality of IoT devices has become an urgent technical problem to be solved.

[0003] In existing technologies, gateway power consumption is primarily regulated by real-time monitoring of the gateway's overall temperature. When the temperature reaches a preset high-temperature threshold, all operating hardware modules within the gateway are uniformly frequency-reduced. However, different hardware modules exhibit varying heat generation and thermal coupling effects, and the services transmitted by IoT devices connected to the gateway have different frequency requirements for these modules. Therefore, existing technologies suffer from insufficient intelligence in gateway power consumption regulation and an inability to ensure the stability of service data transmission. Summary of the Invention

[0004] This application provides a gateway power consumption adjustment method and system for IoT devices, which solves the problems of insufficient intelligence in the existing gateway power consumption adjustment methods and the inability to ensure the stability of business data transmission. By calculating the heat impact value of each heat-generating module on the gateway, the priority of frequency reduction adjustment of the heat-generating modules is determined, the heat-generating modules to be frequency reduced are screened, a first frequency reduction amplitude is determined based on multiple heat impact values, the amplitude of the first frequency reduction amplitude is adjusted according to business flow information and communication link information to obtain a second frequency reduction amplitude, and the module frequency of the heat-generating modules to be frequency reduced is reduced according to the second frequency reduction amplitude to complete the power consumption adjustment of the gateway. It can achieve the purpose of adjusting the gateway power consumption based on the heat contribution of the heat-generating modules while ensuring stable transmission of business data, thereby improving the rationality of the gateway power consumption adjustment method and ensuring the stability of the gateway for business data transmission.

[0005] In a first aspect, embodiments of this application provide a gateway power consumption adjustment method for Internet of Things (IoT) devices, the method comprising: The power data of each heat-generating module inside the gateway and the thermal coupling coefficient between multiple heat-generating modules are acquired in real time. Based on the power data and thermal coupling coefficient, the heat-generating impact value of each heat-generating module on the gateway is calculated. Determine the frequency reduction adjustment priority corresponding to each heat-affecting value, filter the heat-affecting modules to be frequency-reduced from multiple heat-affecting modules based on the preset minimum frequency reduction priority and frequency reduction adjustment priority, and determine the first frequency reduction amplitude of the heat-affecting modules to be frequency-reduced based on multiple heat-affecting values; Determine the service flow information and communication link information of each IoT device associated with the frequency reduction and heat generation module to be reduced, and determine the frequency reduction impact coefficient of each IoT device on the frequency reduction and heat generation module to be reduced based on the service flow information and communication link information; The first frequency reduction amplitude is adjusted based on the frequency reduction influence coefficient to obtain the second frequency reduction amplitude. The frequency reduction of the module to be reduced and the heat generated is reduced according to the second frequency reduction amplitude to complete the power consumption adjustment of the gateway.

[0006] Furthermore, the first frequency reduction amplitude of the heating module to be frequency-reduced is determined based on multiple heat-affecting values, including: Determine the maximum heat-affecting value among multiple heat-affecting values, and the target heat-affecting value corresponding to the heat-affecting module to be frequency-reduced; Calculate the ratio of the target heat-generating effect value to the maximum heat-generating effect value, and multiply the ratio by the preset global frequency reduction factor to obtain the initial frequency reduction amplitude; The initial frequency reduction amplitude is compared with the preset maximum allowable frequency reduction amplitude. Based on the comparison result, the minimum frequency reduction amplitude is determined and used as the first frequency reduction amplitude of the heat-generating module to be frequency reduced.

[0007] Furthermore, after determining the maximum heat-affecting value among multiple heat-affecting values, and the target heat-affecting value corresponding to the heat-affecting module to be frequency-reduced, the method also includes: Calculate the average heat impact value of multiple heat impact values, and determine the heat impact threshold based on the preset contribution fluctuation ratio coefficient and the average heat impact value; If the target heat-affecting value is less than the heat-affecting threshold, the first frequency reduction amplitude of the heat-affecting module to be frequency-reduced is determined to be 0.

[0008] Furthermore, the service flow information includes real-time service traffic, service type information, and service latency thresholds; the communication link information includes link transmission signal strength and link retransmission rate. Based on service flow information and communication link information, determine the frequency reduction impact coefficient of each IoT device on the frequency reduction and heat generation module to be reduced, including: Determine the business security level corresponding to the business type information, calculate the reciprocal of the business latency threshold, and normalize the business security level, real-time business traffic, and the reciprocal of the latency threshold to obtain multiple normalization results. The service transmission tolerance is obtained by calculating the weighted sum of multiple normalized processing results based on multiple preset service impact weights. The link transmission signal strength is calculated by linear interpolation based on the preset maximum signal strength threshold and the preset minimum signal strength threshold, and the link quality assessment value is determined based on the linear interpolation result and the link retransmission rate. The sum of the service transmission tolerance and the link quality assessment value is calculated. If the sum is less than 1, the sum is used as the frequency reduction impact coefficient of the frequency reduction and heat generation module for each IoT device.

[0009] Furthermore, based on the down-frequency impact coefficient, the first down-frequency amplitude is adjusted to obtain the second down-frequency amplitude, including: The minimum frequency reduction impact coefficient among all IoT devices corresponding to the frequency reduction heat generation module is determined based on the frequency reduction impact coefficient. Obtain the backhaul link status information corresponding to the gateway, determine the backhaul link correction coefficient corresponding to the backhaul link status information, and correct the minimum frequency reduction impact coefficient based on the backhaul link correction coefficient to obtain the correction result of the minimum frequency reduction impact coefficient. The second frequency reduction amplitude is obtained by multiplying the corrected result of the minimum frequency reduction effect coefficient with the first frequency reduction amplitude.

[0010] Furthermore, based on power data and thermal coupling coefficients, the heat-generating impact of each heat-generating module on the gateway is calculated, including: Based on the thermal coupling coefficient, identify multiple associated modules that have a thermal coupling relationship with the current heating module among multiple heating modules, and calculate the product of the first power data of each associated module and the corresponding thermal coupling coefficient to obtain the thermal coupling influence parameters of each associated module on the current heating module. The sum of the second power data of the current heat-generating module and the thermal coupling influence parameters of all associated modules is calculated to obtain the heat generation influence value of the current heat-generating module on the gateway. Iterate through all heat-generating modules to obtain the heat impact value of each module on the gateway.

[0011] Furthermore, the thermal coupling coefficient includes the initial thermal coupling coefficient; Before calculating the heat impact of each heat-generating module on the gateway based on power data and thermal coupling coefficient, the method also includes: The temperature data of each heating module is acquired in real time. When the temperature data is greater than the preset temperature fluctuation threshold, the first heating module and the second heating module associated with each initial thermal coupling coefficient are determined. Based on the preset temperature-power correlation fitting algorithm, multiple temperature data of the first heating module and multiple power consumption data of the second heating module, the actual thermal coupling coefficient corresponding to each initial thermal coupling coefficient is calculated. The thermal coupling coefficient deviation between each initial thermal coupling coefficient and the corresponding actual thermal coupling coefficient is calculated, and each initial thermal coupling coefficient is corrected based on the preset correction step size and the thermal coupling coefficient deviation to obtain the final thermal coupling coefficient.

[0012] Secondly, embodiments of this application provide a gateway power consumption adjustment system for Internet of Things (IoT) devices, the system comprising: The heat generation impact value calculation module is used to obtain the power data of each heat generation module inside the gateway and the thermal coupling coefficient between multiple heat generation modules in real time, and calculate the heat generation impact value of each heat generation module on the gateway based on the power data and thermal coupling coefficient. The first frequency reduction amplitude determination module is used to determine the frequency reduction adjustment priority corresponding to each heat-affecting value, filter the heat-affecting modules to be frequency reduced from multiple heat-affecting modules based on the preset minimum frequency reduction priority and frequency reduction adjustment priority, and determine the first frequency reduction amplitude of the heat-affecting modules to be frequency reduced based on multiple heat-affecting values. The frequency reduction impact coefficient determination module is used to determine the service flow information and communication link information of each IoT device associated with the frequency reduction heat generation module, and to determine the frequency reduction impact coefficient of each IoT device on the frequency reduction heat generation module based on the service flow information and communication link information. The power consumption adjustment module is used to adjust the first frequency reduction amplitude based on the frequency reduction influence coefficient to obtain the second frequency reduction amplitude, and to reduce the frequency of the module to be reduced according to the second frequency reduction amplitude, so as to complete the power consumption adjustment of the gateway.

[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 power data of each heat-generating module inside the gateway and the thermal coupling coefficient between multiple heat-generating modules are acquired in real time. Based on the power data and thermal coupling coefficient, the heat-generating impact value of each heat-generating module on the gateway is calculated. The frequency reduction adjustment priority corresponding to each heat-generating impact value is determined. Based on the preset minimum frequency reduction priority and frequency reduction adjustment priority, the heat-generating modules to be frequency-reduced among multiple heat-generating modules are selected, and the first frequency reduction amplitude of the heat-generating modules to be frequency-reduced is determined based on multiple heat-generating impact values. The service flow information and communication link information of each IoT device associated with the heat-generating modules to be frequency-reduced are determined, and the frequency reduction impact coefficient of each IoT device on the heat-generating modules to be frequency-reduced is determined based on the service flow information and communication link information. The first frequency reduction amplitude is adjusted based on the frequency reduction impact coefficient to obtain the second frequency reduction amplitude, and the module frequency of the heat-generating modules to be frequency-reduced is reduced according to the second frequency reduction amplitude to complete the power consumption adjustment of the gateway. The aforementioned gateway power consumption adjustment method for IoT devices solves the problems of insufficient intelligence in gateway power consumption adjustment and inability to ensure the stability of business data transmission in existing technologies. By calculating the heat impact value of each heat-generating module on the gateway, the priority of frequency reduction adjustment for heat-generating modules is determined, heat-generating modules to be frequency-reduced are selected, and a first frequency reduction amplitude is determined based on multiple heat impact values. The amplitude of the first frequency reduction amplitude is adjusted according to business flow information and communication link information to obtain a second frequency reduction amplitude. The frequency reduction of the heat-generating modules to be frequency-reduced is then performed according to the second frequency reduction amplitude to complete the power consumption adjustment of the gateway. This achieves the goal of adjusting gateway power consumption based on the heat contribution of heat-generating modules while ensuring stable transmission of business data, improving the rationality of the gateway power consumption adjustment method and ensuring the stability of gateway for business data transmission. Attached Figure Description

[0017] Figure 1 This is a flowchart of a gateway power consumption adjustment method for IoT devices provided in an embodiment of this application; Figure 2 This is a flowchart of a method for determining the frequency reduction amplitude of a heating module to be frequency-reduced, provided in an embodiment of this application; Figure 3 This is a flowchart of a method for determining the frequency reduction influence coefficient provided in an embodiment of this application; Figure 4 This is a structural block diagram of a gateway power consumption regulation system for Internet of Things (IoT) devices 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 in scenarios where gateway power consumption is adjusted to reduce the gateway's operating temperature, especially for portable gateways with compact structures and limited internal heat dissipation space. This is to avoid thermal coupling effects between internal heat-generating hardware components and reduce the gateway's operating temperature. By calculating the heat impact of each heat-generating module on the gateway, the priority of frequency reduction adjustment for these modules is determined. Modules to be frequency-reduced are then selected, and a first frequency reduction magnitude is determined based on multiple heat impact values. This first frequency reduction magnitude is then adjusted according to service flow information and communication link information to obtain a second frequency reduction magnitude. The frequency of the heat-generating modules to be frequency-reduced is then reduced according to this second magnitude to complete the gateway power consumption adjustment. This achieves the goal of adjusting gateway power consumption based on the heat contribution of heat-generating modules while ensuring stable transmission of service data, improving the rationality of the gateway power consumption adjustment method and ensuring the stability of the gateway's service data transmission.

[0022] Based on the above usage scenarios, it is understandable that the entity executing each step in this solution can be a gateway with data reception, data processing, and power consumption adjustment functions. For example, a portable gateway such as a portable Wi-Fi device.

[0023] The following description, in conjunction with the accompanying drawings, details a gateway power consumption adjustment method and system for IoT devices provided in this application, through specific embodiments and application scenarios.

[0024] Figure 1 This is a flowchart illustrating a gateway power consumption adjustment method for IoT devices provided in an embodiment of this application. Figure 1 As shown, the specific steps include the following: S101 acquires the power data of each heat-generating module inside the gateway and the thermal coupling coefficient between multiple heat-generating modules in real time, and calculates the heat-generating impact value of each heat-generating module on the gateway based on the power data and thermal coupling coefficient.

[0025] The heat-generating modules can be hardware modules within the gateway that generate heat during operation. Examples include processors, Wi-Fi chips, 5G modules, power management chips, and memory chips. The power consumption of these modules is converted into heat, causing their own and the surrounding environment's temperature to rise. The thermal coupling coefficient is a physical parameter that quantifies the impact of a power consumption change in one heat-generating module on the temperature change of another. For example, if a 1W increase in CPU power consumption results in a 0.3°C increase in Wi-Fi chip temperature, then the thermal coupling coefficient from the CPU to Wi-Fi is 0.3°C / W. The thermal coupling coefficient is directional; the impact of the CPU on Wi-Fi and the impact of Wi-Fi on the CPU can differ. The heat-generating impact value is a quantitative indicator that comprehensively characterizes the contribution of a particular heat-generating module to the overall temperature rise of the gateway. For example, a heat-generating impact value of 2.425W means that the sum of the module's own power consumption and the equivalent power consumption transferred to other modules through thermal coupling is 2.425W. A larger heat-generating impact value indicates a greater contribution of the module to the gateway's temperature rise, and its frequency should be reduced as a priority.

[0026] In one embodiment, the specific method for real-time acquisition of power data of each heat-generating module within the gateway and the thermal coupling coefficient between multiple heat-generating modules can be as follows: Periodically read the current power consumption value of each heat-generating module through the current sampling register of the power management chip or the power consumption statistics interface of the operating system to obtain the power data of each heat-generating module. Obtain the initial thermal coupling coefficient between multiple heat-generating modules by reading the thermal coupling coefficient matrix pre-stored in the gateway's non-volatile memory. The initial thermal coupling coefficient is obtained through experimental calibration before the gateway leaves the factory. The specific method for calculating the heat-generating impact value of each heat-generating module on the gateway based on the power data and thermal coupling coefficient can be as follows: Based on the thermal coupling coefficient, determine multiple associated modules that have a thermal coupling relationship with the current heat-generating module, and calculate the product of the first power data of each associated module and the corresponding thermal coupling coefficient to obtain the thermal coupling impact parameter of each associated module on the current heat-generating module; calculate the sum of the second power data of the current heat-generating module and the thermal coupling impact parameters of all associated modules to obtain the heat-generating impact value of the current heat-generating module on the gateway; traverse all heat-generating modules to obtain the heat-generating impact value of each heat-generating module on the gateway.

[0027] The associated module can be any other heat-generating module that has a thermal coupling relationship with the current heat-generating module. The criterion for this determination can be a heat-generating module whose thermal coupling coefficient with the current heat-generating module is greater than a preset threshold. For example, there is significant thermal coupling between the CPU and the adjacent Wi-Fi chip. When the CPU heats up, it transfers heat to the Wi-Fi chip through the PCB and air; therefore, the CPU is an associated module of the Wi-Fi chip. Conversely, the heat generated by the Wi-Fi chip also affects the CPU; therefore, the Wi-Fi chip is also an associated module of the CPU. Thermal coupling is directional; A being an associated module of B does not necessarily mean that B is an associated module of A. The first power data can be the real-time power consumption value of the associated module at the current moment. The first power data can be obtained periodically by reading the current sampling register of the power management chip or the power consumption statistics interface of the operating system. The thermal coupling impact parameter can be the equivalent power consumption value that quantifies the impact of the associated module on the temperature of the current heat-generating module through thermal coupling. For example, if the thermal coupling coefficient between the CPU and Wi-Fi is 0.3, and the CPU's current power consumption is 2.0W, then the thermal coupling effect parameter between the CPU and Wi-Fi is 0.3 × 2.0 = 0.6W. This parameter indicates that 0.6W of heat from the CPU's own power consumption is transferred to the Wi-Fi chip, causing the Wi-Fi temperature to rise. The larger the thermal coupling effect parameter, the greater the thermal impact of the associated module on the current heat-generating module. The second power data can be the real-time power consumption value of the current heat-generating module itself.

[0028] In one embodiment, the specific method for determining multiple associated modules that have a thermal coupling relationship with the current heating module based on the thermal coupling coefficient can be as follows: Obtain the thermal coupling coefficient matrix among all heating modules in the gateway. For the current heating module, iterate through all other heating modules and check whether the thermal coupling coefficient of the other module to the current heating module in the thermal coupling coefficient matrix is ​​greater than a preset thermal coupling threshold. If it is greater than the threshold, the other module is determined as an associated module of the current heating module; if it is less than or equal to the threshold, the thermal coupling is considered negligible and it is not included in the associated modules. The specific method for calculating the thermal coupling influence parameter of each associated module on the current heating module by multiplying the first power data of each associated module with the corresponding thermal coupling coefficient can be as follows: Obtain the first power data of each associated module at the current time and the thermal coupling coefficient of the associated module to the current heating module in the thermal coupling coefficient matrix, and multiply the two to obtain the thermal coupling influence parameter of the associated module. The specific method for calculating the heat impact value of the current heat-generating module on the gateway by summing its second power data with the thermal coupling influence parameters of all associated modules can be as follows: Obtain the second power data (i.e., its own real-time power consumption) of the current heat-generating module itself, and add it to the thermal coupling influence parameters of all associated modules calculated above to obtain the heat impact value of the current heat-generating module. The specific method for iterating through all heat-generating modules to obtain the heat impact value of each heat-generating module on the gateway can be as follows: Take each heat-generating module in the gateway as the current heat-generating module in turn, and repeat the steps of determining associated modules, calculating thermal coupling influence parameters, and calculating heat impact values ​​to obtain the heat impact value of each heat-generating module.

[0029] In one embodiment, the thermal coupling coefficient includes an initial thermal coupling coefficient. Before calculating the heat generation impact value of each heat-generating module on the gateway based on power data and thermal coupling coefficient, the method further includes: acquiring the temperature data of each heat-generating module in real time; if the temperature data is greater than a preset temperature fluctuation threshold, determining the first heat-generating module and the second heat-generating module associated with each initial thermal coupling coefficient; calculating the actual thermal coupling coefficient corresponding to each initial thermal coupling coefficient based on a preset temperature-power consumption correlation fitting algorithm, multiple temperature data of the first heat-generating module, and multiple power consumption data of the second heat-generating module; calculating the thermal coupling coefficient deviation between each initial thermal coupling coefficient and the corresponding actual thermal coupling coefficient, and correcting each initial thermal coupling coefficient based on a preset correction step size and the thermal coupling coefficient deviation to obtain the final thermal coupling coefficient.

[0030] The preset temperature fluctuation threshold can be the minimum temperature fluctuation value used to determine the trigger for online correction of the thermal coupling coefficient, and can be preset according to the gateway's heat dissipation characteristics. The first heat-generating module can be the heat-generating module whose temperature change is currently being analyzed, i.e., the affected party in the thermal coupling coefficient. For example, when analyzing the impact of CPU heat on Wi-Fi temperature, Wi-Fi is the module affected by heat, therefore Wi-Fi is the first heat-generating module. The temperature data of the first heat-generating module is used to reflect the actual result of the thermal coupling effect. The second heat-generating module can be a heat-generating module that has a thermal coupling relationship with the first heat-generating module, i.e., the affected party in the thermal coupling coefficient. For example, when analyzing the impact of CPU heat on Wi-Fi temperature, the CPU is the module that generates heat, therefore the CPU is the second heat-generating module. The power consumption data of the second heat-generating module is used to reflect the input source of the thermal coupling effect. The first and second heat-generating modules are jointly associated with an initial thermal coupling coefficient, which represents the degree of influence of the power consumption change of the second heat-generating module on the temperature change of the first heat-generating module. The preset temperature-power consumption correlation fitting algorithm can be a mathematical method used to fit the actual thermal coupling coefficient from the temperature data and power consumption data. For example: recursive least squares algorithm, least squares fitting algorithm, gradient descent algorithm, or Kalman filter algorithm. This algorithm establishes a correlation between temperature changes and power consumption changes to obtain a coefficient value reflecting the actual thermal coupling strength. The actual thermal coupling coefficient can be the current true thermal coupling coefficient obtained by fitting real-time temperature and power consumption data using a preset algorithm. For example, the initial thermal coupling coefficient (factory calibration value) is 0.3, and after online correction, the actual thermal coupling coefficient is 0.28. The actual thermal coupling coefficient reflects the true thermal coupling state of the equipment under current aging, heat dissipation conditions, and ambient temperature. The thermal coupling coefficient deviation can be the difference between the initial thermal coupling coefficient and the actual thermal coupling coefficient. The larger the deviation, the greater the difference between the initial coefficient and the current true state, requiring a larger correction. The final thermal coupling coefficient can be the calibrated thermal coupling coefficient used for subsequent calculations of heat generation impact values ​​after one or more iterative corrections.

[0031] In one embodiment, the method for determining the first and second heating modules associated with each initial thermal coupling coefficient when the temperature data exceeds a preset temperature fluctuation threshold can be as follows: Temperature data of each heating module is periodically collected using built-in or external temperature sensors, and the temperature change at adjacent sampling times is calculated. When the temperature change of any heating module exceeds the preset temperature fluctuation threshold, an online correction process for the thermal coupling coefficient is triggered. All initial thermal coupling coefficients are iterated over, and for each initial thermal coupling coefficient, its associated two heating modules are determined: the heating module affected by heat is designated as the first heating module, and the heating module emitting heat is designated as the second heating module. Based on a preset temperature-power consumption correlation fitting algorithm, multiple temperature data of the first heating module, and multiple power consumption data of the second heating module, the method for calculating the actual thermal coupling coefficient corresponding to each initial thermal coupling coefficient can be as follows: The temperature data of the first heating module and the power consumption data of the second heating module at the corresponding times of each initial thermal coupling coefficient are input into the preset temperature-power consumption correlation fitting algorithm to obtain the actual thermal coupling coefficient corresponding to each initial thermal coupling coefficient. The specific method for calculating the thermal coupling coefficient deviation between each initial thermal coupling coefficient and the corresponding actual thermal coupling coefficient, and correcting each initial thermal coupling coefficient based on the preset correction step size and the thermal coupling coefficient deviation to obtain the final thermal coupling coefficient can be as follows: calculate the difference between the initial thermal coupling coefficient and the corresponding actual thermal coupling coefficient to obtain the thermal coupling coefficient deviation, multiply the thermal coupling coefficient deviation by the preset correction step size to obtain a correction amount, add this correction amount to the initial thermal coupling coefficient to obtain a new thermal coupling coefficient, and obtain the final thermal coupling coefficient.

[0032] This solution monitors the temperature fluctuations of the heat-generating module in real time and triggers corrections. After collecting temperature and power consumption data from related modules, it uses a fitting algorithm to calculate the actual thermal coupling coefficient. Then, it calculates the deviation between the initial coefficient and the actual coefficient and iteratively corrects it according to a preset step size to obtain the final thermal coupling coefficient. This allows the thermal coupling coefficient to be dynamically adjusted as an adaptive parameter based on the aging of hardware modules, dust accumulation, degradation of heat dissipation materials, and changes in ambient temperature. This solves the problem of inaccurate calculation of heat contribution value caused by the drift of the initial calibration coefficient over time, ensuring that subsequent frequency reduction decisions are always based on the current real thermal coupling state, thus improving the accuracy of power consumption adjustment and environmental adaptability.

[0033] S102, determine the frequency reduction adjustment priority corresponding to each heat-affecting value, filter the heat-affecting modules to be frequency reduced from multiple heat-affecting modules based on the preset minimum frequency reduction priority and frequency reduction adjustment priority, and determine the first frequency reduction amplitude of the heat-affecting modules to be frequency reduced based on multiple heat-affecting values.

[0034] The frequency reduction adjustment priority can be determined by sorting each heat-generating module according to its heat-affecting value. A larger heat-affecting value indicates a greater contribution of that module to the overall temperature rise of the gateway, and therefore, it should be prioritized for frequency reduction adjustment. The preset minimum frequency reduction priority can be a priority threshold value used to filter heat-affecting modules to be frequency-reduced. Heat-affecting modules to be frequency-reduced can be those that meet the filtering criteria and are determined to require frequency reduction. The first frequency reduction amplitude can be the initial frequency reduction ratio or amount calculated for the heat-affecting modules to be frequency-reduced. The first frequency reduction amplitude is calculated based on the heat-affecting value; the larger the heat-affecting value, the larger the first frequency reduction amplitude.

[0035] In one embodiment, determining the frequency reduction adjustment priority corresponding to each heat-affecting value, and selecting the heat-affecting modules to be frequency-reduced from multiple heat-affecting modules based on a preset minimum frequency reduction priority and a frequency reduction adjustment priority, can be achieved by: sorting the heat-affecting modules according to their heat-affecting values ​​from largest to smallest, obtaining a frequency reduction adjustment priority number for each heat-affecting module; comparing the preset minimum frequency reduction priority with the frequency reduction adjustment priority number of each heat-affecting module; if the frequency reduction adjustment priority number of a heat-affecting module is less than or equal to the preset minimum frequency reduction priority, the module is selected as a heat-affecting module to be frequency-reduced; if the frequency reduction adjustment priority number is greater than the preset minimum frequency reduction priority, the module is not selected as a heat-affecting module to be frequency-reduced. The specific method for determining the first frequency reduction amplitude of the heat-affecting module to be frequency-reduced based on multiple heat-affecting values ​​can be as follows: Figure 2 As shown. Figure 2 This is a flowchart illustrating a method for determining the frequency reduction amplitude of a heat-generating module to be frequency-reduced, as provided in an embodiment of this application. Figure 2 As shown, the specific steps include the following: S201, determine the maximum heat-affecting value among multiple heat-affecting values, and the target heat-affecting value corresponding to the heat-affecting module to be frequency-reduced.

[0036] The maximum heat-generating impact value can be the maximum value of the heat-generating impact values ​​among all heat-generating modules. The target heat-generating impact value can be the heat-generating impact value of the module to be frequency-reduced. The ratio of the target heat-generating impact value to the maximum heat-generating impact value reflects the degree of heat contribution of the module relative to the module with the largest heat source. The larger the ratio, the closer the heat contribution of the module is to the largest heat source, and the greater the frequency reduction should be.

[0037] In one embodiment, determining the maximum heat-affected value among multiple heat-affected values ​​can be achieved by: after calculating the heat-affected values ​​of all heat-affected modules, iterating through all heat-affected values ​​and finding the maximum value through comparison. Determining the target heat-affected value corresponding to the heat-affected module to be frequency-reduced can be achieved by: after determining the heat-affected module to be frequency-reduced, finding the corresponding heat-affected value from the heat-affected value calculation results, and using this as the target heat-affected value.

[0038] S202, calculate the ratio of the target heat-affecting value to the maximum heat-affecting value, and calculate the product of the ratio and the preset global frequency reduction factor to obtain the initial frequency reduction amplitude.

[0039] The preset global frequency reduction factor can be a pre-defined proportional coefficient used to control the overall frequency reduction intensity. The preset global frequency reduction factor typically ranges from 0 to 1, with a typical value of 0.3. The initial frequency reduction amplitude can be the preliminary frequency reduction ratio calculated based on the ratio of the heat-affected value and the preset global frequency reduction factor.

[0040] In one embodiment, the specific method for calculating the ratio of the target heat-affecting value to the maximum heat-affecting value, and then multiplying this ratio by a preset global frequency reduction factor to obtain the initial frequency reduction amplitude, can be as follows: Divide the target heat-affecting value of the heat-affecting module to be frequency-reduced by the maximum heat-affecting value among all heat-affecting modules to obtain a ratio between 0 and 1. Multiply this ratio by the preset global frequency reduction factor, and the product result is the initial frequency reduction amplitude.

[0041] S203, compare the initial frequency reduction amplitude with the preset maximum allowable frequency reduction amplitude, determine the minimum frequency reduction amplitude based on the comparison result, and use the minimum frequency reduction amplitude as the first frequency reduction amplitude of the heat-generating module to be frequency reduced.

[0042] The preset maximum allowable frequency reduction can be a pre-set upper limit value for the frequency reduction that no heat-generating module can exceed. For example, a preset maximum allowable frequency reduction of 40% means that no matter how large the heat-generating impact, the actual frequency reduction of any module cannot exceed 40%. This parameter is a safety protection threshold used to prevent excessive frequency reduction from causing module malfunction or system crash. The minimum frequency reduction can be the smaller value between the initial frequency reduction and the preset maximum allowable frequency reduction. For example, if the initial frequency reduction is 30% and the preset maximum allowable frequency reduction is 40%, then the smaller value is 30%, which is the minimum frequency reduction; if the initial frequency reduction is 50% and the preset maximum allowable frequency reduction is 40%, then the smaller value is 40%. The first frequency reduction can be the initial frequency reduction ratio of the heat-generating module to be frequency-reduced, determined after the upper limit constraint. For example, a first frequency reduction of 30% means reducing the current operating frequency of the module by 30%.

[0043] In one embodiment, comparing the initial frequency reduction amplitude with the preset maximum allowable frequency reduction amplitude, and determining the minimum frequency reduction amplitude based on the comparison result, the specific method for using the minimum frequency reduction amplitude as the first frequency reduction amplitude of the heat-generating module to be frequency-reduced can be as follows: The calculated initial frequency reduction amplitude is compared numerically with the preset maximum allowable frequency reduction amplitude. If the initial frequency reduction amplitude is less than or equal to the preset maximum allowable frequency reduction amplitude, the initial frequency reduction amplitude itself is safe, and it is directly used as the first frequency reduction amplitude. If the initial frequency reduction amplitude is greater than the preset maximum allowable frequency reduction amplitude, the initial frequency reduction amplitude exceeds the safety limit and needs to be restricted; the preset maximum allowable frequency reduction amplitude is then used as the first frequency reduction amplitude. In other words, the smaller of the two values ​​is taken as the first frequency reduction amplitude.

[0044] In one embodiment, after determining the maximum heat-affecting value among multiple heat-affecting values ​​and the target heat-affecting value corresponding to the heat-affecting module to be frequency-reduced, the method further includes: calculating the average heat-affecting value of the multiple heat-affecting values, determining a heat-affecting threshold based on a preset contribution floating ratio coefficient and the average heat-affecting value; and determining the first frequency reduction amplitude of the heat-affecting module to be frequency-reduced to 0 if the target heat-affecting value is less than the heat-affecting threshold.

[0045] The average heat impact value can be the arithmetic mean of the heat impact values ​​of all heat-generating modules. The average heat impact value reflects the average heat contribution level of each heat-generating module in the gateway. The preset contribution fluctuation ratio coefficient can be pre-set and used to adjust the scaling factor of the heat impact threshold relative to the average value. The preset contribution fluctuation ratio coefficient is usually between 0 and 1, with a typical value of 0.8. For example, a preset contribution fluctuation ratio coefficient of 0.8 means that only modules whose heat impact value reaches more than 80% of the average value are considered significant heat sources and require frequency reduction; modules below this ratio are considered low-contribution modules and do not require frequency reduction. The heat impact threshold can be a threshold value used to determine whether a heat-generating module needs frequency reduction, equal to the average heat impact value multiplied by the preset contribution fluctuation ratio coefficient. When the heat impact value of a module is below this threshold, it is considered that the module's contribution to the overall heat of the gateway is small, and frequency reduction has limited effect on heat dissipation; therefore, frequency reduction should not be performed.

[0046] In one embodiment, the specific method for calculating the average heat-affected value of multiple heat-affected values ​​and determining the heat-affected threshold based on a preset contribution floating ratio coefficient and the average heat-affected value can be as follows: After calculating the heat-affected value of all heat-affected modules, add all heat-affected values ​​together and divide by the total number of heat-affected modules to obtain the average heat-affected value. Multiply this average heat-affected value by the preset contribution floating ratio coefficient, and the product is the heat-affected threshold. When the target heat-affected value is less than the heat-affected threshold, the specific method for determining the first frequency reduction amplitude of the heat-affected module to be frequency-reduced to 0 can be as follows: Compare the target heat-affected value of the heat-affected module to be frequency-reduced with the calculated heat-affected threshold. If the target heat-affected value is less than the heat-affected threshold, the first frequency reduction amplitude of the module is directly set to 0, indicating that no frequency reduction is performed on the module; if the target heat-affected value is greater than or equal to the heat-affected threshold, the first frequency reduction amplitude is calculated according to the normal procedure.

[0047] This solution calculates the average value of the heat-generating impact of all heat-generating modules and determines the heat-generating impact threshold by combining it with a preset contribution floating ratio coefficient. The first frequency reduction amplitude of modules whose target heat-generating impact value is lower than the threshold is directly set to 0. This avoids ineffective frequency reduction of modules with small heat generation, thereby concentrating limited frequency reduction resources on processing the real heat source modules, ensuring heat dissipation while reducing unnecessary performance loss.

[0048] This solution uses the maximum heat impact value as a normalization benchmark, calculates the ratio of the target heat impact value to the maximum value, multiplies it by a preset global frequency reduction factor to obtain the initial frequency reduction amplitude, and then compares this initial frequency reduction amplitude with the preset maximum allowable frequency reduction amplitude, taking the smaller value as the first frequency reduction amplitude. This ensures that modules with greater heat contribution receive a larger frequency reduction amplitude, while using the preset maximum allowable frequency reduction amplitude as a safety upper limit to prevent excessive frequency reduction. This achieves fine-grained adjustment of frequency reduction resources allocated according to contribution, avoiding insufficient heat dissipation or excessive performance loss caused by applying a uniform frequency reduction amplitude to all heat-generating modules.

[0049] S103, determine the service flow information and communication link information of each IoT device associated with the frequency reduction and heat generation module, and determine the frequency reduction impact coefficient of each IoT device on the frequency reduction and heat generation module based on the service flow information and communication link information.

[0050] The service flow information can be a set of data describing the characteristics of the services transmitted by the IoT device. The communication link information can be a set of data describing the real-time status of the wireless channel between the IoT device and the gateway. The frequency reduction impact coefficient can be a quantitative indicator that comprehensively reflects the degree of influence of the IoT device's service characteristics and link status on the frequency reduction decision. The larger the frequency reduction impact coefficient, the higher the device's tolerance for frequency reduction (service is not sensitive and link quality is good), and the more permissible it is to reduce the frequency of the module that is causing heat generation. The smaller the frequency reduction impact coefficient, the lower the device's tolerance for frequency reduction (service is sensitive or link quality is poor), and the less permissible it is to reduce the frequency of the module that is causing heat generation.

[0051] In one embodiment, the specific method for determining the service flow information and communication link information of each IoT device associated with the frequency reduction and heat generation module can be as follows: Based on the function of the frequency reduction and heat generation module in the gateway and the functions required by each IoT device when communicating with the gateway, determine each IoT device associated with the frequency reduction and heat generation module; parse the transmission data packets of each IoT device using deep packet inspection technology to obtain service type information; read the real-time service traffic of each IoT device communicating with the gateway using a traffic statistics module; obtain the service latency threshold using service configuration information; obtain the link transmission signal strength by reading the statistical information of the wireless driver layer; and calculate the link retransmission rate by counting the number of data packet retransmissions per unit time. The specific method for determining the frequency reduction impact coefficient of each IoT device on the frequency reduction and heat generation module based on the service flow information and communication link information can be as follows: Figure 3 As shown. Figure 3 This is a flowchart illustrating a method for determining the frequency reduction influence coefficient provided in an embodiment of this application. Figure 3 As shown, the specific steps include the following: S301, determine the business security level corresponding to the business type information, calculate the reciprocal of the business latency threshold, and normalize the business security level, real-time business traffic and the reciprocal of the latency threshold respectively to obtain multiple normalization results.

[0052] The service type information can identify the category to which the IoT device's current service belongs. Examples include: real-time control (alarm commands, industrial control commands), video transmission (video calls, video surveillance), audio transmission (voice calls), file transmission (firmware upgrades, log uploads), and periodic sensing (temperature and humidity reporting, heartbeat packets). The service security level can be a quantitative level reflecting the importance and sensitivity of the service, mapped from the service type information. The service latency threshold can be the maximum end-to-end transmission latency that the service can tolerate.

[0053] In one embodiment, the specific method for determining the service security level corresponding to the service type information can be: using a preset service type-security level mapping table, the corresponding service security level is found based on the service type information. This mapping table is pre-configured during system design and can be adjusted according to actual needs. The specific method for calculating the reciprocal of the service latency threshold can be: taking the reciprocal of the service latency threshold value. For example, if the voice call service latency threshold is 50ms, the reciprocal of the latency threshold is 1 / 50 = 0.02. The specific method for normalizing the service security level, real-time service traffic, and the reciprocal of the latency threshold to obtain multiple normalization results can be: the service security level itself is designed as a value between 0 and 1, so it can be used directly without additional normalization; for real-time service traffic, it is divided by a preset maximum service traffic reference value to obtain the normalized traffic value. The preset maximum service traffic reference value can be set according to the gateway's maximum processing capacity or the network bandwidth limit. For example, if the gateway's Wi-Fi maximum theoretical bandwidth is 100Mbps, then a real-time service traffic of 2.5Mbps normalized to 2.5 / 100 = 0.025. The reciprocal of the latency threshold is divided by a preset maximum latency sensitivity reference value to obtain the normalized reciprocal of the latency. The preset maximum latency sensitivity reference value can be set according to the latency requirements of the service type.

[0054] S302, based on multiple preset service impact weights, calculates the weighted sum of multiple normalized processing results to obtain the service transmission tolerance.

[0055] The preset business impact weights can be pre-defined coefficients used to measure the importance of each normalized parameter in the business transmission tolerance calculation. Each normalized parameter (business security level, real-time business traffic, reciprocal of latency threshold) corresponds to a preset business impact weight. Business transmission tolerance can be a quantitative indicator that comprehensively reflects the degree to which the IoT device's business characteristics tolerate gateway frequency reduction. The higher the business transmission tolerance, the less sensitive the device's business is to frequency reduction (e.g., file download, sensor reporting), and the more permissible it is to reduce the frequency of the module that is causing the heat from frequency reduction; the lower the business transmission tolerance, the more sensitive the device's business is to frequency reduction (e.g., voice calls, industrial control), and the less permissible it is to reduce the frequency of the module that is causing the heat from frequency reduction.

[0056] In one embodiment, the specific way to obtain the service transmission tolerance by calculating the weighted sum of multiple normalized processing results based on multiple preset service influence weights can be as follows: multiply each normalized processing result by its corresponding preset service influence weight, add all the product results, and the resulting weighted sum is the service transmission tolerance.

[0057] S303 performs linear interpolation calculation on the link transmission signal strength based on the preset maximum signal strength threshold and the preset minimum signal strength threshold, and determines the link quality assessment value based on the linear interpolation calculation result and the link retransmission rate.

[0058] The preset maximum signal strength threshold can be pre-set, representing the optimal signal strength threshold for link signal quality. The preset minimum signal strength threshold can also be pre-set, representing the acceptable signal strength threshold for link signal quality. Link transmission signal strength can be the strength indication value of the gateway's wireless signal received by the IoT device, typically in dBm. Link retransmission rate can be the proportion of data packets transmitted between the IoT device and the gateway that need to be retransmitted per unit time, ranging from 0 to 1. For example, a retransmission rate of 0.05 (5%) means that 5 out of every 100 data packets need to be retransmitted; a retransmission rate of 0.30 (30%) means that nearly one-third of the data packets need to be retransmitted. A higher retransmission rate indicates a less stable link. The link quality assessment value can be a quantitative indicator that comprehensively reflects the wireless link quality between the IoT device and the gateway, ranging from 0 to 1. A higher link quality assessment value indicates better link quality, allowing for frequency reduction of the heat-generating module on which the device relies; a lower link quality assessment value indicates worse link quality, making frequency reduction less permissible.

[0059] In one embodiment, the specific method for linearly interpolating the link transmission signal strength based on a preset maximum signal strength threshold and a preset minimum signal strength threshold can be as follows: when the link transmission signal strength is greater than or equal to the preset maximum signal strength threshold, the linear interpolation result is 1.0; when the link transmission signal strength is less than or equal to the preset minimum signal strength threshold, the linear interpolation result is 0; when the link transmission signal strength is between the preset minimum signal strength threshold and the preset maximum signal strength threshold, the linear interpolation result is obtained by subtracting the preset minimum signal strength threshold from the link transmission signal strength and then dividing by the difference between the preset maximum signal strength threshold and the preset minimum signal strength threshold. The specific method for determining the link quality assessment value based on the linear interpolation result and the link retransmission rate can be as follows: calculate 1 minus the link retransmission rate to obtain the successful transmission ratio, multiply the linear interpolation result by the successful transmission ratio to obtain the link quality assessment value. When the link retransmission rate exceeds a preset retransmission threshold, the link quality assessment value is forcibly reset to zero. For example, if the preset retransmission threshold is 0.30 (30%), when the link retransmission rate reaches or exceeds 30%, the link quality assessment value will be directly set to 0 regardless of the linear interpolation result.

[0060] S304 calculates the sum of the service transmission tolerance and the link quality assessment value. If the sum is less than 1, the sum is used as the frequency reduction impact coefficient of each IoT device on the frequency reduction and heat generation module.

[0061] In one embodiment, the specific method for calculating the sum of the service transmission tolerance and the link quality assessment value, and using the sum as the frequency reduction impact coefficient of each IoT device for the frequency reduction and heat generation module when the sum is less than 1, can be as follows: directly add the service transmission tolerance and the link quality assessment value calculated for the same IoT device to obtain the sum of the two, and determine whether the sum is less than 1. If the sum is less than 1, then the sum is used as the frequency reduction impact coefficient of the IoT device for the frequency reduction and heat generation module; if the sum is greater than or equal to 1, then the frequency reduction impact coefficient is taken as 1, so as to ensure that the value range of the frequency reduction impact coefficient is limited to between 0 and 1, and to avoid the occurrence of a coefficient greater than 1, which would amplify the frequency reduction amplitude when multiplied with the first frequency reduction amplitude.

[0062] This solution obtains the service transmission tolerance by normalizing the service security level, real-time service traffic, and the reciprocal of the latency threshold, and then summing them according to preset weights. At the same time, it obtains the link quality assessment value by linearly interpolating the link signal strength and multiplying it by the successful transmission ratio. Finally, it adds the two together and limits the upper limit to obtain the frequency reduction impact coefficient. This integrates the two dimensions of service sensitivity and link vulnerability into a comprehensive quantitative indicator, ensuring differentiated protection for critical services and vulnerable links in frequency reduction decisions. It avoids sensitive service interruptions or weak signal equipment outages caused by frequency reduction, and achieves intelligent power consumption adjustment based on service awareness and link awareness.

[0063] S104, adjust the first frequency reduction amplitude based on the frequency reduction influence coefficient to obtain the second frequency reduction amplitude, and reduce the frequency of the module to be reduced according to the second frequency reduction amplitude to complete the power consumption adjustment of the gateway.

[0064] The second frequency reduction amplitude can be the final actual frequency reduction ratio after adjustment based on service flow information and communication link information.

[0065] In one embodiment, the specific method for adjusting the first frequency reduction amplitude based on the frequency reduction impact coefficient to obtain the second frequency reduction amplitude can be as follows: determine the minimum frequency reduction impact coefficient among all IoT devices corresponding to the heat dissipation module to be frequency reduced based on the frequency reduction impact coefficient; obtain the backhaul link status information corresponding to the gateway, determine the backhaul link correction coefficient corresponding to the backhaul link status information, and perform coefficient correction on the minimum frequency reduction impact coefficient based on the backhaul link correction coefficient to obtain the correction result of the minimum frequency reduction impact coefficient; calculate the product of the correction result of the minimum frequency reduction impact coefficient and the first frequency reduction amplitude to obtain the second frequency reduction amplitude.

[0066] The minimum frequency reduction impact coefficient can be the minimum frequency reduction impact coefficient among all IoT devices associated with the module to be frequency-reduced and prone to overheating. For example, if a Wi-Fi chip is associated with three IoT devices with frequency reduction impact coefficients of 0.83, 0.70, and 0.95 respectively, then the minimum frequency reduction impact coefficient is 0.70. Taking the minimum value is to protect the most sensitive or worst-performing device, preventing any vulnerable device from being harmed by frequency reduction. Backhaul link status information can be data describing the communication link status between the gateway and the upstream network. The backhaul link is the connection between the gateway and upstream network devices such as base stations and routers, and its status reflects the congestion level of the external network. The backhaul link correction coefficient can be determined based on the backhaul link status information and is used to adjust the minimum frequency reduction impact coefficient. The more congested the backhaul link (the higher the bandwidth utilization), the smaller the correction coefficient, indicating that more frequency reduction can be allowed (because the external network has become a bottleneck, frequency reduction within the gateway does not affect the user experience); the more idle the backhaul link, the closer the correction coefficient is to 1.

[0067] In one embodiment, the specific method for determining the minimum frequency reduction impact coefficient among all IoT devices corresponding to the frequency reduction heat dissipation module based on the frequency reduction impact coefficient can be as follows: traverse all IoT devices associated with the frequency reduction heat dissipation module, obtain the frequency reduction impact coefficient corresponding to each device, and find the minimum value through comparison. Obtain the backhaul link status information corresponding to the gateway, determine the backhaul link correction coefficient corresponding to the backhaul link status information, and correct the minimum frequency reduction impact coefficient based on the backhaul link correction coefficient to obtain the corrected minimum frequency reduction impact coefficient. The specific method for obtaining the corrected minimum frequency reduction impact coefficient can be as follows: obtain the current bandwidth utilization or current available bandwidth of the backhaul link by reading the statistical information of the gateway's uplink interface or sending a query request to the network device. Based on the congestion level of the backhaul link, determine the backhaul link correction coefficient according to a preset mapping rule: when the backhaul link is idle, the correction coefficient is 1.0; when the backhaul link is congested, the correction coefficient is less than 1; when the backhaul link is extremely congested, the correction coefficient is an even smaller value. Multiply the minimum frequency reduction impact coefficient by the backhaul link correction coefficient to obtain the corrected minimum frequency reduction impact coefficient. The specific method for calculating the second frequency reduction magnitude by multiplying the corrected minimum frequency reduction influence coefficient by the first frequency reduction magnitude can be as follows: multiply the corrected minimum frequency reduction influence coefficient by the first frequency reduction magnitude; the product is the second frequency reduction magnitude. For example, if the first frequency reduction magnitude is 30% and the corrected minimum frequency reduction influence coefficient is 0.56, then the second frequency reduction magnitude is 30% × 0.56 = 16.8%.

[0068] This solution takes the minimum frequency reduction impact coefficient from all IoT devices associated with the frequency reduction module, determines a correction coefficient based on the congestion level of the backhaul link, corrects this minimum value, and multiplies the correction result by the first frequency reduction magnitude to obtain the second frequency reduction magnitude. This solution can simultaneously take into account the service-sensitive devices on the access link side and the bandwidth bottleneck on the backhaul link side, achieving end-to-end perception and joint optimization from the terminal device to the upper-level network, maximizing the frequency reduction benefits of the gateway while ensuring end-to-end communication quality.

[0069] In one embodiment, the specific method for adjusting the power consumption of the gateway by reducing the frequency of the heat-generating module to be reduced according to the second frequency reduction amplitude can be as follows: Based on a preset frequency reduction amplitude and frequency direct conversion relationship, the calculated second frequency reduction amplitude is converted into a specific frequency value. Then, the frequency reduction operation is performed on the heat-generating module to be reduced by calling a hardware interface or operating system interface. For example, the CPU operating frequency is reduced to the frequency value corresponding to the second frequency reduction amplitude through the CPU frequency modulation interface of the operating system. Through this frequency reduction operation, the actual operating frequency and dynamic power consumption of each heat-generating module inside the gateway are reduced, thereby reducing the overall heat generation of the gateway.

[0070] The technical solution provided in this application embodiment acquires the power data of each heat-generating module inside the gateway and the thermal coupling coefficient between multiple heat-generating modules in real time. Based on the power data and thermal coupling coefficient, it calculates the heat-generating impact value of each heat-generating module on the gateway; determines the frequency reduction adjustment priority corresponding to each heat-generating impact value; filters the heat-generating modules to be frequency-reduced from multiple heat-generating modules based on the preset minimum frequency reduction priority and frequency reduction adjustment priority; and determines the first frequency reduction amplitude of the heat-generating modules to be frequency-reduced based on multiple heat-generating impact values; determines the service flow information and communication link information of each IoT device associated with the heat-generating modules to be frequency-reduced, and determines the frequency reduction impact coefficient of each IoT device on the heat-generating modules to be frequency-reduced based on the service flow information and communication link information; adjusts the first frequency reduction amplitude based on the frequency reduction impact coefficient to obtain the second frequency reduction amplitude, and performs module frequency reduction on the heat-generating modules to be frequency-reduced according to the second frequency reduction amplitude to complete the power consumption adjustment of the gateway. The aforementioned gateway power consumption adjustment method for IoT devices solves the problems of insufficient intelligence in gateway power consumption adjustment and inability to ensure the stability of business data transmission in existing technologies. By calculating the heat impact value of each heat-generating module on the gateway, the priority of frequency reduction adjustment for heat-generating modules is determined, heat-generating modules to be frequency-reduced are selected, and a first frequency reduction amplitude is determined based on multiple heat impact values. The amplitude of the first frequency reduction amplitude is adjusted according to business flow information and communication link information to obtain a second frequency reduction amplitude. The frequency reduction of the heat-generating modules to be frequency-reduced is then performed according to the second frequency reduction amplitude to complete the power consumption adjustment of the gateway. This achieves the goal of adjusting gateway power consumption based on the heat contribution of heat-generating modules while ensuring stable transmission of business data, improving the rationality of the gateway power consumption adjustment method and ensuring the stability of gateway for business data transmission.

[0071] Figure 4 This is a structural block diagram of a gateway power consumption regulation system for IoT devices provided in an embodiment of this application. Figure 4 As shown, it specifically includes: The heat generation impact value calculation module 401 is used to acquire the power data of each heat generation module inside the gateway and the thermal coupling coefficient between multiple heat generation modules in real time, and calculate the heat generation impact value of each heat generation module on the gateway based on the power data and thermal coupling coefficient. The first frequency reduction amplitude determination module 402 is used to determine the frequency reduction adjustment priority corresponding to each heat-affecting value, filter the heat-affecting modules to be frequency reduced from multiple heat-affecting modules based on the preset minimum frequency reduction priority and frequency reduction adjustment priority, and determine the first frequency reduction amplitude of the heat-affecting modules to be frequency reduced based on multiple heat-affecting values. The frequency reduction impact coefficient determination module 403 is used to determine the service flow information and communication link information of each IoT device associated with the frequency reduction heat generation module, and to determine the frequency reduction impact coefficient of each IoT device on the frequency reduction heat generation module based on the service flow information and communication link information. The power consumption adjustment module 404 is used to adjust the first frequency reduction amplitude based on the frequency reduction influence coefficient to obtain the second frequency reduction amplitude, and to reduce the frequency of the heat-generating module to be reduced according to the second frequency reduction amplitude, so as to complete the power consumption adjustment of the gateway.

[0072] Furthermore, the first frequency reduction amplitude determination module 402 is specifically used for: Determine the maximum heat-affecting value among multiple heat-affecting values, and the target heat-affecting value corresponding to the heat-affecting module to be frequency-reduced; Calculate the ratio of the target heat-generating effect value to the maximum heat-generating effect value, and multiply the ratio by the preset global frequency reduction factor to obtain the initial frequency reduction amplitude; The initial frequency reduction amplitude is compared with the preset maximum allowable frequency reduction amplitude. Based on the comparison result, the minimum frequency reduction amplitude is determined and used as the first frequency reduction amplitude of the heat-generating module to be frequency reduced.

[0073] Furthermore, the system also includes: The heating impact threshold determination module is used to calculate the average heating impact value of multiple heating impact values ​​and determine the heating impact threshold based on the preset contribution fluctuation ratio coefficient and the average heating impact value. The first frequency reduction amplitude determination module is used to determine that the first frequency reduction amplitude of the heating module to be frequency reduced is 0 when the target heating influence value is less than the heating influence threshold.

[0074] Furthermore, the service flow information includes real-time service traffic, service type information, and service latency thresholds; the communication link information includes link transmission signal strength and link retransmission rate. The frequency reduction impact coefficient determination module 403 is specifically used for: Determine the business security level corresponding to the business type information, calculate the reciprocal of the business latency threshold, and normalize the business security level, real-time business traffic, and the reciprocal of the latency threshold to obtain multiple normalization results. The service transmission tolerance is obtained by calculating the weighted sum of multiple normalized processing results based on multiple preset service impact weights. The link transmission signal strength is calculated by linear interpolation based on the preset maximum signal strength threshold and the preset minimum signal strength threshold, and the link quality assessment value is determined based on the linear interpolation result and the link retransmission rate. The sum of the service transmission tolerance and the link quality assessment value is calculated. If the sum is less than 1, the sum is used as the frequency reduction impact coefficient of the frequency reduction and heat generation module for each IoT device.

[0075] Furthermore, the power consumption regulation module 404 is specifically used for: The minimum frequency reduction impact coefficient among all IoT devices corresponding to the frequency reduction heat generation module is determined based on the frequency reduction impact coefficient. Obtain the backhaul link status information corresponding to the gateway, determine the backhaul link correction coefficient corresponding to the backhaul link status information, and correct the minimum frequency reduction impact coefficient based on the backhaul link correction coefficient to obtain the correction result of the minimum frequency reduction impact coefficient. The second frequency reduction amplitude is obtained by multiplying the corrected result of the minimum frequency reduction effect coefficient with the first frequency reduction amplitude.

[0076] Furthermore, the heat effect value calculation module 401 is specifically used for: Based on the thermal coupling coefficient, identify multiple associated modules that have a thermal coupling relationship with the current heating module among multiple heating modules, and calculate the product of the first power data of each associated module and the corresponding thermal coupling coefficient to obtain the thermal coupling influence parameters of each associated module on the current heating module. The sum of the second power data of the current heat-generating module and the thermal coupling influence parameters of all associated modules is calculated to obtain the heat generation influence value of the current heat-generating module on the gateway. Iterate through all heat-generating modules to obtain the heat impact value of each module on the gateway.

[0077] Furthermore, the thermal coupling coefficient includes the initial thermal coupling coefficient; The system also includes: The association determination module is used to acquire the temperature data of each heating module in real time, and determine the first heating module and the second heating module associated with each initial thermal coupling coefficient when the temperature data is greater than the preset temperature fluctuation threshold. The actual thermal coupling coefficient calculation module is used to calculate the actual thermal coupling coefficient corresponding to each initial thermal coupling coefficient based on a preset temperature-power correlation fitting algorithm, multiple temperature data of the first heating module, and multiple power consumption data of the second heating module. The thermal coupling coefficient correction module is used to calculate the thermal coupling coefficient deviation between each initial thermal coupling coefficient and the corresponding actual thermal coupling coefficient, and to correct each initial thermal coupling coefficient based on the preset correction step size and thermal coupling coefficient deviation to obtain the final thermal coupling coefficient.

[0078] The technical solution provided in this application includes a heat-affected value calculation module, used to acquire in real time the power data of each heat-affected module inside the gateway and the thermal coupling coefficient between multiple heat-affected modules, and calculate the heat-affected value of each heat-affected module on the gateway based on the power data and thermal coupling coefficient; a first frequency reduction amplitude determination module, used to determine the frequency reduction adjustment priority corresponding to each heat-affected value, filter the heat-affected modules to be frequency-reduced among multiple heat-affected modules based on the preset minimum frequency reduction priority and frequency reduction adjustment priority, and determine the first frequency reduction amplitude of the heat-affected modules to be frequency-reduced based on multiple heat-affected values; a frequency reduction influence coefficient determination module, used to determine the service flow information and communication link information of each IoT device associated with the heat-affected module to be frequency-reduced, and determine the frequency reduction influence coefficient of each IoT device on the heat-affected module to be frequency-reduced based on the service flow information and communication link information; and a power consumption adjustment module, used to adjust the first frequency reduction amplitude based on the frequency reduction influence coefficient to obtain a second frequency reduction amplitude, and reduce the frequency of the heat-affected module to be frequency-reduced according to the second frequency reduction amplitude to complete the power consumption adjustment of the gateway. The aforementioned gateway power consumption adjustment system for IoT devices solves the problems of insufficient intelligence in gateway power consumption adjustment and inability to ensure the stability of business data transmission in existing technologies. By calculating the heat impact value of each heat-generating module on the gateway, the priority of frequency reduction adjustment for heat-generating modules is determined, heat-generating modules to be frequency-reduced are selected, and a first frequency reduction amplitude is determined based on multiple heat impact values. The amplitude of the first frequency reduction amplitude is adjusted according to business flow information and communication link information to obtain a second frequency reduction amplitude. The frequency reduction of the heat-generating modules to be frequency-reduced is then performed according to the second frequency reduction amplitude to complete the power consumption adjustment of the gateway. This achieves the goal of adjusting gateway power consumption based on the heat contribution of heat-generating modules while ensuring stable transmission of business data, improving the rationality of the gateway power consumption adjustment method and ensuring the stability of gateway for business data transmission.

[0079] The gateway power consumption regulation system for IoT devices in this application embodiment can be configured in the system or in a component, integrated circuit, or chip in the terminal. The system 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 embodiment does not impose specific limitations.

[0080] The gateway power consumption regulation 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 it.

[0081] The gateway power consumption adjustment system for IoT devices 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.

[0082] 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 a gateway power consumption adjustment method for IoT devices and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0083] 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.

[0084] 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 embodiment of a gateway power consumption adjustment method for IoT devices and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0085] 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.

[0086] 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 gateway power consumption adjustment method for Internet of Things (IoT) devices as described in this application embodiment. The program product can be implemented using any combination of one or more readable media.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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 gateway power consumption adjustment method for Internet of Things (IoT) devices, characterized in that, The method includes: The power data of each heat-generating module inside the gateway and the thermal coupling coefficient between multiple heat-generating modules are acquired in real time, and the heat-generating impact value of each heat-generating module on the gateway is calculated based on the power data and the thermal coupling coefficient. Determine the frequency reduction adjustment priority corresponding to each of the aforementioned heat-affecting values, filter the heat-affecting modules to be frequency-reduced among the plurality of heat-affecting modules based on the preset minimum frequency reduction priority and the frequency reduction adjustment priority, and determine the first frequency reduction amplitude of the heat-affecting modules to be frequency-reduced based on the plurality of heat-affecting values; Determine the service flow information and communication link information of each IoT device associated with the frequency reduction and heat generation module to be reduced, and determine the frequency reduction impact coefficient of each IoT device on the frequency reduction and heat generation module to be reduced based on the service flow information and the communication link information; The first frequency reduction amplitude is adjusted based on the frequency reduction influence coefficient to obtain the second frequency reduction amplitude, and the frequency reduction of the heat-generating module to be frequency reduced is performed according to the second frequency reduction amplitude to complete the power consumption adjustment of the gateway.

2. The gateway power consumption adjustment method for IoT devices according to claim 1, characterized in that, The determination of the first frequency reduction amplitude of the heating module to be frequency-reduced based on multiple heat-affecting values ​​includes: Determine the maximum heat-affecting value among multiple heat-affecting values, and the target heat-affecting value corresponding to the heat-affecting module to be frequency-reduced; Calculate the ratio of the target heat-generating impact value to the maximum heat-generating impact value, and multiply the ratio by a preset global frequency reduction factor to obtain the initial frequency reduction amplitude; The initial frequency reduction amplitude is compared with the preset maximum allowable frequency reduction amplitude. Based on the comparison result, the minimum frequency reduction amplitude is determined and the minimum frequency reduction amplitude is used as the first frequency reduction amplitude of the heating module to be frequency reduced.

3. The gateway power consumption adjustment method for IoT devices according to claim 2, characterized in that, After determining the maximum heat-affecting value among multiple heat-affecting values, and the target heat-affecting value corresponding to the heat-affecting module to be frequency-reduced, the method further includes: Calculate the average heat-generating impact value of the multiple heat-generating impact values, and determine the heat-generating impact threshold based on the preset contribution fluctuation ratio coefficient and the average heat-generating impact value; If the target heat-affecting value is less than the heat-affecting threshold, the first frequency reduction amplitude of the heat-affecting module to be frequency-reduced is determined to be 0.

4. The gateway power consumption adjustment method for IoT devices according to claim 1, characterized in that, The service flow information includes real-time service traffic, service type information, and service latency threshold; the communication link information includes link transmission signal strength and link retransmission rate. The step of determining the frequency reduction impact coefficient of each IoT device on the frequency reduction-to-heat module based on the service flow information and the communication link information includes: Determine the service security level corresponding to the service type information, calculate the reciprocal of the latency threshold of the service latency threshold, and normalize the service security level, the real-time service traffic and the reciprocal of the latency threshold respectively to obtain multiple normalization results; The service transmission tolerance is obtained by calculating the weighted sum of the multiple normalized processing results based on multiple preset service impact weights. The link transmission signal strength is linearly interpolated based on a preset maximum signal strength threshold and a preset minimum signal strength threshold, and the link quality assessment value is determined based on the linear interpolation result and the link retransmission rate. Calculate the sum of the service transmission tolerance and the link quality assessment value. If the sum is less than 1, use the sum as the frequency reduction impact coefficient of each IoT device on the frequency reduction heating module to be reduced.

5. The gateway power consumption adjustment method for IoT devices according to claim 4, characterized in that, The step of adjusting the first frequency reduction amplitude based on the frequency reduction influence coefficient to obtain the second frequency reduction amplitude includes: Based on the frequency reduction impact coefficient, determine the minimum frequency reduction impact coefficient among all the IoT devices corresponding to the heating module to be frequency reduced; Obtain the backhaul link status information corresponding to the gateway, determine the backhaul link correction coefficient corresponding to the backhaul link status information, and perform coefficient correction on the minimum frequency reduction impact coefficient based on the backhaul link correction coefficient to obtain the correction result of the minimum frequency reduction impact coefficient. The second frequency reduction amplitude is obtained by multiplying the corrected result of the minimum frequency reduction influence coefficient with the first frequency reduction amplitude.

6. The gateway power consumption adjustment method for IoT devices according to claim 1, characterized in that, The calculation of the heat-generating impact value of each heat-generating module on the gateway based on the power data and the thermal coupling coefficient includes: Based on the thermal coupling coefficient, multiple associated modules that have a thermal coupling relationship with the current heating module are determined among the multiple heating modules, and the product of the first power data of each associated module and the corresponding thermal coupling coefficient is calculated to obtain the thermal coupling influence parameters of each associated module on the current heating module. The sum of the second power data of the current heating module and the thermal coupling influence parameters of all the associated modules is calculated to obtain the heating influence value of the current heating module on the gateway; By iterating through all the heat-generating modules, the heat-generating impact value of each heat-generating module on the gateway is obtained.

7. The gateway power consumption adjustment method for IoT devices according to claim 1, characterized in that, The thermal coupling coefficient includes the initial thermal coupling coefficient; Before calculating the heat-generating impact of each heat-generating module on the gateway based on the power data and the thermal coupling coefficient, the method further includes: The temperature data of each of the heating modules is acquired in real time. If the temperature data is greater than a preset temperature fluctuation threshold, the first heating module and the second heating module associated with each of the initial thermal coupling coefficients are determined. Based on a preset temperature-power correlation fitting algorithm, multiple temperature data of the first heating module and multiple power consumption data of the second heating module, the actual thermal coupling coefficient corresponding to each of the initial thermal coupling coefficients is calculated. The thermal coupling coefficient deviation between each initial thermal coupling coefficient and the corresponding actual thermal coupling coefficient is calculated, and each initial thermal coupling coefficient is corrected based on a preset correction step size and the thermal coupling coefficient deviation to obtain the final thermal coupling coefficient.

8. A gateway power consumption regulation system for Internet of Things (IoT) devices, characterized in that, The system includes: The heat generation impact value calculation module is used to acquire the power data of each heat generation module inside the gateway and the thermal coupling coefficient between multiple heat generation modules in real time, and calculate the heat generation impact value of each heat generation module on the gateway based on the power data and the thermal coupling coefficient. The first frequency reduction amplitude determination module is used to determine the frequency reduction adjustment priority corresponding to each of the heat-affecting values, filter the heat-affecting modules to be frequency reduced among the plurality of heat-affecting modules based on the preset minimum frequency reduction priority and the frequency reduction adjustment priority, and determine the first frequency reduction amplitude of the heat-affecting modules to be frequency reduced based on the plurality of heat-affecting values. The frequency reduction impact coefficient determination module is used to determine the service flow information and communication link information of each IoT device associated with the frequency reduction heat generation module, and to determine the frequency reduction impact coefficient of each IoT device on the frequency reduction heat generation module based on the service flow information and the communication link information. The power consumption adjustment module is used to adjust the first frequency reduction amplitude based on the frequency reduction influence coefficient to obtain a second frequency reduction amplitude, and to reduce the frequency of the heat-generating module to be reduced according to the second frequency reduction amplitude, so as to complete the power consumption adjustment of the gateway.

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 when the program or instructions are executed by the processor, they implement the steps of a gateway power consumption adjustment method for 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 gateway power consumption adjustment method for IoT devices as described in any one of claims 1-7.

Images