Adaptive adjustment method for acquisition and reporting frequency of Internet of Things equipment and related device

Abstract

The invention discloses a self-adaptive adjustment method for the collection and reporting frequency of Internet of Things equipment and a related device, and relates to the technical field of Internet of Things equipment, and the method comprises the steps: obtaining the weather data in a future preset time period when the Internet of Things equipment is awakened, and obtaining the collection and reporting frequency of the Internet of Things equipment according to the weather data at the current moment and the weather data in the future preset time period; the method comprises the following steps: determining a first wake-up moment, determining an acquisition and reporting frequency according to weather data at the current moment and weather data in a future preset time period, determining a second wake-up moment according to the acquisition and reporting frequency, and if the first wake-up moment is reached firstly, calculating the next acquisition and reporting frequency. If the second wake-up moment is reached firstly or the first wake-up moment and the second wake-up moment are reached at the same time, the sensing data are collected and reported, and the next collection and reporting frequency is calculated. The Internet of Things equipment can autonomously determine the collection and reporting frequency according to the weather condition, so that the manual burden is greatly reduced, and the working efficiency is improved. And the sensing data acquisition flexibility is improved.

Description

Technical Field

[0001] The present application relates to the technical field of Internet of Things devices, and in particular to a method and related device for adaptively adjusting the frequency of data collection and reporting of Internet of Things devices based on a weather API (Application Programming Interface). Background Art

[0002] With the development of IoT technology, more and more IoT devices are widely used in various industries for data collection and monitoring. Among them, IoT devices such as outdoor smart level meters and sewage pipe flow meters are mostly powered by lithium-ion batteries. The more frequent the collection and reporting frequency, the higher the power consumption of IoT devices. Obviously, the collection and reporting frequency of IoT devices seriously affects the use time of IoT devices. Under the same conditions, the power consumption of IoT devices can be reduced by reducing unnecessary high collection and reporting frequencies, thereby extending the use time of IoT devices.

[0003] In different regions and under different weather conditions, the collection and reporting frequency of IoT devices such as outdoor smart level meters and sewage pipe flow meters varies. For example, in clear weather conditions, IoT devices such as outdoor smart level meters and sewage pipe flow meters may collect and report sensor data once every 24 hours; in heavy rain or torrential rain weather conditions, IoT devices such as outdoor smart level meters and sewage pipe flow meters may report sensor data once every 10 minutes, or once every 5 minutes, or even collect and report sensor data at a faster frequency.

[0004] At present, the collection and reporting frequency of each IoT device can only be controlled manually on the system platform, which is very cumbersome and inflexible. If IoT devices such as outdoor smart level meters and sewage pipe flow meters can independently determine the collection and reporting frequency according to different weather conditions, it will be of great significance to improve the flexibility of sensor data collection and reduce the manual burden. Summary of the invention

[0005] The purpose of this application is to provide a method and related device for adaptively adjusting the collection and reporting frequency of an Internet of Things device. The Internet of Things device can independently determine the collection and reporting frequency according to weather conditions, greatly reducing the manual burden and improving the flexibility of sensor data collection.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for adaptively adjusting the frequency of data collection and reporting of an Internet of Things device, the method comprising:

[0008] When the IoT device is awakened, the weather data for the preset time period in the future is obtained;

[0009] Determine the first awakening time according to the current weather data and the weather data in the future preset time period;

[0010] Determine the collection and reporting frequency according to the current weather data and the weather data in the future preset time period, and determine the second wake-up time according to the collection and reporting frequency;

[0011] If the first wake-up time is reached first, the process returns to the step of "obtaining weather data for a preset time period in the future when the IoT device is awakened"; if the second wake-up time is reached first or both the first wake-up time and the second wake-up time are reached at the same time, the sensor data is collected and reported, and the process returns to the step of "obtaining weather data for a preset time period in the future when the IoT device is awakened".

[0012] Optionally, determining the first awakening time according to the weather data at the current moment and the weather data within a preset time period in the future specifically includes:

[0013] Determine the weather at the current moment based on the weather data at the current moment, and determine the weather within a preset time period in the future based on the weather data within a preset time period in the future;

[0014] If the weather in the future preset time period changes compared to the current time, the time when the weather first changes in the future preset time period is selected as the first wake-up time;

[0015] If the weather in the future preset time period does not change compared to the current time, the end time of the future preset time period is selected as the first wake-up time.

[0016] Optionally, the calculation formula for the collection and reporting frequency is:

[0017]

[0018] Among them, E f is the collection and reporting frequency; β is the cycle scaling coefficient; α1 is the weather coefficient of the current weather, which is determined based on the weather data at the current time; α2 is the weather coefficient of the weather in the future preset time period, which is determined based on the weather data at the current time and the weather data in the future preset time period; θ is the weather severity coefficient, which is determined based on the weather data at the current time and the weather data in the future preset time period.

[0019] Optionally, α1 is determined as follows:

[0020] The current weather is determined based on the current weather data. If the current weather is sunny, α1=1.0; if the current weather is cloudy, overcast or foggy, α1=0.5; if the current weather is raining, snowing or hail, α1=0.25.

[0021] Optionally, α2 is determined as follows:

[0022] The current weather is determined based on the weather data at the current moment, and the weather in the future preset time period is determined based on the weather data in the future preset time period. If the current weather is non-precipitation weather and the weather in the future preset time period has not changed compared to the current weather, then α2=1.0; if the current weather is non-precipitation weather and the weather in the future preset time period has changed compared to the current weather, when the current weather is sunny, but there is no precipitation weather in the future preset time period, and it changes to cloudy, overcast or foggy, then α2=0.5, when the current weather is cloudy , cloudy or foggy, but there is no precipitation weather in the preset time period in the future, then α2=1.0; when the current weather is sunny, but there is precipitation weather in the preset time period in the future, it first changes to cloudy, overcast or foggy, then α2=0.5; when the current weather is non-precipitation weather, but there is precipitation weather in the preset time period in the future, it first changes to rain or snow, then α2=0.25; if the current weather is precipitation weather, then α2=0.4; among which, non-precipitation weather is sunny, cloudy, overcast and foggy; precipitation weather is rain, snow and hail.

[0023] Optionally, θ is determined as follows:

[0024] The current weather is determined based on the current weather data, and the weather in the future preset time period is determined based on the weather data in the future preset time period. If the weather in the future preset time period is non-precipitation weather, θ=1.0; if the current weather is non-precipitation weather, but there is precipitation weather in the future preset time period, θ=2.0; if the current weather is light rain, and there is precipitation weather in the future preset time period, θ=4.0; if the current weather is moderate rain, and there is precipitation weather in the future preset time period, θ=8.0; if the current weather is heavy rain, and there is precipitation weather in the future preset time period, θ=16.0; if the current weather is heavy rain, and there is precipitation weather in the future preset time period, θ=24. 0; if the current weather is freezing rain, and there is precipitation in the future preset time period, θ=8.0; if the current weather is sleet, and there is precipitation in the future preset time period, θ=4.8; if the current weather is light snow, and there is precipitation in the future preset time period, θ=3.6; if the current weather is moderate snow, and there is precipitation in the future preset time period, θ=7.2; if the current weather is heavy to blizzard, and there is precipitation in the future preset time period, θ=14.4; if the current weather is thunderstorm, and there is precipitation in the future preset time period, θ=28.8; among them, non-precipitation weather is sunny, cloudy, overcast and foggy; precipitation weather is rain, snow and hail.

[0025] In a second aspect, the present application provides an Internet of Things device collection and reporting frequency adaptive adjustment system, the Internet of Things device collection and reporting frequency adaptive adjustment system includes: an Internet of Things device, the Internet of Things device includes: a processor, a sensor module, a communication module and a power management module, the processor is respectively connected to the sensor module and the communication module, the communication module is respectively connected to the weather API and the cloud server, and the power management module is respectively electrically connected to the processor, the sensor module and the communication module;

[0026] The power management module is used to wake up the processor, sensor module and communication module;

[0027] The sensor module is used to collect sensor data and send the sensor data to the processor;

[0028] The processor is used to execute any of the above-mentioned methods for adaptively adjusting the frequency of IoT device collection and reporting, obtain weather data within a future preset time period from the weather API through the communication module, and upload the sensor data to the cloud server through the communication module.

[0029] Optionally, the Internet of Things device also includes: a local storage module, the local storage module is communicatively connected to the processor, and the local storage module is used to store sensor data sent by the processor.

[0030] Optionally, the Internet of Things device also includes: a protective shell, and the processor, sensor module, communication module, power management module and local storage module are all located in the protective shell.

[0031] In a third aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for adaptively adjusting the collection and reporting frequency of an Internet of Things device.

[0032] According to the specific embodiments provided in this application, this application has the following technical effects:

[0033] The present application provides a method for adaptively adjusting the collection and reporting frequency of an Internet of Things device and a related device. When the Internet of Things device is awakened, the weather data within a future preset time period is obtained, and the first awakening time is determined according to the weather data at the current time and the weather data within the future preset time period. The collection and reporting frequency is determined according to the weather data at the current time and the weather data within the future preset time period, and the second awakening time is determined according to the collection and reporting frequency. If the first awakening time is reached first, the next collection and reporting frequency is calculated. If the second awakening time is reached first or the first awakening time and the second awakening time are reached at the same time, the sensor data is collected and reported, and the next collection and reporting frequency is calculated. The Internet of Things device of the present application can autonomously determine the collection and reporting frequency according to the weather conditions, which greatly reduces the manual burden and improves the flexibility of sensor data collection. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 A method flow chart of a method for adaptively adjusting the frequency of data collection and reporting of an Internet of Things device provided in Example 1 of the present application.

[0036] Figure 2 A schematic diagram of the workflow of a system for adaptively adjusting the frequency of data collection and reporting of an Internet of Things device provided in Example 2 of the present application.

[0037] Figure 3 A schematic diagram of the structure of a computer device provided in Example 3 of the present application. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0039] Example 1

[0040] This embodiment provides a method for adaptively adjusting the frequency of data collection and reporting of an Internet of Things device, such as Figure 1 As shown, the method for adaptively adjusting the collection and reporting frequency of the Internet of Things device includes:

[0041] S1: When the IoT device is awakened, the weather data within the future preset time period is obtained.

[0042] S2: Determine a first wake-up time according to the current weather data and the weather data in a future preset time period.

[0043] S3: Determine a collection and reporting frequency based on the current weather data and the weather data in a preset time period in the future, and determine a second wake-up time based on the collection and reporting frequency.

[0044] S4: If the first wake-up time is reached first, return to the step of "when the IoT device is awakened, obtain weather data within a preset time period in the future"; if the second wake-up time is reached first or the first wake-up time and the second wake-up time are reached at the same time, collect and report the sensor data, and return to the step of "when the IoT device is awakened, obtain weather data within a preset time period in the future".

[0045] In this embodiment, the IoT device is awakened in two cases: one is when the IoT device is started, and the other is when the IoT device is started but in a dormant state (also called a low power consumption state) and is awakened again. When the IoT device is awakened, weather data within a future preset time period is obtained. The future preset time period may be the next 24 hours, and the start time of the future preset time period is the next moment of the current moment.

[0046] After obtaining weather data within a future preset time period, this embodiment determines the first wake-up time according to the weather data at the current moment and the weather data within the future preset time period.

[0047] Determine the first wake-up time according to the current weather data and the weather data in the future preset time period, specifically including:

[0048] (1) The current weather is determined based on the current weather data, and the weather in a future preset time period is determined based on the weather data in the future preset time period.

[0049] The weather includes sunny, cloudy, overcast, fog, rain, snow and hail. Sunny, cloudy, overcast and fog belong to non-precipitation weather, while rain, snow and hail belong to precipitation weather. Rainfall includes light rain, moderate rain, heavy rain, torrential rain, freezing rain and thunderstorms. Snowfall includes sleet, light snow, moderate snow and heavy to heavy snow.

[0050] (2) If the weather changes in the future preset time period compared to the current time, the time when the weather changes for the first time in the future preset time period is selected as the first wake-up time.

[0051] (3) If the weather in the future preset time period does not change compared to the current time, the end time of the future preset time period is selected as the first wake-up time.

[0052] After obtaining weather data within a future preset time period, this embodiment determines a collection and reporting frequency based on the weather data at the current moment and the weather data within the future preset time period, and determines a second wake-up time based on the collection and reporting frequency.

[0053] This embodiment designs a weather API-based IoT device collection and reporting frequency self-adjustment model (referred to as the weather API model), which is used to adjust the collection and reporting frequency of IoT devices. The calculation formula for the collection and reporting frequency is:

[0054]

[0055] Among them, E f is the collection and reporting frequency, which is the expected value of the collection and reporting frequency, in hours; β is the period scaling factor, which is the period scaling factor controlled by the platform and is 1.0 by default; α1 is the weather coefficient of the current weather, which is determined based on the weather data at the current time; α2 is the weather coefficient of the weather in the future preset time period, which is determined based on the weather data at the current time and the weather data in the future preset time period; θ is the weather severity coefficient, which is determined based on the weather data at the current time and the weather data in the future preset time period.

[0056] This embodiment determines different values ​​of weather coefficients and weather severity coefficients based on different weather conditions, as follows:

[0057] (1) α1 is determined as follows:

[0058] The current weather is determined based on the current weather data. If the current weather is sunny, α1=1.0; if the current weather is cloudy, overcast or foggy, α1=0.5; if the current weather is raining, snowing or hail, α1=0.25.

[0059] (2) α2 is determined as follows:

[0060] The weather at the current moment is determined based on the weather data at the current moment, and the weather in the future preset time period is determined based on the weather data in the future preset time period. If the weather at the current moment is non-precipitation weather and the weather in the future preset time period has not changed compared with the weather at the current moment, then α2=1.0; if the weather at the current moment is non-precipitation weather and the weather in the future preset time period has changed compared with the weather at the current moment, when the weather at the current moment is sunny, but there is no precipitation weather in the future preset time period, it changes to cloudy, overcast or foggy, then α2=0.5, when the weather at the current moment is cloudy, overcast or foggy, but there is no precipitation weather in the future preset time period, then α2=1.0, when the weather at the current moment is sunny, but there is precipitation weather in the future preset time period, it first changes to cloudy, overcast or foggy, then α2=0.5, when the weather at the current moment is non-precipitation weather, but there is precipitation weather in the future preset time period, it first changes to rain or snow, then α2=0.25; if the weather at the current moment is precipitation weather, then α2=0.4. Among them, non-precipitation weather includes sunny, cloudy, overcast and foggy days, and precipitation weather includes rain, snow and hail.

[0061] (3)θ is determined as follows:

[0062] The current weather is determined based on the weather data at the current moment, and the weather in the future preset time period is determined based on the weather data in the future preset time period. If the weather in the future preset time period is non-precipitation weather, that is, there will be no precipitation, then θ=1.0; if the current weather is non-precipitation weather, but there is precipitation weather in the future preset time period, then θ=2.0; if the current weather is light rain, and there is precipitation weather in the future preset time period, then θ=4.0; if the current weather is moderate rain, and there is precipitation weather in the future preset time period, then θ=8.0; if the current weather is heavy rain, and there is precipitation weather in the future preset time period, then θ=16.0; if the current weather is torrential rain, and there is precipitation weather in the future preset time period, then θ= 24.0; if the current weather is freezing rain, and there is precipitation in the future preset time period, θ=8.0; if the current weather is sleet, and there is precipitation in the future preset time period, θ=4.8; if the current weather is light snow, and there is precipitation in the future preset time period, θ=3.6; if the current weather is moderate snow, and there is precipitation in the future preset time period, θ=7.2; if the current weather is heavy to blizzard, and there is precipitation in the future preset time period, θ=14.4; if the current weather is thunderstorm, and there is precipitation in the future preset time period, θ=28.8; among them, non-precipitation weather is sunny, cloudy, overcast and foggy, and precipitation weather is rain, snow and hail.

[0063] The following example takes the future preset time period of the next 24 hours as an example to provide examples of calculating the collection and reporting frequency under various weather conditions:

[0064] Case 1: Sunny weather for 24 hours.

[0065]

[0066] Set the collection and reporting frequency of IoT devices to 24 hours / time, then perform other tasks such as collecting and reporting sensor data, and finally sleep.

[0067] Case 2: It is not sunny all the time within 24 hours, and there is no rain, snow, hail and other weather conditions. However, it is sunny now and turns into cloudy, overcast or foggy after a few hours.

[0068]

[0069] Set the hour when the weather changes for the first time as the next wake-up time of the device (i.e., the first wake-up time), and set the collection and reporting frequency to 12 hours / time, then perform other tasks such as collection and reporting of sensor data, and finally sleep.

[0070] Case 3: It is not sunny all the time within 24 hours. There is no rain, snow, hail and other weather conditions. It is currently cloudy, overcast or foggy.

[0071]

[0072] Set the collection and reporting frequency to 12 hours / time, then perform other tasks such as collecting and reporting sensor data, and finally sleep.

[0073] Case 4: It is not sunny all the time within 24 hours. There may be rain, snow, hail and other weather conditions. It is sunny now, but it will turn into cloudy, overcast or foggy after a few hours.

[0074]

[0075] Set the hour when the weather changes for the first time as the next wake-up time for the device, and set the collection and reporting frequency to 6 hours / time, then perform other tasks such as collecting and reporting sensor data, and finally sleep.

[0076] Case 5: It is not sunny all the time within 24 hours. There may be rain, snow, hail and other weather conditions. It is currently sunny, but it will turn into rain or snow after a few hours.

[0077]

[0078] Set the hour when the weather changes for the first time as the next wake-up time for the device, and set the collection and reporting frequency to 3 hours / time, then perform other tasks such as collecting and reporting sensor data, and finally sleep.

[0079] Case 6: It is not sunny all the time within 24 hours. There will be rain, snow, hail and other weather conditions. It is currently cloudy, overcast or foggy, and will turn into rain or snow after a few hours.

[0080]

[0081] Set the hour when the weather changes for the first time as the next wake-up time for the device, and set the collection and reporting frequency to 1.5 hours / time, then perform other tasks such as collecting and reporting sensor data, and finally sleep.

[0082] Situation 7: It will not be sunny all the time within 24 hours. There will be rain, snow, hail and other weather conditions. Currently it is light rain.

[0083]

[0084] Set the collection and reporting frequency to 0.6 hours / time, that is, 36 minutes / time, and then perform other tasks such as collecting and reporting sensor data, and finally sleep.

[0085] Situation 8: It will not be sunny all the time within 24 hours. There will be rain, snow, hail and other weather conditions. Currently it is moderate rain.

[0086]

[0087] Set the collection and reporting frequency to 0.3 hours / time, that is, 18 minutes / time, and then perform other tasks such as collecting and reporting sensor data, and finally sleep.

[0088] Situation 9: It will not be sunny all the time within 24 hours. There will be rain, snow, hail and other weather conditions. Currently it is heavy rain.

[0089]

[0090] Set the collection and reporting frequency to 0.15 hours / time, that is, 9 minutes / time, and then perform other tasks such as collecting and reporting sensor data, and finally sleep.

[0091] Case 10: It is not sunny all the time within 24 hours. There will be rain, snow, hail and other weather conditions. Currently it is heavy rain.

[0092]

[0093] Set the collection and reporting frequency to 0.1 hours / time, that is, 6 minutes / time, and then perform other tasks such as collecting and reporting sensor data, and finally sleep.

[0094] Situation 11: It will not be sunny all the time within 24 hours. There will be rain, snow, hail and other weather conditions. Currently it is freezing rain.

[0095]

[0096] Set the collection and reporting frequency to 0.3 hours / time, that is, 18 minutes / time, and then perform other tasks such as collecting and reporting sensor data, and finally sleep.

[0097] Situation 12: It will not be sunny all the time within 24 hours. There will be rain, snow, hail and other weather conditions. Currently it is sleet.

[0098]

[0099] Set the collection and reporting frequency to 0.5 hours / time, that is, 30 minutes / time, and then perform other tasks such as collecting and reporting sensor data, and finally sleep.

[0100] Situation 13: It will not be sunny all the time within 24 hours. There will be rain, snow, hail and other weather conditions. Currently it is light snow.

[0101]

[0102] Set the collection and reporting frequency to 0.67 hours / time, that is, 40 minutes / time, and then perform other tasks such as collecting and reporting sensor data, and finally sleep.

[0103] Situation 14: It will not be sunny all the time within 24 hours. There will be rain, snow, hail and other weather conditions. Currently it is moderate snow.

[0104]

[0105] Set the collection and reporting frequency to 0.33 hours / time, that is, 20 minutes / time, and then perform other tasks such as collecting and reporting sensor data, and finally sleep.

[0106] Situation 15: It will not be sunny all the time within 24 hours. There will be rain, snow, hail and other weather conditions. Currently it is heavy to severe snow.

[0107]

[0108] Set the collection and reporting frequency to 0.17 hours / time, that is, 10 minutes / time, and then perform other tasks such as collecting and reporting sensor data, and finally sleep.

[0109] Situation 16: It will not be sunny all the time within 24 hours. There will be rain, snow, hail and other weather conditions. Currently there is a thunderstorm.

[0110]

[0111] Set the collection and reporting frequency to 0.08 hours / time, that is, 5 minutes / time, and then perform other tasks such as collecting and reporting sensor data, and finally sleep.

[0112] In this embodiment, for the case where the collection and reporting frequency is within 30 minutes, sensor data is reported once for every two sensor data are collected (in this case, the two sensor data are reported together); for the case where the collection and reporting frequency is greater than 30 minutes, sensor data is reported once for every one sensor data is collected.

[0113] After determining the collection and reporting frequency, this embodiment further determines the second wake-up time according to the collection and reporting frequency, and specifically takes the sum of the current time and the time corresponding to the collection and reporting frequency as the second wake-up time. For example, if the current time is 2 o'clock, the collection and reporting frequency is 1 hour / time, that is, the time corresponding to the collection and reporting frequency is 1 hour, then the second wake-up time is 3 o'clock.

[0114] After determining the first wake-up time and the second wake-up time, if the first wake-up time is reached first, the sensor data will not be collected and reported, only the collection and reporting frequency will be calculated again to determine the next first wake-up time and the second wake-up time. At this time, directly return to the step of "when the Internet of Things device is awakened, obtain weather data within the future preset time period"; if the second wake-up time is reached first or the first wake-up time and the second wake-up time are reached at the same time, not only the sensor data will be collected and reported, but the collection and reporting frequency will be calculated again to determine the next first wake-up time and the second wake-up time. At this time, the sensor data will be collected and reported, and the step of "when the Internet of Things device is awakened, obtain weather data within the future preset time period" will be returned.

[0115] This embodiment can dynamically adjust the collection and reporting frequency of IoT devices according to weather conditions, reduce manual burden, and improve the flexibility of sensor data collection.

[0116] Example 2

[0117] This embodiment is used to propose an adaptive adjustment system for the collection and reporting frequency of IoT devices based on weather API, which can obtain real-time weather conditions and future weather conditions through weather API, and calculate the collection and reporting frequency of IoT devices according to the real-time weather conditions and future weather conditions. Specifically, when the IoT device is started and connected to the wireless network (which can be a 4G wireless network), it will access the weather API through the communication module to obtain the weather data at the current moment and the weather data within the future preset time period. After obtaining the weather data from the weather API, the weather data is parsed, and the collection and reporting frequency is set according to the parsed weather conditions, and the second wake-up time is determined. At the same time, if there is a weather change within the preset time period in the future, the next weather change time is set as the first wake-up time, and the IoT device is woken up after the first wake-up time or the second wake-up time. After the next wake-up, the collection and reporting frequency is reset according to the real-time weather and future weather at the wake-up time.

[0118] To ensure that IoT devices can access the weather API, adjust the collection and reporting frequency according to the weather API data, and complete the collection and upload of sensor data, the hardware requirements of IoT devices include but are not limited to:

[0119] (1) Processor (MCU / microcontroller), which executes data processing and acquisition and reporting frequency adjustment logic, controls the adjustment of the acquisition frequency of the sensor module, obtains weather API data through cooperation with the communication module, and reports the sensor data collected by the sensor module to the cloud server.

[0120] (2) Communication module, ensuring that it can connect to the Internet, is used to communicate with the weather API and the cloud server, receive weather data from the weather API, and report the sensor data collected by the sensor module to the cloud server.

[0121] (3) Power management module, which ensures that IoT devices can run for a long time in low-power mode, especially when the collection and reporting frequency is reduced. It supports an automatic wake-up mechanism to automatically wake up IoT devices at a predetermined time or according to changes in weather API information.

[0122] (4) A local storage module is used to temporarily store the sensor data of the sensor module to ensure that the sensor data will not be lost when the network is unstable or the upload interval is long.

[0123] (5) The protective casing needs to have a certain level of waterproof and dustproof to ensure that the IoT device can work normally under various weather conditions.

[0124] Specifically, the present embodiment provides a system for adaptively adjusting the frequency of data collection and reporting of an Internet of Things device, the system comprising: an Internet of Things device, the Internet of Things device comprising: a processor, a sensor module, a communication module and a power management module, the processor is respectively communicated with the sensor module and the communication module, the communication module is respectively communicated with the weather API and the cloud server, and the power management module is respectively electrically connected to the processor, the sensor module and the communication module.

[0125] The power management module is used to wake up the processor, sensor module and communication module.

[0126] The sensor module is used to collect sensor data and send the sensor data to the processor. The sensor data refers to the data obtained by the sensor module through actual measurement.

[0127] The processor is used to execute the method for adaptively adjusting the collection and reporting frequency of the Internet of Things device described in Example 1, obtain weather data within a future preset time period from the weather API through the communication module, and upload the sensor data to the cloud server through the communication module.

[0128] In this embodiment, the Internet of Things device also includes: a local storage module, the local storage module is communicatively connected to the processor, and the local storage module is used to store the sensor data sent by the processor.

[0129] In this embodiment, the Internet of Things device also includes: a protective shell, and the processor, sensor module, communication module, power management module and local storage module are all located in the protective shell.

[0130] like Figure 2 As shown, the working process of the above system is: when the Internet of Things device is started or the Internet of Things device reaches the first wake-up time or the second wake-up time, if it is started or reaches the first wake-up time, the processor and the communication module are woken up first, and the processor communicates with the weather API through the communication module to obtain the weather data at the current time and the weather data in the future preset time period, determine the first wake-up time, the collection and reporting frequency, and the second wake-up time, enter the sleep state, and wait for the next wake-up; if it reaches the second wake-up time, the processor, the communication module and the sensor module are woken up first, the sensor module collects the sensor data, and sends the sensor data to the processor, the processor communicates with the cloud server through the communication module, uploads the sensor data to the cloud server, and can be synchronously stored in the local storage module, the processor communicates with the weather API through the communication module, obtains the weather data at the current time and the weather data in the future preset time period, determines the first wake-up time, the collection and reporting frequency, and the second wake-up time, enters the sleep state, and waits for the next wake-up.

[0131] Taking the outdoor radar level meter as an example, after the device is powered on or awakened from low-power mode, it connects to the 4G wireless network, then connects to the MQTT server (which is a cloud server) and obtains the device's expected values ​​(such as liquid level reference value, liquid level threshold, etc.). Then access the weather API to obtain the current weather data and weather data in the future preset time period, and calculate the collection and reporting frequency of the outdoor radar level meter according to the weather API model. Then the outdoor radar level meter powers the sensor module and processor, configures the sensor module parameters, starts the radar real-time ranging, reads the radar measurement value after the ranging is completed, calculates the actual liquid level after the verification is completed, and sends the liquid level information to the cloud server. Finally, turn off the power of the sensor module and processor to enter low-power mode.

[0132] The IoT device of this embodiment can dynamically adjust the collection and reporting frequency to save resources. Through the weather API model, the IoT device can dynamically adjust the collection and reporting frequency according to real-time weather conditions. For example, when encountering bad weather (such as heavy rain or extreme temperature), the IoT device can increase the collection and reporting frequency to provide more sensor data to support decision-making. When the weather is stable, the IoT device can reduce the collection and reporting frequency to reduce power and data transmission consumption, reduce hardware loss, and thus extend the service life of the IoT device.

[0133] This embodiment can enhance the intelligence of IoT devices and reduce human intervention and management costs. IoT devices usually work according to a fixed collection and reporting frequency. Without the introduction of the weather API model, the collection and reporting frequency cannot be dynamically adjusted, and it is difficult to adapt to the dynamic changes in the environment. Through the weather API model, IoT devices can respond to changes in the external environment more intelligently. This adaptive mechanism can not only reduce human intervention, but also make IoT devices more efficient and reduce operating and management costs, especially in scenarios where large-scale IoT devices are deployed. For example, on days when there is no rainfall, water level monitoring equipment can maintain a lower collection and reporting frequency to reduce redundant data. When the risk of heavy rain or flooding increases, the water level monitoring equipment can automatically increase the collection and reporting frequency to capture rapidly changing water level data and help users respond to emergencies in a timely manner.

[0134] This embodiment can reduce unnecessary collection of IoT devices and reduce the loss of power and traffic of IoT devices. Without a weather API model, IoT devices may collect sensor data at a fixed frequency regardless of how the weather changes, which may waste resources or cause unnecessary data transmission. The weather API model can help IoT devices reduce the frequency when frequent collection is not needed, thereby reducing the use of bandwidth and cloud storage. By reducing unnecessary frequent collection through the weather API model, the operating time of IoT devices is reduced, and the loss of hardware is reduced accordingly, thereby reducing the maintenance frequency of IoT devices. This is especially important for IoT devices distributed in remote or hard-to-reach areas, reducing the need and cost of on-site maintenance.

[0135] This embodiment can achieve preventive maintenance and early warning, and quickly provide data support for emergency management personnel. Through the weather API model, IoT devices can timely adjust the collection and reporting frequency and report sensor data in advance when severe weather is approaching, helping users to perform preventive maintenance or take necessary early warning measures. For example, when a rainstorm is about to come, water level monitoring equipment can increase the collection and reporting frequency to timely detect flood signs, help relevant departments to take precautions in advance, help to ensure public safety and reduce losses caused by disasters, and timely acquisition of sensor data can provide support for emergency management personnel and help to respond quickly.

[0136] After applying the weather API-based IoT device collection and reporting frequency self-adjustment model provided in this embodiment, the equipment adjustment efficiency of maintenance personnel is greatly improved, and the problem of insensitive collection of IoT devices such as outdoor smart liquid level meters and sewage pipe flow meters in severe weather and the high power consumption of IoT devices in areas with changeable weather are effectively reduced.

[0137] Example 3

[0138] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for adaptively adjusting the collection and reporting frequency of an Internet of Things device is implemented.

[0139] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0140] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the method for adaptively adjusting the collection and reporting frequency of the Internet of Things device in Example 1 is implemented.

[0141] Example 4

[0142] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which, when executed by a processor, implements the method for adaptively adjusting the collection and reporting frequency of the Internet of Things device in Example 1.

[0143] Example 5

[0144] In an exemplary embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the method for adaptively adjusting the collection and reporting frequency of an Internet of Things device in Embodiment 1.

[0145] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0146] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0147] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for adaptively adjusting the collection and reporting frequency of an Internet of Things device, characterized in that: The method for adaptively adjusting the frequency of data collection and reporting of the Internet of Things device includes: When the IoT device is awakened, the weather data for the preset time period in the future is obtained; Determine the first awakening time according to the current weather data and the weather data in the future preset time period; Determine the collection and reporting frequency according to the current weather data and the weather data in the future preset time period, and determine the second wake-up time according to the collection and reporting frequency; If the first wake-up time is reached first, the process returns to the step of "obtaining weather data for a preset time period in the future when the IoT device is awakened"; if the second wake-up time is reached first or both the first wake-up time and the second wake-up time are reached at the same time, the sensor data is collected and reported, and the process returns to the step of "obtaining weather data for a preset time period in the future when the IoT device is awakened".

2. The method for adaptively adjusting the collection and reporting frequency of Internet of Things devices according to claim 1 is characterized in that: Determine the first wake-up time according to the current weather data and the weather data in the future preset time period, specifically including: Determine the weather at the current moment based on the weather data at the current moment, and determine the weather within a preset time period in the future based on the weather data within a preset time period in the future; If the weather in the future preset time period changes compared to the current time, the time when the weather first changes in the future preset time period is selected as the first wake-up time; If the weather in the future preset time period does not change compared to the current time, the end time of the future preset time period is selected as the first wake-up time.

3. The method for adaptively adjusting the collection and reporting frequency of Internet of Things devices according to claim 1 is characterized in that: The calculation formula for the collection and reporting frequency is: Among them, E f is the collection and reporting frequency; β is the cycle scaling coefficient; α1 is the weather coefficient of the current weather, which is determined based on the weather data at the current time; α2 is the weather coefficient of the weather in the future preset time period, which is determined based on the weather data at the current time and the weather data in the future preset time period; θ is the weather severity coefficient, which is determined based on the weather data at the current time and the weather data in the future preset time period.

4. The method for adaptively adjusting the collection and reporting frequency of Internet of Things devices according to claim 3 is characterized in that: α1 is determined as follows: The current weather is determined based on the current weather data. If the current weather is sunny, α1=1.0; if the current weather is cloudy, overcast or foggy, α1=0.5; if the current weather is raining, snowing or hail, α1=0.

25.

5. The method for adaptively adjusting the collection and reporting frequency of Internet of Things devices according to claim 3 is characterized in that: α2 is determined as follows: The current weather is determined based on the weather data at the current moment, and the weather in the future preset time period is determined based on the weather data in the future preset time period. If the current weather is non-precipitation weather and the weather in the future preset time period has not changed compared to the current weather, then α2=1.0; if the current weather is non-precipitation weather and the weather in the future preset time period has changed compared to the current weather, when the current weather is sunny, but there is no precipitation weather in the future preset time period, and it changes to cloudy, overcast or foggy, then α2=0.5, when the current weather is cloudy , cloudy or foggy, but there is no precipitation weather in the preset time period in the future, then α2=1.0; when the current weather is sunny, but there is precipitation weather in the preset time period in the future, it first changes to cloudy, overcast or foggy, then α2=0.5; when the current weather is non-precipitation weather, but there is precipitation weather in the preset time period in the future, it first changes to rain or snow, then α2=0.25; if the current weather is precipitation weather, then α2=0.4; among which, non-precipitation weather is sunny, cloudy, overcast and foggy; precipitation weather is rain, snow and hail.

6. The method for adaptively adjusting the collection and reporting frequency of Internet of Things devices according to claim 3 is characterized in that: θ is determined as follows: The current weather is determined based on the current weather data, and the weather in the future preset time period is determined based on the weather data in the future preset time period. If the weather in the future preset time period is non-precipitation weather, θ=1.0; if the current weather is non-precipitation weather, but there is precipitation weather in the future preset time period, θ=2.0; if the current weather is light rain, and there is precipitation weather in the future preset time period, θ=4.0; if the current weather is moderate rain, and there is precipitation weather in the future preset time period, θ=8.0; if the current weather is heavy rain, and there is precipitation weather in the future preset time period, θ=16.0; if the current weather is heavy rain, and there is precipitation weather in the future preset time period, θ=24. 0; if the current weather is freezing rain, and there is precipitation in the future preset time period, θ=8.0; if the current weather is sleet, and there is precipitation in the future preset time period, θ=4.8; if the current weather is light snow, and there is precipitation in the future preset time period, θ=3.6; if the current weather is moderate snow, and there is precipitation in the future preset time period, θ=7.2; if the current weather is heavy to blizzard, and there is precipitation in the future preset time period, θ=14.4; if the current weather is thunderstorm, and there is precipitation in the future preset time period, θ=28.8; among them, non-precipitation weather is sunny, cloudy, overcast and foggy; precipitation weather is rain, snow and hail.

7. An Internet of Things device collection and reporting frequency adaptive adjustment system, characterized in that: The IoT device collection and reporting frequency adaptive adjustment system comprises: an IoT device, the IoT device comprises: a processor, a sensor module, a communication module and a power management module, the processor is respectively connected to the sensor module and the communication module, the communication module is respectively connected to the weather API and the cloud server, and the power management module is respectively electrically connected to the processor, the sensor module and the communication module; The power management module is used to wake up the processor, sensor module and communication module; The sensor module is used to collect sensor data and send the sensor data to the processor; The processor is used to execute the method for adaptively adjusting the collection and reporting frequency of the Internet of Things device according to any one of claims 1-6, obtain weather data within a future preset time period from the weather API through the communication module, and upload the sensor data to the cloud server through the communication module.

8. The system for adaptively adjusting the frequency of data collection and reporting of Internet of Things devices according to claim 7 is characterized in that: The Internet of Things device also includes: a local storage module, the local storage module is communicatively connected to the processor, and the local storage module is used to store the sensor data sent by the processor.

9. The Internet of Things device collection and reporting frequency adaptive adjustment system according to claim 8 is characterized in that: The IoT device also includes a protective shell, in which a processor, a sensor module, a communication module, a power management module and a local storage module are all located.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for adaptively adjusting the collection and reporting frequency of an Internet of Things device according to any one of claims 1 to 6 is implemented.

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