Iot device remote control method, system and medium

Abstract

The application provides a kind of Internet of Things equipment remote control method, system and medium, belong to Internet of Things technical field, specifically includes: the data monitoring of power equipment is used as monitoring device for Internet of Things equipment, when the data reliability of basic monitoring equipment does not meet the requirement, according to the type and the number of monitoring points of the monitoring data of other monitoring equipment, the determination of the monitoring timeliness of the other monitoring equipment, the determination of the monitoring opening period and the monitoring time of the monitoring opening period of different other monitoring equipment is carried out through the data reliability of basic monitoring equipment and the monitoring timeliness and data volume of other monitoring equipment, and other monitoring equipment is controlled through monitoring opening period and monitoring time, so as to further improve the reliability of the operation of power equipment Internet of Things equipment.

Description

Technical Field

[0001] This invention belongs to the field of Internet of Things (IoT) technology, and particularly relates to a method, system and medium for remote control of IoT devices. Background Technology

[0002] To achieve real-time monitoring of power equipment, existing technical solutions often use IoT devices to collect and upload real-time operating data. Specifically, transformers are typically equipped with various types of IoT devices for monitoring temperature, current, and gas fault signals. How to achieve remote control of these IoT devices has become an urgent technical problem to be solved.

[0003] To address the aforementioned technical issues, the invention patent "A Remote Control Method, System, and Related Equipment for IoT Devices" allocates computing resources to each piece of data to be processed based on its volume and complexity, obtains and outputs fault analysis results for each IoT device based on each piece of data, thereby achieving control over each IoT device. However, the following technical problems remain: For the same power equipment, there are often multiple IoT devices used to monitor the operating status of the power equipment. At the same time, the data of different IoT devices are often correlated. For transformers, when the operating voltage and operating current do not change, it is unnecessary to monitor the operating temperature, gas fault signals and oil chromatography online. Therefore, if the monitoring results of some IoT devices cannot be combined to monitor and control the remaining IoT devices, it will not only lead to excessive data processing and storage pressure on the server, but also make it impossible to guarantee the operating reliability of the remaining IoT devices.

[0004] For transformers, the reliability of IoT devices that monitor operating voltage and current varies to some extent. At the same time, the abnormality of monitoring data from the remaining IoT devices also has a significant impact on the operational safety of transformers. Therefore, if the activation cycle and monitoring duration of the monitoring area of ​​the remaining IoT devices are not controlled in combination with the above factors, the operational safety and reliability of power equipment cannot be guaranteed.

[0005] To address the aforementioned technical problems, this invention provides a method, system, and medium for remote control of Internet of Things (IoT) devices. Summary of the Invention

[0006] To achieve the objectives of this invention, the following technical solution is adopted: According to one aspect of the present invention, a method for remote control of an Internet of Things (IoT) device is provided.

[0007] A method for remotely controlling an Internet of Things (IoT) device, characterized in that it specifically includes: S1 uses IoT devices for data monitoring of power equipment as monitoring devices, and determines the data parsing difficulty based on the type and quantity of monitoring data from the monitoring devices. When the data parsing difficulty is greater than a preset difficulty value, proceed to the next step. S2 proceeds to the next step when it is determined, based on the monitoring data of the power equipment, that there are no abnormalities in the operating voltage and operating current of the power equipment. S3 takes the monitoring equipment used to monitor the operating voltage and operating current as the basic monitoring equipment, and determines the data reliability of the basic monitoring equipment based on real-time monitoring data and historical monitoring data. The monitoring equipment excluding the basic monitoring equipment is taken as other monitoring equipment. When the data reliability of the basic monitoring equipment does not meet the requirements, the monitoring timeliness of the other monitoring equipment is determined based on the type of monitoring data and the number of monitoring points of the other monitoring equipment, and then proceeds to the next step. S4 obtains the operating load of the parsing server corresponding to the monitoring device, and determines the monitoring start cycle and monitoring time of different other monitoring devices by combining the data reliability of the basic monitoring device and the monitoring timeliness and data volume of other monitoring devices, and controls other monitoring devices by the monitoring start cycle and monitoring time.

[0008] The beneficial effects of this invention are as follows: By first determining the difficulty of data parsing for power equipment, the identification of power equipment with higher data parsing difficulty was achieved. This ensured both the real-time and comprehensive parsing of monitoring data for power equipment with lower data parsing difficulty, and the real-time parsing of monitoring data for power equipment with higher data parsing difficulty.

[0009] By assessing the reliability of data from basic monitoring equipment, the technical problem of low fault analysis efficiency caused by low data reliability of basic monitoring equipment is avoided, thereby improving the reliability of monitoring data and enhancing the operational stability of power equipment.

[0010] By controlling the monitoring cycle and monitoring time of other monitoring devices, the timeliness of data analysis of other monitoring devices is ensured, while avoiding the problem of high power consumption and low operational reliability caused by frequent activation.

[0011] A further technical solution is that the monitoring equipment includes, but is not limited to, Internet of Things (IoT) devices used for monitoring the operating temperature, operating current, operating voltage, and fault data of the power equipment.

[0012] A further technical solution is that when either the operating voltage or the operating current of the power equipment is abnormal, there is no need to control the monitoring cycle and the monitoring time of the monitoring equipment, and the monitoring data of the monitoring equipment is analyzed in real time by the analysis server.

[0013] A further technical solution is that the number of abnormal changes in the monitoring data is determined based on the amount of change in the monitoring data of the basic monitoring equipment within a unit time. Specifically, when the amount of change in the monitoring data of the basic monitoring equipment within a unit time is greater than a preset reasonable range, it is determined that the monitoring data has undergone abnormal changes.

[0014] On the other hand, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, it executes the above-described method for remote control of an Internet of Things device.

[0015] On the other hand, this application provides a computer-readable storage medium storing a computer program thereon, which, when executed in a computer, causes the computer to perform the aforementioned method for remote control of an Internet of Things device.

[0016] Other features and advantages will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0019] Figure 1 This is a flowchart of a method for remotely controlling Internet of Things (IoT) devices. Figure 2 This is a flowchart illustrating the method for determining the data reliability of basic monitoring equipment; Figure 3 It is a framework diagram of a computer system. Detailed Implementation

[0020] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that the invention will be thorough and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0021] The terms “a,” “one,” “the,” and “the” are used to indicate the existence of one or more elements / components / etc.; the terms “including” and “having” are used to indicate an open-ended meaning of inclusion and that other elements / components / etc. may exist in addition to the listed elements / components / etc.

[0022] The applicant discovered that, in order to ensure real-time monitoring of the operating status of power equipment, many power equipment are equipped with a large number of IoT devices. Since the monitoring data of different IoT devices have a certain correlation, when the operating voltage and operating current of the power equipment do not show obvious abnormalities, the operating temperature generally remains normal as well. At the same time, for IoT devices used for operating voltage and operating current, there may be problems with insufficient reliability of the monitoring data. Therefore, for IoT devices with insufficient reliability for operating voltage and operating current, it is necessary to dynamically implement differentiated control of the activation cycle of the remaining IoT devices to ensure the reliability of power equipment data monitoring.

[0023] Example 1 To solve the above problems, according to one aspect of the present invention, such as Figure 1 As shown, according to one aspect of the present invention, a method for remotely controlling an Internet of Things (IoT) device is provided, characterized in that it specifically includes: S1 uses IoT devices for data monitoring of power equipment as monitoring devices, and determines the data parsing difficulty based on the type and quantity of monitoring data from the monitoring devices. When the data parsing difficulty is greater than a preset difficulty value, proceed to the next step. It should be noted that the monitoring equipment mentioned in step S1 above includes, but is not limited to, Internet of Things (IoT) devices used for monitoring the operating temperature, operating current, operating voltage, and fault data of the power equipment.

[0024] Specifically, the method for determining the data parsing difficulty in step S1 above is as follows: S11 Obtain the number of monitoring devices for the power equipment and determine whether the number of monitoring devices is greater than a preset number. If yes, it is determined that the data parsing difficulty of the monitoring devices for the power equipment does not meet the requirements. If no, proceed to the next step. S12 obtains the type of monitoring data of the power equipment monitoring device, and determines the type monitoring complexity of the power equipment monitoring device by the type of monitoring data and the number of monitoring data types of the monitoring device, and judges whether the type monitoring complexity meets the requirements. If yes, proceed to the next step; otherwise, proceed to step S14. S13 determines the number of monitoring devices and monitoring points corresponding to different types of monitoring data based on the type of monitoring data of the power equipment monitoring devices. It also determines the parsing complexity of different types of monitoring data and the number of complex monitoring data types based on the number of monitoring devices and monitoring points corresponding to different types of monitoring data. Finally, it determines the comprehensive parsing complexity of the power equipment monitoring devices by combining the number of monitoring devices and monitoring points for the complex monitoring data types of the power equipment. It then determines whether the comprehensive parsing complexity meets the requirements. If yes, it is determined that the data parsing difficulty of the power equipment monitoring devices does not meet the requirements. If no, it proceeds to the next step. S14 determines the difficulty of data parsing by considering the overall parsing complexity of the power equipment monitoring devices, the type of monitoring complexity, and the number of monitoring devices.

[0025] In another possible embodiment, before determining the monitoring complexity of the power equipment type in step S12, it is necessary to further determine the number of power equipment monitoring devices. When the number of power equipment monitoring devices is less than the minimum number limit, it is not necessary to determine the monitoring complexity of the power equipment type, and it can be directly determined that the data parsing difficulty of the power equipment monitoring devices meets the requirements.

[0026] Specifically, when the data parsing difficulty meets the requirements or the data parsing difficulty is not greater than the preset difficulty value, there is no need to control the monitoring start cycle and the monitoring time of the monitoring start cycle of the power equipment, and the monitoring data of the monitoring equipment is parsed in real time through the parsing server.

[0027] In this embodiment, by first determining the data parsing difficulty of the power equipment, the identification of power equipment with higher data parsing difficulty is achieved. This ensures both the real-time and comprehensive parsing of monitoring data for power equipment with lower data parsing difficulty, and the real-time parsing of monitoring data for power equipment with higher data parsing difficulty.

[0028] S2 proceeds to the next step when it is determined, based on the monitoring data of the power equipment, that there are no abnormalities in the operating voltage and operating current of the power equipment. Furthermore, when either the operating voltage or the operating current of the power equipment is abnormal, there is no need to control the monitoring cycle and the monitoring time of the monitoring equipment, and the monitoring data of the monitoring equipment can be analyzed in real time by the analysis server.

[0029] S3 takes the monitoring equipment used to monitor the operating voltage and operating current as the basic monitoring equipment, and determines the data reliability of the basic monitoring equipment based on real-time monitoring data and historical monitoring data. The monitoring equipment excluding the basic monitoring equipment is taken as other monitoring equipment. When the data reliability of the basic monitoring equipment does not meet the requirements, the monitoring timeliness of the other monitoring equipment is determined based on the type of monitoring data and the number of monitoring points of the other monitoring equipment, and then proceeds to the next step. In one possible embodiment, such as Figure 2 As shown, the method for determining the data reliability of the basic monitoring equipment in step S3 above is as follows: The real-time monitoring data of the basic monitoring equipment is obtained, and the reliability of the real-time monitoring data of the basic monitoring equipment is determined by the real-time monitoring data of the basic monitoring equipment and the preset range of the monitoring data. If it is reliable, the process proceeds to the next step; otherwise, the reliability of the data of the basic monitoring equipment does not meet the requirements. Generally, the real-time monitoring data of basic monitoring equipment will remain within a reasonable preset range. When the real-time monitoring data of basic monitoring equipment changes significantly, it is determined that the data reliability of basic monitoring equipment does not meet the requirements.

[0030] The monitoring data of the basic monitoring equipment is obtained within the most recent preset time period. The reliability of the monitoring data of the basic monitoring equipment is determined by the number of abnormal changes and the number of data distortions in the monitoring data of the basic monitoring equipment within the most recent preset time period. If it is reliable, proceed to the next step; otherwise, the reliability of the data of the basic monitoring equipment does not meet the requirements. Specifically, the number of abnormal changes in the monitoring data is determined based on the amount of change in the monitoring data of the basic monitoring equipment within a unit of time. Specifically, when the amount of change in the monitoring data of the basic monitoring equipment within a unit of time exceeds a preset reasonable range, it is determined that the monitoring data has undergone abnormal changes.

[0031] The historical monitoring data of the basic monitoring equipment is obtained, and the reliability of the monitoring data of the basic monitoring equipment is determined by the number of abnormal changes and the number of data distortions in the historical monitoring data of the basic monitoring equipment. If it is reliable, proceed to the next step; otherwise, the reliability of the data of the basic monitoring equipment does not meet the requirements. The historical monitoring duration and historical failure count of the basic monitoring equipment are obtained, and the data reliability of the basic monitoring equipment is determined by combining the number of abnormal changes and data distortions in the historical monitoring data of the basic monitoring equipment, as well as the number of abnormal changes and data distortions in the monitoring data within the most recent preset time period.

[0032] In one possible embodiment, the method for determining the monitoring timeliness of the other monitoring devices is as follows: The type of monitoring data from the other monitoring devices is obtained, and the associated fault types of the other monitoring devices are determined by the correlation between the type of monitoring data from the other monitoring devices and the different fault types of the power equipment. It is then determined whether the associated fault types of the other monitoring devices include a specific fault type. If so, the monitoring timeliness of the other monitoring devices is determined by the number of specific fault types of the other monitoring devices. If not, proceed to the next step. Based on the historical monitoring data of the other monitoring devices, the probability of occurrence of associated fault types and high-probability fault types are determined, and it is determined whether there are high-probability fault types among the associated fault types of the other monitoring devices. If so, the monitoring importance of the other monitoring devices is determined by the number of associated fault types, the number of historical occurrences and their severity, and the number of high-probability fault types and their severity. If not, the monitoring importance of the other monitoring devices is determined by the number of historical occurrences of the associated fault types of the other monitoring devices. The analytical complexity evaluation value of the other monitoring devices is determined based on the number of monitoring points, the type and quantity of monitoring data, and the monitoring timeliness of the other monitoring devices is determined based on the analytical complexity evaluation value and the monitoring importance of the other monitoring devices.

[0033] In this embodiment, by assessing the data reliability of the basic monitoring equipment, the technical problem of low fault analysis efficiency caused by the low data reliability of the basic monitoring equipment is avoided, thereby improving the reliability of the monitoring data and also improving the operational stability of the power equipment.

[0034] S4 obtains the operating load of the parsing server corresponding to the monitoring device, and determines the monitoring start cycle and monitoring time of different other monitoring devices by combining the data reliability of the basic monitoring device and the monitoring timeliness and data volume of other monitoring devices, and controls other monitoring devices by the monitoring start cycle and monitoring time.

[0035] Specifically, the method for determining the monitoring activation cycle and the monitoring time within the monitoring activation cycle is as follows: The system obtains the operational load of the parsing server corresponding to the monitoring device, and, in conjunction with the data reliability of the basic monitoring device and the monitoring timeliness and data volume of other monitoring devices, determines the monitoring activation cycle and the monitoring time of the monitoring activation cycle for different other monitoring devices. When the data reliability of the basic monitoring equipment is greater than the preset reliability threshold, the first monitoring cycle is adopted as the basic monitoring start cycle. The basic monitoring start cycle is then corrected by the monitoring timeliness and data volume of the other monitoring equipment and the operating busyness of the parsing server corresponding to the other monitoring equipment to obtain the monitoring start cycle of the other monitoring equipment. The monitoring time of the monitoring start cycle of the other monitoring equipment is determined by the monitoring timeliness of the other monitoring equipment. When the data reliability of the basic monitoring equipment is not greater than the preset reliability threshold, the second monitoring cycle is adopted as the basic monitoring start cycle. The basic monitoring start cycle is then corrected by the monitoring timeliness and data volume of the other monitoring equipment and the operating load of the parsing server corresponding to the other monitoring equipment to obtain the monitoring start cycle of the other monitoring equipment. The monitoring time of the monitoring start cycle of the other monitoring equipment is determined by the monitoring timeliness of the other monitoring equipment.

[0036] In this embodiment, by controlling the monitoring start-up cycle and the monitoring time of different other monitoring devices, the timeliness of data parsing of other monitoring devices is ensured, while avoiding the problem of high power consumption and low operational reliability caused by frequent start-up.

[0037] Example 2 like Figure 3 As shown, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, it executes the above-described method for remote control of an Internet of Things (IoT) device.

[0038] Example 3 This application provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed in a computer, it causes the computer to perform the aforementioned method for remote control of an Internet of Things (IoT) device.

[0039] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

Claims

1. A method for remote control of an Internet of Things (IoT) device, characterized in that, Specifically, it includes: The IoT device used for data monitoring of power equipment is used as the monitoring device, and the data parsing difficulty is determined according to the type and quantity of the monitoring data of the monitoring device. When the data parsing difficulty is greater than the preset difficulty value, the process proceeds to the next step. If the monitoring data of the power equipment determines that there are no abnormalities in the operating voltage and operating current of the power equipment, proceed to the next step. The monitoring equipment used to monitor operating voltage and operating current is used as the basic monitoring equipment. The data reliability of the basic monitoring equipment is determined based on real-time monitoring data and historical monitoring data. The monitoring equipment excluding the basic monitoring equipment is used as other monitoring equipment. When the data reliability of the basic monitoring equipment does not meet the requirements, the monitoring timeliness of the other monitoring equipment is determined based on the type of monitoring data and the number of monitoring points. Proceed to the next step. The system obtains the operating load of the parsing server corresponding to the monitoring device, and determines the monitoring start cycle and monitoring time of different other monitoring devices by combining the data reliability of the basic monitoring device and the monitoring time and data volume of other monitoring devices. The system then controls other monitoring devices based on the monitoring start cycle and monitoring time. When the data reliability of the basic monitoring equipment is greater than a preset reliability threshold, a first monitoring cycle is used as the basic monitoring start cycle. The basic monitoring start cycle is then adjusted based on the monitoring timeliness and data volume of the other monitoring equipment, as well as the operating load of the corresponding parsing server, to obtain the monitoring start cycle for the other monitoring equipment. The monitoring time of the monitoring start cycle for the other monitoring equipment is then determined based on the monitoring timeliness of the other monitoring equipment. When the data reliability of the basic monitoring equipment is not greater than the preset reliability threshold, a second monitoring cycle is used as the basic monitoring start cycle. The basic monitoring start cycle is then adjusted based on the monitoring timeliness and data volume of the other monitoring equipment, as well as the operating load of the corresponding parsing server, to obtain the monitoring start cycle for the other monitoring equipment. The monitoring time of the monitoring start cycle for the other monitoring equipment is then determined based on the monitoring timeliness of the other monitoring equipment.

2. The method for remote control of an Internet of Things (IoT) device as described in claim 1, characterized in that, The monitoring equipment includes Internet of Things (IoT) devices for monitoring the operating temperature, operating current, operating voltage, and fault data of the power equipment.

3. The method for remote control of an Internet of Things (IoT) device as described in claim 1, characterized in that, The method for determining the difficulty of data parsing is as follows: S11 Obtain the number of monitoring devices for the power equipment and determine whether the number of monitoring devices is greater than a preset number. If yes, it is determined that the data parsing difficulty of the monitoring devices for the power equipment does not meet the requirements. If no, proceed to the next step. S12 obtains the type of monitoring data of the power equipment monitoring device, and determines the type monitoring complexity of the power equipment monitoring device by the type of monitoring data and the number of monitoring data types of the monitoring device, and judges whether the type monitoring complexity meets the requirements. If yes, proceed to the next step; otherwise, proceed to step S14. S13 determines the number of monitoring devices and monitoring points corresponding to different types of monitoring data based on the type of monitoring data of the power equipment monitoring devices. It also determines the parsing complexity of different types of monitoring data and the number of complex monitoring data types based on the number of monitoring devices and monitoring points corresponding to different types of monitoring data. Finally, it determines the comprehensive parsing complexity of the power equipment monitoring devices by combining the number of monitoring devices and monitoring points for the complex monitoring data types of the power equipment. It then determines whether the comprehensive parsing complexity meets the requirements. If yes, it is determined that the data parsing difficulty of the power equipment monitoring devices does not meet the requirements. If no, it proceeds to the next step. S14 determines the difficulty of data parsing by considering the overall parsing complexity of the power equipment monitoring devices, the type of monitoring complexity, and the number of monitoring devices.

4. The method for remote control of an Internet of Things (IoT) device as described in claim 3, characterized in that, Before determining the monitoring complexity of the power equipment type, it is also necessary to further determine the number of power equipment monitoring devices. When the number of power equipment monitoring devices is less than the minimum number limit, it is not necessary to determine the monitoring complexity of the power equipment type, and it can be directly determined that the data parsing difficulty of the power equipment monitoring devices meets the requirements.

5. The method for remote control of an Internet of Things (IoT) device as described in claim 4, characterized in that, When the data parsing difficulty meets the requirements or the data parsing difficulty is not greater than the preset difficulty value, there is no need to control the monitoring start cycle and the monitoring time of the monitoring start cycle of the power equipment, and the monitoring data of the monitoring equipment is parsed in real time through the parsing server.

6. The method for remote control of an Internet of Things (IoT) device as described in claim 1, characterized in that, When either the operating voltage or the operating current of the power equipment is abnormal, there is no need to control the monitoring cycle and the monitoring time of the monitoring equipment, and the monitoring data of the monitoring equipment is analyzed in real time by the analysis server.

7. The method for remote control of an Internet of Things (IoT) device as described in claim 1, characterized in that, The method for determining the data reliability of the basic monitoring equipment is as follows: obtain the real-time monitoring data of the basic monitoring equipment, and determine whether the real-time monitoring data of the basic monitoring equipment is reliable by using the real-time monitoring data of the basic monitoring equipment and the preset range of the monitoring data. If it is reliable, proceed to the next step; otherwise, the data reliability of the basic monitoring equipment does not meet the requirements. The monitoring data of the basic monitoring equipment is obtained within the most recent preset time period. The reliability of the monitoring data of the basic monitoring equipment is determined by the number of abnormal changes and the number of data distortions in the monitoring data of the basic monitoring equipment within the most recent preset time period. If it is reliable, proceed to the next step; otherwise, the reliability of the data of the basic monitoring equipment does not meet the requirements. The historical monitoring data of the basic monitoring equipment is obtained, and the reliability of the monitoring data of the basic monitoring equipment is determined by the number of abnormal changes and the number of data distortions in the historical monitoring data of the basic monitoring equipment. If it is reliable, proceed to the next step; otherwise, the reliability of the data of the basic monitoring equipment does not meet the requirements. The historical monitoring duration and historical failure count of the basic monitoring equipment are obtained, and the data reliability of the basic monitoring equipment is determined by combining the number of abnormal changes and data distortions in the historical monitoring data of the basic monitoring equipment, as well as the number of abnormal changes and data distortions in the monitoring data within the most recent preset time period.

8. The method for remote control of an Internet of Things (IoT) device as described in claim 1, characterized in that, The number of abnormal changes in the monitoring data is determined based on the amount of change in the monitoring data of the basic monitoring equipment within a unit of time. Specifically, when the amount of change in the monitoring data of the basic monitoring equipment within a unit of time exceeds a preset reasonable range, it is determined that the monitoring data has undergone abnormal changes.

9. A computer system, comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, it executes a remote control method for an Internet of Things device according to any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform a remote control method for an Internet of Things (IoT) device according to any one of claims 1-8.

Images