A self-adaptive collection method and system for Internet of Things data

Abstract

The present invention relates to an adaptive collection method and system for Internet of Things data, which aims to optimize data quality and system efficiency by adaptively adjusting the data collection frequency. The system includes an acquisition module, a processing module, an acquisition module, and a transmission module, and can dynamically adjust the data collection frequency according to multiple factors such as network conditions, sensor status, data quality, data urgency, and device power. The system status evaluation adopts a comprehensive evaluation formula, combined with the weight coefficients of various factors, to calculate the system quality evaluation value in real time. The data collection strategy dynamically adjusts the data collection interval through a series of formulas based on the evaluation results to respond to environmental changes or changes in device status. This method helps to optimize resource utilization, improve the efficiency and accuracy of data collection, and is suitable for Internet of Things application scenarios of various scales. At the same time, its adaptive data collection algorithm can effectively cope with various environments and device states, and maintain the long-term stability and reliability of the system.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things, and in particular, to an adaptive collection method and system for Internet of Things data. Background Art

[0002] With the rapid development of IoT technology, a large number of devices are connected through the network, generating massive amounts of data. Effectively managing this data and improving the security, reliability and real-time performance of the data are crucial to the success of IoT systems. In existing technologies, the data collection frequency is usually statically set and cannot be adjusted according to actual conditions, which may lead to inefficient data transmission or waste of resources. Summary of the invention

[0003] The present invention relates to an adaptive data collection method and system for the Internet of Things, which includes an acquisition module, a processing module, and a collection module. By utilizing a self-created adaptive collection algorithm, the data collection interval of the sensor can be dynamically adjusted to respond to environmental changes or device state changes, which helps to optimize resource usage, improve the efficiency and accuracy of data collection, and is suitable for Internet of Things application scenarios of various scales.

[0004] In order to achieve the above object, the technical solution of the present invention is as follows:

[0005] An adaptive collection method for Internet of Things data comprises the following steps:

[0006] Step 1: Obtain input parameters of the adaptive acquisition algorithm, and determine a system quality evaluation value according to the input parameters;

[0007] Step 2: The adaptive collection algorithm determines the data collection interval of the sensor of the IoT device according to the system quality evaluation value;

[0008] Step 3: The sensor collects data according to the data collection interval;

[0009] The step 1 is specifically as follows: obtaining input parameters at time t, wherein the input parameters include: a network status evaluation value N(t), a sensor status evaluation value S(t), a data quality evaluation value D(t), a data urgency evaluation value U(t) and a device power evaluation value E(t); and determining a system quality evaluation value Q(t) at time t according to the input parameters. The specific formula is as follows:

[0010] Q(t)=w n ·N(t)+w s ·S(t)+w d ·D(t)+w u ·U(t)+w e ·E(t) (1)

[0011] In formula (1), w n : Weight coefficient of network status evaluation value N(t); w s : Weight coefficient of sensor state evaluation value S(t); w d : weight coefficient of data quality assessment value D(t); w u : weight coefficient of data urgency assessment value U(t); w e : Weight coefficient of equipment power evaluation value E(t);

[0012] The step 2 specifically includes: determining the data collection interval of the sensor according to the system quality evaluation value, using the following formula:

[0013]

[0014] I(t+1)=I(t)·(1+k·δ(t)) (3)

[0015] In formulas (2)-(3), δ(t) represents the change in system state relative to the last evaluation; Q max : Theoretical maximum value of system quality assessment; I(t): data collection interval at time t; I(t+1): data collection interval at the next time point t+1; k: adjustment coefficient, used to control the sensitivity of data collection frequency adjustment.

[0016] Furthermore, the method also includes: Step 4: The IoT device sends the data collected by the sensor to the server through an encrypted channel, and the server stores the collected data in a distributed database system.

[0017] Furthermore, the data collected by the sensor may be environmental data or machine operation data.

[0018] The present invention also discloses an adaptive collection system for Internet of Things data, the system comprising:

[0019] Acquisition module: acquires input parameters of the adaptive acquisition algorithm, and determines the system quality evaluation value according to the input parameters;

[0020] Processing module: The adaptive acquisition algorithm determines the data acquisition interval of the sensor of the IoT device according to the system quality evaluation value;

[0021] Collection module: the sensor collects data according to the data collection interval;

[0022] The acquisition module is specifically configured to acquire input parameters at time t, wherein the input parameters include: network status evaluation value N(t), sensor status evaluation value S(t), data quality evaluation value D(t), data urgency evaluation value U(t) and device power evaluation value E(t), and determine the system quality evaluation value Q(t) at time t according to the input parameters. The specific formula is as follows:

[0023] Q(t)=w n ·N(t)+w s ·S(t)+w d ·D(t)+w u ·U(t)+w e ·E(t) (1)

[0024] In formula (1), w n : Weight coefficient of network status evaluation value N(t); w s : Weight coefficient of sensor state evaluation value S(t); w d : weight coefficient of data quality assessment value D(t); w u : weight coefficient of data urgency assessment value U(t); w e : Weight coefficient of equipment power evaluation value E(t);

[0025] The processing module specifically determines the data collection interval of the sensor according to the system quality evaluation value, using the following formula:

[0026]

[0027] I(t+1)=I(t)·(1+k·δ(t)) (3)

[0028] In formulas (2)-(3), δ(t) represents the change in system state relative to the last evaluation; Q max : Theoretical maximum value of system quality assessment; I(t): data collection interval at time t; I(t+1): data collection interval at the next time point t+1; k: adjustment coefficient, used to control the sensitivity of data collection frequency adjustment.

[0029] Furthermore, the system also includes: a transmission module: the IoT device sends the data collected by the sensor to the server through an encrypted channel, and the server stores the collected data in a distributed database system.

[0030] Furthermore, the data collected by the sensor may be environmental data or machine operation data.

[0031] To achieve the above objectives, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0032] To achieve the above object, the present invention also provides a computer-readable storage medium on which a computer program is stored, and the computer program implements the steps of the above method when executed by a processor.

[0033] The beneficial effects of the present invention are as follows:

[0034] 1. Adaptability: The system can adaptively adjust the data collection frequency by evaluating the network status, sensor status, data quality, data urgency, and device power in real time. The system status evaluation uses a comprehensive evaluation formula, combined with the weight coefficients of various factors, to calculate the system quality evaluation value in real time. The data collection strategy dynamically adjusts the data collection interval through a series of formulas based on the evaluation results to respond to changes in the environment or device status.

[0035] 2. Optimize resource usage: Dynamically adjusting the data collection frequency helps reduce unnecessary data transmission, thereby reducing energy consumption and network load and optimizing overall resource usage.

[0036] 3. Improve data quality: By precisely controlling the frequency of data collection, the system can provide high-quality data when needed and reduce data noise and errors.

[0037] 4. Enhance system stability: Adaptive algorithms can reduce the burden on sensors, extend sensor life, and improve the long-term stability and reliability of the system.

[0038] 5. Wide applicability: The system is suitable for IoT application scenarios of various scales, from smart homes to industrial monitoring, and can effectively improve the efficiency and quality of data management.

[0039] 6. Support real-time decision-making: The system's real-time data processing and analysis capabilities support rapid decision-making, especially in emergency situations, and can provide critical data in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0041] Figure 1 It is a work flow chart of the present invention;

[0042] Figure 2 It is a system configuration diagram of the present invention. DETAILED DESCRIPTION

[0043] The present application will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present application and are not intended to limit the scope of the present application. In addition, it should be understood that after reading the content taught in the present application, those skilled in the art may make various changes or modifications to the present application, and these equivalent forms also fall within the scope limited by the present application.

[0044] like Figure 1 , an adaptive collection method for Internet of Things data, comprising the following steps:

[0045] Step 1: Obtain input parameters of the adaptive acquisition algorithm, and determine a system quality evaluation value according to the input parameters;

[0046] Step 2: The adaptive acquisition algorithm determines the data acquisition interval of the sensor of the IoT device according to the system quality evaluation value:

[0047] Step 3: The sensor collects data according to the data collection interval;

[0048] The step 1 is specifically as follows: obtaining input parameters at time t, wherein the input parameters include: a network status evaluation value N(t), a sensor status evaluation value S(t), a data quality evaluation value D(t), a data urgency evaluation value U(t) and a device power evaluation value E(t); and determining a system quality evaluation value Q(t) at time t according to the input parameters. The specific formula is as follows:

[0049] Q(t)=w n ·N(t)+w s ·S(t)+w d ·D(t)+w u ·U(t)+w e ·E(t) (1)

[0050] In formula (1), w n : Weight coefficient of network status evaluation value N(t); w s : Weight coefficient of sensor state evaluation value S(t); w d : weight coefficient of data quality assessment value D(t); w u : weight coefficient of data urgency assessment value U(t); w e : Weight coefficient of equipment power evaluation value E(t);

[0051] The step 2 specifically includes: determining the data collection interval of the sensor according to the system quality evaluation value, using the following formula:

[0052]

[0053] I(t+1)=I(t)·(1+k·δ(t)) (3)

[0054] In formulas (2)-(3), δ(t) represents the change in system state relative to the last evaluation; Q max : Theoretical maximum value of system quality assessment; I(t): data collection interval at time t; I(t+1): data collection interval at the next time point t+1; k: adjustment coefficient, used to control the sensitivity of data collection frequency adjustment.

[0055] The network status evaluation value N(t) can be evaluated based on the network speed and packet loss rate. The sensor status evaluation value S(t) can be evaluated based on one or more factors of the sensor's accuracy, stability, failure rate, and maintenance record. The data quality evaluation value D(t) can be evaluated based on one or more factors of the data's completeness, accuracy, and timeliness. The data urgency evaluation value U(t) can be evaluated based on the degree of impact of the data on real-time decision-making. The device power evaluation value E(t) can be evaluated based on the remaining power and consumption rate of the IoT device.

[0056] Furthermore, the data collected by the sensor may be environmental data or machine operation data.

[0057] Furthermore, if Figure 1 , the method also includes: Step 4: the IoT device sends the data collected by the sensor to the server through an encrypted channel, and the server stores the collected data in a distributed database system.

[0058] For example, in smart home scenarios, environmental data such as temperature and humidity are collected dynamically through various sensors and transmitted to cloud servers through encrypted channels. The cloud servers process the data and store it in a distributed database. Users can view the environmental conditions of their homes in real time through mobile phone applications. Another example is in industrial monitoring scenarios, machine operation data is collected through sensors and transmitted to data centers through dedicated networks. The data centers monitor and analyze the data in real time, detect abnormal machine conditions in a timely manner, and issue warnings.

[0059] Furthermore, the method further includes executing steps 1-4 according to a trigger operation of a user, or executing steps 1-4 periodically, so as to dynamically adjust the value of the data collection interval.

[0060] like Figure 2 The present invention also discloses an adaptive data collection system for the Internet of Things, the system comprising an acquisition module, a processing module, a collection module, and a transmission module. The functions performed by the above modules are as follows:

[0061] Acquisition module: acquires input parameters of the adaptive acquisition algorithm, and determines the system quality evaluation value according to the input parameters;

[0062] Processing module: The adaptive acquisition algorithm determines the data acquisition interval of the sensor of the IoT device according to the system quality evaluation value;

[0063] Collection module: the sensor collects data according to the data collection interval;

[0064] The acquisition module is specifically configured to acquire input parameters at time t, wherein the input parameters include: network status evaluation value N(t), sensor status evaluation value S(t), data quality evaluation value D(t), data urgency evaluation value U(t) and device power evaluation value E(t), and determine the system quality evaluation value Q(t) at time t according to the input parameters. The specific formula is as follows:

[0065] Q(t)=w n ·N(t)+w s ·S(t)+w d ·D(t)+w u ·U(t)+w e ·E(t) (1)

[0066] In formula (1), w n : Weight coefficient of network status evaluation value N(t); w s : Weight coefficient of sensor state evaluation value S(t); w d : weight coefficient of data quality assessment value D(t); w u : weight coefficient of data urgency assessment value U(t); w e : Weight coefficient of equipment power evaluation value E(t);

[0067] The processing module specifically determines the data collection interval of the sensor according to the system quality evaluation value, using the following formula:

[0068]

[0069] I(t+1)=I(t)·(1+k·δ(t)) (3)

[0070] In formulas (2)-(3), δ(t) represents the change in system state relative to the last evaluation; Q max : Theoretical maximum value of system quality assessment; I(t): data collection interval at time t; I(t+1): data collection interval at the next time point t+1; k: adjustment coefficient, used to control the sensitivity of data collection frequency adjustment.

[0071] The network status evaluation value N(t) can be evaluated based on the network speed and packet loss rate. The sensor status evaluation value S(t) can be evaluated based on one or more factors of the sensor's accuracy, stability, failure rate, and maintenance record. The data quality evaluation value D(t) can be evaluated based on one or more factors of the data's completeness, accuracy, and timeliness. The data urgency evaluation value U(t) can be evaluated based on the degree of impact of the data on real-time decision-making. The device power evaluation value E(t) can be evaluated based on the remaining power and consumption rate of the IoT device.

[0072] Furthermore, the system also includes: a transmission module: the IoT device sends the data collected by the sensor to the server through an encrypted channel, and the server stores the collected data in a distributed database system.

[0073] Furthermore, the data collected by the sensor may be environmental data or machine operation data.

[0074] For example, in smart home scenarios, environmental data such as temperature and humidity are collected dynamically through various sensors and transmitted to cloud servers through encrypted channels. The cloud servers process the data and store it in a distributed database. Users can view the environmental conditions of their homes in real time through mobile phone applications. Another example is in industrial monitoring scenarios, machine operation data is collected through sensors and transmitted to data centers through dedicated networks. The data centers monitor and analyze the data in real time, detect abnormal machine conditions in a timely manner, and issue warnings.

[0075] To achieve the above objectives, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0076] To achieve the above object, the present invention also provides a computer-readable storage medium on which a computer program is stored, and the computer program implements the steps of the above method when executed by a processor.

[0077] The above description is only a specific embodiment of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An adaptive collection method for Internet of Things data, characterized in that: The following steps are involved: Step 1: Obtain input parameters of the adaptive acquisition algorithm, and determine a system quality evaluation value according to the input parameters; Step 2: The adaptive collection algorithm determines the data collection interval of the sensor of the IoT device according to the system quality evaluation value; Step 3: The sensor collects data according to the data collection interval; the data collected by the sensor is environmental data or machine operation data; Step 4: The IoT device sends the data collected by the sensor to the server through an encrypted channel, and the server stores the collected data in a distributed database system; The step 1 is specifically as follows: obtaining input parameters at time t, wherein the input parameters include: network status evaluation value N(t), sensor status evaluation value S(t), data quality evaluation value D(t), data urgency evaluation value U(t) and device power evaluation value E(t), and determining the system quality evaluation value Q(t) at time t according to the input parameters. The specific formula is as follows: Q(t)=w n ·N(t)+w s ·S(t)+w d ·D(t)+w u ·U(t)+w e ·E(t)(1) In formula (1), w n : Weight coefficient of network status evaluation value N(t); w s : Weight coefficient of sensor state evaluation value S(t); w d : weight coefficient of data quality assessment value D(t); w u : weight coefficient of data urgency assessment value U(t); w e : Weight coefficient of equipment power evaluation value E(t); The step 2 specifically includes: determining the data collection interval of the sensor according to the system quality evaluation value, using the following formula: I(t+1)=I(t)·(1+k·δ(t)) (3) In formulas (2)-(3), δ(t) represents the change in system state relative to the last evaluation; Q max : Theoretical maximum value of system quality assessment; I(t): data collection interval at time t; I(t+1): data collection interval at the next time point t+1; k: Adjustment coefficient, used to control the sensitivity of data acquisition frequency adjustment.

2. An adaptive collection system for Internet of Things data, characterized in that: The system includes the following modules: Acquisition module: acquires input parameters of the adaptive acquisition algorithm, and determines the system quality evaluation value according to the input parameters; Processing module: The adaptive acquisition algorithm determines the data acquisition interval of the sensor of the IoT device according to the system quality evaluation value; Collection module: the sensor collects data according to the data collection interval; the data collected by the sensor is environmental data or machine operation data; Transmission module: The IoT device sends the data collected by the sensor to the server through an encrypted channel, and the server stores the collected data in a distributed database system; The acquisition module is specifically configured to acquire input parameters at time t, wherein the input parameters include: a network status evaluation value N(t), a sensor status evaluation value S(t), a data quality evaluation value D(t), a data urgency evaluation value U(t) and a device power evaluation value E(t), and determine a system quality evaluation value Q(t) at time t according to the input parameters. The specific formula is as follows: Q(t)=w n ·N(t)+w s ·S(t)+w d ·D(t)+w u ·U(t)+w e ·E(t)(1) In formula (1), w n : Weight coefficient of network status evaluation value N(t); w s : Weight coefficient of sensor state evaluation value S(t); w d : weight coefficient of data quality assessment value D(t); w u : weight coefficient of data urgency assessment value U(t); w e : Weight coefficient of equipment power evaluation value E(t); The processing module specifically determines the data collection interval of the sensor according to the system quality evaluation value, using the following formula: I(t+1)=I(t)·(1+k·δ(t)) (3) In formulas (2)-(3), δ(t) represents the change in system state relative to the last evaluation; Q max : Theoretical maximum value of system quality assessment; I(t): data collection interval at time t; I(t+1): data collection interval at the next time point t+1; k: Adjustment coefficient, used to control the sensitivity of data acquisition frequency adjustment.

3. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to claim 1 are implemented.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to claim 1 are implemented.

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