Electronic product dynamic detection control method based on adaptive algorithm

Abstract

The invention relates to the technical field of electronic products, in particular to an electronic product dynamic detection control method based on a self-adaptive algorithm, and solves the problem that in the prior art, a traditional detection control method is lack of real-time sensing and self-adaptive adjusting capacity for external environment and internal state changes, and consequently the real-time sensing and self-adaptive adjusting capacity cannot be achieved under complex and changeable operation conditions. And the detection precision and the control effect are greatly reduced. An electronic product dynamic detection control method based on a self-adaptive algorithm comprises the steps that S1, operation data of an electronic product are collected in real time, and the operation data comprise environment parameters, load information and internal component states; the adaptive algorithm can dynamically adjust the detection parameters and the control strategy according to the real-time data in the operation process of the electronic product, effectively deals with the changes of the external environment and the internal state, and ensures that the detection control effect is always kept in the optimal state.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic products, and in particular to a dynamic detection and control method of electronic products based on an adaptive algorithm. Background Art

[0002] With the rapid development of electronic technology, electronic products have significantly improved in function, performance and complexity. However, this also puts higher requirements on the dynamic detection and control of electronic products. Traditional detection methods are often based on fixed parameters and preset rules, which are difficult to adapt to the dynamic changes of electronic products in actual operation, such as environmental parameter fluctuations, load changes, and aging of internal components. These problems lead to the defects of slow response speed, low accuracy, poor stability and other defects in traditional detection and control methods in practical applications, which seriously affect the performance and reliability of electronic products.

[0003] Poor dynamic adaptability: Traditional detection and control methods lack the ability to perceive and adaptively adjust the external environment and internal state changes in real time, resulting in greatly reduced detection accuracy and control effects under complex and changeable operating conditions.

[0004] Slow response: When electronic products experience abnormal conditions, traditional methods often take a long time to respond, which may cause the problem to deteriorate further or even cause the system to crash.

[0005] Insufficient stability: Due to the inability to effectively deal with various uncertain factors, the stability of traditional detection and control systems is poor and they are easily affected by external interference, which affects the long-term stable operation of electronic products. Summary of the invention

[0006] The purpose of the present invention is to provide a dynamic detection and control method for electronic products based on an adaptive algorithm, which solves the problem that traditional detection and control methods in the prior art lack the ability to perceive and adaptively adjust the external environment and internal state changes in real time, resulting in greatly reduced detection accuracy and control effects under complex and changeable operating conditions.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A dynamic detection and control method for electronic products based on an adaptive algorithm comprises S1: real-time collection of operating data of the electronic products, wherein the operating data comprises environmental parameters, load information and internal component status;

[0009] S2: Building a dynamic model of the electronic product based on the data collected in step S1, combined with historical operation records and expert knowledge base;

[0010] S3: Apply adaptive algorithms to analyze the dynamic model and dynamically adjust detection parameters and control strategies based on the analysis results;

[0011] S4: Execute the adjusted detection and control strategy to monitor and control the electronic products in real time;

[0012] S5: Feedback the monitoring results to the adaptive algorithm to form a closed-loop control and continuously optimize the detection and control effect;

[0013] S6: When an electronic product is abnormal, the adaptive algorithm quickly identifies the abnormal type and automatically triggers the emergency response mechanism;

[0014] S7: Record and store the data of the detection and control process for subsequent performance analysis and system optimization.

[0015] Preferably, the adaptive algorithm includes but is not limited to BP neural network, fuzzy logic control or reinforcement learning algorithm, which is used to achieve adaptive adjustment and optimization of parameters.

[0016] Preferably, the dynamic model in S2 also includes a function of predicting the future operating state of the electronic product, based on current operating data and historical trends.

[0017] Preferably, the detection parameter adjustment in S3 includes sampling frequency, detection threshold and sensitivity setting, and the control strategy adjustment includes control algorithm selection, control parameter optimization and execution logic change.

[0018] Preferably, the emergency handling mechanism in S6 includes but is not limited to automatic power-off protection, fault isolation and emergency notification functions to ensure the safety of electronic products in abnormal conditions.

[0019] Preferably, S8: regularly evaluate the performance of the adaptive algorithm, optimize and upgrade the algorithm according to the evaluation results, and improve the overall effectiveness of the detection and control method.

[0020] Preferably, the electronic products include but are not limited to smart phones, tablet computers, wearable devices, industrial control systems and smart home devices.

[0021] The present invention has at least the following beneficial effects:

[0022] This method introduces advanced adaptive algorithms, such as BP neural network, fuzzy logic control or reinforcement learning algorithm, to achieve real-time monitoring and intelligent adjustment of the operating status of electronic products, thereby significantly improving detection accuracy, accelerating response speed and enhancing system stability. The specific beneficial effects are as follows:

[0023] Improve dynamic adaptability: Adaptive algorithms can dynamically adjust detection parameters and control strategies based on real-time data during the operation of electronic products, effectively respond to changes in the external environment and internal status, and ensure that the detection and control effects are always maintained in the optimal state.

[0024] Faster response speed: Through real-time perception and rapid analysis, the adaptive algorithm can respond instantly to abnormal situations in electronic products and take corresponding measures in time to prevent the problem from expanding and ensure the safe and stable operation of the system.

[0025] Enhance system stability: The adaptive algorithm has strong anti-interference ability and robustness, which can maintain the stable operation of the system in a complex and changing environment and reduce system fluctuations and failures caused by external interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0027] Figure 1 It is a schematic diagram of the electronic product dynamic detection control method of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0029] Embodiment 1, refer to Figure 1 , a dynamic detection and control method for electronic products based on an adaptive algorithm, comprising S1: real-time collection of operating data of the electronic products, the operating data including environmental parameters, load information and internal component status;

[0030] S2: Building a dynamic model of the electronic product based on the data collected in step S1, combined with historical operation records and expert knowledge base;

[0031] S3: Apply adaptive algorithms to analyze the dynamic model and dynamically adjust detection parameters and control strategies based on the analysis results;

[0032] S4: Execute the adjusted detection and control strategy to monitor and control the electronic products in real time;

[0033] S5: Feedback the monitoring results to the adaptive algorithm to form a closed-loop control and continuously optimize the detection and control effect;

[0034] S6: When an electronic product is abnormal, the adaptive algorithm quickly identifies the abnormal type and automatically triggers the emergency response mechanism;

[0035] S7: Record and store the data of the detection and control process for subsequent performance analysis and system optimization.

[0036] Data collection and processing: Use sensors and other equipment to collect real-time operating data such as environmental parameters (such as temperature and humidity), load information, and internal component status of electronic products, and pre-process the data, such as filtering and denoising, to improve data quality.

[0037] Dynamic model establishment: Based on the collected operation data, combined with historical operation records and expert knowledge base, a dynamic model of electronic products is established. This model can reflect the performance of electronic products under different operating conditions and provide a basis for subsequent adaptive adjustments.

[0038] Adaptive algorithm application: input the preprocessed data into the adaptive algorithm for analysis. The adaptive algorithm dynamically adjusts the detection parameters (such as sampling frequency, detection threshold) and control strategies (such as control algorithm selection, control parameter optimization) according to the analysis results. Through continuous iterative optimization, the detection and control effect is always kept in the optimal state.

[0039] Real-time monitoring and control: Execute the adjusted detection and control strategy to monitor and control electronic products in real time. During the monitoring process, if an abnormal situation is found, the emergency response mechanism will be triggered immediately to ensure the safety of electronic products in abnormal conditions.

[0040] Closed-loop control and optimization: Feedback to the adaptive algorithm based on the monitoring results forms a closed-loop control. The adaptive algorithm further optimizes the detection and control strategy based on the feedback results, continuously improving the detection accuracy and control effect. At the same time, the performance of the adaptive algorithm is regularly evaluated, and the algorithm is optimized and upgraded based on the evaluation results.

[0041] Data storage and analysis: The data of the detection and control process is recorded and stored in the database. By analyzing the stored data, we can gain a deep understanding of the operation rules and performance characteristics of electronic products, providing strong support for subsequent system optimization and new product development.

[0042] According to the above embodiments, the method can realize real-time monitoring and intelligent adjustment of the operating status of electronic products by introducing advanced adaptive algorithms, such as BP neural network, fuzzy logic control or reinforcement learning algorithm, thereby significantly improving detection accuracy, accelerating response speed and enhancing system stability. The specific beneficial effects are as follows:

[0043] Improve dynamic adaptability: Adaptive algorithms can dynamically adjust detection parameters and control strategies based on real-time data during the operation of electronic products, effectively respond to changes in the external environment and internal status, and ensure that the detection and control effects are always maintained in the optimal state.

[0044] Faster response speed: Through real-time perception and rapid analysis, the adaptive algorithm can respond instantly to abnormal situations in electronic products and take corresponding measures in time to prevent the problem from expanding and ensure the safe and stable operation of the system.

[0045] Enhance system stability: The adaptive algorithm has strong anti-interference ability and robustness, which can maintain the stable operation of the system in a complex and changing environment and reduce system fluctuations and failures caused by external interference.

[0046] Example 2, refer to Figure 1 ,The adaptive algorithms include but are not limited to BP neural network, fuzzy logic control or ,reinforcement learning algorithm for realizing adaptive adjustment and optimization of ,parameters.

[0047] The dynamic model in S2 also includes the ability to predict the future operating status of electronic products based on current operating data and historical trends.

[0048] The detection parameter adjustment in S3 includes sampling frequency, detection threshold and sensitivity setting, and the control strategy adjustment includes control algorithm selection, control parameter optimization and execution logic change.

[0049] The emergency response mechanism in S6 includes but is not limited to automatic power-off protection, fault isolation and emergency notification functions to ensure the safety of electronic products in abnormal conditions.

[0050] S8: Regularly evaluate the performance of the adaptive algorithm, optimize and upgrade the algorithm based on the evaluation results, and improve the overall effectiveness of the detection and control method.

[0051] Electronic products include but are not limited to smartphones, tablets, wearable devices, industrial control systems and smart home devices.

[0052] According to the above embodiments, the electronic product dynamic detection and control method based on adaptive algorithm proposed in the present invention significantly improves the performance and reliability of electronic products by solving the problems existing in traditional methods in terms of dynamic adaptability, response speed and stability, and injects new vitality into the development of the electronic product industry.

[0053] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A dynamic detection and control method for electronic products based on an adaptive algorithm, characterized in that: include: S1: real-time collection of operating data of electronic products, the operating data including environmental parameters, load information and internal component status; S2: Building a dynamic model of the electronic product based on the data collected in step S1, combined with historical operation records and expert knowledge base; S3: Apply adaptive algorithms to analyze the dynamic model and dynamically adjust detection parameters and control strategies based on the analysis results; S4: Execute the adjusted detection and control strategy to monitor and control the electronic products in real time; S5: Feedback the monitoring results to the adaptive algorithm to form a closed-loop control and continuously optimize the detection and control effect; S6: When an electronic product is abnormal, the adaptive algorithm quickly identifies the abnormal type and automatically triggers the emergency response mechanism; S7: Record and store the data of the detection and control process for subsequent performance analysis and system optimization.

2. The electronic product dynamic detection control method based on adaptive algorithm according to claim 1, characterized in that: The adaptive algorithm includes but is not limited to BP neural network, fuzzy logic control or reinforcement learning algorithm, which is used to achieve adaptive adjustment and optimization of parameters.

3. The electronic product dynamic detection control method based on adaptive algorithm according to claim 2 is characterized in that: The dynamic model in S2 also includes the ability to predict the future operating status of electronic products based on current operating data and historical trends.

4. The electronic product dynamic detection and control method based on an adaptive algorithm according to claim 3 is characterized in that: The detection parameter adjustment in S3 includes sampling frequency, detection threshold and sensitivity setting, and the control strategy adjustment includes control algorithm selection, control parameter optimization and execution logic change.

5. The electronic product dynamic detection and control method based on adaptive algorithm according to claim 1, characterized in that: The emergency response mechanism in S6 includes but is not limited to automatic power-off protection, fault isolation and emergency notification functions to ensure the safety of electronic products in abnormal conditions.

6. The electronic product dynamic detection and control method based on adaptive algorithm according to claim 5, characterized in that: S8: Regularly evaluate the performance of the adaptive algorithm, optimize and upgrade the algorithm based on the evaluation results, and improve the overall effectiveness of the detection and control method.

7. The electronic product dynamic detection and control method based on adaptive algorithm according to claim 6 is characterized in that: The electronic products include but are not limited to smart phones, tablet computers, wearable devices, industrial control systems and smart home devices.

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