An AI-based intelligent recorder management method and system

Abstract

The present invention discloses an AI-based intelligent recorder management method and system, which relates to the technical field of intelligent device management. The AI-based intelligent recorder management method and system obtain environmental interference data and recorder resource status data through sensors, extract environmental interference features by combining convolutional neural networks, analyze interference types and calculate environmental interference factors. According to the environmental interference factors, the matching degree between the recorder workload and the environment is evaluated, and an environmental matching index is obtained to measure the working efficiency of the recorder in the current environment. Further, the feasibility of task execution is judged through the environmental matching index, and the task priority is dynamically adjusted according to the resource status. Finally, through the fuzzy control algorithm, the working mode of the recorder is dynamically adjusted, the task scheduling and resource allocation are optimized, intelligent management is realized, and the adaptability and working stability of the recorder are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent device management, and particularly to an AI-based intelligent recorder management method and system. Background Art

[0002] With the rapid development of information technology, intelligent devices are increasingly widely used in all walks of life. Especially in the field of data collection and recording, intelligent recorders, with their efficient and accurate monitoring capabilities, are widely used in multiple fields such as environmental monitoring, industrial control, and medical diagnosis. To ensure that intelligent recorders can continuously and stably perform tasks in complex and changing environments, environmental adaptability and resource management become crucial. Especially in high-interference working environments, external environmental factors such as temperature fluctuations, electromagnetic interference, and low-frequency vibrations often significantly affect the working efficiency and task execution of recorders. Therefore, how to improve the environmental adaptability of intelligent recorders and optimize their workload scheduling has become a key research direction for improving the performance and reliability of intelligent recorders.

[0003] Most of the existing intelligent recorder management methods rely on traditional hardware optimization or static environmental adaptation strategies, and these methods are usually difficult to effectively cope with the challenges brought by real-time environmental changes. The existing resource scheduling methods often do not consider the dynamic impact of environmental interference on the performance of recorders, resulting in inflexible task priority management in the case of strong interference or resource shortage, which may lead to task execution failure or low efficiency. In addition, traditional environmental adaptation mechanisms usually cannot evaluate the matching degree between the recorder and the external environment in real time, and lack intelligent dynamic adjustment of task priorities, and cannot make full use of the resources of the recorder for optimization. Therefore, the existing methods are difficult to meet the requirements of efficiently performing tasks in complex environments, and there are problems such as low efficiency and slow response. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides an AI-based intelligent recorder management method and system, which solves the problems in the above background art.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: An AI-based intelligent recorder management method, comprising the following steps: S1. Obtain the external environment interference data and the recorder resource status data of the recorder through sensors, extract the environmental interference features through a convolutional neural network, identify the environmental interference types, analyze the impact of the environmental interference on the workload of the recorder, and obtain the environmental interference factors; S2. According to the environmental interference factors, analyze the matching degree between the workload of the recorder and the external environment, obtain the environmental matching index, which is used to measure the working efficiency of the recorder in the current environment; S3. According to the environmental matching index, judge the feasibility of the recorder task execution, and dynamically adjust the task priority according to the environmental interference and the recorder resource status; S4. According to the environmental matching index and the priority of the task execution, dynamically adjust the working mode of the recorder through a fuzzy control algorithm.

[0006] Further, the external environment interference data includes temperature gradient data, electromagnetic interference data, and low-frequency vibration data; the recorder resource status data includes processor load, memory usage, storage space, network bandwidth, and battery power.

[0007] Further, the specific process of extracting the environmental interference features through a convolutional neural network and identifying the environmental interference types is as follows: Preprocess the temperature gradient data, electromagnetic interference data, and low-frequency vibration data, including standardization and normalization processing. For the vibration data, convert the time-domain signal into a frequency-domain signal through a fast Fourier transform; Input the preprocessed data into multiple convolutional layers of the convolutional neural network, perform a sliding window operation on the input data through multiple filters, and extract the environmental interference features, including temperature change features, electromagnetic interference features, and low-frequency vibration features; The environmental interference features extracted by the convolutional layer are processed by the pooling layer, and the data dimension is reduced through downsampling to generate a feature map. Through the fully connected layer, the feature maps obtained from the convolutional layer and the pooling layer are flattened into vectors and sent to the classifier to perform a weighted combination of different features to identify the environmental interference types of temperature change, electromagnetic interference, and vibration; Convert the output into a probability distribution through an activation function, and finally output the labels of the environmental interference types, including temperature interference, electromagnetic interference, and vibration interference.

[0008] Further, the specific process of analyzing the impact of the environmental interference on the workload of the recorder and obtaining the environmental interference factors is as follows: Through the environmental interference features extracted by the convolutional neural network, analyze the weights of the environmental interference features through a hierarchy, and calculate the impact factors of each environmental interference on the workload of the recorder, including the temperature impact factor, the electromagnetic interference impact factor, and the low-frequency vibration impact factor; Perform a comprehensive operation process on the temperature impact factor, the electromagnetic interference impact factor, and the low-frequency vibration impact factor to obtain the environmental interference factors.

[0009] Furthermore, the specific process of analyzing the workload of the recorder and the external environment to obtain the environmental matching index is as follows: Based on the recorder workload data and environmental interference factors, each interference factor is analyzed in relation to the recorder's workload to evaluate the impact of each interference factor on the various resources of the recorder; a model is built for the impact relationship between each environmental interference and the workload, and the impact degree of each environmental interference on the recorder's workload is calculated through regression analysis; the impact degrees of the interference factors are compared with the load status of the recorder to obtain the environmental matching index, which is used as a standard to measure the working efficiency of the recorder in the current environment.

[0010] Furthermore, the specific process of determining whether the recorder can execute tasks in the current environment based on the environmental matching degree index is as follows: According to the environmental matching degree index, a minimum environmental matching degree threshold for task execution is set, and the environmental matching degree index is compared with the minimum environmental matching degree threshold for task execution: If the environmental matching degree index is greater than or equal to the minimum environmental matching degree threshold, it indicates that the recorder can execute tasks in the current environment, and task scheduling continues; if the environmental matching degree index is less than the minimum environmental matching degree threshold, it indicates that the recorder cannot effectively execute tasks in the current environment.

[0011] Furthermore, the specific process of dynamically adjusting the task priority according to environmental interference and the recorder's resource status is as follows: If the recorder cannot effectively execute tasks in the current environment, based on the environmental interference factors and the recorder's resource status, the current working state of the recorder is dynamically evaluated to obtain a task priority adjustment factor; according to the task priority adjustment factor, the priority of the task is determined.

[0012] Furthermore, the adjustment logic for dynamically adjusting the working mode of the recorder through a fuzzy control algorithm is as follows: The environmental matching degree index and the task priority are used as input variables; the environmental matching degree index and the task priority are respectively mapped to fuzzy sets, and a fuzzy rule base is established; according to the fuzzy rule base, combined with the current environmental matching degree index and the task priority, fuzzy inference is performed to generate an adaptive working mode output to dynamically adjust the working mode of the recorder.

[0013] An AI-based intelligent recorder management system includes the following modules: an environmental interference factor extraction module, an environmental matching degree evaluation module, a task priority adjustment module, and a working mode adjustment module; the environmental interference factor extraction module is used to obtain the external environmental interference data and the recorder resource status data of the recorder through sensors, extract the environmental interference features through a convolutional neural network, identify the environmental interference types, analyze the impact of the environmental interference on the workload of the recorder, and obtain the environmental interference factors; the environmental matching degree evaluation module is used to analyze the matching degree between the workload of the recorder and the external environment according to the environmental interference factors, obtain the environmental matching index, and use it to measure the working efficiency of the recorder in the current environment; the task priority adjustment module is used to judge the feasibility of the recorder task execution according to the environmental matching degree index, and dynamically adjust the task priority according to the environmental interference and the recorder resource status; the working mode adjustment module is used to dynamically adjust the working mode of the recorder through a fuzzy control algorithm according to the environmental matching degree index and the priority of the task execution.

[0014] The present invention has the following beneficial effects:

[0015] (1) For the AI-based intelligent recorder management method, by obtaining the external environmental interference data and the recorder resource status data through sensors and using a convolutional neural network to extract the environmental interference features, it can accurately identify the environmental interference types, thereby deeply analyzing the impact of the environment on the workload of the recorder and obtaining the environmental interference factors. It improves the accuracy of the environmental adaptability analysis, helps to optimize the operation performance of the recorder under various environmental conditions, and effectively reduces the negative impact of external interference on the performance of the recorder.

[0016] (2) For the AI-based intelligent recorder management system, by analyzing the environmental matching degree index, combining the task priority of the recorder, and using a fuzzy control algorithm to dynamically adjust the working mode of the recorder, it can optimize the task scheduling according to the real-time environmental changes and task requirements. This method improves the task execution efficiency of the recorder under different working conditions, enhances the intelligence and adaptability of the system, ensures the reasonable allocation of task priorities and the flexible adjustment of the working mode, and greatly improves the use performance and stability of the recorder.

[0017] Of course, it is not necessary for any product implementing the present invention to achieve all the above advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of an AI-based intelligent recorder management method of the present invention.

[0019] Figure 2 It is a flowchart of an AI-based intelligent recorder management system of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0020] In the embodiment of the present application, a method and system for managing an intelligent recorder based on AI are provided, which solve the problems of low task execution efficiency, resource waste, and poor stability caused by the impact of external interference on the workload of the recorder in a complex environment. By intelligently analyzing the environmental interference factors and the resource status of the recorder, dynamically adjusting the task priority and optimizing the working mode, the adaptability and task execution efficiency of the recorder under different environmental conditions are improved, and the intelligent management and autonomous adjustment capabilities of the recorder are significantly enhanced.

[0021] The general idea of the solution in the embodiment of the present application is as follows:

[0022] Obtain the external environmental interference data of the recorder and the recorder resource status data through sensors, extract the environmental interference features through a convolutional neural network, identify the environmental interference type, analyze the impact of the environmental interference on the workload of the recorder, and obtain the environmental interference factors.

[0023] According to the environmental interference factors, analyze the matching degree between the workload of the recorder and the external environment, obtain the environmental matching index, which is used to measure the working efficiency of the recorder in the current environment.

[0024] According to the environmental matching index, judge the feasibility of the recorder task execution, and dynamically adjust the task priority according to the environmental interference and the recorder resource status.

[0025] According to the environmental matching index and the task execution priority, dynamically adjust the working mode of the recorder through a fuzzy control algorithm.

[0026] Please refer to Figure 1 , the embodiment of the present invention provides a technical solution: a method for managing an intelligent recorder based on AI, including the following steps: S1. Obtain the external environmental interference data of the recorder and the recorder resource status data through sensors, extract the environmental interference features through a convolutional neural network, identify the environmental interference type, analyze the impact of the environmental interference on the workload of the recorder, and obtain the environmental interference factors; S2. According to the environmental interference factors, analyze the matching degree between the workload of the recorder and the external environment, obtain the environmental matching index, which is used to measure the working efficiency of the recorder in the current environment; S3. According to the environmental matching index, judge the feasibility of the recorder task execution, and dynamically adjust the task priority according to the environmental interference and the recorder resource status; S4. According to the environmental matching index and the task execution priority, dynamically adjust the working mode of the recorder through a fuzzy control algorithm.

[0027] In this implementation scheme: S1: In this step, the recorder collects in real time the interference data of the external environment (such as temperature, electromagnetic interference, vibration, etc.) and the resource status data of the recorder itself (such as processor load, memory usage, storage space, network bandwidth, battery power, etc.) through the installed sensors. These data will provide a basis for subsequent analysis. The interference data is processed by a convolutional neural network (CNN) to extract the features related to environmental interference and identify specific types of environmental interference (such as temperature fluctuations, electromagnetic interference, etc.), and then analyze the impact of the interference on the workload of the recorder to obtain environmental interference factors. The sensor is a device used to collect information on the external environment of the recorder and the internal resource status data. Common types of sensors include temperature sensors, electromagnetic field sensors, acceleration sensors (for detecting vibration), humidity sensors, etc. In the present invention, the sensor is used to obtain the interference data (such as temperature, electromagnetic interference, and vibration, etc.) in the environment where the recorder is located and the resource status data inside the recorder (such as processor load, memory, storage space, battery power, etc.). The convolutional neural network (CNN) is a deep learning model widely used in image and signal processing. CNN extracts features in the data through multiple convolutional layers and pooling layers. In the present invention, it is used to extract important features from the environmental interference data collected by the sensor. CNN can efficiently identify and classify different types of interference (such as temperature fluctuations, electromagnetic interference, and low-frequency vibration), thus helping to judge its impact on the operation of the recorder. The environmental interference factor refers to various factors in the external environment that may affect the normal operation of the recorder. In the present invention, the environmental interference factors include temperature changes, electromagnetic interference, and low-frequency vibration, etc. These factors are used to analyze the impact of the environment on the workload of the recorder in order to optimize the working mode and task scheduling of the recorder in the future. S2: According to the environmental interference factors extracted in step S1, this step will further analyze the matching degree between the workload of the recorder and the external environment. By calculating the environmental matching index, it is used to measure the working efficiency of the recorder in the current environment. This matching degree index can help identify the compatibility between the environment and the device state, so as to judge whether the recorder can efficiently execute tasks in the existing environment. The environmental matching index is a quantitative index to measure the working efficiency of the recorder in a specific environment. By analyzing the environmental interference factors and the resource status of the recorder (such as processor load, memory usage, etc.), this index reflects the adaptability of the recorder to the current environment. When the environmental matching index is relatively high, it means that the recorder can efficiently execute tasks in the current environment; while when the matching index is relatively low, it may be necessary to adjust the task execution strategy or optimize the working mode. S3: According to the environmental matching index, the system will judge whether the recorder can complete the task in the current environment. If the environmental matching index indicates that the current environment has a greater negative impact on task execution (such as high temperature, electromagnetic interference, etc.), the system will dynamically adjust the priority of the task according to the situation of environmental interference and recorder resources.Adjusting the task priority helps ensure that high-priority tasks are processed first, while avoiding the situation where low-priority tasks cannot be executed smoothly due to excessive environmental interference. Task priority refers to the criterion for determining the order of task execution when multiple tasks are executed in parallel. The dynamic adjustment of task priority is based on the current resource status of the recorder, environmental interference, and the importance of the tasks. If the environment is unfavorable or resources are scarce, the system will adjust the execution order according to the task priority to ensure that the most important tasks can be executed first with limited resources, avoiding task failures or delays due to insufficient resources.

[0028] S4: Dynamically adjust the working mode of the recorder through a fuzzy control algorithm. After the priority of task execution is determined, the system will adjust the working mode of the recorder through a fuzzy control algorithm. The fuzzy control algorithm can dynamically adjust the working state of the recorder (such as reducing power consumption, adjusting processing speed, changing the working mode, etc.) according to the environmental matching index and task priority to adapt to environmental changes and improve the stability and efficiency of task execution. Fuzzy control allows for flexible processing of input conditions (such as environmental interference, task priority, etc.), thus achieving more precise regulation and optimization.

[0029] Specifically, the external environmental interference data includes temperature gradient data, electromagnetic interference data, and low-frequency vibration data; the recorder resource status data includes processor load, memory usage, storage space, network bandwidth, and battery power.

[0030] In this implementation, the external environmental interference data includes the following important dimensions: Temperature gradient data: The temperature gradient refers to the rate of change and distribution of temperature in the environment where the recorder is located. Temperature changes directly affect the working state of the recorder. Especially when the device overheats, it may lead to performance degradation, hardware failures, or system crashes. By collecting temperature gradient data, the system can monitor the temperature changes around the recorder in real time and identify temperature fluctuations that may cause device failures in advance. Electromagnetic interference data: Electromagnetic interference (EMI) refers to the impact of electromagnetic waves caused by external electronic devices or natural phenomena in the environment (such as lightning, radio waves, etc.) on the signal transmission of the recorder. Electromagnetic interference will reduce the signal quality of the recorder and even affect data storage and processing functions. Collecting electromagnetic interference data helps to identify and avoid interference sources to ensure the stable operation of the recorder. Low-frequency vibration data: Low-frequency vibrations usually originate from factors such as mechanical movements in the external environment, traffic flow, or natural earthquakes. Excessive vibrations may affect the sensor accuracy of the recorder, resulting in measurement errors or device damage. Collecting low-frequency vibration data helps to monitor the vibration intensity in the environment in real time, thus avoiding negative impacts on the accuracy of the recorder. Recorder resource status data: Processor load: The processor load of the recorder represents the current usage of the processor's computing power, usually measured by the CPU usage rate or the occupancy rate of processor cycles. High load may mean that the device is performing complex tasks or there are performance bottlenecks, which may cause the recorder to respond slowly or task execution to be delayed. Memory usage: Memory usage represents the occupancy of the current memory resources of the recorder. Excessive memory occupancy may cause the system to be unable to load new tasks or perform data processing operations. The sufficiency of memory directly affects the processing speed of tasks and the response ability of the system. Storage space: Storage space refers to the available hard disk or flash memory space of the recorder. Sufficient storage space is crucial for the normal operation of the recorder, especially when performing a large number of data collection and storage tasks. Insufficient space may lead to data loss, task interruption, or device crashes. Network bandwidth: Network bandwidth refers to the communication ability between the recorder and external systems (such as cloud platforms, other devices, etc.). Limited bandwidth may cause slow or interrupted data transmission, affecting real-time data synchronization and task execution efficiency. Battery power: Battery power refers to the remaining energy of the battery in the recorder device. Low battery power will limit the operating time of the device and may cause the device to shut down at critical moments. Therefore, battery power is an important indicator for evaluating whether the recorder can continue to perform tasks.

[0031] Specifically, the specific process of extracting environmental interference features and identifying environmental interference types through a convolutional neural network is as follows: Preprocess the temperature gradient data, electromagnetic interference data, and low-frequency vibration data, including standardization and normalization processing. For vibration data, convert the time-domain signal into a frequency-domain signal through fast Fourier transform; Input the preprocessed data into multiple convolutional layers of the convolutional neural network, and perform sliding window operations on the input data through multiple filters to extract environmental interference features, including temperature change features, electromagnetic interference features, and low-frequency vibration features; The environmental interference features extracted by the convolutional layer are processed by the pooling layer, and the data dimension is reduced through downsampling to generate a feature map. Through the fully connected layer, the feature maps obtained from the convolutional layer and the pooling layer are flattened into vectors and sent to the classifier to perform weighted combination of different features to identify environmental interference types such as temperature change, electromagnetic interference, and vibration; Convert the output into a probability distribution through the activation function, and finally output the labels of environmental interference types, including temperature interference, electromagnetic interference, and vibration interference.

[0032] In this implementation, data preprocessing: standardization and normalization: Data preprocessing is to eliminate the influence of different dimensions on the analysis results. Temperature gradient, electromagnetic interference, and low-frequency vibration data usually have different numerical ranges. Standardization usually adjusts the data to zero mean and unit variance, while normalization scales the data to a specific range (such as 0 to 1). This preprocessing helps the convolutional neural network (CNN) effectively learn the patterns of the data and avoid certain features having an excessive impact on the learning process due to large numerical values. Fourier transform of low-frequency vibration data: Vibration data is usually a time-domain signal. To convert it into a form that can reveal frequency characteristics, the fast Fourier transform (FFT) is used to convert the time-domain signal into a frequency-domain signal. FFT can extract periodic components, that is, frequency characteristics, from the vibration signal to help identify the interference caused by vibration. Input data into the convolutional layer: The preprocessed data (including temperature gradient, electromagnetic interference, and low-frequency vibration data) will be fed as input into the convolutional layer of the convolutional neural network. The role of the convolutional layer is to perform a sliding window operation through multiple filters (convolution kernels) to extract features from the input data. The filter slides over the input data to extract local features, and the convolutional layer will learn specific patterns in the image or signal (such as temperature changes, electromagnetic wave frequency components, vibration patterns, etc.). Convolution operation: The convolution operation is to perform a weighted sum through the filter (matrix) and the input data (such as temperature, vibration, etc.), and then pass it through an activation function (such as ReLU) to the next layer. Different filters can identify different local patterns, such as the rise of temperature and the periodic fluctuations of vibration. Role of the pooling layer: The feature maps extracted by the convolutional layer usually have a high dimension. The main role of the pooling layer is to reduce the dimension of the data through downsampling (such as max pooling or average pooling), thereby reducing the computational amount and the risk of overfitting, while retaining important features. The pooling operation reduces the spatial size of the data, significantly reducing the number of network parameters and improving the training efficiency. Max pooling: In the pooling operation, the max pooling technique is usually adopted, that is, the maximum value in each window is selected as the representative of the window. This helps to retain the most significant features in the input data. Flatten the feature map and input it into the fully connected layer: The pooled feature map is flattened into a one-dimensional vector and fed into the fully connected layer. The role of the fully connected layer is to perform a weighted combination of different features through linear transformation to generate high-level abstract features. At this time, the network has a deeper understanding of the influence of each feature in the input data. Weighted combination: In the fully connected layer, each neuron is connected to all neurons in the previous layer, and the output is calculated by weighted sum. This process is equivalent to performing a weighted sum on the features extracted from the convolutional layer and the pooling layer to comprehensively consider various interference factors. Classification operation: After the fully connected layer, the output is fed into the classifier.The classifier is usually a softmax layer that converts the features extracted from the network into a probability distribution, thereby assigning a probability value to each type of environmental interference (such as temperature interference, electromagnetic interference, vibration interference). Each output class corresponds to an environmental interference type. Activation function: In the classification stage, the activation function (softmax) is used to convert the network output into a probability distribution to ensure that the classification results meet expectations. The Softmax function converts the original numerical output (un-normalized scores) into a probability value representing the occurrence probability of each environmental interference type. Final output: Based on the results of the activation function, the network outputs the probability distribution of each environmental interference type and determines the final environmental interference label according to the maximum probability value. For example, if the probability of the temperature interference category predicted by the network is the largest, then "temperature interference" is output as the final result.

[0033] Specifically, the specific process of analyzing the impact of environmental interference on the recorder workload and obtaining the environmental interference factor is as follows: The environmental interference features extracted by the convolutional neural network are used to analyze the weights of the environmental interference features through the analytic hierarchy process, and the impact factors of each environmental interference on the recorder workload are calculated, including the temperature impact factor, the electromagnetic interference impact factor, and the low-frequency vibration impact factor; the temperature impact factor, the electromagnetic interference impact factor, and the low-frequency vibration impact factor are comprehensively processed to obtain the environmental interference factor.

[0034] In this implementation plan, to analyze the impact of environmental interference on the recorder workload and calculate the environmental interference factor, the following steps can be followed for calculation. The design of the formula needs to combine the environmental interference features extracted by the convolutional neural network and the analysis of the weights of the environmental interference features by the analytic hierarchy process (AHP). The calculation formula for the environmental interference factor is: ; Explanation of the formula: : The environmental interference factor, representing the total impact of temperature, electromagnetic interference, and low-frequency vibration on the recorder workload. The working environment temperature of the recorder. : The reference temperature, usually the standard working temperature of the recorder. : The temperature change range, used to normalize the temperature change. : The frequency of temperature interference. : The maximum value of the temperature interference frequency. : The weight coefficient of temperature interference. The gain index of temperature impact, used to adjust the impact of temperature on the interference factor. : The electromagnetic field intensity. The reference electromagnetic field intensity. : The change range of the electromagnetic field intensity. : The frequency of the electromagnetic interference signal, : The maximum frequency of the electromagnetic interference. The weight coefficient of the electromagnetic interference. The influence index of electromagnetic interference intensity on interference factors. : Vibration amplitude. : The maximum value of vibration amplitude. Vibration frequency (unit: Hertz). The weight coefficient of low-frequency vibration. The gain index of low-frequency vibration on interference factors. Weight coefficient: , , : Respectively represent the weighted coefficients of temperature, electromagnetic interference, and low-frequency vibration on the environmental interference factors. These coefficients are adjusted according to specific application scenarios to ensure that the impacts of different interference factors are reflected proportionally. The recorder works.

[0035] Specifically, to analyze the workload of the recorder and the external environment, the specific process of obtaining the environmental matching index is as follows: According to the recorder workload data and environmental interference factors, each interference factor is analyzed in relation to the recorder's workload to evaluate the impact of each interference factor on the recorder's various resources; model the impact relationship between each environmental interference and the workload, and calculate the degree of impact of each environmental interference on the recorder's workload through regression analysis; compare the degree of impact of each interference factor with the load status of the recorder to obtain the environmental matching index, which is used as a standard to measure the working efficiency of the recorder in the current environment.

[0036] In this implementation plan, the calculation process: Evaluation of the impact of interference factors: Determine the impact of each environmental interference (such as temperature, electromagnetic interference, vibration) on the recorder's workload through regression analysis. The results of the regression analysis can be expressed by the coefficient and environmental interference data . Modeling the relationship between load and interference: The impact of each interference factor on the recorder's workload is modeled through this difference metric to ensure that the impact of interference on the workload increases with the increase in the degree of difference. Comprehensive evaluation and calculation of the matching index: Multiply the degree of impact of each interference factor by the recorder's workload, and calculate the final environmental matching index through the adjustment of a non-linear regression function . The result reflects the working efficiency of the recorder in the current environment. Obtain the environmental matching index. The following is the specific formula representation based on regression analysis. The calculation formula of the environmental matching index ; Formula explanation: Environmental matching index, a standard to measure the working efficiency of the recorder in the current environment. The higher the value, the more matched the environment is with the recorder's workload and the higher the working efficiency. Environmental interference factor Includes the impacts of environmental factors such as temperature, electromagnetic interference, and low-frequency vibration. For different interference factors, the corresponding numbers can be used To distinguish the recorder workload : The resource loads of the recorder, including processor load, memory usage, storage space, network bandwidth, and battery power. Different workload data are numbered as . Regression function: Through regression analysis, we model the relationship between the influence degree of each interference factor and the recorder workload. In the regression function: : The numerical value corresponding to the load status data of the recorder The numerical value corresponding to the environmental interference factor. The coefficient obtained from the regression analysis, representing the influence weight of environmental interference on the recorder workload. Nonlinear adjustment: By taking the difference between the workload and environmental interference and performing exponential adjustment, the matching index decreases in the case of a large difference and increases in the case of a small difference. This adjustment uses a logistic regression model (Sigmoid function), where: : Through this exponential term, the environmental matching degree rapidly decreases as the difference between the load and the interference factor increases.

[0037] Specifically, according to the environmental matching degree index, the specific process of determining whether the recorder can execute tasks in the current environment is as follows: Set the minimum environmental matching degree threshold for task execution according to the environmental matching degree index, and compare the environmental matching degree index with the minimum environmental matching degree threshold for task execution: If the environmental matching degree index is greater than or equal to the minimum environmental matching degree threshold, it means that the recorder can execute tasks in the current environment, and task scheduling continues; if the environmental matching degree index is less than the minimum environmental matching degree threshold, it means that the recorder cannot effectively execute tasks in the current environment.

[0038] In this implementation plan, the calculation of the environmental matching degree index: The environmental matching degree index is calculated through the aforementioned steps, and this index reflects the working efficiency and adaptability of the recorder in the current environment. Set the minimum environmental matching degree threshold: Set a minimum environmental matching degree threshold, which represents the minimum environmental conditions required for the recorder to effectively execute tasks. Generally, this value is preset according to the complexity of the task and the requirements of the environment. Matching degree comparison: Compare the calculated environmental matching degree index with the set minimum environmental matching degree threshold: If the environmental matching degree index is greater than or equal to the minimum threshold: It indicates that the environmental conditions are suitable for task execution, and the recorder can continue task scheduling. If the environmental matching degree index is less than the minimum threshold: It indicates that the current environment is not suitable for task execution, and the recorder cannot effectively execute tasks and may need to adjust the task or environmental settings.

[0039] Specifically, the specific process of dynamically adjusting task priorities according to environmental interference and recorder resource status is as follows: If the recorder cannot effectively execute tasks in the current environment, dynamically evaluate the current working status of the recorder based on the environmental interference factor and the recorder's resource status to obtain a task priority adjustment factor; determine the task priority according to the task priority adjustment factor.

[0040] In this implementation plan, evaluate the current working status of the recorder: If the recorder cannot effectively execute tasks in the current environment (such as excessive environmental interference or insufficient resources), it is first necessary to evaluate the working status of the recorder. This involves comprehensively analyzing environmental interference factors (such as temperature, electromagnetic interference, vibration, etc.) and the recorder's resource status (such as processor load, memory, storage, bandwidth, battery power, etc.) to understand the adequacy of the current working environment and the recorder's resources. To dynamically adjust task priorities according to environmental interference factors and the recorder's resource status, define a task priority adjustment factor to reflect the change in task execution priority. This factor needs to consider the impact of environmental interference on the recorder's performance and the usage of the recorder's resources. The following is the calculation formula and explanation of this factor: The formula for the task priority adjustment factor is: ; Parameter explanation The task priority adjustment factor represents the adjustment value of the current task relative to the original priority. If , the task priority is increased; if , the task priority is decreased. The environmental interference weight factor is used to represent the degree of influence of environmental interference on task priority. This factor reflects the influence intensity of different environmental interferences (such as temperature, electromagnetic interference, vibration, etc.) on the recorder's resources and task execution. The change in the environmental interference factor measures the impact of the current environmental interference on the recorder's work. For example, a higher temperature or strong electromagnetic interference may cause the performance of the recorder to decline, thus requiring adjustment of the task priority. Specifically, can be obtained through the quantification of the environmental interference type and interference intensity, usually expressed as a negative or positive value (a negative value indicates severe interference and requires a decrease in task priority, and a positive value indicates a suitable environment and the task priority can be increased). : The recorder resource status weight factor represents the degree of influence of the recorder's available resources (such as processor load, memory, bandwidth, battery power, etc.) on task priority. This factor is used to measure whether task priority needs to be adjusted when resources are scarce. The change in the recorder resource status measures the impact of the current resource usage on the task. For example, if the processor or memory load of the recorder is high, the task priority needs to be adjusted to prevent system overload. Specifically, Indicates the change of the current resource status relative to the maximum available resources, usually expressed as a negative or positive value (a negative value indicates insufficient resources and the task priority needs to be reduced, and a positive value indicates sufficient resources and the task priority can be increased). Once the task priority adjustment factor is obtained , the task priority can be adjusted according to its value; if , it means that the task priority needs to be increased and the task can be executed preferentially. If , it means that the task priority needs to be reduced and the execution of the task can be postponed. If , it means that the environment and resource status have no impact on the task execution and the task priority remains unchanged.

[0041] Specifically, the adjustment logic for dynamically adjusting the working mode of the recorder through the fuzzy control algorithm is as follows: taking the environment matching degree index and the task priority as input variables; mapping the environment matching degree index and the task priority to fuzzy sets respectively and establishing a fuzzy rule base; according to the fuzzy rule base, combining the current environment matching degree index and the task priority, performing fuzzy inference to generate an adaptive working mode output and dynamically adjusting the working mode of the recorder.

[0042] In this implementation plan, the input variables are defined as follows: the input variables of the fuzzy control algorithm are the environment matching degree index and the task priority. These two variables reflect the working efficiency of the recorder in the current environment and the urgency of the task. Fuzzy set mapping: The environment matching degree index and the task priority are mapped to fuzzy sets respectively. For example: the environment matching degree index is mapped to: high, medium, low (indicating environmental adaptability). The task priority is mapped to: high, medium, low (indicating the urgency of the task). These mappings will be converted into fuzzy sets according to the predetermined rules and the membership functions in the fuzzy control algorithm, representing the fuzzy nature of the environment and the task. Establishing a fuzzy rule base: Define the working modes that should be adopted under different combinations of the environment matching degree index and the task priority. For example: if the environment matching degree is high and the task priority is high, a more efficient and full-power working mode can be selected. If the environment matching degree is low and the task priority is low, an energy-saving or low-power consumption working mode can be selected. If the task priority is high but the environment matching degree is low, a protection mode may be adopted to reduce risks. Fuzzy inference and working mode output: According to the current environment matching degree index and the task priority, through fuzzy inference, combined with the fuzzy rule base, generate a working mode output. Fuzzy inference is to perform operations on the input variables of the fuzzy set through the rule base to obtain a fuzzy conclusion (adaptive working mode). The output adaptive working mode will indicate the specific working mode that the recorder should adopt (for example: full-power mode, energy-saving mode, protection mode). Dynamically adjusting the working mode of the recorder:

[0043] Finally, according to the results of fuzzy inference, the working mode of the recorder is dynamically adjusted to optimize the working efficiency and stability of the recorder under the current environment and task conditions. Through dynamic adjustment, the recorder can operate in an optimal manner under different environmental interferences and resource conditions.

[0044] An AI-based intelligent recorder management system includes the following modules: an environmental interference factor extraction module, an environmental matching degree evaluation module, a task priority adjustment module, and a working mode adjustment module; the environmental interference factor extraction module is used to obtain the external environmental interference data and the recorder resource status data of the recorder through sensors, extract the environmental interference features through a convolutional neural network, identify the environmental interference types, analyze the impact of environmental interference on the recorder workload, and obtain the environmental interference factors; the environmental matching degree evaluation module is used to analyze the matching degree between the workload of the recorder and the external environment according to the environmental interference factors, obtain the environmental matching index, and be used to measure the working efficiency of the recorder in the current environment; the task priority adjustment module is used to judge the feasibility of the recorder task execution according to the environmental matching degree index, and dynamically adjust the task priority according to the environmental interference and the recorder resource status; the working mode adjustment module is used to dynamically adjust the working mode of the recorder through a fuzzy control algorithm according to the environmental matching degree index and the task execution priority.

[0045] In this implementation plan, for the environmental interference factor extraction module: this module collects the interference data (such as temperature, electromagnetic interference, vibration, etc.) of the external environment of the recorder and the resource status data (such as processor load, memory usage, storage space, etc.) of the recorder through sensors. Then, the environmental interference features in these data are extracted through a convolutional neural network (CNN), the interference types are identified, and the impact of the interference on the recorder workload is analyzed. Finally, the environmental interference factors are obtained. For the environmental matching degree evaluation module: this module uses the information obtained from the environmental interference factors to analyze the matching degree between the workload of the recorder and the current external environment, and calculates the environmental matching index. The environmental matching index is an indicator to measure the working efficiency of the recorder in the current environment and can reflect the adaptation of the environmental conditions to the recorder performance. For the task priority adjustment module: based on the environmental matching degree index, this module judges whether the recorder can effectively execute the task in the current environment. If the environmental matching degree index is low, it means that the environmental conditions are not suitable for task execution, and the system will dynamically adjust the task priority according to the environmental interference and the recorder resource status. The tasks with higher priority will be executed first, and the low-priority tasks may be delayed or abandoned. For the working mode adjustment module: this module adjusts the working mode of the recorder through a fuzzy control algorithm according to the environmental matching degree index and the task priority. The fuzzy control algorithm can flexibly adjust the working state of the recorder (for example, reduce power consumption, improve performance, or adjust other system parameters) according to different situations of the environmental matching degree and the task priority to ensure the smooth execution of the task.

[0046] In summary, the present application has at least the following effects:

[0047] An AI-based intelligent recorder management method and system. Through the extraction and analysis of environmental interference factors, the system can monitor and evaluate the impact of the external environment on the recorder's performance in real time, thereby dynamically adjusting the working mode of the recorder to ensure its stable operation under different environmental conditions. Through environmental matching degree evaluation and task priority adjustment, the system can reasonably arrange task priorities according to environmental changes and the recorder's resource status, avoid the execution of inefficient tasks, and improve the utilization efficiency of system resources. By dynamically adjusting the working mode through the fuzzy control algorithm, the system can optimize the working state of the recorder according to the environmental matching degree and task priority, thereby reducing unnecessary energy consumption and improving work efficiency at the same time.

[0048] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0049] The present invention is described with reference to the flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce a device for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0050] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device that realizes the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0051] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the functions specified in one process or a plurality of processes and / or blocks Figure 1 one process or a plurality of processes and / or blocks Figure 1 steps for implementing the functions specified in one block or a plurality of blocks.

[0052] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to cover the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0053] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. An AI-based intelligent recorder management method, characterized in that, It includes the following steps: S1. Obtain the external environment interference data and the recorder resource status data through sensors, extract the environmental interference features through a convolutional neural network, identify the environmental interference types, analyze the impact of environmental interference on the recorder workload, and obtain the environmental interference factors; S2. According to the environmental interference factors, analyze the matching degree between the recorder workload and the external environment, obtain the environmental matching index, which is used to measure the working efficiency of the recorder in the current environment; S3. According to the environmental matching degree index, judge the feasibility of the recorder task execution, and dynamically adjust the task priority according to the environmental interference and the recorder resource status; S4. According to the environmental matching degree index and the task execution priority, dynamically adjust the working mode of the recorder through a fuzzy control algorithm; The specific process of extracting the environmental interference features through a convolutional neural network and identifying the environmental interference types is as follows: Preprocess the temperature gradient data, electromagnetic interference data, and low-frequency vibration data, including standardization and normalization processing. For vibration data, transform the time-domain signal into a frequency-domain signal through fast Fourier transform; Input the preprocessed data into multiple convolutional layers of the convolutional neural network, perform a sliding window operation on the input data through multiple filters, and extract the environmental interference features, including temperature change features, electromagnetic interference features, and low-frequency vibration features; The environmental interference features extracted by the convolutional layer are processed by the pooling layer, reduce the data dimension through downsampling to generate a feature map, and through the fully connected layer, flatten the feature map obtained from the convolutional layer and the pooling layer into a vector, send it to the classifier, and perform a weighted combination of different features to identify the environmental interference types of temperature change, electromagnetic interference, and vibration; Convert the output into a probability distribution through an activation function, and finally output the labels of the environmental interference types, including temperature interference, electromagnetic interference, and vibration interference; The specific process of analyzing the impact of environmental interference on the recorder workload and obtaining the environmental interference factors is as follows: Through the environmental interference features extracted by the convolutional neural network, analyze the weights of the environmental interference features hierarchically, and calculate the impact factors of each environmental interference on the recorder workload, including the temperature impact factor, the electromagnetic interference impact factor, and the low-frequency vibration impact factor; Perform a comprehensive arithmetic processing on the temperature impact factor, the electromagnetic interference impact factor, and the low-frequency vibration impact factor to obtain the environmental interference factor; The calculation formula for the environmental interference factor is as follows: Explanation of the formula: : The environmental interference factor, representing the total impact of temperature, electromagnetic interference, and low-frequency vibration on the working load of the recorder. : The gain index of temperature influence, used to adjust the influence of temperature on the interference factor. The working environmental temperature of the recorder. : The reference temperature, usually the standard working temperature of the recorder. : The temperature change range, used to standardize the temperature change. : The frequency of temperature interference. : The maximum value of the temperature interference frequency. : The weight coefficient of temperature interference, the gain index of temperature influence, used to adjust the influence of temperature on the interference factor. : The electromagnetic field strength. The reference electromagnetic field strength. : The change range of the electromagnetic field strength. : The frequency of the electromagnetic interference signal. : The maximum frequency of the electromagnetic interference. The weight coefficient of the electromagnetic interference. The influence index of the electromagnetic interference intensity on the interference factor. : The vibration amplitude. : The maximum value of the vibration amplitude. : The frequency of the low-frequency vibration. The vibration frequency. The weight coefficient of the low-frequency vibration. The gain index of the low-frequency vibration on the interference factor, weight coefficient: , , : Respectively represent the weighting coefficients of temperature, electromagnetic interference, and low-frequency vibration on the environmental interference factor; The adjustment logic of dynamically adjusting the working mode of the recorder through a fuzzy control algorithm is as follows: Use the environmental matching degree index and the task priority as input variables; Map the environmental matching degree index and the task priority to fuzzy sets respectively, and establish a fuzzy rule base; According to the fuzzy rule base, combine the current environmental matching degree index and the task priority, perform fuzzy reasoning, generate an adaptive working mode output, and dynamically adjust the working mode of the recorder.

2. The method for managing an AI-based intelligent recorder according to claim 1, wherein: The external environment interference data includes temperature gradient data, electromagnetic interference data, and low-frequency vibration data; The recorder resource status data includes processor load, memory usage, storage space, network bandwidth, and battery power.

3. The intelligent recorder management method based on AI according to claim 2, characterized in that: The specific process of analyzing the recorder workload and the external environment and obtaining the environmental matching index is as follows: Based on the recorder workload data and environmental interference factors, each interference factor is analyzed in relation to the recorder's workload to evaluate the impact of each interference factor on the recorder's various resources. Model the impact relationship between each environmental interference and the workload, and calculate the degree of impact of each environmental interference on the recorder's workload through regression analysis. Compare the degree of impact of each interference factor with the recorder's load status to obtain an environmental matching index, which is used as a standard to measure the working efficiency of the recorder in the current environment.

4. The method for managing an AI-based intelligent recorder according to claim 3, wherein: The specific process of determining whether the recorder can execute tasks in the current environment based on the environmental matching index is as follows: According to the environmental matching index, set the minimum environmental matching threshold for task execution, and compare the environmental matching index with the minimum environmental matching threshold for task execution: If the environmental matching index is greater than or equal to the minimum environmental matching threshold, it means that the recorder can execute tasks in the current environment, and task scheduling continues. If the environmental matching index is less than the minimum environmental matching threshold, it means that the recorder cannot effectively execute tasks in the current environment.

5. The intelligent recorder management method based on AI according to claim 4, characterized in that: The specific process of dynamically adjusting task priorities according to environmental interference and the recorder's resource status is as follows: If the recorder cannot effectively execute tasks in the current environment, dynamically evaluate the recorder's current working status based on the environmental interference factors and the recorder's resource status to obtain a task priority adjustment factor. Determine the task priority according to the task priority adjustment factor.

6. An AI-based intelligent recorder management system, which is applied to an AI-based intelligent recorder management method described in any one of claims 1-5, and is characterized in that, It includes the following modules: environmental interference factor extraction module, environmental matching degree evaluation module, task priority adjustment module, and working mode adjustment module. The environmental interference factor extraction module is used to obtain the recorder's external environmental interference data and recorder resource status data through sensors, extract environmental interference features through a convolutional neural network, identify the types of environmental interference, analyze the impact of environmental interference on the recorder's workload, and obtain environmental interference factors. The environmental matching degree evaluation module is used to analyze the matching degree between the recorder's workload and the external environment based on the environmental interference factors, obtain an environmental matching index, and use it to measure the working efficiency of the recorder in the current environment. The task priority adjustment module is used to judge the feasibility of the recorder's task execution according to the environmental matching index, and dynamically adjust the task priority according to the environmental interference and the recorder's resource status. The working mode adjustment module is used to dynamically adjust the working mode of the recorder through a fuzzy control algorithm according to the environmental matching index and the priority of task execution.

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