Smart home equipment management method and system

Abstract

The invention discloses a smart home equipment management method and system, and relates to the technical field of data service value evaluation and decision optimization, and the method comprises the steps: collecting equipment data, and carrying out the fusion to form a unified equipment state data set; performing feature extraction based on the equipment state data set, and combining to generate a multi-dimensional life prediction feature vector; according to the multi-dimensional life prediction feature vector, constructing an equipment life prediction model, and outputting an equipment residual life prediction value in real time; based on the equipment residual life prediction value, identifying an equipment life abnormal decrease condition, and triggering equipment maintenance early warning information; and receiving user feedback according to the equipment maintenance early warning information, and adaptively adjusting the parameters of the equipment life prediction model. According to the method, the service life model is constructed by fusing vibration thermoelectric multiple features, self-adaptive updating and graded early warning are performed, advanced maintenance of household appliances, fault shutdown risk reduction and operation and maintenance cost saving are realized, the energy consumption monitoring optimization benefit is remarkable, and the user experience is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart home device management and health prediction modeling, and specifically provides a method and system for smart home device management. Background Art

[0002] With the continuous development of Internet of Things (IoT) technology and artificial intelligence (AI), smart home systems have evolved from the initial stage of remote device control to the current comprehensive management stage of multi-device collaboration, status perception, and intelligent decision-making in parallel. More and more home terminal devices (such as smart air conditioners, smart lighting, security cameras, etc.) have achieved networking and data collection functions, providing users with convenient and comfortable living experiences. At the same time, the types and quantities of smart devices continue to increase, significantly raising the complexity of device management. To improve device operation efficiency and service life, device health management technology has gradually been introduced into the home scenario, forming a device life prediction and fault warning mechanism based on data analysis. However, most existing methods focus on single devices and lack cross-device status fusion and systematic management means, unable to fully unleash the data potential of smart home systems.

[0003] Although some studies in the prior art have attempted to introduce data-driven health assessment and prediction models, there are still several key technical bottlenecks. First, traditional methods usually rely on single-dimensional or static features and lack the ability to fuse multi-dimensional and time-series features, resulting in low accuracy of life prediction models. Second, existing device status monitoring means are mostly based on regular maintenance or post-fault processing and fail to achieve early warning and proactive maintenance. In addition, existing models generally adopt fixed parameter structures and lack a mechanism for dynamically adapting and optimizing based on actual operation feedback, unable to effectively cope with actual environmental changes such as user behavior differences and different degrees of device aging. These technical limitations prevent existing device management methods from achieving efficient and personalized device health management and intelligent warning, and it is also difficult to build a continuously evolving and feedback-driven management system. The present invention constructs a unified data fusion framework, multi-dimensional feature extraction mechanism, and prediction model with self-learning ability to achieve high-precision prediction of device status and proactive maintenance suggestions in a multi-device environment, breaking through the limitations of the prior art in data dimension fusion, model evolution ability, and warning accuracy, and having significant practical value and innovative advantages. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are as follows: The existing smart home device management methods lack a multi-device status fusion mechanism, have low accuracy of the life prediction model and are difficult to dynamically optimize, the device maintenance and feedback mechanism is imperfect and it is difficult to form a closed-loop management, and how to dynamically adjust the parameters of the life prediction model based on user feedback to achieve the adaptive evolution of the prediction model.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides a smart home device management method, including collecting device data and fusing it to form a unified device status data set; Based on the device status data set, feature extraction is performed to combine and generate a multi-dimensional life prediction feature vector; According to the multi-dimensional life prediction feature vector, a device life prediction model is constructed to output the predicted remaining life value of the device in real time; Based on the predicted remaining life value of the device, the abnormal decline of the device life is identified, and a device maintenance warning message is triggered; According to the device maintenance warning message, user feedback is received, and the parameters of the device life prediction model are adaptively adjusted.

[0007] As a preferred solution of the smart home device management method of the present invention, among them: the collection of device data includes respectively deploying data collection units in each device in the smart home environment, and the data collection units include vibration sensors, temperature sensors, acceleration sensors, current sensors and power sensors; The vibration sensor is used to collect the vibration signal during the operation of the device and generate a multi-band vibration envelope energy sequence through multi-band envelope analysis; The temperature sensor is used to collect the temperature change of the key components of the device in real time to form a temperature time series curve; The acceleration sensor is used to measure the acceleration change of the device and obtain the speed change rate time series through numerical calculation; The current sensor is used to measure the current change during the operation of the device in real time to generate current waveform data; The power sensor is used to measure the power output of the device in real time to generate instantaneous power data; The data collection unit periodically sends the multi-band vibration envelope energy sequence, temperature time series curve, speed change rate time series, current waveform data and instantaneous power data to the smart home local gateway through the local wireless communication device for data storage, and the smart home local gateway performs preliminary time marking processing on the received data to form the original device operation data with a unified time stamp.

[0008] As a preferred solution of the smart home device management method described in the present invention, wherein: the fusion to form a unified set of device status data includes, based on the original device operation data stored in the smart home local gateway, using a time series data interpolation algorithm to eliminate the difference in data sampling frequencies of different devices and unify the data sampling frequency; Standardize the original device operation data through a data standardization algorithm to eliminate the differences in dimension and value range between the data, and generate standardized device operation data; Use a multi-source data spatio-temporal alignment algorithm for the standardized device operation data, with a unified device identifier and a unified timestamp as the data matching benchmark, and integrate the data item by item according to the timestamp to form multi-dimensional device operation data that is spatio-temporally consistent; Perform data noise reduction processing on the multi-dimensional device operation data using the Kalman filtering algorithm to eliminate the measurement errors of various sensors and environmental noise interference, and generate noise-reduced multi-dimensional device operation data; Fuse the noise-reduced multi-dimensional device operation data according to a preset weight through a multi-dimensional data weighted fusion algorithm to obtain a unified data format and output it as a unified set of device status data; The set of device status data includes a multi-band vibration envelope energy sequence after data processing, a temperature time series curve after data processing, a speed change rate time series after data processing, current waveform data after data processing, and instantaneous power data after data processing.

[0009] As a preferred solution of the smart home device management method described in the present invention, wherein: the generation of the multi-dimensional life prediction feature vector includes, performing weighted summation on the multi-band vibration envelope energy sequence after data processing according to a preset weight to obtain a vibration fatigue feature; Perform integral processing on the temperature time series curve after data processing to obtain an accumulated heat load feature; Perform square integral on the speed change rate time series after data processing to obtain a speed excitation feature; Accumulate the absolute values of the differences between adjacent sampling points of the current waveform data after data processing to obtain a current disturbance feature; Perform logarithmic mapping and normalization processing on the instantaneous power data after data processing to obtain a power response feature; Combine the vibration fatigue feature, the accumulated heat load feature, the speed excitation feature, the current disturbance feature, and the power response feature in a preset order to form a multi-dimensional life prediction feature vector.

[0010] As a preferred solution of the smart home device management method described in the present invention, wherein: constructing the device life prediction model includes inputting the multi-dimensional life prediction feature vector into a preset life prediction function group, and the life prediction function group includes a non-linear mapping function for vibration fatigue response characteristics, an incomplete integration function for cumulative heat load characteristics and speed excitation characteristics, a logarithmic normalization function for power response characteristics, and a radical attenuation function for adjusting the influence degree of current disturbance characteristics; Perform weighted combination on the output results of the life prediction function group, perform integral accumulation, and form a health loss value of the device under the current operating state; Combine the state disturbance complexity factor during the device operation, normalize the health loss value, and output a device remaining life prediction value representing the remaining life status of the device.

[0011] As a preferred solution of the smart home device management method described in the present invention, wherein: identifying the abnormal decline of the device life includes constructing a dynamic health index baseline for the device health level according to the type of the target device and historical operation data; Periodically calculate the residual difference between the current device remaining life prediction value and the dynamic health index baseline, and use a sliding time window to accumulate the residuals. If the continuous period exceeds the set threshold and the cumulative residual value exceeds the set threshold, it is determined that there is an abnormal decline trend in life; Divide multiple levels of maintenance warning levels according to the residual degree, including primary warning, intermediate warning, and emergency warning. The primary warning is used to trigger device self-check and user prompt, the intermediate warning is used to limit the device operation ability and record abnormal energy consumption, and the emergency warning is used to automatically cut off the power of high-risk devices and push remote maintenance notifications; After the warning level is confirmed, automatically generate device maintenance warning information, and the device maintenance warning information includes the prediction value change trend, the current level classification, and recommended handling measures.

[0012] As a preferred solution of the smart home device management method described in the present invention, wherein: adaptively adjusting the parameters of the device life prediction model includes, after the device maintenance warning information is pushed, receiving feedback information provided by the user through the smart home terminal, and the feedback information includes the maintenance execution situation confirmed by the user, the actual operating state of the device, the user's subjective evaluation, and other relevant information; Compare the feedback information with the device remaining life prediction value. If it is found that there is a deviation between the prediction value and the actual device state, use an adaptive adjustment algorithm to optimize the parameters of the life prediction model.

[0013] In a second aspect, an embodiment of the present invention provides a smart home device management system, including: Data acquisition module: Collect device data and fuse it to form a unified set of device status data; Feature extraction module: Based on the set of device status data, perform feature extraction and combine to generate a multi-dimensional life prediction feature vector; Life prediction module: According to the multi-dimensional life prediction feature vector, construct a device life prediction model and real-time output the predicted value of the remaining life of the device; Abnormality identification and warning module: Based on the predicted value of the remaining life of the device, identify the abnormal decline of the device life and trigger the device maintenance warning information; Feedback learning module: According to the device maintenance warning information, receive user feedback and adaptively adjust the parameters of the device life prediction model.

[0014] Advantages of the present invention: By constructing a unified set of device status data, combining multi-dimensional feature vectors and life prediction models, the present invention can achieve high-precision real-time evaluation of the operating status of multiple types of devices in a smart home and prediction of the remaining life. The multi-level maintenance warning mechanism triggered based on the prediction results effectively improves the initiative and safety of device maintenance, and avoids interference or losses caused to users by sudden device failures. At the same time, the present invention introduces an adaptive parameter optimization mechanism driven by user feedback, enabling the life prediction model to have the ability of dynamic adjustment and personalized evolution, significantly enhancing the adaptability and robustness of the system, constructing an intelligent management closed-loop from data acquisition, status evaluation, warning trigger to feedback correction, and comprehensively improving the management efficiency and user experience of smart home devices. Description of the drawings

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings, where: Figure 1 It is the overall flowchart of a smart home device management method provided by the first embodiment of the present invention. Detailed implementation manners

[0016] To make the above objects, features and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific implementation manners of the present invention in conjunction with the drawings of the specification. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0017] Example 1, referring to Figure 1, is an embodiment of the present invention, and provides a smart home device management method, comprising: S1: Collect equipment data and integrate them into a unified equipment status data set.

[0018] S11: deploying a data acquisition unit in each device in the smart home environment, wherein the data acquisition unit includes a vibration sensor, a temperature sensor, an acceleration sensor, a current sensor, and a power sensor; The vibration sensor is used to collect vibration signals during the operation of the equipment and generate a multi-band vibration envelope energy sequence through multi-band envelope analysis; The temperature sensor is used to collect temperature changes of key components of the equipment in real time to form a temperature time series curve; The acceleration sensor is used to measure the acceleration change of the device and obtain the speed change rate time series through numerical calculation; The current sensor is used to measure the current change of the device in real time when it is running and generate current waveform data; The power sensor is used to measure the power output of the device in real time and generate instantaneous power data; The data acquisition unit periodically sends the multi-band vibration envelope energy sequence, temperature timing curve, speed change rate timing, current waveform data and instantaneous power data to the smart home local gateway through the local wireless communication device for data storage. The smart home local gateway performs preliminary time stamp processing based on the received data to form the original equipment operation data with a unified time stamp.

[0019] Specifically, vibration sensors are used to collect vibration signals during the operation of the equipment, and multi-band envelope analysis is performed to generate a multi-band vibration envelope energy sequence. This sequence can effectively capture the vibration characteristics of the equipment at different frequencies, especially the slight changes in the high-frequency band, which helps to identify problems such as fatigue damage or looseness of mechanical parts at an early stage.

[0020] The temperature sensor collects the temperature changes of key components of the equipment in real time and forms a temperature time series curve. This curve reflects the heat load of the equipment under different working conditions and can reveal potential problems such as overheating and poor heat dissipation.

[0021] The acceleration change of the equipment is measured by the acceleration sensor, and the speed change rate time series is obtained by numerical calculation. This time series data can reflect the mechanical impact load during the dynamic process of equipment start-up and stop, which helps to evaluate the fatigue degree of the equipment during frequent start-up and stop.

[0022] Current sensors and power sensors are respectively used to measure the current changes and power output during device operation in real time, generating current waveform data and instantaneous power data. These data can reveal the electrical performance changes of the device under different load conditions and help identify abnormalities in the electrical system, such as overload, short circuit, etc.

[0023] The data acquisition unit periodically sends the above-mentioned various data to the smart home local gateway through the local wireless communication device for data storage and performs preliminary time stamping processing to form the original device operation data with a unified time stamp. This unified data format helps with subsequent data fusion and analysis, improving the efficiency and accuracy of data processing.

[0024] S12: Based on the original device operation data stored in the smart home local gateway, use the time series data interpolation algorithm to eliminate the differences in data sampling frequencies of different devices and unify the data sampling frequency; Standardize the original device operation data through the data standardization algorithm to eliminate the differences in dimension and value range between the data, and generate the standardized device operation data; Use the multi-source data spatio-temporal alignment algorithm for the standardized device operation data. With the unified device identifier and unified time stamp as the data matching benchmark, integrate the data item by item according to the time stamp to form the multi-dimensional device operation data with consistent space and time; Use the Kalman filter algorithm to perform data noise reduction processing on the multi-dimensional device operation data to eliminate the measurement errors of various sensors and environmental noise interference, and generate the noise-reduced multi-dimensional device operation data; Fuse the noise-reduced multi-dimensional device operation data according to the preset weights through the multi-dimensional data weighted fusion algorithm to obtain a unified data format and output it as a unified device status data set; The device status data set includes the multi-band vibration envelope energy sequence after data processing, the temperature time series curve after data processing, the speed change rate time series after data processing, the current waveform data after data processing, and the instantaneous power data after data processing.

[0025] It should be noted that in the smart home environment, sensors of different devices may have different sampling frequencies, resulting in inconsistencies in data on the time axis. By using the time series data interpolation algorithm, such as linear interpolation or spline interpolation, data with different frequencies can be unified to a standard sampling frequency to ensure the time series consistency of subsequent processing.

[0026] Since the data collected by various sensors have different dimensions and value ranges, such as temperature (°C), current (A), power (W), etc., directly performing data fusion may cause the influence of some features on the model to be amplified or reduced. Through standardization processing, such as Z-score standardization or Min-Max normalization, the dimension differences can be eliminated, enabling each feature to be on the same scale, which is convenient for subsequent analysis and modeling.

[0027] In the data collection of multiple devices and multiple sensors, there may be problems such as inconsistent timestamps or confused device identifiers. By unifying the device identifiers and timestamps and adopting a spatio-temporal alignment algorithm, the consistency of data from different sources in time and space can be ensured, forming a complete multi-dimensional device operation data.

[0028] When sensors collect data, they may be affected by environmental noise or their own errors, resulting in noise in the data. Kalman filtering is an effective noise reduction method that can filter out noise and obtain more accurate device operation state data through the processes of prediction and update.

[0029] After obtaining the noise-reduced data of multiple sensors, it is necessary to fuse them into a unified device state data set. By setting the preset weights of the data of each sensor and adopting a weighted fusion algorithm, the characteristics of various types of data can be integrated to form a unified data format, providing a reliable data basis for subsequent feature extraction and model construction.

[0030] S2: Based on the device state data set, perform feature extraction and combine to generate a multi-dimensional life prediction feature vector.

[0031] Perform weighted summation on the multi-band vibration envelope energy sequence obtained by processing the data in the device state data set according to the preset weight to obtain the vibration fatigue feature; specifically, process the multi-band vibration envelope energy sequence. This sequence is obtained by performing multi-band envelope analysis on the vibration signal during the operation of the device and reflects the vibration energy distribution of the device in different frequency ranges. In order to extract the vibration fatigue feature, it is necessary to perform weighted summation on the energy values of each frequency band according to the preset weight. The setting of the weight should be based on the sensitivity of each frequency band to the fatigue damage of the device and is usually determined through experiments or historical data analysis. The result of the weighted summation is the vibration fatigue feature, which can effectively reflect the influence of the vibration energy of the device in each frequency band on its life.

[0032] Integrate the temperature time-series curve after data processing to obtain the cumulative heat load feature; specifically, process the temperature time-series curve. This curve records the temperature changes of the key components of the device during operation. By integrating this curve, the cumulative heat load feature of the device within a certain period of time can be obtained. This feature reflects the cumulative degree of thermal stress endured by the device during operation and is of great significance for evaluating the thermal aging and lifespan of the device.

[0033] Square-integrate the rate-of-change-of-speed time-series after data processing to obtain the speed excitation feature; specifically, process the rate-of-change-of-speed time-series. This time-series data is obtained by numerically calculating the acceleration signal of the device and reflects the dynamic characteristics of the device's speed change. By performing square-integration on the rate-of-change-of-speed time-series, the speed excitation feature can be obtained. This feature can quantify the intensity of the speed changes experienced by the device during operation and plays an important role in evaluating the mechanical shock and fatigue damage of the device.

[0034] Accumulate the absolute differences between adjacent sampling points of the current waveform data after data processing to obtain the current perturbation feature; specifically, this data records the current changes of the device during operation. By calculating the absolute differences between adjacent sampling points and accumulating them, the current perturbation feature can be obtained. This feature reflects the degree of current fluctuation of the device during operation and is of great significance for evaluating the electrical stability and potential faults of the device.

[0035] Perform logarithmic mapping and normalization on the instantaneous power data after data processing to obtain the power response feature; specifically, process the instantaneous power data. This data records the power output of the device during operation. By performing logarithmic mapping and normalization on this data, the power response feature can be obtained. This feature can reflect the power response characteristics of the device under different load conditions and plays an important role in evaluating the energy efficiency and operating status of the device.

[0036] Combine the vibration fatigue feature, cumulative heat load feature, speed excitation feature, current perturbation feature, and power response feature in a preset order to form a multi-dimensional life prediction feature vector.

[0037] S3: Construct a device life prediction model based on the multi-dimensional life prediction feature vector and real-time output the predicted value of the remaining life of the device.

[0038] Input the multi-dimensional life prediction feature vector into a preset set of life prediction functions, which includes a non-linear mapping function for the vibration fatigue response feature, an incomplete integration function for the cumulative heat load feature and speed excitation feature, a logarithmic normalization function for the power response feature, and a radical decay function for adjusting the influence degree of the current perturbation feature; Specifically: Vibration fatigue characteristics Obtained through wavelet packet decomposition and energy weighting: ; Wherein, is the time moment of vibration fatigue characteristics, is the vibration envelope energy of the frequency band at time is the vibration weight of the th frequency band, representing its diagnostic importance or feature sensitivity, and the acquisition method is generally determined by experience or the weighting coefficient based on entropy weight; is the frequency band index number, and the value range is ; is the total number of wavelet packet frequency bands; The non - linear fatigue mapping function is defined as: ; Wherein, is the non - linear mapped vibration energy response, which constitutes the fatigue index; is the square value of used to construct the non - linear fatigue index; ; Where, is the cumulative integral value of temperature in the interval (cumulative heat load characteristic), reflecting the heat load; is the temperature at the time point ; is the rate of change of velocity at the time point is the square value ofused to evaluate the contribution of mechanical excitation energy, indicating the impact of shock or mutation on life; is the square integral value of the rate of change of velocity in the interval (velocity excitation characteristic); The non - linear life response function is expressed in the form of incomplete Gamma function as: ; Wherein, respectively represent the cumulative amounts of heat load and velocity change; is the incomplete Gamma function, used to express non - linear damage accumulation; is the integration variable, which is an intermediate variable used to traverse the domain in the mathematical integration process; Introduce a power response adjustment term simultaneously: ; Among them, is a normalization adjustment function based on power, used to adjust the loss growth in the high-power interval; is time the instantaneous power (power response characteristic) at the moment; is the normalization scaling coefficient, controlling the "compression" degree of the adjustment function for the high-power interval, the larger it is, the stronger the "adjustment" for high power, making the instantaneous power have a gradually weakened influence on , thus reflecting the adaptability characteristics of the system to power influence.

[0039] constitute a thermal-motion-power composite fatigue mapping function: ; Among them, is the damage intensity value under the coupling of heat-velocity-power; During the sampling period, calculate the current fluctuation characteristics: ; Among them, is the quantization value of the current disturbance intensity per unit time (current disturbance characteristic); is time at the moment, the current value at the th sampling point; is the sampling point index, ; is the power coefficient of the disturbance intensity, reflecting the sensitivity of the system to the current fluctuation amplitude. The larger it is, the more sensitive it is to large fluctuations, mainly used to enhance the response ability to spike-type disturbances or non-linear changes, and plays a non-linear amplification or suppression effect when measuring the "current disturbance characteristic".

[0040] Weight and combine the output results of the life prediction function group, and perform integral accumulation to form the health loss value of the equipment under the current operating state ; Specifically: ; Combine the state disturbance complexity factor during the operation of the equipment, normalize the health loss value, and output the equipment remaining life prediction value characterizing the remaining life status of the equipment; Specifically: ; Among them, is the equipment in the time window The predicted remaining life value of the device within is used as the core output value for life assessment; is the evaluation time window length, is the normalization adjustment factor; is the normalized term of state perturbation complexity, defined as: ; Among them, is the index of the number of device operation state switches, ; represents the th current change rate before and after state switching, is the th load difference corresponding to state switching, is the th coupling sensitivity coefficient of temperature rise and jump signal during state switching.

[0041] S4: Based on the predicted remaining life value of the device, identify the abnormal decline of the device life and trigger the device maintenance warning information.

[0042] According to the type and historical operation data of the target device, construct a dynamic health index baseline for the device health level, and based on the actual operation duration of the device, generate a theoretical life decay curve in combination with the Weibull distribution model as the reference for remaining life judgment; Periodically calculate the residual difference between the current predicted remaining life value of the device and the dynamic health index baseline, and use a sliding time window to accumulate the residuals. If the continuous period exceeds the set threshold and the cumulative residual value exceeds the set threshold, it is judged that there is an abnormal decline trend in life; Set the residual tolerance coefficient according to the structural sensitivity of different types of devices, relax the threshold for high-vibration devices, and tighten the threshold for precision devices to adapt to the performance degradation characteristics of multi-type devices; Divide multiple levels of maintenance warning levels according to the degree of residuals, including primary warning, intermediate warning and emergency warning. The primary warning is used to trigger device self-check and user prompt, the intermediate warning is used to limit the device operation ability and record abnormal energy consumption, and the emergency warning is used to automatically cut off the power of high-risk devices and push remote maintenance notifications; After the warning level is confirmed, automatically generate the device maintenance warning information, and the device maintenance warning information includes the predicted value change trend, the current level classification and the recommended handling measures.

[0043] It should be noted that the "dynamic health index baseline" is not a static threshold setting, but a benchmark model established based on the operating history and type differences of the target device, which reflects the dynamic evolution of the device's health status and avoids misjudgment problems under fixed thresholds. The "Weibull distribution model", as a widely used life statistical model in reliability analysis, can more accurately reflect the probability distribution characteristics of the life decay of most devices. Its rationality lies in its fitting advantage for right-skewed life data, which enhances the rigor of theoretical life judgment. The "residual difference" represents the degree of deviation between the predicted value and the health baseline, and is a sensitive indicator for measuring sudden changes in the device state; its accumulation method is processed through a "sliding time window", so that short-term fluctuations will not trigger false alarms, while retaining trend information and enhancing the robustness of judgment. Setting the "residual tolerance coefficient" reflects the adaptability design of the algorithm, that is, tolerance correction is introduced for the high-frequency fluctuations that may exist in high-vibration devices, reflecting the generalization ability of the model; for precision devices, higher discrimination sensitivity is required to improve monitoring accuracy. The division of the "multi-level maintenance warning level" enables the device maintenance strategy to have a response level, which can not only intervene in advance, but also respond according to the risk progression, helping to achieve refined device management.

[0044] S5: According to the device maintenance warning information, receive user feedback and adaptively adjust the parameters of the device life prediction model.

[0045] After the device maintenance warning information is pushed, receive the feedback information provided by the user through the smart home terminal. The feedback information includes the maintenance execution situation confirmed by the user, the actual operating state of the device, the user's subjective evaluation and other relevant information. Compare the feedback information with the predicted value of the remaining life of the device. If a deviation is found between the predicted value and the actual device state, use an adaptive adjustment algorithm to optimize the parameters of the life prediction model.

[0046] It should be noted that after the device maintenance warning information is pushed, the system not only receives the active feedback information from the user, such as the maintenance execution situation confirmed by the user through the smart home terminal, but also includes unstructured information such as passive monitoring data of the device operating state and the user's subjective evaluation. These feedback data are diverse in type and structure, but together they constitute an important information source for the true operating state of the device, and are the core basis for verifying the accuracy of device life prediction and adjusting model parameters.

[0047] Furthermore, the system compares the above feedback information with the remaining life prediction value generated by the current life prediction model, and automatically judges the accuracy of the model prediction based on the error value between the actual operating state and the predicted data. If there is a systematic deviation between the actual state indicated by the feedback information and the model prediction in multiple consecutive cycles, the system will trigger an error-driven adaptive optimization algorithm to fine-tune the key parameters in the life model. The adjusted parameters can involve the scale and shape parameters in the life distribution model, or can also include model environment parameters such as the sliding window size, residual threshold, and tolerance coefficient for state recognition, so as to achieve more accurate matching of the degradation characteristics of different devices.

[0048] Moreover, the system conducts intelligent classification and weight assignment for the type and quality of the feedback information. Contents such as whether the equipment returns to normal after the user confirms maintenance, whether new abnormal signs appear, and the running smoothness in the user's subjective perception are all included in the evaluation scope. For voice or text-based feedback information, a natural language processing model can also be introduced to extract keywords and recognize emotions, and convert them into parameters with semantic dimensions to assist the model correction process.

[0049] In terms of the model parameter adjustment mechanism, the system can adopt a periodic training method. By constructing a sample set with historical feedback data, global or local search for model parameters is carried out using techniques such as genetic algorithms, Bayesian updates, or gradient optimization, and finally a prediction model with better convergence and stronger generalization ability is output. Compared with static models, such methods can significantly improve the application stability and accuracy in the actual environment.

[0050] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

[0051] Embodiment 2 is the second embodiment of the present invention. The difference from the previous embodiment is: When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the current technical solution can be embodied in the form of a software product. The current computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0052] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0053] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memories), fiber optic devices, and portable compact disc read-only memories (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0054] Embodiment 3, an embodiment of the present invention, provides a smart home device management system, including a data acquisition module, a feature extraction module, a life prediction module, an anomaly recognition and warning module, and a feedback learning module.

[0055] Data acquisition module: Acquire device data and fuse it to form a unified set of device status data; Feature extraction module: Based on the device status data set, perform feature extraction and combine to generate a multi-dimensional life prediction feature vector; Life prediction module: Based on the multi-dimensional life prediction feature vector, construct a device life prediction model and output the predicted remaining life value of the device in real time; Abnormality recognition and warning module: Based on the predicted remaining life value of the device, recognize the abnormal decline in the device life and trigger the device maintenance warning information; Feedback learning module: According to the device maintenance warning information, receive user feedback and adaptively adjust the parameters of the device life prediction model.

Claims

1. A smart home device management method, characterized in that: include: Collect equipment data and integrate them into a unified equipment status data set; Based on the equipment status data set, feature extraction is performed to combine and generate a multi-dimensional life prediction feature vector; Based on the multi-dimensional life prediction feature vector, a device life prediction model is constructed to output the remaining life prediction value of the device in real time; Based on the predicted value of the remaining life of the equipment, an abnormal decrease in the life of the equipment is identified, and equipment maintenance warning information is triggered; Based on the equipment maintenance warning information, user feedback is received, and the equipment life prediction model parameters are adaptively adjusted.

2. The smart home device management method according to claim 1, characterized in that: The collecting device data comprises respectively deploying a data collection unit in each device in the smart home environment, wherein the data collection unit comprises a vibration sensor, a temperature sensor, an acceleration sensor, a current sensor and a power sensor; The vibration sensor is used to collect vibration signals during the operation of the equipment and generate a multi-band vibration envelope energy sequence through multi-band envelope analysis; The temperature sensor is used to collect temperature changes of key components of the equipment in real time to form a temperature time series curve; The acceleration sensor is used to measure the acceleration change of the device and obtain the speed change rate time series through numerical calculation; The current sensor is used to measure the current change of the device in real time when it is running and generate current waveform data; The power sensor is used to measure the power output of the device in real time and generate instantaneous power data; The data acquisition unit periodically sends the multi-band vibration envelope energy sequence, temperature timing curve, speed change rate timing, current waveform data and instantaneous power data to the smart home local gateway through the local wireless communication device for data storage. The smart home local gateway performs preliminary time stamp processing based on the received data to form the original equipment operation data with a unified time stamp.

3. The smart home device management method according to claim 2, characterized in that: The fusion to form a unified device status data set includes, based on the original device operation data stored in the smart home local gateway, using a time series data interpolation algorithm to eliminate the difference in data sampling frequencies of different devices and unify the data sampling frequency; The original data of equipment operation is standardized through data standardization algorithm to eliminate the differences in dimensions and value ranges between data and generate standardized data of equipment operation; A multi-source data spatiotemporal alignment algorithm is used for standardized equipment operation data. A unified equipment identifier and a unified timestamp are used as the data matching benchmark. The data are integrated one by one according to the timestamp to form multi-dimensional equipment operation data that is consistent in time and space. The Kalman filter algorithm is used to perform data noise reduction on multi-dimensional equipment operation data to eliminate the measurement errors of various sensors and environmental noise interference, and generate multi-dimensional equipment operation data after noise reduction; The multi-dimensional equipment operation data after noise reduction is fused according to preset weights through a multi-dimensional data weighted fusion algorithm to obtain a unified data format and output it as a unified equipment status data set; The equipment status data set includes a multi-band vibration envelope energy sequence after data processing, a temperature timing curve after data processing, a speed change rate timing after data processing, a current waveform data after data processing, and an instantaneous power data after data processing.

4. The smart home device management method according to claim 3, characterized in that: Generating the multi-dimensional life prediction feature vector includes performing weighted summation on the multi-band vibration envelope energy sequence after data processing according to preset weights to obtain the vibration fatigue feature; Integrate the temperature time series curve after data processing to obtain the cumulative heat load characteristics; The speed excitation characteristics are obtained by performing square integration on the speed change rate time series after data processing; Accumulating the absolute values ​​of the differences between adjacent sampling points of the current waveform data after data processing to obtain the current disturbance characteristics; Perform logarithmic mapping and normalization on the instantaneous power data after data processing to obtain power response characteristics; The vibration fatigue characteristics, cumulative heat load characteristics, speed excitation characteristics, current disturbance characteristics and power response characteristics are combined in a preset order to form a multi-dimensional life prediction feature vector.

5. The smart home device management method according to claim 4, characterized in that: The constructing of the equipment life prediction model includes inputting the multidimensional life prediction feature vector into a preset life prediction function group, wherein the life prediction function group includes a nonlinear mapping function for vibration fatigue response characteristics, an incomplete integration function for cumulative heat load characteristics and speed excitation characteristics, a logarithmic normalization function for power response characteristics, and a root attenuation function for adjusting the influence degree of current disturbance characteristics; The output results of the life prediction function group are weighted and combined, and integrated to form a health loss value of the equipment under the current operating state; Combined with the state disturbance complexity factor during equipment operation, the health loss value is normalized and the equipment remaining life prediction value representing the equipment remaining life status is output.

6. The smart home device management method according to claim 5, characterized in that: The identifying of abnormal decline in equipment life includes constructing a dynamic health index baseline for equipment health level based on the type and historical operation data of the target equipment; The residual difference between the current equipment remaining life prediction value and the dynamic health index baseline is periodically calculated, and the residual is accumulated using a sliding time window. If the continuous period exceeds the set threshold and the accumulated residual value exceeds the set threshold, it is judged that there is an abnormal downward trend in life; According to the degree of residual error, the maintenance warning level is divided into multiple levels, including primary warning, intermediate warning and emergency warning. The primary warning is used to trigger equipment self-check and user prompts, the intermediate warning is used to limit the equipment's operating capacity and record abnormal energy consumption, and the emergency warning is used to automatically cut off the power supply of high-risk equipment and push remote maintenance notifications. After the warning level is confirmed, equipment maintenance warning information is automatically generated, and the equipment maintenance warning information includes the predicted value change trend, current level classification and recommended treatment measures.

7. The smart home device management method according to claim 6, characterized in that: The adaptive adjustment of the equipment life prediction model parameters includes receiving feedback information provided by the user through the smart home terminal after the equipment maintenance warning information is pushed, and the feedback information includes the maintenance execution status confirmed by the user, the actual operation status of the equipment, the user's subjective evaluation and other relevant information; The feedback information is compared with the predicted value of the remaining life of the equipment. If a deviation is found between the predicted value and the actual equipment state, an adaptive adjustment algorithm is used to optimize the parameters of the life prediction model.

8. A smart home device management system, used to implement the smart home device management method according to any one of claims 1 to 7, characterized in that: include: Data acquisition module: collects equipment data and integrates it into a unified equipment status data set; Feature extraction module: based on the equipment status data set, feature extraction is performed to combine and generate a multi-dimensional life prediction feature vector; Life prediction module: constructs an equipment life prediction model based on the multi-dimensional life prediction feature vector, and outputs the equipment remaining life prediction value in real time; Abnormal identification and early warning module: based on the predicted value of the remaining life of the equipment, it identifies the abnormal decline of the equipment life and triggers the equipment maintenance early warning information; Feedback learning module: receiving user feedback based on the equipment maintenance warning information, and adaptively adjusting the equipment life prediction model parameters.

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