Smart furniture status analysis method, system and storage medium based on the Internet of Things

Abstract

The present application relates to the technical field of electronic module detection, and discloses a method, system and storage medium for analyzing the state of smart furniture based on the Internet of Things. The method regularly collects the first sound data of the electronic module within a set time period, and performs spectrum conversion on it to obtain first spectrum data. At the same time, a background sound library of the environment in which the electronic module is located is obtained, which contains background sound data and corresponding background spectrum data. The first spectrum data is then filtered based on the background spectrum data to obtain second spectrum data. Thereafter, the incremental frequency, incremental energy, average value and discrete value of the incremental frequency, and cumulative value and fluctuation value of the incremental energy of multiple second spectrum data are calculated. The frequency confidence value and energy confidence value are then calculated using these values, and then a comprehensive confidence value is obtained. If the comprehensive confidence value is greater than the preset reference confidence value, a status abnormality prompt is issued, thereby achieving effective monitoring and early warning of the operating status of the electronic module of the smart furniture.

Description

Technical Field

[0001] The present application relates to the technical field of electronic module detection, and in particular to a method, system and storage medium for analyzing the status of smart furniture based on the Internet of Things. Background Art

[0002] The development of smart furniture relies on the deep integration of the Internet of Things and energy management technologies, with the core being the innovative integration of electronic modules. Sensor technology enables basic environmental perception, while advancements in microprocessors and wireless communication technologies enable device connectivity and remote control.

[0003] Electronic modules are evolving from single functions to multi-technology integration, now offering features such as wireless charging and appliance control. The circuitry within these modules features an independent power supply and control unit. The power supply is used for charging or powering the control unit, while status indicators within the circuitry indicate the module's operating status. For example, when the module is dormant, the status indicator is off; when it's operational, the indicator flashes.

[0004] Electronic modules contain circuits with various components, such as resistors, capacitors, inductors, and integrated circuit chips. Over time, these components age, causing a current whine during operation. Causes of this whine include excessive power supply ripple, capacitor leakage, or high dielectric loss. This whine becomes louder and more frequent over time. Initially, users may not notice this whine. By the time they begin to notice it, it's already been a significant amount of time, leaving room for improvement in the user experience. Summary of the Invention

[0005] In order to let users know the current howling status of electronic modules as early as possible, the present application provides a smart furniture status analysis method, system and storage medium based on the Internet of Things.

[0006] In the first aspect, the present application provides a method for analyzing the status of smart furniture based on the Internet of Things, which adopts the following technical solutions:

[0007] A method for analyzing the state of smart furniture based on the Internet of Things comprises the following steps:

[0008] Within a set time period, regularly collect first sound data of the electronic module;

[0009] Performing spectrum conversion on the first sound data to obtain first spectrum data;

[0010] Acquire a background sound library of the environment in which the electronic module is located, wherein the background sound library includes background sound data and background spectrum data corresponding to the background sound data;

[0011] filtering the first spectrum data according to the background spectrum data to obtain second spectrum data;

[0012] Calculating a plurality of incremental frequencies of the second spectrum data and incremental energies corresponding to the incremental frequencies;

[0013] Calculating an average value and a discrete value of the incremental frequency, and calculating an accumulated value and a fluctuation value of the incremental energy;

[0014] Calculating a frequency confidence value based on the average value and the discrete value, and calculating an energy confidence value based on the accumulated value and the fluctuation value;

[0015] Calculating a comprehensive confidence value based on the frequency confidence value and the energy confidence value;

[0016] If the comprehensive confidence value is greater than the preset reference confidence value, an abnormal status prompt is issued.

[0017] By adopting the above technical solution and leveraging sound wave and spectrum analysis technology, sound signals can be accurately analyzed. Spectral conversion converts sound data into spectrum data, which allows the frequency composition to be analyzed, allowing for sensitive identification of current whistling caused by faulty components such as capacitors. This unique spectrum signature can be compared with normal conditions. This enables effective early warning and significantly improves the user experience. By regularly collecting and continuously analyzing sound data, various confidence values are calculated based on key parameters in the very early stages of current whistling, when it is difficult for the human ear to detect. Once the comprehensive confidence value exceeds the preset value, an abnormality is indicated, allowing users to address the problem at the early stages of the fault, reducing noise interference and improving the accuracy and reliability of analysis.

[0018] Optionally, the method further comprises the following steps:

[0019] Calculating a first time-domain waveform of the comprehensive confidence value, and extracting a first change node sequence from the first time-domain waveform;

[0020] Calculating a fluctuation degree value in the first change node sequence;

[0021] If the fluctuation degree value is greater than a preset first classification value, a non-continuous howling warning is issued.

[0022] By adopting the above technical solution, for smart furniture, non-continuous howling may mean that the component failure is in the initial unstable stage or is subject to intermittent interference. Timely warning can allow users or maintenance personnel to pay attention in advance and take targeted measures to reduce the severity of the problem, further improving the accuracy and comprehensiveness of smart furniture operation status monitoring.

[0023] Optionally, the method further comprises the following steps:

[0024] Based on the non-continuous howling warning, calculating the frequency confidence value and obtaining the indication parameter of the electronic module;

[0025] Calculating a second time domain waveform of the indicative parameter, and extracting a second change node sequence from the second time domain waveform;

[0026] Calculating a node matching value between the first changed node sequence and the second changed node sequence;

[0027] If the node matching value is greater than a preset first reference matching value, a non-continuous howling warning is issued.

[0028] By employing this technical solution, the current whine characteristics are correlated and analyzed with other operating status parameters of the electronic module (such as indicator parameters). For example, if changes such as light flickering match the changing nodes of the current whine, it likely indicates a systemic problem in the circuit rather than a single component failure. By matching and analyzing multiple types of parameters, the root cause of smart furniture failures can be more accurately determined, providing more valuable information for precise repairs, reducing unnecessary troubleshooting and improving maintenance efficiency. It also provides users with more targeted and reliable warnings, ensuring the stable operation of smart furniture.

[0029] Optionally, the method further comprises the following steps:

[0030] Based on the non-continuous howling warning, adjusting the working state of the electronic module according to a preset change strategy to change the indication parameter to obtain an adjustment parameter;

[0031] Calculating a third time-domain waveform of the adjustment parameter, and extracting a third change node sequence from the third time-domain waveform;

[0032] Calculating a second time-domain waveform of the latest comprehensive confidence value, and extracting a latest second change node sequence from the second time-domain waveform;

[0033] calculating an adjusted matching value between the third change node sequence and the second change node sequence;

[0034] If the adjustment matching value is greater than the preset adjustment reference matching value, a non-continuous howling abnormality prompt is issued.

[0035] By adopting the above technical solution, it is possible to further accurately determine whether there are real anomalies in smart furniture. Traditional judgment methods may lead to misjudgment due to factors such as environmental interference. This method actively changes the working state of the electronic module and observes the changes in related parameters, which is like performing a "stress test." For example, in a lighting change scenario, if the lighting change after adjusting the working state is highly matched with the comprehensive confidence value change node and exceeds the preset reference value, then the reliability of determining the anomaly is greatly improved. It reduces misjudgment and can more accurately locate the root cause of the problem when determining an anomaly, providing clear direction for maintenance personnel, greatly optimizing the maintenance process of smart furniture, ensuring the long-term stable operation of the equipment, and improving the user experience.

[0036] Optionally, the method further comprises the following steps:

[0037] Calculating a first time domain waveform of the comprehensive confidence value, and extracting a continuous state sequence from the first time domain waveform;

[0038] Calculating a ratio of duration of a continuous state in the continuous state sequence;

[0039] If the duration ratio of the continuous state is greater than a preset second classification value, a continuous howling abnormality prompt is issued.

[0040] By adopting the above technical solutions, the dimensions for determining current howling faults have been enriched and refined. In the complex operating environment of smart furniture, different types of current howling correspond to electronic module faults of varying degrees and natures. Continuous howling often indicates a serious and persistent problem within the electronic module, such as a short circuit or severe aging of key components. By specifically analyzing the continuous howling state, it can be effectively distinguished from non-continuous howling conditions, making fault diagnosis more accurate and improving the user experience. It also helps maintenance personnel quickly identify problems, develop targeted maintenance plans, improve maintenance efficiency, and extend equipment life.

[0041] Optionally, the method further comprises the following steps:

[0042] When the fluctuation degree value is less than the first classification value, the second classification value is adjusted according to the positive correlation of the fluctuation degree value; the larger the fluctuation degree value, the larger the second classification value; the smaller the fluctuation degree value, the smaller the second classification value.

[0043] By adopting the above technical solutions, in the complex and changeable operating environment of smart furniture, the current howling characteristics in different scenarios are different. When the fluctuation degree value is less than the first classification value, it indicates that the current current howling mode is different from the common non-continuous howling situation, and may be in a relatively mild or special state. At this time, the second classification value is adjusted according to the positive correlation of the fluctuation degree value, and the threshold for judging the continuous howling sound can be flexibly adjusted. This adjustment mechanism improves the adaptability of the system judgment, makes the fault judgment more consistent with the actual operating status, enhances the accuracy of the smart furniture status analysis, and thus provides users with more reliable equipment status monitoring and early warning services.

[0044] Optionally, the process of establishing the background sound library includes the following steps:

[0045] Creating the background sound library;

[0046] collecting background sound when the electronic module is not powered on to obtain the background sound data, and collecting background sound when the electronic module is powered on but no howling occurs to obtain the background sound data;

[0047] Performing spectrum conversion on the background sound data to obtain background spectrum data;

[0048] The frequency data and energy data corresponding to the background spectrum data are stored in the background sound library.

[0049] By implementing the above technical solution, a background sound library was created to establish the infrastructure for sound data analysis. This library collects background sound when the electronic module is unpowered and when powered but not whistling, comprehensively covering the ambient sound of different device states and ensuring accurate background sound data. After converting the background sound data spectrum, the frequency and energy data are stored, providing key support for accurately filtering out background noise. This significantly improves the accuracy of current whistling identification, ensures the reliability of smart furniture status analysis, and facilitates the timely detection of electronic module failures.

[0050] Optionally, the optimization process of the background sound library includes the following steps:

[0051] collecting howling sounds of the other electronic modules that generate howling when powered on to obtain howling data;

[0052] Performing spectrum conversion on the howling data to obtain howling spectrum data;

[0053] The background spectrum data corresponding to the howling spectrum data in the background sound library is deleted.

[0054] By adopting the above technical solution, the corresponding background spectrum data in the background sound library is deleted to avoid misjudging the howling of other electronic modules as background sound, thereby improving the purity of the background sound library, enabling smart furniture status analysis to more accurately focus on current howling anomalies, and enhancing the reliability of fault warning.

[0055] In a second aspect, the present application provides a smart furniture status analysis system based on the Internet of Things, which adopts the following technical solutions:

[0056] A smart furniture status analysis system based on the Internet of Things includes a processor, wherein the processor executes the steps of any one of the smart furniture status analysis methods based on the Internet of Things described above.

[0057] In a third aspect, the present application provides a storage medium that adopts the following technical solution:

[0058] A storage medium stores a program, which, when executed by a processor, implements the steps of any one of the above-mentioned methods for analyzing the state of smart furniture based on the Internet of Things.

[0059] In summary, this application includes at least one of the following beneficial technical effects:

[0060] Through comprehensive and detailed sound data collection, spectrum conversion and multi-dimensional analysis, such as calculating incremental frequency, incremental energy, various confidence values, etc., it is possible to accurately identify the current howling caused by aging and failure of components such as capacitors in the electronic modules of smart furniture, distinguish between continuous and non-continuous howling, accurately judge the type and severity of the fault, and provide a reliable basis for maintenance.

[0061] At the earliest possible stage of electrical whistling, when it's difficult for the human ear to detect, data analysis can be used to provide status anomalies, significantly improving the user experience of smart furniture. Furthermore, early warning and alert mechanisms for both non-continuous and continuous whistling allow users to promptly understand device status and take proactive measures to address potential faults.

[0062] The establishment and optimization of the background sound library comprehensively collects background sound data under different states, effectively filters out environmental noise interference, accurately extracts current howling signals, and can flexibly adjust the judgment threshold according to the fluctuation degree value, thereby improving the system's adaptability and analysis accuracy in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a step diagram of a smart furniture status analysis method based on the Internet of Things.

[0064] Figure 2 This is a diagram showing the steps for determining whether to issue a non-continuous howling warning.

[0065] Figure 3 This is a diagram showing the steps for determining whether to issue a non-continuous howling warning. DETAILED DESCRIPTION

[0066] Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings.

[0067] Throughout this specification, reference to the terms "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0068] The present application embodiment discloses a method for analyzing the state of smart furniture based on the Internet of Things. Figure 1 , including the following steps:

[0069] Over a set period of time, the microphone sensor regularly collects first sound data from the electronic module. This first sound data represents all sound data from the smart device's surrounding environment. This collected first sound data is then transmitted via the IoT communication module. The IoT communication module, which can be Wi-Fi, Bluetooth, or ZigBee, converts the analog sound signals collected by the microphone sensor into digital signals and transmits the data to a cloud server or local data analysis device according to a specific communication protocol. During transmission, the data is encrypted using an encryption algorithm to ensure its security and integrity. The data is then stored in a high-capacity data storage device, such as a hard drive array or cloud storage, for subsequent spectrum conversion and analysis.

[0070] The Fast Fourier Transform (FFT) algorithm is used to perform spectrum conversion on the first sound data to obtain the first spectrum data. In practical applications, the collected first sound data is segmented according to a specific time window. The Fast Fourier Transform algorithm is applied to each segment of data to calculate the frequency components and corresponding energy distribution of the sound signal within that time period. In this way, the complex sound signal is decomposed into a series of superimposed sine waves of different frequencies, intuitively displaying the frequency characteristics of the sound signal and providing a key basis for subsequent filtering and analysis.

[0071] A background sound library of the environment in which the electronic module is located is obtained, where the background sound library includes background sound data and background spectrum data corresponding to the background sound data.

[0072] An adaptive filtering algorithm is used to filter the first spectrum data based on the background spectrum data. The algorithm continuously compares the first spectrum data with the background spectrum data, identifies the frequency components belonging to the background noise, and removes them from the first spectrum data to obtain second spectrum data; the second spectrum data is the spectrum data corresponding to the current howling sound.

[0073] The incremental frequencies and corresponding incremental energies of multiple pieces of second spectrum data are calculated. The incremental frequencies are calculated by taking the difference between the same frequency components in the second spectrum data at two adjacent time points, reflecting how the frequency changes over time. The incremental energies are calculated by taking the difference between the energies of the same frequency components at adjacent time points, reflecting the dynamic changes in energy. During the calculation process, a sliding window approach is used to process the second spectrum data, ensuring that the results reflect the real-time operating status of the electronic module.

[0074] The average and variance of the incremental frequency are calculated, as are the cumulative and fluctuation values of the incremental energy. The average is calculated by summing the incremental frequency data over a period of time and dividing it by the number of data points. It reflects the overall level of the incremental frequency. The variance is calculated using the standard deviation, which measures the dispersion of the incremental frequency data relative to the average and reflects the stability of the frequency changes. The cumulative value of the incremental energy is calculated by adding up the incremental energies over a period of time, reflecting the overall accumulation of energy. The fluctuation value is calculated by calculating the variance of the incremental energy, indicating the degree of energy fluctuation over time. These statistics provide important input parameters for the subsequent confidence value calculation.

[0075] The frequency confidence value is calculated based on the average and discrete values, while the energy confidence value is calculated based on the cumulative and fluctuation values. A weighted fusion algorithm is used to calculate the frequency and energy confidence values. This algorithm assigns weights to different parameters based on their importance, combining the average, discrete, cumulative, and fluctuation values to produce the frequency and energy confidence values. The weights are optimized using extensive experimental data and machine learning algorithms to ensure that the confidence values accurately reflect the operating status of the electronic module.

[0076] A comprehensive confidence value is calculated based on the frequency confidence value and the energy confidence value. Similarly, a weighted fusion method is adopted to perform weighted summation on the frequency confidence value and the energy confidence value to obtain a comprehensive indicator to evaluate the overall operating status of the electronic module.

[0077] If the comprehensive confidence value is greater than the preset reference confidence value, an abnormal status prompt will be issued. The reference confidence value is an empirical value obtained through long-term monitoring and data analysis of a large number of smart furniture in normal operation. In actual application, the comprehensive confidence value calculated in real time is compared with the reference confidence value. When the comprehensive confidence value exceeds the reference confidence value, the system determines that the electronic module may be in an abnormal state and immediately triggers the abnormal prompt mechanism. Abnormal prompts can be implemented in various ways, such as displaying a warning message on the control panel of the smart furniture, pushing notifications through mobile phone apps, or sending text messages to users, reminding users to take timely measures for inspection and maintenance.

[0078] With the help of sound wave and spectrum analysis technology, sound signals can be accurately analyzed. Through spectrum conversion, sound data is converted into spectrum data, and then the frequency composition is analyzed. By comparing the unique spectrum characteristics of the current howling caused by component failures such as capacitors with the normal state, the fault can be keenly identified. This technology regularly collects and continuously analyzes sound data. In the very early stages of current howling, when it is still difficult for the human ear to detect, various confidence values are calculated based on key parameters. Once the comprehensive confidence value exceeds the preset value, an abnormality is immediately prompted. This can achieve effective early warning, allowing users to deal with problems in a timely manner at the early stage of the fault, reduce noise interference, greatly improve the accuracy and reliability of smart furniture status analysis, and significantly improve the user experience.

[0079] Reference Figure 2 , the method further comprises the steps of:

[0080] The first time-domain waveform of the integrated confidence value is calculated, and a first sequence of change nodes is extracted from the first time-domain waveform. The integrated confidence value is a comprehensive evaluation indicator of the electronic module's operating status, integrating information from multiple key parameters such as the frequency confidence value and the energy confidence value. By presenting the changes in the integrated confidence value over time as a waveform, its dynamic trend can be intuitively observed. During the calculation process, the system samples the integrated confidence value at regular intervals based on the collected sound data and various previously calculated characteristic values. These sampled points are then connected to form the first time-domain waveform. Change nodes are points in the waveform where the slope changes significantly. These points often indicate significant fluctuations in the integrated confidence value and may be related to changes in the electronic module's operating status. The system uses specialized algorithms, such as slope detection algorithms, to analyze the waveform point by point, identifying points where the slope change exceeds a certain threshold. These points are extracted as first change nodes, forming a first change node sequence. This sequence records significant changes in the integrated confidence value at different time points, providing an important data foundation for subsequent fluctuation analysis.

[0081] The fluctuation level is calculated for the first sequence of change nodes. This level measures the severity of fluctuations in the integrated confidence value at these change nodes, reflecting the instability of the electronic module's operating state. The system uses various statistical methods to calculate the fluctuation level, such as calculating the absolute value of the difference in integrated confidence values between adjacent change nodes and performing statistical analysis on these differences to obtain a numerical value representative of the degree of fluctuation.

[0082] If the fluctuation value exceeds the preset first classification value, a non-continuous howling warning is issued. The first classification value is an empirical threshold value obtained through experiments and data analysis of a large number of smart furniture in normal and faulty operating states. It serves as an important basis for determining whether non-continuous howling exists. When the fluctuation value exceeds this threshold, it indicates that the comprehensive confidence value is fluctuating significantly, and non-continuous howling may occur.

[0083] For smart furniture, non-continuous howling is of great significance. It may indicate that a component failure is in the early stages of instability, such as a slight leakage in a capacitor causing abnormal current fluctuations and triggering non-continuous howling. It may also be caused by intermittent interference to the electronic module, such as nearby electromagnetic interference or momentary fluctuations in the power supply voltage. Timely warning of non-continuous howling allows users and maintenance personnel to pay attention to equipment operation in advance. Users can pay attention to subtle anomalies in furniture, such as flickering lights and temporary functional failures. Maintenance personnel can prepare testing equipment and repair tools based on this information and conduct a comprehensive inspection and diagnosis of the electronic module. Taking targeted measures in advance can reduce the severity of the problem and prevent the escalation of the fault from rendering the furniture unusable. This early warning mechanism improves the accuracy and comprehensiveness of operational status monitoring, detecting both obvious faults and potential incipient problems, providing more reliable protection for the stable operation of smart furniture.

[0084] Reference Figure 3 , the method further comprises the steps of:

[0085] Based on the non-continuous howling warning, the system calculates the frequency confidence value and obtains the electronic module's indicator parameters. Once the non-continuous howling warning is triggered, a new round of in-depth analysis begins. First, the system recalculates the frequency confidence value. As a key indicator of the stability of the frequency characteristics of the electronic module's current howling, accurate calculation at this stage helps further analyze the detailed changes in the frequency dimension of the current howling. By re-analyzing previously collected sound data, applying more sophisticated spectrum analysis algorithms, and combining them with the current warning situation, the system can derive a more accurate frequency confidence value, providing solid data support for subsequent comprehensive judgments. Simultaneously, the system comprehensively obtains the electronic module's indicator parameters. These indicator parameters cover various aspects of the electronic module's operating status, such as the motor speed controlled by the electronic module, the values fed back by various sensors, and the operating parameters of smart furniture devices such as lights and displays. These indicator parameters reflect the real-time operating status of the electronic module from different perspectives. Taking a smart lighting system as an example, the brightness adjustment value, flicker frequency, and color temperature changes of the light all fall into the category of indicator parameters. Changes in these parameters may be closely related to the operating status of the electronic module.

[0086] A second time domain waveform of the indicating parameter is calculated, and a second sequence of change nodes is extracted from the second time domain waveform. Similar to the first time domain waveform of the comprehensive confidence value, the second time domain waveform uses time as the horizontal axis to intuitively present the change of the indicating parameter over time. By continuously sampling the indicating parameter and plotting it in a coordinate system in chronological order, a curve that can reflect its dynamic change is formed. By detecting the mutation points of characteristics such as slope or curvature in the waveform, these mutation points are extracted as change nodes to form a second sequence of change nodes. These change nodes represent the moments when the indicating parameter changes significantly on the time axis, which may correspond to the state transition inside the electronic module or the interference of external environmental factors.

[0087] Calculate the node matching value for the first and second change node sequences; this node matching value calculation is intended to measure the degree of synchronization and similarity between the comprehensive confidence value change node associated with current squeal and the electronic module indicator parameter change node. The system utilizes multiple matching algorithms, such as the Dynamic Time Warping (DTW) algorithm, which can find the most similar correspondence between the two sequences while taking into account differences in time series lengths and calculate a quantitative matching value. For example, if the time point representing a sudden change in current squeal frequency in the first change node sequence is close on the timeline to the time point representing a sudden change in light brightness in the second change node sequence, and their change trends are somewhat similar, then the node matching value will be relatively high.

[0088] If the node matching value exceeds the preset first reference matching value, a non-continuous whistling warning will be issued. The first reference matching value is a threshold determined through extensive experimental data and real-world case analysis, combined with node matching conditions under normal operation and fault conditions of smart furniture. When the node matching value exceeds this threshold, it indicates a strong correlation between the current whistling characteristics and other operating parameters of the electronic module, indicating the possibility of a deeper systemic problem.

[0089] Correlating current whine characteristics with other operating parameters of the electronic module is extremely valuable. For example, if light flickering matches a current whine variation, it's likely a systemic circuit problem, such as unstable power supply voltage, causing abnormal lighting control in the electronic module and triggering current whine. This cross-parameter analysis transcends the limitations of single-parameter fault diagnosis and considers the electronic module's operating status from multiple perspectives. This allows for more accurate identification of the root cause of smart furniture failures. Unlike traditional component-by-component troubleshooting, this method can quickly pinpoint the problem area and provide maintenance personnel with precise repair guidance. For example, if a non-continuous whine alert is received and the lighting indicator parameters strongly correlate with the current whine, maintenance personnel can prioritize the lighting control circuit and power supply system, reducing unnecessary troubleshooting and significantly improving maintenance efficiency. Furthermore, it provides users with more targeted and reliable alerts. When receiving an alert, users can understand the abnormal parameters and their correlation with the current whine, better understanding the nature of the problem and taking temporary measures to prevent further escalation while awaiting repair. This comprehensively ensures the stable operation of smart furniture, enhancing user safety and user experience.

[0090] The method further comprises the steps of:

[0091] Based on a non-continuous howling warning, the electronic module's operating state is adjusted according to a preset change strategy, causing the indicated parameters to change, resulting in the adjusted parameters. These preset change strategies were developed through extensive experimentation and data analysis, taking into account the diverse usage scenarios and potential fault conditions of smart furniture. For example, in a smart lighting system, if a non-continuous howling warning occurs, the system might gradually reduce the light brightness or change the flashing frequency according to a preset strategy to observe the electronic module's response under different operating conditions. By adjusting the electronic module's operating state, the indicated parameters will change accordingly, resulting in the adjusted parameters. The indicated parameters reflect various status information of the electronic module during normal operation, such as voltage, current, and power, while the adjusted parameters represent the new values of these parameters after the operating state is changed. For example, in a smart air conditioner, when the system issues a non-continuous howling warning, it adjusts the compressor speed, causing changes in the air conditioner's indicated parameters, such as current and cooling capacity. These changed values are the adjusted parameters.

[0092] A third time-domain waveform of the adjustment parameter is calculated, and a third sequence of change nodes is extracted from the third time-domain waveform. A time-domain waveform can intuitively display how the adjustment parameter changes over time. It uses time as the horizontal axis and the adjustment parameter value as the vertical axis, graphically presenting the dynamic changes of the parameter during the adjustment process. The system continuously samples the adjustment parameter, recording the parameter values at regular intervals and connecting these values into a curve to form the third time-domain waveform.

[0093] The system calculates the second time-domain waveform of the latest comprehensive confidence value and extracts the latest second change node sequence from the second time-domain waveform. The system also extracts the third change node sequence from the third time-domain waveform. Change nodes are points in the waveform where the slope changes significantly. These points represent sudden changes in the control parameters over time and may be related to internal faults or external interference in the electronic module. The system uses specialized algorithms, such as differential algorithms, to analyze the waveform point by point, identifying points where the slope change exceeds a certain threshold. These points are extracted as third change nodes and form a sequence. Simultaneously, the system calculates the second time-domain waveform of the latest comprehensive confidence value. The comprehensive confidence value is a comprehensive assessment indicator of the overall operating status of the electronic module, combining multiple key parameters such as the frequency confidence value and the energy confidence value. The system calculates the change in the latest comprehensive confidence value over time to obtain the second time-domain waveform. The system then extracts the latest second change node sequence from the second time-domain waveform using a similar method for extracting the third change node sequence.

[0094] Calculate the adjustment match value between the third change node sequence and the second change node sequence. The adjustment match value measures the correlation and synchronization between the changes in the adjustment parameters and the changes in the integrated confidence value. The system uses correlation analysis algorithms, such as the Pearson correlation coefficient, to compare the two sequences and obtain a quantitative match value. A higher match value indicates a greater synchronization between the changes in the adjustment parameters and the integrated confidence value, potentially indicating a more serious anomaly.

[0095] If the adjustment match value exceeds the preset adjustment reference match value, a non-continuous howling anomaly alert is issued. The adjustment reference match value is a threshold determined through extensive experiments and real-world cases, and it serves as an important basis for determining whether a true anomaly exists. When the adjustment match value exceeds this threshold, it indicates that changes in the adjustment parameters and the overall confidence value after adjusting the operating state of the electronic module are highly correlated, indicating a high likelihood of a true anomaly in the smart furniture. This method accurately determines whether a true anomaly exists in the smart furniture. Traditional judgment relies on data from a single time point or fixed conditions, making it susceptible to misjudgment due to environmental interference. This method proactively changes the operating state of the electronic module, similar to a "stress test" of the furniture, revealing hidden issues. For example, in a lighting scenario, after detecting a non-continuous howling alert, the light brightness or flashing frequency can be adjusted. If the degree of match between the lighting change and the overall confidence value change exceeds a preset value, an anomaly can be confirmed. Multi-parameter matching significantly improves the reliability of anomaly detection. Furthermore, it not only reduces false positives but also accurately locates the root cause of the problem when an anomaly is identified. When maintenance personnel receive abnormality alerts, they can quickly narrow the scope of investigation based on changes in adjustment parameters and comprehensive confidence values. For example, if the current and comprehensive confidence value are abnormal after adjusting the light brightness, they can focus on checking the light power supply and control circuits. This greatly optimizes the maintenance process, reduces maintenance time and costs, ensures long-term and stable operation of the equipment, reduces user inconvenience, and improves user experience and satisfaction.

[0096] The method further comprises the steps of:

[0097] The system calculates the first time-domain waveform of the integrated confidence value and extracts a persistent state sequence from the first time-domain waveform. The integrated confidence value is derived by integrating multiple key information, such as the frequency confidence value and the energy confidence value, and is a comprehensive indicator that fully reflects the operating status of the electronic module. When calculating the first time-domain waveform, the system samples the integrated confidence value at regular intervals and connects these sampling points in chronological order to form a curve that visually displays the integrated confidence value over time, clearly demonstrating its state fluctuations at different moments. A persistent state refers to a state in which the integrated confidence value remains relatively stable over a period of time. This may correspond to normal operation of the electronic module, abnormal whistling, or other specific operating states. The system uses advanced signal processing algorithms to analyze the waveform segment by segment. By examining waveform characteristics such as slope and amplitude changes, it identifies time periods where the integrated confidence value changes by less than a certain threshold. These time periods are defined as persistent states and extracted to form a persistent state sequence. For example, when the electronic module of smart furniture continues to howl, the comprehensive confidence value may remain at a high and relatively stable level for a long time, and the system will extract this period of time as a continuous state.

[0098] Calculate the duration ratio of each continuous state in the continuous state sequence; this ratio represents the proportion of the total duration of all continuous states to the entire analysis period. The calculation process is relatively straightforward: the system accumulates the duration of each continuous state in the continuous state sequence to obtain the total duration, then divides this total by the duration of the entire analysis period to obtain the duration ratio. This ratio quantifies the importance of the continuous state throughout the operation and provides a clear quantitative indicator for subsequent fault diagnosis.

[0099] If the proportion of the duration of the continuous state is greater than the preset second classification value, a continuous howling abnormality prompt will be issued. The second classification value is an empirical threshold obtained by analyzing and statistically analyzing a large number of experimental data of smart furniture in normal operation and fault conditions. It is the key boundary for distinguishing normal operation from continuous howling faults. When the proportion of the duration of the continuous state exceeds this threshold, it means that the electronic module has been in a continuous abnormal state for a long time, and there is a high possibility of a continuous howling problem. The system will issue a continuous howling abnormality prompt in a variety of ways, such as displaying a prominent warning message on the control panel of the smart furniture, pushing notifications to users through mobile phone APP, and even triggering the voice prompt function to promptly inform users that there are potential serious problems with the smart furniture.

[0100] This analysis method offers significant advantages in the complex operating environments of smart furniture. Different levels of howling current correspond to different electronic module faults. Persistent howling is often caused by serious and persistent hardware issues within the electronic module, such as short circuits or severe aging of key components. If not promptly addressed, this can lead to complete module damage, impacting the normal operation of the furniture. This method specifically analyzes persistent howling conditions and effectively distinguishes between persistent and non-persistent howling. Traditional methods struggle to accurately distinguish between these conditions and are prone to misjudgments and omissions. However, this method, through in-depth analysis of the persistence of the comprehensive confidence value, accurately identifies the characteristics of persistent howling over time, improving the accuracy of fault diagnosis. For users, timely notification of persistent howling anomalies enhances the user experience, allowing them to contact maintenance promptly and avoid long-term noise interference. For maintenance personnel, receiving notifications allows them to quickly identify key troubleshooting points, focus on key components, and develop targeted maintenance plans, reducing troubleshooting time, improving maintenance efficiency, and promptly repairing faults, extending equipment life, and ensuring the long-term stable operation of smart furniture.

[0101] The method further comprises the steps of:

[0102] When the fluctuation level is less than the first category value, it indicates that the current whistling pattern generated by the smart furniture electronic module is significantly different from the typical non-continuous whistling. Typical non-continuous whistling is often accompanied by large fluctuations, while the smaller fluctuation level in this case may indicate that the electronic module is in a more moderate or special state. For example, a component in the electronic module may be experiencing a slight performance degradation, but not yet reaching the level of causing severe fluctuations; or it may be affected by some intermittent, relatively weak interference factors.

[0103] The second classification value is adjusted based on the positive correlation between the fluctuation level. The greater the fluctuation level, the larger the second classification value; the smaller the fluctuation level, the smaller the second classification value. The first classification value is an important threshold for distinguishing different current howling modes, while the second classification value is an important reference threshold for determining whether a persistent howling sound occurs.

[0104] Consider a smart desk lamp whose electronic module controls the brightness and color temperature of the light. During normal use, the light is stable, and the current whine fluctuation level remains low. For example, in a quiet study, a user uses the desk lamp for extended reading time. The detected current whine fluctuation level is calculated to be 5 (a hypothetical unit value). Because this value is less than the first category value of 10, the system initiates an adjustment mechanism. Due to the low fluctuation level, the second category value is lowered from the initial 30 (a hypothetical unit value) to 25. This means that even if the percentage of duration of the current whine state in the desk lamp does not reach the initial setting of 30, as long as it exceeds 25, the system will determine that a persistent whine anomaly may exist and issue an alert. However, if the desk lamp's power adapter experiences slight aging, causing current fluctuations and the fluctuation level to rise to 8, the system, using the positive correlation adjustment mechanism, will raise the second category value from 25 to 28. This allows the system to dynamically adjust its criteria based on the current fluctuations when determining whether the desk lamp has a persistent whine problem, avoiding false or missed detections due to fixed thresholds.

[0105] In the complex operating environments of smart furniture, different scenarios and external conditions can significantly alter the characteristics of current whine. Conditions like high temperatures or strong electromagnetic interference can alter the frequency and fluctuation of current whine. This system automatically adapts to these changes by adjusting the second-category value based on the fluctuation level. When fluctuations are small, the value is lowered to more accurately detect potential persistent whine; when fluctuations are large, the value is raised to prevent normal fluctuations from being misclassified. Traditional fixed-threshold fault diagnosis methods struggle to adapt to complex situations and are prone to misjudgments and omissions. This adjustment mechanism, however, dynamically adjusts the judgment criteria based on real-time fluctuation levels, ensuring fault diagnosis more accurately reflects actual operating conditions. For example, when fluctuations are small but the duration of the persistent state is high, traditional thresholds may be unable to detect an anomaly. However, the adjustment mechanism reduces the second-category value, enabling accurate identification and prompt notification. This adjustment mechanism significantly enhances the accuracy of smart furniture status analysis, providing users with reliable monitoring and early warning, enabling them to respond promptly to faults. It also helps maintenance personnel quickly locate problems, develop efficient repair plans, and extend the lifespan of smart furniture.

[0106] The process of building a background sound library includes the following steps:

[0107] Create a background sound library; the database structure can efficiently store and manage large amounts of sound data, including background sound data, background spectrum data, and corresponding frequency and energy data. At the same time, consider the scalability and compatibility of the data to facilitate the addition of new data types and analysis functions in the future.

[0108] Background sound data is obtained by collecting background sound when the electronic module is not powered on. Background sound data is also obtained by collecting background sound when the electronic module is powered on and no howling occurs. When the electronic module is not powered on, various sounds still exist in the environment, such as the operating sounds of surrounding electrical equipment and ambient noise. These sounds constitute the basic noise background of the smart furniture's environment. To collect accurate background sound data when the electronic module is not powered on, it is necessary to select appropriate sound collection equipment, such as a highly sensitive microphone. The microphone should be placed near the smart furniture to ensure that it can clearly capture surrounding sound signals. The collection process should continue for a period of time to obtain sufficient sample data. When the electronic module is powered on but not howling, it will produce some normal operating sounds, which are also affected by the surrounding environment. These sounds include the sound characteristics of the electronic module during normal operation, as well as the superposition of ambient noise.

[0109] The Fast Fourier Transform (FFT) algorithm is used to perform spectrum conversion on background sound data to generate background spectrum data. This spectrum conversion process first requires preprocessing the collected background sound data, including noise removal and filtering, to improve data quality. The preprocessed sound data is then segmented according to specific time windows. The FFT algorithm is applied to each segment to calculate the frequency components and corresponding energy distribution of the sound signal within that time period. The resulting background spectrum data, with corresponding frequency and energy data, clearly demonstrates the energy distribution of the background sound at different frequencies.

[0110] Save the frequency and energy data corresponding to the background spectrum data to the background sound library. Store the frequency and energy data in a table format in the database, with each row representing a frequency point and containing the corresponding energy value. Also, add timestamps and environmental parameter information to each data record to facilitate data filtering and comparison in subsequent analysis.

[0111] The background sound library established through a series of steps strongly supports the status analysis of smart furniture. It can greatly improve the accuracy of current whistling recognition, compare the collected electronic module sound data with the spectrum data in the library, accurately identify and filter out the background noise frequency components, highlight the current whistling signal, avoid interference, and make the recognition more accurate and reliable. The background sound library also guarantees the reliability of status analysis. Accurately filtering background noise allows the system to focus on the actual operating status of the electronic module and detect potential faults in a timely manner. If current whistling occurs in the electronic module, the system can quickly determine whether the whistling is abnormal based on the background spectrum data, and issue a timely warning to help users and maintenance personnel take measures to prevent the fault from worsening and ensure the stable operation of smart furniture. The optimization process of the background sound library includes the following steps:

[0112] Acquire howling data by collecting the howling sound of other electronic modules that generate howling when powered on. A microphone is used to extensively collect howling sounds from other electronic modules that generate howling when powered on, thereby acquiring comprehensive howling data. In practice, this data collection is required for a wide range of smart furniture electronic modules of varying types, brands, and ages. These electronic modules include common components such as driver modules in smart lighting systems, compressor control modules in smart air conditioners, and power amplifier modules in smart speakers.

[0113] The fast Fourier transform (FFT) algorithm is used to convert the howling data into a spectrum to obtain the howling spectrum data. The complex time-domain howling signal is decomposed into a series of sine waves with different frequency components, clearly showing the energy distribution of the howling sound at each frequency.

[0114] The howling spectrum data obtained through spectrum conversion is carefully compared with the background spectrum data stored in the background sound library, and the background spectrum data corresponding to the howling spectrum data in the background sound library is deleted.

[0115] In the operating environment of smart furniture, if the background sound library contains data with similar spectrum to the howling of other electronic modules, the status analysis process can easily misinterpret current howling anomalies as normal background noise, seriously affecting accuracy. Removing this interfering data can significantly improve the purity of the background sound library. During subsequent analysis, the system can more closely and precisely focus on current howling anomalies, effectively avoiding misjudgments caused by background noise interference and significantly enhancing the reliability of fault warnings. This effectively supports the timely detection of potential faults, ensures stable furniture operation, and improves the overall performance and practicality of the status analysis system.

[0116] An embodiment of the present application further discloses a smart furniture status analysis system based on the Internet of Things, comprising a processor, wherein the processor executes the steps of any one of the smart furniture status analysis methods based on the Internet of Things described above.

[0117] An embodiment of the present application further discloses a storage medium, in which a program is stored. When the program is executed by a processor, the steps of any one of the above-mentioned methods for analyzing the state of smart furniture based on the Internet of Things are implemented.

[0118] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for analyzing the state of smart furniture based on the Internet of Things, characterized in that: The steps include: Within a set time period, regularly collecting first sound data of the electronic module; Performing spectrum conversion on the first sound data to obtain first spectrum data; Acquire a background sound library of the environment in which the electronic module is located, wherein the background sound library includes background sound data and background spectrum data corresponding to the background sound data; filtering the first spectrum data according to the background spectrum data to obtain second spectrum data; Calculating a plurality of incremental frequencies of the second spectrum data and incremental energies corresponding to the incremental frequencies; Calculating an average value and a discrete value of the incremental frequency, and calculating an accumulated value and a fluctuation value of the incremental energy; Calculating a frequency confidence value based on the average value and the discrete value, and calculating an energy confidence value based on the accumulated value and the fluctuation value; Calculating a comprehensive confidence value based on the frequency confidence value and the energy confidence value; If the comprehensive confidence value is greater than the preset reference confidence value, an abnormal status prompt is issued.

2. The method for analyzing the state of smart furniture based on the Internet of Things according to claim 1, characterized in that: The method further comprises the steps of: Calculating a first time-domain waveform of the comprehensive confidence value, and extracting a first change node sequence from the first time-domain waveform; Calculating a fluctuation degree value in the first change node sequence; If the fluctuation degree value is greater than a preset first classification value, a non-continuous howling warning is issued.

3. The method for analyzing the state of smart furniture based on the Internet of Things according to claim 2, characterized in that: The method further comprises the steps of: Based on the non-continuous howling warning, calculating the frequency confidence value and obtaining the indication parameter of the electronic module; Calculating a second time domain waveform of the indicative parameter, and extracting a second change node sequence from the second time domain waveform; Calculating a node matching value between the first changed node sequence and the second changed node sequence; If the node matching value is greater than a preset first reference matching value, a non-continuous howling warning is issued.

4. The method for analyzing the state of smart furniture based on the Internet of Things according to claim 3, characterized in that: The method further comprises the steps of: Based on the non-continuous howling warning, adjusting the working state of the electronic module according to a preset change strategy to change the indication parameter to obtain an adjustment parameter; Calculating a third time-domain waveform of the adjustment parameter, and extracting a third change node sequence from the third time-domain waveform; Calculating a second time-domain waveform of the latest comprehensive confidence value, and extracting a latest second change node sequence from the second time-domain waveform; calculating an adjusted matching value between the third change node sequence and the second change node sequence; If the adjustment matching value is greater than the preset adjustment reference matching value, a non-continuous howling abnormality prompt is issued.

5. The method for analyzing the state of smart furniture based on the Internet of Things according to claim 3, characterized in that: The method further comprises the steps of: Calculating a first time domain waveform of the comprehensive confidence value, and extracting a continuous state sequence from the first time domain waveform; Calculating a ratio of duration of a continuous state in the continuous state sequence; If the proportion of the duration of the continuous state is greater than a preset second classification value, a continuous howling sound abnormality prompt is issued.

6. The method for analyzing the state of smart furniture based on the Internet of Things according to claim 5, characterized in that: The method further comprises the steps of: When the fluctuation degree value is less than the first classification value, the second classification value is adjusted according to the positive correlation of the fluctuation degree value; the larger the fluctuation degree value is, the larger the second classification value is; The smaller the fluctuation degree value is, the smaller the second classification value is.

7. The method for analyzing the state of smart furniture based on the Internet of Things according to claim 1, characterized in that: The process of establishing the background sound library comprises the following steps: Creating the background sound library; collecting background sound when the electronic module is not powered on to obtain the background sound data, and collecting background sound when the electronic module is powered on but no howling occurs to obtain the background sound data; Performing spectrum conversion on the background sound data to obtain background spectrum data; The frequency data and energy data corresponding to the background spectrum data are stored in the background sound library.

8. The method for analyzing the state of smart furniture based on the Internet of Things according to claim 1, characterized in that: The optimization process of the background sound library includes the following steps: Collect the howling sound of other electronic modules that generate howling when powered on to obtain howling data; Performing spectrum conversion on the howling data to obtain howling spectrum data; The background spectrum data corresponding to the howling spectrum data in the background sound library is deleted.

9. A smart furniture status analysis system based on the Internet of Things, characterized in that: The method comprises a processor, wherein the processor executes the steps of the method for analyzing the state of smart furniture based on the Internet of Things as described in any one of claims 1 to 8.

10. A storage medium, characterized in that: The medium stores a program, and when the program is executed by the processor, the steps of the smart furniture status analysis method based on the Internet of Things according to any one of claims 1 to 8 are implemented.

Images