Intelligent household equipment monitoring system based on data analysis

Abstract

The invention discloses an intelligent household equipment monitoring system based on data analysis, which relates to the technical field of intelligent household equipment monitoring and comprises a data acquisition module, a central processing unit, an analysis module, a comparison module and an early warning module. And the data acquisition module is used for acquiring state data information, including system performance information and heat dissipation performance information, of the controller used by the smart home equipment during operation, and transmitting the system performance information and the heat dissipation performance information to the central processing unit after acquisition. According to the invention, by intelligently sensing the abnormal hidden danger occurring during the operation of the controller, a smart home user can conveniently know the abnormal hidden danger possibly existing in the controller in time, the maintenance processing work is timely arranged for the controller in advance, the controller is ensured to efficiently control the smart home, and the user experience is improved. The operation efficiency of the controller is effectively prevented from being affected more seriously or the controller is prevented from being directly damaged, and intelligent home users can conveniently and efficiently use the controller.

Description

Technical Field

[0001] This invention relates to the field of smart home device monitoring technology, and more specifically to a smart home device monitoring system based on data analysis. Background Technology

[0002] Smart home devices are home appliances that utilize the internet, wireless communication, and sensing technologies to achieve automation, remote control, and intelligent management. These devices aim to improve home comfort, convenience, security, and energy efficiency. By connecting to the internet and communicating with each other, smart home devices allow users to remotely control and monitor various devices in their homes, thus achieving a more intelligent, convenient, and comfortable home experience.

[0003] Smart home devices are typically equipped with controllers. These controllers coordinate multiple smart home devices to perform complex automated tasks, ensuring a smooth home experience. Therefore, the controller plays a crucial role in smart home devices. As one of the core components of a smart home device, the controller is responsible for processing sensor data, executing intelligent algorithms, and controlling the operation of actuators. Through the controller, users can remotely control smart home devices via mobile applications, the internet, etc. The controller serves as the interface for communication between the devices and the outside world, allowing users to control and monitor their home from anywhere.

[0004] The existing technology has the following shortcomings: However, during use, the controller will age over time. When the controller ages, some abnormalities may occur in its operation. Since the existing technology cannot intelligently sense these abnormalities, it cannot promptly detect the potential aging problems of the controller. Over time, the controller's operating efficiency may be more severely affected or it may even be damaged, making it inconvenient to efficiently control and use smart home devices.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a smart home device monitoring system based on data analysis. This invention intelligently senses potential anomalies in the controller during operation, enabling smart home users to be aware of possible anomalies and problems in a timely manner. This allows for timely scheduling of maintenance and repairs, ensuring efficient control of the smart home by the controller and effectively preventing further impact on the controller's operating efficiency or direct damage. This facilitates efficient use of the controller by smart home users, thus solving the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a smart home device monitoring system based on data analysis, comprising a data acquisition module, a central processing unit, an analysis module, a comparison module, and an early warning module;

[0008] The data acquisition module collects the status data information of the controller used by the smart home device during operation, including system performance information and heat dissipation performance information. After collection, the system performance information and heat dissipation performance information are transmitted to the central processing unit.

[0009] The central processing unit (CPU) comprehensively analyzes system performance and heat dissipation performance information, generates an anomaly assessment index, and transmits the anomaly assessment index to the analysis module.

[0010] The analysis module establishes an analysis set of several abnormal evaluation indices generated during controller runtime, compares and analyzes the abnormal evaluation indices in the analysis set with the reference thresholds of the abnormal evaluation indices, generates a deviation evaluation index, and transmits the deviation evaluation index to the comparison module.

[0011] The comparison module compares the deviation evaluation index generated within the subset using the anomaly evaluation index and the anomaly evaluation index reference threshold with the deviation evaluation index reference threshold, generates an anomaly risk signal, and transmits the anomaly risk signal to the early warning module, which then generates an early warning prompt.

[0012] Preferably, the system performance information of the controller used in the smart home device includes the signal output stability coefficient and the response speed abnormal fluctuation coefficient. After collection, the data acquisition module calibrates the signal output stability coefficient and the response speed abnormal fluctuation coefficient as X respectively. wd u and Y fd u, the heat dissipation performance information includes the temperature anomaly deviation coefficient. After acquisition, the data acquisition module calibrates the temperature anomaly deviation coefficient as W. yc u.

[0013] Preferably, the logic for obtaining the signal output stability coefficient is as follows:

[0014] A101. Obtain the signal output strength of the controller at different times within time T during its operation. Simultaneously, obtain the signal output rate of the controller at different time intervals within time T during its operation. Define the signal output strength and signal output rate as Q. d j and V s h,j represent the number of the signal output strength at different times during time T when the controller is running, j = 1, 2, 3, 4, ..., g, where g is a positive integer; h represents the number of the signal output rate at different time intervals during time T when the controller is running, h = 1, 2, 3, 4, ..., G, where G is a positive integer;

[0015] A102. Calculate the signal output strength Q of the controller at different times within time T during operation. d Calculate the standard deviation s1 of j to determine the signal output rate V of the controller at different time intervals within time T. s The formulas for calculating the standard deviations s2, s1, and s2 of h are:

[0016]

[0017] ,in, The signal output strength Q at different times within time T during controller operation d The average value of j is obtained by the following expression: The signal output rate V at different time periods within time T during controller operation s The average value of h is obtained by the following expression:

[0018] A103. Calculate the signal output stability coefficient. The expression for the calculation is: In the formula, e1 and e2 are the signal output strength Q of the controller at different times during time T. d The standard deviation of j, s1, and the signal output rate V of the controller during different time periods within time T. s The weighting factors of the standard deviation s2 of h all have values ​​greater than 0.

[0019] Preferably, the logic for obtaining the abnormal fluctuation coefficient of response speed is as follows:

[0020] B101. Obtain the optimal time range for the controller to respond to the input signal, and calibrate the optimal time range as t. bes min ~t bes max ;

[0021] B102. Obtain the actual response time of the controller in response to several input signals within time T during operation, and calibrate the actual response time as t. bes x x represents the number of the actual response time of the controller in response to several input signals within time T, x = 1, 2, 3, 4, ..., n, where n is a positive integer;

[0022] B103, t is less than the optimal time range bes min The actual response time is calibrated as t bes v v indicates that t is less than the optimal time range. besmin The actual response time is numbered, v = 1, 2, 3, 4, ..., N, where N is a positive integer;

[0023] B104. Calculate the abnormal fluctuation coefficient of the response speed. The expression for the calculation is:

[0024] Preferably, the logic for obtaining the temperature anomaly deviation coefficient is as follows:

[0025] C101. Obtain the maximum temperature value when the controller is operating normally, and calibrate the maximum temperature value as t. max ;

[0026] C102. Obtain the internal temperature values ​​of the controller at different times within time T during controller operation, and calibrate the internal temperature values ​​of the controller as W. d y represents the number of the internal temperature value of the controller at different times during the time T during the operation of the controller, y = 1, 2, 3, 4, ..., m, where m is a positive integer;

[0027] C103, will be greater than the maximum temperature value t max The internal temperature value W of the controller d y is calibrated as W d 'k, k represents a temperature greater than the maximum temperature t max The controller's internal temperature values ​​are numbered, k = 1, 2, 3, 4, ..., M, where M is a positive integer;

[0028] C104. Calculate the temperature anomaly deviation coefficient. The expression for the calculation is:

[0029] Preferably, the central processing unit obtains the signal output stability coefficient X. wd u, response speed abnormal fluctuation coefficient Y fd u and the temperature anomaly deviation coefficient are calibrated as W. yc After u, a data analysis model is established to generate the anomaly assessment index XS, based on the following formula:

[0030]

[0031] In the formula, w1, w2, and w3 are the signal output stability coefficients X. wd u, response speed abnormal fluctuation coefficient Y fd u and the temperature anomaly deviation coefficient are calibrated as W. yc The preset scaling factor of u, and w1, w2, and w3 are all greater than 0.

[0032] Preferably, the analysis module establishes an analysis set from several anomaly evaluation indices generated during controller runtime, and labels the data set as V, then V = {XS} r} = {XS1, XS2, ..., XS} q}, where r represents the index number of the anomaly assessment index within the analysis set, r = 1, 2, 3, 4, ..., q, and q is a positive integer;

[0033] The anomaly assessment index reference threshold is defined as XS0, and anomaly assessment indices greater than the anomaly assessment index reference threshold XS0 are defined as XS. a 'a' represents the anomaly assessment index greater than the anomaly assessment index reference threshold XS0, where a = 1, 2, 3, 4, ..., F, and F is a positive integer. The anomaly assessment index is determined by the anomaly assessment index reference threshold XS0 and the anomaly assessment index XS0. a The deviation evaluation index is calculated using the following formula: In the formula, PG represents the deviation evaluation index.

[0034] Preferably, the comparison module compares the deviation evaluation index generated within the subset using the anomaly assessment index and the anomaly assessment index reference threshold with the deviation evaluation index reference threshold. If the deviation evaluation index is greater than or equal to the deviation evaluation index reference threshold, the comparison module generates a high anomaly risk signal and transmits the signal to the early warning module. The early warning module then generates an early warning prompt and transmits the prompt to the mobile monitoring terminal. If the deviation evaluation index is less than the deviation evaluation index reference threshold, the comparison module generates a low anomaly risk signal and transmits the signal to the early warning module, but the early warning module does not generate an early warning prompt.

[0035] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0036] This invention enables intelligent sensing of potential anomalies during controller operation, allowing smart home users to be aware of any possible issues and schedule timely maintenance. This ensures efficient control of the smart home system and prevents further damage to the controller, facilitating its efficient use.

[0037] This invention establishes an analysis set based on the anomaly evaluation index generated during controller operation, performs comprehensive analysis, and determines whether to generate an early warning prompt. This effectively prevents accidental triggering of early warning prompts, improves the accuracy of analyzing potential anomalies in the controller, and thereby increases the trust of smart home users in the controller's early warning capabilities, ensuring the stable and efficient operation of the controller. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0039] Figure 1 This is a schematic diagram of the modules of the smart home device monitoring system based on data analysis of the present invention. Detailed Implementation

[0040] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0041] This invention provides, for example Figure 1 The data analysis-based smart home device monitoring system shown includes a data acquisition module, a central processing unit, an analysis module, a comparison module, and an early warning module.

[0042] The data acquisition module collects the status data information of the controller used by the smart home device during operation, including system performance information and heat dissipation performance information. After collection, the system performance information and heat dissipation performance information are transmitted to the central processing unit.

[0043] The system performance information of the controller used in smart home devices includes the signal output stability coefficient and the response speed anomaly fluctuation coefficient. After collection, the data acquisition module calibrates the signal output stability coefficient and the response speed anomaly fluctuation coefficient as X, respectively. wd u and Y fd u;

[0044] When the stability of the controller output signal used by smart home devices deteriorates, it can have various serious impacts on the intelligent control of the smart home. The specific extent of the impact depends on the type of device, the usage scenario, and the importance of the controller output signal. The following are some possible impacts:

[0045] Automated task failure or error: Stable signals are the foundation for the realization of automated tasks. When the controller output signal is unstable, the device may not be able to execute the automated task as expected, resulting in task failure, incorrect execution, or incomplete execution.

[0046] Time delay: Unstable signals may cause delays in command transmission, affecting the response speed of smart home devices, which may impact user experience and make operation sluggish;

[0047] Misoperation: Signal instability may cause device misoperation, such as performing operations without user instructions or failing to perform the operations expected by the user;

[0048] Scene execution issues: Scenes in a smart home system may be executed collaboratively by multiple devices. If the controller output signal is unstable, different devices in the scene may not be able to execute synchronously, thus ruining the scene effect.

[0049] Security risks: The security of smart home devices may be compromised. Unstable signals may cause security devices (such as security systems) to fail to trigger alarms or notifications correctly, thereby affecting home security.

[0050] Energy waste: If the controller cannot accurately control high-energy-consuming equipment, it may lead to energy waste and increase household energy costs;

[0051] Degraded user experience: Unstable signals may prevent users from controlling the device as expected, reducing user satisfaction;

[0052] System crash: Severe signal instability may cause the entire smart home system to crash, requiring a restart or reconfiguration of the devices;

[0053] Therefore, by monitoring the signal output of the controller, we can promptly detect problems such as deterioration in the stability of the controller signal output, and thus discover potential abnormalities during the operation of the controller.

[0054] The logic for obtaining the signal output stability coefficient is as follows:

[0055] A101. Obtain the signal output strength of the controller at different times within time T during its operation, and simultaneously obtain the signal output rate of the controller at different time intervals (equal time intervals) within time T during its operation. Define the signal output strength and signal output rate as Q. d j and V s h,j represent the number of the signal output strength at different times during time T when the controller is running, j = 1, 2, 3, 4, ..., g, where g is a positive integer; h represents the number of the signal output rate at different time intervals during time T when the controller is running, h = 1, 2, 3, 4, ..., G, where G is a positive integer;

[0056] It should be noted that by using appropriate communication protocols (such as Modbus, CAN, etc.) or connecting and communicating with the controller's specific interface (such as RS-232, Ethernet, etc.), real-time values ​​of the controller's output signals can be obtained, including the strength and rate of the controller's output signals.

[0057] A102. Calculate the signal output strength Q of the controller at different times within time T during operation. dCalculate the standard deviation s1 of j to determine the signal output rate V of the controller at different time intervals within time T. s The formulas for calculating the standard deviations s2, s1, and s2 of h are:

[0058]

[0059] ,in, The signal output strength Q at different times within time T during controller operation d The average value of j is obtained by the following expression: The signal output rate V at different time periods within time T during controller operation s The average value of h is obtained by the following expression:

[0060] A103. Calculate the signal output stability coefficient. The expression for the calculation is: In the formula, e1 and e2 are the signal output strength Q of the controller at different times during time T. d The standard deviation of j, s1, and the signal output rate V of the controller during different time periods within time T. s The weighting factor of the standard deviation s2 of h takes values ​​greater than 0. The weighting factor is used to balance the proportion of each data in the formula, thereby improving the accuracy of the calculation results.

[0061] As can be seen from the calculated expression, the larger the value of the signal output stability coefficient generated by the controller within time T, the worse the stability of the controller signal output, and the greater the probability of abnormal potential problems during controller operation. Conversely, the smaller the value, the better the stability of the controller signal output, and the lower the probability of abnormal potential problems during controller operation.

[0062] When the controller used in smart home devices becomes slow to respond, it can have a variety of serious impacts on the intelligent control of the smart home. The extent of the impact depends on the type of device, the usage scenario, and the importance of the controller's response speed. The following are some possible impacts:

[0063] Degraded user experience: Slow controller response speed will cause users to feel a delay when operating the device, reducing user satisfaction and experience;

[0064] Automation task failure: Smart home devices often perform preset automated tasks, such as timed switching of lights or temperature adjustment. If the controller is slow to respond, the automated task may fail to be executed as planned.

[0065] Delayed scene execution: In smart home scenarios, multiple devices may need to execute synchronously. If the controller responds slowly, the devices in the scene may not be able to execute synchronously in time, affecting the scene's effect.

[0066] Security risks: The response speed of smart security devices is directly related to home security. If the controller responds slowly, the security devices may not be able to trigger alarms or notifications in time, thus affecting home security.

[0067] Misoperation and instability: Users may attempt to operate multiple times while waiting for a response, leading to misoperation. In addition, delayed response may cause the device to operate in an unstable state, affecting system stability.

[0068] Energy waste: Slow response times may prevent equipment from performing energy-saving operations in a timely manner, resulting in energy waste;

[0069] Time management issues: If smart home devices fail to perform scheduled tasks on time, such as morning alarms or temperature adjustments, it may affect the user's time management.

[0070] Therefore, monitoring the controller's response speed can promptly identify problems such as a slowdown in the controller's response speed, and thus discover potential abnormalities during the controller's operation.

[0071] The logic for obtaining the abnormal fluctuation coefficient of response speed is as follows:

[0072] B101. Obtain the optimal time range for the controller to respond to the input signal, and calibrate the optimal time range as t. bes min ~t bes max ;

[0073] It should be noted that controller manufacturers typically provide information about response time in product specifications or technical documents. These documents include the controller's response time metrics, maximum response time, or typical response time range. By consulting these documents, the optimal response time range of the controller can be obtained. However, the optimal response time range of a controller usually depends on multiple factors, including the controller's hardware and software design, processing power, complexity of the control algorithm, and characteristics of the input signal. Therefore, the optimal response time range may vary depending on the application scenario and performance requirements. Thus, the setting of the optimal time range is not specifically limited here, but should be set according to the requirements.

[0074] B102. Obtain the actual response time of the controller in response to several input signals within time T during operation, and calibrate the actual response time as t. bes xx represents the number of the actual response time of the controller in response to several input signals within time T, x = 1, 2, 3, 4, ..., n, where n is a positive integer;

[0075] B103, t is less than the optimal time range bes min The actual response time is calibrated as t bes v v indicates that t is less than the optimal time range. bes min The actual response time is numbered, v = 1, 2, 3, 4, ..., N, where N is a positive integer;

[0076] B104. Calculate the abnormal fluctuation coefficient of the response speed. The expression for the calculation is:

[0077] As can be seen from the calculated expression, the larger the performance value of the abnormal fluctuation coefficient of the response speed generated by the controller within time T, the worse the operating state of the controller is, and the greater the probability of abnormal hidden dangers during the operation of the controller. Conversely, the better the operating state of the controller is, and the lower the probability of abnormal hidden dangers during the operation of the controller.

[0078] Heat dissipation performance information includes a temperature anomaly deviation coefficient. After acquisition, the data acquisition module calibrates the temperature anomaly deviation coefficient as W. yc u;

[0079] When the heat dissipation performance of the controllers used in smart home devices deteriorates, it can affect the intelligent control of the smart home, especially for devices that have been running for extended periods. This is because heat dissipation issues can limit the performance of the controller, potentially affecting the stability and performance of the devices. The following are some possible impacts:

[0080] Performance degradation: High temperatures may reduce the performance of the controller because electronic components may not function properly at high temperatures, which may slow down the controller's processing speed and delay response time.

[0081] Stability issues: Overheating may cause the controller to become unstable, easily leading to crashes, freezes, or restarts, thus affecting the normal operation of smart home devices;

[0082] Automated task failure: An unstable controller may fail to execute automated tasks accurately, resulting in task failure or execution errors;

[0083] Reduced energy efficiency: High temperatures may cause the controller to consume more energy to maintain normal operation, thereby reducing energy efficiency;

[0084] Response latency: At high temperatures, the controller may need more time to process sensor data and execute algorithms, resulting in increased response time.

[0085] System crash: Prolonged high temperatures may cause serious malfunctions in the controller, or even trigger a system crash, requiring restart or equipment repair;

[0086] Therefore, by monitoring the temperature of the controller, abnormal temperature can be detected in a timely manner, thereby identifying potential problems that may occur during the operation of the controller.

[0087] The logic for obtaining the temperature anomaly deviation coefficient is as follows:

[0088] C101. Obtain the maximum temperature value when the controller is operating normally, and calibrate the maximum temperature value as t. max ;

[0089] It should be noted that controller manufacturers usually provide product specifications and technical documents, which contain detailed information about the controller, including its operating temperature range. By consulting the product manual, technical specification sheet, or manufacturer's website, you can obtain the controller's temperature specifications and thus the maximum internal temperature value during normal operation.

[0090] C102. Obtain the internal temperature values ​​of the controller at different times within time T during controller operation, and calibrate the internal temperature values ​​of the controller as W. d y represents the number of the internal temperature value of the controller at different times during the time T during the operation of the controller, y = 1, 2, 3, 4, ..., m, where m is a positive integer;

[0091] It should be noted that a temperature sensor is installed inside the controller to obtain the internal temperature of the controller;

[0092] C103, will be greater than the maximum temperature value t max The internal temperature value W of the controller d y is calibrated as W d 'k, k represents a temperature greater than the maximum temperature t max The controller's internal temperature values ​​are numbered, k = 1, 2, 3, 4, ..., M, where M is a positive integer;

[0093] C104. Calculate the temperature anomaly deviation coefficient. The expression for the calculation is:

[0094] As can be seen from the calculation formula, the larger the performance value of the temperature anomaly deviation coefficient generated by the controller within time T during operation, the worse the controller's operating status is, and the greater the probability of potential anomalies during controller operation. Conversely, the smaller the value is, the better the controller's operating status is, and the lower the probability of potential anomalies during controller operation.

[0095] The central processing unit (CPU) comprehensively analyzes system performance and heat dissipation performance information, generates an anomaly assessment index, and transmits the anomaly assessment index to the analysis module.

[0096] The central processing unit obtains the signal output stability coefficient X. wd u, response speed abnormal fluctuation coefficient Y fd u and the temperature anomaly deviation coefficient are calibrated as W. yc After u, a data analysis model is established to generate the anomaly assessment index XS, based on the following formula:

[0097]

[0098] In the formula, w1, w2, and w3 are the signal output stability coefficients X. wd u, response speed abnormal fluctuation coefficient Y fd u and the temperature anomaly deviation coefficient are calibrated as W. yc The preset scaling factor of u, and w1, w2, and w3 are all greater than 0;

[0099] As can be seen from the calculation formula, the larger the signal output stability coefficient, response speed abnormal fluctuation coefficient, and temperature abnormal deviation coefficient generated by the controller within time T during operation, that is, the larger the performance value of the abnormality evaluation index XS, the worse the operating status of the controller, and the greater the probability of abnormal potential problems during the operation of the controller. Conversely, the smaller the signal output stability coefficient, response speed abnormal fluctuation coefficient, and temperature abnormal deviation coefficient generated by the controller within time T during operation, that is, the smaller the performance value of the abnormality evaluation index XS, the better the operating status of the controller, and the lower the probability of abnormal potential problems during the operation of the controller.

[0100] It should be noted that the above-mentioned time T is a relatively short time period. The time within this period is not specifically limited and can be set according to the actual situation. The purpose is to monitor the operating status of the controller used by the smart home device within time T, so as to intelligently monitor the status of the controller used by the smart home device in different time periods (within time T).

[0101] The analysis module establishes an analysis set of several abnormal evaluation indices generated during controller runtime, compares and analyzes the abnormal evaluation indices in the analysis set with the reference thresholds of the abnormal evaluation indices, generates a deviation evaluation index, and transmits the deviation evaluation index to the comparison module.

[0102] The analysis module establishes an analysis set from several anomaly evaluation indices generated during controller runtime, and labels the data set as V, then V = {XS} r} = {XS1, XS2, ..., XS} q}, where r represents the index number of the anomaly assessment index within the analysis set, r = 1, 2, 3, 4, ..., q, and q is a positive integer;

[0103] The anomaly assessment index reference threshold is defined as XS0, and anomaly assessment indices greater than the anomaly assessment index reference threshold XS0 are defined as XS. a 'a' represents the anomaly assessment index greater than the anomaly assessment index reference threshold XS0, where a = 1, 2, 3, 4, ..., F, and F is a positive integer. The anomaly assessment index is determined by the anomaly assessment index reference threshold XS0 and the anomaly assessment index XS0. a The deviation evaluation index is calculated using the following formula: In the formula, PG represents the deviation evaluation index;

[0104] As can be seen from the calculated expression, the larger the performance value of the deviation evaluation index PG generated by the anomaly evaluation index and the anomaly evaluation index reference threshold within the subset, the greater the probability of anomalies occurring during controller operation; conversely, the smaller the performance value, the greater the probability of anomalies occurring during controller operation.

[0105] The comparison module compares the deviation evaluation index generated by the anomaly evaluation index and the anomaly evaluation index reference threshold within the subset with the deviation evaluation index reference threshold to generate an anomaly risk signal, and transmits the anomaly risk signal to the early warning module, which then generates an early warning prompt.

[0106] The comparison module compares the deviation evaluation index generated within the subset using the anomaly evaluation index and the anomaly evaluation index reference threshold with the deviation evaluation index reference threshold. If the deviation evaluation index is greater than or equal to the deviation evaluation index reference threshold, it indicates that there is a high probability of an anomaly or potential problem inside the controller. In this case, the comparison module generates a high anomaly risk signal and transmits the signal to the early warning module. The early warning module then generates an early warning prompt and transmits the prompt to the mobile monitoring terminal. This allows smart home users to be aware of potential anomalies or potential problems with the controller in a timely manner, arrange maintenance and repair work in advance, effectively prevent the controller's operating efficiency from being more seriously affected or directly damaged, and facilitate the controller's efficient control of smart home devices. This, in turn, facilitates the efficient use of the controller by smart home users. If the deviation evaluation index is less than the deviation evaluation index reference threshold, the comparison module generates a low anomaly risk signal and transmits the signal to the early warning module. No early warning prompt is generated by the early warning module.

[0107] This invention enables intelligent sensing of potential anomalies during controller operation, allowing smart home users to be aware of any possible issues and schedule timely maintenance. This ensures efficient control of the smart home system and prevents further damage to the controller, facilitating its efficient use.

[0108] This invention establishes an analysis set based on the anomaly evaluation index generated during controller operation, performs comprehensive analysis, and determines whether to generate an early warning prompt. This effectively prevents accidental triggering of early warning prompts, improves the accuracy of analyzing potential anomalies in the controller, and thereby increases the trust of smart home users in the controller's early warning capabilities, ensuring the stable and efficient operation of the controller.

[0109] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0110] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0111] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0112] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0113] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0114] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0115] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0116] If the aforementioned functions are implemented as 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 this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0117] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0118] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A smart home device monitoring system based on data analysis, characterized in that, It includes a data acquisition module, a central processing unit, an analysis module, a comparison module, and an early warning module; The data acquisition module collects the status data information of the controller used by the smart home device during operation, including system performance information and heat dissipation performance information. After collection, the system performance information and heat dissipation performance information are transmitted to the central processing unit. The central processing unit (CPU) comprehensively analyzes system performance and heat dissipation performance information, generates an anomaly assessment index, and transmits the anomaly assessment index to the analysis module. The analysis module establishes an analysis set of several abnormal evaluation indices generated during controller runtime, compares and analyzes the abnormal evaluation indices in the analysis set with the reference thresholds of the abnormal evaluation indices, generates a deviation evaluation index, and transmits the deviation evaluation index to the comparison module. The comparison module compares the deviation evaluation index generated within the subset using the anomaly evaluation index and the anomaly evaluation index reference threshold with the deviation evaluation index reference threshold, generates an anomaly risk signal, and transmits the anomaly risk signal to the early warning module, which then generates an early warning prompt.

2. The smart home device monitoring system based on data analysis according to claim 1, characterized in that, The system performance information of the controller used in smart home devices includes the signal output stability coefficient and the response speed anomaly fluctuation coefficient. After collection, the data acquisition module calibrates the signal output stability coefficient and the response speed anomaly fluctuation coefficient as X respectively. wd u and Y fd u, the heat dissipation performance information includes the temperature anomaly deviation coefficient. After acquisition, the data acquisition module calibrates the temperature anomaly deviation coefficient as W. yc u.

3. The smart home device monitoring system based on data analysis according to claim 2, characterized in that, The logic for obtaining the signal output stability coefficient is as follows: A101. Obtain the signal output strength of the controller at different times within time T during its operation, and simultaneously obtain the signal output rate of the controller at different time intervals within time T during its operation. Define the signal output strength and signal output rate as Q, respectively. d j and V s h,j represent the number of the signal output strength at different times during time T when the controller is running, j = 1, 2, 3, 4, ..., g, where g is a positive integer; h represents the number of the signal output rate at different time intervals during time T when the controller is running, h = 1, 2, 3, 4, ..., G, where G is a positive integer; A102. Calculate the signal output strength Q of the controller at different times within time T during operation. d Calculate the standard deviation s1 of j to determine the signal output rate V of the controller at different time intervals within time T. s The formulas for calculating the standard deviations s2, s1, and s2 of h are: ， in, The signal output strength Q at different times within time T during controller operation d The average value of j is obtained by the following expression: The signal output rate V at different time periods within time T during controller operation s The average value of h is obtained by the following expression: A103. Calculate the signal output stability coefficient. The expression for the calculation is: X wd u= In the formula, e1 and e2 are the signal output strength Q of the controller at different times during time T. d The standard deviation of j, s1, and the signal output rate V of the controller during different time periods within time T. s The weighting factors of the standard deviation s2 of h all have values ​​greater than 0.

4. The smart home device monitoring system based on data analysis according to claim 3, characterized in that, The logic for obtaining the abnormal fluctuation coefficient of response speed is as follows: B101. Obtain the optimal time range for the controller to respond to the input signal, and calibrate the optimal time range as t. bes min ~t bes max ; B102. Obtain the actual response time of the controller in response to several input signals within time T during operation, and calibrate the actual response time as t. bes x, where x represents the number of the actual response time of the controller in response to several input signals within time T, x = 1, 2, 3, 4, ..., n, where n is a positive integer; B103, t is less than the optimal time range bes min The actual response time is calibrated as t bes v v indicates that t is less than the optimal time range. bes min The actual response time is numbered, v = 1, 2, 3, 4, ..., N, where N is a positive integer; B104. Calculate the abnormal fluctuation coefficient of the response speed. The expression for the calculation is:

5. The smart home device monitoring system based on data analysis according to claim 4, characterized in that, The logic for obtaining the temperature anomaly deviation coefficient is as follows: C101. Obtain the maximum temperature value when the controller is operating normally, and calibrate the maximum temperature value as t. max ; C102. Obtain the internal temperature values ​​of the controller at different times within time T during controller operation, and calibrate the internal temperature values ​​of the controller as W. d y represents the number of the internal temperature value of the controller at different times during the time T during the operation of the controller, y = 1, 2, 3, 4, ..., m, where m is a positive integer; C103, will be greater than the maximum temperature value t max The internal temperature value W of the controller d y is calibrated as W d 'k, k represents a temperature greater than the maximum temperature t max The controller's internal temperature values ​​are numbered, k = 1, 2, 3, 4, ..., M, where M is a positive integer; C104. Calculate the temperature anomaly deviation coefficient. The expression for the calculation is:

6. The smart home device monitoring system based on data analysis according to claim 5, characterized in that, The central processing unit obtains the signal output stability coefficient X. wd u, response speed abnormal fluctuation coefficient Y fd u and the temperature anomaly deviation coefficient are calibrated as W. yc After u, a data analysis model is established to generate the anomaly assessment index XS, based on the following formula: ， In the formula, w1, w2, and w3 are the signal output stability coefficients X. wd u, response speed abnormal fluctuation coefficient Y fd u and the temperature anomaly deviation coefficient are calibrated as W. yc The preset scaling factor of u, and w1, w2, and w3 are all greater than 0.

7. The smart home device monitoring system based on data analysis according to claim 6, characterized in that, The analysis module establishes an analysis set from several anomaly evaluation indices generated during controller runtime, and labels the data set as V, then V = {XS} r } = {XS1, XS2, ..., XS} q }, where r represents the index number of the anomaly assessment index within the analysis set, r = 1, 2, 3, 4, ..., q, and q is a positive integer; The anomaly assessment index reference threshold is defined as XS0, and anomaly assessment indices greater than the anomaly assessment index reference threshold XS0 are defined as XS. a 'a' represents the anomaly assessment index greater than the anomaly assessment index reference threshold XS0, where a = 1, 2, 3, 4, ..., F, and F is a positive integer. The anomaly assessment index is determined by the anomaly assessment index reference threshold XS0 and the anomaly assessment index XS0. a The deviation evaluation index is calculated using the following formula: In the formula, PG represents the deviation evaluation index.

8. The smart home device monitoring system based on data analysis according to claim 7, characterized in that, The comparison module compares the deviation evaluation index generated within the subset using the anomaly assessment index and the anomaly assessment index reference threshold with the deviation evaluation index reference threshold. If the deviation evaluation index is greater than or equal to the deviation evaluation index reference threshold, the comparison module generates a high anomaly risk signal and transmits the signal to the early warning module. The early warning module then generates an early warning prompt and transmits the prompt to the mobile monitoring terminal. If the deviation evaluation index is less than the deviation evaluation index reference threshold, the comparison module generates a low anomaly risk signal and transmits the signal to the early warning module. No early warning prompt is generated by the early warning module.

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