A smart home appliance controller data processing system

Abstract

This invention discloses a data processing system for an intelligent home appliance controller, relating to the field of intelligent home appliance control technology. It includes: a disturbance monitoring unit for real-time monitoring of grid voltage fluctuations, wireless signal interference intensity, and the number of concurrent devices; a risk assessment unit for receiving wireless signal interference intensity to generate a normalized interference level and performing risk assessment calculations to generate a comprehensive disturbance index; a control strategy unit for generating system adjustment coefficients based on the comprehensive disturbance index; and a collaborative execution unit for generating dynamic data sampling frequency, dynamic power compensation gain, and a dynamic upper limit for communication retransmission times based on the system adjustment coefficients. This invention achieves flexible control by comprehensively predicting multiple disturbances and integrating and collaboratively controlling sampling, power, and communication parameters, avoiding conflicting oscillations caused by independent compensation, and significantly improving the system's stability and reliability in complex environments.

Description

Technical Field

[0001] This invention relates to the field of smart home appliance control technology, and more specifically to a smart home appliance controller data processing system. Background Technology

[0002] With the popularization of IoT technology and the concept of smart homes, the stability and reliability of smart appliance controllers, as the core hub connecting users and home appliances, are of paramount importance. In daily operation, these controllers not only need to execute user commands and adjust appliance status, but also need to monitor and adapt to complex and ever-changing external environments in real time. These environmental factors mainly include fluctuations in grid voltage, signal interference in wireless communication channels, and changes in the number of concurrent devices in the network. To ensure that home appliances operate at their optimal state, controllers typically integrate power compensation mechanisms to cope with voltage instability and communication retransmission mechanisms to ensure data transmission reliability. Ideally, these mechanisms should work together to maintain a stable, efficient, and comfortable user experience. For example, when the grid power quality is poor, the controller can automatically adjust the power input to ensure the safe and stable operation of appliances; when wireless signals are interfered with, it can retransmit commands to ensure that commands are successfully received and executed, thereby ensuring the normal implementation of functions and the user's operating experience.

[0003] Existing smart home appliance controllers suffer from significant design flaws when handling the aforementioned multiple disturbances. The core issue lies in the fact that key adjustment mechanisms such as power compensation and communication retransmission are typically independent and operate independently. When multiple adverse factors, such as grid voltage fluctuations and wireless signal interference, occur simultaneously, these independent adjustment modules conflict due to the lack of a unified coordination strategy. For example, the power compensation module may increase instantaneous power consumption to cope with voltage drops, potentially exacerbating the processor load; simultaneously, the communication module may initiate high-frequency retransmissions due to poor signal strength, further consuming system resources and potentially causing data congestion. This conflict in processing mechanisms creates a vicious cycle, known as "positive feedback oscillation," leading to a sharp decline in overall system performance. Specifically, this manifests as a significant increase in the controller's response delay to user commands, and drastic and disorderly fluctuations in device energy consumption, ultimately severely impacting the user's experience of environmental comfort. This passive, lagging, and conflicting compensation method cannot fundamentally anticipate and avoid the systemic risks brought about by complex disturbances, constituting a critical technical bottleneck that urgently needs to be addressed in the current field of smart home appliance controllers. Summary of the Invention

[0004] The purpose of this invention is to provide a data processing system for intelligent home appliance controllers, which solves the problems existing in the background art.

[0005] To address the aforementioned technical problems, this invention provides a smart home appliance controller data processing system, comprising: a disturbance monitoring unit for real-time monitoring of grid voltage fluctuations, wireless signal interference intensity, and the number of concurrent devices;

[0006] The risk assessment unit is used to receive the wireless signal interference intensity and perform normalization processing to generate a normalized interference degree; the risk assessment unit is also used to combine the normalized interference degree, the grid voltage fluctuation value and the number of concurrent devices to perform risk assessment calculations to generate a comprehensive disturbance index.

[0007] The control strategy unit is used to perform nonlinear smooth control calculations based on the comprehensive disturbance index to generate system control coefficients;

[0008] The collaborative execution unit is used to generate a dynamic data sampling frequency, a dynamic power compensation gain, and a dynamic upper limit for the number of communication retransmissions based on the system adjustment coefficient.

[0009] Preferably, the normalization process of the risk assessment unit is as follows:

[0010] The system acquires the wireless signal interference intensity and performs linear mapping processing on the wireless signal interference intensity based on the preset best and worst signal strengths to generate a normalized interference degree.

[0011] Preferably, the risk assessment unit performs the risk assessment calculation as follows:

[0012] The system combines normalized interference, grid voltage fluctuation, and the number of concurrent devices; and incorporates preset rated voltage and preset maximum number of devices derived from system hardware specifications and communication protocols, as well as disturbance weighting coefficients derived from offline stress tests; to perform multi-dimensional vector magnitude calculations to generate a comprehensive disturbance index.

[0013] Preferably, the calibration process for the perturbation weight coefficients is as follows:

[0014] A single variable is scanned in a controlled environment; and the variable value that causes system response delay or energy consumption fluctuation rate to reach a preset failure threshold is recorded; the result is calibrated after assessing the relative impact of different disturbances on system stability.

[0015] Preferably, the process of the nonlinear smooth control calculation performed by the control strategy unit is as follows:

[0016] Obtain the comprehensive disturbance index; combine it with the preset adjustment sensitivity coefficient derived from the simulation during the product design phase; and perform mapping calculations using the hyperbolic tangent function to generate the system adjustment coefficient.

[0017] Preferably, the process for generating the dynamic data sampling frequency is as follows:

[0018] Based on the system adjustment coefficient, a linear interpolation calculation is performed between the preset minimum sampling frequency and the preset maximum sampling frequency derived from the controller hardware performance to generate a dynamic data sampling frequency.

[0019] Preferably, the process for generating the dynamic power compensation gain is as follows:

[0020] Based on the system adjustment coefficient, a linear interpolation calculation is performed between the preset minimum gain and the preset maximum gain derived from the hardware design specifications to generate a dynamic power compensation gain.

[0021] Preferably, the process for generating the upper limit of the dynamic communication retransmission count is as follows:

[0022] Based on the system adjustment coefficient, linear interpolation is performed between the preset minimum retransmission count and the preset maximum retransmission count derived from the communication protocol specification; and the calculation result is rounded up to generate a dynamic upper limit for the number of communication retransmissions.

[0023] Beneficial effects

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1. This invention establishes a forward-looking risk assessment mechanism, realizing a shift from passive response to proactive prediction. It can monitor and comprehensively analyze various environmental disturbance sources of different natures, such as power grids, wireless signals, and equipment loads, in real time. Through standardized data processing and quantitative calculation, it generates a unified index that can macroscopically characterize the overall operational risk of the system. This allows the controller to anticipate potential risks before instability manifests, rather than waiting for performance degradation to take remedial measures. This provides a basis for decision-making for early intervention and proactive control, greatly improving adaptability and operational stability in dynamically changing environments.

[0026] 2. This invention introduces a nonlinear flexible control strategy to ensure a smooth and stable system response to external disturbances. Based on the precise quantification of overall risk, a smoothly changing adjustment coefficient is generated. The adjustment range of this coefficient is precisely matched with the risk level. When faced with minor disturbances, only minor adjustments are made, avoiding energy waste and unnecessary fluctuations caused by overreaction. When faced with severe disturbances, the system can intervene decisively and stably. This nonlinear control method fundamentally suppresses system oscillations caused by sudden changes in control commands, ensuring that the controller can achieve efficient and stable operation under various operating conditions.

[0027] 3. This invention realizes an integrated collaborative execution mechanism, integrating multiple previously isolated control modules into an organic whole. Based on a unified system adjustment coefficient, the controller can synchronously and harmoniously adjust multiple core operating parameters such as data sampling frequency, power compensation gain, and communication retransmission strategy. This collaborative action ensures that system resources are optimally configured under different pressures, avoids internal conflicts and resource contention between modules, and demonstrates higher reliability and robustness in complex and extreme environments by intelligently balancing response speed, energy consumption, and communication efficiency, thus ensuring the long-term, safe, and comfortable operation of smart home appliances. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a logic block diagram of a smart home appliance controller data processing system according to the present invention. Detailed Implementation

[0030] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0031] Example 1:

[0032] Please see Figure 1 This invention provides a data processing system for an intelligent home appliance controller, comprising: a disturbance monitoring unit for real-time monitoring of grid voltage fluctuations, wireless signal interference intensity, and the number of concurrent devices; a risk assessment unit for receiving wireless signal interference intensity and performing normalization processing to generate a normalized interference degree; the risk assessment unit is further configured to combine the normalized interference degree, grid voltage fluctuations, and the number of concurrent devices to perform risk assessment calculations to generate a comprehensive disturbance index; a control strategy unit for performing nonlinear smooth control calculations based on the comprehensive disturbance index to generate system adjustment coefficients; and a collaborative execution unit for generating a dynamic data sampling frequency, a dynamic power compensation gain, and a dynamic upper limit for the number of communication retransmissions based on the system adjustment coefficients.

[0033] This invention discloses a data processing system for an intelligent home appliance controller. Its technical objective is to provide a predictive, integrated dynamic control mechanism to avoid positive feedback oscillations caused by the superposition of independent power compensation and communication retransmission mechanisms in existing technologies. The system achieves closed-loop control from environmental perception and risk quantification to collaborative execution through a series of logically coupled data processing steps. The initial steps of system operation are performed by a disturbance monitoring unit, whose responsibility is to continuously capture key environmental variables that pose a potential threat to system stability. Specifically, the monitoring objects are the real-time fluctuation value of the grid voltage, the signal interference intensity in the wireless communication environment, and the number of concurrent devices in the current network. This raw data is then delivered to a risk assessment unit for in-depth processing. This unit is responsible for quantifying multi-dimensional disturbance information into a unified risk index. Based on this risk index, a control strategy unit generates a core control coefficient. Finally, the collaborative execution unit, based on this coefficient, synchronously adjusts multiple key operating parameters within the controller to form an integrated response action, thereby maintaining stable system operation in a changing external environment.

[0034] The normalization process of the risk assessment unit is as follows: obtain the wireless signal interference intensity; and perform linear mapping processing on the wireless signal interference intensity based on the preset best signal intensity and worst signal intensity to generate normalized interference degree.

[0035] The risk assessment unit performs the risk assessment calculation as follows: combining normalized interference degree, grid voltage fluctuation value and number of concurrent devices; and introducing preset rated voltage and preset maximum number of devices from system hardware specifications and communication protocols, as well as disturbance weight coefficient from offline stress test; and performing multi-dimensional vector magnitude calculation to generate a comprehensive disturbance index.

[0036] To ensure a unified quantitative assessment of disturbance sources with different physical dimensions, the risk assessment unit must first normalize the received wireless signal interference intensity. This aims to eliminate the inconsistency in dimensions when performing mathematical operations on the logarithmic scale of wireless signal interference intensity (dBm) with other linear unit variables. The normalization process follows the following linear mapping formula:

[0037] ;

[0038] The normalized disturbance degree represents the dimensionless value, and its range is [0, 1].

[0039] This represents the real-time interference strength of the wireless signal acquired by the disturbance monitoring unit, measured in dBm.

[0040] and These represent the optimal signal strength and the minimum available signal strength thresholds preset according to the system design, respectively. For example, they can be set... , These preset values ​​collectively define the signal quality boundary that enables the system to operate stably;

[0041] According to this formula, when the real-time signal The optimal value hour, This indicates no risk of interference; when the real-time signal Degrade to the lowest threshold hour, This indicates the highest risk of interference; through this calculation, the risk assessment unit will determine the original signal strength. Transform it into a standardized indicator that can accurately reflect the degree of interference. This laid a correct mathematical foundation for the subsequent fusion and evaluation of multi-source heterogeneous data;

[0042] Based on the above results, the risk assessment unit uses this normalized disturbance degree, combined with other disturbance variables, to perform risk assessment calculations. The theoretical model for this risk assessment calculation is built upon the physical concept of multidimensional vector magnitude. It aims to abstract the three disturbance sources—grid voltage, signal interference, and equipment load—which have distinct properties, into orthogonal components in a multidimensional space. By calculating the magnitude of their combined vector, a comprehensive disturbance index is generated that macroscopically characterizes the overall risk faced by the system. The risk assessment calculation follows the formula below:

[0043] ;

[0044] Represents the comprehensive disturbance index;

[0045] These are dimensionless perturbation weighting coefficients that correspond to voltage, signal, and number of devices, respectively, and have been calibrated through offline stress testing.

[0046] It is the absolute value of the real-time monitored grid voltage fluctuation; its specific calculation method is the current grid voltage measurement value. With preset rated voltage The absolute value of the difference, i.e. ;

[0047] It is the preset rated voltage derived from the system hardware specifications;

[0048] It is the normalized interference degree calculated by the aforementioned formula;

[0049] It is the number of concurrent devices in real time;

[0050] It originates from the preset maximum number of devices in the communication protocol; this formula performs a weighted sum of squares and takes the square root of each dimensionless variable to generate the final comprehensive disturbance index. A single, continuous numerical value accurately quantifies the severity of the complex disturbance environment currently experienced by the smart home appliance controller.

[0051] The calibration process for the disturbance weight coefficient is as follows: a single variable is scanned in a controlled environment; and the variable values ​​that cause system response delay or energy consumption fluctuation rate to reach a preset failure threshold are recorded; the coefficient is calibrated after assessing the relative impact of different disturbances on system stability.

[0052] Perturbation weighting coefficient The value of the index has a definite engineering basis, and its accurate calibration is essential to ensuring the comprehensive disturbance index. This calibration process accurately reflects the prerequisites for physical disturbances. It is implemented through offline stress testing during product development, using a method that involves independently scanning a single disturbance variable in a strictly controlled laboratory environment. The process records the variable values ​​when system performance indicators, such as response latency or energy consumption fluctuation rate, reach preset failure thresholds, for example, 800 milliseconds or 40% respectively. By comparing the magnitudes required for each of the three disturbance sources to reach the system failure threshold, the relative impact of different disturbances on system stability can be assessed in reverse. Based on this, the corresponding weighting coefficients are calibrated and embedded in the controller. This experimental calibration process gives the weighting coefficients a clear and reproducible physical meaning.

[0053] Specifically, this calibration method follows the principle that the weights are inversely proportional to the normalized norm of the disturbance critical value; it records each disturbance critical value that leads to system failure, and then performs dimensionless processing on these critical values ​​to obtain the normalized critical value: voltage. Signal interference and number of devices Since a smaller critical value indicates a more severe impact from the disturbance, its weight should be higher. Therefore, the weighting coefficient is inversely proportional to the normalization critical value. For example, a set of initial weights can be defined. , , To facilitate calculation, this initial set of weights can be further normalized, for example, by setting their sum to 1, to calculate the final weight coefficients: , and And so on; this method ensures that the disturbance factors that have the greatest impact on system stability receive the highest weight in the calculation of the comprehensive disturbance index.

[0054] The process of nonlinear smooth control calculation by the control strategy unit is as follows: obtain the comprehensive disturbance index; combine it with the preset adjustment sensitivity coefficient obtained from the simulation in the product design stage; perform mapping calculation through the hyperbolic tangent function to generate the system adjustment coefficient;

[0055] The function of the control strategy unit is to take the quantitative risk indicators generated in the previous step and apply them to the control strategy unit. The mapping is a smooth control coefficient that can be directly used for execution-level regulation. To avoid abrupt changes and oscillations in the system response that may be caused by directly using the disturbance exponent for control, this embodiment uses the hyperbolic tangent function for nonlinear smooth regulation. The calculation of this nonlinear smooth regulation is defined by the following mapping formula:

[0056] ;

[0057] The system adjustment coefficient represents a dimensionless system.

[0058] It is the comprehensive disturbance index output by the risk assessment unit, which constitutes a direct logical input relationship with the calculation in the previous stage;

[0059] It is a dimensionless preset adjustment sensitivity coefficient;

[0060] This coefficient The source is system simulation during the product design phase, achieved by adjusting the simulation model. The convergence speed and overshoot of the system under different disturbances are measured and observed. Finally, an optimal value that balances response speed and operational stability is selected. As the disturbance index... The increase, The value of approaches 1 from 0, causing the system regulation coefficient to... The value smoothly decreases from 1 and approaches 0; this nonlinear mapping relationship generates a system adjustment coefficient that can precisely guide downstream units to perform fine-tuning. ;

[0061] The selection of this coefficient k requires a trade-off between the dynamic response characteristics of the system. If the value of k is set too high, the system adjustment coefficient λ will react violently to small changes in the disturbance index Ψ, making the control action too sensitive and potentially causing high-frequency oscillations near the stable point. Conversely, if the value of k is set too low, the system response will be too sluggish, and it will be unable to make timely and effective adjustments when the disturbance increases significantly, thus losing the advantage of proactive risk avoidance. Therefore, the optimal value of k is selected so that the change curve of the system adjustment coefficient λ has a moderate slope within the main activity range of the disturbance index Ψ.

[0062] The process of generating the dynamic data sampling frequency is as follows: based on the system adjustment coefficient, linear interpolation is performed between the preset minimum sampling frequency and the preset maximum sampling frequency derived from the controller hardware performance to generate the dynamic data sampling frequency;

[0063] The dynamic power compensation gain is generated as follows: based on the system adjustment coefficient, linear interpolation is performed between the preset minimum gain and the preset maximum gain derived from the hardware design specifications to generate the dynamic power compensation gain.

[0064] The process for generating the upper limit of the dynamic communication retransmission count is as follows: based on the system adjustment coefficient; perform linear interpolation calculation between the preset minimum retransmission count and the preset maximum retransmission count derived from the communication protocol specification; and round up the calculation result to generate the upper limit of the dynamic communication retransmission count.

[0065] To embody the integrated control design concept of this technical solution, the collaborative execution unit uses the received system adjustment coefficients. Instead of adjusting a single parameter in isolation, it uses it as a unified control benchmark to generate and apply three dynamic operating parameters simultaneously.

[0066] The generation of the dynamic data sampling frequency is a linear interpolation calculation based on the system adjustment coefficient, and its formula is as follows:

[0067] ;

[0068] It is the dynamic data sampling frequency;

[0069] It is the system adjustment coefficient;

[0070] and These are the minimum and maximum sampling frequencies preset based on the controller's hardware performance;

[0071] When external disturbances intensify, leading to When the sampling frequency is reduced, The response time will decrease linearly. This approach, at the cost of moderately sacrificing response speed, effectively reduces the computational load on the microprocessor and the system power consumption.

[0072] The generation of dynamic power compensation gain also follows linear interpolation calculation, and its formula is:

[0073] ;

[0074] It is a dimensionless dynamic power compensation gain;

[0075] It is the system adjustment coefficient;

[0076] and These are the minimum and maximum gain values ​​preset according to the hardware design specifications; in the event of severe fluctuations in the mains voltage, i.e. When the value is low, the system will automatically reduce the compensation gain. This adjustment can effectively avoid power system oscillations caused by overcompensation;

[0077] The generation of the upper limit for dynamic communication retransmission count involves an additional rounding step after the linear interpolation calculation. The formula is as follows:

[0078] ;

[0079] This is the upper limit of the number of retransmissions in dynamic communication;

[0080] It is the system adjustment coefficient;

[0081] and The minimum and maximum number of retransmissions are preset according to the communication protocol specifications used;

[0082] The symbol represents the floor function, ensuring that the output is an integer;

[0083] In areas with severe wireless signal interference When the value decreases, the system will proactively reduce the maximum allowed number of retransmissions. This strategy aims to alleviate channel congestion caused by repeated retransmission failures and ensure network communication efficiency.

[0084] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A data processing system for an intelligent home appliance controller, characterized in that, include: The disturbance monitoring unit is used to monitor grid voltage fluctuations, wireless signal interference intensity, and the number of concurrent devices in real time. The risk assessment unit is used to receive the wireless signal interference intensity and perform normalization processing to generate a normalized interference degree; the risk assessment unit is also used to combine the normalized interference degree, the grid voltage fluctuation value and the number of concurrent devices to perform risk assessment calculations to generate a comprehensive disturbance index. The control strategy unit is used to perform nonlinear smooth control calculations based on the comprehensive disturbance index to generate system control coefficients; The collaborative execution unit is used to generate a dynamic data sampling frequency, a dynamic power compensation gain, and a dynamic upper limit for the number of communication retransmissions based on the system adjustment coefficient. The risk assessment unit performs risk assessment calculations as follows: The system combines normalized interference, grid voltage fluctuation, and the number of concurrent devices; and incorporates preset rated voltage and preset maximum number of devices derived from system hardware specifications and communication protocols, as well as disturbance weighting coefficients derived from offline stress tests; to perform multi-dimensional vector magnitude calculations to generate a comprehensive disturbance index. The process of nonlinear smoothing control calculation by the control strategy unit is as follows: Obtain the comprehensive disturbance index; combine it with the preset adjustment sensitivity coefficient derived from the simulation during the product design phase; and perform mapping calculations using the hyperbolic tangent function to generate the system adjustment coefficient.

2. The intelligent home appliance controller data processing system according to claim 1, characterized in that, The normalization process for the risk assessment unit is as follows: The system acquires the wireless signal interference intensity and performs linear mapping processing on the wireless signal interference intensity based on the preset best and worst signal strengths to generate a normalized interference degree.

3. The intelligent home appliance controller data processing system according to claim 2, characterized in that, The calibration process for the perturbation weight coefficients is as follows: A single variable is scanned in a controlled environment; and the variable value that causes system response delay or energy consumption fluctuation rate to reach a preset failure threshold is recorded; the result is calibrated after assessing the relative impact of different disturbances on system stability.

4. The intelligent home appliance controller data processing system according to claim 1, characterized in that, The process of generating the dynamic data sampling frequency is as follows: Based on the system adjustment coefficient, a linear interpolation calculation is performed between the preset minimum sampling frequency and the preset maximum sampling frequency derived from the controller hardware performance to generate a dynamic data sampling frequency.

5. The intelligent home appliance controller data processing system according to claim 1, characterized in that, The process of generating the dynamic power compensation gain is as follows: Based on the system adjustment coefficient, a linear interpolation calculation is performed between the preset minimum gain and the preset maximum gain derived from the hardware design specifications to generate a dynamic power compensation gain.

6. The intelligent home appliance controller data processing system according to claim 1, characterized in that, The process for generating the upper limit of the dynamic communication retransmission count is as follows: Based on the system adjustment coefficient, linear interpolation is performed between the preset minimum retransmission count and the preset maximum retransmission count derived from the communication protocol specification; and the calculation result is rounded up to generate a dynamic upper limit for the number of communication retransmissions.

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