Intelligent household electrical appliance controller data processing system

Abstract

The invention discloses an intelligent household electrical appliance controller data processing system, and relates to the technical field of intelligent household electrical appliance control, and the system comprises a disturbance monitoring unit which is used for monitoring the voltage fluctuation value of a power grid, the interference intensity of wireless signals and the number of concurrent devices in real time; the risk assessment unit is used for receiving the interference intensity of the wireless signal to generate a normalized interference degree; performing risk assessment calculation to generate a comprehensive disturbance index; the regulation and control strategy unit is used for generating a system regulation coefficient based on the comprehensive disturbance index; and the cooperative execution unit is used for generating a dynamic data sampling frequency, a dynamic power compensation gain and a dynamic communication retransmission time upper limit based on the system adjustment coefficient. According to the method, by comprehensively pre-judging various disturbances and integrally and cooperatively regulating and controlling parameters such as sampling, power and communication, flexible control is achieved, conflict oscillation of independent compensation is avoided, and the stability and reliability of the system in a complex environment are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent home appliance control, and in particular to a data processing system for an intelligent home appliance controller. Background Art

[0002] With the increasing popularity of IoT technology and the smart home concept, the stability and reliability of smart appliance controllers, the core hub connecting users and appliances, are crucial. In daily operation, these controllers not only need to execute user commands and adjust appliance status, but also monitor and adapt to complex and changing external environments in real time. These environmental factors primarily include grid voltage fluctuations, signal interference in wireless communication channels, and the increase or decrease in the number of concurrent devices on the network. To ensure optimal appliance operation, controllers often integrate power compensation mechanisms to address voltage instability and communication retransmission mechanisms to ensure data transmission reliability. Ideally, these mechanisms should work together to maintain system stability, efficiency, and a comfortable user experience. For example, when grid power quality is poor, controllers can automatically adjust power input to ensure safe and stable appliance operation. When wireless signals are interfered with, they resend commands to ensure successful command reception and execution, thereby ensuring proper functionality and a comfortable user experience.

[0003] Existing smart appliance controllers suffer from significant design flaws when handling these multiple disturbances. The core issue lies in the fact that key regulation 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 regulation modules, lacking a unified coordinated strategy, can conflict. For example, the power compensation module increases instantaneous power consumption to cope with voltage drops, potentially increasing processor load. Simultaneously, the communication module initiates high-frequency retransmissions due to poor signal conditions, further consuming system resources and potentially leading to data congestion. This conflicting processing mechanism creates a vicious cycle, known as "positive feedback oscillation," leading to a sharp decline in overall system performance. Specifically, this manifests as significantly increased latency in the controller's response to user commands, drastic and chaotic fluctuations in device energy consumption, and ultimately, a severe impact on user comfort. This passive, lagging, and conflicting compensation approach fails to fundamentally anticipate and mitigate the systemic risks associated with complex disturbances, constituting a critical technical bottleneck in the field of smart appliance controllers. Summary of the Invention

[0004] The purpose of the present invention is to provide a data processing system for an intelligent home appliance controller, which solves the problems existing in the background technology.

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

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

[0007] A control strategy unit is used to perform nonlinear smooth control calculation based on the comprehensive disturbance index to generate a system regulation coefficient;

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

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

[0010] Obtaining wireless signal interference strength; and performing linear mapping processing on the wireless signal interference strength based on preset best signal strength and worst signal strength to generate a normalized interference degree.

[0011] Preferably, the process of the risk assessment unit performing risk assessment calculation is as follows:

[0012] The normalized interference degree, grid voltage fluctuation value and number of concurrent devices are combined; the preset rated voltage and preset maximum number of devices derived from the system hardware specifications and communication protocol, as well as the disturbance weight coefficient derived from the offline stress test, are introduced; and multi-dimensional vector modulus calculation is performed to generate a comprehensive disturbance index.

[0013] Preferably, the calibration process of the disturbance weight coefficient 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 reaches the preset failure threshold is recorded; and the relative impact of different disturbances on system stability is evaluated and calibrated.

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

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

[0017] Preferably, the generation process of the dynamic data sampling frequency is as follows:

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

[0019] Preferably, the generation process of the dynamic power compensation gain is as follows:

[0020] Based on the system adjustment coefficient; linearly interpolating between a preset minimum gain and a preset maximum gain derived from the hardware design specifications to generate a dynamic power compensation gain.

[0021] Preferably, the process of generating the upper limit of the number of dynamic communication retransmissions is as follows:

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

[0023] Beneficial effects

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

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

[0026] 2. The present invention introduces a nonlinear flexible control strategy to ensure that the system responds smoothly and stably to external disturbances. Based on the precise quantification of the overall risk, a smoothly changing adjustment coefficient is generated; the adjustment range of this coefficient accurately matches the risk level. In the face of minor disturbances, only slight 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 instructions, ensuring that the controller can achieve efficient and stable operation under various working conditions.

[0027] 3. The present invention implements an integrated collaborative execution mechanism, integrating multiple originally 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 competition among modules, and demonstrates higher reliability and robustness in complex and extreme environments by intelligently balancing response speed, energy consumption, and communication efficiency, ensuring the long-term, safe, and comfortable operation of smart home appliances. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0029] Figure 1 This is a logic block diagram of a data processing system for an intelligent home appliance controller according to the present invention. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0031] Example 1:

[0032] See also Figure 1 The present invention provides a data processing system for a smart home appliance controller, comprising: a disturbance monitoring unit for real-time monitoring of grid voltage fluctuation values, wireless signal interference intensity, and the number of concurrent devices; a risk assessment unit for receiving the 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 fluctuation values, and the number of concurrent devices to perform risk assessment calculations to generate a comprehensive disturbance index; a control strategy unit is configured to perform nonlinear smoothing control calculations based on the comprehensive disturbance index to generate a system adjustment coefficient; and a collaborative execution unit is configured to generate a dynamic data sampling frequency, a dynamic power compensation gain, and an upper limit on the number of dynamic communication retransmissions based on the system adjustment coefficient.

[0033] An embodiment of the present invention discloses a data processing system for a smart home appliance controller. Its technical objective is to provide a predictive, integrated dynamic control mechanism to avoid the positive feedback oscillations caused by the superposition of independent power compensation and communication retransmission mechanisms in the existing technology. The system implements closed-loop control from environmental perception, risk quantification, to collaborative execution through a series of logically coupled data processing steps. The initial steps of the system operation are performed by a disturbance monitoring unit, which is responsible for continuously capturing key environmental variables that pose potential threats to system stability. Its specific monitoring targets are the real-time fluctuations in 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, the control strategy unit generates a core control coefficient. Finally, the collaborative execution unit uses this coefficient to synchronously adjust multiple key operating parameters within the controller, forming an integrated response action, thereby maintaining stable system operation under changing external environments.

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

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

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

[0037] ;

[0038] represents the dimensionless normalized interference degree, and its value range is [0, 1];

[0039] Represents the wireless signal interference strength obtained in real time by the disturbance monitoring unit, in dBm;

[0040] and They represent the optimal signal strength and the minimum available signal strength thresholds preset according to system design. For example, , ,The preset values ​​jointly define the signal quality boundary at which the system can work stably;

[0041] According to this formula, when the real-time signal is the optimal value hour, , indicating no interference risk; when the real-time signal Degraded to the lowest threshold hour, , indicating the greatest interference risk; through this calculation, the risk assessment unit converts the original signal strength Converted into a standardized indicator that accurately reflects the degree of interference , which laid a correct mathematical foundation for the subsequent fusion evaluation of multi-source heterogeneous data;

[0042] Based on the above results, the risk assessment unit uses this normalized interference degree in combination with other disturbance variables to perform risk assessment calculations. The theoretical model of this risk assessment calculation is based on the physical concept of multidimensional vector modulus. It aims to abstract the three disturbance sources of different properties—grid voltage, signal interference, and equipment load—into orthogonal components in multidimensional space. By calculating the modulus of their resultant vector, it generates a comprehensive disturbance index that can macroscopically characterize the overall risk faced by the system. Its risk assessment calculation follows the following formula:

[0043] ;

[0044] represents the comprehensive disturbance index;

[0045] are dimensionless disturbance weight coefficients corresponding to voltage, signal, and number of devices, respectively, and 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, ;

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

[0048] is the normalized interference calculated by the above formula;

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

[0050] is the preset maximum number of devices from the communication protocol; this formula performs a square root operation on the weighted sum of squares of each dimensionless variable to generate a comprehensive disturbance index With a single, continuous value, it accurately quantifies the severity of the complex disturbance environment in which the smart home appliance controller is currently located.

[0051] The calibration process of the disturbance weight coefficient is as follows: a single variable is scanned in a controlled environment; and the variable value when the system response delay or energy consumption fluctuation rate reaches a preset failure threshold is recorded; and the relative impact of different disturbances on system stability is evaluated and then calibrated;

[0052] Perturbation weight coefficient The value of has a definite engineering basis, and its accurate calibration is to ensure the comprehensive disturbance index The prerequisite for truly reflecting physical disturbances is to conduct a calibration process during offline stress testing during the product development phase. The method involves independently scanning a single disturbance variable in a strictly controlled laboratory environment. The process records the corresponding variable value when the system performance indicator, such as response delay or energy consumption fluctuation rate, reaches a preset failure threshold, such as 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 reversely assessed. Ultimately, the corresponding weight coefficient value is calibrated and fixed in the controller. This experimental calibration process gives the weight coefficient a clear and reproducible physical meaning.

[0053] Specifically, the calibration method follows the principle that the weight is inversely proportional to the normalized norm of the disturbance critical value; the critical values ​​of each disturbance that causes system failure are recorded, and these critical values ​​are dimensionless processed to obtain the normalized critical value: voltage , signal interference , and the number of devices ; Since the smaller the critical value, the more severe the impact of the disturbance, and the higher the weight, the weight coefficient is inversely proportional to the normalized critical value; for example, a set of initial weights can be defined , , ; To facilitate calculation, this set of initial weights can be further normalized, for example, by making their sum 1, and calculating the final weight coefficient: , and And so on; this method ensures that the disturbance factor that has the greatest impact on system stability receives the highest weight in the calculation of the comprehensive disturbance index.

[0054] The process of the control strategy unit performing nonlinear smooth control calculation is as follows: obtaining a comprehensive disturbance index; combining it with a preset control sensitivity coefficient derived from a simulation in the product design phase; performing a mapping calculation using a hyperbolic tangent function to generate a system control coefficient;

[0055] The function of the control strategy unit is to convert the quantitative risk indicators generated in the previous step into is mapped into a smooth control coefficient that can be directly used for execution layer control. To avoid sudden changes and oscillations in system response that may be caused by directly using the disturbance index for control, this embodiment uses a hyperbolic tangent function for nonlinear smooth control. The nonlinear smooth control calculation is defined by the following mapping formula:

[0056] ;

[0057] represents the dimensionless system adjustment coefficient;

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

[0059] is the dimensionless preset adjustment sensitivity coefficient;

[0060] The coefficient The source is the system simulation in the product design stage. By adjusting The value is adjusted and the convergence speed and overshoot of the system under different disturbance shocks are observed, and finally an optimal value that can achieve a balance between response speed and operation stability is selected; as the disturbance index The increase, The value of goes from 0 to 1, resulting in the system adjustment coefficient The value of decreases smoothly from 1 and approaches 0; this nonlinear mapping relationship generates a system regulation coefficient that can accurately guide the downstream units to perform delicate regulation. ;

[0061] The selection of the coefficient k requires a trade-off between the dynamic response characteristics of the system. If the k value 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 possibly causing the system to produce high-frequency oscillations near the stable point. Conversely, if the k value is set too low, the system response will be too sluggish, and timely and effective adjustments will not be possible when the disturbance increases significantly, thus losing the advantage of forward-looking risk avoidance. Therefore, the selection of the optimal k value aims to ensure 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 dynamic data sampling frequency is generated by performing a linear interpolation calculation between a preset minimum sampling frequency and a preset maximum sampling frequency based on the controller hardware performance based on the system adjustment coefficient to generate the dynamic data sampling frequency.

[0063] The dynamic power compensation gain is generated by performing a linear interpolation calculation between a preset minimum gain and a preset maximum gain based on the hardware design specification based on the system adjustment coefficient to generate the dynamic power compensation gain;

[0064] The dynamic communication retransmission limit is generated by performing a linear interpolation calculation between a preset minimum retransmission number and a preset maximum retransmission number based on a system adjustment coefficient; and rounding up the result of the calculation to generate the dynamic communication retransmission limit.

[0065] In order to reflect the design concept of integrated control of this technical solution, the collaborative execution unit is based on the received system adjustment coefficient , rather than adjusting a single parameter in isolation, it is used as a unified control benchmark to simultaneously generate and apply three dynamic operating parameters;

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

[0067] ;

[0068] is the dynamic data sampling frequency;

[0069] is the system regulation coefficient;

[0070] and It is the minimum and maximum sampling frequency preset according to the controller hardware performance;

[0071] When external disturbances intensify When the sampling frequency decreases It will decrease linearly, which effectively reduces the computing load of the microprocessor and system power consumption at the expense of moderate response speed;

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

[0073] ;

[0074] is the dimensionless dynamic power compensation gain;

[0075] is the system regulation coefficient;

[0076] and The minimum and maximum gain values ​​are preset according to the hardware design specifications; when the grid voltage fluctuates violently, that is, When the value is low, the system will actively lower the compensation gain ,This regulation can effectively avoid the oscillation of the power system caused by overcompensation;

[0077] The generation of the upper limit of dynamic communication retransmission times adds a rounding step after the linear interpolation calculation. The formula is:

[0078] ;

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

[0080] is the system regulation coefficient;

[0081] and It is the minimum and maximum number of retransmissions preset according to the adopted communication protocol specification;

[0082] The symbol represents the ceiling function, which ensures that the output result is an integer;

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

[0084] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution 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 the grid voltage fluctuation value, wireless signal interference intensity and the number of concurrent devices in real time; a risk assessment unit configured to receive wireless signal interference strength and perform normalization processing to generate a normalized interference degree; the risk assessment unit is further configured to combine the normalized interference degree, grid voltage fluctuation value, and number of concurrent devices to perform risk assessment calculations to generate a comprehensive disturbance index; A control strategy unit is used to perform nonlinear smooth control calculation based on the comprehensive disturbance index to generate a system regulation coefficient; The collaborative execution unit is used to generate a dynamic data sampling frequency, a dynamic power compensation gain, and an upper limit on the number of dynamic communication retransmissions based on a system adjustment coefficient.

2. The intelligent home appliance controller data processing system according to claim 1, characterized in that: The process of normalization processing performed by the risk assessment unit is as follows: Obtaining wireless signal interference strength; and performing linear mapping processing on the wireless signal interference strength based on preset best signal strength and worst signal strength to generate a normalized interference degree.

3. The intelligent home appliance controller data processing system according to claim 1, characterized in that: The process of the risk assessment unit performing risk assessment calculation is as follows: Combine the normalized interference level, grid voltage fluctuation value, and number of concurrent devices; introduce the preset rated voltage and preset maximum number of devices derived from system hardware specifications and communication protocols, as well as the disturbance weight coefficient derived from offline stress testing; Perform multi-dimensional vector modulus calculations to generate a comprehensive perturbation index.

4. The intelligent home appliance controller data processing system according to claim 3, characterized in that: The calibration process of the disturbance weight coefficient 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 reaches the preset failure threshold is recorded; and the relative impact of different disturbances on system stability is evaluated and calibrated.

5. The intelligent home appliance controller data processing system according to claim 1, characterized in that: The process of the control strategy unit performing nonlinear smooth control calculation is as follows: Obtain a comprehensive disturbance index; combine it with the preset adjustment sensitivity coefficient derived from the simulation in the product design phase; and perform mapping calculations using the hyperbolic tangent function to generate a system adjustment coefficient.

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

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

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

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