An electronic control system for smart home

Abstract

The present invention discloses an electronic control system for smart homes, which specifically relates to the field of joint control of smart devices, including: marking the power status of devices as abnormal through a faulty device identification module and establishing a faulty device list; a power defect calculation module extracting device functional attribute parameters, calculating power differences and constructing a power defect matrix; a functional defect assessment module generating a mapping relationship between power defects and functional defect degrees based on a fitting curve; a compensation device identification module identifying available compensation devices through functional feature matching and spatial attenuation adjustment; a compensation strategy construction module allocating power indicators and generating functional compensation strategies based on a multi-objective optimization model; a control instruction issuing module sending a control instruction set to the compensation device for power regulation; realizing functional compensation and optimization of the smart home system, solving the problem of lack of adaptive compensation capability of the devices, and improving the intelligence level of the smart home devices.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent device joint control, and more specifically, to an electronic control system for smart homes. Background Art

[0002] In current smart home electronic control systems, various home appliances are typically interconnected and controlled through a unified network architecture. However, the control logic of these devices is often independent of each other. When a device fails or performs abnormally, the system usually relies on manual user intervention or predefined redundant backup strategies for repair and replacement. For example, when a corridor light in the lighting system fails, the existing system cannot automatically brighten the lights in adjacent areas to compensate for the lighting. Similarly, when a humidifier fails, the system struggles to automatically adjust the air conditioner power or turn on the fan to replace the humidity control. This lack of adaptive compensation capability seriously affects the robustness and user satisfaction of smart home systems in practical applications.

[0003] Therefore, how to design an electronic control system for smart homes that can identify equipment failures and automatically compensate through functional similarity matching and optimized scheduling has become an important issue that needs to be urgently addressed in the current smart home field.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an electronic control system for smart homes to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An electronic control system for smart homes, including a faulty device identification module, a power defect calculation module, a function defect assessment module, a compensation device identification module, a compensation strategy construction module, and a control instruction issuing module;

[0008] The faulty device identification module marks the power status of devices in the smart home network as abnormal and creates a list of faulty devices;

[0009] The power deficiency calculation module extracts the functional attribute parameters of all devices in the faulty device list, calculates the power difference based on the rated power of the device according to the difference calculation rule, and constructs the power deficiency matrix of the faulty device;

[0010] The functional defect assessment module constructs a fitting curve of the relationship between power and device performance, substitutes it into the power defect matrix, and outputs the mapping relationship between power defect and functional defect degree;

[0011] The compensation device identification module identifies the available compensation device set by matching functional feature similarity. Based on the relationship between the spatial position of the compensation device and the attenuation of the device performance response, it performs spatial attenuation adjustment on the fitting curve of the relationship between power and device performance.

[0012] The compensation strategy construction module generates the target value for the functional defect compensation of the faulty equipment, performs reverse calculation on the fitting curve after spatial attenuation adjustment, allocates power indicators to the compensation equipment by constructing a multi-objective optimization model, and establishes a functional compensation control strategy.

[0013] The control instruction issuing module constructs a control instruction set for controlling the power output of the device based on the compensation strategy, and issues the control instruction set to each compensation device for power regulation.

[0014] In a preferred embodiment, marking the power status of devices in the smart home network as abnormal and establishing a list of faulty devices specifically includes:

[0015] Extract the MAC addresses of all devices in the smart home network as unique device identifiers, and construct a device power status dataset through real-time sampling and recording;

[0016] Perform data format conversion on the power data in the power status record set and apply a data normalization algorithm to map the power values to a unified numerical range;

[0017] The power value of each device in the power data is calculated point by point through differential calculation to extract the power change rate of the device;

[0018] Devices whose power change rate reaches the set sudden drop threshold, or whose power deviates from the device's rated power by more than the set deviation threshold, are marked as abnormal and a list of faulty devices is generated.

[0019] In a preferred embodiment, extracting functional attribute parameters of all devices in the faulty device list, calculating power differences based on the rated power of the devices according to the difference calculation rule, and constructing a power defect matrix of the faulty devices specifically includes:

[0020] Extract all device function attribute parameters from the faulty device list and construct a standardized data structure describing the device function characteristics in the form of feature vectors, forming a high-dimensional feature description matrix for the device functions. The function attribute parameters include the device function type and power output capacity.

[0021] The power output capability parameters in the high-dimensional feature vector are used to calculate the power difference based on the rated power of the equipment according to the difference calculation rule, and the power difference is constructed into a power defect matrix in a unified data format.

[0022] In a preferred embodiment, a fitting curve of the relationship between power and device performance is constructed and substituted into the power defect matrix. The mapping relationship between the output power defect and the functional defect degree specifically includes:

[0023] A generalized additive model and the least squares method are used to construct a fitting curve for the functional attribute parameters of the device, which shows the relationship between power and device performance. The curve fitting process for different functional types is based on the linear and nonlinear response characteristics of the device function.

[0024] Converting the fitting curve into a function model of different device types, wherein the device performance is a core capability expression of the device function;

[0025] The power defect in the power defect matrix is input into the function model, and a data indicator for real-time quantification of the functional defect degree is formed according to the model output result, and a mapping relationship between the power defect and the functional defect degree is established.

[0026] In a preferred embodiment, identifying a set of available compensation devices by matching functional feature similarity, and performing spatial attenuation adjustment on the fitting curve of the relationship between power and device performance based on the relationship between the spatial position of the compensation device and the attenuation of the device performance response specifically includes:

[0027] In the high-dimensional feature description matrix, using the device with the abnormal status mark as the benchmark, similarity matching is performed on the functional features of all other devices without the abnormal status mark, identifying the set of available compensation devices and recording the original performance indicators of the devices in the compensation device set;

[0028] Based on the spatial position of the compensation device set and the functional type between devices as the association condition, the spatial distance of each compensation device is calculated through the three-dimensional Euclidean distance;

[0029] A piecewise linear fitting algorithm is used to construct an attenuation curve that shows the relationship between spatial distance and device performance response attenuation, and mathematically characterize the performance attenuation of devices with different functional types.

[0030] By combining the attenuation curve with the spatial position parameters of the compensation device, a spatial attenuation parameter set of each compensation device is generated, and a fitting curve of the relationship between power and device performance is adjusted based on the spatial attenuation parameter set.

[0031] In a preferred embodiment, a functional defect compensation target value for a faulty device is generated, a fitting curve after spatial attenuation adjustment is reversely calculated, and a multi-objective optimization model is constructed to allocate power indicators to the compensation devices. Establishing a functional compensation control strategy specifically includes:

[0032] Extract the power defect value of the faulty device in the faulty device list, obtain the functional defect degree of the faulty device based on the mapping relationship between the power defect and the functional defect degree, and generate the functional defect degree compensation target value of the faulty device;

[0033] Perform reverse calculation on the fitting curve after spatial attenuation adjustment, and allocate power indicators according to the target value based on the available power output capacity of each compensation device;

[0034] By using the compensation target value and the power output capacity parameters of the compensation equipment, a multi-objective optimization model is constructed with compensation output power, spatial performance degradation, and load balancing as optimization objectives.

[0035] The power index allocation of the compensation equipment is solved through a multi-objective optimization algorithm, and the adjustment value of the power output of the compensation equipment is generated based on the calculation result, forming a compensation control strategy for multiple compensation devices used for scheduling to simultaneously assume the function of a single faulty device.

[0036] In a preferred embodiment, a control instruction set for controlling the power output of a device is constructed based on a compensation strategy, and the control instruction set is sent to each compensation device for power regulation. Specifically, the control instruction set includes:

[0037] Extract the device power output parameters and spatial position parameters based on the compensation strategy, and construct a control instruction set for controlling the device power output;

[0038] Obtain the MAC address of the compensation device, send power output control instructions to each compensation device through the communication interface, and the compensation device performs power regulation based on the control instructions;

[0039] Monitor the faulty equipment in the faulty equipment list. If the equipment is detected to have recovered from the fault, perform compensation control recovery.

[0040] In a preferred embodiment, the monitoring of the faulty devices in the faulty device list and performing compensation control recovery if a device that has recovered from the fault is detected specifically includes:

[0041] After regulation, the faulty equipment in the faulty equipment list is monitored in real time. When the power change rate of the faulty equipment does not reach the set sudden drop threshold within a set monitoring time window, and the power deviation from the equipment rated power does not exceed the set deviation threshold, the abnormal status flag of the equipment is cleared;

[0042] In the process of clearing the abnormal status identification of the device, the faulty device node broadcasts the device's original performance indicator recovery instruction to end the performance compensation control of the compensation device.

[0043] The technical effects and advantages of the electronic control system for smart homes of the present invention are as follows:

[0044] The system first detects devices with abnormal power and creates a fault list, then generates a power defect matrix by calculating the power difference. A generalized additive model is used to construct a fitting curve between power and device performance, converting the power defect matrix into a functional defect degree. A mapping relationship between power and functional response is established. The system matches the available set of compensation devices and adjusts the fitting curve based on spatial location and performance attenuation characteristics to improve compensation accuracy. A compensation strategy is constructed based on a multi-objective optimization model to allocate and regulate power for the compensation devices. Finally, the system sends the generated control instruction set to each compensation device, enabling real-time compensation and optimization of the equipment. The system has the capabilities of automatic identification, intelligent optimization, and efficient regulation, significantly improving stability and adaptability in smart home environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a schematic diagram of the structure of an electronic control system for smart homes in the present invention. DETAILED DESCRIPTION

[0046] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0047] Example 1, Figure 1 The present invention provides an electronic control system for smart homes, which includes a fault device identification module, a power defect calculation module, a function defect assessment module, a compensation device identification module, a compensation strategy construction module, and a control instruction issuing module;

[0048] The faulty device identification module marks the power status of devices in the smart home network as abnormal and creates a list of faulty devices;

[0049] The power deficiency calculation module extracts the functional attribute parameters of all devices in the faulty device list, calculates the power difference based on the rated power of the device according to the difference calculation rule, and constructs the power deficiency matrix of the faulty device;

[0050] The functional defect assessment module constructs a fitting curve of the relationship between power and device performance, substitutes it into the power defect matrix, and outputs the mapping relationship between power defect and functional defect degree;

[0051] The compensation device identification module identifies the available compensation device set by matching functional feature similarity. Based on the relationship between the spatial position of the compensation device and the attenuation of the device performance response, it performs spatial attenuation adjustment on the fitting curve of the relationship between power and device performance.

[0052] The compensation strategy construction module generates the target value for the functional defect compensation of the faulty equipment, performs reverse calculation on the fitting curve after spatial attenuation adjustment, allocates power indicators to the compensation equipment by constructing a multi-objective optimization model, and establishes a functional compensation control strategy.

[0053] The control instruction issuing module constructs a control instruction set for controlling the power output of the device based on the compensation strategy, and issues the control instruction set to each compensation device for power regulation.

[0054] Mark abnormal power status of devices in the smart home network and create a list of faulty devices.

[0055] The MAC addresses of all online devices on the smart home network are extracted and stored as unique identifiers in the device identification table. All online devices are sampled in real time at a predetermined sampling interval (once per minute), recording the output power data of each device. Each sampling data generation includes the device's MAC address, timestamp, and power value, and is stored in the power status dataset.

[0056] Convert raw power data collected by devices from various data formats (e.g., floating point, integer, and string) to a standardized numerical format (e.g., floating point). This process ensures that power data from all devices is stored and processed in a unified format. A Min-Max normalization algorithm is used to map power values from different devices to a unified numerical range (0 to 1).

[0057] The power value of each device is differentially calculated point by point and the results are recorded in the power change rate dataset. The result of each calculation includes the device's MAC address, timestamp, and power change rate.

[0058] When the power change rate of a device reaches the preset sudden drop threshold (30% by default), or the difference between the power value of a device and the rated power exceeds the preset deviation threshold (20% by default), the system marks the device as abnormal. All devices marked as abnormal are recorded to form a faulty device list. Each record contains the following fields: device ID (MAC address), abnormal state type, timestamp, power change rate or power deviation value.

[0059] Extract the functional attribute parameters of all devices in the faulty device list, calculate the power difference based on the rated power of the device according to the difference calculation rule, and construct the power defect matrix of the faulty device.

[0060] In order to quantitatively analyze and structure the degree of functional defects of faulty equipment, the system extracts the functional attribute parameters of all equipment from the faulty equipment list and constructs a standardized data structure in the form of feature vectors to describe the functional characteristics of the equipment.

[0061] The extracted functional attribute parameters include the functional type and power output capacity of the device. The functional type is used to distinguish the working categories of different devices, such as lighting equipment, audio equipment, humidification equipment, ventilation equipment, etc. The power output capacity represents the rated power or maximum power output value of the device under normal working conditions. The system extracts and normalizes the functional attribute parameters of each device to generate a high-dimensional feature vector composed of the functional type and power output capacity. The high-dimensional feature vectors of each device are uniformly constructed into a high-dimensional feature description matrix, where the rows of the matrix represent different devices and the columns of the matrix represent attribute parameters such as functional type and power output capacity. This feature description matrix provides a complete input data set for power defect calculation. The high-dimensional feature vector consists of functional type, power output capacity, spatial location parameters (completed by the compensation device identification module), communication protocol parameters, device status parameters, etc.

[0062] After completing the construction of the high-dimensional feature description matrix, the system extracts the power output capability parameters from the matrix, and based on the rated power of the equipment, quantifies the power difference according to the difference calculation rules to construct a power defect matrix.

[0063] The power defect matrix is generated by comparing the power output capability of each device with its rated power and calculating the power difference. For each device in the matrix, the system calculates the difference between its current power output and its rated power and records the result as the power defect value. The power defect values of all devices are recorded in the power defect matrix using a unified data format, with rows representing different devices and columns representing power defect values. The power defect matrix provides an accurate numerical basis for subsequent functional defect assessment and compensation strategy generation, ensuring the system can quantitatively analyze and effectively preemptively handle faulty devices under abnormal conditions.

[0064] To accurately assess the degree of functional impairment in smart home electronic control systems, the system uses a generalized additive model (GAM) to model the functional attribute parameters of the devices, construct a fitting curve that shows the relationship between power and device performance, and convert this curve into a functional model for different device types. By inputting the power impairment from the power impairment matrix into the functional model, the system generates a data indicator for real-time quantification of functional impairment, thereby establishing a mapping relationship between power impairment and functional impairment. The entire process includes the collection and processing of device characteristic data, GAM model training and fitting curve generation, functional model construction, and functional impairment degree calculation.

[0065] For different types of equipment, the system uses both linear fitting and generalized additive models to model the relationship between power and equipment performance based on the differences in their functional characteristics. For equipment types that exhibit a strong linear relationship, such as lighting, humidification, and ventilation, the system uses a linear fitting algorithm to build the model. The linear fitting algorithm uses the least squares method to generate a linear expression for the relationship between input power and equipment performance. The formula is as follows:

[0066] ;

[0067] Where F1 is the equipment performance index item of the equipment type with strong linear relationship, P is the power output, a and b are the parameters of linear fitting. The system obtains linear fitting models for different equipment types by training the original data set.

[0068] For equipment types that exhibit nonlinear relationships, such as the power-loudness relationship of audio equipment, the system uses a generalized additive model for modeling. The GAM model fits the performance response curve under different power outputs through an adaptive smoothing function. The mathematical expression is:

[0069] ;

[0070] Where, F2 is the equipment performance index item of the equipment type with nonlinear relationship, is a constant term, which represents the baseline performance value when the power is zero or other variables are invalid, p is the total number of factors affecting the function of the device, is the independent variable corresponding to the jth factor affecting equipment performance, is a smooth function of the nonlinear response of the equipment performance.

[0071] The form of the smoothing function is automatically selected during model training and is typically one of the following: spline functions (for smooth and steadily changing nonlinear relationships), Gaussian processes (for highly nonlinear and complex relationships), and kernel regression (for relationships with significant local variations). Data is fitted using methods such as penalized spline regression or gradient boosting. During the fitting process, the shape and complexity of the smoothing function are adjusted by minimizing a loss function (such as mean squared error).

[0072] The power defect values in the power defect matrix are input into the constructed function model. By calculating the performance output of the device in the power defect state and comparing the performance output of the device in the normal power state with the power defect state, the system generates data indicators for quantifying the degree of functional defect and establishes a mapping relationship between power defect and functional defect.

[0073] Among them, the defect degree has different expressions according to the core capabilities of the equipment function (corresponding to F1 or F2 in the formula). For lighting equipment, it represents brightness (unit: lumen) defect, for audio equipment, it represents loudness (unit: decibel dB) defect, and for humidifiers, it represents humidification speed (unit: L / h) defect. Other types of equipment are also expressed in this way.

[0074] The available compensation device set is identified by functional feature similarity matching, and the fitting curve of the relationship between power and device performance is spatially attenuated based on the relationship between the spatial position of the compensation device and the attenuation of the device performance response.

[0075] In a smart home network, the system performs similarity matching on the functional characteristics of all devices based on a high-dimensional feature description matrix. In this matrix, each device's data vector consists of information such as its function type, power output capability, spatial location parameters, communication protocol parameters, and device status parameters. For each device with an abnormal status indicator (i.e., a device listed in the faulty device list), the system uses its function type and power output capability as a matching benchmark, and then performs similarity matching on all other devices in the matrix that do not carry abnormal status indicators.

[0076] The similarity matching process is completed by calculating the similarity of functional features between devices. The matching formula is as follows:

[0077] ;

[0078] Where, is the similarity of device functional characteristics, n is the dimension of the feature vector, and the dimension includes at least four dimensions: functional type, power output capability, spatial location parameters, and communication protocol parameters. is the weight coefficient of different features (for example, the weight of functional type is higher, and the weight of spatial location parameter is lower, which is set specifically according to the functional type). is the matching degree of the kth feature. The specific calculation method is as follows: the matching degree of numerical feature vectors (such as power output capacity) is calculated by the numerical proportional deviation, and the matching degree of Boolean feature vectors (such as function type) is calculated by the exclusive OR operation. When the two input values are the same, the output is 1, and when they are different, the output is 0.

[0079] Based on matching calculation results The compensation equipment corresponding to the faulty equipment is screened out through the threshold comparison method.

[0080] After identifying the set of compensation devices, the system calculates the spatial distance between each compensation device and the faulty device based on the device's spatial location parameters. Spatial location parameters are represented by the X, Y, and Z coordinates in a three-dimensional coordinate system. Distance calculation is completed using the three-dimensional Euclidean distance calculation formula.

[0081] Because device performance decays in space, especially for lighting, audio, and ventilation equipment, its output gradually decreases with increasing distance. To mathematically characterize this attenuation, the system uses a piecewise linear fitting algorithm to model the relationship between spatial distance and device performance response.

[0082] The piecewise linear fitting algorithm divides the entire spatial range into several intervals and uses linear regression within each interval for fitting. For each compensatory device, its performance output at different spatial distances is calculated using the same linear fitting algorithm used in the Functional Defect Assessment module. The attenuation parameter is obtained from the device manufacturer's specifications (e.g., a 20lm drop in bulb brightness at a distance exceeding 5m) or from specific experiments.

[0083] By combining the attenuation curve with the spatial position parameters of the compensation device, a spatial attenuation parameter set for each compensation device is generated. Based on the spatial attenuation parameter set, the system adjusts the performance response of devices of different functional types and generates a fitting curve for subsequent compensation strategy generation.

[0084] Generate the target value of functional defect compensation for the faulty equipment, perform reverse calculation on the fitting curve after spatial attenuation adjustment, allocate power indicators to the compensation equipment by constructing a multi-objective optimization model, and establish a functional compensation control strategy.

[0085] The system first extracts the power defect values of all faulty devices in the faulty device list, and calculates the functional defect degree of each faulty device based on the established mapping relationship between power defect and functional defect degree, and uses the calculated functional defect degree as the compensation target value.

[0086] After generating the target value for functional impairment compensation, the system allocates power output indicators to each compensatory device based on the fitted curve adjusted for spatial attenuation of different device types. This process determines the power output of each compensatory device by performing a reverse calculation on the fitted curve.

[0087] By performing reverse calculation on the generated spatial attenuation adjusted fitting curve, the performance response of each compensation device under different power output conditions is determined. The reverse calculation formula of the fitting curve is:

[0088] ;

[0089] Where, Represents the power output of each compensation device, Indicates the compensation demand for the target functional defect, It represents the inverse function of the fitting curve L, and the power output is obtained by inverse calculation.

[0090] To optimize the effectiveness of the compensation strategy, a multi-objective optimization model was constructed, simultaneously considering three optimization objectives: compensation output power, spatial performance degradation, and load balancing. These objectives were to minimize compensation output power to improve the power consumption ratio of the compensation process, minimize spatial performance degradation to reduce device performance degradation due to spatial distance, and balance power loads to avoid overloading a single compensation device and improve coordination and stability among devices. The power output of each compensation device did not exceed its maximum power output capacity.

[0091] A multi-objective optimization algorithm (such as NSGA-II or MOEA / D) solves the constructed multi-objective optimization model to obtain the optimal power allocation strategy. The optimization algorithm solution process includes individual initialization, fitness evaluation, selection, crossover, and mutation operations. The final optimization result is the optimal power output of each compensation device.

[0092] The optimization solution results are converted into adjustment values for the power output of the compensation device, and a compensation control strategy is formed for scheduling multiple compensation devices to simultaneously assume the functions of a single faulty device. The strategy includes the power output of each compensation device and the MAC address of the corresponding device, and is recorded and stored in a unified data format.

[0093] Based on the compensation strategy, a control instruction set is constructed to control the power output of the device, and the control instruction set is sent to each compensation device for power regulation.

[0094] After generating the compensation strategy and optimizing the power allocation strategy, a control instruction set for controlling the device's power output is constructed based on the device's power output parameters, spatial position parameters (if the power output compensation amount cannot be met, a position adjustment instruction is sent to the removable device as a backup compensation control method; this process is not implemented in this solution), and the device's MAC address. The control instruction set generation process includes three steps: parameter extraction, instruction formatting, and instruction set construction. The constructed control instruction set is formatted and recorded and stored according to a predetermined data format. The control instruction set serves as the input source for subsequent instruction issuance and device control.

[0095] Based on the established control instruction set, the system issues power output control commands to each compensation device through a communication interface. This communication interface is based on smart home network protocols, such as Wi-Fi, Bluetooth, and Zigbee, ensuring that each device can accurately receive and execute control commands. The compensation device is identified based on the MAC address in the control instruction set, and a communication path is established for command transmission.

[0096] Based on the power output and spatial position parameters in the control instruction set, a command sequence for controlling the device's power output is generated and distributed to each compensation device via a communication interface. Upon receiving the command, the compensation device regulates its power output according to the parameters in the command. The power regulation process is based on the device's internal control mechanism, adjusting the power output to meet the requirements of the compensation strategy.

[0097] After regulation, the faulty devices in the faulty device list are monitored in real time. When the power change rate of the faulty device does not reach the set sudden drop threshold within the set monitoring time windows (the window size is the length of the power collection cycle. The number of consecutive monitoring windows is set according to the power collection frequency and is 5 by default for the device), and the power deviation from the device's rated power does not exceed the set deviation threshold, the device's abnormal status indicator is cleared.

[0098] When clearing a device's abnormal status indicator, the faulty device node broadcasts a command to restore the device's original performance indicators. Upon receiving the command, each compensation device automatically terminates its current power control and returns to its original performance state. The device's power output and spatial location parameters are reset to their initial configurations, and the device ceases to work with the faulty device.

[0099] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0100] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially performed. 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 means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0101] Those skilled in the art will appreciate that the modules and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel 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.

[0102] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0104] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0105] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0106] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0107] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0108] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An electronic control system for smart home, characterized in that: It includes a faulty equipment identification module, a power defect calculation module, a functional defect assessment module, a compensation equipment identification module, a compensation strategy construction module, and a control instruction issuing module; The faulty device identification module marks the power status of devices in the smart home network as abnormal and creates a list of faulty devices; The power deficiency calculation module extracts the functional attribute parameters of all devices in the faulty device list, calculates the power difference based on the rated power of the device according to the difference calculation rule, and constructs the power deficiency matrix of the faulty device; The functional defect assessment module constructs a fitting curve of the relationship between power and device performance, substitutes it into the power defect matrix, and outputs the mapping relationship between power defect and functional defect degree; The compensation device identification module identifies the available compensation device set by matching functional feature similarity. Based on the relationship between the spatial position of the compensation device and the attenuation of the device performance response, it performs spatial attenuation adjustment on the fitting curve of the relationship between power and device performance. The compensation strategy construction module generates the target value for the functional defect compensation of the faulty equipment, performs reverse calculation on the fitting curve after spatial attenuation adjustment, allocates power indicators to the compensation equipment by constructing a multi-objective optimization model, and establishes a functional compensation control strategy. The control instruction issuing module constructs a control instruction set for controlling the power output of the equipment based on the compensation strategy, and issues the control instruction set to each compensation device for power regulation; The fitting curve of the relationship between power and device performance is constructed and substituted into the power defect matrix. The mapping relationship between the output power defect and the functional defect degree specifically includes: A generalized additive model and the least squares method are used to construct a fitting curve for the functional attribute parameters of the device, which shows the relationship between power and device performance. The curve fitting process for different functional types is based on the linear and nonlinear response characteristics of the device function. Converting the fitting curve into a function model of different device types, wherein the device performance is a core capability expression of the device function; The power defect in the power defect matrix is input into the function model, and a data indicator for real-time quantification of the functional defect degree is formed according to the model output result, and a mapping relationship between the power defect and the functional defect degree is established.

2. The electronic control system for smart home according to claim 1, characterized in that: Mark the power status of devices in the smart home network as abnormal and establish a list of faulty devices, including: Extract the MAC addresses of all devices in the smart home network as unique device identifiers, and construct a device power status dataset through real-time sampling and recording; Perform data format conversion on the power data in the power status record set and apply a data normalization algorithm to map the power values to a unified numerical range; The power value of each device in the power data is calculated point by point through differential calculation to extract the power change rate of the device; Devices whose power change rate reaches the set sudden drop threshold, or whose power deviates from the device's rated power by more than the set deviation threshold, are marked as abnormal and a list of faulty devices is generated.

3. The electronic control system for smart home according to claim 2, characterized in that: Extract all device function attribute parameters from the faulty device list, calculate the power difference based on the rated power of the device according to the difference calculation rule, and construct the power defect matrix of the faulty device. Specifically, the following steps are performed: Extract all device function attribute parameters from the faulty device list and construct a standardized data structure describing the device function characteristics in the form of feature vectors, forming a high-dimensional feature description matrix for the device functions. The function attribute parameters include the device function type and power output capacity. The power output capability parameters in the high-dimensional feature vector are used to calculate the power difference based on the rated power of the equipment according to the difference calculation rule, and the power difference is constructed into a power defect matrix in a unified data format.

4. The electronic control system for smart home according to claim 3, characterized in that: The available compensation device set is identified through functional feature similarity matching. Based on the relationship between the spatial position of the compensation device and the attenuation of the device performance response, the fitting curve of the relationship between power and device performance is adjusted for spatial attenuation. Specifically, the following steps are performed: In the high-dimensional feature description matrix, using the device with the abnormal status mark as the benchmark, similarity matching is performed on the functional features of all other devices without the abnormal status mark, identifying the set of available compensation devices and recording the original performance indicators of the devices in the compensation device set; Based on the spatial position of the compensation device set and the functional type between devices as the association condition, the spatial distance of each compensation device is calculated through the three-dimensional Euclidean distance; A piecewise linear fitting algorithm is used to construct an attenuation curve that shows the relationship between spatial distance and device performance response attenuation, and mathematically characterize the performance attenuation of devices with different functional types. By combining the attenuation curve with the spatial position parameters of the compensation device, a spatial attenuation parameter set of each compensation device is generated, and a fitting curve of the relationship between power and device performance is adjusted based on the spatial attenuation parameter set.

5. The electronic control system for smart home according to claim 4, characterized in that: Generate the target value of functional defect compensation for the faulty equipment, perform reverse calculation on the fitting curve after spatial attenuation adjustment, allocate power indicators to the compensation equipment by building a multi-objective optimization model, and establish a functional compensation control strategy, which specifically includes: Extract the power defect value of the faulty device in the faulty device list, obtain the functional defect degree of the faulty device based on the mapping relationship between the power defect and the functional defect degree, and generate the functional defect degree compensation target value of the faulty device; Perform reverse calculation on the fitting curve after spatial attenuation adjustment, and allocate power indicators according to the target value based on the available power output capacity of each compensation device; By using the compensation target value and the power output capacity parameters of the compensation equipment, a multi-objective optimization model is constructed with compensation output power, spatial performance degradation, and load balancing as optimization objectives. The power index allocation of the compensation equipment is solved through a multi-objective optimization algorithm, and the adjustment value of the power output of the compensation equipment is generated based on the calculation result, forming a compensation control strategy for multiple compensation devices used for scheduling to simultaneously assume the function of a single faulty device.

6. The electronic control system for smart home according to claim 5, characterized in that: Based on the compensation strategy, a control instruction set is constructed to control the power output of the device. The control instruction set is sent to each compensation device for power regulation. Specifically, the following steps are involved: Extract the device power output parameters and spatial position parameters based on the compensation strategy, and construct a control instruction set for controlling the device power output; Obtain the MAC address of the compensation device, send power output control instructions to each compensation device through the communication interface, and the compensation device performs power regulation based on the control instructions; Monitor the faulty equipment in the faulty equipment list. If the equipment is detected to have recovered from the fault, perform compensation control recovery.

7. The electronic control system for smart home according to claim 6, characterized in that: The monitoring of the faulty devices in the faulty device list and performing compensation control recovery if a device recovering from the fault is detected specifically includes: After regulation, the faulty equipment in the faulty equipment list is monitored in real time. When the power change rate of the faulty equipment does not reach the set sudden drop threshold within a set monitoring time window, and the power deviation from the equipment rated power does not exceed the set deviation threshold, the abnormal status flag of the equipment is cleared; In the process of clearing the abnormal status identification of the device, the faulty device node broadcasts the device's original performance indicator recovery instruction to end the performance compensation control of the compensation device.

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