Electronic control system for smart home

Abstract

The invention discloses a smart home-oriented electronic control system, and particularly relates to the field of smart device combined control, comprising the following steps: carrying out abnormal marking on a device power state through a fault device identification module and establishing a fault device list; the power defect calculation module extracts equipment function attribute parameters, calculates power difference and constructs a power defect matrix; the functional defect evaluation module generates a mapping relation between the power defect and the functional defect degree based on the fitted curve; the compensation equipment identification module identifies available compensation equipment through functional feature matching and spatial attenuation adjustment; the compensation strategy construction module carries out power index distribution and function compensation strategy generation based on a multi-objective optimization model; the control instruction issuing module sends the control instruction set to the compensation equipment for power regulation and control; functional compensation and optimization of the smart home system are realized, the problem that the equipment lacks self-adaptive compensation capability is solved, and the intelligent degree of the smart home equipment is improved.

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 home. Background Art

[0002] In the current electronic control system for smart home, various furniture devices are usually interconnected and controlled through a unified network architecture. However, the control logics of these devices are often independent. When a device fails or its performance is abnormal, the system usually can only rely on manual intervention by the user or predefined redundant backup strategies for repair and replacement. For example, when the corridor light in the lighting system fails, the existing system cannot automatically brighten the lamps in adjacent areas to achieve light compensation; another example is that when the humidifier fails, it is difficult for the system to replace the humidity control by automatically adjusting the power of the air conditioner or turning on the fan. This problem of lacking adaptive compensation ability seriously affects the robustness and user satisfaction of the smart home system in practical applications.

[0003] Therefore, how to design an electronic control system for smart home that can identify device failures and perform automatic compensation through function similarity matching and optimized scheduling has become an important problem to be solved urgently in the current smart home field.

[0004] To solve the above problems, a technical solution is provided as follows. 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 home to solve the problems raised in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solutions: An electronic control system for smart home, comprising a faulty device identification module, a power deficit calculation module, a function deficit evaluation module, a compensatory device identification module, a compensatory strategy construction module, and a control instruction issuing module; The faulty device identification module marks the power states of devices in the smart home network as abnormal states and establishes a list of faulty devices; The power deficit calculation module extracts all device function attribute parameters in the list of faulty devices, calculates the power difference according to the difference calculation rule based on the rated power of the devices, and constructs a power deficit matrix of the faulty devices; The function deficit evaluation module constructs a fitting curve of the relationship between power and device performance, substitutes the power deficit matrix, and outputs the mapping relationship between power deficit and function deficit degree; The compensation device identification module identifies the available set of compensation devices through functional feature similarity matching, and based on the relationship between the spatial position of the compensation device and the attenuation of device performance response, adjusts the spatial attenuation of the fitting curve of the power-device performance relationship; The compensation strategy construction module generates the compensation target value of the functional defect degree of the faulty device, performs back-calculation on the fitting curve after spatial attenuation adjustment, allocates power indicators to the compensation devices 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 devices based on the compensation strategy, and issues the control instruction set to each compensation device for power regulation.

[0007] In a preferred embodiment, abnormal state marks are set for the power states of the devices in the smart home network, and a faulty device list is established, which specifically includes: Extract the MAC addresses of all devices in the smart home network as the unique device identifiers, and construct a device power state data set through real-time sampling records; Perform data format conversion processing on the power data in the power state record set, and apply a data normalization algorithm to map the power values to a unified numerical range; Perform point-by-point difference calculation on the power values of each device in the power data through difference calculation, and extract the power change rate of the device; Set abnormal state marks for the devices whose power change rate reaches the set sudden drop threshold or the deviation between the power and the rated power of the device exceeds the set deviation threshold, and form a faulty device list.

[0008] In a preferred embodiment, extract all the device functional attribute parameters in the faulty device list, calculate the power difference according to the difference calculation rule based on the rated power of the device, and construct a power defect matrix of the faulty device, which specifically includes: Extract all the device functional attribute parameters in the faulty device list, construct a standardized data structure describing the device functional features in the form of feature vectors, and form a high-dimensional feature description matrix for the device functions. Among them, the functional attribute parameters include the device function type and the power output ability; Through the power output ability parameter in the high-dimensional feature vector, calculate the power difference according to the difference calculation rule based on the rated power of the device, and construct the power difference into a power defect matrix in a unified data format.

[0009] In a preferred embodiment, construct a fitting curve of the power-device performance relationship, substitute the power defect matrix, and output the mapping relationship between the power defect and the functional defect degree, which specifically includes: Using a generalized additive model and the least squares method, a fitting curve of the relationship between power and device performance is constructed for the functional attribute parameters of the device. Among them, the curve fitting process for different functional types is based on the linear and non-linear response characteristics of the device function; The fitting curve is transformed into a function model for different device types, and the device performance is the core ability expression of the device function; The power deficit in the power deficit matrix is input into the function model, and a data index for real-time quantifying the function deficit degree is formed according to the model output result, and a mapping relationship between the power deficit and the function deficit degree is established.

[0010] In a preferred embodiment, a set of available replacement devices is identified through functional feature similarity matching. Based on the relationship between the spatial position of the replacement device and the attenuation of the device performance response, the spatial attenuation adjustment of the fitting curve of the relationship between power and device performance specifically includes: In the high-dimensional feature description matrix, taking the device carrying the abnormal state identifier as a reference, the functional features of all other devices not carrying the abnormal state identifier are subjected to similarity matching to identify a set of available replacement devices, and the original performance indicators of the devices in the set of replacement devices are recorded; Based on the spatial position of the set of replacement devices, taking the functional type between devices as the association condition, the spatial distance of each replacement device is calculated through three-dimensional Euclidean distance; A piecewise linear fitting algorithm is used to construct an attenuation curve of the relationship between the spatial distance and the attenuation of the device performance response, and the attenuation of the device performance of different functional types is mathematically characterized; By combining the attenuation curve with the spatial position parameters of the replacement device, a spatial attenuation parameter set for each replacement device is generated, and the fitting curve of the relationship between power and device performance is adjusted based on the spatial attenuation parameter set.

[0011] In a preferred embodiment, a compensation target value for the function deficit degree of the faulty device is generated, and the fitting curve after spatial attenuation adjustment is inversely calculated. By constructing a multi-objective optimization model, power index allocation is performed on the replacement device, and a functional compensation control strategy is established, specifically including: Extract the power deficit value of the faulty device in the faulty device list, obtain the function deficit degree of the faulty device according to the mapping relationship between the power deficit and the function deficit degree, and generate a compensation target value for the function deficit degree of the faulty device; Perform inverse calculation on the fitting curve after spatial attenuation adjustment, and allocate power indexes according to the target value according to the available power output capacity of each replacement device; Through the compensation target value and the power output capacity parameters of the replacement device, a multi-objective optimization model with the compensation output power, spatial performance attenuation, and load balance as optimization objectives is constructed; Solve the power index allocation amount of the compensation device through a multi-objective optimization algorithm, generate an adjustment value for the power output of the compensation device based on the calculation result, and form a functional compensation control strategy for multiple compensation devices to simultaneously undertake the function of a single faulty device for scheduling.

[0012] In a preferred embodiment, constructing a control instruction set for controlling the power output of the device based on the compensation strategy, and sending the control instruction set to each compensation device for power regulation specifically includes: Extract the device power output parameters and spatial position parameters based on the compensation strategy, and construct a control instruction set for controlling the power output of the device; Obtain the MAC address of the compensation device, send the power output control instruction to each compensation device through the communication interface, and the compensation device performs power regulation based on the control instruction; Monitor the faulty devices in the faulty device list. If a device with recovered faults is detected, perform compensation control recovery.

[0013] In a preferred embodiment, the monitoring of the faulty devices in the faulty device list and performing compensation control recovery if a device with recovered faults is detected specifically includes: Monitor the faulty devices in the faulty device list in real time after regulation. When the power change rate of the faulty device within a continuous set number of monitoring time windows does not reach the set sudden drop threshold and the deviation between the power and the rated power of the device does not exceed the set deviation threshold, clear the abnormal state identifier of the device; During the process of clearing the abnormal state identifier of the device, the faulty device node broadcasts and sends a device original performance index recovery instruction to end the performance compensation control of the compensation device.

[0014] Technical effects and advantages of an electronic control system for smart home according to the present invention: The system first detects power-abnormal devices and establishes a fault list, generates a power deficiency matrix by calculating the power difference. Utilize the generalized additive model to construct a fitting curve of power and device performance, convert the power deficiency matrix into a functional deficiency degree, and establish a mapping relationship between power and functional response. The system matches the available set of compensation devices and adjusts the fitting curve according to the spatial position and performance attenuation characteristics to improve the compensation accuracy. Construct a compensation strategy based on the multi-objective optimization model, perform power allocation and regulation on the compensation devices. Finally, the system sends the generated control instruction set to each compensation device to achieve real-time compensation and optimization of the devices. This system has the capabilities of automatic identification, intelligent optimization, and efficient regulation, significantly improving the stability and adaptability in the smart home environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic structural diagram of an electronic control system for smart home according to the present invention. Detailed implementation manners

[0016] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a 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 those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0017] Embodiment 1 Figure 1 An electronic control system for smart home is provided, including a faulty device identification module, a power deficit calculation module, a function deficit evaluation module, a replacement device identification module, a replacement strategy construction module, and a control instruction issuing module; The faulty device identification module marks the abnormal state of the power state of the devices in the smart home network and establishes a list of faulty devices; The power deficit calculation module extracts the function attribute parameters of all devices in the faulty device list, calculates the power difference according to the difference calculation rule based on the rated power of the devices, and constructs a power deficit matrix of the faulty devices; The function deficit evaluation module constructs a fitting curve of the relationship between power and device performance, substitutes the power deficit matrix, and outputs the mapping relationship between power deficit and function deficit degree; The replacement device identification module identifies the available set of replacement devices through functional feature similarity matching, and based on the relationship between the spatial position of the replacement devices and the attenuation of device performance response, performs spatial attenuation adjustment on the fitting curve of the relationship between power and device performance; The replacement strategy construction module generates the compensation target value of the function deficit degree of the faulty devices, performs inverse calculation on the spatially attenuated adjusted fitting curve, allocates power indicators to the replacement devices by constructing a multi-objective optimization model, and establishes a function compensation control strategy; The control instruction issuing module constructs a control instruction set for controlling the power output of the devices based on the replacement strategy, and issues the control instruction set to each replacement device for power regulation.

[0018] Mark the abnormal state of the power state of the devices in the smart home network and establish a list of faulty devices.

[0019] Extract the MAC addresses of all online devices from the smart home network and store them as unique identifiers in the device identification table. Perform real-time sampling on all online devices at a predetermined sampling period (once per minute), and record the output power data of each device. The data generated each time sampling contains the MAC address, timestamp, and power value of the device, and stores them in the power state dataset.

[0020] Convert the original power data collected by the device from multiple data formats (such as floating-point, integer, string) to a standardized numerical format (such as floating-point). This process ensures that the power data of all devices is stored and processed in a unified format. The Min-Max normalization algorithm is used to map the power values of different devices to a unified numerical range (0 to 1).

[0021] Perform point-by-point difference calculation on the power values of each device, and record the results in the power change rate dataset. The result of each calculation includes the MAC address, timestamp, and power change rate of the device.

[0022] When the power change rate of the device reaches the preset sudden drop threshold (default set to 30%), or when the difference between the power value of the device and the rated power of the device exceeds the predetermined deviation threshold (default set to 20%), the system marks the device as an abnormal state, records all devices marked as abnormal states, and forms a list of faulty devices. Each record contains the following fields: device identifier (MAC address), abnormal state type, timestamp, power change rate, or power deviation value.

[0023] Extract all device functional attribute parameters in the list of faulty devices, calculate the power difference according to the difference calculation rule based on the rated power of the device, and construct the power deficiency matrix of the faulty devices.

[0024] In order to perform quantitative analysis and structured representation of the functional deficiency degree of faulty devices, the system extracts all device functional attribute parameters from the list of faulty devices and constructs a standardized data structure for describing device functional characteristics in the form of a feature vector.

[0025] The extracted functional attribute parameters include the functional type and power output capacity of the device. The functional type is used to distinguish different device working categories, such as lighting devices, audio devices, humidifying devices, ventilation devices, etc.; the power output capacity represents the rated power or maximum power output value of the device under normal working conditions. The system generates a high-dimensional feature vector composed of the functional type and power output capacity by extracting and normalizing the functional attribute parameters of each device. The high-dimensional feature vectors of each device are uniformly constructed into a high-dimensional feature description matrix. The rows of the matrix represent different devices, and the columns represent attribute parameters such as functional type and power output capacity. This feature description matrix provides a complete input dataset for power deficiency calculation. The composition of the high-dimensional feature vector includes functional type, power output capacity, spatial position parameters (completed by the compensation device identification module), communication protocol parameters, device status parameters, etc.

[0026] After completing the construction of the high-dimensional feature description matrix, the system constructs a power deficiency matrix by extracting the power output capacity parameters from the matrix and quantitatively calculating the power difference according to the difference calculation rule based on the rated power of the device.

[0027] The generation process of the power deficiency matrix is based on calculating the power difference by comparing the power output capacity of each device with its rated power. 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 deficiency value. The power deficiency values of all devices are recorded in the power deficiency matrix in a unified data format, where the rows of the matrix represent different devices and the columns represent the power deficiency values. The power deficiency matrix provides an accurate numerical basis for subsequent functional deficiency degree evaluation and compensation strategy generation, ensuring the quantitative analysis and effective preprocessing of faulty devices in abnormal states of the system.

[0028] In the smart home electronic control system, in order to accurately evaluate the degree of functional deficiency of devices, the system uses the Generalized Additive Model (GAM) to model the functional attribute parameters of devices, constructs a fitting curve of the relationship between power and device performance, and converts this curve into a function model for different device types. By inputting the power deficiency in the power deficiency matrix into the function model, the system generates data indicators for real-time quantifying the functional deficiency degree, thereby establishing a mapping relationship between power deficiency and functional deficiency degree. The whole process includes steps such as device characteristic data collection and processing, GAM model training and fitting curve generation, function model construction and functional deficiency degree calculation.

[0029] For different types of devices, according to the differences in their functional characteristics, the system uses two methods, linear fitting and generalized additive model, to model the relationship between power and device performance. For device types showing a strong linear relationship, such as lighting devices, humidifying devices, and ventilation devices, the system uses a linear fitting algorithm to construct the model. The linear fitting algorithm is based on the least squares method and generates an expression of the linear relationship between the input power and device performance. The formula is as follows: ; In the formula, F1 is the device performance index term of the device type with a strong linear relationship, P is the power output, and a and b are the parameters of linear fitting. The system obtains the linear fitting models for different device types through training on the original data set.

[0030] For device types showing a non-linear relationship, such as the relationship between the power and loudness of audio devices, the system uses the generalized additive model for modeling. The GAM model fits the performance response curve at different power outputs through an adaptive smoothing function, and the mathematical expression is: ; In the formula, F2 is the device performance index term of the device type with a non-linear relationship, is a constant term, representing the baseline performance value when the power is zero or other variables are invalid. p is the total number of influencing factors that affect the device's function. is the independent variable corresponding to the j-th influencing factor that affects the device's performance. is a smoothing function for the non-linear response of the device's performance.

[0031] Among them, the form of the smoothing function is automatically selected during the model training process, usually the following function forms: spline function (used to handle smooth and steadily changing non-linear relationships), Gaussian process (used to handle highly non-linear complex relationships), and kernel regression (used to handle relationships with significant local changes). Methods such as penalized spline regression or gradient boosting are used to fit the data. During the fitting process, the shape and complexity of the smoothing function are adjusted by minimizing the loss function (such as mean squared error).

[0032] Input the power deficiency values in the power deficiency matrix into the constructed function model. By calculating the performance output of the device in the power deficiency state and comparing the performance outputs of the device in the normal power state and the power deficiency state, the system generates data indicators for quantifying the function deficiency degree and establishes a mapping relationship between power deficiency and function deficiency degree.

[0033] Among them, the deficiency degree has different manifestation forms according to the core capabilities of the device's function (corresponding to F1 or F2 in the formula). For lighting devices, it represents the deficiency of brightness (unit: lumen lm), for audio devices, it represents the deficiency of loudness (unit: decibel dB), for humidifiers, it represents the deficiency of humidification speed (unit: L / h). Other types of devices also express the function deficiency degree in this identification method.

[0034] Identify the set of available compensatory devices through functional feature similarity matching. Based on the spatial position of the compensatory devices and the relationship between the device performance response attenuation, perform spatial attenuation adjustment on the fitting curve of the power and device performance relationship.

[0035] In the smart home network, the system performs similarity matching on the functional features of all devices based on the high-dimensional feature description matrix. In the high-dimensional feature description matrix, the data vector of each device is composed of information such as the device's function type, power output ability, spatial position parameters, communication protocol parameters, and device status parameters. For each device carrying an abnormal status identifier (i.e., the device recorded in the faulty device list), the system uses its function type and power output ability as the matching benchmark to perform similarity matching on all other devices in the high-dimensional feature description matrix that do not carry abnormal status identifiers.

[0036] The process of similarity matching is completed by calculating the functional feature similarity between devices. The matching formula is as follows: ; Wherein, is the similarity of device function features, n is the dimension of the feature vector, and the dimension includes at least 4 dimensions: function type, power output ability, spatial position parameter, and communication protocol parameter. is the weight coefficient of different features (for example, the weight of the function type is higher, and the weight of the spatial position parameter is lower, which is specifically set according to the function type). is the matching degree of the kth feature. The specific calculation method is as follows: for the numerical feature vector (such as power output ability), the matching degree is calculated through the numerical ratio deviation; for the boolean feature vector (such as function type), the XNOR operation is used to calculate the matching degree, and the output is 1 when the two input values are the same, and 0 when they are different.

[0037] Based on the matching calculation result The replacement device corresponding to the faulty device is screened out through the threshold comparison method.

[0038] After completing the identification of the replacement device set, the system calculates the spatial distance between each replacement device and the faulty device based on the spatial position parameter of the device. The spatial position parameter is represented by the position coordinates X, Y, Z in the three-dimensional coordinate system, and the distance calculation is completed through the three-dimensional Euclidean distance calculation formula.

[0039] Since the performance of the device decays in space, especially for lighting devices, audio devices, and ventilation devices, etc., their performance output gradually weakens with the increase of distance. In order to mathematically characterize this attenuation characteristic, the system uses the piecewise linear fitting algorithm to model the relationship between the spatial distance and the device performance response.

[0040] The piecewise linear fitting algorithm divides the entire spatial range into several intervals and uses linear regression for fitting within each interval. For each replacement device, the performance output at different spatial distances is realized by the same calculation method of the linear fitting algorithm in the function defect assessment module, where the attenuation parameter is obtained through the specification annotation of the device manufacturer (such as when the distance exceeds 5m, the brightness drop of the bulb is 20lm) or specific experiments.

[0041] By combining the attenuation curve with the spatial position parameter of the replacement device, a spatial attenuation parameter set of each replacement device is generated. Based on the spatial attenuation parameter set, the system adjusts the device performance response of different function types and generates a fitting curve for subsequent replacement strategy generation.

[0042] Generate the function defect degree compensation target value of the faulty device, perform inverse calculation on the fitting curve after spatial attenuation adjustment, allocate power indicators to the replacement device by constructing a multi-objective optimization model, and establish a function compensation control strategy.

[0043] The system first extracts the power deficiency values of all faulty devices in the faulty device list, and calculates the functional deficiency degree of each faulty device based on the established mapping relationship between power deficiency and functional deficiency degree, and uses the calculated functional deficiency degree as the compensation target value.

[0044] After generating the compensation target value of the functional deficiency degree, the system distributes the power output indicators of each compensating device according to the fitting curve adjusted by the spatial attenuation of different device types. This process determines the power output of each compensating device through inverse calculation of the fitting curve.

[0045] By performing inverse calculation on the generated fitting curve adjusted by spatial attenuation, the performance response of each compensating device under different power output conditions is determined. The inverse calculation formula of the fitting curve is: ; In the formula, represents the power output of each compensating device, represents the compensation requirement for the target functional deficiency degree, represents the inverse function of the fitting curve L, and the power output is obtained through inverse calculation.

[0046] To optimize the effect of the compensation strategy, by constructing a multi-objective optimization model, three optimization objectives of compensation output power, spatial performance attenuation and load balance are considered at the same time, which are minimizing the compensation output power to improve the power consumption ratio of the compensation process, minimizing the spatial performance attenuation to reduce the device performance attenuation caused by the spatial distance, and balancing the power load, avoiding excessive load on a single compensating device, and improving the coordination and stability between devices. Among them, the power output of each compensating device does not exceed its maximum power output capacity.

[0047] The multi-objective optimization algorithm (such as NSGA-II or MOEA / D) solves the constructed multi-objective optimization model to obtain the optimal power distribution strategy. The solution process of the optimization algorithm includes steps such as individual initialization, fitness evaluation, selection, crossover and mutation operations, and the final optimization result is the optimal power output of each compensating device.

[0048] The optimized solution result is converted into the adjustment value of the power output of the compensating device, and a functional compensation control strategy for multiple compensating devices to simultaneously bear a single faulty device is formed for scheduling. This strategy includes the power output of each compensating device and the MAC address of the corresponding device, and is recorded and stored in a unified data format.

[0049] Based on the compensation strategy, a control instruction set for controlling the power output of the device is constructed, and the control instruction set is sent to each compensating device for power regulation.

[0050] After the generation of the compensation strategy and the optimization of the power distribution strategy, a control instruction set for controlling the power output of the devices is constructed based on the device power output parameters, spatial position parameters (when the power output compensation amount cannot be satisfied, a position adjustment instruction is sent to the movable device as a backup means for compensation control, which is not implemented in this solution), and the MAC addresses of the devices in the optimization results. The generation process of the control instruction set includes three links: parameter extraction, instruction formatting, and instruction set construction. The constructed control instruction set is formatted and recorded and stored in accordance with a predetermined data format. The control instruction set serves as the input source for subsequent instruction issuance and device control.

[0051] Based on the constructed control instruction set, the system issues power output control instructions to each compensation device through the communication interface. The communication interface is constructed based on the communication protocols of the smart home network, such as Wi-Fi, Bluetooth, Zigbee, etc., to ensure that each device can accurately receive and execute the control instructions. The compensation devices are identified according to the MAC addresses in the control instruction set, and a communication path for instruction transmission is constructed.

[0052] According to the power output amount and spatial position parameters in the control instruction set, an instruction sequence for controlling the power output of the devices is generated and sent to each compensation device through the communication interface. After receiving the instructions, the compensation devices adjust their own power outputs according to the parameters in the instructions. The power regulation process is based on the internal control mechanism of the devices, and the power output amount is adjusted to meet the requirements of the compensation strategy.

[0053] The faulty devices in the faulty device list are monitored in real time after regulation. When the power change rate of the faulty devices within a continuous set number of monitoring time windows (the window size is the length of the power acquisition period, and the number of continuous monitoring windows is specifically set according to the power acquisition frequency, with the default device being 5) does not reach the set sudden drop threshold and the deviation between the power and the rated power of the device does not exceed the set deviation threshold, the abnormal state flag of the device is cleared.

[0054] During the process of clearing the abnormal state flag of the device, the faulty device node broadcasts and sends a device original performance index restoration instruction. After each compensation device receives the restoration instruction, it automatically terminates the current power regulation and restores to the original performance state. The power output parameters and spatial position parameters of the device are reset according to the initial configuration, and the collaborative work with the faulty device is stopped.

[0055] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula that is closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0056] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. 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 includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. 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 by wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0057] Those of ordinary skill in the art can realize that the modules and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0058] Those skilled in the art can 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 foregoing method embodiments and will not be elaborated herein.

[0059] In several embodiments provided in the present 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 illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For example, 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 displayed or discussed couplings, direct couplings, or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0060] The module described as a separation component may or may not be physically separated. The component shown as a module may or may not be a physical module. It may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0061] In addition, in each embodiment of the present application, each functional module may be integrated in a processing module, may exist separately as individual physical modules, or two or more modules may be integrated in one module.

[0062] If the described function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0063] As described above, this is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claimed rights.

[0064] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope 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 establishes a list of faulty devices; The power defect calculation module extracts the function 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 defect matrix of the faulty device; The functional defect assessment module constructs a fitting curve of the relationship between power and equipment 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 the functional characteristics similarity, and performs spatial attenuation adjustment on the fitting curve of the relationship between power and device performance based on the spatial position of the compensation device and the attenuation relationship between the device performance response; The compensation strategy building module generates the compensation target value of the functional defect degree of the faulty equipment, performs reverse calculation on the fitting curve after spatial attenuation adjustment, allocates power indicators to the compensation equipment by building a multi-objective optimization model, and establishes a functional compensation control strategy; The control instruction sending module constructs a control instruction set for controlling the power output of the device based on the compensation strategy, and sends the control instruction set to each compensation device for power regulation.

2. The electronic control system for smart home according to claim 1, characterized in that: The power status of devices in the smart home network is marked as abnormal, and a list of faulty devices is established, including: Extract the MAC addresses of all devices in the smart home network as the unique device identifier, and construct a device power status dataset through real-time sampling records; Perform data format conversion processing on the power data in the power status record set, and apply a data normalization algorithm to map the power value 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 deviation from the device rated power exceeds the set deviation threshold, are marked as abnormal and a list of faulty devices is formed.

3. The electronic control system for smart home according to claim 2, characterized in that: Extract all equipment function attribute parameters in the faulty equipment list, calculate the power difference based on the rated power of the equipment according to the difference calculation rule, and construct the power defect matrix of the faulty equipment, including: Extract all equipment function attribute parameters in the faulty equipment list, construct a standardized data structure describing the equipment function characteristics in the form of feature vectors, and form a high-dimensional feature description matrix for equipment functions, where the function attribute parameters include equipment function type and power output capacity; The power output capacity 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: Construct a fitting curve of the relationship between power and equipment performance and substitute it into the power defect matrix. The mapping relationship between output power defect and functional defect degree includes: The generalized additive model and the least square method are used to construct a fitting curve of the relationship between power and equipment performance for the functional attribute parameters of the equipment. The curve fitting process of different functional types is based on the linear and nonlinear response characteristics of the equipment function. Convert 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.

5. The electronic control system for smart home according to claim 4, characterized in that: The available compensation equipment set is identified through functional feature similarity matching. Based on the spatial position of the compensation equipment and the attenuation relationship between the equipment performance response, the fitting curve of the relationship between power and equipment performance is adjusted for spatial attenuation. Specifically, the following are included: In the high-dimensional feature description matrix, the device carrying the abnormal state mark is used as the benchmark, and the functional characteristics of all other devices that do not carry the abnormal state mark are similarly matched to identify the available compensation device set and record the original performance indicators of the compensation device set; Based on the spatial position of the set of compensation devices, the spatial distance of each compensation device is calculated by three-dimensional Euclidean distance, taking the functional type between the devices as the association condition; The attenuation curve of the relationship between spatial distance and equipment performance response attenuation is constructed using a piecewise linear fitting algorithm, and the performance attenuation of equipment of different functional types is mathematically characterized; 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.

6. The electronic control system for smart home according to claim 5, characterized in that: Generate the target value of the functional defect compensation of 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 according to 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; Through the compensation target value and the power output capacity parameters of the compensation equipment, a multi-objective optimization model with compensation output power, spatial performance attenuation and load balancing as optimization objectives is constructed; 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 functional compensation control strategy for multiple compensation devices used for scheduling to simultaneously assume the function of a single faulty device.

7. The electronic control system for smart home according to claim 6, characterized in that: Based on the compensation strategy, a control instruction set is constructed to control the power output of the equipment, and the control instruction set is sent to each compensation device for power regulation, which includes: Extract equipment power output parameters and spatial position parameters based on compensation strategy, and construct a control instruction set for controlling equipment 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; The faulty equipment in the faulty equipment list is monitored, and if the equipment is detected to have recovered from the fault, compensation control recovery is performed.

8. The electronic control system for smart home according to claim 7, characterized in that: The monitoring of the faulty equipment in the faulty equipment list and, if a fault-recovered equipment is detected, performing compensation control recovery 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 within a set monitoring time window does not reach the set sudden drop threshold, and the power deviation from the rated power of the equipment does not exceed the set deviation threshold, the abnormal status mark of the equipment is cleared; In the process of clearing the abnormal status mark of the device, the faulty device node broadcasts and sends the original performance indicator recovery instruction of the device to end the performance compensation control of the compensation device.

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