Simulation method and system suitable for multi-level home energy management system

Abstract

The application relates to the technical field of smart home, in particular to a simulation method and system suitable for a multi-level family energy management system. The method comprises the following steps: obtaining smart home data, and extracting communication protocol data; detecting an equipment abnormal state based on the communication protocol data, and obtaining the equipment abnormal state; constructing a smart home model based on the smart home data, and performing smart home control simulation by using preset control instructions to generate smart home control data; performing energy consumption analysis according to the smart home control data to obtain energy consumption data; performing circuit board soldering hot state fault decoding according to the equipment abnormal state to obtain circuit board fault data; performing antenna impedance etching change diagnosis according to the equipment abnormal state to obtain antenna damage data, and performing signal decay spectrum deduction to generate signal attenuation data. The application improves the equipment fault detection, the self-adaptive ability of the system and the overall energy efficiency management rate based on the smart home technology.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart home, and particularly relates to a simulation method and system suitable for a multi-level home energy management system. BACKGROUND

[0002] The data acquisition and device recognition capabilities of traditional systems are insufficient, usually relying on basic sensor data or manual input, which cannot provide accurate anomaly monitoring required by high-complexity smart home systems, and lack effective real-time dynamic monitoring mechanisms in large-scale devices and complex networks. The energy consumption analysis capability is limited, and traditional systems often analyze based on fixed models or historical data, which cannot flexibly respond to dynamic changes in home environment and device state, and lack accurate prediction and optimization of energy efficiency and peak energy consumption stages. Traditional methods are also relatively simple in hardware fault diagnosis, mainly relying on troubleshooting processes, which are difficult to achieve in-depth analysis, and have weak diagnostic capabilities for complex hardware problems such as circuit board soldering problems and signal attenuation. In terms of signal processing, traditional methods usually use simple signal strength evaluation and filtering techniques, which cannot effectively handle signal attenuation and distortion, and lack real-time signal enhancement and optimization. The level of intelligence is also one of the weaknesses of traditional systems, and traditional methods mostly use fixed parameters and control instructions, which cannot be adjusted according to the real-time state of the device, and it is more difficult to achieve intelligent scheduling of multiple devices, especially lacking self-adaptive ability in dynamic environment. SUMMARY

[0003] Therefore, it is necessary to provide a simulation method and system suitable for a multi-level home energy management system to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a simulation method suitable for a multi-level home energy management system comprises the following steps:

[0005] Step S1: Obtain smart home data and extract communication protocol data; detect device abnormal state based on communication protocol data to obtain device abnormal state;

[0006] Step S2: Construct a smart home model based on smart home data, and perform smart home control simulation using a pre-set control instruction to generate smart home control data; perform energy consumption analysis according to the smart home control data to obtain energy consumption data;

[0007] Step S3: Perform circuit board soldering hot fault decoding according to the device abnormal state to obtain circuit board fault data; perform antenna impedance erosion diagnosis according to the device abnormal state to obtain antenna damage data; perform signal decay spectrum deduction on the antenna damage data to generate signal attenuation data;

[0008] Step S4: signal distortion correlation analysis is performed on the energy consumption data according to the signal attenuation data, signal distortion data is obtained, signal enhancement is performed based on the signal distortion data, signal enhancement data is obtained, and the signal enhancement data is uploaded to the multi-level home energy management system.

[0009] The present application can ensure real-time updating of the state of the device by acquiring and extracting communication protocol data, and provides accurate data sources for smart home control. Using the device anomaly detection mechanism, the system can detect potential problems in the device early, provide early warning, and avoid damage and failure. By building a smart home model and performing control simulation, not only can the energy consumption of the home environment be accurately analyzed, but also the energy consumption can be optimized under different devices and environmental conditions, thereby improving the energy efficiency of the system. In-depth diagnosis of device failure, including circuit board soldering problems and antenna impedance etching diagnosis, can accurately identify hardware failure, avoid the lag of traditional troubleshooting, and generate effective attenuation data through signal attenuation spectrum deduction to help further analyze the source of the problem. Signal distortion correlation analysis can reveal the deep relationship between energy consumption and signal attenuation, thereby providing data support for signal enhancement and optimization, solving the problem of insufficient signal strength evaluation and filtering technology in traditional methods. Through signal enhancement and optimization, not only can the system signal quality be improved and transmission delay be reduced, but also the overall communication quality can be improved to ensure stable operation of the device and system. After uploading to the multi-level home energy management system, the intelligent management level of the smart home system is further improved, so that the system can adaptively adjust according to the actual situation, optimize device scheduling and energy distribution in real time, reduce energy consumption, and improve overall efficiency.

[0010] Preferably, step S1 is specifically:

[0011] Step S11: acquiring smart home data and extracting communication protocol data;

[0012] Step S12: network sniffing is performed on the communication protocol data to obtain network data packets;

[0013] Step S13: detecting the storage performance heartbeat packet in the network data packet;

[0014] Step S14: device flash failure analysis is performed according to the storage performance heartbeat packet to obtain device flash failure data;

[0015] Step S15: monitoring the device abnormal state of the device flash failure data.

[0016] The application provides an accurate data source for subsequent fault diagnosis and performance optimization by acquiring and extracting communication protocol data. Through network sniffing and packet analysis, the communication of devices in the network can be tracked in detail, ensuring that the system can monitor the changes in device status in real time. The detection of storage performance heartbeat packets can effectively determine whether the device is in a normal operating state. Once an anomaly is found, the system can analyze flash memory faults through storage performance heartbeat packet data, identify potential device fault points in advance, and avoid the limitations of traditional methods that rely on manual inspection and troubleshooting. Through the monitoring of device flash memory fault data, the device state can be continuously tracked and diagnosed, ensuring that the system responds to device faults in a timely manner and handles them, thereby improving the reliability of the device and the stability of the system. This series of steps not only enhances the real-time recognition ability of the traditional system for the dynamic state of the device, but also improves the efficiency of hardware fault warning and processing, providing a more accurate and intelligent management method for the system, thereby optimizing the operation efficiency of the smart home system and avoiding the negative impact of device failure on household energy consumption and user experience.

[0017] Preferably, step S14 specifically comprises:

[0018] Step S141: packet loss detection is performed according to the storage performance heartbeat packet, storage performance heartbeat packet loss data is obtained, and a storage performance heartbeat packet loss device is screened out;

[0019] Step S142: flash memory access data of the storage performance heartbeat packet loss device is extracted;

[0020] Step S143: flash memory read-write errors of the flash memory access data are detected;

[0021] Step S144: file system damage checking is performed based on the flash memory read-write errors, and file system damage data is obtained;

[0022] Step S145: device flash memory fault identification is performed according to the file system damage data, and device flash memory fault data is obtained.

[0023] The application can identify abnormal communication of the device in the early stage through the detection of the lost packet of the storage performance heartbeat packet, and timely screen the device with problems, which provides an important basis for subsequent troubleshooting and early warning. The flash access data extraction and analysis of the lost packet device can deeply understand the storage state of the device, so as to detect the read-write error of the flash memory layer and ensure that the system can accurately identify the storage failure. In addition, through the file system damage check based on the flash read-write error, the system can more accurately locate the existing system level problem, avoiding the limitation of relying on manual troubleshooting in the traditional method. Finally, through the accurate implementation of the device flash failure identification, the system can not only timely discover and handle the device failure, but also provide comprehensive data support for subsequent maintenance and optimization, further improving the reliability, stability and intelligent level of the smart home system, reducing the device downtime and system operation and maintenance cost. The intelligent improvement of this process avoids the lag of the traditional method, and provides an efficient and automated solution for device failure detection and processing.

[0024] Preferably, step S145 specifically comprises:

[0025] extracting the write error log in which 5-10 errors occur per minute in the file system damage data;

[0026] identifying the high-frequency error log area in which the number of errors exceeds 50 in the write error log;

[0027] detecting the flash read-write exception of the high-frequency error log area to obtain a flash read-write exception unit;

[0028] mapping the high-frequency error log area according to the flash read-write exception unit to obtain a bad block unit, wherein the bad block unit standard is set as the number of storage blocks or pages of the exception unit exceeding 10%;

[0029] evaluating the device flash failure of the bad block unit to obtain device flash failure data.

[0030] The application can effectively identify and solve the storage failure problem in the smart home device by fine monitoring and analysis of the file system. First, by extracting the log of multiple write errors occurring within each minute, the system can capture the abnormal performance of the storage device in real time, thereby providing first-hand data support for subsequent problem positioning. Further, the high-frequency error log area is identified, which helps to discover the potential storage problem area and guide the maintenance and repair of the device. The detected high-frequency error log area flash read-write exception can deeply excavate the root cause of the problem, locate the specific storage unit, and then process the problem area through the bad block mapping method. By setting standards to screen out the bad block unit with greater impact, the failure degree of the storage device can be accurately evaluated, avoiding the limitations of traditional methods that cannot accurately identify high-frequency failure areas. Finally, the bad block unit is evaluated for device flash failure, which not only improves the accuracy of fault detection, but also provides a scientific basis for the maintenance and replacement of the device. The implementation of these steps enables the system to actively monitor, diagnose and repair storage failures, avoiding the impact of device failures on the overall stability of the system, improving the reliability and intelligence level of smart home devices, and ensuring the continuous stability of system operation.

[0031] Preferably, step S2 is specifically:

[0032] Step S21: constructing a smart home model based on smart home data;

[0033] Step S22: generating smart home control data by simulating smart home control of the smart home model using a preset control instruction;

[0034] Step S23: calculating the control success rate of the smart home control data;

[0035] Step S24: statistically analyzing the device power of the smart home model based on the control success rate;

[0036] Step S25: drawing a power curve using the device power and identifying the peak power consumption data;

[0037] Step S26: calculating the energy consumption of the peak power consumption data to obtain energy consumption data.

[0038] The application can simulate the operation and interaction of devices in a simulation environment by constructing an intelligent home model, thereby providing a basis for subsequent optimization of control strategies. The use of preset control instructions to simulate the model can accurately generate intelligent home control data, thereby verifying the effectiveness and performance of the control system. By calculating the control success rate, the execution effect of the control instructions can be evaluated, providing data support for adjustment and optimization. Based on the control success rate, the power consumption of the devices is further calculated, which can effectively identify the energy demand of each device and provide a basis for energy efficiency management and optimization. Using these power data to draw power curves can clearly show the power fluctuations of devices in different operating states, thereby identifying peak power consumption data, which helps to accurately predict and optimize high-energy consumption stages. Finally, by calculating the energy consumption of peak power consumption data, accurate data support can be provided for the energy management of the overall intelligent home system, ensuring that energy consumption is within a controllable range and improving the energy efficiency and sustainability of the system. This series of steps effectively improves the intelligent level and resource utilization efficiency of the intelligent home system by deeply analyzing and optimizing the energy use of intelligent home devices, solving the shortcomings of traditional methods in dynamic monitoring and energy efficiency optimization.

[0039] Preferably, the circuit board soldering hot state fault decoding in step S3 comprises:

[0040] Extracting an abnormal state code of the abnormal state of the device;

[0041] Matching the short-circuit fault code of the abnormal state code with the preset short-circuit state code, and marking the short-circuit fault device of the intelligent home model;

[0042] Scanning the printed circuit board of the short-circuit fault device with a thermal imager to generate a circuit board thermal image;

[0043] Identifying the high-temperature area in the circuit board thermal image;

[0044] Positioning the soldering point area of the high-temperature area, and obtaining the hollow area data by ultrasonic detection;

[0045] Calculating the soldering uniformity of the soldering point area;

[0046] Calculating the roughness of the soldering point area;

[0047] Determining the soldering data of the soldering point based on the roughness and the soldering uniformity;

[0048] Integrating the soldering data of the soldering point and the hollow area data to obtain the circuit board fault data.

[0049] The application can quickly identify whether the equipment is abnormal by extracting the abnormal state code of the equipment abnormal state, providing an important basis for troubleshooting. By matching the short circuit state code and the abnormal state code, the equipment with short circuit fault can be effectively marked, so that timely fault diagnosis and processing can be carried out. By scanning the printed circuit board of the short circuit fault equipment with a thermal imager, the temperature distribution of the equipment can be accurately obtained, so that the abnormally high temperature area can be found, and the troubleshooting range can be further narrowed. After identifying the high temperature area in the circuit board thermal imaging diagram, the problem part in the circuit board can be located, and the fault area can be effectively diagnosed. By locating the soldering point area and combining the cavity area data obtained by ultrasonic detection, the soldering quality and mechanical defects can be deeply understood, and the identification of the root cause of the fault can be ensured. The soldering uniformity and roughness of the soldering point area can be calculated, the welding quality can be evaluated, important reference data can be provided for maintenance personnel, and the subsequent welding process can be optimized. Based on the uniformity and roughness of the soldering point, whether the soldering point has a potential fault can be accurately judged, and the soldering data of the soldering point can be further determined, so that the reliability of the circuit board welding can be ensured. Integrating the soldering data of the soldering point and the cavity area data can provide more comprehensive information support for circuit board fault diagnosis, improve the accuracy of fault identification and the self-adaptive ability of the system, avoid the diagnostic limitations of traditional systems on complex hardware problems, and effectively improve the ability of the smart home system in equipment maintenance and fault warning.

[0050] Preferably, the antenna impedance metamorphism diagnosis in step S3 comprises:

[0051] Statistical signal abnormal state data of equipment abnormal state;

[0052] Marking signal abnormal home equipment of the smart home model according to the signal abnormal state data;

[0053] Using an impedance analyzer to test the antenna impedance of the signal abnormal home equipment to obtain antenna impedance data;

[0054] Comparing the preset standard antenna impedance data with the antenna impedance data to obtain impedance deviation data;

[0055] Based on the impedance deviation data, the antenna oxidation of the signal abnormal home equipment is detected to obtain antenna oxidation data;

[0056] Carrying out corrosion damage analysis on the antenna oxidation data to obtain antenna damage data.

[0057] The application can effectively capture signal problems of the device in the running process by counting the signal abnormal state data of the device abnormal state, and identify potential fault sources in time. By marking the signal abnormal home device, the system can determine the device that appears abnormal, and facilitate subsequent detection and maintenance work. The antenna impedance data obtained by using the impedance analyzer can provide accurate physical data for subsequent fault analysis, so as to effectively evaluate the transmission capacity of the signal. By comparing the deviation of the standard antenna impedance data and the actual antenna impedance data, the deviation of the antenna can be found out, and the root cause of the potential problem can be further locked. Based on the impedance deviation data, the antenna oxidation detection of the signal abnormal home device can accurately identify whether there is oxidation problem on the surface of the antenna, and prevent signal attenuation or distortion. Through corrosion damage analysis of the antenna oxidation data, valuable information can be provided for equipment maintenance to judge whether the antenna has been seriously damaged, and then decide whether to replace or repair. These steps can improve the fault diagnosis capability and real-time monitoring capability of the smart home system in the complex device network through accurate signal analysis, impedance comparison, oxidation detection and corrosion analysis, avoid the limitations of traditional methods on signal processing and fault diagnosis, and further improve the stability of the device and the intelligent management level of the system.

[0058] Preferably, the signal decay spectrum deduction in step S3 comprises:

[0059] quantifying the damage degree of the antenna damage data;

[0060] According to the antenna damage data, signal transmission simulation is performed, and the transmission frequency range is set to 2.4GHz-5GHz, and the simulation accuracy is set to 0.1dB-0.3dB;

[0061] calculating the signal transmission distance in the signal transmission simulation process;

[0062] constructing a signal attenuation model according to the damage degree and the signal transmission distance;

[0063] inputting the antenna damage data into the signal attenuation model and performing signal attenuation prediction, setting the damage degree range to 0-1, the signal attenuation value to 10dB-50dB, and the environmental influence coefficient to 0.2-0.8, and generating signal attenuation data.

[0064] The application can more intuitively evaluate the health condition of the antenna by quantifying the damage degree of the antenna damage data, and provide accurate basic data for subsequent signal transmission analysis. According to the damage data, signal transmission simulation can be performed to simulate the signal transmission performance under different damage degrees, thereby helping to predict the performance of the device in the actual environment. By setting appropriate transmission frequency range and simulation accuracy, the accuracy and reliability of the simulation results can be ensured, and a strong basis for subsequent performance evaluation is provided. By calculating the signal transmission distance in the signal transmission simulation process, the effective range of signal propagation under different damage conditions can be intuitively understood, and the use scenario of the device is provided for reference. Based on the damage degree and the signal transmission distance, a signal attenuation model is constructed, which helps to understand the relationship between signal attenuation and device damage from a system perspective, and provides accurate theoretical support for fault prediction. By inputting the antenna damage data into the signal attenuation model and performing signal attenuation prediction, the changes of the device performance can be reflected in real time, and by setting reasonable damage range, signal attenuation value and environmental influence coefficient, the effective prediction of complex factors in the environment can be realized, thereby further optimizing the signal transmission quality. This series of steps accurately analyzes the influence of antenna damage on signal transmission, improves the system's ability to predict signal attenuation and faults, and provides important technical support for the efficient operation of smart home systems, avoiding the limitations of traditional methods in dealing with complex signal problems.

[0065] Preferably, step S4 is specifically:

[0066] Step S41: statistics signal attenuation time of signal attenuation data;

[0067] Step S42: statistics energy consumption time of energy consumption data, wherein the high energy consumption threshold is greater than 3W, and the high consumption time is 30 seconds-3 minutes;

[0068] Step S43: time matching according to signal attenuation time and energy high consumption time, wherein the overlap degree of signal attenuation and high energy consumption is greater than 50%, and the signal attenuation-high energy consumption time is obtained;

[0069] Step S44: calculating the retransmission number of signal attenuation-high energy consumption time;

[0070] Step S45: calculating the signal strength of signal attenuation-high energy consumption time;

[0071] Step S46: signal distortion analysis according to the retransmission number and the signal strength, wherein the signal-to-noise ratio is 15dB-30dB, and the signal distortion amount is 0%-50%, and the signal distortion data is obtained;

[0072] Step S47: adjusting the signal equalization degree of signal distortion data;

[0073] Step S48: adjusting the filtering parameter of the signal distortion data;

[0074] Step S49: integrating the signal equalization degree and the filtering parameter to obtain signal enhancement data, and uploading to the multi-level home energy management system.

[0075] By statistically analyzing the signal attenuation time of the signal attenuation data, the duration of signal attenuation and its impact on device performance can be comprehensively understood, providing data support for subsequent optimization. By statistically analyzing the high energy consumption time of the energy consumption data, the high energy consumption period of the device can be determined, helping to identify the source of energy waste and providing a basis for subsequent energy-saving measures. According to the time matching of the signal attenuation time and the high energy consumption time, the correlation between signal attenuation and high energy consumption can be effectively identified, revealing the performance problems of the device in a specific time period, helping to adjust the device working state in time. By calculating the retransmission times of the signal attenuation-high energy consumption time, the impact of signal attenuation on signal transmission can be quantified, helping to identify the system performance degradation caused by signal attenuation. By calculating the signal strength of the signal attenuation-high energy consumption time, the change trend of the signal strength can be accurately evaluated, providing a technical basis for subsequent signal optimization. According to the signal distortion analysis of the signal retransmission times and the signal strength, setting a reasonable range of bit error rate, signal-to-noise ratio and signal distortion, the change of signal quality can be deeply analyzed, and the future signal distortion trend can be predicted, and signal enhancement measures can be taken in time. By adjusting the signal equalization degree and the filtering parameter of the signal distortion data, the signal distortion can be effectively reduced, and the quality and stability of the signal can be improved, thereby avoiding the situation of insufficient signal attenuation processing capacity in traditional methods. Finally, by integrating the signal equalization degree and the filtering parameter, signal enhancement data is generated and uploaded to the multi-level home energy management system, which helps to adjust the device state in real time in a wider smart home system, ensuring energy use optimization and efficient device operation. Overall, this series of steps enhances the adaptive ability and real-time response ability of the system in complex environments, effectively improves the intelligent level of device management, and promotes the upgrading of traditional methods to the smart home field.

[0076] Preferably, the present specification also provides a simulation system suitable for a multi-level home energy management system, for executing the simulation method suitable for a multi-level home energy management system as described above, the simulation system suitable for a multi-level home energy management system comprising:

[0077] a device abnormal state detection module for obtaining smart home data and extracting communication protocol data; detecting the device abnormal state based on the communication protocol data to obtain the device abnormal state;

[0078] The energy consumption analysis module is used for constructing a smart home model based on the smart home data, and performing smart home control simulation by using preset control instructions to generate smart home control data; and performing energy consumption analysis according to the smart home control data to obtain energy consumption data.

[0079] The circuit board soldering hot fault decoding module decodes the circuit board soldering hot fault according to the equipment abnormal state to obtain circuit board fault data; diagnoses the antenna impedance metamorphosis according to the equipment abnormal state to obtain antenna damage data; and performs signal decay spectrum deduction on the antenna damage data to generate signal attenuation data.

[0080] The signal enhancement module is used for performing signal distortion correlation analysis on the energy consumption data according to the signal attenuation data to obtain signal distortion data; performing signal enhancement based on the signal distortion data to obtain signal enhancement data, and uploading the signal enhancement data to the multi-level home energy management system.

[0081] The simulation system suitable for the multi-level home energy management system of the application can realize any simulation method suitable for the multi-level home energy management system of the application, is used as a medium for joint operation and signal transmission between modules, and is used for completing the simulation method suitable for the multi-level home energy management system. The modules in the system cooperate with each other, improve the accuracy and efficiency of equipment fault detection, energy optimization and signal enhancement, and thus improve the self-adaptive ability and overall energy efficiency management rate of the system. BRIEF DESCRIPTION OF DRAWINGS

[0082] Other features, objects and advantages of the application will become more apparent from the following detailed description of non-limiting embodiments made with reference to the accompanying drawings:

[0083] Fig. 1 The figure is a step flow diagram of the simulation method suitable for the multi-level home energy management system of the application.

[0084] Fig. 2 The figure is a detailed step flow diagram of step S1 in the application.

[0085] Fig. 3 The figure is a detailed step flow diagram of step S14 in the application.

[0086] The implementation, functional features and advantages of the application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0087] The technical method of the patent of the present application will be described clearly and completely in combination with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0088] In addition, the drawings are only schematic illustrations of the present application, and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated description thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities, which do not necessarily have to correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0089] It should be understood that although the terms "first", "second" and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the exemplary embodiments, a first element can be called a second element, and similarly a second element can be called a first element. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0090] To achieve the above-mentioned object, please refer to Figs. 1 to 3 The present application provides a simulation method suitable for a multi-level home energy management system, the method comprising the following steps:

[0091] Step S1: obtaining smart home data and extracting communication protocol data; detecting device abnormal state based on the communication protocol data to obtain the device abnormal state;

[0092] In this embodiment, when acquiring smart home data, connect to each smart device through wireless communication protocols such as Zigbee, Wi-Fi or Bluetooth, collect device status, sensor data, etc. Data extraction uses corresponding protocol analysis tools (such as Wireshark or custom analysis programs) to parse communication data, obtain the original data packets sent by the device, and extract device status information. These data include voltage, current, temperature, humidity, etc. By comparing with the device data in normal state, set the abnormal threshold for judgment. If the device data exceeds the set normal range (such as temperature exceeding 75℃ or current higher than 0.5A), it is determined that the device enters an abnormal state, records the device abnormal state and marks the specific abnormal category, such as overheating, short circuit, signal loss, etc. The specific threshold depends on the device specifications and can be provided by device testing or technical manual.

[0093] Step S2: Construct a smart home model based on smart home data, and use pre-set control instructions for smart home control simulation to generate smart home control data; analyze energy consumption based on smart home control data to obtain energy consumption data;

[0094] In this embodiment, the collected smart home data (such as temperature, humidity, light intensity, power consumption, etc.) is used to construct a smart home model. This model is based on device type and function, and can include the relationship between devices, energy efficiency level, control instructions, etc. Use simulation tools (such as MATLAB / Simulink, Simatic S7) to control the simulation of the home model, and set pre-set control instructions such as timing switch, temperature adjustment, etc. In the simulation, by setting control parameters (such as setting temperature threshold 20℃-25℃, humidity range 40%-60%, etc.), the running condition of the device and its energy consumption process are simulated, and the simulated energy consumption data is generated. During the simulation process, the collection of energy consumption data is based on power meter or real-time energy efficiency monitoring device, and by analyzing the control instructions and device response, the energy efficiency data including power consumption, standby power, etc. is obtained, and finally the specific energy consumption data is obtained.

[0095] Step S3: Decode the circuit board soldering hot fault according to the device abnormal state to obtain circuit board fault data; diagnose the antenna impedance metamorphism according to the device abnormal state to obtain antenna damage data, and perform signal decay spectrum deduction to generate signal attenuation data;

[0096] In this embodiment, according to the abnormal state of the device, a special tool is used for soldering heat decoding of the circuit board. First, an infrared thermal imager (such as FLIR T1K) is used to detect the temperature distribution of the circuit board, and the overheating of the soldering joint is analyzed. By setting a temperature threshold (for example, 80℃ is the abnormal temperature standard), the heat decoding image is analyzed to find soldering defects or poor contact problems. Then, antenna impedance anomaly diagnosis is carried out, and the impedance characteristics of the antenna are measured by using a network analyzer (such as Agilent 8753E). By comparing the impedance of the normal antenna (for example, 50Ω), if the measured impedance value deviates from the standard value, it is determined that the antenna is damaged, and the damage location and type are recorded. Further signal attenuation spectrum deduction is carried out, and different frequency signals (such as 2.4GHz, 5GHz) are transmitted by using a radio frequency signal source (such as Keysight N5182B), and the received signal strength at the antenna is measured. The signal attenuation value is recorded by using a spectrum analyzer (such as Rohde & Schwarz FSH4), the frequency spectrum measurement range is adjusted according to the antenna damage degree (such as crack, corrosion), and the signal attenuation data is generated.

[0097] Step S4: According to the signal attenuation data, signal distortion correlation analysis is carried out on the energy consumption data to obtain signal distortion data; based on the signal distortion data, signal enhancement is carried out to obtain signal enhancement data, and the signal enhancement data is uploaded to the multi-level home energy management system.

[0098] In this embodiment, according to the signal attenuation data and the energy consumption data, signal distortion correlation analysis is carried out, and the correlation between signal attenuation and energy consumption is calculated by using signal processing software (such as MATLAB or SciPy library in Python). By setting a correlation threshold (such as signal attenuation greater than 30dB and energy consumption greater than 5W for correlation analysis), the coincidence period of the two is analyzed, the relationship between signal distortion and energy consumption is identified, and signal distortion data is obtained. Next, the distorted signal is enhanced by using a signal enhancement algorithm (such as an adaptive filtering algorithm, a frequency compensation technique, etc.). Adjust the enhancement parameters, such as filter bandwidth (for example, 2.4GHz-5GHz), signal-to-noise ratio (such as 15dB or more), and bit error rate (such as 10^-5). For the filter parameters, adjust the frequency response of the filter (such as low pass, high pass, etc.) to restore the signal to a better state, and obtain signal enhancement data. Finally, the signal enhancement data is uploaded to the multi-level home energy management system through the wireless communication module, and the system optimizes and adjusts the state of the home device in real time according to the enhanced signal, thereby improving the overall energy efficiency.

[0099] Preferably, step S1 is specifically:

[0100] Step S11: Obtain smart home data and extract communication protocol data;

[0101] In this embodiment, when obtaining smart home data, connect to smart home devices through wireless communication protocols (such as Zigbee, Wi-Fi or Bluetooth) to obtain device status information, sensor data and communication data between devices. These data include voltage, current, temperature, humidity, device control instructions, etc. The data is captured by the corresponding protocol analysis tool (such as Wireshark, tcpdump) to parse the transmitted data packet and obtain the communication protocol data. The data packet contains the device status information and control instructions, for example, in the Zigbee protocol, the device sends a message that includes the source device address, target device address and control instruction field, and the temperature sensor data is transmitted in the data segment. This step relies on the communication protocol standard provided by the device specification, such as the MAC layer and application layer format in the Zigbee protocol, to ensure that the data is complete and accurate.

[0102] Step S12: Network sniffing of communication protocol data to obtain network data packets;

[0103] In this embodiment, based on the obtained communication protocol data, the network traffic is monitored and analyzed using a network sniffing tool (such as Wireshark, tcpdump). First, the network data packets are captured by setting a filter (for example, the filter condition is "ip.addr==192.168.1.100" to specify the IP address of a specific device). This step also requires configuring the sniffing tool to filter traffic of specific protocol types, such as TCP, UDP packets, or packets for specific ports (such as port 80 for HTTP and port 443 for HTTPS). After the data packet capture is complete, the network sniffing tool is used to analyze the data packets and extract the specific communication content. According to the header information of the data packet, the source address, target address and timestamp are obtained, and real-time data monitoring and abnormal traffic or communication interruption detection are performed by relying on traffic analysis.

[0104] Step S13: Detecting the storage performance heartbeat packet in the network data packet;

[0105] In this embodiment, when detecting the storage performance heartbeat packet in the network data packet, the content of the packet is further analyzed by using the network sniffing tool. The storage performance heartbeat packet is usually a small data packet periodically sent between the device and the control center, which is used to confirm whether the device is normally online. First, the filter condition is set to identify the characteristics of the storage performance heartbeat packet (such as setting the “icmp” filter condition to capture the ICMP protocol storage performance heartbeat packet, or filtering the specific application layer heartbeat protocol). The storage performance heartbeat packet usually contains the source device identifier, timestamp and confirmation information, and the storage performance heartbeat packet is analyzed in detail by using Wireshark or a custom protocol analysis tool. By setting a reasonable time interval threshold (such as 5 minutes) to identify missing or delayed storage performance heartbeat packets. If no storage performance heartbeat packet is detected within the set time window or the content of the storage performance heartbeat packet is abnormal (such as loss of device identifier or abnormal timestamp), it is considered that the device communication is abnormal, and is marked as device failure.

[0106] Step S14: Device flash failure analysis is performed according to the storage performance heartbeat packet, and device flash failure data is obtained;

[0107] In this embodiment, device flash failure analysis is performed according to the captured storage performance heartbeat packet information. Flash failure usually manifests as the device being unable to normally read or write data, or the device responding with delay or error due to flash damage. First, the timestamp transmitted in the storage performance heartbeat packet is compared and analyzed with the response time of the device, and a response timeout threshold (such as more than 5 seconds without receiving a response) is set. If the device response is timed out or the response data is erroneous (such as the packet content being empty or having a format error), it is considered that the device has flash failure. At this time, the flash state of the device can be directly read by using an embedded debugging tool (such as a JTAG debugger) or a flash detection tool (such as FlashBench), and it is checked whether the read / write function of the device is normal. If it is found that a specific area of the flash is damaged (such as block erase failure, write failure, etc.), the flash failure data is further recorded, including the specific position of the flash damage, the error type, etc.

[0108] Step S15: Monitor the device abnormal state of the device flash failure data.

[0109] In this embodiment, when monitoring device flash failure data, the device state is tracked through the real-time data monitoring system, combined with storage performance heartbeat packet detection and flash failure analysis results, and the device abnormal state is recorded. Specifically, by setting a device abnormal threshold (such as three consecutive storage performance heartbeat packet losses or response timeouts), a device abnormal alarm is triggered and flash failure diagnosis is performed. System monitoring software (such as Zabbix, Nagios) can be used for device health check, and the abnormal state of the device is tracked in real time through the preset device monitoring rules (such as device response timeout, flash damage event). When the device enters a fault state, the system automatically records the fault time, device type, fault type and other information, and displays the fault alarm information on the system interface. At this time, the operator can determine whether the device needs to be repaired or replaced according to the fault data, to ensure the stable operation of the smart home system.

[0110] Preferably, step S14 is specifically:

[0111] Step S141: Packet loss detection is performed according to the storage performance heartbeat packet, storage performance heartbeat packet loss data is obtained, and storage performance heartbeat packet loss devices are screened;

[0112] In this embodiment, when packet loss detection is performed according to the storage performance heartbeat packet, the network traffic of the device is captured through a network sniffing tool (such as Wireshark), and the storage performance heartbeat packet data packet is screened out. Through the specific identifier of the storage performance heartbeat packet (such as the ICMP protocol or the application layer protocol specific to the device), the source device address, the target device address and the timestamp are extracted in the data packet. Then a time window (for example, 10 seconds) is set, the number of storage performance heartbeat packet losses of each device in the window is calculated, and it is determined whether there is a packet loss. If packet loss is detected within the set threshold (such as 3 consecutive storage performance heartbeat packet losses), the device is marked as a storage performance heartbeat packet loss device, and its device ID, packet loss number, packet loss time and communication path information of the packet loss device are recorded. The data can be stored through a database (such as MySQL, SQLite) for further analysis and subsequent tracking.

[0113] Step S142: Extracting flash access data of the storage performance heartbeat packet loss device;

[0114] In this embodiment, when extracting the flash access data of the storage performance heartbeat packet loss device, the flash log of the device is accessed based on the storage performance heartbeat packet loss device identified in step S141. The flash access records of the device are extracted through the device management system (such as through the SNMP protocol to manage the device), including the flash read and write operations of the device before and after the loss of the storage performance heartbeat packet. These data include the timestamp of the flash operation, the operation type (read / write), the data block or file identifier of the operation, etc. These flash logs are regularly pulled through an automated script or interface (such as the PySNMP library of Python, REST API), ensuring the accuracy of the complete recording of each flash operation. If the device has frequent flash access or the flash cannot be normally read and written, the flash state of the device can be further analyzed.

[0115] Step S143: detecting flash read / write errors of the flash access data;

[0116] In this embodiment, when detecting the flash read / write errors of the flash access data, the flash access log of the device is used to check the error information in the read and write operations line by line through an analysis program (such as a Python script combined with a regular expression). For example, when detecting flash read errors, the error data can be obtained by filtering according to the error code or error message (such as “Read Error”) in the log. The error judgment standard is set (for example, error code “0x01” represents a read error, and error code “0x02” represents a write error), and the error code of each flash operation is detected. When the error code of the flash operation appears multiple times or exceeds the set threshold (for example, 5 consecutive read / write errors), it can be determined that the device has a flash failure, and the key data such as the error type, frequency, and time period of occurrence is recorded.

[0117] Step S144: performing file system damage check based on the flash read / write errors to obtain file system damage data;

[0118] In this embodiment, when performing file system damage check based on the flash read / write errors, the flash access log of the device is first obtained, and all flash blocks or file areas with read / write errors are identified. The consistency of the flash file system is checked through a file system check tool (such as the fsck tool under Linux). The file system repair command (for example, “fsck / dev / sda”) can be executed on the device to scan the flash and detect the damage degree of the file system. File system damage usually manifests as directory structure errors, data block loss, file system mounting failure, etc. The damage judgment standard is set, such as if the file system errors exceed the set threshold (for example, more than 50 errors) during the scanning process, it is determined that the file system has serious damage. In addition, the time point of system damage is marked in combination with the timestamp and error frequency of file access, providing a basis for subsequent device maintenance.

[0119] Step S145: Device flash failure identification according to file system damage data, thereby obtaining device flash failure data.

[0120] In this embodiment, when identifying device flash failure according to file system damage data, the file system damage data obtained in step S144 is further analyzed. Based on the file system damage situation (such as the number of file system errors, damage type, etc.), in combination with the device's flash access data, it is determined whether the device's flash has failed. If the file system damage data indicates that some flash blocks are not recoverable in multiple operations, and the damage degree reaches a preset threshold (such as more than 70% of file system errors), it can be determined that the device's flash has failed. Further hardware detection is performed through the device management system or flash diagnosis tool (such as FlashBench) to assess the health of the flash, and record the device's failure data, including failure type, failure time, affected device area, etc.

[0121] Preferably, step S145 specifically comprises:

[0122] Extracting the write error log of 5-10 errors per minute in the file system damage data;

[0123] In this embodiment, when extracting the write error log of 5-10 errors per minute in the file system damage data, first, the write error log is obtained through the device's log management system (such as syslog, dmesg, etc.) or flash diagnosis system. The log recording system should include timestamp, error type, error code, and device area where the error occurred, etc. information. All logs are screened and grouped according to timestamp, and the number of write errors per minute is calculated. If the number of write errors in a minute falls within a certain range (such as 5-10 times), these write error logs are recorded. The error type should be set to "write error" (for example, error code 0x02), and the write error type is identified according to the specific error code. All error logs that meet the condition should be stored in a database (such as MySQL or SQLite), with data fields including time, device ID, error type, and error number, etc., to facilitate subsequent analysis.

[0124] Identifying high-frequency error log area with more than 50 errors in the write error log;

[0125] In this embodiment, when a high-frequency error log area with more than 50 error logs is identified, the write error logs extracted in step S151 are grouped by device ID and error log area, and the number of error logs in each area is calculated. If a device area has more than 50 write errors within a certain time range (e.g. 30 minutes or 1 hour), and the error is a high-frequency error, it is marked as a high-frequency error log area. To implement this step, a threshold of 50 error logs can be set, and a log aggregation tool such as Elasticsearch can be used for quick queries and statistical analysis to filter out high-frequency area log data. The recorded high-frequency error area should include device ID, area address (such as storage block or page), error number, etc., to help subsequent fault location.

[0126] Detecting flash read-write exceptions in high-frequency error log areas, obtaining flash read-write exception units;

[0127] In this embodiment, when detecting flash read-write exceptions in high-frequency error log areas, the flash read-write data of the high-frequency error log areas marked in step S152 is accessed, and the flash read-write operations of these areas are detected through a flash diagnosis tool (such as FlashBench) or a hardware monitoring system provided by the device. By reading the flash access logs, it is identified whether abnormal behavior occurs in the read-write process of the area, and the abnormal behavior is represented by read failure, write timeout, data loss, etc. The abnormal data is filtered and recorded using a set of error codes (such as "0x01" for read failure and "0x03" for write failure). Specific technical measures include setting a time window (such as 1 second) to detect frequent read-write operations, and if the number of abnormal read-write operations within the window exceeds a set threshold (such as 5 times), it is considered that the area has a flash read-write exception. The results of the detection include the address of the flash unit, the number of errors, and the type of exception, etc.

[0128] Mapping bad blocks to high-frequency error log areas based on flash read-write exception units, obtaining bad block units, wherein the bad block unit standard is set to more than 10% of the number of storage blocks or pages where the exception units are located;

[0129] In this embodiment, when mapping the bad block of the high-frequency error log area according to the flash read-write abnormal unit, the bad block is mapped by a bad block management tool (such as Flash Translation Layer, FTL) based on the flash read-write abnormal unit detected in step S153. According to the flash diagnosis data, the position of the storage block or page of the read-write abnormal unit is identified, and the bad block standard is set to be that the damage degree of the storage block or page where the abnormal unit is located exceeds 10%. If flash read-write abnormality occurs in more than 10% of the units in a certain storage block, the storage block is marked as a bad block, and the ID, position and abnormal times of the bad block are recorded. The bad block mapping process screens the bad block by setting a threshold (such as 10% damage rate), and isolates the bad block from the normal storage block by mapping algorithm, to prevent the area from continuing to store data.

[0130] The device flash failure data is obtained by evaluating the bad block unit.

[0131] In this embodiment, when evaluating the device flash failure of the bad block unit, the flash failure is evaluated by a device failure evaluation tool (such as an SSD health detection tool) based on the bad block data obtained in step S154. According to the number of bad blocks, the position and damage degree of the bad blocks, the overall health status of the flash is evaluated. The evaluation standard can be set to be that when the total number of bad blocks exceeds 2% of the total storage capacity of the device, it is considered that the device has a serious failure; if the bad block area exceeds a set threshold (such as 10% of the storage block has a bad block), it is confirmed that the flash of the device has a failure. The device flash failure data includes the failure type, the bad block position, the bad block number and the state of the flash unit. The evaluation result will be stored and reported for subsequent device maintenance or replacement decision.

[0132] Preferably, step S2 is specifically:

[0133] Step S21: constructing an intelligent home model based on intelligent home data;

[0134] In this embodiment, when constructing the smart home model based on smart home data, first, collect various sensor data in the smart home system (such as temperature, humidity, lighting, door and window status, etc.), and clean and standardize these data. The construction of the smart home model is realized through data analysis tools (such as Pandas, NumPy libraries in Python, etc.), which specifically includes time series analysis, missing value processing, and outlier detection of sensor data. The constructed model should be able to reflect the mutual relationship between various smart devices and their dynamic changes under different environmental conditions, use machine learning methods (such as K-means clustering analysis) to group devices, and classify devices by function (such as lighting, air conditioning, home appliance control, etc.). Finally, use these data and model parameters (such as sensor operating range, device response time, etc.) to construct a mathematical model reflecting the state of the entire smart home system, set device performance indicators and response strategies.

[0135] Step S22: Use the preset control instruction to simulate the smart home control of the smart home model, and generate smart home control data;

[0136] In this embodiment, when simulating the smart home control of the smart home model using the preset control instruction, first, input the preset control instruction into the constructed smart home model through the control system (such as Zigbee, Wi-Fi protocol, etc.). The control instruction should include the on-off state of the target device, the adjustment value (such as temperature setting, brightness adjustment, etc.) and the execution time, etc. The sending of the control instruction follows the predetermined trigger rule (such as time trigger, event trigger, etc.), and the response of the device is simulated through the simulation platform (such as MATLAB / Simulink). During the simulation process, the control operation and response state of each device are recorded to generate smart home control data. These data include the on-off state of the device, the response time, the execution result (whether successful), the actual adjustment value, etc. The matching degree of the control instruction and the device response data will also be recorded to evaluate the control effect of the system.

[0137] Step S23: Calculate the control success rate of the smart home control data;

[0138] In this embodiment, when calculating the control success rate of smart home control data, first analyze the smart home control data generated in step S22. According to whether the device successfully executes the scheduled task after receiving the control instruction, define the control success rate as the ratio of the number of devices that successfully execute the control instruction to the total number of control devices. If a device does not perform the operation as expected within a specified time (such as failing to adjust to the specified temperature or brightness), it is recorded as a control failure. By writing a program (such as a loop statement in Python) to traverse the control logs of all devices, the number of successes and failures is counted, and the control success rate is calculated. If the control success rate is below a certain predetermined threshold (for example, 90%), it is considered that the control effect is poor, and the control strategy or device response mechanism needs to be further optimized.

[0139] Step S24: Statistically analyze the power consumption of the devices in the smart home model based on the control success rate;

[0140] In this embodiment, when calculating the power consumption of the devices in the smart home model based on the control success rate, first collect the power consumption data of each device during the execution of the control instruction. The power data can be obtained through the built-in power meter of the device (such as a smart socket or power monitoring module). The calculation method of device power is: power (W) = voltage (V) x current (A). The power consumption of each device in different time periods should be recorded according to the actual control data, taking into account the power fluctuations of different devices before and after the execution of the control instruction. The statistical process needs to aggregate the power data of each device in different control states, and weight the power data according to the control success rate to obtain the power consumption of each device in different scenarios.

[0141] Step S25: Draw a power curve using the device power and identify the peak power consumption data;

[0142] In this embodiment, when drawing a power curve using the device power, first organize the power data obtained in step S24 by time period or operation state. Use data visualization tools (such as Excel, Matplotlib, etc.) to draw a power curve graph. The graph should show the power consumption changes of each device in different time periods or control conditions. For example, you can draw the power consumption change curve of multiple devices within a day (grouped by hours), or compare the power consumption of different devices in the same time period. According to the power curve graph, you can analyze the power consumption trend of the devices in different time periods and identify the peak period of power consumption. Specifically, the peak power consumption data can be identified by setting a power threshold (such as the time period when the maximum power consumption exceeds the set value).

[0143] Step S26: Calculate the energy consumption of the peak power consumption data to obtain the energy consumption data.

[0144] In this embodiment, when calculating the peak power consumption data, first, based on the peak power consumption data identified in step S25, the power consumption value of each time period is obtained. The energy consumption of each time period can be calculated by the formula Energy consumption (Wh) = Power (W) x Time (h). The power data of the peak period is integrated (such as the summation of the time interval), and the total energy consumption data in the period is obtained. It should be noted that the peak period should be set as the period in which the power consumption is greater than a certain set threshold (such as 50W). The calculation result should include the total energy consumption of each device in the peak period, and finally the energy consumption data of the entire smart home system in the peak period is obtained, and the energy management or optimization strategy design is based on this data.

[0145] Preferably, the circuit board soldering hot state fault decoding in step S3 includes:

[0146] Extracting the abnormal state code of the abnormal state of the device;

[0147] In this embodiment, when extracting the abnormal state code of the abnormal state of the device, first, according to the running state monitoring data (such as voltage, current, temperature, etc.) of each device in the smart home system, data comparison is carried out through the set abnormal state code rule. For example, when the current of a certain device exceeds the pre-set normal range (such as current exceeding 5A), the abnormal state code of the device will be marked as "001". The abnormal state code usually adopts a fixed format, such as a 3-digit code (for example, "001", "002", etc.), wherein each digit represents a different type of fault or device state. For example, the first digit represents the device category, the second digit represents the device state type, and the third digit represents the fault severity. The device state code is generated by the monitoring system by collecting and analyzing device running data in real time, and by the logical judgment rule in the state monitoring module, the running parameters of the device are compared with the pre-set normal value, so as to extract the corresponding abnormal state code.

[0148] Using the pre-set short-circuit state code to match the short-circuit fault code of the abnormal state code, and marking the short-circuit fault device of the smart home model;

[0149] In this embodiment, when matching the short-circuit fault code with the preset short-circuit state code, first, set the short-circuit state code (such as "003") in the state management system of the device, which is used to mark the short-circuit fault of the device. After the device state monitoring system detects the abnormal state code, it is matched with the preset short-circuit fault code. If the abnormal state code belongs to the short-circuit related fault (such as excessive current or unstable voltage), it is classified as a short-circuit fault device. At this time, the system will mark the corresponding device as "short-circuit fault" state. The matching rule is based on the pre-defined coding standard. By comparing the abnormal state code, the system automatically identifies the devices with short-circuit fault characteristics and traces and processes these devices through corresponding markers. In the system, the abnormal state code "003" is compared with other related fault codes such as "005" to ensure accurate short-circuit device calibration.

[0150] Scan the printed circuit board of the short-circuit fault device with a thermal imager to generate a thermal imaging image of the circuit board.

[0151] In this embodiment, when scanning the printed circuit board of the short-circuit fault device with a thermal imager, first, physically locate the device that has short-circuited and use a thermal imager (such as FLIR T640) to scan the printed circuit board of the device. The thermal imager detects the surface temperature of the circuit board through infrared sensors to form a temperature distribution map. At this time, it should be ensured that the setting parameters of the thermal imager, such as temperature range (for example, -20℃ to +150℃), resolution (such as 320x240 pixels) and scanning speed (such as 100 points per second), have been optimized. During the scanning process, the device should be stationary to ensure the accuracy of the device surface temperature data. The thermal imaging image obtained by the thermal imager can clearly show the temperature changes on the surface of the circuit board, thereby quickly locating the fault area, such as the part with excessively high temperature.

[0152] Identify high temperature areas in the thermal imaging image of the circuit board.

[0153] In this embodiment, when identifying high temperature areas in the thermal imaging image of the circuit board, use the temperature information on the thermal imaging image to set a temperature threshold (such as areas exceeding 70℃) as the standard for high temperature areas. By using image processing algorithms (such as edge detection or region growing algorithms), compare the temperature values in the thermal imaging image with the set threshold to identify all areas with temperature exceeding the set threshold. These high temperature areas are usually caused by abnormal heating phenomena such as short-circuit or current overload. Image processing software (such as the image processing toolbox in MATLAB) can identify areas with high temperature and display them as red or yellow areas in the thermal imaging image. The key to this step is to select an appropriate temperature threshold and image processing algorithm to ensure accurate identification of high temperature areas.

[0154] Locate the solder joint area in the high-temperature region and obtain the void area data using ultrasonic testing.

[0155] In this embodiment, when locating the solder joint area in the high-temperature region and obtaining the void area data using ultrasonic testing, first, in the high-temperature region, the soldering area is located according to the circuit board layout. The soldering area usually includes solder joints, solder connection parts, etc. The positions of these areas are determined through visual inspection or mechanical positioning. Then, the soldering area is scanned using an ultrasonic flaw detector (such as CT-1200). The ultrasonic sensor sends high-frequency sound waves and receives echo signals. By analyzing the intensity and time delay of the echo signals, it can be determined whether there are voids or cracks inside the solder joint. After processing the ultrasonic detection data through a signal processing algorithm, detailed information such as the size and position of the voids can be provided. The advantage of this step using ultrasonic flaw detection technology is that it can non-destructively detect defects inside the solder joint area and obtain specific data of the void area.

[0156] Calculate the solder uniformity of the solder joint area;

[0157] In this embodiment, when calculating the solder uniformity of the solder joint area, first, the image data of the solder joint is obtained through a microscope or a high-resolution scanner. Then, using image processing software (such as MATLAB, OpenCV), the solder joint image is processed to extract the contour of the solder distribution and calculate the variation of the solder thickness. The calculation method of solder uniformity is: uniformity = (maximum thickness of solder - minimum thickness of solder) / average thickness of solder. The smaller the value of solder uniformity calculated by this formula, the better the uniformity of the solder. According to this standard, the uniformity data of each solder joint can be obtained through automated image analysis to evaluate the soldering quality.

[0158] Calculate the roughness of the solder joint area;

[0159] In this embodiment, when calculating the roughness of the solder joint area, first, high-resolution image data of the solder joint surface is obtained through a scanning electron microscope (SEM) or an optical microscope. Then, using surface roughness analysis software, the image is analyzed to extract the microstructure of the solder joint surface. The calculation of roughness can be done by Ra (arithmetic mean roughness) or Rq (root mean square roughness), with the formula Ra = 1 / n∑|Zi-Zmean|, where Zi is the height of each measurement point, Zmean is the average height of the surface, and n is the number of measurement points. Through this method, the roughness of the solder joint surface can be quantitatively evaluated to judge the soldering quality.

[0160] Determine the soldering data of the solder joint based on roughness and solder uniformity;

[0161] In this embodiment, when determining the soldering data of the welding points based on the roughness and soldering uniformity, first, the soldering uniformity and the roughness of the welding points are combined, and the quality standards (such as the uniformity should be less than 10%, and the roughness should be less than 0.5 μm) are screened according to the set quality standards to determine the welding point data meeting the quality requirements. Specifically, when the uniformity and roughness of the welding points meet the standards at the same time, it is considered that the soldering quality of the welding points is good, otherwise it is determined as unqualified welding points. The soldering quality data of these welding points is integrated into a complete welding quality report as the basis for subsequent maintenance or optimization.

[0162] The soldering data of the welding points and the hollow area data are integrated to obtain the circuit board fault data.

[0163] In this embodiment, when the soldering data of the welding points and the hollow area data are integrated to obtain the circuit board fault data, first, the soldering uniformity data and the hollow area data are combined. The specific method is: the soldering uniformity of each welding point is associated with the hollow data at its location, if the uniformity of a certain welding point is poor and the hollow area is large, it is marked as a fault point. By analyzing the soldering quality and hollow data of all welding points, the overall fault data report of the circuit board is generated. This report includes the welding quality of each welding point, the hollow position and its size, etc., which provides a basis for the maintenance and optimization of the circuit board.

[0164] Preferably, the antenna impedance metamorphic diagnosis in step S3 comprises:

[0165] Statistical device abnormal state signal abnormal state data;

[0166] In this embodiment, the device abnormal state data is collected in real time by the built-in sensor. The working state of each device, such as temperature, humidity, voltage, signal strength and other parameters, will be recorded when the abnormality occurs. For signal abnormal state, special attention is paid to wireless signal strength, signal frequency, signal noise and other data. The data collector sets a trigger threshold to automatically mark any abnormal data exceeding the normal range. For example, if the signal strength is lower than a certain preset threshold (such as -70 dBm) or the signal noise is higher than the standard value (such as 30 dB), these data are marked as abnormal state. The abnormal state data is uploaded to the central server through wireless transmission for summarization and storage, which is convenient for subsequent analysis and troubleshooting.

[0167] According to the signal abnormal state data, the signal abnormal home device of the smart home model is marked;

[0168] In this embodiment, by analyzing the signal abnormal state data transmitted by the device, using data filtering algorithm to eliminate irrelevant or normal state data, and focusing on the signal strength, frequency and other values detected in the signal abnormal state data. For example, when the signal strength is continuously lower than the set threshold value (such as -70dBm) or appears a large amplitude fluctuation, it is marked as "signal abnormality". Through model algorithm or database matching, relevant devices (such as routers, wireless cameras in smart home, etc.) are screened out and marked as "signal abnormal home device". This marking will be used as troubleshooting data in the subsequent steps to confirm the location of the abnormal device and develop appropriate handling measures.

[0169] Antenna impedance data of the signal abnormal home device is obtained by using an impedance analyzer to test the antenna impedance of the signal abnormal home device.

[0170] In this embodiment, the impedance analyzer is used to detect the device marked as signal abnormality. The probe of the impedance analyzer is connected to the antenna port of the device to obtain the impedance data of the antenna. According to the working frequency band of the device (such as 2.4GHz or 5GHz), appropriate test frequency and frequency band width are set. The test frequency is usually 1GHz above the required working frequency, and the frequency accuracy requirement is within 1MHz. The impedance analyzer will output the reflection loss, impedance curve and related parameters of the antenna in this frequency band, record the real working state of the device antenna, and facilitate subsequent impedance deviation analysis and antenna performance detection.

[0171] Impedance deviation data is obtained by comparing the preset standard antenna impedance data with the antenna impedance data.

[0172] In this embodiment, the standard antenna impedance data is usually obtained from the technical specifications provided by the device manufacturer or by laboratory measurement. The standard data usually includes reflection loss, impedance value (such as 50Ω or 75Ω) in ideal state, and performance of the antenna in different frequency bands. Then, the actual impedance data of the signal abnormal home device antenna obtained by testing is compared with the standard impedance data. Specifically, the impedance deviation degree is calculated using the impedance comparison algorithm to obtain the difference between the two. If the deviation between the test antenna impedance and the standard value exceeds the preset threshold value (for example, 5%), it is marked as impedance deviation. These impedance deviation data will be used for subsequent antenna oxidation detection.

[0173] Antenna oxidation data of the signal abnormal home device is obtained based on the impedance deviation data.

[0174] In this embodiment, by analyzing the impedance deviation data of the device antenna, the degree of oxidation of the antenna can be inferred. When the surface of the antenna is oxidized, it usually leads to a decline in electrical performance and a deviation in impedance value from the standard value. A threshold value (e.g., impedance deviation exceeding 10%) is set to determine whether the antenna has been oxidized. By testing the impedance changes under different frequency bands, combined with the data of the actual use environment (such as humidity, temperature, etc.), it is determined whether there is an oxidation problem. If the impedance deviation exceeds the set threshold value is detected, the system will record the antenna oxidation condition and generate an oxidation data report, which will be provided to the subsequent corrosion damage analysis.

[0175] Corrosion damage analysis is performed on the antenna oxidation data to obtain antenna damage data.

[0176] In this embodiment, based on the antenna oxidation data, a corrosion damage model is used to analyze the damage degree of the antenna. The corrosion damage model takes into account multiple factors such as the degree of oxidation of the antenna, the humidity and temperature of the use environment, and the working frequency to make predictions and calculations. For example, by analyzing the antenna oxidation data and the impedance changes in the working frequency band, it is inferred whether the metal layer of the antenna has been severely corroded, leading to a decline in performance. A set damage threshold (e.g., impedance value exceeding 15% of the standard value) is used as the criterion for damage. Once this threshold is exceeded, the antenna is marked as "damaged" and antenna damage data is generated and input into the subsequent repair or replacement workflow.

[0177] Preferably, the signal decay spectrum deduction in step S3 includes:

[0178] Quantifying the damage degree of the antenna damage data;

[0179] In this embodiment, based on the aforementioned antenna oxidation detection data, the impedance deviation of the antenna is calculated by comparing the impedance values of the damaged antenna and the standard antenna. A specific impedance deviation threshold is set, for example, if the difference between the antenna impedance and the standard value is greater than 10% (e.g., standard value 50Ω, actual measurement 55Ω), it is considered that the antenna has slight damage; if the deviation exceeds 20% (e.g., standard value 50Ω, actual measurement 60Ω), it is considered that the antenna has moderate damage; if the deviation exceeds 30% (e.g., standard value 50Ω, actual measurement 65Ω), it is considered that the antenna is severely damaged. Combined with environmental data (such as humidity, temperature, etc.), a weight coefficient can be set for each damage degree (e.g., slight damage weight 0.2, moderate damage weight 0.5, severe damage weight 1) to quantify the degree of antenna damage.

[0180] According to the antenna damage data, signal transmission simulation is performed, with a transmission frequency range of 2.4GHz-5GHz and a simulation accuracy of 0.1dB-0.3dB;

[0181] In this embodiment, when simulating signal transmission, first set the transmission frequency range to 2.4GHz-5GHz, which is suitable for common wireless communication frequency bands. The simulation accuracy is set to 0.1dB-0.3dB, which is to ensure the accuracy of the simulation results, while considering the efficiency of simulation calculation. Use electromagnetic simulation software (such as Ansys HFSS or CST Microwave Studio) to simulate the transmission process of the signal under different damage levels. When simulating, input the impedance data of the damaged antenna, set the corresponding damage parameters (such as impedance deviation, damage level, etc.), and calculate the signal attenuation at different frequencies through the software, with a result accuracy of 0.1dB-0.3dB.

[0182] Calculate the signal transmission distance during the signal transmission simulation process.

[0183] In this embodiment, during the signal transmission simulation process, the maximum effective transmission distance of the signal is calculated according to the set frequency range and antenna damage level. First, the propagation model of wireless communication (for example, free-space propagation model or ground wave propagation model) needs to be obtained, and the transmission loss is adjusted according to the damage level of the antenna. Set the attenuation constant and path loss parameters, and calculate the signal attenuation and effective transmission distance based on the simulation results. For each simulation frequency, according to different antenna damage levels (such as light, moderate, and severe damage), the maximum effective distance of signal transmission is calculated. The formulas used in this process are, for example, the Free-Space Path Loss (FSPL) model and actual wave propagation parameters such as frequency, antenna gain, etc., and the final result is the effective transmission distance.

[0184] Construct a signal attenuation model based on the damage level and signal transmission distance.

[0185] In this embodiment, a signal attenuation model is established based on the data of antenna damage level and signal transmission distance. The main factors of signal attenuation are set, including antenna damage (such as impedance deviation), transmission frequency, transmission distance, etc. Specifically, a linear relationship between transmission loss and antenna damage level is set, and a regression model or interpolation model is constructed to describe the attenuation characteristics using the known corresponding data of signal transmission distance and damage level. The relationship between each damage level and signal attenuation is obtained through experimental data, assuming that the damage severity is proportional to the attenuation value. Set the damage parameters in the model (such as 0-1 damage) and the range of signal attenuation (such as 10dB-50dB), and then calculate the attenuation model under different conditions.

[0186] Input the antenna damage data into the signal attenuation model and perform signal attenuation prediction. Set the damage range to 0-1, the signal attenuation value to 10dB-50dB, and the environmental influence coefficient to 0.2-0.8, and generate signal attenuation data.

[0187] In this embodiment, the antenna damage data (including damage degree and corresponding impedance deviation) is taken as an input parameter and substituted into the constructed signal attenuation model for simulation. The damage degree range is set to 0 to 1, where 0 represents no damage and 1 represents complete damage; the signal attenuation value range is set to 10 dB to 50 dB, which is used to represent the quantized value of signal strength attenuation; and the environmental influence coefficient range is set to 0.2 to 0.8, which takes into account the influence of environmental factors such as humidity and temperature on signal transmission. On this basis, combined with the actually measured damage data (for example, antenna damage degree 0.4), the signal attenuation value under this damage state is calculated through the model, and then the corresponding attenuation data report is generated. According to the corresponding relationship between the damage degree and the attenuation value, the signal attenuation prediction of each device can be generated, so as to further maintain and optimize the device.

[0188] Preferably, step S4 specifically comprises:

[0189] Step S41: statistics signal attenuation time of signal attenuation data;

[0190] In this embodiment, according to the real-time monitoring data, the change of signal strength is collected. In the transmission process, when the signal strength is lower than the set threshold (for example, the signal attenuation value exceeds 30 dB), the time period is recorded. Through the real-time monitoring system, the signal strength is periodically sampled, and the signal attenuation value is recorded once per second. When the signal attenuation value is higher than 30 dB, the timing starts until the signal strength recovers to above the threshold (for example, the signal attenuation value is lower than 20 dB). The duration of each attenuation will be counted as signal attenuation time. The statistical results include the signal attenuation time of each device in a certain time period, and finally these data are summarized to obtain the total signal attenuation time of each device.

[0191] Step S42: statistics energy high consumption time of energy consumption data, wherein the high energy consumption threshold is set to be greater than 3W and the high consumption time is 30 seconds to 3 minutes;

[0192] In this embodiment, according to the power consumption data of the device, the threshold of high energy consumption is set to 3W. The power consumption data of each device is collected in real time through the power monitoring system. If the power consumption of a device exceeds 3W, it is recorded as a high energy consumption event. Then, the duration of high energy consumption is set to 30 seconds to 30 minutes. During the time period when the device is in a high energy consumption state, the device power consumption is checked every second, and if it exceeds 3W, the timing starts until the power consumption is lower than 3W. These high energy consumption periods are counted and the duration is calculated. Finally, the high energy consumption time data of the device is generated by summarizing the high energy consumption time of all devices in a certain time.

[0193] Step S43: Time matching according to signal attenuation time and energy high consumption time, where the overlap of signal attenuation and high energy consumption is greater than 50%, obtaining signal attenuation-high energy consumption time;

[0194] In this embodiment, when performing time matching, the signal attenuation time and energy high consumption time data need to be processed first. For each device, use the overlap of time period to match. Through time alignment technology (such as sliding window algorithm), calculate the overlapping part of signal attenuation time and high energy consumption time. When the overlapping time is greater than 50%, it is considered that the time period belongs to signal attenuation-high energy consumption time period. For each device, calculate the overlapping interval of signal attenuation and high energy consumption time of the device in a specific time, and record these overlapping time periods.

[0195] Step S44: Calculate the number of signal retransmissions in signal attenuation-high energy consumption time;

[0196] In this embodiment, the calculation of signal retransmission number is based on transmission protocol data. By analyzing the signal transmission log of the device, especially the time period overlapping with signal attenuation and high energy consumption time, it is counted whether the signal is retransmitted in these overlapping time periods. In wireless communication, when the signal is attenuated, the communication protocol usually triggers the retransmission mechanism. By analyzing the transmission log of the data packet, record the signal retransmission event, and count the number of retransmissions in the overlapping time period. Finally, the total number of retransmissions in the signal attenuation-high energy consumption time period is obtained.

[0197] Step S45: Calculate the signal strength in signal attenuation-high energy consumption time;

[0198] In this embodiment, the calculation of signal strength is based on the real-time signal data of the device in the signal attenuation-high energy consumption time period. In the attenuation period of each device, the signal strength is recorded in real time, and the signal strength analyzer (such as network analyzer or signal monitoring device) is used for detection. The signal strength at each time point in the signal attenuation-high energy consumption time period is weighted and averaged to obtain the signal strength value of the time period. Assuming that the signal strength range is -70dBm to -100dBm when the signal is attenuated, the signal strength is calculated and adjusted in real time according to the length of each attenuation and environmental conditions. Finally, the average signal strength of the attenuation-high energy consumption period is obtained.

[0199] Step S46: Signal distortion analysis according to the number of retransmissions and signal strength, setting the signal-to-noise ratio to 15dB-30dB and the signal distortion amount to 0%-50%, obtaining signal distortion data;

[0200] In this embodiment, during the signal distortion analysis process, the signal-to-noise ratio (SNR) range is set to 15 dB to 30 dB. According to the retransmission number and signal strength of the device, the signal distortion analysis is performed using the bit error rate calculation formula (e.g., BER = 1 / 2*(1-SNR)). According to the retransmission number of each device and the calculated signal strength, the corresponding signal-to-noise ratio is calculated. Through the bit error rate calculation formula, the corresponding bit error rate is obtained, and the signal distortion amount is set according to the error range. According to the retransmission number and signal strength value of the device, the signal distortion data of the device in the signal attenuation-high energy consumption period is finally calculated.

[0201] Step S47: Adjust the signal equalization degree of the signal distortion data.

[0202] In this embodiment, according to the calculated signal distortion data, the signal equalization degree is adjusted. The signal equalization degree adjustment uses a signal processing algorithm (such as a digital signal processor (DSP) algorithm) to optimize the signal. Through gain control, filter adjustment and other means, the distortion is reduced. During the adjustment process, the signal distortion amount is set to a range of 0% to 50%, and the signal equalizer parameters (such as gain, delay time) are gradually adjusted to optimize the signal quality and reduce distortion. At each signal distortion data point, the signal equalization degree is gradually adjusted until the set equalization standard is reached, and the equalization degree value during the adjustment process is recorded.

[0203] Step S48: Adjust the filter parameters of the signal distortion data.

[0204] In this embodiment, the filter parameter adjustment is based on the obtained signal distortion data. First, select an appropriate filter type (such as a low-pass filter, a high-pass filter, or a band-pass filter), and set the corresponding filter parameters (such as filter order, cutoff frequency) according to the frequency response requirements of the device. Use a filter algorithm (such as a FIR or IIR filter) to filter the signal to remove noise and distortion. During this process, adjust the gain and bandwidth parameters of the filter to restore the signal to an optimal state. After filtering, the signal output is detected to ensure that the signal distortion amount after filtering is controlled within the set range (e.g., 0%-50%).

[0205] Step S49: Integrate the signal equalization degree and the filter parameters to obtain signal enhancement data and upload to the multi-level home energy management system.

[0206] In this embodiment, the signal equalization degree is integrated with the filtering parameters to generate the final signal enhancement data. Specifically, the equalized signal and the filtered signal are combined to form the optimized signal output data. In this process, the signal enhancement data needs to record the adjusted gain, signal distortion degree, filter type and related parameters. Through the data upload interface of the home energy management system, the signal enhancement data is uploaded to the system to ensure that the data can be monitored and managed in real time on the home energy management platform.

[0207] Preferably, the present specification also provides a simulation system suitable for a multi-level home energy management system for executing the simulation method suitable for a multi-level home energy management system as described above, the simulation system suitable for a multi-level home energy management system comprising:

[0208] The device abnormal state detection module is configured to obtain smart home data and extract communication protocol data, detect a device abnormal state based on the communication protocol data, and obtain device abnormal state data.

[0209] The energy consumption analysis module is configured to construct a smart home model based on the smart home data, perform smart home control simulation using a preset control instruction, and generate smart home control data, and perform energy consumption analysis based on the smart home control data to obtain energy consumption data.

[0210] The circuit board soldering thermal fault decoding module decodes the circuit board soldering thermal fault based on the device abnormal state to obtain circuit board fault data, diagnoses antenna impedance corrosion based on the device abnormal state to obtain antenna damage data, and performs signal decay spectrum deduction on the antenna damage data to generate signal attenuation data.

[0211] The signal enhancement module is configured to perform signal distortion correlation analysis on the energy consumption data based on the signal attenuation data to obtain signal distortion data, perform signal enhancement based on the signal distortion data to obtain signal enhancement data, and upload the signal enhancement data to the multi-level home energy management system.

[0212] The simulation system suitable for a multi-level home energy management system of the present application can implement any simulation method suitable for a multi-level home energy management system of the present application, and is used to jointly operate and transmit signals between various modules to complete the simulation method suitable for a multi-level home energy management system. The modules in the system cooperate with each other, improving the accuracy and efficiency of device fault detection, energy optimization and signal enhancement, thereby improving the self-adaptability and overall energy efficiency management rate of the system.

[0213] Therefore, the embodiments should be regarded, at any point, as being exemplary and not limiting, the scope of the application being defined by the appended claims and not by the above description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

[0214] The foregoing is considered as illustrative only of the principles of the application. Numerous modifications and changes will readily occur to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Therefore, the application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A simulation method suitable for a multi-tiered home energy management system, characterized by, The method comprises the following steps: Step S1: obtaining smart home data and extracting communication protocol data; detecting device abnormal state based on the communication protocol data to obtain device abnormal state; Step S2: constructing a smart home model based on the smart home data, and performing smart home control simulation using a preset control instruction to generate smart home control data; performing energy consumption analysis based on the smart home control data to obtain energy consumption data; Step S3: decoding circuit board soldering thermal state fault based on the device abnormal state to obtain circuit board fault data; diagnosing antenna impedance etching based on the device abnormal state to obtain antenna damage data; performing signal decay spectrum deduction on the antenna damage data to generate signal attenuation data; Step S4: performing signal distortion correlation analysis on the energy consumption data based on the signal attenuation data to obtain signal distortion data; performing signal enhancement based on the signal distortion data to obtain signal enhancement data, and uploading the signal enhancement data to a multi-level home energy management system, and step S4 specifically comprises: Step S41: counting signal attenuation time of the signal attenuation data; Step S42: counting energy high consumption time of the energy consumption data, wherein a high energy consumption threshold is greater than 3W, and the high consumption time is 30 seconds-3 minutes; Step S43: time matching based on the signal attenuation time and the energy high consumption time, wherein the overlap degree of signal attenuation and high energy consumption is greater than 50%, to obtain signal attenuation-high energy consumption time; Step S44: calculating retransmission times of the signal attenuation-high energy consumption time; Step S45: calculating signal strength of the signal attenuation-high energy consumption time; Step S46: performing signal distortion analysis based on the retransmission times and the signal strength, wherein a signal-to-noise ratio is 15dB-30dB, and a signal distortion amount is 0%-50%, to obtain signal distortion data; Step S47: adjusting signal equalization degree of the signal distortion data; Step S48: adjusting filtering parameters of the signal distortion data; Step S49: integrating the signal equalization degree and the filtering parameters to obtain signal enhancement data, and uploading the signal enhancement data to the multi-level home energy management system.

2. The simulation method suitable for multi-tiered home energy management system according to claim 1, wherein, Step S1 specifically comprises: Step S11: obtaining smart home data and extracting communication protocol data; Step S12: performing network sniffing on the communication protocol data to obtain network data packets; Step S13: detecting storage performance heartbeat packets in the network data packets; Step S14: performing device flash memory fault analysis based on the storage performance heartbeat packets to obtain device flash memory fault data; Step S15: monitoring device abnormal state of the device flash memory fault data.

3. The simulation method suitable for multi-tiered home energy management system according to claim 2, wherein, Step S14 specifically comprises: Step S141: performing packet loss detection based on the storage performance heartbeat packets to obtain storage performance heartbeat packet loss data, and screening storage performance heartbeat packet loss devices; Step S142: extracting flash memory access data of the storage performance heartbeat packet loss devices; Step S143: detecting flash memory read / write errors of the flash memory access data; Step S144: performing file system damage checking based on the flash memory read / write errors to obtain file system damage data; Step S145: performing device flash memory fault identification based on the file system damage data to obtain device flash memory fault data.

4. The simulation method suitable for multi-tiered home energy management system according to claim 3, wherein, Step S145 specifically comprises: Extracting the write error log of 5-10 errors per minute in the file system damage data; Identifying the high-frequency error log area with more than 50 errors in the write error log; Detecting the flash read-write exception of the high-frequency error log area to obtain the flash read-write exception unit; Mapping the high-frequency error log area according to the flash read-write exception unit to obtain the bad block unit, wherein the bad block unit standard is set to more than 10% of the number of storage blocks or pages of the exception unit; Evaluating the device flash failure of the bad block unit to obtain the device flash failure data.

5. The simulation method suitable for multi-tiered home energy management system according to claim 1, wherein, Step S2 specifically comprises: Step S21: constructing a smart home model based on smart home data; Step S22: generating smart home control data by simulating smart home control of the smart home model using a preset control instruction; Step S23: calculating the control success rate of the smart home control data; Step S24: calculating the device power of the smart home model based on the control success rate; Step S25: drawing a power curve using the device power and identifying the peak power consumption data; Step S26: calculating the energy consumption of the peak power consumption data to obtain the energy consumption data.

6. The simulation method suitable for multi-tiered home energy management system according to claim 1, wherein, The circuit board soldering thermal state fault decoding in step S3 comprises: Extracting the abnormal state code of the device abnormal state; Matching the short-circuit fault code of the abnormal state code using a preset short-circuit state code, and marking the short-circuit fault device of the smart home model; Scanning the printed circuit board of the short-circuit fault device using a thermal imager to generate a circuit board thermal image; Identifying the high-temperature area in the circuit board thermal image; Positioning the soldering point area of the high-temperature area and obtaining the hollow area data using ultrasonic detection; Calculating the soldering uniformity of the soldering point area; Calculating the roughness of the soldering point area; Determining the soldering data of the soldering point based on the roughness and the soldering uniformity; Integrating the soldering data of the soldering point and the hollow area data to obtain the circuit board fault data.

7. The simulation method suitable for multi-tiered home energy management system according to claim 1, wherein, The antenna impedance alteration diagnosis in step S3 comprises: Statistically analyzing the signal abnormal state data of the device abnormal state; Marking the signal abnormal home device of the smart home model according to the signal abnormal state data; Performing antenna impedance testing on the signal abnormal home device using an impedance analyzer to obtain antenna impedance data; Comparing the preset standard antenna impedance data with the antenna impedance data to obtain impedance deviation data; Performing antenna oxidation detection on the signal abnormal home device based on the impedance deviation data to obtain antenna oxidation data; Performing corrosion damage analysis on the antenna oxidation data to obtain antenna damage data.

8. The simulation method suitable for multi-tiered home energy management system according to claim 1, wherein, The signal decay spectrum deduction in step S3 comprises: Quantifying the damage degree of the antenna damage data; Performing signal transmission simulation according to the antenna damage data, setting the transmission frequency range to 2.4GHz-5GHz and the simulation accuracy to 0.1dB-0.3dB; Calculating the signal transmission distance during the signal transmission simulation; Constructing a signal attenuation model according to the damage degree and the signal transmission distance; Inputting the antenna damage data into the signal attenuation model and performing signal attenuation prediction, setting the damage degree range to 0-1, the signal attenuation value to 10dB-50dB, and the environmental influence coefficient to 0.2-0.8 to generate signal attenuation data.

9. A simulation system suitable for multi-level home energy management systems, characterized in that, The simulation method for the multi-level home energy management system is used to execute the method as claimed in claim 1, and the simulation system for the multi-level home energy management system comprises: An equipment abnormal state detection module is configured to acquire smart home data and extract communication protocol data, detect an equipment abnormal state based on the communication protocol data, and obtain equipment abnormal state data; An energy consumption analysis module is configured to construct a smart home model based on the smart home data, perform smart home control simulation using a preset control instruction, generate smart home control data, perform energy consumption analysis based on the smart home control data, and obtain energy consumption data; A circuit board soldering hot fault decoding module is configured to perform circuit board soldering hot fault decoding based on the equipment abnormal state, obtain circuit board fault data, perform antenna impedance erosion diagnosis based on the equipment abnormal state, obtain antenna damage data, and perform signal decay spectrum deduction on the antenna damage data to generate signal attenuation data; A signal enhancement module is configured to perform signal distortion correlation analysis on the energy consumption data based on the signal attenuation data, obtain signal distortion data, perform signal enhancement based on the signal distortion data, obtain signal enhancement data, and upload the signal enhancement data to the multi-level home energy management system.

Images