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

Abstract

The invention relates to the technical field of smart home, in particular to a simulation method and system suitable for a multi-level home energy management system. The method comprises the following steps: acquiring smart home data, and extracting communication protocol data; detecting an equipment abnormal state based on the communication protocol data to obtain the equipment abnormal state; constructing a smart home model based on the smart home data, and performing smart home control simulation by using a preset control instruction to generate smart home control data; performing energy consumption analysis according to the smart home control data to obtain energy consumption data; circuit board soldering tin thermal state fault decoding is carried out according to the equipment abnormal state, and circuit board fault data are obtained; and performing antenna impedance alteration diagnosis according to the abnormal state of the equipment to obtain antenna damage data, and performing signal decay spectrum deduction to generate signal attenuation data. According to the invention, based on the smart home technology, the equipment fault detection, the self-adaptive capability of the system and the overall energy efficiency management rate are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart home, and particularly to a simulation method and system applicable to a multi-level home energy management system. Background Art

[0002] The data acquisition and device identification capabilities of traditional systems are relatively insufficient. They usually rely on basic sensor data or manual input, and cannot provide the accurate anomaly monitoring required by high-complexity smart home systems. Moreover, in large-scale devices and complex networks, there is a lack of an effective real-time dynamic monitoring mechanism. The energy consumption analysis ability is limited. Traditional systems often analyze based on fixed models or historical data, and cannot flexibly cope with the dynamic changes of the home environment and device status, lacking accurate prediction and optimization for energy efficiency and peak energy consumption stages. Traditional methods are also relatively simple in hardware fault diagnosis. They mainly rely on troubleshooting processes, 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, cannot effectively process 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. Traditional methods mostly use fixed parameters and control instructions, cannot adjust according to the real-time status of devices, and are even more difficult to achieve intelligent scheduling of multiple devices, especially lacking adaptability in dynamic environments. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a simulation method and system applicable to a multi-level home energy management system to solve at least one of the above technical problems.

[0004] To achieve the above object, a simulation method applicable to a multi-level home energy management system includes the following steps:

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

[0006] Step S2: Build a smart home model based on the smart home data, and perform smart home control simulation using preset control instructions to generate smart home control data; perform energy consumption analysis based on the smart home control data to obtain energy consumption data;

[0007] Step S3: Decode the hot state fault of the circuit board soldering according to the device abnormal state to obtain circuit board fault data; diagnose the antenna impedance alteration 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: 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 it to the multi-level home energy management system.

[0009] By acquiring and extracting communication protocol data, the present invention can ensure the real-time update of the device status, providing an accurate data source for smart home control. Using the device anomaly detection mechanism, the system can detect potential problems of the device early, give early warnings, and avoid damage and failures. By constructing a smart home model and conducting 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 device and environmental conditions, thereby improving the energy efficiency of the system. Conducting in-depth diagnosis of device failures, including diagnosing problems with circuit board soldering and antenna impedance alteration, can accurately identify hardware failures, avoid the lag of traditional fault troubleshooting, and generate effective attenuation data through signal attenuation spectrum deduction to help further analyze the problem source. Signal distortion correlation analysis can reveal the deep relationship between energy consumption and signal attenuation, thereby providing data support for signal enhancement and optimization, and solving the problems of insufficient signal strength evaluation and filtering technology in traditional methods. Through signal enhancement and optimization, not only can the signal quality of the system be improved, transmission delay be reduced, but also the overall communication quality can be improved to ensure the stable operation of the device and the system. After being uploaded to the multi-level home energy management system, the intelligent management level of the smart home system is further improved, enabling the system to adaptively adjust according to the actual situation, optimize device scheduling and energy distribution in real time, reduce energy consumption, and improve the overall efficiency.

[0010] Preferably, step S1 is specifically as follows:

[0011] Step S11: Acquire smart home data and extract communication protocol data;

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

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

[0014] Step S14: Perform device flash memory fault analysis based on the storage performance heartbeat packet to obtain device flash memory fault data;

[0015] Step S15: Monitor the device anomaly status of the device flash memory fault data.

[0016] The present invention 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 carefully tracked to ensure that the system can monitor the changes in the 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 perform flash fault analysis through the storage performance heartbeat packet data to detect potential fault points of the device in advance, avoiding the limitations of relying on manual inspection and fault troubleshooting in traditional methods. Through the monitoring of device flash fault data, continuous tracking and diagnosis of the device status can be achieved, ensuring that the system promptly responds to and processes device failures, thereby improving the reliability of the device and the stability of the system. This series of steps not only enhances the traditional system's real-time recognition ability of the device's dynamic status but also improves the early warning and processing efficiency of hardware failures, 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 impacts of device failures on home energy consumption and user experience.

[0017] Preferably, step S14 is specifically as follows:

[0018] Step S141: Perform packet loss detection based on the storage performance heartbeat packet to obtain storage performance heartbeat packet loss data, and screen out devices with lost storage performance heartbeat packets;

[0019] Step S142: Extract the flash access data of the devices with lost storage performance heartbeat packets;

[0020] Step S143: Detect flash read / write errors in the flash access data;

[0021] Step S144: Based on the flash read / write errors, perform a file system corruption check to obtain file system corruption data;

[0022] Step S145: Identify device flash faults based on the file system corruption data to obtain device flash fault data.

[0023] Through the detection of packet loss of the storage performance heartbeat packet, the system can identify abnormal device communication at an early stage, and screen out the devices with problems in a timely manner, which provides an important basis for subsequent troubleshooting and early warning. By extracting and analyzing the flash access data of the packet loss devices, the storage status of the devices can be deeply understood, so as to detect read and write errors at the flash level, ensuring that the system can accurately identify storage failures. In addition, through the file system corruption check based on flash read and write errors, the system can more accurately locate the existing system-level problems, avoiding the limitation of only relying on manual troubleshooting in traditional methods. Finally, through the accurate implementation of device flash failure identification, the system can not only detect and handle device failures in a timely manner, but also provide comprehensive data support for subsequent maintenance and optimization, further improving the reliability, stability and intelligence level of the smart home system, reducing device downtime and system operation and maintenance costs. The intelligent improvement of this process avoids the lag of traditional methods and provides an efficient and automated solution for device fault detection and handling.

[0024] Preferably, step S145 is specifically as follows:

[0025] Extract the write error logs that occur 5-10 times per minute in the file system corruption data;

[0026] Identify the high-frequency error log area where the number of errors in the write error log exceeds 50 times;

[0027] Detect the flash read and write anomalies in the high-frequency error log area to obtain flash read and write anomaly units;

[0028] Perform bad block mapping on the high-frequency error log area according to the flash read and write anomaly units to obtain bad block units, where the bad block unit standard is set to that the storage block or page number where the anomaly unit is located exceeds 10%;

[0029] Perform device flash failure assessment on the bad block units to obtain device flash failure data.

[0030] Through the refined monitoring and analysis of the file system, the present invention can effectively identify and solve the storage failure problems in smart home devices. First, by extracting the logs with multiple write errors occurring within each minute, the system can capture the abnormal performance of the storage device in real time, providing first-hand data support for subsequent problem localization. Further, by identifying the areas of high-frequency error logs, this process helps to discover potential storage problem areas, guiding the maintenance and repair of the device. The abnormal flash read and write in the detected high-frequency error log area can deeply explore the root cause of the problem, locate specific storage units, and then process the problem area through bad block mapping. By setting criteria to screen out the bad block units with greater impact, the failure degree of the storage device can be accurately evaluated, avoiding the limitation of the traditional method in accurately identifying high-frequency failure areas. Finally, the device flash failure assessment of the bad block units not only improves the accuracy of failure 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 as follows:

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

[0033] Step S22: Use the preset control instructions to perform smart home control simulation on the smart home model to generate smart home control data;

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

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

[0036] Step S25: Use the device power to draw a power curve and identify the peak power consumption data;

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

[0038] By constructing a smart home model, the present invention can comprehensively simulate the operation and interaction of devices in a simulation environment, thereby providing a basis for the subsequent optimization of control strategies. Using preset control instructions to simulate the model can accurately generate smart 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, further statistical analysis of the power consumption of devices can effectively identify the energy requirements of each device, providing a basis for energy efficiency management and optimization. Using these power data to draw a power curve can clearly show the power fluctuations of the device in different operating states, and then identify the peak power consumption data, which helps to accurately predict and optimize the high-energy consumption stage. Finally, by calculating the energy consumption of the peak power consumption data, precise data support can be provided for the energy management of the overall smart home system, ensuring that the 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 system by deeply analyzing and optimizing the energy use of smart home devices, and solves the deficiencies of traditional methods in dynamic monitoring and energy efficiency optimization.

[0039] Preferably, the decoding of the printed circuit board soldering thermal state failure in step S3 includes:

[0040] Extracting the abnormal state code of the device's abnormal state;

[0041] Using the preset short-circuit state code to match the short-circuit fault code of the abnormal state code, and marking the short-circuit fault devices of the smart home model;

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

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

[0044] Locating the solder joint area of the high-temperature area and obtaining the void area data by ultrasonic detection;

[0045] Calculating the solder uniformity of the solder joint area;

[0046] Calculating the roughness of the solder joint area;

[0047] Determining the solder data of the solder joint based on the roughness and the solder uniformity;

[0048] Integrating the solder data of the solder joint and the void area data to obtain the printed circuit board fault data.

[0049] By extracting the abnormal status codes of device abnormal statuses, the present invention can quickly identify whether a device is abnormal, providing an important basis for troubleshooting. By matching the short - circuit status codes and abnormal status codes, devices with short - circuit faults can be effectively marked, enabling timely fault diagnosis and handling. Using a thermal imager to scan the printed circuit board of a short - circuit fault device can accurately obtain the temperature distribution of the device, thereby discovering abnormal high - temperature areas and further narrowing the scope of troubleshooting. After identifying the high - temperature areas in the thermal imaging diagram of the circuit board, the problematic parts in the circuit board can be located to ensure effective diagnosis of the fault area. By locating the solder joint area and combining ultrasonic detection to obtain data on the void area, the solder joint quality and mechanical defects can be deeply understood to ensure the identification of the root cause of the fault. Calculating the solder uniformity and roughness of the solder joint area can evaluate the welding quality, providing important reference data for maintenance personnel and helping to optimize subsequent welding processes. Based on the uniformity and roughness of the solder joints, it is possible to accurately determine whether there are potential faults in the solder joints and further determine the solder data of the solder joints, ensuring the reliability of circuit board welding. Integrating the solder data of the solder joints and the data of the void area provides more comprehensive information support for circuit board fault diagnosis, improving the accuracy of fault identification and the adaptive ability of the system, avoiding the diagnostic limitations of traditional systems for complex hardware problems, and effectively improving the capabilities of the smart home system in device maintenance and fault warning.

[0050] Preferably, the antenna impedance alteration diagnosis in step S3 includes:

[0051] Statistically analyze the signal abnormal status data of the device abnormal status;

[0052] Mark the signal - abnormal home devices of the smart home model according to the signal abnormal status data;

[0053] Use an impedance analyzer to perform antenna impedance tests on the signal - abnormal home devices to obtain antenna impedance data;

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

[0055] Based on the impedance deviation data, perform antenna oxidation detection on the signal - abnormal home devices to obtain antenna oxidation data;

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

[0057] By statistically analyzing the signal anomaly status data of device anomaly statuses, the present invention can effectively capture signal problems occurring during the operation of the device and timely identify potential fault sources. By marking signal-anomalous home appliances, the system can be helped to identify the devices with anomalies, facilitating subsequent detection and repair work. Using an impedance analyzer to test and obtain antenna impedance data can provide accurate physical data for subsequent fault analysis, thereby effectively evaluating the signal transmission ability. By comparing the deviation between the standard antenna impedance data and the actual antenna impedance data, the deviation of the antenna can be discovered, further locking in the root cause of potential problems. Based on the impedance deviation data, antenna oxidation detection is performed on signal-anomalous home appliances to accurately identify whether there is an oxidation problem on the antenna surface, preventing signal attenuation or distortion. By performing corrosion damage analysis on the antenna oxidation data, valuable information can be provided for device maintenance, determining whether the antenna has suffered serious damage, and then deciding whether replacement or repair is needed. These steps can improve the fault diagnosis ability and real-time monitoring ability of the smart home system in a complex device network through precise signal analysis, impedance comparison, oxidation detection, and corrosion analysis, avoiding the limitations of traditional methods for signal processing and fault diagnosis, and further enhancing the stability of the device and the intelligent management level of the system.

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

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

[0060] Performing signal transmission simulation based on the antenna damage data, setting the transmission frequency range to 2.4 GHz - 5 GHz and the simulation accuracy to 0.1 dB - 0.3 dB;

[0061] Calculating the signal transmission distance during the signal transmission simulation;

[0062] Constructing a signal attenuation model based on 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 range to 0 - 1, the signal attenuation value to 10 dB - 50 dB, and the environmental impact coefficient to 0.2 - 0.8, to generate signal attenuation data.

[0064] By quantifying the degree of damage to the antenna damage data, the present invention can more intuitively evaluate the health status of the antenna, providing accurate basic data for subsequent signal transmission analysis. Based on these damage data, signal transmission simulation can be carried out to simulate the signal transmission performance under different degrees of damage, thereby helping to predict the performance of the device in the actual environment. Setting an appropriate transmission frequency range and simulation accuracy can ensure the accuracy and credibility of the simulation results, providing a strong basis for subsequent performance evaluation. By calculating the signal transmission distance in the signal transmission simulation process, it is possible to intuitively understand the effective range of signal propagation under different damage conditions, providing a reference for the usage scenario of the device. Constructing a signal attenuation model based on the degree of damage and the signal transmission distance helps to understand the relationship between signal attenuation and device damage from a system perspective, providing accurate theoretical support for fault prediction. Inputting the antenna damage data into the signal attenuation model and performing signal attenuation prediction can reflect the changes in device performance in real time. Setting a reasonable range of badness, signal attenuation value, and environmental impact coefficient helps to effectively predict complex factors in the environment, thereby further optimizing the signal transmission quality. This series of steps improves the system's ability to signal attenuation and fault prediction by precisely analyzing the impact of antenna damage on signal transmission, providing an important technical guarantee for the efficient operation of the smart home system and avoiding the limitations of traditional methods in dealing with complex signal problems.

[0065] Preferably, step S4 is specifically as follows:

[0066] Step S41: Statistically analyze the signal attenuation time of the signal attenuation data;

[0067] Step S42: Statistically analyze the high energy consumption time of the energy consumption data, where the high energy consumption threshold is set to be greater than 3W and the high consumption time is 30 seconds to 3 minutes;

[0068] Step S43: Perform time matching based on the signal attenuation time and the high energy consumption time, where the overlap degree of signal attenuation and high energy consumption is greater than 50%, to obtain the signal attenuation - high energy consumption time;

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

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

[0071] Step S46: Perform signal distortion analysis based on the number of retransmissions and the signal strength, where the signal - to - noise ratio is set to 15dB - 30dB and the signal distortion amount is 0% - 50%, to obtain the signal distortion data;

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

[0073] Step S48: Adjust the filtering parameters of the signal distortion data;

[0074] Step S49: Integrate the signal equalization degree and the filtering parameters to obtain signal enhancement data, and upload it to the multi-level home energy management system.

[0075] By statistically analyzing the signal attenuation time of the signal attenuation data, the present invention can comprehensively understand the duration of signal attenuation and its impact on device performance, 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 energy waste sources and providing a basis for subsequent energy-saving measures. By performing time matching based on the signal attenuation time and the high energy consumption time, the correlation between signal attenuation and high energy consumption can be effectively identified, revealing performance problems that occur in the device during a specific period, and helping to timely adjust the working state of the device. Calculating the number of retransmissions of the signal attenuation - high energy consumption time can quantify the impact of signal attenuation on signal transmission, helping to identify system performance degradation caused by signal attenuation. Calculating the signal strength of the signal attenuation - high energy consumption time can accurately evaluate the change trend of the signal strength, providing a technical basis for subsequent signal optimization. By performing signal distortion analysis based on the number of signal retransmissions and the signal strength, setting reasonable ranges for the bit error rate, signal-to-noise ratio, and signal distortion amount, 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 a timely manner. By adjusting the signal equalization degree and filtering parameters of the signal distortion data, signal distortion can be effectively reduced, and the quality and stability of the signal can be improved, thus avoiding the situation of insufficient signal attenuation processing ability in traditional methods. Finally, by integrating the signal equalization degree and the filtering parameters, generating signal enhancement data, and uploading it to the multi-level home energy management system, it 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 a complex environment, effectively improves the intelligent level of device management, and promotes the upgrade of traditional methods to the smart home field.

[0076] Preferably, this specification also provides a simulation system applicable to a multi-level home energy management system for executing the simulation method applicable to the multi-level home energy management system as described above. The simulation system applicable to the multi-level home energy management system includes:

[0077] An abnormal device state detection module, configured to obtain smart home data and extract communication protocol data; detect the abnormal device state based on the communication protocol data to obtain the abnormal device state;

[0078] The energy consumption analysis module is used to build a smart home model based on smart home data, perform smart home control simulation using preset control instructions to generate smart home control data, conduct energy consumption analysis based on the smart home control data to obtain energy consumption data.

[0079] The circuit board soldering hot state fault decoding module decodes the circuit board soldering hot state fault according to the device abnormal state to obtain circuit board fault data, diagnoses the antenna impedance alteration according to 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.

[0080] The signal enhancement module is used to perform signal distortion correlation analysis on the energy consumption data according to 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 it to the multi-level home energy management system.

[0081] The simulation system of the present invention applicable to the multi-level home energy management system can implement any simulation method of the present invention applicable to the multi-level home energy management system, and is used as a medium for coordinating the operations and signal transmissions between various modules to complete the simulation method applicable to the multi-level home energy management system. The internal modules of the system cooperate with each other, improving the accuracy and efficiency of device fault detection, energy optimization, and signal enhancement, thereby enhancing the adaptive ability and overall energy efficiency management rate of the system. Brief Description of the Drawings

[0082] By reading the detailed description of the non-restrictive embodiments with reference to the following drawings, other features, objectives, and advantages of the present invention will become more apparent:

[0083] Figure 1 It is a schematic flowchart of the steps of the simulation method of the present invention applicable to the multi-level home energy management system;

[0084] Figure 2 It is a detailed schematic flowchart of step S1 in the present invention;

[0085] Figure 3 It is a detailed schematic flowchart of step S14 in the present invention;

[0086] The realization of the objectives, functional characteristics, and advantages of the present invention will be further described with reference to the embodiments and the drawings. Detailed Embodiments

[0087] The technical method of the present invention patent will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work belong to the scope of protection of the present invention.

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

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

[0090] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides a simulation method applicable to a multi-level home energy management system, and the method includes the following steps:

[0091] Step S1: Obtain smart home data and extract communication protocol data; detect the abnormal state of the device based on the communication protocol data to obtain the abnormal state of the device;

[0092] In this embodiment, when obtaining smart home data, it is connected to each smart device through a wireless communication protocol (such as Zigbee, Wi-Fi, or Bluetooth) to collect device status, sensor data, etc. Data extraction uses corresponding protocol parsing tools (such as Wireshark or a custom parsing program) to parse the communication data, obtain the original data packets sent by the devices, and extract the device status information therein. These data include parameters such as voltage, current, temperature, and humidity. By comparing with the device data under normal conditions, an abnormal threshold is set for judgment. If the device data exceeds the set normal range (for example, the temperature exceeds 75°C or the current is higher than 0.5 A), it is determined that the device enters an abnormal state, the abnormal state of the device is recorded, and the specific abnormal category is marked, such as overheating, short circuit, signal loss, etc. The specific threshold depends on the device specifications and can be provided through device testing or technical manuals.

[0093] Step S2: Build a smart home model based on the smart home data, and use the preset control instructions to perform smart home control simulation to generate smart home control data; perform energy consumption analysis based on the 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 build a home smart home model. This model is based on the device type and function and can include the relationships between devices, energy efficiency levels, control instructions, etc. Simulation tools (such as MATLAB / Simulink, Simatic S7) are used to perform control simulation on the home model, and preset control instructions are set, such as timed switching on and off, temperature adjustment, etc. In the simulation, by setting control parameters (such as the set temperature threshold of 20°C - 25°C, humidity range of 40% - 60%, etc.), the operation conditions of the devices and their energy consumption processes are simulated to generate simulated energy consumption data. During the simulation process, the collection of energy consumption data is based on a power meter or a real-time energy efficiency monitoring device. By parsing the control instructions and device responses, energy efficiency data is obtained, including power consumption, standby power, etc., and finally specific energy consumption data is obtained.

[0095] Step S3: Decode the hot state fault of the circuit board soldering according to the device abnormal state to obtain circuit board fault data; diagnose the antenna impedance alteration 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 dedicated tool is used to perform solder hot-state decoding on the circuit board. First, an infrared thermal imager (such as FLIR T1K) is used to detect the temperature distribution of the circuit board and analyze the overheating of the solder joints. By setting a temperature threshold (for example, 80°C is the abnormal temperature standard), the hot-state decoding image is analyzed to detect welding defects or poor contact problems. Then, antenna impedance alteration diagnosis is carried out, and a network analyzer (such as Agilent 8753E) is used to measure the impedance characteristics of the antenna. By comparing the impedance of a 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. A radio frequency signal source (such as Keysight N5182B) is used to transmit signals of different frequencies (such as 2.4GHz, 5GHz), and the received signal strength is measured at the antenna. The signal attenuation value is recorded by a spectrum analyzer (such as Rohde&Schwarz FSH4), and the spectrum measurement range is adjusted according to the degree of antenna damage (such as cracks, corrosion) to generate signal attenuation data.

[0097] Step S4: Perform signal distortion correlation analysis on the energy consumption data according to 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 it to the multi-level home energy management system.

[0098] In this embodiment, signal distortion correlation analysis is performed according to the signal attenuation data and the energy consumption data. A signal processing software (such as the SciPy library in MATLAB or Python) is used to calculate the correlation between the signal attenuation and the energy consumption. By setting a correlation threshold (for example, when the signal attenuation is greater than 30dB and the energy consumption is greater than 5W for correlation analysis), the overlapping period of the two is analyzed to identify the relationship between the signal distortion and the energy consumption, and signal distortion data is obtained. Next, a signal enhancement algorithm (such as an adaptive filtering algorithm, frequency compensation technology, etc.) is used to enhance the distorted signal. The enhancement parameters are adjusted, such as the filter bandwidth (for example, 2.4GHz - 5GHz), signal-to-noise ratio (such as above 15dB), and bit error rate (such as 10^-5). For the filtering parameters, by adjusting the frequency response of the filter (such as low-pass, high-pass, etc.), the signal is restored to a better state to obtain signal enhancement data. Finally, the signal enhancement data is uploaded to the multi-level home energy management system through a wireless communication module. The system performs real-time optimization and adjustment on the status of home devices 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 a wireless communication protocol (such as Zigbee, Wi-Fi, or Bluetooth) to obtain the device status information, sensor data, and communication data between devices. These data include voltage, current, temperature, humidity, device control instructions, etc. The data captures the transmitted data packets through corresponding protocol parsing tools (such as Wireshark, tcpdump) and parses the communication protocol data therein. The data packets will contain the device status information and control instructions. For example, in the Zigbee protocol, the messages sent by the device will include the source device address, target device address, and control instruction field, and the data of the temperature sensor will be transmitted in the data segment. This step depends on the communication protocol standards provided by the device specifications, such as the MAC layer and application layer formats in the Zigbee protocol, to ensure the complete and accurate extraction of data.

[0102] Step S12: Perform network sniffing on the communication protocol data to obtain network data packets;

[0103] In this embodiment, based on the obtained communication protocol data, use a network sniffing tool (such as Wireshark, tcpdump) to monitor and analyze network traffic. First, capture network data packets 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 screen traffic of specific protocol types, such as TCP, UDP data packets, or screen data packets for specific ports (such as port 80 for HTTP and port 443 for HTTPS). After the data packet capture is completed, use the analysis function of the network sniffing tool to parse the data packets into specific communication content. Obtain the source address, target address, and timestamp according to the data packet header information, and rely on traffic analysis to perform real-time data monitoring and detect abnormal traffic or communication interruption situations.

[0104] Step S13: Detect 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, a network sniffing tool is used to further analyze the content of the data packet. The storage performance heartbeat packet is usually a small data packet regularly sent between the device and the control center to confirm whether the device is normally online. First, set filtering conditions to identify the characteristics of the storage performance heartbeat packet (such as setting the "icmp" filtering condition to capture the ICMP protocol storage performance heartbeat packet, or filtering a specific application layer heartbeat protocol). The storage performance heartbeat packet usually contains a source device identifier, a timestamp, and confirmation information, and a detailed analysis of the storage performance heartbeat packet is performed using Wireshark or a custom protocol parsing tool. By setting a reasonable time interval threshold (such as 5 minutes) to identify missing or delayed storage performance heartbeat packets. If the storage performance heartbeat packet is not detected within the set time window or the content of the storage performance heartbeat packet is abnormal (such as the device identifier is missing or the timestamp is abnormal), it is regarded as abnormal device communication and marked as a device failure.

[0106] Step S14: Perform device flash memory failure analysis based on the storage performance heartbeat packet to obtain device flash memory failure data;

[0107] In this embodiment, device flash memory failure analysis is performed based on the captured storage performance heartbeat packet information. Flash memory failure usually manifests as the device being unable to read or write data normally, or the device response being delayed or incorrect due to flash memory damage. First, a comparison analysis is performed between the timestamp transmitted in the storage performance heartbeat packet and the response time of the device, and a response timeout threshold is set (such as not receiving a response after more than 5 seconds). If the device response times out or the response data is incorrect (such as the data packet content is empty or the format is incorrect), it is considered that the device has a flash memory failure. At this time, the flash memory status of the device can be directly read through an embedded debugging tool (such as a JTAG debugger) or a flash memory detection tool (such as FlashBench) to check whether its read / write function is normal. If it is found that a specific area of the flash memory is damaged (such as block erase failure, write failure, etc.), the flash memory failure data is further recorded, including the specific location of the flash memory damage, the error type, etc.

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

[0109] In this embodiment, when monitoring the flash failure data of the device, the device status is tracked through the real-time data monitoring system. By combining the storage performance heartbeat packet detection and the flash failure analysis results, the abnormal status of the device is recorded. Specifically, by setting the device abnormal threshold (such as the device continuously loses three storage performance heartbeat packets or the response times out), the device abnormal alarm is triggered and the flash failure diagnosis is carried out. System monitoring software (such as Zabbix, Nagios) can be used to check the device health, and the abnormal status 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 the failure state, the system will automatically record information such as the failure time, device type, and failure type, and display the failure alarm information on the system interface. At this time, the operator can judge whether the device needs to be repaired or replaced according to the failure data to ensure the stable operation of the smart home system.

[0110] Preferably, step S14 is specifically as follows:

[0111] Step S141: Perform packet loss detection according to the storage performance heartbeat packet, obtain the storage performance heartbeat packet loss data, and screen the devices with storage performance heartbeat packet loss;

[0112] In this embodiment, when performing packet loss detection 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 packets are screened out. Through the specific identifier of the storage performance heartbeat packet (such as the ICMP protocol or the device-specific application layer protocol), the source device address, target device address, and timestamp are extracted from the data packet. Then, a time window is set (for example, 10 seconds), and by calculating the number of storage performance heartbeat packet losses of each device within this window, it is determined whether there is a packet loss situation. If a packet loss phenomenon is detected within the set threshold (such as continuously losing 3 storage performance heartbeat packets), the device is marked as a device with storage performance heartbeat packet loss, and its device ID, packet loss times, packet loss time, and communication path information of the packet loss device are recorded. The data can be stored in a database (such as MySQL, SQLite) for further analysis and subsequent tracking.

[0113] Step S142: Extract the flash access data of the devices with storage performance heartbeat packet loss;

[0114] In this embodiment, when extracting the flash access data of the storage performance heartbeat packet loss device, based on the storage performance heartbeat packet loss device identified in step S141, access the flash log of the device. Extract the flash access records of the device through the device management system (such as managing the device through the SNMP protocol), including the flash read and write operations performed by the device before and after losing the storage performance heartbeat packet. These data include the timestamps of flash operations, operation types (read / write), data block or file identifiers of operations, etc. Regularly pull these flash logs through an automated script or interface (such as the PySNMP library of Python, REST API) to ensure that the integrity of each flash operation can be accurately recorded. If the device has frequent flash access or the flash cannot be read or written normally, the flash status of the device can be further analyzed.

[0115] Step S143: Detect flash read and write errors in the flash access data;

[0116] In this embodiment, when detecting flash read and write errors in the flash access data, based on the flash access log of the device, check the error information in the read and write operations line by line by writing an analysis program (such as a Python script combined with regular expressions). For example, when detecting flash read errors, the error data can be obtained by filtering according to the error code or error message in the log (such as "Read Error"). Set error judgment criteria (for example, an error code of "0x01" indicates a read error, and an error code of "0x02" indicates a write error), and detect the error code for each flash operation. When the error code of the flash operation appears multiple times or exceeds the set threshold (for example, 5 consecutive read and write errors), it can be determined that the device has a flash failure, and key data such as the error type, frequency, and occurrence time period are recorded.

[0117] Step S144: Based on the flash read and write errors, perform a file system corruption check to obtain file system corruption data;

[0118] In this embodiment, when performing a file system corruption check based on the flash read and write errors, first obtain the flash access log of the device and identify all flash blocks or file areas where read and write errors occur. Perform a consistency check on the flash file system through a file system check tool (such as the fsck tool under Linux). The file system repair command (such as "fsck / dev / sda") can be executed on the device to scan the flash and detect the degree of file system corruption. File system corruption usually manifests as directory structure errors, data block loss, file system mount failure, etc. Set corruption judgment criteria. For example, if the file system errors exceed the set threshold (for example, more than 50 errors) during the scan, it is determined that the file system is severely damaged. In addition, combine the timestamp of file access with the error frequency to mark the time point of system corruption, providing a basis for subsequent device maintenance.

[0119] Step S145: Identify device flash failure based on the file system corrupted data, so as to obtain device flash failure data.

[0120] In this embodiment, when identifying device flash failure based on the file system corrupted data, further analyze the file system corrupted data obtained in step S144. Based on the file system corruption situation (such as the number of file system errors, corruption types, etc.), combined with the flash access data of the device, determine whether the device flash has failed. If the corrupted data of the file system indicates that some flash blocks are irrecoverable during multiple operations and the corruption degree reaches a preset threshold (such as more than 70% file system errors), it can be determined that the device flash has failed. Perform further hardware detection through the device management system or flash diagnostic tool (such as FlashBench), evaluate the health of the flash, and record the device failure data, including the failure type, failure occurrence time, affected device area, etc.

[0121] Preferably, step S145 is specifically:

[0122] Extract the write error logs with 5 - 10 errors per minute in the file system corrupted data;

[0123] In this embodiment, when extracting the write error logs with 5 - 10 errors per minute in the file system corrupted data, first obtain the write error logs through the device's log management system (such as syslog, dmesg, etc.) or flash diagnostic system. The log recording system should include information such as timestamp, error type, error code, and the device area where the error occurs. Filter all the logs, group them according to the timestamp, and calculate the number of write errors per minute. If the number of write errors in a certain minute falls within the set range (such as 5 to 10 times), then record these write error logs. The error type should be set to "write error" (for example, the error code is 0x02), and the write error type is identified according to the specific error code. All error logs that meet this condition should be stored in a database (such as MySQL or SQLite), and the data fields include time, device ID, error type, and error quantity, etc., for subsequent analysis.

[0124] Identify the high - frequency error log area in the write error logs where the number of errors exceeds 50 times;

[0125] In this embodiment, when identifying the high-frequency error log area where the number of errors in the written error log exceeds 50 times, based on the written error log extracted in step S151, group by device ID and error log area, and calculate the occurrence times of the error logs in each area. If a certain device area has more than 50 write errors within a certain time range (such as 30 minutes or 1 hour), and this error belongs to a high-frequency error, then mark it 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 query and statistical analysis to filter out the log data of the high-frequency area. The recorded high-frequency error areas should include device ID, area address (such as storage block or page), number of errors, etc., to help locate the fault position in the subsequent process.

[0126] Detect the flash read / write anomaly in the high-frequency error log area to obtain the flash read / write anomaly unit;

[0127] In this embodiment, when detecting the flash read / write anomaly in the high-frequency error log area, for the high-frequency error log area marked in step S152, access the flash read / write data of this area, and detect the flash read / write operations of these areas through a flash diagnostic tool (such as FlashBench) or the built-in hardware monitoring system of the device. By reading the flash access log, identify whether there are abnormal behaviors during the read / write process of this area. The abnormal behaviors are manifested as read failure, write timeout, data loss, etc. Use the set error codes (such as "0x01" indicating read failure, "0x03" indicating write failure) to filter and record the abnormal data. Specific technical measures include setting a time window (such as within 1 second) to detect frequent read / write operations. If the number of abnormal read / writes within this window exceeds the set threshold (such as 5 times), then it is considered that there is a flash read / write anomaly in this area. The detection results include the address of the flash unit, the number of errors, and the type of anomaly, etc.

[0128] Perform bad block mapping on the high-frequency error log area according to the flash read / write anomaly unit to obtain the bad block unit, where the bad block unit standard is set to that the number of storage blocks or pages where the abnormal unit is located exceeds 10%;

[0129] In this embodiment, when performing bad block mapping on the high-frequency error log area according to the flash memory read / write abnormal units, based on the flash memory read / write abnormal units detected in step S153, bad block mapping is performed through a bad block management tool (such as Flash Translation Layer, FTL). According to the flash memory diagnostic data, the location of the storage block or page of the read / write abnormal unit is identified, and the bad block standard is set that the damage degree of the storage block or page where the abnormal unit is located exceeds 10%. If more than 10% of the units in a certain storage block have flash memory read / write abnormalities, then this storage block is marked as a bad block, and the ID, location, and number of abnormalities of this bad block are recorded. The bad block mapping process screens bad blocks by setting a threshold (such as a 10% damage rate), and isolates the bad blocks from the storage blocks normally in use through a mapping algorithm to prevent continued use of this area for storing data.

[0130] Perform device flash memory fault assessment on the bad block units to obtain device flash memory fault data.

[0131] In this embodiment, when performing device flash memory fault assessment on the bad block units, based on the bad block data obtained in step S154, a flash memory fault assessment is performed using the device's fault assessment tool (such as an SSD health detection tool). According to the number of bad blocks, the location of the bad blocks, and the damage degree, the overall health status of the flash memory is evaluated. The evaluation criteria can be set that when the total number of bad blocks reaches more than 2% of the total storage capacity of the device, it is considered that the device has a serious fault; if the bad block area exceeds the set threshold (such as 10% of the storage blocks have bad blocks), then it is confirmed that the flash memory of the device has failed. The device's flash memory fault data includes the fault type, the location of the bad blocks, the number of bad blocks, and the status of the flash memory units. This evaluation result will be stored and reported for subsequent device maintenance or replacement decisions.

[0132] Preferably, step S2 is specifically as follows:

[0133] Step S21: Construct a smart home model based on smart home data;

[0134] In this embodiment, when constructing a smart home model based on smart home data, various sensor data in the smart home system (such as temperature, humidity, lighting, door and window status, etc.) are first collected, and these data are cleaned and standardized. The construction of the smart home model is achieved through data analysis tools (such as libraries like Pandas and NumPy in Python), specifically including operations such as time series analysis, missing value processing, and outlier detection of sensor data. The constructed model should be able to reflect the mutual relationships between smart devices and their dynamic changes under different environmental conditions. Machine learning methods (such as K-means clustering analysis) are used to group the devices, and the devices are classified according to their functions (such as lighting, air conditioning, home appliance control, etc.). Finally, a mathematical model reflecting the state of the entire smart home system is constructed using these data and model parameters (such as the working range of sensors, response time of devices, etc.), and the performance indicators and response strategies of the devices are set.

[0135] Step S22: Use the preset control instructions to perform smart home control simulation on the smart home model to generate smart home control data;

[0136] In this embodiment, when using the preset control instructions to perform smart home control simulation on the smart home model, the preset control instructions are first input into the constructed smart home model through a control system (such as Zigbee, Wi-Fi protocol, etc.). The control instructions should include the on / off state of the target device, adjustment values (such as temperature setting, brightness adjustment, etc.), and execution time, etc. The issuance of the control instructions follows a predetermined trigger rule (such as time trigger, event trigger, etc.), and the response of the device is simulated through a simulation platform (such as MATLAB / Simulink). During the simulation process, the control operations and response status of each device are recorded to generate smart home control data. These data include the on / off state of the device, response time, execution result (whether successful), actual achieved adjustment value, etc. The matching degree between the control instructions 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 the smart home control data, first analyze the smart home control data generated in step S22. According to whether the device successfully executes the predetermined 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 controlled devices. If a device fails to perform the operation as expected within the 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, count the number of successes and failures, and calculate the control success rate. When the control success rate should be set 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 calculate the device power of the smart home model based on the control success rate;

[0140] In this embodiment, when statistically calculating the device power of the smart home model based on the control success rate, first collect the power consumption situation of the device according to the power consumption data of each device during the execution of the control instruction. The power data can be obtained through the built-in electricity meter of the device (such as a smart socket or a power monitoring module). The calculation method of the device power is: Power (W) = Voltage (V) × Current (A). The power consumption of each device in different time periods should be recorded according to the actual control data, considering the power fluctuations that occur before and after the execution of the control instruction by different devices. The statistical process needs to summarize the power data of each device in different control states and perform a weighted average on the power data according to the control success rate to obtain the power consumption of each device in different scenarios.

[0141] Step S25: Use the device power to draw a power curve and identify the peak power consumption data;

[0142] In this embodiment, when using the device power to draw a power curve, first organize the power data obtained in step S24 according to the time period or operation state. Use a data visualization tool (such as Excel, Matplotlib, etc.) to draw a power curve graph. The curve graph should show the power consumption changes of each device in different time periods or control conditions. For example, it is possible to draw the power consumption change curves of multiple devices throughout the day (grouped by hour), or compare the power consumption situations of different devices within the same time period. According to the power curve graph, the power consumption trend of the device in different time periods can be analyzed, and the peak period of power consumption can be identified. 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 energy consumption of 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) × Time (h). Integrate the power data of the peak time period (such as summing the time intervals) to obtain the total energy consumption data during this time period. It should be noted that the peak time period should be set as the time period when 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 during the peak time period, and finally obtain the energy consumption data of the entire smart home system during the peak time period, and based on this data, energy management or optimization strategy design is carried out.

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

[0146] Extract the abnormal status code of the device abnormal status;

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

[0148] Use the preset short-circuit status code to match the short-circuit fault code of the abnormal status code, and mark the short-circuit fault device of the smart home model;

[0149] In this embodiment, when using a preset short - circuit status code to match the short - circuit fault code of the abnormal status code, first set a short - circuit status code (such as "003") in the device's status management system. This status code is used to mark that the device has a short - circuit fault. When the device status monitoring system detects an abnormal status code, it matches it with the preset short - circuit fault code. If the abnormal status code belongs to a short - circuit - related fault (such as excessive current or unstable voltage), it classifies the device as a short - circuit - fault device. At this time, the system marks the corresponding device as in the "short - circuit fault" state. The matching rule is based on a predefined coding standard. By comparing the abnormal status code, the system automatically identifies the devices with short - circuit fault characteristics and conducts fault tracking and processing on these devices through corresponding markings. In the system, the abnormal status code "003" is compared with other relevant fault codes such as "005" to ensure the accurate calibration of short - circuit devices.

[0150] Use a thermal imager to scan the printed circuit board of the short - circuit fault device to generate a thermal image of the circuit board;

[0151] In this embodiment, when using a thermal imager to scan the printed circuit board of the short - circuit fault device, first physically locate the device with a short - circuit, 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 an infrared sensor to form a temperature distribution map. At this time, it should be ensured that the setting parameters of the thermal imager, such as the temperature range (for example, - 20°C to + 150°C), resolution (such as 320×240 pixels), and scanning speed (such as scanning 100 points per second), have been optimized. During the scanning process, the device should be stationary to ensure the accuracy of the surface temperature data of the device. The thermal image obtained by the thermal imager can clearly show the change of the surface temperature of the circuit board, so as to quickly locate the existing fault area, such as the part with too high temperature.

[0152] Identify the high - temperature areas in the thermal image of the circuit board;

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

[0154] Locate the solder joint area in the high-temperature region and obtain the data of the void area by ultrasonic detection;

[0155] In this embodiment, when locating the solder joint area in the high-temperature region and obtaining the data of the void area by ultrasonic detection, first, in the high-temperature region, locate the welding area according to the circuit board layout. The welding area usually includes solder joints, solder connection parts, etc. Determine the positions of these areas through visual inspection or mechanical positioning. Then, use an ultrasonic flaw detector (such as CT-1200) to scan the welding area. The ultrasonic sensor will send high-frequency sound waves and receive the echo signals. By analyzing the intensity and time delay of the echo signals, detect whether there are voids or cracks inside the solder joints. After the ultrasonic detection data is processed by the signal processing algorithm, detailed information such as the size and position of the voids can be provided. The advantage of using ultrasonic flaw detection technology in this step is that it can non-destructively detect the defects inside the solder joint area and obtain the 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 obtain the image data of the solder joints through a microscope or a high-resolution scanner. Then, use image processing software (such as MATLAB, OpenCV) to process the solder joint images, extract the contour of the solder distribution, and calculate the change in the solder thickness. The calculation method of the solder uniformity is: uniformity = (maximum solder thickness - minimum solder thickness) / average solder thickness. The smaller the solder uniformity value 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 welding quality.

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

[0159] In this embodiment, when calculating the roughness of the solder joint area, first obtain the high-resolution image data of the solder joint surface through a scanning electron microscope (SEM) or an optical microscope. Then, use surface roughness analysis software to analyze the image and extract the microscopic morphology of the solder joint surface. The roughness can be calculated by Ra (arithmetic mean roughness) or Rq (root mean square roughness). The formula is Ra = 1 / n∑|Zi - Zmean|, where Zi is the height of each measurement point, Zmean is the average surface height, 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 welding quality.

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

[0161] In this embodiment, when determining the solder data of solder joints based on roughness and solder uniformity, first, combine the solder uniformity and the roughness of the solder joints, and screen according to the set quality standards (for example, the uniformity should be less than 10% and the roughness should be less than 0.5 μm) to determine the solder joint data that meets the quality requirements. Specifically, when the uniformity and roughness of a solder joint simultaneously meet the standards, it is considered that the solder quality of this solder joint is good; otherwise, it is determined as a non-conforming solder joint. Integrate the solder quality data of these solder joints into a complete welding quality report as the basis for subsequent maintenance or optimization.

[0162] Integrate the solder data of solder joints and the data of the void area to obtain the circuit board fault data.

[0163] In this embodiment, when integrating the solder data of solder joints and the data of the void area to obtain the circuit board fault data, first combine the solder uniformity data and the data of the void area. The specific method is as follows: Associate the solder uniformity of each solder joint with the void data at its location. If the uniformity of a certain solder joint is poor and the void area is large, it is marked as a fault point. By analyzing the solder quality and void data of all solder joints, generate an overall fault data report of the circuit board. This report includes information such as the welding quality of each solder joint, the void location and its size, etc., providing a basis for the repair and optimization of the circuit board.

[0164] Preferably, the antenna impedance alteration diagnosis in step S3 includes:

[0165] Statistically analyze the signal abnormal state data of the device in the abnormal state;

[0166] In this embodiment, the device abnormal state data is collected in real time through built-in sensors. The working states of each device, such as parameters like temperature, humidity, voltage, signal strength, etc., are recorded when an abnormality occurs. For the signal abnormal state, particularly pay attention to data such as wireless signal strength, signal frequency, signal noise, etc. The data collector sets a trigger threshold to automatically mark any abnormal data that exceeds 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), this data is marked as an abnormal state. The abnormal state data is uploaded to the central server for aggregation and storage through wireless transmission, facilitating subsequent analysis and fault troubleshooting.

[0167] Mark the signal-abnormal smart home devices in the smart home model according to the signal abnormal state data;

[0168] In this embodiment, by analyzing the signal abnormal state data transmitted by the device, the data filtering algorithm is used to eliminate irrelevant or normal state data, and key markings are made for values such as signal strength and frequency detected in the signal abnormal state data. For example, when the signal strength is continuously lower than the set threshold (such as -70 dBm) for multiple times or there are large fluctuations, it is marked as "signal abnormal". Through model algorithms or database matching, relevant devices (such as routers and wireless cameras in smart homes) are screened out and marked as "home devices with abnormal signals". This marking will enter the subsequent steps as troubleshooting data for confirming the location of the abnormal device and formulating corresponding treatment measures.

[0169] Use an impedance analyzer to test the antenna impedance of the home device with abnormal signals to obtain antenna impedance data;

[0170] In this embodiment, an impedance analyzer is used to detect the device marked as having abnormal signals. Connect the probe of the impedance analyzer to the antenna port of the device to obtain the impedance data of the antenna. According to the operating frequency band of the device (such as 2.4 GHz or 5 GHz), set appropriate test frequencies and frequency bandwidths. Usually, the test frequency is more than 1 GHz of the required operating frequency, and the frequency accuracy requirement is within 1 MHz. The impedance analyzer will output the reflection loss, impedance curve, and related parameters of the antenna within this frequency band, recording the actual operating state of the device antenna for subsequent impedance deviation analysis and antenna performance detection.

[0171] Compare the preset standard antenna impedance data with the antenna impedance data for impedance deviation to obtain impedance deviation data;

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

[0173] Based on the impedance deviation data, perform antenna oxidation detection on the home device with abnormal signals to obtain antenna oxidation data;

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

[0175] Perform corrosion damage analysis 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 makes predictions and calculations based on multiple factors such as the oxidation degree of the antenna, the humidity and temperature of the use environment, and the operating frequency. For example, by analyzing the antenna oxidation data and the impedance changes in the operating frequency band, it is inferred whether the metal layer of the antenna has been severely corroded, resulting in a decline in performance. The set damage threshold (such as the impedance value exceeding 15% of the standard) will be used as the judgment criterion for damage. Once this threshold is exceeded, the antenna will be marked as in the "damaged" state, generating antenna damage data and inputting it into the subsequent workflow for repair or replacement.

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

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

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

[0180] Perform signal transmission simulation according to the antenna damage data, and set the transmission frequency range to 2.4GHz - 5GHz and the simulation accuracy to 0.1dB - 0.3dB;

[0181] In this embodiment, when performing signal transmission simulation, the transmission frequency range is first set to 2.4GHz to 5GHz to adapt to common wireless communication frequency bands. The simulation accuracy is set to 0.1dB to 0.3dB, which is to ensure the accuracy of the simulation results while considering the efficiency of the simulation calculation. Electromagnetic simulation software (such as Ansys HFSS or CST Microwave Studio) is used to simulate the transmission process of signals under different degrees of damage. During simulation, the impedance data of the damaged antenna is input, the corresponding damage parameters (such as impedance deviation, damage level, etc.) are set, and the signal attenuation at different frequencies is calculated by the software, and the result accuracy is between 0.1dB and 0.3dB.

[0182] Calculate the signal transmission distance during the signal transmission simulation;

[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 the degree of antenna damage. First, it is necessary to obtain a propagation model of wireless communication (for example, a free space propagation model or a ground wave propagation model), and adjust the transmission loss according to the degree of antenna damage. Set the attenuation constant and path loss parameters, and calculate the attenuation and effective transmission distance of the signal in combination with the simulation results. For each simulation frequency, the maximum effective distance of signal transmission is calculated based on different antenna damage levels (such as mild, moderate, and severe damage). The formulas used in this process include the Free-Space Path Loss (FSPL) model and actual radio 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 degree of damage and signal transmission distance;

[0185] In this embodiment, a signal attenuation model is established based on the data of the antenna damage degree and the 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 degree 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 severity of damage is proportional to the attenuation value. Set the damage parameters (such as 0-1 degree of damage) and the range of signal attenuation (such as 10dB-50dB) in the model, and then deduce the attenuation model under different conditions.

[0186] The antenna damage data is input into the signal attenuation model, and the signal attenuation prediction is performed. The badness range is set to 0-1, the signal attenuation value is set to 10dB-50dB, and the environmental impact coefficient is set to 0.2-0.8 to generate the signal attenuation data.

[0187] In this embodiment, the antenna damage data (including the damage degree and the corresponding impedance deviation) is used as an input parameter and substituted into the constructed signal attenuation model for simulation. The damage degree range is set from 0 to 1, where 0 represents no damage and 1 represents complete damage; the signal attenuation value range is set from 10 dB to 50 dB, which is used to represent the quantization value of the signal strength attenuation; the environmental impact coefficient range is set from 0.2 to 0.8, considering 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, the antenna damage degree is 0.4), the signal attenuation value in 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, a signal attenuation prediction for each device can be generated for further equipment maintenance and optimization.

[0188] Preferably, step S4 is specifically as follows:

[0189] Step S41: Statistically analyze the signal attenuation time of the signal attenuation data;

[0190] In this embodiment, according to the real-time monitoring data, the change of the signal strength is collected. During the transmission process, when the signal strength is lower than the set threshold (for example, the signal attenuation value exceeds 30 dB), this time period is recorded. Through the real-time monitoring system, the signal strength is sampled periodically, 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 returns above the threshold (for example, the signal attenuation value is lower than 20 dB). Each attenuation duration is statistically analyzed as the signal attenuation time. The statistical result includes the signal attenuation duration of each device within a specific time period, and finally these data are summarized to obtain the total signal attenuation time of each device.

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

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

[0193] Step S43: Perform time matching based on the signal attenuation time and the high energy consumption time, where the overlap degree between the signal attenuation and the high energy consumption is greater than 50%, to obtain the signal attenuation - high energy consumption time;

[0194] In this embodiment, when performing time matching, it is first necessary to process the signal attenuation time and the high energy consumption time data separately. For each device, the overlap degree of time periods is used for matching. Through time alignment techniques (such as the sliding window algorithm), calculate the overlapping part between the signal attenuation time and the high energy consumption time. When the overlapping time is greater than 50%, this time period is considered to belong to the signal attenuation - high energy consumption time period. For each device, calculate the overlapping interval between the signal attenuation and the high energy consumption time within a specific time, and record these overlapping time periods.

[0195] Step S44: Calculate the retransmission times of the signal attenuation - high energy consumption time;

[0196] In this embodiment, the calculation of the signal retransmission times is based on the transmission protocol data. By analyzing the signal transmission logs of the device, especially the time periods overlapping with the signal attenuation and the high energy consumption time, count whether the signal has been retransmitted during these overlapping time periods. In wireless communication, when the signal attenuates, the communication protocol usually triggers the retransmission mechanism. By analyzing the transmission logs of data packets, record the signal retransmission events, and count the retransmission times within the overlapping time periods. Finally, obtain the total retransmission times within the signal attenuation - high energy consumption time period.

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

[0198] In this embodiment, the calculation of the signal strength is based on the real - time signal data of the device within the signal attenuation - high energy consumption time period. During the attenuation period of each device, record the signal strength in real - time, and use a signal strength analyzer (such as a network analyzer or a signal monitoring device) for detection. Perform weighted averaging on the signal strength at each time point within the signal attenuation - high energy consumption time period to obtain the signal strength value of this time period. Assume that when the signal attenuates, the signal strength range is from - 70dBm to - 100dBm, and calculate and adjust the signal strength in real - time according to the duration of each attenuation and the environmental conditions. Finally, obtain the average signal strength of the attenuation - high energy consumption time period.

[0199] Step S46: Perform signal distortion analysis based on the retransmission times and the signal strength, set the signal - to - noise ratio to 15dB - 30dB and the signal distortion amount to 0% - 50%, to obtain the signal distortion data;

[0200] In this embodiment, during the signal distortion analysis, the signal-to-noise ratio (SNR) range is set to 15 dB to 30 dB. According to the retransmission times and signal strength of the device, the bit error rate calculation formula (such as BER = 1 / 2 * (1 - SNR)) is used for signal distortion analysis. Based on the retransmission times of each device and the calculated signal strength, the corresponding signal-to-noise ratio is deduced. Through the bit error rate calculation formula, the corresponding bit error rate is obtained, and the signal distortion amount is set according to this error range. Based on the retransmission times and signal strength values of the device, the signal distortion data of the device during 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, based on 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. By means of gain control, filter adjustment, etc., the distortion is reduced. During the adjustment process, the signal distortion amount is set in the 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 the 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 filtering parameters of the signal distortion data;

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

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

[0206] In this embodiment, the signal equalization degree and the filtering parameters are integrated to generate the final signal enhancement data. Specifically, the equalized signal and the filtered signal are combined to form the optimized signal output data. During 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, this specification also provides a simulation system applicable to a multi-level home energy management system for performing the simulation method applicable to a multi-level home energy management system as described above. The simulation system applicable to a multi-level home energy management system includes:

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

[0209] The energy consumption analysis module is used to construct a smart home model based on the smart home data and perform smart home control simulation using preset control instructions to generate smart home control data; perform energy consumption analysis based on the smart home control data to obtain energy consumption data;

[0210] The circuit board soldering thermal state fault decoding module decodes the circuit board soldering thermal state fault according to the device abnormal state to obtain the circuit board fault data; diagnoses the antenna impedance alteration according to the device abnormal state to obtain the antenna damage data; performs signal decay spectrum deduction on the antenna damage data to generate signal attenuation data;

[0211] The signal enhancement module is used to perform signal distortion correlation analysis on the energy consumption data according to 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 it to the multi-level home energy management system.

[0212] The simulation system of the present invention applicable to a multi-level home energy management system can implement any simulation method of the present invention applicable to a multi-level home energy management system, and is used as a medium for coordinating the operations and signal transmissions between various modules to complete the simulation method applicable to a multi-level home energy management system. The internal modules of the system cooperate with each other, improving the accuracy and efficiency of device fault detection, energy optimization and signal enhancement, thereby enhancing the adaptive ability and overall energy efficiency management rate of the system.

[0213] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Thus, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0214] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A simulation method suitable for a multi-level home energy management system, characterized in that: The following steps are involved: Step S1: Acquire smart home data and extract communication protocol data; detect device abnormality based on the communication protocol data to obtain the device abnormality; Step S2: constructing a smart home model based on the smart home data, and using preset control instructions to perform smart home control simulation 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 the circuit board solder thermal fault according to the abnormal state of the equipment to obtain circuit board fault data; diagnosing antenna impedance alteration according to the abnormal state of the equipment to obtain antenna damage data; performing signal decay spectrum deduction on the antenna damage data to generate signal attenuation data; Step S4: Perform signal distortion correlation analysis on the energy consumption data according to 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.

2. The simulation method for a multi-level home energy management system according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: Acquire smart home data and extract communication protocol data; Step S12: Perform network sniffing on the communication protocol data to obtain network data packets; Step S13: Detecting the storage performance heartbeat packet in the network data packet; Step S14: performing device flash memory failure analysis according to the storage performance heartbeat packet to obtain device flash memory failure data; Step S15: monitor the abnormal state of the device flash memory fault data.

3. The simulation method for a multi-level home energy management system according to claim 2, characterized in that: Step S14 is specifically as follows: Step S141: performing packet loss detection according to the storage performance heartbeat packet, obtaining storage performance heartbeat packet loss data, and screening storage performance heartbeat packet loss devices; Step S142: extracting flash memory access data of the device whose storage performance heartbeat packet is lost; Step S143: Detecting flash memory read and write errors of flash memory access data; Step S144: performing a file system damage check based on the flash memory read and write errors to obtain file system damage data; Step S145: performing device flash memory failure identification according to the file system damage data, thereby obtaining device flash memory failure data.

4. The simulation method for a multi-level home energy management system according to claim 3, characterized in that: Step S145 is specifically as follows: Extract write error logs with 5-10 errors per minute in the file system corruption data; Identify high-frequency error log areas where the number of errors written to the error log exceeds 50; Detect flash memory read and write anomalies in a high-frequency error log area to obtain a flash memory read and write anomaly unit; According to the flash memory read and write abnormal units, bad block mapping is performed on the high-frequency error log area to obtain bad block units, wherein the bad block unit standard is set as the number of storage blocks or pages where the abnormal units are located exceeds 10%; The device flash memory failure evaluation is performed on the bad block unit to obtain the device flash memory failure data.

5. The simulation method for a multi-level home energy management system according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: constructing a smart home model based on smart home data; Step S22: using preset control instructions to perform smart home control simulation on the smart home model to generate smart home control data; Step S23: Calculate the control success rate of the smart home control data; Step S24: Counting the device power of the smart home model based on the control success rate; Step S25: Draw a power curve using the device power, and identify and obtain peak power consumption data; Step S26: Calculate the energy consumption of the peak power consumption data to obtain energy consumption data.

6. The simulation method for a multi-level home energy management system according to claim 1, characterized in that: The circuit board solder thermal fault decoding in step S3 includes: Extract the abnormal status code of the abnormal status of the device; Use the preset short-circuit status code to match the short-circuit fault code of the abnormal status code, and mark the short-circuit fault device of the smart home model; Use a thermal imager to scan the printed circuit board of the short-circuit fault device to generate a thermal image of the circuit board; Identify high temperature areas in thermal images of circuit boards; Locate the solder joint area in the high temperature area and use ultrasonic testing to obtain void area data; Calculate the solder uniformity of the solder joint area; Calculate the roughness of the solder joint area; Determine solder joint data based on roughness and solder uniformity; Integrate solder joint data and void area data to obtain circuit board failure data.

7. The simulation method for a multi-level home energy management system according to claim 1, characterized in that: The antenna impedance alteration diagnosis in step S3 includes: Statistics of abnormal signal status data of equipment abnormal status; Mark the signal abnormal home devices of the smart home model according to the signal abnormal status data; Use an impedance analyzer to test the antenna impedance of household devices with abnormal signals to obtain antenna impedance data; Comparing the preset standard antenna impedance data with the antenna impedance data for impedance deviation to obtain impedance deviation data; Based on the impedance deviation data, antenna oxidation detection is performed on the home equipment with abnormal signals to obtain antenna oxidation data; Corrosion damage analysis is performed on the antenna oxidation data to obtain antenna damage data.

8. The simulation method for a multi-level home energy management system according to claim 1, characterized in that: The signal decay spectrum deduction in step S3 includes: Quantify the damage degree of antenna damage data; Perform signal transmission simulation based on antenna damage data, set the transmission frequency range to 2.4GHz-5GHz, and the simulation accuracy to 0.1dB-0.3dB; Calculate the signal transmission distance during the signal transmission simulation; Construct a signal attenuation model based on the degree of damage and signal transmission distance; The antenna damage data is input into the signal attenuation model, and the signal attenuation prediction is performed. The badness range is set to 0-1, the signal attenuation value is set to 10dB-50dB, and the environmental impact coefficient is set to 0.2-0.8 to generate the signal attenuation data.

9. The simulation method for a multi-level home energy management system according to claim 1, characterized in that: Step S4 is specifically as follows: Step S41: Counting the signal attenuation time of the signal attenuation data; Step S42: Counting the high energy consumption time of the energy consumption data, wherein the high energy consumption threshold is set to be greater than 3W and the high consumption time is set to be 30 seconds to 3 minutes; Step S43: performing time matching according to the signal attenuation time and the energy high consumption time, wherein the overlap between the signal attenuation and the high energy consumption is greater than 50%, and obtaining the signal attenuation-high energy consumption time; Step S44: Calculate the number of retransmissions during the signal attenuation-high energy consumption time; Step S45: Calculate the signal strength of signal attenuation-high energy consumption time; Step S46: Perform signal distortion analysis based on the number of retransmissions and signal strength, set the signal-to-noise ratio to 15dB-30dB and the signal distortion amount to 0%-50%, and obtain signal distortion data; Step S47: adjusting the signal balance of the signal distortion data; Step S48: adjusting the filtering parameters of the signal distortion data; Step S49: Integrate the signal balance and filtering parameters to obtain signal enhancement data, and upload it to the multi-level home energy management system.

10. A simulation system suitable for a multi-level home energy management system, characterized in that: The method for performing the simulation of a multi-level home energy management system according to claim 1, wherein the simulation system for the multi-level home energy management system comprises: The device abnormal state detection module is used to obtain smart home data and extract communication protocol data; detect the device abnormal state based on the communication protocol data to obtain the device abnormal state; The energy consumption analysis module is used to build a smart home model based on smart home data, and use preset control instructions to perform smart home control simulation to generate smart home control data; perform energy consumption analysis based on the smart home control data to obtain energy consumption data; The circuit board soldering thermal fault decoding module decodes the circuit board soldering thermal fault according to the abnormal state of the equipment to obtain the circuit board fault data; diagnoses the antenna impedance alteration according to the abnormal state of the equipment to obtain the antenna damage data; and performs signal decay spectrum deduction on the antenna damage data to generate signal attenuation data; The signal enhancement module is used to perform signal distortion correlation analysis on energy consumption data according to 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 a multi-level home energy management system.

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