Information security protection method and system for household energy management system

Abstract

The invention relates to the technical field of information encryption, in particular to an information security protection method and system for a household energy management system. The method comprises the following steps: acquiring smart home data; performing communication simulation according to the smart home data, identifying electromagnetic leakage in the communication simulation process, and generating electromagnetic leakage data; identifying a side channel attack based on the electromagnetic leakage data; the shielding effectiveness of the material is tested through side channel attack; predicting the shielding material life of the smart home data according to the material shielding effectiveness; performing multi-layer shielding design based on the service life of the shielding material to obtain multi-layer shielding data; and performing transient electromagnetic pulse simulation based on the multilayer shielding data, and detecting the breakdown damage of the inverter in the simulation process to obtain the breakdown damage data of the inverter. According to the invention, based on the information encryption technology, the security protection capability of the smart home system facing electromagnetic leakage and side channel attacks is improved, and the shielding effectiveness and the anti-interference capability of the system are significantly improved.

Description

Technical Field

[0001] The present invention relates to the field of information encryption technology, and in particular to an information security protection method and system for a home energy management system. Background Art

[0002] Traditional smart home information security solutions have exposed numerous shortcomings in the face of increasingly complex security threats. Most rely on simple encryption technologies and firewalls, failing to fully address the complex multi-dimensional data flows and multi-dimensional attacks inherent in modern home energy management systems. These approaches often lack effective preventative measures against emerging attacks (such as side-channel attacks and electromagnetic leakage attacks), making it difficult to promptly detect and respond to new security threats. Traditional protection measures often fail to fully consider the risk of electromagnetic leakage. Communication between home devices generates leakage signals, and the lack of electromagnetic leakage detection mechanisms makes them vulnerable to attacks. Traditional approaches ignore the aging effects of devices or shielding materials, focusing only on the current security status without fully considering the durability of the shielding materials. This leads to a decrease in protection effectiveness after long-term use. Furthermore, traditional shielding designs typically use a single shielding method (such as a metal casing) and fail to fully account for electromagnetic interference and side-channel attacks in the home environment, resulting in poor protection. Traditional approaches also fail to integrate dynamic simulation and optimization, lack predictive measures and response measures for emergencies such as electromagnetic pulses and inverter damage, and are unable to effectively respond to sudden system failures or attacks. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide an information security protection method and system for a home energy management system to solve at least one of the above technical problems.

[0004] To achieve the above objectives, an information security protection method for a home energy management system includes the following steps:

[0005] Step S1: Acquire smart home data; perform communication simulation based on the smart home data, identify electromagnetic leakage during the communication simulation, and generate electromagnetic leakage data;

[0006] Step S2: Identify side channel attacks based on electromagnetic leakage data; test material shielding effectiveness using side channel attacks; predict the lifespan of shielding materials for smart home data based on the shielding effectiveness of the materials; perform multi-layer shielding design based on the lifespan of the shielding materials to obtain multi-layer shielding data;

[0007] Step S3: performing transient electromagnetic pulse simulation based on the multi-layer shielding data, and detecting inverter breakdown damage during the simulation process to obtain inverter breakdown damage data; identifying parasitic effect characteristics of the smart home data based on the inverter breakdown damage data;

[0008] Step S4: Optimize electromagnetic shielding based on parasitic effect characteristics to obtain electromagnetic shielding optimization data; encrypt the electromagnetic shielding information of the electromagnetic shielding optimization data and upload it to the home energy management system to perform electromagnetic shielding information security protection tasks.

[0009] By focusing on traditional security measures during data communication, this invention can also identify electromagnetic leakage between smart home devices, effectively detecting potential side-channel attack risks and improving defenses against new attacks. Furthermore, by identifying side-channel attacks and combining them with testing the shielding effectiveness of materials, the lifespan of shielding materials can be accurately predicted, enabling optimized shielding designs to avoid degradation of shielding effectiveness over time. Transient electromagnetic pulse simulation based on a multi-layer shielding structure can simulate dynamic changes in the electromagnetic environment and detect inverter breakdown damage during the simulation, helping to proactively identify potential system failures and improve device reliability. Regarding parasitic effects in smart home data, this method analyzes electromagnetic coupling data to identify parasitic currents and their effects, accurately assessing parasitic effect characteristics and preventing information leakage caused by parasitic effects. Further electromagnetic shielding optimization based on parasitic effect characteristics can effectively improve electromagnetic compatibility, enabling the smart home system to maintain stable operation in complex electromagnetic environments. An encryption mechanism for electromagnetic shielding information is introduced to ensure enhanced security during transmission and storage of optimized electromagnetic shielding data. The optimized electromagnetic shielding data is uploaded to the home energy management system, enabling it to perform information security protection tasks, achieving overall electromagnetic security optimization for the smart home environment. Compared with traditional methods that rely on single shielding materials or static protection strategies, this method combines dynamic simulation, shielding material life prediction, multi-layer shielding optimization, parasitic effect analysis and other means to build a more comprehensive electromagnetic safety protection system. It not only improves the resistance to sudden electromagnetic interference, but also can more effectively prevent complex security threats such as side channel attacks and electromagnetic leakage, and enhance the data security and reliability of the home energy management system.

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

[0011] Step S11: Acquire smart home data;

[0012] Step S12: extracting the communication protocol of the smart home data and setting a communication process based on the communication protocol;

[0013] Step S13: Identify communication channel characteristics of smart home data;

[0014] Step S14: performing communication simulation according to the communication channel characteristics and the communication process to obtain communication simulation data;

[0015] Step S15: Identify electromagnetic leakage of communication simulation data and generate electromagnetic leakage data.

[0016] The present invention conducts an in-depth analysis of the communication characteristics of smart home data to construct an efficient detection mechanism for electromagnetic leakage, thereby overcoming the limitations of traditional information security protection methods. This method can effectively extract the communication protocol of smart home data and construct a reasonable communication process based on the communication protocol, thereby optimizing the data transmission structure and improving the stability and security of communication. At the same time, it accurately identifies the characteristics of the communication channel, making the channel status during the communication process more transparent, and providing more reliable data support for subsequent security protection. Communication simulation based on the communication channel characteristics and communication process enables the smart home system to detect potential safety hazards in advance in a simulated environment, thereby improving the adaptability and predictability of protective measures. Through this simulation process, the communication mode between smart home devices can be deeply analyzed, the existing information leakage risk can be detected, and a data basis can be provided for the accurate identification of electromagnetic leakage. Ultimately, it is possible to efficiently identify electromagnetic leakage in communication simulation data and generate electromagnetic leakage data, thereby establishing an electromagnetic safety early warning mechanism for the smart home system.

[0017] Preferably, step S15 is specifically as follows:

[0018] Step S151: Identify the signal propagation path of the communication analog data;

[0019] Step S152: Calculating the electromagnetic field strength of the signal propagation path;

[0020] Step S153: converting the electromagnetic field intensity into an electromagnetic intensity spectrum;

[0021] Step S154: Identify the electromagnetic energy concentration area;

[0022] Step S155: Counting abnormally strong signals in the electromagnetic energy concentration area, and identifying stray signals based on the abnormally strong signals;

[0023] Step S156: Perform electromagnetic leakage determination on the stray signal to obtain electromagnetic leakage data.

[0024] The present invention makes the analysis of electromagnetic leakage more accurate and traceable by accurately identifying the signal propagation path of communication simulation data, which helps to locate potential leakage sources. Calculating the electromagnetic field strength of the signal propagation path enables the system to quantify the degree of electromagnetic interference at different locations, providing data support for subsequent leakage risk assessment. Converting the electromagnetic field strength into an electromagnetic intensity spectrum enables the system to intuitively analyze the distribution of electromagnetic energy in different frequency ranges and improve the ability to distinguish abnormal signals. Identifying areas where electromagnetic energy is concentrated enables the system to focus on areas where leakage occurs, improving the pertinence and efficiency of detection. Counting abnormally strong signals in areas where electromagnetic energy is concentrated can effectively eliminate interference from normal signals and ensure the reliability of the detection results. Identifying stray signals based on abnormally strong signals enables the system to further screen out electromagnetic leakage sources, thereby improving the accuracy of detection. Finally, electromagnetic leakage judgment is performed on stray signals to ensure that true electromagnetic leakage data can be accurately identified and extracted, thereby enhancing the system's security protection capabilities in complex smart home environments.

[0025] Preferably, the identifying of the side channel attack in step S2 includes:

[0026] Reversely infer the encryption key of electromagnetic leakage data;

[0027] Recover the full key space based on the encryption key;

[0028] Using the full key space to identify attack fingerprints of electromagnetic leakage data;

[0029] Track attack paths based on attack fingerprints;

[0030] Locate key attack fingerprints of attack paths;

[0031] Obtain a side-channel attack fingerprint library;

[0032] Based on the key attack fingerprint, fingerprint matching is performed on the side channel attack fingerprint library to obtain the side channel attack fingerprint;

[0033] Identify side-channel attacks on electromagnetic leakage data based on side-channel attack fingerprints.

[0034] The present invention reversely infers the encryption key of electromagnetic leakage data, enabling the system to infer key information based on the leaked signal, thereby analyzing encryption weaknesses exploited by attackers. Restoring the full key space based on the encryption key enables the system to restore all key combinations, improving its ability to deduce potential attack methods. Using the full key space to identify attack fingerprints in electromagnetic leakage data allows the system to extract features from multiple attack modes, improving the accuracy of attack type determination. Tracking the attack path based on the attack fingerprint enables the system to reconstruct the attacker's trajectory, effectively tracing the attack source. Locating the key attack fingerprint of the attack path enables the system to focus on the most critical attack features, improving the accuracy and efficiency of subsequent detection. Acquiring a side-channel attack fingerprint library allows the system to perform comparisons based on existing attack datasets, improving the breadth and adaptability of detection. Fingerprint matching is performed against the side-channel attack fingerprint library based on the key attack fingerprint, ensuring that the system can accurately identify the matching attack type, thereby improving the reliability and automation level of identification. Identifying side-channel attacks in electromagnetic leakage data based on the side-channel attack fingerprint enables the system to intelligently analyze the electromagnetic leakage data, quickly determine the attack type, and enhance the real-time response and early warning capabilities of the protection system.

[0035] Preferably, the testing of the shielding effectiveness of the material in step S2 includes:

[0036] Extract smart camera data for smart home data;

[0037] Use side channel attacks to simulate attacks on smart camera data to obtain attack simulation data;

[0038] Collect electromagnetic signal waveforms of attack simulation data;

[0039] Determine electromagnetic leakage based on the electromagnetic signal waveform and obtain camera electromagnetic leakage data;

[0040] Screen electromagnetic leakage cameras based on camera electromagnetic leakage data, and use a preset 0.1mm-0.3mm thin aluminum foil to perform a shielding test on the electromagnetic leakage cameras to obtain thin aluminum foil shielding data;

[0041] The electromagnetic leakage camera was shielded using a preset 0.5mm-1mm thick aluminum foil to obtain thick aluminum foil shielding data;

[0042] Calculate the shielding effectiveness of thin aluminum foil using thin aluminum foil shielding data;

[0043] Calculate the shielding effectiveness of thick aluminum foil using thick aluminum foil shielding data;

[0044] The shielding effectiveness of the material is obtained by integrating the shielding effectiveness of thin aluminum foil and thick aluminum foil.

[0045] By extracting smart camera data from smart home data, the present invention enables the system to focus on the electromagnetic leakage risks of smart cameras, providing an accurate data foundation for subsequent analysis. Using side-channel attacks to simulate attacks on smart camera data, realistic attack scenarios can be constructed, helping the system evaluate the electromagnetic leakage characteristics of the camera under attack. Collecting the electromagnetic signal waveforms from the simulated attack data enables the system to accurately measure the electromagnetic radiation emitted by the camera under attack, providing high-precision data support for leakage determination. Determining electromagnetic leakage based on the electromagnetic signal waveforms allows efficient identification of electromagnetic leakage in cameras, improving the automation and accuracy of detection. Screening cameras for electromagnetic leakage based on camera electromagnetic leakage data enables the system to accurately identify devices with leakage risks, avoid interference from unrelated devices, and improve testing efficiency. Using a preset 0.1mm-0.3mm thin aluminum foil to perform shielding tests on electromagnetic leakage cameras, the system can evaluate the thin foil's shielding ability against low-intensity electromagnetic leakage, generating thin foil shielding data. Using a preset 0.5mm-1mm thick aluminum foil to perform shielding tests on electromagnetic leakage cameras, the system can further verify the shielding effectiveness of thicker foil against high-intensity electromagnetic leakage, generating thick foil shielding data. Calculating the shielding effectiveness of thin aluminum foil using thin foil shielding data allows the system to quantify its shielding effectiveness, providing a reference for protection against different leakage intensities. Calculating the shielding effectiveness of thick aluminum foil using thick foil shielding data allows the system to evaluate the actual shielding effectiveness of higher-strength shielding materials, providing data support for selecting appropriate shielding materials. Integrating the shielding effectiveness of thin and thick aluminum foil allows for a comprehensive comparison of the shielding performance of aluminum foils of different thicknesses, providing a scientific basis for optimizing electromagnetic protection for smart home devices.

[0046] Preferably, the predicting of the shielding material life in step S2 includes:

[0047] Extracting shielding data of low-efficiency materials for material shielding effectiveness;

[0048] Perform thermal simulation based on low-efficiency material shielding data to obtain thermal simulation data;

[0049] Calculate electrical conductivity from thermal simulation data;

[0050] Conduct corrosion experiments based on low-efficiency material shielding data to obtain corrosion data;

[0051] Identify surface oxidation of corrosion data and obtain surface oxidation data;

[0052] Predicting the lifespan of shielding materials for smart home data based on conductivity and surface oxidation data.

[0053] The present invention extracts shielding data of low-efficiency materials, enabling the system to accurately identify materials with poor shielding effects, providing basic data support for subsequent optimization and screening. Thermal simulation based on the shielding data of low-efficiency materials can simulate the performance changes of materials in high-temperature environments, evaluate their stability during long-term use, and provide a basis for optimizing heat resistance. Calculating the conductivity of thermal simulation data enables the system to quantify the conductive properties of materials at different temperatures, ensuring that their electromagnetic shielding capabilities in smart home devices will not decrease significantly due to temperature changes. Corrosion experiments based on the shielding data of low-efficiency materials can analyze the corrosion resistance of materials under different environmental conditions, providing data support for reliability in long-term use. Identifying surface oxidation of corrosion data enables the system to quantify the degree of oxidation of materials, thereby evaluating their antioxidant capacity and avoiding the reduction of shielding effectiveness of materials due to oxidation. Predicting the life of shielding materials based on conductivity and surface oxidation data enables the system to establish a shielding material aging prediction model based on the changing trends of key physical properties, providing more scientific protection measures for smart home devices.

[0054] Preferably, the multi-layer shielding design in step S2 includes:

[0055] Collect data on low-life materials of shielding material life;

[0056] Dividing the low-life material data into material layers to obtain a conductive metal layer, a conductive polymer layer, and an absorbing layer;

[0057] Design of nickel-plated aluminum alloy shielding material based on conductive metal layer;

[0058] Design of conductive rubber shielding materials based on conductive polymer layers;

[0059] Design of carbon-based shielding materials based on absorbing layers;

[0060] Shielding materials, conductive rubber shielding materials, and carbon-based shielding materials are integrated, and the integrated material data is recorded to obtain multi-layer shielding data.

[0061] The present invention uses statistics on low-life material data to enable the system to accurately identify shielding materials with short lifespans, providing data support for optimizing material design. Dividing the material hierarchy of low-life material data allows different types of materials to be used in a targeted manner in the shielding structure, thereby improving the stability and adaptability of the overall shielding effect. Designing nickel-plated aluminum alloy shielding materials based on the conductive metal layer can effectively improve the material's electrical conductivity and corrosion resistance, thereby enhancing the electromagnetic shielding effect and extending its service life. Designing conductive rubber shielding materials based on the conductive polymer layer allows the shielding materials to maintain good conductivity while also being flexible, making them suitable for complex structures and flexible components in smart home devices. Designing carbon-based shielding materials based on the absorbing layer allows the shielding materials to have strong absorbing capabilities while reducing electromagnetic wave reflections, thereby reducing electromagnetic leakage and improving electromagnetic compatibility. Integrating different types of shielding materials and recording the integrated material data can form a multi-layer shielding structure, enabling the system to provide effective shielding in both high-frequency and low-frequency electromagnetic interference environments, improving the comprehensive protection capabilities and long-term stability of the shielding materials, and overcoming the limitations of traditional single shielding methods.

[0062] Preferably, step S3 is specifically as follows:

[0063] Step S31: performing transient electromagnetic pulse simulation based on the multi-layer shielding data, wherein the electromagnetic pulse frequency is set to 0.1 MHz–10 GHz and the electromagnetic pulse intensity is set to 10 V / m–200 kV / m, to obtain electromagnetic pulse simulation data;

[0064] Step S32: calculating the inverter electromagnetic field of the electromagnetic pulse simulation data and drawing a distribution diagram of the inverter electromagnetic field;

[0065] Step S33: Identify the inverter electromagnetic overload area in the inverter electromagnetic field distribution map, where the magnetic field strength threshold is set to 10A / m and the power density threshold is set to 10W / m 2 ;

[0066] Step S34: calculating the magnetic field concentration in the electromagnetic overload area of ​​the inverter;

[0067] Step S35: Calculating the dielectric loss in the electromagnetic overload area of ​​the inverter;

[0068] Step S36: determining inverter breakdown damage based on the electromagnetic pulse simulation data according to the magnetic field concentration and the dielectric loss, and obtaining inverter breakdown damage data;

[0069] Step S37: extracting electromagnetic coupling data of smart home data;

[0070] Step S38: Identifying parasitic current of electromagnetic coupling data based on the inverter breakdown damage data;

[0071] Step S39: Identify parasitic effect characteristics of smart home data based on the parasitic current.

[0072] Through transient electromagnetic pulse simulation, this invention can simulate the effects of different electromagnetic environments across a wide range of frequencies and intensities, providing accurate electromagnetic interference data for subsequent analysis. Calculating the inverter's electromagnetic field and mapping its distribution visualizes the spatial distribution of electromagnetic interference, facilitating the identification of high-risk areas. Identifying inverter electromagnetic overload areas helps identify key electromagnetic anomalies that cause equipment failures and precisely locates high-risk areas based on set magnetic field strength and power density thresholds. Calculating magnetic field concentration enables the system to quantify the localized concentration of the electromagnetic field and identify potential overload risks. Calculating dielectric loss enables the system to assess the electromagnetic absorption characteristics of materials and determine whether excessive losses will lead to localized overheating or damage. Determining inverter breakdown damage based on magnetic field concentration and dielectric loss enables the system to accurately predict the inverter's failure risk in high-intensity electromagnetic environments, enhancing electromagnetic protection capabilities. Extracting electromagnetic coupling data enables the system to analyze the electromagnetic interference characteristics between smart home devices, providing data support for subsequent optimization. Identifying parasitic currents based on inverter breakdown damage data enables the system to detect unexpected currents caused by electromagnetic interference, improving device safety. By identifying the characteristics of parasitic effects based on parasitic currents, the system can accurately assess the potential impact of electromagnetic interference on smart home devices, optimize shielding strategies, and improve overall electromagnetic compatibility and safety.

[0073] Preferably, step S4 is specifically as follows:

[0074] Step S41: Calculating the frequency of the electromagnetic wave based on the parasitic effect characteristics;

[0075] Step S42: Dividing the frequency bands of the electromagnetic waves to obtain low-frequency electromagnetic waves and high-frequency electromagnetic waves;

[0076] Step S43: selecting ferrite material based on low-frequency electromagnetic waves;

[0077] Step S44: selecting a carbon nanotube composite material based on high-frequency electromagnetic waves;

[0078] Step S45: integrating the ferrite material and the carbon nanotube composite material, and recording the integrated material data to obtain electromagnetic shielding optimization data;

[0079] Step S46: Encrypt the electromagnetic shielding information of the electromagnetic shielding optimization data and upload it to the home energy management system to perform the electromagnetic shielding information security protection task.

[0080] The present invention provides an effective frequency identification method for subsequent electromagnetic shielding optimization by calculating the characteristics of parasitic effects and their electromagnetic wave frequencies, ensuring that the shielding material can effectively shield electromagnetic waves in specific frequency bands. Dividing the electromagnetic wave frequency into low-frequency bands and high-frequency bands helps to more accurately select materials suitable for the characteristics of each frequency band and optimize the shielding effect. Based on the low-frequency band, ferrite materials are selected to provide efficient absorption and shielding capabilities for low-frequency electromagnetic waves, which helps to reduce the impact of low-frequency interference on the equipment. Based on the high-frequency band, carbon nanotube composite materials are selected to enable high-frequency electromagnetic waves to obtain better shielding and anti-interference capabilities, thereby improving the protection effect of the high-frequency band. Ferrite materials and carbon nanotube composite materials are integrated to form a composite shielding material with wider applicability, ensuring that effective protection can be provided in different electromagnetic wave frequency bands. The integrated material data is encrypted with electromagnetic shielding information to improve the security of the information, and uploaded to the home energy management system to ensure that the electromagnetic shielding optimization data can be safely executed within the system, thereby effectively preventing electromagnetic leakage and side-channel attacks, and improving the overall information security protection capabilities of the smart home system.

[0081] Preferably, this specification also provides an information security protection system for a home energy management system, which is used to execute the information security protection method for a home energy management system as described above. The information security protection system for a home energy management system includes:

[0082] A communication simulation module is used to obtain smart home data; perform communication simulation based on the smart home data, identify electromagnetic leakage during the communication simulation process, and generate electromagnetic leakage data;

[0083] A multi-layer shielding design module is used to identify side-channel attacks based on electromagnetic leakage data; test the shielding effectiveness of materials using side-channel attacks; predict the lifespan of shielding materials for smart home data based on the shielding effectiveness of the materials; and perform multi-layer shielding design based on the shielding material lifespan to obtain multi-layer shielding data.

[0084] The parasitic effect identification module is used to perform transient electromagnetic pulse simulation based on multi-layer shielding data, detect inverter breakdown damage during the simulation process, and obtain inverter breakdown damage data; based on the inverter breakdown damage data, it identifies the parasitic effect characteristics of smart home data;

[0085] The electromagnetic shielding information encryption module is used to optimize electromagnetic shielding based on parasitic effect characteristics to obtain electromagnetic shielding optimization data; encrypt the electromagnetic shielding information of the electromagnetic shielding optimization data and upload it to the home energy management system to perform electromagnetic shielding information security protection tasks.

[0086] The information security protection system for a home energy management system of the present invention can implement any one of the information security protection methods for a home energy management system of the present invention, and is used to combine the medium for operation and signal transmission between various modules to be used for the information security protection method of the home energy management system. The internal modules of the system cooperate with each other, thereby improving the security protection capability of the smart home system in the face of electromagnetic leakage and side channel attacks, and significantly improving the shielding effectiveness and anti-interference capability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0088] Figure 1 A schematic flow chart of the steps of the information security protection method for a home energy management system according to the present invention;

[0089] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0090] Figure 3 Detailed step flow diagram of step S15 in the present invention;

[0091] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0092] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative work are within the scope of protection of the present invention.

[0093] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0094] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0095] To achieve this, please refer to Figures 1 to 3 The present invention provides an information security protection method for a home energy management system, the method comprising the following steps:

[0096] Step S1: Acquire smart home data; perform communication simulation based on the smart home data, identify electromagnetic leakage during the communication simulation, and generate electromagnetic leakage data;

[0097] In the present embodiment, it is necessary to collect the real-time operating data of each device in the home through a sensor array, including but not limited to information such as temperature, humidity, power consumption, and device status. These data will be transmitted to the central control system via a wireless communication network. In this process, communication simulation is performed using a communication toolbox such as MATLAB to simulate the signal transmission between devices within the smart home system. By setting an electromagnetic wave frequency range (e.g., 0.1MHz to 10GHz), the system can simulate and identify electromagnetic leakage during the communication process. Electromagnetic leakage data is generated by measuring parameters such as signal leakage intensity, frequency, and direction between devices. When leaking data, it is necessary to set a leakage threshold (such as a signal intensity below -60dBm as a leakage judgment criterion), and perform signal monitoring by a spectrum analyzer to record the time spectrum of electromagnetic leakage and generate electromagnetic leakage data for subsequent analysis.

[0098] Step S2: Identify side channel attacks based on electromagnetic leakage data; test material shielding effectiveness using side channel attacks; predict the lifespan of shielding materials for smart home data based on the shielding effectiveness of the materials; perform multi-layer shielding design based on the lifespan of the shielding materials to obtain multi-layer shielding data;

[0099] In this embodiment, based on the identification of electromagnetic leakage data, time-domain signal analysis techniques are used to perform a detailed analysis of the electromagnetic leakage waveform and extract its spectral characteristics. Based on the spectral characteristics of the leakage signal (such as frequency and amplitude), the principles of side-channel attacks are utilized to simulate a hacker attack process by setting an attack window (e.g., a time window set to 500ms). Appropriate electromagnetic leakage attack tools (e.g., dedicated side-channel attack simulation software) are then used to test the shielding effectiveness of different materials in the corresponding frequency bands. During the shielding effectiveness test, the electromagnetic absorption rate of different materials is considered, and the material thickness (e.g., 2mm for steel plate and 5mm for ferrite) and the electromagnetic wave incident angle are set. The leakage signal intensity after passing through the material is measured. Using these test results, a model is calculated to predict the service life of the shielding material in a specified operating environment. For example, when the electromagnetic wave frequency is 1GHz, the attenuation coefficient of the metal shielding material is 30dB. It can be expected that the shielding effectiveness of the material will decay to 50% after 5 years. Based on the predicted results, a multi-layer shielding design is completed, where each layer of shielding material must meet specific electromagnetic blocking standards, ultimately obtaining multi-layer shielding data.

[0100] Step S3: performing transient electromagnetic pulse simulation based on the multi-layer shielding data, and detecting inverter breakdown damage during the simulation process to obtain inverter breakdown damage data; identifying parasitic effect characteristics of the smart home data based on the inverter breakdown damage data;

[0101] In this embodiment, based on the multi-layer shielding data, a transient electromagnetic pulse simulation is performed using simulation software such as ANSYS. The frequency range of the electromagnetic pulse is set to 0.1MHz to 10GHz, and the intensity range is 10V / m to 200kV / m. Different electromagnetic wave sources are selected for simulation. During the simulation process, the impact of electromagnetic waves on the smart home inverter is simulated, and the current and voltage changes on the inverter circuit board are focused on. According to the set electromagnetic pulse intensity (for example, the intensity is 50kV / m) and frequency, the finite element method is used to perform electromagnetic field simulation, calculate the electromagnetic field distribution and monitor whether the electrical breakdown of the inverter occurs. The threshold value of the inverter breakdown damage is set to the case where the voltage exceeds 30V. If the simulation data exceeds the threshold, it is recorded as breakdown damage and the inverter breakdown damage data is generated. By analyzing the electromagnetic field distribution map, the potential parasitic effect characteristics in the smart home system are further identified, and data such as parasitic current and electric field strength are recorded.

[0102] Step S4: Optimize electromagnetic shielding based on parasitic effect characteristics to obtain electromagnetic shielding optimization data; encrypt the electromagnetic shielding information of the electromagnetic shielding optimization data and upload it to the home energy management system to perform electromagnetic shielding information security protection tasks.

[0103] In this embodiment, the parasitic current characteristics identified in the above steps are used as optimization input to adjust the materials and structures of the electromagnetic shielding. During the shielding optimization process, ferrite materials are selected as shielding materials for low-frequency electromagnetic waves, and carbon nanotube composite materials are selected as shielding materials for high-frequency electromagnetic waves. The electromagnetic compatibility analysis of the materials is performed in simulation software. The relative magnetic permeability of the ferrite material is set to 1000, and the conductivity of the carbon nanotube composite material is 10^7 S / m. The shielding effect is simulated using simulation tools, and the target parameters are set to reduce signal leakage and improve the material's anti-electromagnetic interference capability. Finally, the ferrite material and carbon nanotube composite material are integrated and the data is recorded. The electromagnetic shielding optimization data is encrypted using the AES encryption algorithm to ensure the security of the information. The encrypted data is uploaded to the home energy management system via a secure network. The system performs electromagnetic shielding information security protection tasks based on the encrypted data, and responds to potential electromagnetic leakage attacks in a timely manner.

[0104] Preferably, step S1 is specifically as follows:

[0105] Step S11: Acquire smart home data;

[0106] In this embodiment, data is collected through sensors installed in smart home devices. These sensors include temperature sensors, humidity sensors, motion sensors, light intensity sensors, etc. This data is wirelessly transmitted to the central control system through the communication module of the smart home device. During the data collection process, all device data is sorted and recorded according to timestamps to ensure the timeliness and accuracy of the data. The data collection frequency is set to once per minute, and the collected data includes basic information such as device ID, device type, device status, sensor readings, etc. All data will be encrypted and transmitted using IoT protocols (such as MQTT and CoAP) to ensure data security.

[0107] Step S12: extracting the communication protocol of the smart home data and setting a communication process based on the communication protocol;

[0108] In this embodiment, the communication protocols of various smart home devices are analyzed to extract key fields, such as device type identification, data transmission mode (such as WIFI, ZigBee, Bluetooth, etc.), message format, etc. The specific operation is to use network packet capture tools such as Wireshark to monitor the communication of the devices and capture the communication data packets between devices. By analyzing the content of the data packet, the protocol stack in the communication protocol (such as TCP / IP protocol, UDP protocol, etc.) can be identified, and the message header and data field of the communication can be extracted. On this basis, the communication process of the smart home system is defined, including the handshake process between devices, the data transmission order, and the retransmission mechanism that occurs. The specific design of the communication process refers to the standard communication protocol format and takes into account the actual topology of the home network to ensure that each device can exchange data according to the predetermined communication process.

[0109] Step S13: Identify communication channel characteristics of smart home data;

[0110] In this embodiment, a network analyzer is used to monitor the wireless communication signals between smart home devices. By selecting an appropriate frequency band (such as 2.4GHz or 5GHz band), the quality and strength of the signal transmission between devices are analyzed. For Wi-Fi signals, a device such as Signal Analyzer can be used to capture the spectrum data of the signal and detect characteristics such as signal attenuation and frequency drift. The specific approach is to perform spectrum analysis on each signal transmission cycle and record parameters such as signal strength, frequency, and noise ratio to form channel characteristic data. Channel characteristics include signal propagation loss (for example, setting the maximum signal attenuation to -60dBm), delay (such as setting it to 2ms), and frequency offset, etc. These characteristic data will be used as input parameters in subsequent communication simulations.

[0111] Step S14: performing communication simulation according to the communication channel characteristics and the communication process to obtain communication simulation data;

[0112] In this embodiment, in the process of performing communication simulation based on the communication channel characteristics and the communication process, simulation tools such as MATLAB and NS3 are used to input the communication protocol, channel characteristic data and communication process of the device to perform detailed signal transmission simulation. The communication simulation needs to take into account factors such as attenuation, interference and signal shielding of the wireless signal. For each simulation, simulation parameters such as the signal propagation loss is set to -50dB, the power of the signal interference source is set to -40dBm, and the simulation period is set to 5 minutes. During the simulation process, a channel model (such as the Rayleigh fading model or the Nyquist model) is used to simulate the signal transmission process. The simulation data includes the transmission time, delay and packet loss rate of each data packet. Ultimately, the results obtained through the communication simulation can provide information about the quality of the network connection, the stability of data transmission, and the like.

[0113] Step S15: Identify electromagnetic leakage of communication simulation data and generate electromagnetic leakage data.

[0114] In this embodiment, the spectrum of the communication signal is analyzed, and the frequency distribution of the signal is monitored using a spectrum analyzer (such as Keysight's X-Series Signal Analyzers). By setting a threshold for the leakage signal intensity (such as -70dBm), the signal below the threshold is recorded as an electromagnetic leakage signal. The process includes real-time monitoring of whether the electromagnetic waves emitted by the device exceed the standard shielding limit during each data transmission cycle of the communication simulation. For example, when the signal intensity exceeds -70dBm, it is considered that electromagnetic leakage exists. Through further analysis of the leakage signal, the frequency range, intensity and its relationship with the location of the device are identified. When recording electromagnetic leakage data, parameters such as the frequency, intensity and duration of the leakage signal are included. These data will be used in subsequent steps to analyze the effectiveness of shielding materials and optimize shielding designs.

[0115] Preferably, step S15 is specifically as follows:

[0116] Step S151: Identify the signal propagation path of the communication analog data;

[0117] In this embodiment, when identifying the signal propagation path of communication simulation data, it is first necessary to spatially track the communication signal of the device. A high-precision signal positioning system (such as using GPS-assisted positioning or radio frequency-based positioning technology) is used to determine the propagation trajectory of the signal emitted by the device in space. By installing multiple receivers, the path of the signal from the transmitting device to the receiving device is recorded, and data including signal strength, propagation time, transmission distance, etc. are obtained. For wireless signals, a ray tracing method is used for path modeling, and the signal propagation path is calculated based on the geometric layout of the device and the setting of obstacles (such as walls, metal objects, etc.). For example, the signal propagation is modeled using the Hata model or the Okumura model, and corrections are made based on actual environmental factors. For each signal propagation path, the maximum propagation range is set to 200 meters. The attenuation coefficient of the wall needs to be considered in the path calculation, and the attenuation coefficient of the wall is set to -5dB.

[0118] Step S152: Calculating the electromagnetic field strength of the signal propagation path;

[0119] In this embodiment, when calculating the electromagnetic field strength of the signal propagation path, electromagnetic field calculation is first performed using electromagnetic field simulation software (such as COMSOL Multiphysics or ANSYS HFSS) based on the propagation path parameters in the communication simulation data. According to the path data and the power value of the equipment used, the electric field strength of the signal at each transmission path point is calculated. The distribution of the electric field strength is calculated in units of meter. Assuming that the output power of the transmitting device is 20dBm, the electric field strength threshold is set to -50dBμV / m during the calculation process, and the electromagnetic field strength during the signal propagation process is recorded. During the simulation, environmental factors such as signal attenuation and reflection must also be considered. Assuming that the attenuation factor is 2.5dB / km, the electromagnetic wave frequency used is set to 2.4GHz. Through this method, the electromagnetic field strength data of each point on the path can be obtained.

[0120] Step S153: converting the electromagnetic field intensity into an electromagnetic intensity spectrum;

[0121] In this embodiment, when converting the electromagnetic field intensity into the electromagnetic intensity spectrum, the electromagnetic field intensity data is first converted into frequency domain data using Fourier transform (FFT). After the collected electromagnetic field intensity data is processed, spectrum analysis is performed to obtain the intensity distribution of the signal at different frequencies. Specifically, the electric field intensity data is input into a spectrum analyzer, and the frequency scanning range is set to 1 MHz to 5 GHz, the step is 1 MHz, and the scanning time is set to 1 second. In this process, all measured electromagnetic field intensity values ​​are converted into an electromagnetic intensity spectrum, and the signal intensity in each frequency band is recorded. The electromagnetic spectrum data will be presented in the form of a two-dimensional graph of frequency and intensity to identify the distribution of signal intensity in the frequency domain. After the spectrum analysis is completed, the frequency bandwidth is set to 20 MHz and the standard signal frequency is set to 2.4 GHz.

[0122] Step S154: Identify the electromagnetic energy concentration area;

[0123] In this embodiment, when identifying electromagnetic energy concentration areas, it is first necessary to perform cluster analysis on the electromagnetic spectrum data. A clustering algorithm (such as the K-means algorithm or the DBSCAN algorithm) is used to analyze the signal strength in the electromagnetic spectrum to identify the energy concentration areas in the spectrum. The threshold of the signal strength is set to -60dBm. Signals below this threshold are not considered to be energy concentration areas. According to the strong signal intervals in the electromagnetic spectrum, the frequency ranges corresponding to these areas are identified, and the frequency segments of each energy concentration area are marked. The identification process of the electromagnetic energy concentration area needs to take into account multiple interference sources, so the number of clusters set is 5 to ensure that strong signal clusters under different frequency bandwidths can be captured. Ultimately, all identified energy concentration areas will be saved as data files for subsequent processing.

[0124] Step S155: Counting abnormally strong signals in the electromagnetic energy concentration area, and identifying stray signals based on the abnormally strong signals;

[0125] In this embodiment, when counting abnormally strong signals in the electromagnetic energy concentration area and identifying spurious signals based on the abnormally strong signals, the signal strength in the energy concentration area is first detected as an outlier. Statistical analysis methods (such as Z-score or threshold judgment based on standard deviation) are used to identify abnormally strong signals whose signal strength deviates significantly from the normal range. For each identified abnormally strong signal, the signal strength threshold is set to a signal exceeding -40dBm, these signals are marked as abnormal signals, and compared with the surrounding signals to determine whether they are spurious signals. In order to further improve the recognition accuracy, a time-frequency analysis method can be used to perform a detailed analysis of the abnormal signal to filter out the frequency range of the spurious signal. Assuming that the frequency range of the spurious signal is between 2.3GHz and 2.5GHz, it is regarded as a spurious signal when the signal strength is higher than -40dBm.

[0126] Step S156: Perform electromagnetic leakage determination on the stray signal to obtain electromagnetic leakage data.

[0127] In this embodiment, when determining electromagnetic leakage of stray signals, it is first necessary to set the electromagnetic leakage standard and use electromagnetic compatibility (EMC) test equipment (such as Keysight's E4440A spectrum analyzer) to perform spectrum scanning on the stray signal to determine whether the signal exceeds the specified electromagnetic leakage standard. The set electromagnetic leakage standard is a signal with a signal strength exceeding -50dBm and lasting for more than 1 second. Each stray signal is judged, and if its intensity and duration exceed the threshold, the signal is considered to be electromagnetic leakage. Through testing and comparison, the frequency, intensity and leakage area of ​​the leakage signal are recorded. All leakage signal data will be archived for subsequent electromagnetic shielding design and optimization.

[0128] Preferably, the identifying of the side channel attack in step S2 includes:

[0129] Reversely infer the encryption key of electromagnetic leakage data;

[0130] In this embodiment, when reversely inferring the encryption key of electromagnetic leakage data, the collected electromagnetic leakage data is first subjected to signal decoding processing to extract useful information from the leakage signal. The frequency domain characteristics of the signal are analyzed using Fast Fourier Transform (FFT) to identify the spectral components of the electromagnetic wave. The encryption key bits are inferred by comparing the frequency domain data of the leakage signal with the electromagnetic interference pattern of a known encryption algorithm. For each encryption operation, the encryption algorithm type of the leakage signal (such as AES, RSA, etc.) is extracted, and the analysis window is set to 10 milliseconds. For each electromagnetic leakage signal, the relationship between the electric field strength and the encryption bit is analyzed through linear regression, and each part of the key is gradually inferred. The bit width of the encryption key is set to 128 bits. During the inference process, the analysis time window width is adjusted to ensure that the electromagnetic information related to the encryption operation is captured.

[0131] Recover the full key space based on the encryption key;

[0132] In this embodiment, when recovering the full key space based on the encryption key, first use the partial encryption key as a known input. Use a cryptographic algorithm library (such as OpenSSL or Cryptlib) to brute force the known partial key, combine it with the existing encryption algorithm type (such as AES or DES), and gradually recover the full key space by increasing the inferred key bits. This process uses an exhaustive method, setting the maximum key space for brute force cracking to 2^128 times, and limiting the calculation time to within 48 hours. The recovered full key space is confirmed by comparing the generated key with the encryption result of the actual leakage signal during each attempt. During each operation, a time domain window is used for fine-grained segmented analysis, the window length is set to 50ms, and the maximum tolerance error is 5%.

[0133] Using the full key space to identify attack fingerprints of electromagnetic leakage data;

[0134] In this embodiment, based on the recovered full key space, the known leaked data is reconstructed by running the encryption algorithm multiple times. The known electromagnetic leakage signature is compared with the recovered key data, and the changes in the electromagnetic waveform before and after the encryption operation are calculated. During each reconstruction, the sampling period is set to 10 microseconds. The difference in signal intensity before and after encryption is calculated, and the changes in the electromagnetic wave in different frequency bands are recorded. This method extracts the unique spectral characteristics of the electromagnetic leakage signal, which become the attack fingerprint. The amplitude change threshold for each frequency interval is set to ±2dB to identify spectral characteristics that can effectively indicate attack activity.

[0135] Track attack paths based on attack fingerprints;

[0136] In this embodiment, the time series and spectral characteristics of the fingerprint signal are determined by analyzing the identified attack fingerprint data. These characteristics are matched with the attack model, and the dynamic time warping (DTW) algorithm is used to compare the similarities of different attack fingerprints. At this time, the time window is set to 1 millisecond to accurately capture the changes in the attack fingerprint. During the attack path tracing process, a network traffic analysis tool (such as Wireshark or NetFlow) is used to perform collaborative analysis with the leaked electromagnetic signal to determine the source, path and target of the attack event. During the attack path tracing process, the abnormal signal threshold is set to a signal with a time delay of more than 50ms or an amplitude fluctuation greater than 3dB.

[0137] Locate key attack fingerprints of attack paths;

[0138] In this embodiment, the tracked attack path is accurately time-series analyzed, and signal points with strong electromagnetic fluctuations are selected. The changes in the electromagnetic intensity of these signal points are considered to be the key fingerprints of the attack path. Spectrum analysis is performed using a high-precision signal analyzer (such as the Keysight 9000 series) to determine the key frequency bands and intensities of the signal. When locating key attack fingerprints, the frequency band range is set to 1 GHz to 5 GHz, and a sliding window method is used to analyze each signal on the attack path. By comparing the timestamps and spectrum characteristics of the signals, it is determined which fingerprints are decisive for locating the attack path, and the time position, frequency interval, and intensity changes of each key attack fingerprint are recorded.

[0139] Obtain a side-channel attack fingerprint library;

[0140] In this embodiment, the signal data of side channel attack events are collected and sorted from historical data, and signal processing tools (such as Matlab or Python's NumPy library) are used to extract features of the signal to obtain the electromagnetic spectrum, power spectrum and timing signal of the attack. By classifying different types of attacks (such as timing attacks, power consumption attacks, etc.), a fingerprint library containing different types of attacks is created. The storage range of the signal is set to the frequency range of 1GHz to 10GHz to ensure that all common side channel attack features are covered. Each fingerprint in the fingerprint library will include information such as frequency distribution, intensity fluctuations and time characteristics, and all data will be stored in a standard format (such as CSV or JSON).

[0141] Based on the key attack fingerprint, fingerprint matching is performed on the side channel attack fingerprint library to obtain the side channel attack fingerprint;

[0142] In this embodiment, the located key attack fingerprints are compared one by one with the fingerprints in the side channel attack fingerprint library. By calculating the similarity of the attack fingerprints (for example, using Euclidean distance or cosine similarity), it is determined which fingerprints match the existing fingerprints in the library. The signal is converted into the frequency domain using Fast Fourier Transform (FFT), and then the spectrum is compared with each fingerprint in the library. The similarity threshold of fingerprint matching is set to 0.8, and only fingerprints with a similarity exceeding this value are considered to be matched. Through the matching process, it is possible to quickly determine whether the leaked data is related to known side channel attack activities.

[0143] Identify side-channel attacks on electromagnetic leakage data based on side-channel attack fingerprints.

[0144] In this embodiment, a spectrum analysis is performed on the fingerprint extracted from the electromagnetic leakage data. Using the previously matched side channel attack fingerprint library, the spectrum characteristics of each leakage data are compared to confirm whether there is a fingerprint that matches the known attack pattern. The similarity threshold is set to 90%. If an attack fingerprint with a high similarity is matched, it is determined that a side channel attack has occurred. The identification process of each attack fingerprint needs to take into account the noise level of the signal, and the signal-to-noise ratio (SNR) is set to above 30dB to ensure the accuracy of the data. Finally, by comparing the electromagnetic leakage signal with the known fingerprint, the corresponding side channel attack type is accurately identified and the relevant attack data is recorded.

[0145] Preferably, the testing of the shielding effectiveness of the material in step S2 includes:

[0146] Extract smart camera data for smart home data;

[0147] In this embodiment, the extraction of smart camera data requires direct access to the camera's network interface or wireless transmission link. First, the real-time video stream and sensor data of the camera are extracted using a standard communication protocol (such as ONVIF or RTSP) using the device's management interface. The video stream data will be captured and saved in the original stream format (such as H.264 encoded stream). At the same time, the camera's sensor data (such as temperature, humidity, motion detection, etc.) is extracted through the sensor data interface or internal API interface and saved as structured data. During the extraction process, it is necessary to ensure that the network connection is stable and the transmission rate meets the minimum requirements to avoid data loss. In addition, for the encrypted data of the camera, it is necessary to use the corresponding key to decrypt it to ensure the integrity of the data.

[0148] Use side channel attacks to simulate attacks on smart camera data to obtain attack simulation data;

[0149] In this embodiment, a side channel attack technique is used to simulate an attack on a smart camera. The specific operation is to capture the electromagnetic waves generated by the camera when processing data by configuring an attack simulation device (for example, a radio frequency detector, a spectrum analyzer). The attack simulation process includes generating a series of abnormal inputs to the camera (such as random data packets or abnormal control signals) to simulate the changes in the electromagnetic wave emission of the camera when facing malicious inputs. These abnormal inputs will induce the camera to generate side channel leakage signals, and the signals collected by the spectrum analyzer are recorded as attack simulation data. During this process, the bandwidth of the spectrum analyzer should be set to 10kHz to 1GHz, and the sampling rate should be 5GHz to ensure that signals with rich details are captured.

[0150] Collect electromagnetic signal waveforms of attack simulation data;

[0151] In this embodiment, the RF detection equipment (such as an oscilloscope or spectrum analyzer) is first set to an appropriate frequency range, typically 100 MHz to 1 GHz, to ensure that the frequency band of the electromagnetic signal generated by the camera is covered. Then, the electromagnetic signal waveform during camera operation is collected by the detection instrument. The data acquisition time should be set according to the camera processing cycle, typically 1 second to 5 seconds, to capture sufficient waveform changes. During the acquisition process, it is necessary to minimize the interference of the electromagnetic signal and keep the equipment working under ideal environmental conditions.

[0152] Determine electromagnetic leakage based on the electromagnetic signal waveform and obtain camera electromagnetic leakage data;

[0153] In this embodiment, the electromagnetic waveform signal is converted between the time domain and the frequency domain, and the frequency domain features are extracted using Fourier transform. Then, spectrum analysis is performed to evaluate whether there are abnormal peaks or periodic fluctuations. By setting a threshold (for example, the amplitude of the signal peak exceeding 10dB), it is determined whether there is electromagnetic leakage. Based on the frequency band and amplitude of the leakage, the source and characteristics of the leakage signal are further identified to generate electromagnetic leakage data of the camera. This process requires ensuring that the signal analysis parameters (such as frequency, amplitude, etc.) are accurate.

[0154] Screen electromagnetic leakage cameras based on camera electromagnetic leakage data, and use a preset 0.1mm-0.3mm thin aluminum foil to perform a shielding test on the electromagnetic leakage cameras to obtain thin aluminum foil shielding data;

[0155] In this embodiment, cameras with serious leakage are screened out based on the electromagnetic leakage data, and the screening criterion is set as the electromagnetic leakage signal amplitude exceeds the set threshold (such as 20dB). Then, the screened cameras are shielded using aluminum foil with a thickness of 0.1mm-0.3mm. In the specific implementation, the aluminum foil is evenly wrapped around the outside of the camera to ensure that the aluminum foil is in full contact with the camera surface to avoid missing leakage areas. The electromagnetic signal waveform is collected again using a spectrum analyzer, and the electromagnetic signal data after aluminum foil shielding is recorded. All collected data should be compared with the baseline data without shielding to analyze its changes.

[0156] The electromagnetic leakage camera was shielded using a preset 0.5mm-1mm thick aluminum foil to obtain thick aluminum foil shielding data;

[0157] In this example, a thicker 0.5mm-1mm aluminum foil was used for the shielding test. The aluminum foil was wrapped around the camera using the same method as the thin foil shielding, ensuring complete coverage and no gaps. The spectrum analyzer was then used to collect the electromagnetic signal waveform and compare the signal changes before and after the thick foil shielding. This test focused on evaluating the effectiveness of the thick foil shielding on electromagnetic leakage, particularly its impact across different frequency bands. All data should be compared under the same conditions to eliminate interference from other external factors.

[0158] Calculate the shielding effectiveness of thin aluminum foil using thin aluminum foil shielding data;

[0159] In this embodiment, it is necessary to collect electromagnetic signal strength data before and after thin aluminum foil shielding. The signal strength before shielding refers to the electromagnetic leakage signal strength recorded by the smart camera without shielding. The signal strength after shielding refers to the electromagnetic leakage signal strength re-measured after the thin aluminum foil shielding is applied. To ensure the accuracy of the measurement results, multiple measurements are required, and each measurement records the signal strength before and after shielding and averages it. By comparing these signal strength data, the shielding effectiveness of the thin aluminum foil can be obtained. Specifically, when calculating, it is necessary to determine the standard for signal strength measurement, such as using electromagnetic signal measurement equipment in the same frequency band, testing under the same environment, and other conditions to ensure the consistency and reliability of the data. After multiple measurements, a set of signal strength ratios will be obtained, from which the average value is obtained, and then the shielding effectiveness of the thin aluminum foil is obtained. This process can quantify the effective suppression ability of the thin aluminum foil on electromagnetic leakage.

[0160] Calculate the shielding effectiveness of thick aluminum foil using thick aluminum foil shielding data;

[0161] In this embodiment, the steps for calculating the shielding effectiveness of thick aluminum foil are similar to those for thin aluminum foil. However, for thick aluminum foil, testing is typically required across multiple frequency bands to ensure that its shielding effectiveness meets standard requirements at all relevant frequencies. First, measure the electromagnetic leakage signal strength of the smart camera without shielding, recording this as the pre-shielding signal strength. Next, shield the smart camera with thick aluminum foil, ensuring that the foil completely covers the electromagnetic leakage source of the camera. Then, remeasure the electromagnetic leakage signal strength and record this as the post-shielding signal strength. To obtain accurate shielding effectiveness data, testing must be performed across multiple frequency bands. This is because different electromagnetic frequencies have different effects on shielding effectiveness. Common frequency bands include low, medium, and high frequencies, and the specific frequency band selected should be based on the frequency range of the electromagnetic signal emitted by the camera. Testing in each frequency band should be conducted under the same stable environmental conditions to avoid external electromagnetic interference from affecting the measurement results. During testing, all equipment, such as the signal receiver and spectrum analyzer, should be operating stably, and consistent standards should be used for signal strength measurements. Repeated measurements, particularly at different frequency bands, are required to obtain sufficient measurement data and perform averaging. The shielding effectiveness of thick aluminum foil is calculated by comparing signal strength before and after shielding. Multiple measurements are performed and averaged to ensure the calculated shielding effectiveness reflects the actual protective effect of the thick aluminum foil. This process effectively quantifies the thick aluminum foil's ability to suppress electromagnetic leakage and ensures compliance with standard requirements.

[0162] The shielding effectiveness of the material is obtained by integrating the shielding effectiveness of thin aluminum foil and thick aluminum foil.

[0163] In this embodiment, the shielding effectiveness of thin and thick aluminum foils needs to be measured and calculated. This process measures the electromagnetic leakage signals of the two materials at different frequency bands to obtain their respective shielding effectiveness values. During the measurement process, the test environment should be stable to prevent any external electromagnetic interference from affecting the results. Next, based on the specific application requirements, the shielding effectiveness of the thin and thick aluminum foils needs to be weighted averaged. At this point, corresponding weighting factors w1 and w2 are set based on the characteristics of each material (such as material thickness, shielding effectiveness, and usage environment). These weighting factors reflect the relative importance of each material under different conditions. The weighting factors are generally selected based on the shielding capabilities of the two materials in different frequency bands. For example, thin aluminum foil has better shielding effectiveness in some frequency bands, while thick aluminum foil is more effective in other frequency bands. For example, thin aluminum foil is more suitable for electromagnetic interference shielding in the low-frequency range, while thick aluminum foil performs better in the high-frequency range. Therefore, the weight w1 of the thin aluminum foil and the weight w2 of the thick aluminum foil should be set according to their shielding effectiveness in the relevant frequency bands, typically w1 + w2 = 1. Finally, a weighted average formula is used to calculate the overall shielding effectiveness: Overall Shielding Effectiveness = w1 × Thin Aluminum Foil Effectiveness + w2 × Thick Aluminum Foil Effectiveness, where thin and thick foil effectiveness are the measured shielding effectiveness values, and w1 and w2 are the corresponding weighting factors. Overall shielding effectiveness provides a comprehensive assessment of a material's overall shielding capabilities in different application scenarios, providing a basis for selecting appropriate shielding materials.

[0164] Preferably, the predicting of the shielding material life in step S2 includes:

[0165] Extracting shielding data of low-efficiency materials for material shielding effectiveness;

[0166] In this embodiment, a target material is selected and its shielding effectiveness in a specific frequency band is determined. Using a standard shielding effectiveness test method, the material is placed in a specified electromagnetic environment, and the electromagnetic leakage signal intensity in a specific frequency band is recorded. The shielding effectiveness of the material is determined by the shielding effectiveness calculation formula (ratio of signal intensities). For low-efficiency materials, the ratio of signal intensities will be lower, and the resulting shielding effectiveness value will be lower than the standard requirements. On this basis, shielding data of low-efficiency materials of different thicknesses and different frequency bands are collected, and repeated measurements are performed multiple times and the average value is calculated to ensure the stability and reliability of the data. All test data should comply with international standards, such as ISO14234 (electromagnetic compatibility standard). Finally, the shielding effectiveness data of low-efficiency materials under different conditions are obtained, and a data basis is provided for subsequent steps.

[0167] Perform thermal simulation based on low-efficiency material shielding data to obtain thermal simulation data;

[0168] In this embodiment, finite element analysis (FEA) software, such as ANSYS or COMSOL, is used to simulate the thermal conductivity of the material. Specific material parameters, such as thermal conductivity, specific heat capacity, thickness, etc., are input, and corresponding temperature field boundary conditions are set. For example, the ambient temperature is set to 25°C and the test temperature range is 0°C to 150°C. The goal of thermal simulation is to evaluate the thermal response of the material at different temperatures. During the simulation process, the time step, calculation accuracy, and heat source distribution (such as radiation heating) need to be set. The thermal simulation results will include temperature change curves and thermal conductivity characteristic data at each point, recording the thermal stability of the material in a high temperature environment.

[0169] Calculate electrical conductivity from thermal simulation data;

[0170] In this embodiment, after the thermal simulation data is obtained, the known physical properties of the material are used to calculate the conductivity. By analyzing the relationship between the temperature change of the material and the conductivity during the thermal simulation process, the effect of the material temperature on the conductivity is determined. According to the thermal conductivity model (for example, using the linear relationship between temperature and conductivity), the conductivity of the material under different temperature conditions is calculated. For example, the conductivity of the material is set to a constant value, or linear regression or other statistical methods are used to fit the relationship between conductivity and temperature based on the temperature change in the simulation data. In this way, conductivity data at different temperatures are obtained.

[0171] Conduct corrosion experiments based on low-efficiency material shielding data to obtain corrosion data;

[0172] In this embodiment, a shielding material with known low efficiency is selected to carry out a corrosion experiment. First, the experimental environment is set up, including the corrosive medium (such as salt spray, acidic solution, etc.), temperature and humidity, and exposure time. In the corrosion experiment, the material needs to be exposed to a simulated corrosion environment, such as a 48-hour salt spray corrosion test in a salt spray test chamber. During the experiment, the corrosion changes on the surface of the material are recorded regularly, such as mass loss, the formation of surface corrosion products, etc. Use corrosion assessment standards (such as ASTMB117 salt spray corrosion test standard) and calculate key parameters such as corrosion rate and corrosion layer thickness based on the surface state of the material after the test.

[0173] Identify surface oxidation of corrosion data and obtain surface oxidation data;

[0174] In this embodiment, scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS) analysis are performed on the surface samples of the material obtained in the corrosion experiment to identify the presence and characteristics of the surface oxide layer. The microscopic morphology of the surface corrosion is observed by SEM to identify whether there is oxide deposition on the surface. Then, the chemical composition of the oxide layer on the surface of the material is analyzed by XPS to determine the type of oxide and its thickness. The identification of oxides is based on the ratio of the oxygen element to the metal element (such as aluminum or copper) signal in the surface spectrum. By setting the thickness threshold of the oxide layer (for example, the oxide layer thickness exceeds 0.5 μm, it is considered that there is oxidation), surface oxidation data is obtained.

[0175] Predicting the lifespan of shielding materials for smart home data based on conductivity and surface oxidation data.

[0176] In this embodiment, a material life prediction model is constructed based on conductivity data and surface oxidation data. First, the key influencing factors of material life are set, such as conductivity change, oxide layer thickness, corrosion rate, etc. Through historical experimental data, a regression analysis method (such as multiple linear regression or exponential decay model) is used to construct the relationship between material life and conductivity and oxidation data. For example, a standard life assessment model is set, in which the life is inversely proportional to the ratio of the decrease in conductivity and the change in the thickness of the oxide layer. Finally, the known conductivity and oxidation data are input into the model to obtain the expected life of the material.

[0177] Preferably, the multi-layer shielding design in step S2 includes:

[0178] Collect data on low-life materials of shielding material life;

[0179] In this embodiment, the lifespan data of different materials in long-term use are collected and organized, with a focus on shielding materials with shorter lifespans. The material lifespan assessment standard is adopted, and a series of standardized tests, such as high temperature, high humidity, salt spray corrosion and other environmental tests, are conducted to obtain the service life of the material under these conditions. The recorded data include the service life, failure mode, corrosion rate, and physical property changes (such as conductivity, hardness, etc.) of each material in different environments. By comparing the performance degradation of different materials, short-life materials are screened out. For example, a standard is set to determine the standard of short-life materials by the rate of change of conductivity in the test environment (for example, a decrease of more than 5% per year) and the frequency of failure modes (for example, corrosion cracks appear every 5 years).

[0180] Dividing the low-life material data into material layers to obtain a conductive metal layer, a conductive polymer layer, and an absorbing layer;

[0181] In this embodiment, the materials are layered and analyzed based on the data of low-life materials. The standard layering method is used to classify the performance of the materials according to factors such as conductivity and wave-absorbing properties. The material of the conductive metal layer is selected from a metal material with good conductivity, such as nickel-plated aluminum alloy. The conductive polymer layer should select a conductive rubber or conductive plastic material with high conductivity. The material of the absorbing layer is selected from a composite material with wave-absorbing properties, such as a carbon-based material. The materials are divided according to the conductivity, wave absorption rate and thickness of each material. For example, the material conductivity of the conductive metal layer should not be less than 10^6S / m, the conductivity of the conductive polymer layer should be between 10^3 and 10^4S / m, and the wave-absorbing performance of the absorbing layer should achieve a reflection loss of more than 20dB at a frequency of 2GHz.

[0182] Design of nickel-plated aluminum alloy shielding material based on conductive metal layer;

[0183] In this embodiment, a nickel-plated aluminum alloy shielding material is designed for the conductive metal layer. First, an aluminum alloy substrate is selected, the main component of the substrate is aluminum, and an appropriate amount of alloying elements such as copper and silicon are added. Then, a layer of nickel is plated on the surface of the aluminum alloy using electroplating technology, and the coating thickness is set to 0.05mm to 0.1mm. The nickel plating process in this step is achieved by controlling the current density and the chemical composition of the electroplating solution, and the electroplating time is generally set to 1 to 2 hours. The shielding effectiveness of the nickel-plated aluminum alloy is determined by factors such as the electrical conductivity of the metal, the surface finish, and the thickness of the coating. In the design, the electrical conductivity of the nickel-plated aluminum alloy shielding material is required to be no less than 10^6S / m, and the uniformity and good adhesion of the coating must be guaranteed.

[0184] Design of conductive rubber shielding materials based on conductive polymer layers;

[0185] In this embodiment, a conductive polymer layer is designed and conductive rubber is selected as the material. The selected base material is a polymer rubber, and a conductive filler (such as carbon black, conductive fiber, etc.) is added to achieve the conductive function. The proportion of the filler is adjusted according to the required conductivity. Generally, the mass fraction of the conductive filler needs to be controlled between 15% and 40%. The filler is evenly dispersed in the rubber base material through the mixing process to ensure the elasticity and conductivity of the rubber. The equipment used in this process includes an open mill and a mixing equipment to ensure the uniform distribution of the filler. The conductivity of the conductive rubber should be controlled between 10^3 and 10^4 S / m, and the thickness of the final material is generally 1mm to 5mm.

[0186] Design of carbon-based shielding materials based on absorbing layers;

[0187] In this embodiment, a composite material containing carbon nanotubes, graphene or carbon black is selected. When selecting a carbon-based material, its wave absorbing properties, that is, the wave absorbing ability in a specific frequency band (such as 2GHz to 10GHz) are considered. According to the wave absorbing performance requirements, the carbon filling ratio of the material is set, generally controlled between 30% and 60%. In this step, the carbon-based shielding material is made by mixing carbon nanotubes or graphene with a matrix material (such as a polymer or rubber) to ensure the uniform distribution of the carbon filler. During the molding process, the surface of the material is required to be flat, and the thickness is usually set to 2mm to 4mm. Ultimately, the reflection loss of the absorbing material should reach more than 20dB to ensure the shielding effectiveness at high frequencies.

[0188] Shielding materials, conductive rubber shielding materials, and carbon-based shielding materials are integrated, and the integrated material data is recorded to obtain multi-layer shielding data.

[0189] In this embodiment, the conductive metal layer, the conductive polymer layer and the absorbing layer are combined in a specific order and proportion according to the design requirements. When combining, it is necessary to ensure the adhesion and structural stability between the layers. Normally, the conductive metal layer is located in the outermost layer, the conductive polymer layer is in the middle, and the absorbing layer is located in the innermost layer. In order to ensure good interlayer bonding, a strong adhesive is used to firmly connect the materials of each layer. After the combination is completed, the shielding effectiveness of the integrated material is tested, including conductivity, absorbency, thickness and overall electromagnetic shielding effect. This data includes the specific parameters of each layer (such as thickness, conductivity, etc.) and the comprehensive performance of the integrated material (such as shielding effectiveness and absorbing ability). For example, the total shielding effectiveness of the integrated material should reach more than 30dB, which can effectively block electromagnetic interference in different frequency bands.

[0190] Preferably, step S3 is specifically as follows:

[0191] Step S31: performing transient electromagnetic pulse simulation based on the multi-layer shielding data, wherein the electromagnetic pulse frequency is set to 0.1 MHz–10 GHz and the electromagnetic pulse intensity is set to 10 V / m–200 kV / m, to obtain electromagnetic pulse simulation data;

[0192] In this embodiment, a transient electromagnetic pulse simulation is performed based on multi-layer shielding data. First, the frequency range of the electromagnetic pulse is set to 0.1MHz to 10GHz, and the electromagnetic pulse intensity is set to 10V / m to 200kV / m. The simulation is performed using an electromagnetic field analysis tool, such as COMSOL Multiphysics or ANSYS HFSS, and the data of the multi-layer shielding material, including its dielectric constant, magnetic permeability, wave absorption characteristics and other parameters, are input. During the simulation process, based on the set frequency range and intensity, the electromagnetic pulse signal is gradually applied to the multi-layer shielding structure. The transient electromagnetic field is calculated using the finite difference time domain (FDTD) method to obtain the electric field and magnetic field data of the entire time series. In this process, it is necessary to set an appropriate time step, spatial grid accuracy and boundary conditions, such as using a perfectly matched layer (PML) boundary to reduce reflections. The simulation results include the propagation characteristics, reflection coefficient and penetration effect of the electromagnetic pulse in the multi-layer shielding structure, and finally generate electromagnetic pulse simulation data.

[0193] Step S32: calculating the inverter electromagnetic field of the electromagnetic pulse simulation data and drawing a distribution diagram of the inverter electromagnetic field;

[0194] In this embodiment, the electromagnetic field of the inverter of the electromagnetic pulse simulation data is calculated and the electromagnetic field distribution map is drawn. First, the electromagnetic pulse simulation results are used to extract the electric field and magnetic field data of the inverter area. The field analysis module in the electromagnetic field analysis tool is used to calculate the electric field distribution in the inverter area point by point to obtain the electric field strength and direction distribution. Combined with the magnetic field data, the magnetic field distribution map around the inverter is calculated. The intensity and distribution of the magnetic field and the hot spots in the area are indicated in the figure. When drawing, use a three-dimensional graphics tool, set appropriate color mapping, and clearly mark the high-intensity magnetic field area for subsequent analysis. This distribution map not only shows the electric and magnetic field strengths, but also shows the electromagnetic wave propagation path inside the inverter.

[0195] Step S33: Identify the inverter electromagnetic overload area in the inverter electromagnetic field distribution map, where the magnetic field strength threshold is set to 10A / m and the power density threshold is set to 10W / m 2 ;

[0196] In this embodiment, the electromagnetic overload area in the electromagnetic field distribution map of the inverter is identified. According to the magnetic field strength data extracted from the electromagnetic field distribution map, the magnetic field strength threshold is set to 10A / m and the power density threshold is set to 10W / m 2 For each data point in the electromagnetic field, the magnetic field strength and power density are first calculated to determine whether they exceed the set threshold. The magnetic field strength exceeding 10A / m and 10W / m 2Areas with high power density are considered electromagnetic overload areas. During this process, all electromagnetic field data is filtered using a threshold determination method to generate a list containing the coordinates and electromagnetic parameters of all electromagnetic overload areas. This step requires batch processing using electromagnetic field simulation software to output data for electromagnetic overload areas that meet the criteria.

[0197] Step S34: calculating the magnetic field concentration in the electromagnetic overload area of ​​the inverter;

[0198] In this embodiment, the concentration formula is used to calculate the magnetic field concentration for each overloaded area using electromagnetic overload area data. This formula is determined by solving for the spatial gradient of the magnetic field intensity within the area and the total magnetic field intensity. The specific method involves calculating the magnetic field intensity distribution within the overloaded area, obtaining the magnetic field focusing within the area, and further optimizing this calculation using a weighted average method. Based on this calculation result, a magnetic field concentration distribution map is generated, which can reveal the focusing characteristics of the magnetic field within the electromagnetic overload area and its potential impact on equipment damage.

[0199] Step S35: Calculating the dielectric loss in the electromagnetic overload area of ​​the inverter;

[0200] In this embodiment, it is necessary to extract electric field strength data from the electromagnetic simulation data, and determine the electric field strength of each electromagnetic overload area based on the distribution of electric field strength given in the simulation results. Next, it is necessary to combine the dielectric constants of the materials used in the inverter, which should be obtained through experiments or material data manuals. For each overload area, the corresponding dielectric loss value is calculated based on its electric field strength and the dielectric constant of the material. During the entire calculation process, it is necessary to ensure that the measured values ​​of the electric field strength and dielectric constant are accurate to ensure that the dielectric loss calculation results of each overload area are accurate. This step requires accurate analysis and combination of the dielectric properties of the material and the regional electric field strength to avoid errors in the calculation affecting the electromagnetic overload area loss analysis of the inverter.

[0201] Step S36: determining inverter breakdown damage based on the electromagnetic pulse simulation data according to the magnetic field concentration and the dielectric loss, and obtaining inverter breakdown damage data;

[0202] In this embodiment, the breakdown damage of the inverter is determined based on the magnetic field concentration and dielectric loss data of the electromagnetic pulse simulation data. The breakdown damage determination criteria are set based on the magnetic field concentration and dielectric loss data obtained in steps S34 and S35. Assume that the magnetic field concentration exceeds a certain critical value (for example, the concentration is greater than 0.5) and the dielectric loss reaches a certain standard (for example, the loss exceeds 10W / m 2), the inverter is determined to have experienced breakdown damage in that area. Using this standard, all electromagnetic overload areas are analyzed one by one, ultimately identifying the damaged area and recording the breakdown damage data, including specific electromagnetic parameters and damage.

[0203] Step S37: extracting electromagnetic coupling data of smart home data;

[0204] In this embodiment, electromagnetic coupling data from smart home devices is extracted. First, based on the electromagnetic field data of smart home devices, a coupling model is used to analyze the electromagnetic coupling relationship between devices. Specifically, electromagnetic field simulation software is used to extract the electric field, magnetic field, and current distribution data within the home devices, and this data is used for coupling analysis. By setting appropriate device spacing, dielectric constant, and magnetic permeability, the interaction of electromagnetic waves is simulated, and the electromagnetic coupling strength between devices is calculated to obtain electromagnetic coupling data.

[0205] Step S38: Identifying parasitic current of electromagnetic coupling data based on the inverter breakdown damage data;

[0206] In this embodiment, based on the inverter breakdown damage data, the areas affected by electromagnetic interference in the smart home system are located and analyzed. These areas are obtained by analyzing the electromagnetic coupling data between devices using an electromagnetic simulation tool. Specifically, the electromagnetic simulation tool is used to perform detailed simulation of the identified electromagnetic overload areas to calculate the current distribution between the devices. By accurately calculating the relationship between the electric field strength and current density between the devices, the areas that cause parasitic currents are identified. For each device, based on the calculation results of its current distribution and electric field strength, it is further confirmed which areas' electromagnetic coupling data cause the generation of parasitic currents. In this process, the data on the electric field strength, current distribution, and current density between all devices must be accurately obtained and input into the simulation tool to ensure that the identified parasitic current areas are sufficiently accurate, and then the relevant data is recorded for subsequent analysis and processing.

[0207] Step S39: Identify parasitic effect characteristics of smart home data based on the parasitic current.

[0208] In this embodiment, data such as the frequency and amplitude of parasitic currents are used to analyze their impact on device performance. In this process, by performing detailed simulation and analysis of parasitic currents, their interference and impact on the internal electrical system of smart home devices are identified. For example, parasitic currents cause additional power consumption of the device, or generate electromagnetic interference, affecting the normal operation of the device. During specific operations, it is necessary to accurately measure and record the operating parameters (such as voltage, current, frequency) and electrical characteristics (such as resistance, capacitance, inductance, etc.) of each device, and then combine these parameters to analyze the specific impact of parasitic currents on the device. By modeling the relationship between parasitic currents and device performance, corresponding parasitic effect characteristic data are extracted, such as additional power consumption, electromagnetic interference of the device, etc. These data will eventually be output as numerical indicators of parasitic effects, providing a reference for further optimizing the design and working performance of smart home devices.

[0209] Preferably, step S4 is specifically as follows:

[0210] Step S41: Calculating the frequency of the electromagnetic wave based on the parasitic effect characteristics;

[0211] In this embodiment, parasitic current data is acquired. Then, combined with the current's frequency characteristics, a Fourier transform is used to perform spectral analysis on the current signal to extract its frequency distribution. This process determines the electromagnetic wave frequency range by analyzing the primary frequency components in the parasitic current signal. The frequency range of the electromagnetic wave needs to be refined based on the current's variations to determine the frequency range. This frequency data provides basic parameters for subsequent material selection.

[0212] Step S42: Dividing the frequency bands of the electromagnetic waves to obtain low-frequency electromagnetic waves and high-frequency electromagnetic waves;

[0213] In this embodiment, electromagnetic wave frequencies are divided into low-frequency and high-frequency bands based on electromagnetic wave frequency data. Low-frequency electromagnetic waves typically range from tens of hertz to several megahertz, while high-frequency electromagnetic waves range from hundreds of megahertz to several gigahertz. During the division process, appropriate thresholds are set based on the distribution of electromagnetic wave frequencies to clearly define the frequency range as low-frequency and high-frequency bands. This division standard is based on actual electromagnetic interference characteristics and material response characteristics, ensuring a reasonable and scientific division of frequency bands.

[0214] Step S43: selecting ferrite material based on low-frequency electromagnetic waves;

[0215] In this embodiment, ferrite materials are selected based on low-frequency electromagnetic waves, and ferrite materials suitable for the low-frequency band are selected according to the divided low-frequency electromagnetic wave frequency range. Ferrite materials have good electromagnetic shielding properties, especially in the low-frequency band, and can effectively absorb electromagnetic waves. At this time, by consulting the electromagnetic performance data of the material, its wave absorption characteristics in the low-frequency band are determined. When selecting ferrite materials, it is necessary to refer to parameters such as the material's magnetic permeability, dielectric constant, and loss factor to ensure that it can effectively shield electromagnetic waves in a specific frequency band. The specifications and performance requirements of the material should be verified by experimental data to ensure that it meets the requirements of electromagnetic shielding.

[0216] Step S44: selecting a carbon nanotube composite material based on high-frequency electromagnetic waves;

[0217] In this embodiment, carbon nanotube composite materials are selected based on the frequency range of high-frequency electromagnetic waves. Based on the frequency range of the high-frequency electromagnetic waves, a carbon nanotube composite material suitable for the high-frequency band is selected. Carbon nanotube composite materials, due to their excellent electrical conductivity and mechanical properties, are particularly outstanding in absorbing electromagnetic waves in the high-frequency band. When selecting this material, consideration is given to the length, diameter, and electrical conductivity of the carbon nanotubes, as well as the proportions of other components in the composite material, to ensure that the material can effectively absorb and shield electromagnetic waves in the high-frequency band. Experimental data analysis of different composite materials is then used to determine the optimal carbon nanotube composite material combination to achieve the goal of optimizing electromagnetic shielding effectiveness.

[0218] Step S45: integrating the ferrite material and the carbon nanotube composite material, and recording the integrated material data to obtain electromagnetic shielding optimization data;

[0219] In this embodiment, ferrite materials and carbon nanotube composite materials are integrated, and the data of the integrated materials are recorded to obtain electromagnetic shielding optimization data. In this step, the ferrite material in the low-frequency band and the carbon nanotube composite material in the high-frequency band are reasonably combined to form a composite shielding material. During the integration process, the mixing ratio of the two materials needs to be precisely controlled to ensure that the composite material has good electromagnetic shielding performance in a wide frequency band. After the materials are integrated, the electromagnetic properties data of the integrated materials are recorded, such as key parameters such as shielding effectiveness, wave absorption capacity, and heat resistance. Through experimental testing and simulation analysis, it is ensured that the integrated materials meet the design requirements and form the final electromagnetic shielding optimization data.

[0220] Step S46: Encrypt the electromagnetic shielding information of the electromagnetic shielding optimization data and upload it to the home energy management system to perform the electromagnetic shielding information security protection task.

[0221] In this embodiment, electromagnetic shielding optimization data is encrypted and uploaded to the home energy management system to perform electromagnetic shielding information security protection tasks. At this stage, an encryption algorithm is used to encrypt the integrated electromagnetic shielding optimization data. This encryption method involves encrypting the data content to prevent theft or tampering during transmission. The encrypted data is uploaded to the home energy management system via a secure protocol to ensure the security of the electromagnetic shielding information. Simultaneously, the home energy management system performs electromagnetic shielding information security protection tasks based on the encrypted data, protecting household electrical devices from external electromagnetic interference and attacks. The encryption process involves selecting appropriate encryption standards and algorithms to ensure data integrity and confidentiality.

[0222] Preferably, this specification also provides an information security protection system for a home energy management system, which is used to execute the information security protection method for a home energy management system as described above. The information security protection system for a home energy management system includes:

[0223] A communication simulation module is used to obtain smart home data; perform communication simulation based on the smart home data, identify electromagnetic leakage during the communication simulation process, and generate electromagnetic leakage data;

[0224] A multi-layer shielding design module is used to identify side-channel attacks based on electromagnetic leakage data; test the shielding effectiveness of materials using side-channel attacks; predict the lifespan of shielding materials for smart home data based on the shielding effectiveness of the materials; and perform multi-layer shielding design based on the shielding material lifespan to obtain multi-layer shielding data.

[0225] The parasitic effect identification module is used to perform transient electromagnetic pulse simulation based on multi-layer shielding data, detect inverter breakdown damage during the simulation process, and obtain inverter breakdown damage data; based on the inverter breakdown damage data, it identifies the parasitic effect characteristics of smart home data;

[0226] The electromagnetic shielding information encryption module is used to optimize electromagnetic shielding based on parasitic effect characteristics to obtain electromagnetic shielding optimization data; encrypt the electromagnetic shielding information of the electromagnetic shielding optimization data and upload it to the home energy management system to perform electromagnetic shielding information security protection tasks.

[0227] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0228] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. An information security protection method for a home energy management system, characterized in that: The following steps are involved: Step S1: Acquire smart home data; perform communication simulation based on the smart home data, identify electromagnetic leakage during the communication simulation, and generate electromagnetic leakage data; Step S2: Identify side channel attacks based on electromagnetic leakage data; test material shielding effectiveness using side channel attacks; and predict the lifespan of shielding materials for smart home data based on the material shielding effectiveness. Conduct multi-layer shielding design based on the life of shielding materials and obtain multi-layer shielding data; Step S3: performing transient electromagnetic pulse simulation based on the multi-layer shielding data, and detecting inverter breakdown damage during the simulation process to obtain inverter breakdown damage data; identifying parasitic effect characteristics of the smart home data based on the inverter breakdown damage data; Step S4: Optimize electromagnetic shielding based on parasitic effect characteristics to obtain electromagnetic shielding optimization data; encrypt the electromagnetic shielding information of the electromagnetic shielding optimization data and upload it to the home energy management system to perform electromagnetic shielding information security protection tasks.

2. The information security protection method for a home energy management system according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: Acquire smart home data; Step S12: extracting the communication protocol of the smart home data and setting a communication process based on the communication protocol; Step S13: Identify communication channel characteristics of smart home data; Step S14: performing communication simulation according to the communication channel characteristics and the communication process to obtain communication simulation data; Step S15: Identify electromagnetic leakage of communication simulation data and generate electromagnetic leakage data.

3. The information security protection method for a home energy management system according to claim 2, characterized in that: Step S15 is specifically as follows: Step S151: Identify the signal propagation path of the communication analog data; Step S152: Calculating the electromagnetic field strength of the signal propagation path; Step S153: converting the electromagnetic field intensity into an electromagnetic intensity spectrum; Step S154: Identify the electromagnetic energy concentration area; Step S155: Counting abnormally strong signals in the electromagnetic energy concentration area, and identifying stray signals based on the abnormally strong signals; Step S156: Perform electromagnetic leakage determination on the stray signal to obtain electromagnetic leakage data.

4. The information security protection method for a home energy management system according to claim 1, characterized in that: The identification of the side channel attack in step S2 includes: Reversely infer the encryption key of electromagnetic leakage data; Recover the full key space based on the encryption key; Using the full key space to identify attack fingerprints of electromagnetic leakage data; Track attack paths based on attack fingerprints; Locate key attack fingerprints of attack paths; Obtain a side-channel attack fingerprint library; Based on the key attack fingerprint, fingerprint matching is performed on the side channel attack fingerprint library to obtain the side channel attack fingerprint; Identify side-channel attacks on electromagnetic leakage data based on side-channel attack fingerprints.

5. The information security protection method for a home energy management system according to claim 1, characterized in that: The testing of the shielding effectiveness of the material in step S2 includes: Extract smart camera data for smart home data; Use side channel attacks to simulate attacks on smart camera data to obtain attack simulation data; Collect electromagnetic signal waveforms of attack simulation data; Determine electromagnetic leakage based on the electromagnetic signal waveform and obtain camera electromagnetic leakage data; Screen electromagnetic leakage cameras based on camera electromagnetic leakage data, and use a preset 0.1mm-0.3mm thin aluminum foil to perform a shielding test on the electromagnetic leakage cameras to obtain thin aluminum foil shielding data; The electromagnetic leakage camera was shielded using a preset 0.5mm-1mm thick aluminum foil to obtain thick aluminum foil shielding data; Calculate the shielding effectiveness of thin aluminum foil using thin aluminum foil shielding data; Calculate the shielding effectiveness of thick aluminum foil using thick aluminum foil shielding data; The shielding effectiveness of the material is obtained by integrating the shielding effectiveness of thin aluminum foil and thick aluminum foil.

6. The information security protection method for a home energy management system according to claim 1, characterized in that: The prediction of shielding material life in step S2 includes: Extracting shielding data of low-efficiency materials for material shielding effectiveness; Perform thermal simulation based on low-efficiency material shielding data to obtain thermal simulation data; Calculate electrical conductivity from thermal simulation data; Conduct corrosion experiments based on low-efficiency material shielding data to obtain corrosion data; Identify surface oxidation of corrosion data and obtain surface oxidation data; Predicting the lifespan of shielding materials for smart home data based on conductivity and surface oxidation data.

7. The information security protection method for a home energy management system according to claim 1, characterized in that: The multi-layer shielding design in step S2 includes: Collect data on low-life materials of shielding material life; Dividing the low-life material data into material layers to obtain a conductive metal layer, a conductive polymer layer, and an absorbing layer; Design of nickel-plated aluminum alloy shielding material based on conductive metal layer; Design of conductive rubber shielding materials based on conductive polymer layers; Design of carbon-based shielding materials based on absorbing layers; Shielding materials, conductive rubber shielding materials, and carbon-based shielding materials are integrated, and the integrated material data is recorded to obtain multi-layer shielding data.

8. The information security protection method for a home energy management system according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: performing transient electromagnetic pulse simulation based on the multi-layer shielding data, wherein the electromagnetic pulse frequency is set to 0.1 MHz–10 GHz and the electromagnetic pulse intensity is set to 10 V / m–200 kV / m, to obtain electromagnetic pulse simulation data; Step S32: calculating the inverter electromagnetic field of the electromagnetic pulse simulation data and drawing a distribution diagram of the inverter electromagnetic field; Step S33: Identify the inverter electromagnetic overload area in the inverter electromagnetic field distribution map, where the magnetic field strength threshold is set to 10A / m and the power density threshold is set to 10W / m 2 ; Step S34: calculating the magnetic field concentration in the electromagnetic overload area of ​​the inverter; Step S35: Calculating the dielectric loss in the electromagnetic overload area of ​​the inverter; Step S36: determining inverter breakdown damage based on the electromagnetic pulse simulation data according to the magnetic field concentration and the dielectric loss, and obtaining inverter breakdown damage data; Step S37: extracting electromagnetic coupling data of smart home data; Step S38: Identifying parasitic current of electromagnetic coupling data based on the inverter breakdown damage data; Step S39: Identify parasitic effect characteristics of smart home data based on the parasitic current.

9. The information security protection method for a home energy management system according to claim 1, characterized in that: Step S4 is specifically as follows: Step S41: Calculating the frequency of the electromagnetic wave based on the parasitic effect characteristics; Step S42: Dividing the frequency bands of the electromagnetic waves to obtain low-frequency electromagnetic waves and high-frequency electromagnetic waves; Step S43: selecting ferrite material based on low-frequency electromagnetic waves; Step S44: selecting a carbon nanotube composite material based on high-frequency electromagnetic waves; Step S45: integrating the ferrite material and the carbon nanotube composite material, and recording the integrated material data to obtain electromagnetic shielding optimization data; Step S46: Encrypt the electromagnetic shielding information of the electromagnetic shielding optimization data and upload it to the home energy management system to perform the electromagnetic shielding information security protection task.

10. An information security protection system for a home energy management system, characterized in that: For executing the information security protection method for a home energy management system according to claim 1, the information security protection system for a home energy management system comprises: A communication simulation module is used to obtain smart home data; perform communication simulation based on the smart home data, identify electromagnetic leakage during the communication simulation process, and generate electromagnetic leakage data; A multi-layer shielding design module is used to identify side-channel attacks based on electromagnetic leakage data; test the shielding effectiveness of materials using side-channel attacks; predict the lifespan of shielding materials for smart home data based on the shielding effectiveness of the materials; and perform multi-layer shielding design based on the shielding material lifespan to obtain multi-layer shielding data. The parasitic effect identification module is used to perform transient electromagnetic pulse simulation based on multi-layer shielding data, detect inverter breakdown damage during the simulation process, and obtain inverter breakdown damage data; based on the inverter breakdown damage data, it identifies the parasitic effect characteristics of smart home data; The electromagnetic shielding information encryption module is used to optimize electromagnetic shielding based on parasitic effect characteristics to obtain electromagnetic shielding optimization data; encrypt the electromagnetic shielding information of the electromagnetic shielding optimization data and upload it to the home energy management system to perform electromagnetic shielding information security protection tasks.

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